  The idea of intersectionality has become a frequent topic of discussion both
in academic sociology, as well as among popular movements for social justice
such as Black Lives Matter, intersectional feminism, and LGBT rights.
Intersectionality proposes that an individual's experience of society has
aspects that are irreducible to the sum of one's various identities considered
individually, but are "greater than the sum of their parts." In this work, we
show that the effects of intersectional identities can be statistically
observed in empirical data using information theory. We show that, when
considering the predictive relationship between various identities categories
such as race, sex, and income (as a proxy for class) on outcomes such as health
and wellness, robust statistical synergies appear. These synergies show that
there are joint-effects of identities on outcomes that are irreducible to any
identity considered individually and only appear when specific categories are
considered together (for example, there is a large, synergistic effect of race
and sex considered jointly on income irreducible to either race or sex). We
then show using synthetic data that the current gold-standard method of
assessing intersectionalities in data (linear regression with multiplicative
interaction coefficients) fails to disambiguate between truly synergistic,
greater-than-the-sum-of-their-parts interactions, and redundant interactions.
We explore the significance of these two distinct types of interactions in the
context of making inferences about intersectional relationships in data and the
importance of being able to reliably differentiate the two. Finally, we
conclude that information theory, as a model-free framework sensitive to
nonlinearities and synergies in data, is a natural method by which to explore
the space of higher-order social dynamics.


  We consider a scenario involving computations over a massive dataset stored
distributedly across multiple workers, which is at the core of distributed
learning algorithms. We propose Lagrange Coded Computing (LCC), a new framework
to simultaneously provide (1) resiliency against stragglers that may prolong
computations; (2) security against Byzantine (or malicious) workers that
deliberately modify the computation for their benefit; and (3)
(information-theoretic) privacy of the dataset amidst possible collusion of
workers. LCC, which leverages the well-known Lagrange polynomial to create
computation redundancy in a novel coded form across workers, can be applied to
any computation scenario in which the function of interest is an arbitrary
multivariate polynomial of the input dataset, hence covering many computations
of interest in machine learning. LCC significantly generalizes prior works to
go beyond linear computations. It also enables secure and private computing in
distributed settings, improving the computation and communication efficiency of
the state-of-the-art. Furthermore, we prove the optimality of LCC by showing
that it achieves the optimal tradeoff between resiliency, security, and
privacy, i.e., in terms of tolerating the maximum number of stragglers and
adversaries, and providing data privacy against the maximum number of colluding
workers. Finally, we show via experiments on Amazon EC2 that LCC speeds up the
conventional uncoded implementation of distributed least-squares linear
regression by up to $13.43\times$, and also achieves a
$2.36\times$-$12.65\times$ speedup over the state-of-the-art straggler
mitigation strategies.


  In this study, we have used point-source approximation with the premise that
it is compatible with the rupture process of the predominant faulting geometry
caused by the initiation of Shonbe seismic sequence. First, all recorded
earthquakes during six months after the main shock, 2013 April 9th, in the
vicinity of khaki anticline near Shonbe, are relocated with the probabilistic
nonlinear method based on Bayesian inference. The synthetic test is done, with
the exact distribution of real stations on hand. Thus, the proper value for the
parameter that can explicitly affect the error results is calculated to be
0.01. After performing relocation task, 98 relocated events with horizontal
errors less than 5 km from all 373 recorded events are taken into account for
further analysis of the spatial characteristics of the sequence. In advance,
point source inversion is implemented for 21 events greater than 4.5 Mn. Then,
supplementary investigations and stability tests are performed for the results
of the main shock. Accordingly, we are convinced that the gathered results are
reliable enough to be used for the seismotectonic analysis of the region
resulting from the sequence. Eventually, the reverse slip vector with
substantial left-lateral strike slip component (in some cases) is modeled
employing the distribution of inverted centroids and resulting faulting
geometry for the chosen events. A southward dip of the probable causative fault
is seen, furthermore depth range of the rupture is localized within 8 to 12 km,
in the lowermost part of the sedimentary cover.


  The therapeutic working alliance is an important predictor of the outcome of
the psychotherapy treatment. In practice, the working alliance is estimated
from a set of scoring questionnaires in an inventory that both the patient and
the therapists fill out. In this work, we propose an analytical framework of
directly inferring the therapeutic working alliance from the natural language
within the psychotherapy sessions in a turn-level resolution with deep
embeddings such as the Doc2Vec and SentenceBERT models. The transcript of each
psychotherapy session can be transcribed and generated in real-time from the
session speech recordings, and these embedded dialogues are compared with the
distributed representations of the statements in the working alliance
inventory. We demonstrate, in a real-world dataset with over 950 sessions of
psychotherapy treatments in anxiety, depression, schizophrenia and suicidal
patients, the effectiveness of this method in mapping out trajectories of
patient-therapist alignment and the interpretability that can offer insights in
clinical psychiatry. We believe such a framework can be provide timely feedback
to the therapist regarding the quality of the conversation in interview
sessions.


  In a region free of currents, magnetostatics can be described by the Laplace
equation of a scalar magnetic potential, and one can apply the same methods
commonly used in electrostatics. Here we show how to calculate the general
vector field inside a real (finite) solenoid, using only the magnitude of the
field along the symmetry axis. Our method does not require integration or
knowledge of the current distribution, and is presented through practical
examples, including a non-uniform finite solenoid used to produce cold atomic
beams via laser cooling. These examples allow educators to discuss the
non-trivial calculation of fields off-axis using concepts familiar to most
students, while offering the opportunity to introduce important advancements of
current modern research.


  Potential energy surfaces of the hydrogen molecular ion H$_2^+$ in the
Born-Oppenheimer approximation are computed by means of the Riccati-Pad\'e
method (RPM). The convergence properties of the method are analyzed for
different states. The equilibrium internuclear distance, as well as the
corresponding electronic plus nuclear energy, and the associated separation
constants, are computed to 40 digits of accuracy for several bound states. For
the ground state the same parameters are computed with more than 100 digits of
accuracy. Additional benchmark values of the electronic energy at different
internuclear distances are given for several additional states. The software
implementation of the RPM is given under a free software license. The results
obtained in the present work are the most accurate available so far, and
further additional benchmarks are made available through the software provided.


  This paper is the continuation of arXiv:0802.1245. We construct the
Hochschild class for coherent modules over a deformation quantization algebroid
on a complex Poisson manifold. We also define the convolution of Hochschild
homologies, and prove that the Hochschild class of the convolution of two
coherent modules is the convolution of their Hochschild classes. We study with
some details the case of symplectic deformations.


  This study analyzed the scar-like localization in the time-average of a
timeevolving wavepacket on the desymmetrized stadium billiard. When a
wavepacket is launched along the orbits, it emerges on classical unstable
periodic orbits as a scar in the stationary states. This localization along the
periodic orbit is clarified through the semiclassical approximation. It
essentially originates from the same mechanism of a scar in stationary states:
the piling up of the contribution from the classical actions of multiply
repeated passes on a primitive periodic orbit. To create this enhancement,
several states are required in the energy range, which is determined by the
initial wavepacket.


  Spectroscopic galaxy-galaxy lens searches are presently the most prolific
method of identifying strong lens systems in large data sets. We study the
probabilities associated with these lens searches, namely the probability of
identifying a candidate with rogue [OII] emission lines in a galaxy's spectrum,
and the probability that the candidate will show features of strong lensing in
follow-up photometric observations. We include selection effects unique to
spectroscopic data, and apply them to the Sloan Lens ACS (SLACS) survey (Bolton
et al. 2006). The most significant selection effect is the finite size of the
spectroscopic fiber which selects against large separation lenses and results
in a non-monotonic dependence of the rogue line probability on velocity
dispersion. For example, with the 3 arcsec diameter SDSS fiber and 2 arcsec
FWHM seeing, we find that the probability that a given LRG has a rogue [OII]
line in its spectrum decreases with velocity dispersion from 150 km/s to 300
km/s and then increases up to 400 km/s for a given source size. The total
probability for observing a rogue line in a single survey spectrum is
~0.9-3.0%, and the total lensing rate is ~0.5-1.3%. The range is due to
uncertainties in the physical size of [OII] emission regions, and in the
evolution of the [OII] luminosity function. Our estimates are a factor of ~5
higher than the results of the SLACS survey, a discrepancy which we attribute
to the SLACS requirement that multiple rogue lines be observed simultaneously.


  Individuals often imitate each other to fall into the typical group, leading
to a self-organized state of typical behaviors in a community. In this paper,
we model self-organization in social tagging systems and illustrate the
underlying interaction and dynamics. Specifically, we introduce a model in
which individuals adjust their own tagging tendency to imitate the average
tagging tendency. We found that when users are of low confidence, they tend to
imitate others and lead to a self-organized state with active tagging. On the
other hand, when users are of high confidence and are stubborn for changes,
tagging becomes inactive. We observe a phase transition at a critical level of
user confidence when the system changes from one regime to the other. The
distributions of post length obtained from the model are compared to real data
which show good agreements.


  We prove heat kernel estimates for the $\bar\partial$-Neumann Laplacian
acting in spaces of differential forms over noncompact, strongly pseudoconvex
complex manifolds with a Lie group symmetry and compact quotient. We also
relate our results to those for an associated Laplace-Beltrami operator on
functions.


  We extend a Gaussian functional inequality to a countable product of Gaussian
measures. This inequality improves on the classical Poincare inequality for
Gaussian measures. As an application, we prove that First Passage Percolation
has sublinear variance when the edge times distribution belongs to a wide class
of continuous distributions, including the exponential one. This extends a
result by Benjamini, Kalai and Schramm, valid for positive Bernoulli edge
times.


  We present the results of a search for relations between observables that are
independent of the Chiral Pertubation Theory (ChPT) Next-to-Next-to-Leading
Order (NNLO) Low-Energy Constants (LECs). We have found some relations between
observables in $\pi\pi$, $\pi K$ scattering and $K_{l4}$ decay which have been
evaluated numerically using the old fit (fit 10 in [1] of the NLO LECs.


