  Let F be a finite set of graphs. In the F-Deletion problem, we are given an
n-vertex graph G and an integer k as input, and asked whether at most k
vertices can be deleted from G such that the resulting graph does not contain a
graph from F as a minor. F-Deletion is a generic problem and by selecting
different sets of forbidden minors F, one can obtain various fundamental
problems such as Vertex Cover, Feedback Vertex Set or Treewidth t-Deletion.
  In this paper we obtain a number of generic algorithmic results about Planar
F-Deletion, when F contains at least one planar graph. The highlights of our
work are - A constant factor approximation algorithm for the optimization
version of Planar F-Deletion. - A randomized linear time and single exponential
parameterized algorithm, that is, an algorithm running in time O(2^{O(k)} n),
for the parameterized version of Planar F-deletion where all graphs in F are
connected. The algorithm can be made deterministic at the cost of making the
polynomial factor in the running time n*log^2 n rather than linear. - A
polynomial kernel for parameterized Planar F-deletion
  These algorithms unify, generalize, and improve a multitude of results in the
literature. Our main results have several direct applications, but also the
methods we develop on the way have applicability beyond the scope of this
paper. Our results -- constant factor approximation, polynomial kernelization
and FPT algorithms -- are stringed together by a common theme of polynomial
time preprocessing.


  A peculiar feature of the majority of three dimensional topological insulator
surface states studied experimentally thus far, namely their particle-hole
asymmetry, makes quantum oscillations (Shubnikov de Haas and de Haas van Alphen
oscillations) in these materials particularly rich. I show that this
peculiarity can be exploited to measure the Chern number, and detect
topological phase transitions in topological insulator surface states from the
quantum spin Hall phase to the quantum anomalous Hall phase. I consider the
behaviour of quantum oscillations in topological insulator thin film surface
states in the presence of a topological exciton condensate, or hybridisation
between the two surfaces. As a function of Zeeman field, the Chern number and
phase transition from a quantum spin Hall to a quantum anomalous Hall phase can
be measured using standard techniques. This effect relies necessarily on the
particle-hole asymmetry which is ubiquitous in currently know materials that
exhibit topological insulator surface states.


  We reduce the embedding problem for hypo SU(2) and SU(3)-structures to the
embedding problem for hypo G2-structures into parallel Spin(7)-manifolds. The
latter will be described in terms of gauge deformations. This description
involves the intrinsic torsion of the initial G2-structure and allows us to
prove that the evolution equations, for all of the above embedding problems, do
not admit non-trivial longtime solutions.


  We demonstrate a 10-GHz bandwidth digitizer with 7-effective bits of
resolution and 52dB SFDR, using photonic time stretch technique. To the best of
our knowledge, this is the highest resolution analog-to-digital converter (ADC)
of the same bandwidth, with at least an order of magnitude higher SNR than
previously achieved. This is made possible by correction of distortion due to
time warps, and non-linearities due to wavelength dependent bias variation in
the Mach-Zehnder modulator. We also demonstrate concatenation of 30 wavelength
interleaved time segments with high fidelity on the path to achieving
continuous time operation.


  We give a fully polynomial-time randomised approximation scheme (FPRAS) for
the number of bases in a bicircular matroids. This is a natural class of
matroids for which counting bases exactly is #P-hard and yet approximate
counting can be done efficiently.


  The process of preparing potentially large and complex data sets for further
analysis or manual examination is often called data wrangling. In classical
warehousing environments, the steps in such a process have been carried out
using Extract-Transform-Load platforms, with significant manual involvement in
specifying, configuring or tuning many of them. Cost-effective data wrangling
processes need to ensure that data wrangling steps benefit from automation
wherever possible. In this paper, we define a methodology to fully automate an
end-to-end data wrangling process incorporating data context, which associates
portions of a target schema with potentially spurious extensional data of types
that are commonly available. Instance-based evidence together with data
profiling paves the way to inform automation in several steps within the
wrangling process, specifically, matching, mapping validation, value format
transformation, and data repair. The approach is evaluated with real estate
data showing substantial improvements in the results of automated wrangling.


  We describe non-trivial $\delta$-derivations of semisimple finite-dimensional
Jordan algebras over an algebraically closed field of characteristic not 2, and
of simple finite-dimensional Jordan superalgebras over an algebraically closed
field of characteristic 0. For these classes of algebras and superalgebras,
non-zero $\delta$-derivations are shown to be missing for $\delta\neq
0,{1}{2},1$, and we give a complete account of ${1}{2}$-derivations.


  We argue that there may exist spacetime defects embedded in Minkowski
spacetime, which have negative active gravitational mass. One such spacetime
defect then repels a test particle, corresponding to what may be called
"antigravity."


  It is increasingly common to solve combinatorial optimisation problems that
are partially-specified. We survey the case where the objective function or the
relations between variables are not known or are only partially specified. The
challenge is to learn them from available data, while taking into account a set
of hard constraints that a solution must satisfy, and that solving the
optimisation problem (esp. during learning) is computationally very demanding.
This paper overviews four seemingly unrelated approaches, that can each be
viewed as learning the objective function of a hard combinatorial optimisation
problem: 1) surrogate-based optimisation, 2) empirical model learning, 3)
decision-focused learning (`predict + optimise'), and 4) structured-output
prediction. We formalise each learning paradigm, at first in the ways commonly
found in the literature, and then bring the formalisations together in a
compatible way using regret. We discuss the differences and interactions
between these frameworks, highlight the opportunities for cross-fertilization
and survey open directions.


  Energy consumption of a wireless sensor node mainly depends on the amount of
time the node spends in each of the high power active (e.g., transmit, receive)
and low power sleep modes. It has been well established that in order to
prolong node's lifetime the duty-cycle of the node should be low. However, low
power sleep modes usually have low current draw but high energy cost while
switching to the active mode with a higher current draw. In this work, we
investigate a MaxWeightlike opportunistic sleep-active scheduling algorithm
that takes into account time- varying channel and traffic conditions. We show
that our algorithm is energy optimal in the sense that the proposed ESS
algorithm can achieve an energy consumption which is arbitrarily close to the
global minimum solution. Simulation studies are provided to confirm the
theoretical results.


  We derive differential equations for the flow of entanglement entropy as a
function of the metric and the couplings of the theory. The variation of the
universal part of entanglement entropy under a local Weyl transformation is
related to the variation under a local change in the couplings. We show that
this relation is in fact equivalent to the trace Ward identity. As a concrete
application of our formalism, we express the entanglement entropy for massive
free fields as a two-point function of the energy-momentum tensor.