  This study aims to accelerate coronary MRI using a novel reconstruction
algorithm, called self-consistent robust artificial-neural-networks for k-space
interpolation (sRAKI). sRAKI performs iterative parallel imaging reconstruction
by enforcing coil self-consistency using subject-specific neural networks. This
approach extends the linear convolutions in SPIRiT to nonlinear interpolation
using convolutional neural networks (CNNs). These CNNs are trained individually
for each scan using the scan-specific autocalibrating signal (ACS) data.
Reconstruction is performed by imposing the learned self-consistency and
data-consistency enabling sRAKI to support random undersampling patterns.
Fully-sampled targeted right coronary artery MRI was acquired in six healthy
subjects for evaluation. The data were retrospectively undersampled, and
reconstructed using SPIRiT, $\ell_1$-SPIRiT and sRAKI for acceleration rates of
2 to 5. Additionally, prospectively undersampled whole-heart coronary MRI was
acquired to further evaluate performance. The results indicate that sRAKI
reduces noise amplification and blurring artifacts compared with SPIRiT and
$\ell_1$-SPIRiT, especially at high acceleration rates in targeted data.
Quantitative analysis shows that sRAKI improves normalized mean-squared-error
(~44% and ~21% over SPIRiT and $\ell_1$-SPIRiT at rate 5) and vessel sharpness
(~10% and ~20% over SPIRiT and $\ell_1$-SPIRiT at rate 5). In addition,
whole-heart data shows the sharpest coronary arteries when resolved using
sRAKI, with 11% and 15% improvement in vessel sharpness over SPIRiT and
$\ell_1$-SPIRiT, respectively. Thus, sRAKI is a database-free neural
network-based reconstruction technique that may further accelerate coronary MRI
with arbitrary undersampling patterns, while improving noise resilience over
linear parallel imaging and image sharpness over $\ell_1$ regularization
techniques.


  This article is present high precision multiwave rectifier circuit operating
in low voltage plus or minus 1.5 Volt current modes by CMOS technology 0.5
\mum, receive input and give output in current mode, respond at high frequency
period. The structure compound with high speed current comparator circuit,
current mirror circuit, and CMOS inverter circuit. PSpice program used for
confirmation the performance of testing. The PSpice program shows operating of
circuit is able to working at maximum input current 400 \muAp p, maximum
frequency responding 200 MHz, high precision and low power losses, and
non-precision zero crossing output signal.


  Domain Adaptation (DA) is important for deep learning-based medical image
segmentation models to deal with testing images from a new target domain. As
the source-domain data are usually unavailable when a trained model is deployed
at a new center, Source-Free Domain Adaptation (SFDA) is appealing for data and
annotation-efficient adaptation to the target domain. However, existing SFDA
methods have a limited performance due to lack of sufficient supervision with
source-domain images unavailable and target-domain images unlabeled. We propose
a novel Uncertainty-aware Pseudo Label guided (UPL) SFDA method for medical
image segmentation. Specifically, we propose Target Domain Growing (TDG) to
enhance the diversity of predictions in the target domain by duplicating the
pre-trained model's prediction head multiple times with perturbations. The
different predictions in these duplicated heads are used to obtain pseudo
labels for unlabeled target-domain images and their uncertainty to identify
reliable pseudo labels. We also propose a Twice Forward pass Supervision (TFS)
strategy that uses reliable pseudo labels obtained in one forward pass to
supervise predictions in the next forward pass. The adaptation is further
regularized by a mean prediction-based entropy minimization term that
encourages confident and consistent results in different prediction heads.
UPL-SFDA was validated with a multi-site heart MRI segmentation dataset, a
cross-modality fetal brain segmentation dataset, and a 3D fetal tissue
segmentation dataset. It improved the average Dice by 5.54, 5.01 and 6.89
percentage points for the three tasks compared with the baseline, respectively,
and outperformed several state-of-the-art SFDA methods.


  We propose local space-time approximation spaces for parabolic problems that
are optimal in the sense of Kolmogorov and may be employed in multiscale and
domain decomposition methods. The diffusion coefficient can be arbitrarily
rough in space and time. To construct local approximation spaces we consider a
compact transfer operator that acts on the space of local solutions and covers
the full time dimension. The optimal local spaces are then given by the left
singular vectors of the transfer operator. To prove compactness of the latter
we combine a suitable parabolic Caccioppoli inequality with the compactness
theorem of Aubin-Lions. In contrast to the elliptic setting [I. Babu\v{s}ka and
R. Lipton, Multiscale Model. Simul., 9 (2011), pp. 373-406] we need an
additional regularity result to combine the two results. Furthermore, we employ
the generalized finite element method to couple local spaces and construct an
approximation of the global solution. Since our approach yields reduced
space-time bases, the computation of the global approximation does not require
a time stepping method and is thus computationally efficient. Moreover, we
derive rigorous local and global a priori error bounds. In detail, we bound the
global approximation error in a graph norm by the local errors in the
$L^2(H^1)$-norm, noting that the space the transfer operator maps to is
equipped with this norm. Numerical experiments demonstrate an exponential decay
of the singular values of the transfer operator and the local and global
approximation errors for problems with high contrast or multiscale structure
regarding space and time.


  IRAC images of M81 show three distinct morphological constituents: a smooth
distribution of evolved stars with bulge, disk, and spiral arm components; a
clumpy distribution of dust emission tracing the spiral arms; and a pointlike
nuclear source. The bulge stellar colors are consistent with M-type giants, and
the disk colors are consistent with a slightly younger population. The dust
emission generally follows the blue and ultraviolet emission, but there are
large areas that have dust emission without ultraviolet and smaller areas with
ultraviolet but little dust emission. The former are presumably caused by
extinction, and the latter may be due to cavities in the gas and dust created
by supernova explosions. The nucleus appears fainter at 8 um than expected from
ground-based 10 um observations made four years ago.


  Anomalous diffusion processes, in particular superdiffusive ones, are known
to be efficient strategies for searching and navigation by animals and also in
human mobility. One way to create such regimes are L\'evy flights, where the
walkers are allowed to perform jumps, the "flights", that can eventually be
very long as their length distribution is asymptotically power-law distributed.
In our work, we present a model in which walkers are allowed to perform, on a
1D lattice, "cascades" of $n$ unitary steps instead of one jump of a randomly
generated length, as in the L\'evy case, where $n$ is drawn from a cascade
distribution $p_n$. We show that this local mechanism may give rise to
superdiffusion or normal diffusion when $p_n$ is distributed as a power law. We
also introduce waiting times that are power-law distributed as well and
therefore the probability distribution scaling is steered by the two PDF's
power-law exponents. As a perspective, our approach may engender a possible
generalization of anomalous diffusion in context where distances are difficult
to define, as in the case of complex networks, and also provide an interesting
model for diffusion in temporal networks.


  For a prime power $q$, $\F$ denotes the finite field of order $q$, and for
$m\geq 2$, $\Fm$ denotes the extension field of degree $m$. We establish a
characteristic function for the set of $(r,\, n)$-free elements of finite
cyclic $R$-module for the Euclidean domain $R$. Furthermore, we explore $(f,\,
g)$-freeness through polynomial values and finally give an expression for the
characteristic function for the set of $(f,\, g)$-free elements.


  We present the first lattice simulation to investigate the nature of
multi-stream inflation. The simulation confirms the physical picture of
multi-stream inflation, and with new findings in parameter space and field
behaviors. Our simulation shows that gradient energy plays a significant role
in multi-stream inflation. For a double field potential with a shifted Gaussian
barrier, bifurcation probability is controlled by the shift distance with an
error function relation. The bubbles created by bifurcation tend to be more
spherical as bifurcation probability decreases. Also, the bifurcation is more
likely to introduce oscillations of field values inside the bubbles than
outside.


  A d-biclique cover of a graph G is a collection of bicliques of G such that
each edge of G is in at least d of the bicliques. The number of bicliques in a
minimum d-biclique cover of G is called the d-biclique covering number of G and
is denoted by ${bc}_d(G)$. In this paper, we present an upper bound for the d-
biclique covering number of the lexicographic product of graphs. Also, we
introduce some bounds of this parameter for some graph constructions and obtain
the exact value of the d-biclique covering number of some graphs.


  Based on the Langevin description of the Continuous Time Random Walk (CTRW),
we consider a generalization of CTRW in which the waiting times between the
subsequent jumps are correlated. We discuss the cases of exponential and slowly
decaying persistent power-law correlations between the waiting times as two
generic examples and obtain the corresponding mean squared displacements as
functions of time. In the case of exponential-type correlations the
(sub)diffusion at short times is slower than in the absence of correlations. At
long times the behavior of the mean squared displacement is the same as in
uncorrelated CTRW. For power-law correlations we find subdiffusion
characterized by the same exponent at all times, which appears to be smaller
than the one in uncorrelated CTRW. Interestingly, in the limiting case of an
extremely long power-law correlations, the (sub)diffusion exponent does not
tend to zero, but is bounded from below by the subdiffusion exponent
corresponding to a short time behavior in the case of exponential correlations.


  We present a systematic investigation of muon-stopping states in
superconductors that reportedly exhibit spontaneous magnetic fields below their
transition temperatures due to time-reversal symmetry breaking. These materials
include elemental rhenium, several intermetallic systems and Sr$_2$RuO$_4$. We
demonstrate that the presence of the muon leads to only a limited and
relatively localized perturbation to the local crystal structure, while any
small changes to the electronic structure occur several electron volts below
the Fermi energy leading to only minimal changes in the charge density on ions
close to the muon. Our results imply that the muon-induced perturbation alone
is unlikely to lead to the observed spontaneous fields in these materials,
whose origin is more likely intrinsic to the time-reversal symmetry broken
superconducting state.


  Let d be a positive integer. We show a finiteness theorem for semialgebraic
RL triviality of a Nash family of Nash functions defined on a Nash manifold,
generalising Benedetti-Shiota's finiteness theorem for semialgebraic RL
equivalence classes appearing in the space of real polynomial functions of
degree not exceeding d. We also prove Fukuda's claim, Theorem 1.3, and its
semialgebraic version Theorem 1.4, on the finiteness of the local R types
appearing in the space of real polynomial functions of real polynomial function
germs of degree not exceeding d.


  This paper presents a convexity analysis for the dynamic snake model based on
the Potential Energy functional and the Hamiltonian formulation of the
classical mechanics. First we see the snake model as a dynamical system whose
singular points are the borders we seek. Next we show that a necessary
condition for a singular point to be an attractor is that the energy functional
is strictly convex in a neighborhood of it, that means, if the singular point
is a local minimum of the potential energy. As a consequence of this analysis,
a local expression relating the dynamic parameters and the rate of convergence
arises. Such results link the convexity analysis of the potential energy and
the dynamic snake model and point forward to the necessity of a physical
quantity whose convexity analysis is related to the dynamic and which
incorporate the velocity space. Such a quantity is exactly the (conservative)
Hamiltonian of the system.


  To mitigate gender bias in contextualized language models, different
intrinsic mitigation strategies have been proposed, alongside many bias
metrics. Considering that the end use of these language models is for
downstream tasks like text classification, it is important to understand how
these intrinsic bias mitigation strategies actually translate to fairness in
downstream tasks and the extent of this. In this work, we design a probe to
investigate the effects that some of the major intrinsic gender bias mitigation
strategies have on downstream text classification tasks. We discover that
instead of resolving gender bias, intrinsic mitigation techniques and metrics
are able to hide it in such a way that significant gender information is
retained in the embeddings. Furthermore, we show that each mitigation technique
is able to hide the bias from some of the intrinsic bias measures but not all,
and each intrinsic bias measure can be fooled by some mitigation techniques,
but not all. We confirm experimentally, that none of the intrinsic mitigation
techniques used without any other fairness intervention is able to consistently
impact extrinsic bias. We recommend that intrinsic bias mitigation techniques
should be combined with other fairness interventions for downstream tasks.


  We give a review and present a comprehensive calculation for the leptonic
constant B_{c} of the low-lying pseudoscalar and vector states of B_{c}-meson
in the framework of static and QCD-motivated nonrelativistic potential models
taking into account the one-loop and two-loop QCD corrections in the short
distance coefficient that governs the leptonic constant of $B_{c}$ quarkonium
system. Further, we use the scaling relation to predict the leptonic constant
of the nS-states of the (b_bar)c system. Our results are compared with other
models to gauge the reliability of the predictions and point out differences.


  Recently, several approaches to solving linear systems on a quantum computer
have been formulated in terms of the quantum adiabatic theorem for a
continuously varying Hamiltonian. Such approaches enabled near-linear scaling
in the condition number $\kappa$ of the linear system, without requiring a
complicated variable-time amplitude amplification procedure. However, the most
efficient of those procedures is still asymptotically sub-optimal by a factor
of $\log(\kappa)$. Here, we prove a rigorous form of the adiabatic theorem that
bounds the error in terms of the spectral gap for intrinsically discrete time
evolutions. We use this discrete adiabatic theorem to develop a quantum
algorithm for solving linear systems that is asymptotically optimal, in the
sense that the complexity is strictly linear in $\kappa$, matching a known
lower bound on the complexity. Our $\mathcal{O}(\kappa\log(1/\epsilon))$
complexity is also optimal in terms of the combined scaling in $\kappa$ and the
precision $\epsilon$. Compared to existing suboptimal methods, our algorithm is
simpler and easier to implement. Moreover, we determine the constant factors in
the algorithm, which would be suitable for determining the complexity in terms
of gate counts for specific applications.


  The purpose of this paper is to understand generic behavior of constraint
functions in optimization problems relying on singularity theory of smooth
mappings. To this end, we will focus on the subgroup $\mathcal{K}[G]$ of the
Mather's group $\mathcal{K}$, whose action to constraint map-germs preserves
the corresponding feasible set-germs (i.e.~the set consisting of points
satisfying the constraints). We will classify map-germs with small stratum
$\mathcal{K}[G]_e$-codimensions, and calculate the codimensions of the
$\mathcal{K}[G]$-orbits of jets represented by germs in the classification
lists and those of the complements of these orbits. Applying these results and
a variant of the transversality theorem, we will show that families of
constraint mappings whose germ at any point in the corresponding feasible set
is $\mathcal{K}[G]$-equivalent to one of the normal forms in the classification
list compose a residual set in the entire space of constraint mappings with at
most $4$-parameters. These results enable us to quantify genericity of given
constraint mappings, and thus evaluate to what extent known test suites are
generic.


  The Fermi GBM catalog provides a large database with many measured variables
that can be used to explore and verify gamma-ray burst classification results.
We have used Principal Component Analysis and statistical clustering techniques
to look for clustering in a sample of 801 gamma-ray bursts described by sixteen
classification variables. The analysis recovers what appears to be the Short
class and two long-duration classes that differ from one another via peak flux,
with negligible variations in fluence, duration and spectral hardness. Neither
class has properties entirely consistent with the Intermediate GRB class.
Spectral hardness has been a critical Intermediate class property. Rather than
providing spectral hardness, Fermi GBM provides a range of fitting variables
for four different spectral models; it is not intuitive how these variables can
be used to support or disprove previous GRB classification results.


  It is argued that the Universe reheating in bouncing cosmologies could be
explained via gravitational particle production, as due to a sudden phase
transition in the contracting regime. To this end, it is shown that
gravitational production of massive particles conformally coupled with gravity
in a matter-ekpyrotic bouncing Universe, where the sudden phase transition
occurs in the contracting regime, yields a reheating temperature which is in
good agreement with cosmological observations.


  Assuming some regularity of the dynamical zeta function, we establish an
explicit formula with an error term for the prime orbit counting function of a
suspended flow. We define the subclass of self-similar flows, for which we give
an extensive analysis of the error term in the corresponding prime orbit
theorem.


  We study self-similar solutions of a multi-phase Stefan problem for a heat
equation on the half-line $x>0$ with a constant initial data and with Dirichlet
or Neumann boundary conditions. In the case of Dirichlet boundary condition we
prove that a nonlinear algebraic system for determination of the free
boundaries is gradient one and the corresponding potential is an explicitly
written strictly convex and coercive function. Therefore, there exists a unique
minimum point of the potential, coordinates of this point determine free
boundaries and provide the desired solution. This result is also extended to
the case of infinitely many phase transitions. In the case of Neumann boundary
condition we demonstrate that the problem may have solutions with different
numbers (called types) of phase transitions. For each fixed type $n$ the system
for determination of the free boundaries is again gradient and the
corresponding potential is proved to be strictly convex and coercive, but in
some wider non-physical domain. On the base of these properties it is proved
that there exists a unique solution of Stefan-Neumann problem, and we also
provide precise conditions to specify the type of the solution. Bibliography$:$
$5$ titles.


  Microarray gene expression data-based tumor classification is an active and
challenging issue. In this paper, an integrated tumor classification framework
is presented, which aims to exploit information in existing available samples,
and focuses on the small sample problem and unbalanced classification problem.
Firstly, an inverse space sparse representation based classification (ISSRC)
model is proposed by considering the characteristics of gene-based tumor data,
such as sparsity and a small number of training samples. A decision information
factors (DIF)-based gene selection method is constructed to enhance the
representation ability of the ISSRC. It is worth noting that the DIF is
established from reducing clinical misdiagnosis rate and dimension of small
sample data. For further improving the representation ability and
classification stability of the ISSRC, feature learning is conducted on the
selected gene subset. The feature learning method is constructed by
complementing the advantages of non-negative matrix factorization (NMF) and
deep learning. Without confusion, the ISSRC combined with gene selection and
feature learning is called the integrated ISSRC, whose stability, optimization
and the corresponding convergence are analyzed. Extensive experiments on six
public microarray gene expression datasets show the integrated ISSRC-based
tumor classification framework is superior to classical and state-of-the-art
methods. There are significant improvements in classification accuracy,
specificity and sensitivity, whether there is a tumor in the early diagnosis,
what kind of tumor, or whether metastasis occurs after tumor surgery.


  We compute and study two determinantal representations of the discriminant of
a cubic quaternary form. The first representation is the Chow form of the
$2$-uple embedding of $\mathbb{P}^3$ and is computed as the Pfaffian of the
Chow form of a rank 2 Ulrich bundle on this Veronese variety. We then consider
the determinantal representation described by Nanson. We investigate the
geometric nature of cubic surfaces whose discriminant matrices satisfy certain
rank conditions. As a special case of interest, we use certain minors of this
matrix to suggest equations vanishing on the locus of $k$-nodal cubic surfaces.


  We study irreversible A-B reaction kinetics at a fixed interface separating
two immiscible bulk phases, A and B. We consider general dynamical exponent
$z$, where $x_t\sim t^{1/z}$ is the rms diffusion distance after time $t$. At
short times the number of reactions per unit area, $R_t$, is {\em 2nd order} in
the far-field reactant densities $n_A^{\infty},n_B^{\infty}$. For spatial
dimensions $d$ above a critical value $d_c=z-1$, simple mean field (MF)
kinetics pertain, $R_t\sim Q_b t n_A^{\infty} n_B^{\infty}$ where $Q_b$ is the
local reactivity. For low dimensions $d<d_c$, this MF regime is followed by 2nd
order diffusion controlled (DC) kinetics, $R_t \approx x_t^{d+1} n_A^{\infty}
n_B^{\infty}$, provided $Q_b > Q_b^* \sim (n_B^{\infty})^{[z-(d+1)]/d}$.
Logarithmic corrections arise in marginal cases. At long times, a cross-over to
{\em 1st order} DC kinetics occurs: $R_t \approx x_t n_A^{\infty}$. A density
depletion hole grows on the more dilute A side. In the symmetric case
($n_A^{\infty}=n_B^{\infty}$), when $d<d_c$ the long time decay of the
interfacial reactant density, $n_A^s$, is determined by fluctuations in the
initial reactant distribution, giving $n_A^s \sim t^{-d/(2z)}$.
Correspondingly, A-rich and B-rich regions develop at the interface analogously
to the segregation effects established by other authors for the bulk reaction
$A+B\to\emptyset$. For $d>d_c$ fluctuations are unimportant: local mean field
theory applies at the interface (joint density distribution approximating the
product of A and B densities) and $n_A^s \sim t^{(1-z)/(2z)}$. We apply our
results to simple molecules (Fickian diffusion, $z=2$) and to several models of
short-time polymer diffusion ($z>2$).