  We use the synergy of infrared, terahertz, and Raman spectroscopies with DFT
calculations to shed light on the magnetic and lattice properties of VI3. The
structural transition at TS1 = 79 K is accompanied by a large splitting of
polar phonon modes. Below TS1, strong ferromagnetic fluctuations are observed.
The variations of phonon frequencies at 55 K induced by magnetoelastic coupling
enhanced by spin-orbit interaction indicate the proximity of long-range
ferromagnetic order. Below TC = 50 K, two Raman modes simultaneously appear and
show dramatic softening in the narrow interval around the temperature TS2 of
the second structural transition associated with the order-order magnetic phase
transition. Below TS2, a magnon in the THz range appears in Raman spectra. The
THz magnon observed in VI3 indicates the application potential of 2D van der
Waals ferromagnets in ultrafast THz spintronics, which has previously been
considered an exclusive domain of antiferromagnets.


  We present a new method based on Functional Data Analysis (FDA) for detecting
associations between one or more scalar covariates and a longitudinal response,
while correcting for other variables. Our methods exploit the temporal
structure of longitudinal data in ways that are otherwise difficult with a
multivariate approach. Our procedure, from an FDA perspective, is a departure
from more established methods in two key aspects. First, the raw longitudinal
phenotypes are assembled into functional trajectories prior to analysis.
Second, we explore an association test that is not directly based on principal
components. We instead focus on quantifying the reduction in $L^2$ variability
as a means of detecting associations. Our procedure is motivated by
longitudinal genome wide association studies and, in particular, the childhood
asthma management program (CAMP) which explores the long term effects of daily
asthma treatments. We conduct a simulation study to better understand the
advantages (and/or disadvantages) of an FDA approach compared to a traditional
multivariate one. We then apply our methodology to data coming from CAMP. We
find a potentially new association with a SNP negatively affecting lung
function. Furthermore, this SNP seems to have an interaction effect with one of
the treatments.


  A semi-centralized joint cell muting and user scheduling scheme for
interference coordination in a multicell network is proposed under two
different temporal fairness criteria. The main principle behind the proposed
scheme is that a central entity selects a cell muting pattern out of a pattern
set at a decision instant, and subsequently the un-muted base stations
opportunistically schedule the users in the associated cells, both decisions
made on a temporal-fair basis. Although some pattern sets are easily obtainable
from static frequency reuse systems, we propose a more general pattern set
construction algorithm in this paper. As for the first fairness criterion, all
cells are assigned to receive the same temporal share with the ratio between
the temporal share of a cell center section and that of the cell edge section
is set to a fixed desired value for all cells. The second fairness criterion is
based on the max-min temporal fairness for which the temporal share of the
network-wide worst-case user is maximized. Numerical results are provided to
validate the effectiveness of the proposed scheme for both criteria. The impact
of choice of the cell muting pattern set is also studied through numerical
examples for various cellular topologies.


  In the context of loop quantum gravity, quantum states of geometry are
mathematically defined as spin networks living on graphs embedded in the
canonical space-like hypersurface. In the effort to study the renormalisation
flow of loop gravity, a necessary step is to understand the coarse-graining of
these states in order to describe their relevant structure at various scales.
Using the spinor network formalism to describe the phase space of loop gravity
on a given graph, we focus on a bounded (connected) region of the graph and
coarse-grain it to a single vertex using a gauge-fixing procedure. We discuss
the ambiguities in the gauge-fixing procedure and their consequences for
coarse-graining spin(or) networks. This allows to define the boundary
deformations of that region in a gauge-invariant fashion and to identify the
area preserving deformations as U(N) transformations similarly to the already
well-studied case of a single intertwiner. The novelty is that the closure
constraint is now relaxed and the closure defect interpreted as a local measure
of the curvature inside the coarse-grained region. It is nevertheless possible
to cancel the closure defect by a Lorentz boost. We further identify a
Lorentz-invariant observable related to the area and closure defect, which we
name "rest area". Its physical meaning remains an open issue.


  We investigate the universal behavior of the Kardar-Parisi-Zhang (KPZ)
equation with temporally correlated noise. The presence of time correlations in
the microscopic noise breaks the statistical tilt symmetry, or Galilean
invariance, of the original KPZ equation with delta-correlated noise (denoted
SR-KPZ). Thus it is not clear whether the KPZ universality class is preserved
in this case. Conflicting results exist in the literature, some advocating that
it is destroyed even in the limit of infinitesimal temporal correlations, while
others find that it persists up to a critical range of such correlations. Using
non-perturbative and functional renormalization group techniques, we study the
influence of two types of temporal correlators of the noise: a short-range one
with a typical time-scale $\tau$, and a power-law one with a varying exponent
$\theta$. We show that for the short-range noise with any finite $\tau$, the
symmetries (the Galilean symmetry, and the time-reversal one in $1+1$
dimension) are dynamically restored at large scales, such that the
long-distance and long-time properties are governed by the SR-KPZ fixed point.
In the presence of a power-law noise, we find that the SR-KPZ fixed point is
still stable for $\theta$ below a critical value $\theta_{\textrm{th}}$, in
accordance with previous renormalization group results, while a long-range
fixed point controls the critical scaling for $\theta>\theta_{\textrm{th}}$,
and we evaluate the $\theta$-dependent critical exponents at this long-range
fixed point, in both $1+1$ and $2+1$ dimensions. While the results in $1+1$
dimension can be compared with previous studies, no other prediction was
available in $2+1$ dimension. We finally report in $1+1$ dimension the
emergence of anomalous scaling in the long-range phase.


  Fine-grained image recognition is very challenging due to the difficulty of
capturing both semantic global features and discriminative local features.
Meanwhile, these two features are not easy to be integrated, which are even
conflicting when used simultaneously. In this paper, a retrieval-based
coarse-to-fine framework is proposed, where we re-rank the TopN classification
results by using the local region enhanced embedding features to improve the
Top1 accuracy (based on the observation that the correct category usually
resides in TopN results). To obtain the discriminative regions for
distinguishing the fine-grained images, we introduce a weakly-supervised method
to train a box generating branch with only image-level labels. In addition, to
learn more effective semantic global features, we design a multi-level loss
over an automatically constructed hierarchical category structure. Experimental
results show that our method achieves state-of-the-art performance on three
benchmarks: CUB-200-2011, Stanford Cars, and FGVC Aircraft. Also,
visualizations and analysis are provided for better understanding.


  The Hardy spaces are defined on the quotient domain of a bounded complete
Reinhardt domain by a finite subgroup of $U(n)$. The Szeg\H{o} projection on
the quotient domain can be studied by lifting to the covering space. This
setting builds on the solution of a boundary value problem for holomorphic
functions. In particular, when the covering space is either the polydisc or the
unit ball in $\mathbb{C}^n$, the boundary value problem can be solved. Applying
this theory in $\mathbb{C}^2$, we further obtain sharp results on the $L^p$
regularity of the Szeg\H{o} projection on the symmetrized bidisc, generalized
Thullen domains, and the minimal ball.