  Theories beyond the standard model often predict the existence of an
additional neutral boson, the $Z^{\prime}$. Using data collected by the Belle
II experiment during 2018 at the SuperKEKB collider, we perform the first
searches for the invisible decay of a $Z^{\prime}$ in the process $e^+ e^- \to
\mu^+ \mu^- Z^{\prime}$ and of a lepton-flavor-violating $Z^{\prime}$ in $e^+
e^- \to e^{\pm} \mu^{\mp} Z^{\prime}$. We do not find any excess of events and
set 90\% credibility level upper limits on the cross sections of these
processes. We translate the former, in the framework of an $L_{\mu}-L_{\tau}$
theory, into upper limits on the $Z^{\prime}$ coupling constant at the level of
$5 \times 10^{-2}$ -- $1$ $M_{Z^\prime}\leq 6$ GeV/$c^2$.


  Network slicing is a key feature in 5G/NG cellular networks that creates
customized slices for different service types with various quality-of-service
(QoS) requirements, which can achieve service differentiation and guarantee
service-level agreement (SLA) for each service type. In Wi-Fi networks, there
is limited prior work on slicing, and a potential solution is based on a
multi-tenant architecture on a single access point (AP) that dedicates
different channels to different slices. In this paper, we define a flexible,
constrained learning framework to enable slicing in Wi-Fi networks subject to
QoS requirements. We specifically propose an unsupervised learning-based
network slicing method that leverages a state-augmented primal-dual algorithm,
where a neural network policy is trained offline to optimize a Lagrangian
function and the dual variable dynamics are updated online in the execution
phase. We show that state augmentation is crucial for generating slicing
decisions that meet the ergodic QoS requirements.


  We consider the quantum evolution of a fermion-hole pair in a d-dimensional
gas of non-interacting fermions in the presence of random phase scattering.
This system is mapped onto an effective Ising model, which enables us to show
rigorously that the probability of recombining the fermion and the hole decays
exponentially with the distance of their initial spatial separation. In the
absence of random phase scattering the recombination probability decays like a
power law, which is reflected by an infinite mean square displacement. The
effective Ising model is studied within a saddle point approximation and yields
a finite mean square displacement that depends on the evolution time and on the
spectral properties of the deterministic part of the evolution operator.


  The extension to millimeter-wave (mmWave) spectrum of communication frequency
band makes it easy to implement a joint radar and communication system using
single hardware. In this paper, we propose radar imaging based on the IEEE
802.11ad waveform for a vehicular setting. The necessary parameters to be
estimated for inverse synthetic aperture radar (ISAR) imaging are sampled
version of round-trip delay, Doppler shift, and vehicular velocity. The delay
is estimated using the correlation property of Golay complementary sequences
embedded on the IEEE 802.11ad preamble. The Doppler shift is first obtained
from least square estimation using radar return signals and refined by
correcting the phase uncertainty of Doppler shift by phase rotation. The
vehicular velocity is determined from the estimated Doppler shifts and an
equation of motion. Finally, an ISAR image is formed with the acquired
parameters. Simulation results show that it is possible to obtain recognizable
ISAR image from a point scatterer model of a realistic vehicular setting.


  Using standard techniques in string/D-brane scattering amplitude
computations, we evaluate the scattering of open strings off D-particles in
brane world scenarios. The D-particles are viewed as D3 branes wrapped up
around three cycles, and their embedding in brane worlds constitutes a case of
intersecting branes, among which strings are stretched, representing various
types of excitations of the Standard Model (SM) particles in the low-energy
limit. Our analysis, reveals interesting and novel selection rules for the
resulting causal time delays, proportional to the energy of the incident matter
state, from the processes of splitting, capture and re-emission of the latter
by the D-particles. In particular, we show that there are relatively large time
delays only for excitations that belong to the Cartan subalgebra of the SM
gauge group, which notably includes photons. We discuss the possible relevance
of these results to the models of space time foam predicting a non-trivial
vacuum refractive index for photons and the associated cosmic \gamma-ray
phenomenology. In particular, we demonstrate how low-string-scale models can be
falsified already in this context, by current astrophysical observations of
cosmic photons, and how such observations serve as a way to discriminate low-
from high-string scale models.


  We compare localization properties of one-dimensional Frenkel excitons with
Gaussian and Lorentzian uncorrelated diagonal disorder. We focus on the states
of the Lifshits tail, which dominate the optical response and low-temperature
energy transport in molecular J-aggregates. The absence of exchange narrowing
in chains with Lorentzian disorder is shown to manifest itself in the disorder
scaling of the localization length distribution. Also, we show that the local
exciton level structure of the Lifshits tail differs substantially for these
two types of disorder: In addition to the singlets and doublets of localized
states near the bare band edge, strongly resembling those found for Gaussian
disorder, for Lorentzian disorder two other types of states are found in this
energy region as well, namely multiplets of three or four states localized on
the same chain segment and isolated states localized on short segments.
Finally, below the Lifshits tail, Lorentzian disorder induces strongly
localized exciton states, centered around low energy sites, with localization
properties that strongly depend on energy. For Gaussian disorder with a
magnitude that does not exceed the exciton bandwidth, the likelihood to find
such very deep states is exponentially small.


  Structure-preserving methods can be derived for the Vlasov-Maxwell system
from a discretisation of the Poisson bracket with compatible finite-elements
for the fields and a particle representation of the distribution function.
These geometric electromagnetic particle-in-cell (GEMPIC) discretisations
feature excellent conservation properties and long-time numerical stability.
This paper extends the GEMPIC formulation in curvilinear coordinates to
realistic boundary conditions. We build a de Rham sequence based on spline
functions with clamped boundaries and apply perfect conductor boundary
conditions for the fields and reflecting boundary conditions for the particles.
The spatial semi-discretisation forms a discrete Poisson system. Time
discretisation is either done by Hamiltonian splitting yielding a semi-explicit
Gauss conserving scheme or by a discrete gradient scheme applied to a Poisson
splitting yielding a semi-implicit energy-conserving scheme. Our system
requires the inversion of the spline finite element mass matrices, which we
precondition with the combination of a Jacobi preconditioner and the spectrum
of the mass matrices on a periodic tensor product grid.


  Site-specific transcription factors (TFs) bind to their target sites on the
DNA, where they regulate the rate at which genes are transcribed. Bacterial TFs
undergo facilitated diffusion (a combination of 3D diffusion around and 1D
random walk on the DNA) when searching for their target sites. Using computer
simulations of this search process, we show that the organisation of the
binding sites, in conjunction with TF copy number and binding site affinity,
plays an important role in determining not only the steady state of promoter
occupancy, but also the order at which TFs bind. These effects can be captured
by facilitated diffusion-based models, but not by standard thermodynamics. We
show that the spacing of binding sites encodes complex logic, which can be
derived from combinations of three basic building blocks: switches, barriers
and clusters, whose response alone and in higher orders of organisation we
characterise in detail. Effective promoter organizations are commonly found in
the E. coli genome and are highly conserved between strains. This will allow
studies of gene regulation at a previously unprecedented level of detail, where
our framework can create testable hypothesis of promoter logic.


  We elaborate the Chern-Simons field theoretic method to obtain colored HOMFLY
invariants of knots and links. Using multiplicity-free quantum 6j-symbols for
U_q(sl_N), we present explicit evaluations of the HOMFLY invariants colored by
symmetric representations for a variety of knots, two-component links and
three-component links.


  We present a general theory to describe two-photon interference, including a
formal description of few photon intereference in terms of single-photon
amplitudes. With this formalism, it is possible to describe both frequency
entangled and separable two-photon interference in terms of single-photon wave
functions. Using this description, we address issues related to the physical
interpretation of two-photon interference experiments. We include a discussion
on how few-photon interference can be interpreted as a bosonic exchange effect,
and how this relates to traditional exchange effects with fermions.


  The generative modeling of data on manifold is an important task, for which
diffusion models in flat spaces typically need nontrivial adaptations. This
article demonstrates how a technique called `trivialization' can transfer the
effectiveness of diffusion models in Euclidean spaces to Lie groups. In
particular, an auxiliary momentum variable was algorithmically introduced to
help transport the position variable between data distribution and a fixed,
easy-to-sample distribution. Normally, this would incur further difficulty for
manifold data because momentum lives in a space that changes with the position.
However, our trivialization technique creates to a new momentum variable that
stays in a simple $\textbf{fixed vector space}$. This design, together with a
manifold preserving integrator, simplifies implementation and avoids
inaccuracies created by approximations such as projections to tangent space and
manifold, which were typically used in prior work, hence facilitating
generation with high-fidelity and efficiency. The resulting method achieves
state-of-the-art performance on protein and RNA torsion angle generation and
sophisticated torus datasets. We also, arguably for the first time, tackle the
generation of data on high-dimensional Special Orthogonal and Unitary groups,
the latter essential for quantum problems.


  The growth of Bi films deposited on both A and B faces of InAs(111) has been
investigated by low-energy electron diffraction, scanning tunneling microscopy,
and photoelectron spectroscopy using synchrotron radiation. The changes upon Bi
deposition of the In 4d and Bi 5d5/2 photoelectron signals allow to get a
comprehensive picture of the Bi/InAs(1 1 1) interface. From the initial stages
the Bi growth on the A face (In-terminated InAs) is epitaxial, contrary to that
on the B face (As- terminated InAs) that proceeds via the formation of islands.
Angle-resolved photoelectron spectra show that the electronic structure of a
$\approx 10$~BL deposit on the A face is identical to that of bulk Bi, while
more than $\approx 30$ BL are needed for the B face. Both bulk and surface
states are well accounted for by fully relativistic ab initio spin-resolved
photoemission calculations.


  We propose a search strategy using the LHC as a photon collider to open
sensitivity to scalar lepton (slepton $\tilde{\ell}$) production with masses
around 15 to 60 GeV above that of neutralino dark matter $\tilde{\chi}^0_1$.
This region is favored by relic abundance and muon $(g-2)_\mu$ arguments.
However, conventional searches are hindered by the irreducible diboson
background. We overcome this obstruction by measuring initial state kinematics
and the missing momentum four-vector in proton-tagged ultraperipheral
collisions using forward detectors. We demonstrate sensitivity beyond LEP for
slepton masses of up to 220 GeV for $ 15 \lesssim \Delta m(\tilde{\ell},
\tilde{\chi}^0_1) \lesssim 60$ GeV with 100 fb$^{-1}$ of 13 TeV proton
collisions. We encourage the LHC collaborations to open this forward frontier
for discovering new physics.