  We study the buckling of an idealized, semiflexible filament along whose
contour magnetic moments are placed. {We give analytic expressions for the
critical stiffness of the filament below which it buckles due to the magnetic
compression. For this, we consider various scenarios of the attachment of the
magnetic particles to the filament. One possible application for this model are
the magnetosome chains of magnetotactic bacteria. An estimate of the critical
bending stiffness indicates that buckling may occur within the range of
biologically relevant parameters and suggests a role for the bending stiffness
of the filament to stabilize the filament against buckling, which would
compromise the functional relevance of the bending stiffness of the used
filament.


  In this study, we explore a range of options and outcomes associated with
using different allocation approaches to operationalise the Planetary
Boundaries (PB) framework at the country, sector, and city scales. We
demonstrate: (i) how to translate the PB framework into various sub-global
scales (countries, cities, industries); and (ii) how to take global/local
aspects (e.g., water use at the watershed level) into account. Finally, we
apply the proposed methodology to derive country, city, and sector-specific
budgets consistent with the PB concept for Switzerland. We then benchmark the
translated PBs for climate, biodiversity, and freshwater use against actual
environmental pressures in Switzerland from both production- and
consumption-based perspectives. This effectively enables us to provide a
comprehensive assessment of whether Switzerland is living within its safe
operating space.


  The paper is a collection of knowledge regarding the phenomenon of climate
change, competitiveness, and literature linking the two phenomena to
agricultural market competitiveness. The objective is to investigate the peer
reviewed and grey literature on the subject to explore the link between climate
change and agricultural market competitiveness and also explore an appropriate
technique to validate the presumed relationship empirically. The paper
concludes by identifying implications for developing an agricultural
competitiveness index while incorporating the climate change impacts, to
enhance the potential of agricultural markets for optimizing the agricultural
sectors competitiveness.


  Brief account of results on the Cauchy problem for the Einstein equations
starting with early the works of Darmois and Lichnerowicz and going up to the
proofs of the existence and uniqueness of solutions global in space and local
in time, in Sobolev spaces, for the general equations either in vacuum or with
classical sources.


  A search for a fermiophobic Higgs boson using diphoton events produced in
proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV is
performed using data corresponding to an integrated luminosity of 4.9/fb
collected by the ATLAS experiment at the Large Hadron Collider. A specific
benchmark model is considered where all the fermion couplings to the Higgs
boson are set to zero and the bosonic couplings are kept at the Standard Model
values (fermiophobic Higgs model). The largest excess with respect to the
background-only hypothesis is found at 125.5 GeV, with a local significance of
2.9 standard deviations, which reduces to 1.6 standard deviations when taking
into account the look-elsewhere effect. The data exclude the fermiophobic Higgs
model in the ranges 110.0 - 118.0 GeV and 119.5 - 121.0 GeV at 95% confidence
level.


  The claustrum (Cl) is a thin grey matter structure located in the center of
each brain hemisphere. Cl has been hypothesized as a central hub of the brain
for multisensory/sensorimotor integration, consciousness, and attention.
Accumulating evidence has suggested that Cl might be important in the
development of severe neurological and psychiatric symptoms including epileptic
seizures and psychosis. However, the specifics of the roles of Cl in human
epilepsy and psychosis are largely unknown, primarily due to methodological
limitations related to the thin morphology of Cl that is challenging to
delineate accurately using conventional methods. The goal of this work is to
develop noninvasive multimodal neuroimaging methods to delineate Cl anatomy by
utilizing a large healthy adult high resolution (0.7mm3) T1-weighted MRI
collected as part of the Washington University-Minnesota Consortium Human
Connectome Project (WU-Minn HCP). We developed a comprehensive manual
segmentation protocol to delineate Cl based on a cellular level brain atlas.
The protocol involves detailed guidelines to delineate the three subregions of
Cl, including the dorsal, ventral, and temporal Cl that can be parcellated
based on a geometric method. As demonstrated in a representative result, Cl is
large in its anterior-posterior, and the dorsal-ventral extent. Also, the
volume is comparable to that of the amygdala. It is required to assess the
reliability of the protocol so that it can be used for future anatomical
studies of neuropsychiatric disorders, including epilepsy and schizophrenia.


  We derive an analytic expression for point-to-point correlation functions of
the Polyakov loop based on the transfer matrix formalism. For the $2d$ Ising
model we show that the results deduced from point-point spin correlators are
coinciding with those from zero momentum correlators. We investigate the
contributions from eigenvalues of the transfer matrix beyond the mass gap and
discuss the limitations and possibilities of such an analysis. The finite size
behaviour of the obtained $2d$ Ising model matrix elements is examined. The
point-to-point correlator formula is then applied to Polyakov loop data in
finite temperature $SU(2)$ gauge theory. The leading matrix element shows all
expected scaling properties. Just above the critical point we find a Debye
screening mass $~\mu_D/T\approx4~$, independent of the volume.


  This paper details the Leibniz generalization of Lie-theoretic results from
Peggy Batten's 1993 dissertation. We first show that the multiplier of a
Leibniz algebra is characterized by its second cohomology group with
coefficients in the field. We then establish criteria for when the center of a
cover maps onto the center of the algebra. Along the way, we obtain a
collection of exact sequences and a brief theory of unicentral Leibniz
algebras.


  In the framework of semihard QCD approach, with the emphasis on the BFKL
evolution of gluon distributions, we calculate the differential cross sections
of inclusive $D^{*\pm}$ mesons electroproduction at HERA and confront our
results with available
  ZEUS experimental data.


  This paper addresses the question of why metal-deficient barium stars are not
yellow symbiotic stars (YSyS). Samples of (suspected) metal-deficient barium
(mdBa) stars and YSyS have been collected from the literature, and their
properties reviewed. It appears in particular that the barium nature of the
suspected mdBa stars needs to be ascertained by detailed abundance analyses.
Abundances are therefore derived for two of them, HD 139409 and HD 148897,
which reveal that HD 148897 should not be considered a barium star. HD 139409
is a mild barium star, with overabundances observed only for elements belonging
to the first s-process peak (Y and Zr). The evidence for binarity among mdBa
stars is then reviewed, using three different methods: (i) radial-velocity
variations (from CORAVEL observations), (ii) Hipparcos astrometric data, and
(iii) a method based on the comparison between the Hipparcos and Tycho-2 proper
motions. A first-time orbit is obtained for HIP 55852, whereas evidence for the
(so far unknown) binary nature of HIP 34795, HIP 76605, HIP 97874 and HIP
107478 is presented. Two stars with no evidence for binarity whatsoever (HIP
58596 and BD +3 2688) are candidates low-metallicity thermally-pulsing
asymptotic giant branch stars, as inferred from their large luminosities. The
reason why mdBa stars are not YSyS is suggested to lie in their different
orbital period distributions: mdBa stars have on average longer orbital periods
than YSyS, and hence their companion accretes matter at a lower rate, for a
given mass loss rate of the giant star. The definite validation of this
explanation should nevertheless await the determination of the orbital periods
for the many mdBa stars still lacking periods, in order to make the comparison
more significant.


  The correlation between the jet power and accretion disk luminosity is
investigated for active galactic nuclei (AGNs) and black hole X-ray binaries
(BHXBs) from the literature. The power-law correlation index is steep ($\mu
\sim$ 1.0--1.4) for radio loud quasars and the `outliers' track of BHXBs, and
it is flatter ($\mu \sim$ 0.3--0.6) for radio loud galaxies and the standard
track of BHXBs. The steep-index groups are mostly at higher accretion rates
(peaked at Eddington ratio $>$ 0.01) and the flatter-index groups are at
relatively low accretion rates (peaked at Eddington ratio $<$ 0.01), implying
that the former groups could be dominated by the inner disk accretion of black
hole, while the jet in latter groups would be a hybrid production of the
accretion and black hole spin. We could still have a fundamental plane of black
hole activity for the BHXBs and AGNs with diverse (maybe two kinds of)
correlation indices. It is noted that the fundamental plane of black hole
activity should be referred to the correlation between the jet power and disk
luminosity or equivalently to the correlation between jet power, Eddington
ratio and black hole mass, rather than the jet power, disk luminosity and black
hole mass.


  In this paper, we present a novel tolerance allocation algorithm for the
assessment and control of geometric variation on system performance that is
applicable to any system of partial differential equations. In particular, we
parameterize the geometric domain of the system in terms of design parameters
and subsequently measure the effect of design parameter variation on system
performance. A surrogate model via a tensor representation is constructed to
map the design parameter variation to the system performance. A set of
optimization problems over this surrogate model restricted to nested
hyperrectangles represents the effect of prescribing design tolerances, where
the maximizer of this restricted function depicts the worst-case member, i.e.
the worst-case design. Moreover, the loci of these tolerance hyperrectangles
with maximizers attaining, but not surpassing, the performance constraint
represents the boundary to the feasible region of allocatable tolerances. Every
tolerance in this domain is measured through a user-specified, weighted norm
which is informed by design considerations such as cost and manufacturability.
The boundary of the feasible set is elucidated as an immersed manifold of
codimension one, over which a suite of optimization routines exist and are
employed to efficiently determine an optimal feasible tolerance with respect to
the specified measure. Examples of this algorithm are presented with
applications to a plate with a hole described by two design parameters, a plate
with a hole described by six design parameters, and an L-Bracket described by
seventeen design parameters.


  By comparing two different evaluations of a modified (\`{a} la Borcherds)
higher Siegel theta lift on even lattices of signature $(r,s)$, we prove
Eichler--Selberg type relations for a wide class of negative weight
vector-valued mock modular forms. In doing so, we detail several properties of
the lift, as well as showing that it produces an infinite family of local (and
locally harmonic) Maa{\ss} forms on Grassmanians in certain signatures.


  In this work we: (1) review likelihood-based inference for parameter
estimation and the construction of confidence regions; and, (2) explore the use
of techniques from information geometry, including geodesic curves and Riemann
scalar curvature, to supplement typical techniques for uncertainty
quantification such as Bayesian methods, profile likelihood, asymptotic
analysis and bootstrapping. These techniques from information geometry provide
data-independent insights into uncertainty and identifiability, and can be used
to inform data collection decisions. All code used in this work to implement
the inference and information geometry techniques is available on GitHub.


  In this paper, we derive new, nearly optimal bounds for the Gaussian
approximation to scaled averages of $n$ independent high-dimensional centered
random vectors $X_1,\dots,X_n$ over the class of rectangles in the case when
the covariance matrix of the scaled average is non-degenerate. In the case of
bounded $X_i$'s, the implied bound for the Kolmogorov distance between the
distribution of the scaled average and the Gaussian vector takes the form $$C
(B^2_n \log^3 d/n)^{1/2} \log n,$$ where $d$ is the dimension of the vectors
and $B_n$ is a uniform envelope constant on components of $X_i$'s. This bound
is sharp in terms of $d$ and $B_n$, and is nearly (up to $\log n$) sharp in
terms of the sample size $n$. In addition, we show that similar bounds hold for
the multiplier and empirical bootstrap approximations. Moreover, we establish
bounds that allow for unbounded $X_i$'s, formulated solely in terms of moments
of $X_i$'s. Finally, we demonstrate that the bounds can be further improved in
some special smooth and zero-skewness cases.


  In this article, we investigate some properties of cyclic coverings of
complex surfaces of general type branched along smooth curves that are
numerically equivalent to a multiple of the canonical class. The main results
concern coverings of surfaces of general type with p_g=0 and Miyaoka--Yau
surfaces; in particular, they provide new examples of multicomponent moduli
spaces of surfaces with given Chern numbers as well as new examples of surfaces
that are not deformation equivalent to their complex conjugates.


  In this paper, a network comprising wireless devices equipped with buffers
transmitting deadline-constrained data packets over a slotted-ALOHA
random-access channel is studied. Although communication protocols facilitating
retransmissions increase reliability, packet transmission from the queue
experiences delays and thus, packets with time constraints might be dropped
before being successfully transmitted, while at the same time causing the queue
size of the buffer to increase. Towards understanding the trade-off between
reliability and delays that might lead to packet drops due to the
deadline-constrained bursty traffic with retransmissions, a scenario of a
wireless network utilizing a slotted-ALOHA random-access channel is
investigated. The main focus is to reveal and investigate further the trade-off
between the number of retransmissions and the packet deadline as a function of
the arrival rate. Hence, we are able to determine numerically the optimal
probability of transmissions and number of retransmissions, given the packet
arrival rate and the packet deadline. The analysis of the system was done by
means of discrete-time Markov chains. Two scenarios are studied: i) the
collision channel model (in which a receiver can decode only when a single
packet is transmitted), and ii) the case for which receivers have multi-packet
reception capabilities. A performance evaluation for a user with different
transmit probability and number of retransmissions is conducted, demonstrating
their impact on the average drop rate and throughput, while at the same time
showing that there exists a set of values, under which improved performance can
be acquired.


  In this paper, I apply the decision theory and empirical Bayesian approach to
construct confidence intervals for selected populations when true parameters
follow a mixture prior distribution. A loss function with two tuning parameters
$k_1$ and $k_2$ is coined to address the mixture prior. One specific choice of
$k_2$ can lead to the procedure in Qiu and Hwang (2007); the other choice of
$k_2$ provides an interval construction which controls the Bayes FCR. Both the
analytical and extensive numerical simulation studies demonstrate that the new
empirical Bayesian FCR controlling approach enjoys great length reduction. At
the end, I apply different methods to a microarray data set. It turns out that
the average length of the new approach is only 57% of that of Qiu and Hwang's
procedure which controls the simultaneous non-coverage probability and 66% of
that of Benjamini and Yekutieli (2005)'s procedure which controls the
frequentist's FCR.