  We compare entanglement with quantum nonlocality employing a geometric
structure of the state space of bipartite qudits. Central object is a regular
simplex spanned by generalized Bell states. The
Collins-Gisin-Linden-Massar-Popescu-Bell inequality is used to reveal states of
this set that cannot be described by local-realistic theories. Optimal
measurement settings necessary to ascertain nonlocality are determined by means
of a recently proposed parameterization of the unitary group U(d) combined with
robust numerical methods. The main results of this paper are descriptive
geometric illustrations of the state space that emphasize the difference
between entanglement and quantum nonlocality. Namely, it is found that the
shape of the boundaries of separability and Bell inequality violation are
essentially different. Moreover, it is shown that also for mixtures of states
sharing the same amount of entanglement, Bell inequality violations and
entanglement measures are non-monotonically related.


  The most detailed existing proposal for the structure of spacetime
singularities originates in the work of Belinskii, Khalatnikov and Lifshitz. We
show rigorously the correctness of this proposal in the case of analytic
solutions of the Einstein equations coupled to a scalar field or stiff fluid.
More specifically, we prove the existence of a family of spacetimes depending
on the same number of free functions as the general solution which have the
asymptotics suggested by the Belinskii-Khalatnikov-Lifshitz proposal near their
singularities. In these spacetimes a neighbourhood of the singularity can be
covered by a Gaussian coordinate system in which the singularity is
simultaneous and the evolution at different spatial points decouples.


  Feature models are commonly used to specify the valid configurations of a
product line. In industry, feature models are often complex due to a large
number of features and constraints. Thus, a multitude of automated analyses
have been proposed. Many of those rely on computing the number of valid
configurations which typically depends on solving a #SAT problem, a
computationally expensive operation. Further, most counting-based analyses
require numerous #SAT computations on the same feature model. In particular,
many analyses depend on multiple computations for evaluating the number of
valid configurations that include certain features or conform to partial
configurations. Instead of using expensive repetitive computations on highly
similar formulas, we aim to improve the performance by reusing knowledge
between these computations. In this work, we are the first to propose reusing
d-DNNFs for performing efficient repetitive queries on features and partial
configurations. Our empirical evaluation shows that our approach is up-to 8,300
times faster (99.99\% CPU-time saved) than the state of the art of repetitively
invoking #SAT solvers. Applying our tool ddnnife reduces runtimes from days to
minutes compared to using #SAT solvers.


  The calculation of bound state properties using renormalization group
techniques to compute the corresponding Regge trajectories is presented. In
particular, we investigate the bound states in different charge sectors of a
scalar theory with interaction (phi^dagger phi chi). The resulting bound state
spectrum is surprisingly rich. Where possible we compare and contrast with
known results of the Bethe-Salpeter equation in the ladder approximation and,
in the non-relativistic limit, with the corresponding Schr"odinger equation.


  In this paper we study the scattering problem for the initial value problem
of the generalized Korteweg-de Vries (gKdV) equation. The purpose of this paper
is to achieve two primary goals. Firstly, we show small data scattering for
(gKdV) in the weighted Sobolev space, ensuring the initial and the asymptotic
states belong to the same class. Secondly, we introduce two equivalent
characterizations of scattering in the weighted Sobolev space. In particular,
this involves the so-called conditional scattering in the weighted Sobolev
space. A key ingredient is incorporation of the scattering criterion for (gKdV)
in the Fourier-Lebesgue space by the authors into the the scattering problem in
the weighted Sobolev space.


  The 4-loop $C_F^3 T_F n_l$ and 5-loop $C_F^4 T_F n_l$ terms in the HQET field
anomalous dimension $\gamma_h$ are calculated analytically (the 4-loop one
agrees with the recent numerical result [arXiv:1801.08292]). The 4-loop $C_F^3
T_F n_l$ and 5-loop $C_F^4 T_F n_l$ terms in the cusp anomalous dimension
$\Gamma(\varphi)$ are calculated analytically, exactly in $\varphi$ (the
$\varphi\to\infty$ asymptotics of the 4-loop one agrees with the recent
numerical result [arXiv:1707.08315]). Combining these results with the recent
4-loop $d_{FF} n_l$ contributions to $\gamma_h$ and to the small-$\varphi$
expansion of $\Gamma(\varphi)$ up to $\varphi^4$ [arXiv:1708.01221] (recently
extended to $\varphi^6$ [arXiv:1807.05145]) we now have the complete analytical
4-loop result for the Bloch--Nordsieck field anomalous dimension in QED, and
the small-$\varphi$ expansion of the 4-loop QED cusp anomalous dimension up to
$\varphi^6$.


  In this paper, we prove a discrete Rubio de Francia extrapolation theorem via
factorization of discrete Muckenhoupt weights and discrete iterated Rubio de
Francia algorithm and its duality.


  Starting from a second-quantized Hamiltonian of many-particle systems, we
derive the Gross-Pitaevskii (GP) equation in momentum space, which is suitable
for studying the multi-wave mixing processes of coherent matter waves. The
coupling equations are then applied to study ideal four-wave mixing (4WM), in
which only four waves with definite wavevectors are involved. Some interesting
problems of 4WM, such as the phase-matching condition, the collapse and revival
behaviour, the effects of relative phase difference, and the conversion
efficiency are discussed in detail. We also show that the main characters of
recent 4WM experiment [Deng et al, Nature 398, 218 (1999)] can be undersood in
the present simplified model.


  We consider the static domain wall braneworld scenario constructed from the
Palatini formalism $f(\mathcal{R})$ theory. We check the self-consistency under
scalar perturbations. By using the scalar-tensor formalism we avoid dealing
with the higher-order equations. We develop the techniques to deal with the
coupled system. We show that under some conditions, the scalar perturbation
simply oscillates with time, which guarantees the stability. We also discuss
the localization condition of the scalar mode by analyzing the effective
potential and the fifth dimensional profile of the scalar mode. We apply these
results to an explicit example, and show that only some of the solutions allow
for stable scalar perturbations. These stable solutions also give
nonlocalizable massless mode. This is important for reproducing a viable
four-dimensional gravity.


  Let $H_0$ and $H$ be self-adjoint operators in a Hilbert space. In the
scattering theory framework, we describe the essential spectrum of the
difference $\varphi(H)-\varphi(H_0)$ for piecewise continuous functions
$\varphi$. This description involves the scattering matrix for the pair $H$,
$H_0$.


  We prove that the free energy of any spherical mixed $p$-spin model converges
as the dimension $N$ tends to infinity. While the convergence is a consequence
of the Parisi formula, the proof we give is independent of the formula and uses
the well-known Guerra-Toninelli interpolation method. The latter was invented
for models with Ising spins to prove that the free energy is super-additive and
therefore (normalized by $N$) converges. In the spherical case, however, the
configuration space is not a product space and the interpolation cannot be
applied directly. We first relate the free energy on the sphere of dimension
$N+M$ to a free energy defined on the product of spheres in dimensions $N$ and
$M$ to which we then apply the interpolation method. This yields an approximate
super-additivity which is sufficient to prove the convergence.


  The quantum approximate optimisation algorithm (QAOA) is at the core of many
scenarios that aim to combine the power of quantum computers and classical
high-performance computing appliances for combinatorial optimisation. Several
obstacles challenge concrete benefits now and in the foreseeable future:
Imperfections quickly degrade algorithmic performance below practical utility;
overheads arising from alternating between classical and quantum primitives can
counter any advantage; and the choice of parameters or algorithmic variant can
substantially influence runtime and result quality. Selecting the optimal
combination is a non-trivial issue, as it not only depends on user
requirements, but also on details of the hardware and software stack.
Appropriate automation can lift the burden of choosing optimal combinations for
end-users: They should not be required to understand technicalities like
differences between QAOA variants, required number of QAOA layers, or necessary
measurement samples. Yet, they should receive best-possible satisfaction of
their non-functional requirements, be it performance or other. We determine
factors that affect solution quality and temporal behaviour of four QAOA
variants using comprehensive density-matrix-based simulations targeting three
widely studied optimisation problems. Our simulations consider ideal quantum
computation, and a continuum of scenarios troubled by realistic imperfections.
Our quantitative results, accompanied by a comprehensive reproduction package,
show strong differences between QAOA variants that can be pinpointed to narrow
and specific effects. We identify influential co-variables and relevant
non-functional quality goals that, we argue, mark the relevant ingredients for
designing appropriate software engineering abstraction mechanisms and automated
tool-chains for devising quantum solutions from high-level problem
specifications.


  With the advance of Web Services technologies and the emergence of Web
Services into the information space, tremendous opportunities for empowering
users and organizations appear in various application domains including
electronic commerce, travel, intelligence information gathering and analysis,
health care, digital government, etc. In fact, Web services appear to be s
solution for integrating distributed, autonomous and heterogeneous information
sources. However, as Web services evolve in a dynamic environment which is the
Internet many changes can occur and affect them. A Web service is affected when
one or more of its associated information sources is affected by schema
changes. Changes can alter the information sources contents but also their
schemas which may render Web services partially or totally undefined. In this
paper, we propose a solution for integrating information sources into Web
services. Then we tackle the Web service synchronization problem by
substituting the affected information sources. Our work is illustrated with a
healthcare case study.


  Digital video-microscopy measurements are reported of both elastic
bandstructures and overdamped phonon decay times in two-dimensional colloidal
crystals. Both quantities together allow to determine the friction coefficients
along various high symmetry directions in q-space. These coefficients contain
valuable information on the hydrodynamic forces acting between the colloidal
particles. We find Stokes-like friction for phonons near the edge of the first
Brillouin zone and vanishing friction coefficients for long wavelength phonons.
The effect of this wavelength dependence in real-space is further investigated
by simulating a crystal with constant friction (Langevin simulation) and
comparing experimentally measured and simulated particle auto-correlation
functions.