  For patients suffering from central nervous system tumors, prognosis
estimation, treatment decisions, and postoperative assessments are made from
the analysis of a set of magnetic resonance (MR) scans. Currently, the lack of
open tools for standardized and automatic tumor segmentation and generation of
clinical reports, incorporating relevant tumor characteristics, leads to
potential risks from inherent decisions' subjectivity. To tackle this problem,
the proposed Raidionics open-source software has been developed, offering both
a user-friendly graphical user interface and stable processing backend. The
software includes preoperative segmentation models for each of the most common
tumor types (i.e., glioblastomas, lower grade gliomas, meningiomas, and
metastases), together with one early postoperative glioblastoma segmentation
model. Preoperative segmentation performances were quite homogeneous across the
four different brain tumor types, with an average Dice around 85% and
patient-wise recall and precision around 95%. Postoperatively, performances
were lower with an average Dice of 41%. Overall, the generation of a
standardized clinical report, including the tumor segmentation and features
computation, requires about ten minutes on a regular laptop. The proposed
Raidionics software is the first open solution enabling an easy use of
state-of-the-art segmentation models for all major tumor types, including
preoperative and postsurgical standardized reports.


  We analyze the network of transactions among cities based on the electronic
invoice database for the municipalities in the Cear\'a state, Brazil. It
consists of approximately 3.7 billion records, registered during the period
between the years 2016 to 2019. All the transactions are grouped in a unique
dataset and represented as an asymmetrical adjacency matrix corresponding to a
directed graph with connections weighted by the number of transactions among
cities. Due to the large size of Cear\'a state, its unequal distribution of
wealth, and spatially heterogeneous population, we initially determine
communities of cities based on their mutual intensity of trades and verify to
which extent their economic interests reflect a community cohesiveness. For the
first task, we use the Infomap algorithm to detect the partition which provides
the shortest description length and captures the optimal community structure of
the network in terms of its associated flow dynamics. Surprisingly, the
partition identified has five modules, whose two-dimensional geographical
projections are all simply-connected domains. We proceed with the analysis of
traded products by building bipartite structures represented in terms of
adjacency matrices between municipalities and products, considering selling and
buying. Using the revealed comparative advantage (RCA) concept, we define a
non-monetary and binary activity index that can distinguish the RCA of a city
in a class of goods or services as evidenced by trade flows. Finally, through
the pairwise Maximum Entropy Model, we can associate to the largest communities
their corresponding binary Ising-like Hamiltonian models. In an analogy with
critical phenomena, our results reveal that each community operates at a
"temperature" that is close to the corresponding "critical point", suggesting a
high degree of "economic cohesiveness" in its trade network of cities.


  We investigate a low-dimensional slow-fast model to understand the dynamical
origin of El Ni\~no-Southern Oscillation. A close inspection of the system
dynamics using several bifurcation plots reveals that a sudden large expansion
of the attractor occurs at a critical system parameter via a type of interior
crisis. This interior crisis evolves through merging of a cascade of
period-doubling and period-adding bifurcations that leads to the origin of
occasional amplitude-modulated extremely large events. More categorically, a
situation similar to homoclinic chaos arises near the critical point, however,
atypical global instability evolves as a channel-like structure in phase space
of the system that modulates variability of amplitude and return time of the
occasional large events and makes a difference from the homoclinic chaos. The
slow-fast timescale of the low-dimensional model plays an important role on the
onset of occasional extremely large events. Such extreme events are
characterized by their heights when they exceed a threshold level measured by a
mean-excess function. The probability density of events' height displays
multimodal distribution with an upper-bounded tail. We identify the dependence
structure of interevent intervals to understand the predictability of return
time of such extreme events using autoregressive integrated moving average
model and box plot analysis.


  We present a constructive recognition algorithm to decide whether a given
black-box group is isomorphic to an alternating or a symmetric group without
prior knowledge of the degree. This eliminates the major gap in known
algorithms, as they require the degree as additional input.
  Our methods are probabilistic and rely on results about proportions of
elements with certain properties in alternating and symmetric groups. These
results are of independent interest; for instance, we establish a lower bound
for the proportion of involutions with small support.


  It is shown that the nuclear Hamiltonian can be separated rigorously as
${\cal H}={\cal H}_m+{\cal H}_M.$ For sufficiently smooth forces the monopole
part ${\cal H}_m $is entirely responsible for Hartree-Fock selfconsistency and
hence saturation properties. The multipole part ${\cal H}_M$ contains the
``residual'' terms - pairing, quadrupole, etc. - that will be analyzed in a
companion paper. We include a review of the basic results often needed when
working with multipole decompositions and average monopole properties.


  This paper addresses forward motion control for trajectory tracking and
mobile formation coordination for a group of non-holonomic vehicles on SE(2).
Firstly, by constructing an intermediate attitude variable which involves
vehicles' position information and desired attitude, the translational and
rotational control inputs are designed in two stages to solve the trajectory
tracking problem. Secondly, the coordination relationships of relative
positions and headings are explored thoroughly for a group of non-holonomic
vehicles to maintain a mobile formation with rigid body motion constraints. We
prove that, except for the cases of parallel formation and translational
straight line formation, a mobile formation with strict rigid-body motion can
be achieved if and only if the ratios of linear speed to angular speed for each
individual vehicle are constants. Motion properties for mobile formation with
weak rigid-body motion are also demonstrated. Thereafter, based on the proposed
trajectory tracking approach, a distributed mobile formation control law is
designed under a directed tree graph. The performance of the proposed
controllers is validated by both numerical simulations and experiments.


  We develop an understanding of the anomalous metal state of the parent
compounds of recently discovered iron based superconductors starting from a
strong coupling viewpoint, including orbital degrees of freedom. On the basis
of an intermediate-spin (S=1) state for the Fe^{2+} ions, we derive a
Kugel-Khomskii spin-orbital Hamiltonian for the active t_{2g} orbitals. It
turns out to be a highly complex model with frustrated spin and orbital
interactions. We compute its classical phase diagrams and provide an
understanding for the stability of the various phases by investigating its
spin-only and orbital-only limits. The experimentally observed spin-stripe
state is found to be stable over a wide regime of physical parameters and can
be accompanied by three different types of orbital orders. Of these the
orbital-ferro and orbital-stripe orders are particularly interesting since they
break the in-plane lattice symmetry -- a robust feature of the undoped
compounds. We compute the magnetic excitation spectra for the effective spin
Hamiltonian, observing a strong reduction of the ordered moment, and point out
that the proposed orbital ordering pattern can be measured in resonant X-ray
diffraction.