  We investigate what kind of structural knowledge learned in neural network
encoders is transferable to processing natural language. We design artificial
languages with structural properties that mimic natural language, pretrain
encoders on the data, and see how much performance the encoder exhibits on
downstream tasks in natural language. Our experimental results show that
pretraining with an artificial language with a nesting dependency structure
provides some knowledge transferable to natural language. A follow-up probing
analysis indicates that its success in the transfer is related to the amount of
encoded contextual information and what is transferred is the knowledge of
position-aware context dependence of language. Our results provide insights
into how neural network encoders process human languages and the source of
cross-lingual transferability of recent multilingual language models.


  We study Severi type inequalities for big line bundles on irregular varieties
via cohomological rank functions. We show that these Severi type inequalities
on an irregular variety $X$ are related to some natural defined birational
invariants of a general fiber $F$ of the Albanese morphism of $X$. As
applications, we provide a new lower bound of volumes of irregular threefolds,
a sharp lower bound of varieties of maximal Albanese dimension and of general
type, and show that the canonical model of such a variety with the minimal
volume should be a flat double covers of a principally polarized abelian
variety $(A, \Theta)$ branched over $D\in |2\Theta|$.


  Measurements of b-hadron masses are performed with the exclusive decay modes
B+ -> J/psi K+, B0 -> J/psi K*0, B0 -> J/psi K0S, B0_s -> J/psi phi and
Lambda_b -> J/psi Lambda using an integrated luminosity of 35 pb-1 collected in
pp collisions at a centre-of-mass energy of 7 TeV by the LHCb experiment. The
momentum scale is calibrated with J/psi -> mu+mu- decays and verified to be
known to a relative precision of 2 x 10^-4 using other two-body decays. The
results are more precise than previous measurements, particularly in the case
of the B0_s and Lambda_b masses.


  This paper studies how to use relation algebras, which are useful for
high-level specification and verification, for proving the correctness of
lower-level array-based implementations of algorithms. We give a simple
relation-algebraic semantics of read and write operations on associative
arrays. The array operations seamlessly integrate with assignments in
computation models supporting while-programs. As a result, relation algebras
can be used for verifying programs with associative arrays. We verify the
correctness of an array-based implementation of disjoint-set forests using the
union-by-rank strategy and find operations with path compression, path
splitting and path halving. All results are formally proved in Isabelle/HOL.
This paper is an extended version of [1].


  We consider the sequential Bayesian optimization problem with bandit
feedback, adopting a formulation that allows for the reward function to vary
with time. We model the reward function using a Gaussian process whose
evolution obeys a simple Markov model. We introduce two natural extensions of
the classical Gaussian process upper confidence bound (GP-UCB) algorithm. The
first, R-GP-UCB, resets GP-UCB at regular intervals. The second, TV-GP-UCB,
instead forgets about old data in a smooth fashion. Our main contribution
comprises of novel regret bounds for these algorithms, providing an explicit
characterization of the trade-off between the time horizon and the rate at
which the function varies. We illustrate the performance of the algorithms on
both synthetic and real data, and we find the gradual forgetting of TV-GP-UCB
to perform favorably compared to the sharp resetting of R-GP-UCB. Moreover,
both algorithms significantly outperform classical GP-UCB, since it treats
stale and fresh data equally.


  Inclusive charged pion, kaon, proton, and deuteron production in 14.6 GeV/c
proton-nucleus collisions measured by BNL experiment E802 is compared with
results from the GEANT3, GEANT4, and FLUKA simulation packages. The FLUKA
package is found to have the best overall agreement.


  The local explanation provides heatmaps on images to explain how
Convolutional Neural Networks (CNNs) derive their output. Due to its visual
straightforwardness, the method has been one of the most popular explainable AI
(XAI) methods for diagnosing CNNs. Through our formative study (S1), however,
we captured ML engineers' ambivalent perspective about the local explanation as
a valuable and indispensable envision in building CNNs versus the process that
exhausts them due to the heuristic nature of detecting vulnerability. Moreover,
steering the CNNs based on the vulnerability learned from the diagnosis seemed
highly challenging. To mitigate the gap, we designed DeepFuse, the first
interactive design that realizes the direct feedback loop between a user and
CNNs in diagnosing and revising CNN's vulnerability using local explanations.
DeepFuse helps CNN engineers to systemically search "unreasonable" local
explanations and annotate the new boundaries for those identified as
unreasonable in a labor-efficient manner. Next, it steers the model based on
the given annotation such that the model doesn't introduce similar mistakes. We
conducted a two-day study (S2) with 12 experienced CNN engineers. Using
DeepFuse, participants made a more accurate and "reasonable" model than the
current state-of-the-art. Also, participants found the way DeepFuse guides
case-based reasoning can practically improve their current practice. We provide
implications for design that explain how future HCI-driven design can move our
practice forward to make XAI-driven insights more actionable.


  Optimum suppression of higher order modes in single-ring hollow-core photonic
crystal fibers (SR-PCFs) occurs when the capillary-to-core diameter ratio d/D =
0.68. Here we report that, in SR-PCFs with sub-optimal values of d/D,
higher-order mode suppression can be recovered by spinning the preform during
fiber drawing, thus introducing a continuous helical twist. This geometrically
increases the effective axial propagation constant (initially too low) of the
LP01-like modes of the capillaries surrounding the core, enabling robust
single-mode operation. The effect is explored by means of extensive numerical
modeling, an analytical model and a series of experiments. Prism-assisted
side-coupling is used to investigate the losses and near-field patterns of
individual fiber modes in both the straight and twisted cases. More than 12
dB/m improvement in higher order mode suppression is achieved experimentally in
a twisted PCF. The measurements also show that the higher order mode profiles
change with twist rate, as predicted by numerical simulations. Helical twisting
offers an additional tool for achieving effectively endlessly single-mode
operation in hollow-core SR-PCFs.


  We discuss corrections to the Yukawa matrices of the Standard Model (SM)
fermions in intersecting D-brane models due to four-point interactions.
Recently, an intersecting D-brane model has been found where it is possible to
obtain correct masses and mixings for all quarks as well as the tau lepton.
However, the masses for the first two charged leptons come close to the right
values but are not quite correct. Since the electron and muon are quite light,
it is likely that there are additional corrections to their masses which cannot
be neglected. With this in mind, we consider contributions to the SM fermion
mass matrices from four-point interactions. In an explicit model, we show that
it is indeed possible to obtain the SM fermion masses and mixings which are a
better match to those resulting from experimental data extrapolated at the
unification scale when these corrections are included. These corrections may
have broader application to other models.


  A slow decaying Kolmogorov n-width of the solution manifold of a parametric
partial differential equation precludes the realization of efficient linear
projection-based reduced-order models. This is due to the high dimensionality
of the reduced space needed to approximate with sufficient accuracy the
solution manifold. To solve this problem, neural networks, in the form of
different architectures, have been employed to build accurate nonlinear
regressions of the solution manifolds. However, the majority of the
implementations are non-intrusive black-box surrogate models, and only a part
of them perform dimension reduction from the number of degrees of freedom of
the discretized parametric models to a latent dimension. We present a new
intrusive and explicable methodology for reduced-order modelling that employs
neural networks for solution manifold approximation but that does not discard
the physical and numerical models underneath in the predictive/online stage. We
will focus on autoencoders used to compress further the dimensionality of
linear approximants of solution manifolds, achieving in the end a nonlinear
dimension reduction. After having obtained an accurate nonlinear approximant,
we seek for the solutions on the latent manifold with the residual-based
nonlinear least-squares Petrov-Galerkin method, opportunely hyper-reduced in
order to be independent from the number of degrees of freedom. New adaptive
hyper-reduction strategies are developed along with the employment of local
nonlinear approximants. We test our methodology on two nonlinear time-dependent
parametric benchmarks involving a supersonic flow past a NACA airfoil with
changing Mach number and an incompressible turbulent flow around the Ahmed body
with changing slant angle.


  A full accounting of biological robustness remains elusive; both in terms of
the mechanisms by which robustness is achieved and the forces that have caused
robustness to grow over evolutionary time. Although its importance to topics
such as ecosystem services and resilience is well recognized, the broader
relationship between robustness and evolution is only starting to be fully
appreciated. A renewed interest in this relationship has been prompted by
evidence that mutational robustness can play a positive role in the discovery
of future adaptive innovations (evolvability) and evidence of an intimate
relationship between robustness and complexity in biology.
  This paper offers a new perspective on the mechanics of evolution and the
origins of complexity, robustness, and evolvability. Here we explore the
hypothesis that degeneracy, a partial overlap in the functioning of
multi-functional components, plays a central role in the evolution and
robustness of complex forms. In support of this hypothesis, we present evidence
that degeneracy is a fundamental source of robustness, it is intimately tied to
multi-scaled complexity, and it establishes conditions that are necessary for
system evolvability.


  We discuss the compressibility in the almost staggered fermionic Harper model
with repulsive interactions in the vicinity of half-filling. It has been shown
by Kraus et al. [33] that for spinless electrons and nearest neighbors
electron-electron interactions the compressibility in the central band is
enhanced by repulsive interactions. Here we would like to investigate the
sensitivity of this conclusion to the spin degree of freedom and longer range
interactions. We use the Hartree-Fock (HF) approximation, as well as density
matrix renormalization group (DMRG) calculation to evaluate the
compressibility. In the almost staggered Harper model, the central energy band
is essentially flat and separated from the other bands by a large gap and
therefore, the HF approximation is rather accurate.In both cases the
compressibility of the system is enhanced compare to the non-interacting case,
although the enhancement is weaker due to the inclusion of Hubbard and longer
ranged interactions.


  Using a three-level algorithm we perform a high-precision lattice computation
of the static force up to 1fm in the 2+1 dimensional SU(5) gauge theory.
Discretization errors and the continuum limit are discussed in detail. By
comparison with existing SU(2) and SU(3) data it is found that \sigma
r_0^2=1.65-\pi/24 holds at an accuracy of 1% for all N>=2, where r_0 is the
Sommer reference scale. The effective central charge c_{eff}(r) is obtained and
an intermediate distance r_s is defined via the property c_{eff}(r_s)=\pi/24.
It separates in a natural way the short-distance regime governed by
perturbation theory from the long-distance regime described by an effective
string theory. The ratio r_s/r_0 decreases significantly from SU(2) to SU(3) to
SU(5), where r_s < r_0. We give a preliminary estimate of its value in the
large-N limit. The static force in the smallest representation of N-ality 2,
which tends to the k=2 string tension as r->oo, is also computed up to 0.7fm.
The deviation from Casimir scaling is positive and grows from 0.1% to 1% in
that range.