  One of the most basic facts related to the famous Ulam reconstruction
conjecture is that the connectedness of a graph can be determined by the deck
of its vertex-deleted subgraphs, which are considered up to isomorphism. We
strengthen this result by proving that connectedness can still be determined
when the subgraphs in the deck are given up to equivalence under the color
refinement isomorphism test. Consequently, this implies that connectedness is
recognizable by Reconstruction Graph Neural Networks, a recently introduced GNN
architecture inspired by the reconstruction conjecture (Cotta, Morris, Ribeiro
2021).


  The first part of this Review describes a few of the main methods that have
been employed in non-linear time series analysis with special reference to
biological applications (biomechanics). The second part treats the physical
basis of posturogram data (human balance) and EMG (electromyography, a measure
of muscle activity).


  Coherence is a fundamental notion in quantum mechanics, defined relative to a
reference basis. As such, it does not necessarily reveal the locality of
interactions nor takes into account the accessible operations in a composite
quantum system. In this paper, we put forward a notion of localizable coherence
as the coherence that can be stored in a particular subsystem, either by
measuring or just by disregarding the rest. We examine its spreading, its
average properties in the Hilbert space and show that it can be applied to
reveal the real-space structure of states of interest in quantum many-body
theory, for example, localized or topological states.


  A microgrid is a new concept that has changed the power systems dramatically.
It is a combination of Distributed Generation Resources (DGR) like Biomass, PV
systems, Wind energy, Fuel cell, Diesel Generator, and so on with different
types of loads (residential or commercial). Microgrids can work in two modes:
autonomous and Interconnected. In the Islanding situation, the loads will be
supported by DGR and without connecting to upstream utility grids. Controlling
power electronic Interfaces between sources and loads has been an important
task between the researchers. Several different strategies have been presented
by researchers. Droop control strategy is one of the which has its pros and
cons. In this paper, the conventional droop strategy has been explained in
detail and formulated. The Simulation results are taken from MATLAB/SIMULINK to
show the capability of the control strategy.


  This paper is part of a program of investigation of the chiral transition in
Nf=2 QCD, started in Phys.Rev.D72:114510,2005. Progress is reported on the
understanding of some possible systematic errors. A direct test of first order
scaling is presented.


  We develop a novel deep learning architecture for naturally complex-valued
data, which is often subject to complex scaling ambiguity. We treat each sample
as a field in the space of complex numbers. With the polar form of a
complex-valued number, the general group that acts in this space is the product
of planar rotation and non-zero scaling. This perspective allows us to develop
not only a novel convolution operator using weighted Fr\'echet mean (wFM) on a
Riemannian manifold, but also a novel fully connected layer operator using the
distance to the wFM, with natural equivariant properties to non-zero scaling
and planar rotation for the former and invariance properties for the latter.
  Compared to the baseline approach of learning real-valued neural network
models on the two-channel real-valued representation of complex-valued data,
our method achieves surreal performance on two publicly available
complex-valued datasets: MSTAR on SAR images and RadioML on radio frequency
signals. On MSTAR, at 8% of the baseline model size and with fewer than 45,000
parameters, our model improves the target classification accuracy from 94% to
98% on this highly imbalanced dataset. On RadioML, our model achieves
comparable RF modulation classification accuracy at 10% of the baseline model
size.


  The $^{12}$C($\alpha,\gamma$)$^{16}$O reaction has a key role in nuclear
astrophysics. A multilevel R-matrix analysis was used to make extrapolations of
the astrophysical S factor for this reaction to the stellar energy of 300 keV.
The statistical precision of the S-factor extrapolation was determined by
performing multiple fits to existing randomized E1 and E2 ground state data,
according to experimental errors. The impact of a future proposed experiment at
Jefferson Laboratory (JLab) was assessed within this framework. The proposed
JLab experiment will make use of a high-intensity low-energy bremsstrahlung
beam that impinges on an oxygen-rich single-fluid bubble chamber in order to
measure the total cross section for the inverse
$^{16}$O($\gamma,\alpha$)$^{12}$C reaction. The importance of low energy data
as well as high precision data was investigated.


  Domain-Specific Languages (DSLs) help practitioners in contributing solutions
to challenges of specific domains. The efficient development of user-friendly
DSLs suitable for industrial practitioners with little expertise in modelling
still is challenging. For such practitioners, who often do not model on a daily
basis, there is a need to foster reduction of repetitive modelling tasks and
providing simplified visual representations of DSL parts. For industrial
language engineers, there is no methodical support for providing such
guidelines or documentation as part of reusable language modules. Previous
research either addresses the reuse of languages or guidelines for modelling.
For the efficient industrial deployment of DSLs, their combination is
essential: the efficient engineering of DSLs from reusable modules that feature
integrated documentation and guidelines for industrial practitioners. To solve
these challenges, we propose a systematic approach for the industrial
engineering of DSLs based on the concept of reusable DSL Building Blocks, which
rests on several years of experience in the industrial engineering of DSLs and
their deployment to various organizations. We investigated our approach via
focus group methods consisting of five participants from industry and research
qualitatively. Ultimately, DSL Building Blocks support industrial language
engineers in developing better usable DSLs and industrial practitioners in more
efficiently achieving their modelling.


  The light flicker problem of high intensity discharge lamps is studied
numerically and experimentally. It is shown that in some respects the systems
behaves very similar to the forced Duffing oscillator with a softening spring.
In particular, the jump phenomenon and hysteresis are observed in the
simulations and in the experiments.


  Cosmic rays are an important source of heating in the interstellar medium, in
particular in dense molecular cloud cores shielded from the external
ultraviolet radiation field. The limits placed on the cosmic-ray ionization
rate from measurements of the gas temperature in dense clouds are unaffected by
the uncertainties associated to the traditional methods based on the analysis
of molecular abundances. However, high-resolution data are required to
determine with sufficient accuracy the spatial temperature distribution within
prestellar cores. In this contribution we illustrate in detail the case of the
well-studied prestellar core L1544, showing that both its thermal structure and
chemical composition are consistent with a cosmic ray ionization rate of $\sim
10^{-17}$~s$^{-1}$, significantly smaller than the value measured in the
diffuse interstellar medium. We also briefly discuss possible applications of
this method to molecular clouds of other galaxies


  A Bose-Einstein condensate of atoms with attractive interactions exhibits
growth and collapse cycles, when it is fed by a thermal cloud. Recently this
phenomenon has been directly observed in a trapped Li-7 gas. We offer a
quantitative explanation of the data, on the basis of a model proposed earlier.
It is shown that the condensate wave function acquires a chaotic component
after the first collapse, indicating superfluid turbulence.


  This paper analyzes customer product-choice behavior based on the recency and
frequency of each customer's page views on e-commerce sites. Recently, we
devised an optimization model for estimating product-choice probabilities that
satisfy monotonicity, convexity, and concavity constraints with respect to
recency and frequency. This shape-restricted model delivered high predictive
performance even when there were few training samples. However, typical
e-commerce sites deal in many different varieties of products, so the
predictive performance of the model can be further improved by integration of
such product heterogeneity. For this purpose, we develop a novel latent-class
shape-restricted model for estimating product-choice probabilities for each
latent class of products. We also give a tailored expectation-maximization
algorithm for parameter estimation. Computational results demonstrate that
higher predictive performance is achieved with our latent-class model than with
the previous shape-restricted model and common latent-class logistic
regression.


  Reflection of near-infrared light is important for preventing heat transfer
in energy saving applications. A large-area, mass-producible reflector that
contains randomly distributed disk-shaped silver nanoparticles and that
exhibits high reflection at near-infrared wavelengths was demonstrated.
Although resonant coupling between incident light and the nanostructure of the
reflector plays some role, what is more important is the geometrical randomness
of the nanoparticles, which serves as the origin of a particle-dependent
localization and hierarchical distribution of optical near-fields in the
vicinity of the nanostructure. Here we show and clarified the unique optical
near-field processes associated with the randomness seen in experimentally
fabricated silver nanostructures by adapting a rigorous theory of optical
near-fields based on an angular spectrum and detailed electromagnetic
calculations.


  GitHub Copilot is an artificial intelligence model for automatically
generating source code from natural language problem descriptions. Since June
2022, Copilot has officially been available for free to all students as a
plug-in to development environments like Visual Studio Code. Prior work
exploring OpenAI Codex, the underlying model that powers Copilot, has shown it
performs well on typical CS1 problems thus raising concerns about the impact it
will have on how introductory programming courses are taught. However, little
is known about the types of problems for which Copilot does not perform well,
or about the natural language interactions that a student might have with
Copilot when resolving errors. We explore these questions by evaluating the
performance of Copilot on a publicly available dataset of 166 programming
problems. We find that it successfully solves around half of these problems on
its very first attempt, and that it solves 60\% of the remaining problems using
only natural language changes to the problem description. We argue that this
type of prompt engineering, which we believe will become a standard interaction
between human and Copilot when it initially fails, is a potentially useful
learning activity that promotes computational thinking skills, and is likely to
change the nature of code writing skill development.


  The European market clearing problem is characterized by a set of
heterogeneous orders and rules that force the implementation of heuristic and
iterative solving methods. In particular, curtailable block orders and the
uniform purchase price (UPP) pose serious difficulties. A block is an order
that spans over multiple hours, and can be either fully accepted or fully
rejected. The UPP prescribes that all consumers pay a common price, i.e., the
UPP, in all the zones, while producers receive zonal prices, which can differ
from one zone to another.
  The market clearing problem in the presence of both the UPP and block orders
is a major open issue in the European context. The UPP scheme leads to a
non-linear optimization problem involving both primal and dual variables,
whereas block orders introduce multi-temporal constraints and binary variables
into the problem. As a consequence, the market clearing problem in the presence
of both blocks and the UPP can be regarded as a non-linear integer programming
problem involving both primal and dual variables with complementary and
multi-temporal constraints.
  The aim of this paper is to present a non-iterative and heuristic-free
approach for solving the market clearing problem in the presence of both
curtailable block orders and the UPP. The solution is exact, with no
approximation up to the level of resolution of current market data. By
resorting to an equivalent UPP formulation, the proposed approach results in a
mixed-integer linear program, which is built starting from a non-linear integer
bilevel programming problem. Numerical results using real market data are
reported to show the effectiveness of the proposed approach. The model has been
implemented in Python, and the code is freely available on a public repository.


  Background forces are linear long-range interactions of the cantilever body
with its surroundings that must be compensated for in order to reveal
tip-surface force, the quantity of interest for determining material properties
in atomic force microscopy. We provide a mathematical derivation of a method to
compensate for background forces, apply it to experimental data, and discuss
how to include background forces in simulation. Our method, based on
linear-response theory in the frequency domain, provides a general way of
measuring and compensating for any background force and it can be readily
applied to different force reconstruction methods in dynamic AFM.


  In this paper we consider two inverse problems on a closed connected
Riemannian manifold $(M,g)$. The first one is a direct analog of the Gel'fand
inverse boundary spectral problem. To formulate it, assume that $M$ is divided
by a hypersurface $\Sigma$ into two components and we know the eigenvalues
$\lambda_j$ of the Laplace operator on $(M,g)$ and also the Cauchy data, on
$\Sigma$, of the corresponding eigenfunctions $\phi_j$, i.e.
$\phi_j|_{\Sigma},\partial_\nu\phi_j|_{\Sigma}$, where $\nu$ is the normal to
$\Sigma$. We prove that these data determine $(M,g)$ uniquely, i.e. up to an
isometry. In the second problem we are given much less data, namely,
$\lambda_j$ and $\phi_j|_{\Sigma}$ only. However, if $\Sigma$ consists of at
least two components, $\Sigma_1, \Sigma_2$, we are still able to determine
$(M,g)$ assuming some conditions on $M$ and $\Sigma$. These conditions are
formulated in terms of the spectra of the manifolds with boundary obtained by
cutting $M$ along $\Sigma_i$, $i=1,2$, and are of a generic nature. We consider
also some other inverse problems on $M$ related to the above with data which is
easier to obtain from measurements than the spectral data described.


  Current solutions mostly focus on how to predict traffic, rather than
observing traffic characteristics in a specific NFV scenario. So, most of them
use a uniform threshold to scale in/out. In real NFV scenario, each VNF may
serve the one or more flows, and the characteristics of these flows are
completely different, a uniform threshold used in this scenario is not
suitable, because each VNF has a distinct processing logic depending on
incident network traffic and events. Even if certain VNFs share packet
processing functionality such as packet header analysis, the differences in
upper-layer processing and implementation can exhibit unique resource usage
patterns.
  We proposes a dynamic threshold scaling mechanism that can tailor thresholds
according to each VNF's characteristic. As setting thresholds is a per-VNF
task, and requires a deep understanding of workload trends and the diversity of
each VNF, so we have added tailor-made features to the traditional dynamic
mechanism. Besides, we also reserve resources by predicting workload and add an
emergency module to cope with anomaly traffic, that is to say we develop a
hybrid scaling policy combining proactive and reactive scaling together.
Moreover, the sharp rise of network traffic not only can be caused by large
amount of new incoming flows, but also can be induced by the growing of
existing flows. If the traffic arises mainly due to the growing of existing
flows, then only rerouting new flows can not alleviate the overload quickly and
SLAs may be violated \cite{zhang2016co}. The only method to avoid SLA
violations is to migrate flows and associated NF internal states quickly and
safely from existing instances to new scaled instances, so state migration is
an important part of the scaling procedure. We achieved the flow migration in
scaling process on openNF to guarantee the accuracy and timeline of scaling.