  In this paper, we define noiseless privacy, as a non-stochastic rival to
differential privacy, requiring that the outputs of a mechanism (i.e., function
composition of a privacy-preserving mapping and a query) can attain only a few
values while varying the data of an individual (the logarithm of the number of
the distinct values is bounded by the privacy budget). Therefore, the output of
the mechanism is not fully informative of the data of the individuals in the
dataset. We prove several guarantees for noiselessly-private mechanisms. The
information content of the output about the data of an individual, even if an
adversary knows all the other entries of the private dataset, is bounded by the
privacy budget. The zero-error capacity of memory-less channels using
noiselessly private mechanisms for transmission is upper bounded by the privacy
budget. The performance of a non-stochastic hypothesis-testing adversary is
bounded again by the privacy budget. Finally, assuming that an adversary has
access to a stochastic prior on the dataset, we prove that the estimation error
of the adversary for individual entries of the dataset is lower bounded by a
decreasing function of the privacy budget. In this case, we also show that the
maximal information leakage is bounded by the privacy budget. In addition to
privacy guarantees, we prove that noiselessly-private mechanisms admit
composition theorem and post-processing does not weaken their privacy
guarantees. We prove that quantization operators can ensure noiseless privacy
if the number of quantization levels is appropriately selected based on the
sensitivity of the query and the privacy budget. Finally, we illustrate the
privacy merits of noiseless privacy using multiple datasets in energy and
transport.


  A three-step laser excitation scheme is used to make absolute frequency
measurements of highly excited nF7/2 Rydberg states in 85Rb for principal
quantum numbers n=33-100. This work demonstrates the first absolute frequency
measurements of rubidium Rydberg levels using a purely optical detection
scheme. The Rydberg states are excited in a heated Rb vapour cell and Doppler
free signals are detected via purely optical means. All of the frequency
measurements are made using a wavemeter which is calibrated against a GPS
disciplined self-referenced optical frequency comb. We find that the measured
levels have a very high frequency stability, and are especially robust to
electric fields. The apparatus has allowed measurements of the states to an
accuracy of 8.0MHz. The new measurements are analysed by extracting the
modified Rydberg-Ritz series parameters.


  Commonsense knowledge is essential for many AI applications, including those
in natural language processing, visual processing, and planning. Consequently,
many sources that include commonsense knowledge have been designed and
constructed over the past decades. Recently, the focus has been on large
text-based sources, which facilitate easier integration with neural (language)
models and application to textual tasks, typically at the expense of the
semantics of the sources and their harmonization. Efforts to consolidate
commonsense knowledge have yielded partial success, with no clear path towards
a comprehensive solution. We aim to organize these sources around a common set
of dimensions of commonsense knowledge. We survey a wide range of popular
commonsense sources with a special focus on their relations. We consolidate
these relations into 13 knowledge dimensions. This consolidation allows us to
unify the separate sources and to compute indications of their coverage,
overlap, and gaps with respect to the knowledge dimensions. Moreover, we
analyze the impact of each dimension on downstream reasoning tasks that require
commonsense knowledge, observing that the temporal and desire/goal dimensions
are very beneficial for reasoning on current downstream tasks, while
distinctness and lexical knowledge have little impact. These results reveal
preferences for some dimensions in current evaluation, and potential neglect of
others.


  We adopt the covariant anomaly cancellation method as well as the effective
action approach to obtain the Hawking radiation from the Reissner-Nordstr\"{o}m
blackhole with a global monopole falling in the class of the most general
spherically symmetric charged blackhole $(\sqrt{-g}\neq1)$, using only
covariant boundary condition at the event horizon.


  Consider a regular $d$-dimensional metric tree $\Gamma$ with root $o$. Define
the Schroedinger operator $-\Delta - V$, where $V$ is a non-negative, symmetric
potential, on $\Gamma$, with Neumann boundary conditions at $o$. Provided that
$V$ decays like $x^{-\gamma}$ at infinity, where $1 < \gamma \leq d \leq 2,
\gamma \neq 2$, we will determine the weak coupling behavior of the bottom of
the spectrum of $-\Delta - V$. In other words, we will describe the
asymptotical behavior of $\inf \sigma(-\Delta - \alpha V)$ as $\alpha \to 0+$


  With the continued growth in field-programmable gate array (FPGA) capacity
and their incorporation into new environments such as datacenters, we have
witnessed the introduction of a new class of reconfigurable acceleration
devices (RADs) that go beyond conventional FPGA architectures. These devices
combine a reconfigurable fabric with coarse-grained domain-specialized
accelerator blocks all connected via a high-performance packet-switched
network-on-chip (NoC) for efficient system-wide communication. However, we lack
the tools necessary to efficiently explore the huge design space for RADs,
study the complex interactions between their different components and evaluate
various combinations of design choices. In this work, we develop RAD-Sim, a
cycle-level architecture simulator that allows rapid application-driven
exploration of the design space of novel RADs. To showcase the capabilities of
RADSim, we map and simulate a state-of-the-art deep learning (DL) inference
overlay on a RAD instance incorporating an FPGA fabric and a complex of hard
matrix-vector multiplication engines, communicating over a system-wide NoC.
Through this example, we show how RAD-Sim can help architects quantify the
effect of changing specific architecture parameters on end-to-end application
performance.


  Galaxies exhibit a variety of non-axisymmetric structure (bars, spiral
structure, lopsided structure, etc.). These suggest the following general
problem: what are the possible stationary configurations of a two-dimensional
self-gravitating fluid other than an axisymmetric razor-thin disc? We address a
modest component of this problem: we seek non-axisymmetric razor-thin discs of
two-dimensional barotropic fluid that are stationary in an inertial frame. We
distinguish between `razor-thin' and `two-dimensional,' applying the latter
term to the equation of state. Furthermore we assume that our systems are
scale-free, which reduces the partial differential equations describing the
system to ordinary differential equations. We also allow for the presence of an
axisymmetric background potential. This simple and highly idealized problem
already exhibits a rich variety of solutions, the richest being for m=1
symmetry.


  Propagation of gamma-quanta in the single crystals, oriented in a region of
the coherent pair production is considered. The qualitative description of the
process is also discussed. The theory of gamma-quanta propagation in the
anisotropic medium is illustrated by the help of the particular calculations of
such a process in silicon single crystals.
  It is shown that the single crystals are sensitive to the initial circular
polarization of gamma-beam despite the fact that the cross section of
absorption is independent of it. The reason is that the normal electromagnetic
waves (an eigenfunctions of the problem) are elliptically polarized. The speeds
of absorption and motion of both the normal waves are different and as a result
the process of gamma-quanta propagation depend on the initial polarization
state. The calculated value of asymmetry is about 20% for 25 GeV gamma-quanta
propagating in 100 cm silicon single crystal.
  The obtained results are useful in creating of polarimeters for high energy
electrons and gamma-quanta.


  We combine density functional theory (DFT) with molecular dynamics
simulations based on an accurate atomistic force field to calculate the
pressure derivative of the melting temperature of magnesium oxide at ambient
pressure - a quantity for which a serious disagreement between theory and
experiment has existed for almost 15 years. We find reasonable agreement with
previous DFT results and with a very recent experimental determination of the
slope. We pay particular attention to areas of possible weakness in theoretical
calculations and conclude that the long-standing discrepancy with experiment
could only be explained by a dramatic failure of existing density functionals
or by flaws in the original experiment.


  Originally, tangles were invented as an abstract tool in mathematical graph
theory to prove the famous graph minor theorem. In this paper, we showcase the
practical potential of tangles in machine learning applications. Given a
collection of cuts of any dataset, tangles aggregate these cuts to point in the
direction of a dense structure. As a result, a cluster is softly characterized
by a set of consistent pointers. This highly flexible approach can solve
clustering problems in various setups, ranging from questionnaires over
community detection in graphs to clustering points in metric spaces. The output
of our proposed framework is hierarchical and induces the notion of a soft
dendrogram, which can help explore the cluster structure of a dataset. The
computational complexity of aggregating the cuts is linear in the number of
data points. Thus the bottleneck of the tangle approach is to generate the
cuts, for which simple and fast algorithms form a sufficient basis. In our
paper we construct the algorithmic framework for clustering with tangles, prove
theoretical guarantees in various settings, and provide extensive simulations
and use cases. Python code is available on github.


  We introduce $\textit{PCFTL (Probabilistic CounterFactual Temporal Logic)}$,
a new probabilistic temporal logic for the verification of Markov Decision
Processes (MDP). PCFTL is the first to include operators for causal reasoning,
allowing us to express interventional and counterfactual queries. Given a path
formula $\phi$, an interventional property is concerned with the satisfaction
probability of $\phi$ if we apply a particular change $I$ to the MDP (e.g.,
switching to a different policy); a counterfactual allows us to compute, given
an observed MDP path $\tau$, what the outcome of $\phi$ would have been had we
applied $I$ in the past. For its ability to reason about \textit{what-if}
scenarios involving different configurations of the MDP, our approach
represents a departure from existing probabilistic temporal logics that can
only reason about a fixed system configuration. From a syntactic viewpoint, we
introduce a generalized counterfactual operator that subsumes both
interventional and counterfactual probabilities as well as the traditional
probabilistic operator found in e.g., PCTL. From a semantics viewpoint, our
logic is interpreted over a structural causal model translation of the MDP,
which gives us a representation amenable to counterfactual reasoning. We
evaluate PCFTL in the context of safe reinforcement learning using a benchmark
of grid-world models.


  A central question in Quantum Computing is how matrices in $SU(2)$ can be
approximated by products over a small set of "generators". A topology will be
defined on $SU(2)$ so as to introduce the notion of a covering exponent
\cite{letter}, which compares the length of products required to covering
$SU(2)$ with $\varepsilon$ balls against the Haar measure of $\varepsilon$
balls. An efficient universal set over $PSU(2)$ will be constructed using the
Pauli matrices, using the metric of the covering exponent. Then, the
relationship between $SU(2)$ and $S^3$ will be manipulated to correlate angles
between points on $S^3$ and give a conjecture on the maximum of angles between
points on a lattice. It will be shown how this conjecture can be used to
compute the covering exponent, and how it can be generalized to universal sets
in $SU(2)$.


  Renormalization-Group (RG) improvement has been frequently applied to capture
the effect of quantum corrections on cosmological and black-hole spacetimes.
This work utilizes an algebraically complete set of curvature invariants to
establish that: On the one hand, RG improvement at the level of the metric is
coordinate-dependent. On the other hand, a newly proposed RG improvement at the
level of curvature invariants is coordinate-independent. Spherically-symmetric
and axially-symmetric black-hole spacetimes serve as physically relevant
examples.


  The charge transport properties of individual, metallic nitrogen doped,
single-walled carbon nanotubes are investigated. It is demonstrated that n-type
conduction can be achieved by nitrogen doping. Evidence was obtained by
appealing to electric-field effect measurements at ambient condition. The
observed temperature dependencies of the zero-bias conductance indicate a
disordered electron system with electric-dipole scattering, caused mainly by
the pyridine-type nitrogen atoms in the honeycomb lattice. These results
illustrate the possibility of creating all-metallic molecular devices, in which
the charge carrier type can be controlled.


  The synthetic dimension, a research topic of both fundamental significance
and practical applications, is attracting increasing attention in recent years.
In this paper, we propose a theoretical framework to construct arbitrary
synthetic dimensions, or N-boson synthetic lattices, using multiple bosons on
one-dimensional lattices. We show that a one-dimensional lattice hosting N
indistinguishable bosons can be mapped to a single boson on a N-dimensional
lattice with high symmetry. Band structure analyses on this N-dimensional
lattice can then be mathematically performed to predict the existence of exotic
eigenstates and the motion of N-boson wavepackets. As illustrative examples, we
demonstrate the edge states in two-boson Su-Schrieffer-Heeger synthetic
lattices without interactions, interface states in two-boson
Su-Schrieffer-Heeger synthetic lattices with interactions, and weakly-bound
triplon states in three-boson tight-binding synthetic lattices with
interactions. The interface states and weakly-bound triplon states have not
been thoroughly understood in previous literatures. Our proposed theoretical
framework hence provides a novel perspective to explore the multi-boson
dynamics on lattices with boson-boson interactions, and opens up a future
avenue in the fields of multi-boson manipulation in quantum engineering.


  In this paper, we discuss about monotone vector fields, which is a typical
extension to the theory of convex functions, by exploiting the tangent space
structure. This new approach to monotonicity in CAT(0) spaces stands in opposed
to the monotonicity defined earlier in CAT(0) spaces by Khatibzadeh and Ranjbar
[14] and Chaipunya and Kumam [8]. In particular, this new concept extends the
theory from both Hilbert spaces and Hadamard manifolds, while the known concept
barely has any obvious relationship to the theory in Hadamard manifolds. We
also study the corresponding resolvents and Yosida approximations of a given
monotone vector field and derive many of their important properties. Finally,
we prove a generation theorem by showing convergence of an exponential formula
applied to resolvents of a monotone vector field. Our findings improve several
known results in the literature including generation theorems of Jost [13,
Theorem 1.3.13], Mayer [19, Theorem 1.13], Stojkovic [22, Theorem 2.18], and
Bac\'ak [4, Theorem 1.5] for proper, convex, lower semicontinuous functions in
the context of complete CAT(0) spaces, and also by Iwamiya and Okochi [11,
Theorem 4.1] for monotone vector fields in the context of Hadamard manifolds.


  In this brief review, we present the results of the fractional differential
approach in cosmology in the context of the exact models of cosmological
accelerated expansion obtained by several authors to date. Most of these
studies are devoted to the problem of introducing fractional derivatives or
fractional integrals into the classical General Relativity (GR). There are
several observational and theoretical motivations to investigate the modified
or alternative theories of GR. Among other things, we cover General Relativity
modified by a phenomenological approach dealing with fractional calculus. At
the same time, a sufficiently large number of exact solutions of the
cosmological equations modified by this approach were obtained. Some of these
models may be especially relevant in the light of solving the problem of late
accelerated expansion of the universe. These studies are largely motivated by
rapid progress in the field of observational cosmology that now allows, for the
first time, precision tests of fundamental physics on the scale of the
observable Universe. The purpose of this review is to provide a reference tool
for researchers and students in cosmology and gravitational physics, as well as
a self-contained, comprehensive, and up-to-date introduction to the subject as
a whole.


  Let $X$ be a smooth irreducible complex algebraic variety of dimension $n$
and $L$ a very ample line bundle on $X$. Given a toric degeneration of $(X,L)$
satisfying some natural technical hypotheses, we construct a deformation
$\{J_s\}$ of the complex structure on $X$ and bases $\mathcal{B}_s$ of
$H^0(X,L, J_s)$ so that $J_0$ is the standard complex structure and, in the
limit as $s \to \infty$, the basis elements approach dirac-delta distributions
centered at Bohr-Sommerfeld fibers of a moment map associated to $X$ and its
toric degeneration. The theory of Newton-Okounkov bodies and its associated
toric degenerations shows that the technical hypotheses mentioned above hold in
some generality. Our results significantly generalize previous results in
geometric quantization which prove "independence of polarization" between
K\"ahler quantizations and real polarizations. As an example, in the case of
general flag varieties $X=G/B$ and for certain choices of $\lambda$, our result
geometrically constructs a continuous degeneration of the (dual) canonical
basis of $V_{\lambda}^*$ to a collection of dirac delta functions supported at
the Bohr-Sommerfeld fibres corresponding exactly to the lattice points of a
Littelmann-Berenstein-Zelevinsky string polytope
$\Delta_{\underline{w}_0}(\lambda) \cap \mathbb{Z}^{\dim(G/B)}$.


  With the aim of determining the statistical properties of relativistic
turbulence and unveiling novel and non-classical features, we present the
results of direct numerical simulations of driven turbulence in an
ultrarelativistic hot plasma using high-order numerical schemes. We study the
statistical properties of flows with average Mach number ranging from $\sim
0.4$ to $\sim 1.7$ and with average Lorentz factors up to $\sim 1.7$. We find
that flow quantities, such as the energy density or the local Lorentz factor,
show large spatial variance even in the subsonic case as compressibility is
enhanced by relativistic effects. The velocity field is highly intermittent,
but its power-spectrum is found to be in good agreement with the predictions of
the classical theory of Kolmogorov.


  The recent and rapid growth in Unmanned Aerial Vehicles (UAVs) deployment for
various computer vision tasks has paved the path for numerous opportunities to
make them more effective and valuable. Object detection in aerial images is
challenging due to variations in appearance, pose, and scale. Autonomous aerial
flight systems with their inherited limited memory and computational power
demand accurate and computationally efficient detection algorithms for
real-time applications. Our work shows the adaptation of the popular YOLOv4
framework for predicting the objects and their locations in aerial images with
high accuracy and inference speed. We utilized transfer learning for faster
convergence of the model on the VisDrone DET aerial object detection dataset.
The trained model resulted in a mean average precision (mAP) of 45.64% with an
inference speed reaching 8.7 FPS on the Tesla K80 GPU and was highly accurate
in detecting truncated and occluded objects. We experimentally evaluated the
impact of varying network resolution sizes and training epochs on the
performance. A comparative study with several contemporary aerial object
detectors proved that YOLOv4 performed better, implying a more suitable
detection algorithm to incorporate on aerial platforms.


  A cell-free network merged with active reconfigurable reflecting surfaces
(RIS) is investigated in this paper. Based on the imperfect channel state
information (CSI), the aggregated channel from the user to the access point
(AP) is initially estimated using the linear minimum mean square error (LMMSE)
technique. The central processing unit (CPU) then detects uplink data from
individual users through the utilization of the maximum ratio combining (MRC)
approach, relying on the estimated channel. Then, a closed-form expression for
uplink spectral efficiency (SE) is derived which demonstrates its reliance on
statistical CSI (S-CSI) alone. The amplitude gain of each active RIS element is
derived in a closed-form expression as a function of the number of active RIS
elements, the number of users, and the size of each reflecting element. A soft
actor-critic (SAC) algorithm is utilized to design the phase shift of the
active RIS to maximize the uplink SE. Simulation results emphasize the
robustness of the proposed SAC algorithm, showcasing its effectiveness in
cell-free networks under the influence of imperfect CSI.


  Component-wise gradient boosting algorithms are popular for their intrinsic
variable selection and implicit regularization, which can be especially
beneficial for very flexible model classes. When estimating generalized
additive models for location, scale and shape (GAMLSS) by means of a
component-wise gradient boosting algorithm, an important part of the estimation
procedure is to determine the relative complexity of the submodels
corresponding to the different distribution parameters. Existing methods either
suffer from a computationally expensive tuning procedure or can be biased by
structural differences in the negative gradients' sizes, which, if encountered,
lead to imbalances between the different submodels. Shrunk optimal step lengths
have been suggested to replace the typical small fixed step lengths for a
non-cyclical boosting algorithm limited to a Gaussian response variable in
order to address this issue. In this article, we propose a new adaptive step
length approach that accounts for the relative size of the fitted base-learners
to ensure a natural balance between the different submodels. The new balanced
boosting approach thus represents a computationally efficient and easily
generalizable alternative to shrunk optimal step lengths. We implemented the
balanced non-cyclical boosting algorithm for a Gaussian, a negative binomial as
well as a Weibull distributed response variable and demonstrate the competitive
performance of the new adaptive step length approach by means of a simulation
study, in the analysis of count data modeling the number of doctor's visits as
well as for survival data in an oncological trial.


  A relativistic Green's function approach to parity-violating quasielastic
electron scattering is presented. The components of the hadron tensor are
expressed in terms of the single particle Green's function, which is expanded
in terms of the eigenfunctions of the non-Hermitian optical potential, in order
to account for final state interactions without any loss of flux. Results for
$^{12}$C, $^{16}$O, and $^{40}$Ca are presented and discussed. The effect of
the strange quark contribution to the nuclear current is investigated.