  We study the shell and alpha cluster structures in the ground and excited
states of 8Be in terms of the tensor-optimized shell model (TOSM). In TOSM, the
tensor correlation is optimized in the full space of 2p2h configurations
involving high-momentum components. The short-range correlation is treated with
the unitary correlation operator method (UCOM). We use the effective
interaction based on the bare nucleon-nucleon interaction AV8$^\prime$. The 8Be
states consist of two groups of ground band states and highly excited states
with the isospin T=0 and T=1. It is found that the tensor contributions of the
ground band states are stronger than the highly excited states and that the
kinetic energies and the central contributions of the ground band states are
almost twice the 4He values. These features suggest two-alpha clustering for
the ground band states in 8Be. We also estimate the correlation energy of the
$\alpha$ clustering using the alpha cluster model. In the highly excited
states, the calculated spectrum in TOSM reproduces the experimental level order
and the relative energies of each level. This agreement suggests that those
states can be interpreted as shell-like states. The level order is found to be
sensitive to the presence of the tensor force in comparison with the results
using the Minnesota effective interaction without the tensor force. It is also
found that the tensor contributions in the T=0 states are stronger than the T=1
states, which is consistent with the state dependence of the tensor force.


  We give a geometric characterization of finite rational groups. In
particular, we prove that a finite group is rational if and only if there
exists a finite geometry $\Gamma$ of type $I$ and action of $G$ on $\Gamma$ as
a group of automorphisms such that if $g$ and $h$ are elements of $G$ fixing
the same number of flags of type $J$ for all subsets $J$ of $I$, then $g$ and
$h$ are conjugate in $G$.


  Using the coalescence model based on the phase-space distributions of
protons, neutrons, Lambdas and their antiparticles from a multiphase transport
(AMPT) model, we study the production of deuteron, triton, helium 3,
hypertriton, hyperhelium 3 and their antinuclei in Pb+Pb collisions at
$\sqrt{s_{NN}}=2.76$ TeV. The resulting transverse momentum spectra, elliptic
flows and coalescence parameters for these nuclei are presented and compared
with available experimental data. We also show the constituent number scaled
elliptic flows of these nuclei and discuss their implications.


  Background: Cannabis use disorder (CUD) is a growing public health problem.
Early identification of adolescents and young adults at risk of developing CUD
in the future may help stem this trend. A logistic regression model fitted
using a Bayesian learning approach was developed recently to predict the risk
of future CUD based on seven risk factors in adolescence and youth. A
nationally representative longitudinal dataset, Add Health was used to train
the model (henceforth referred as Add Health model). Methods: We validated the
Add Health model on two cohorts, namely, Michigan Longitudinal Study (MLS) and
Christchurch Health and Development Study (CHDS) using longitudinal data from
participants until they were approximately 30 years old (to be consistent with
the training data from Add Health). If a participant was diagnosed with CUD at
any age during this period, they were considered a case. We calculated the area
under the curve (AUC) and the ratio of expected and observed number of cases
(E/O). We also explored re-calibrating the model to account for differences in
population prevalence. Results: The cohort sizes used for validation were 424
(53 cases) for MLS and 637 (105 cases) for CHDS. AUCs for the two cohorts were
0.66 (MLS) and 0.73 (CHDS) and the corresponding E/O ratios (after
recalibration) were 0.995 and 0.999. Conclusion: The external validation of the
Add Health model on two different cohorts lends confidence to the model's
ability to identify adolescent or young adult cannabis users at high risk of
developing CUD in later life.


  We present a proof that any continuous function with domain including a
closed interval yields an antiderivative of that function on that interval.
This is done without the need of any integration comparable to that of Riemann,
Cauchy, or Darboux. The proof is based on one given by Lebesgue in 1905.


  Quantum discord (QD) reveals the nonclassical nature of correlations in
bipartite quantum states, going beyond the entanglement-separability paradigm.
In this article we discuss the suitability of QD in what concern its possible
asymmetry with relation to the bipartition we choose to compute it. We obtain
an analytical formula for a symmetrized version of QD (SQD) in Bell-diagonal
states. We observe that if correlation is regarded as a shared property, then
the SQD could be a convenient quantifier for asymmetric states.


  We estimate the heavy quark effective theory parameter \lambda_1 from
inclusive semileptonic B-meson decay spectrum. By using recent CLEO double
lepton tagging data of B -> X e nu, which shows the lepton momentum as low as
0.6 GeV, we extracted \lambda_1 \sim -0.58 GeV^2. We also derived \bar\Lambda
\sim 0.46 GeV and |V_{cb}| = 0.041 \pm 0.002.


  Domain walls are the topological defects that mediate polarization reversal
in ferroelectrics, and they may exhibit quite different geometric and
electronic structures compared to the bulk. Therefore, a detailed atomic-scale
understanding of the static and dynamic properties of domain walls is of
pressing interest. In this work, we use first-principles methods to study the
structures of $180^{\circ}$ domain walls, both in their relaxed state and along
the ferroelectric reversal pathway, in ferroelectrics belonging to the family
of corundum derivatives. Our calculations predict their orientation, formation
energy, and migration energy, and also identify important couplings between
polarization, magnetization, and chirality at the domain walls. Finally, we
point out a strong empirical correlation between the height of the domain-wall
mediated polarization reversal barrier and the local bonding environment of the
mobile $A$ cations as measured by bond valence sums. Our results thus provide
both theoretical and empirical guidance to future searches for ferroelectric
candidates in materials of the corundum derivative family.


  The preprocessing stage of Shor's algorithm generates a class of quantum
states referred to as periodic states, on which the quantum Fourier transform
is applied. Such states also play an important role in other quantum algorithms
that rely on the quantum Fourier transform. Since entanglement is believed to
be a necessary resource for quantum computational speedup, we analyze the
entanglement of periodic states and the way it is affected by the quantum
Fourier transform. To this end, we derive a formula that evaluates the
Groverian entanglement measure for periodic states. Using this formula, we
explain the surprising result that the Groverian entanglement of the periodic
states built up during the preprocessing stage is only slightly affected by the
quantum Fourier transform.


  We present a broadband spectral-timing analysis of SMC X-1 at different
intensity states of its super-orbital variation using 10 Suzaku and 6 NuSTAR
observations. The spectrum in all the states can be described by an absorbed
powerlaw with a high energy cutoff and a black-body component along with an
iron emission line. Compared to other supergiant HMXBs, the Fe K$\alpha$ line
equivalent width is low in SMC X-1 - from less than 10 eV in high state to up
to $\sim$ 270 eV in the low states. The spectral shape is dependent on flux
with the hard X-ray spectrum steepening with increasing flux. We also report a
highly variable normalisation of the power-law component across these 16
super-orbital states. Pulsations in the hard X-rays for both the instruments
were detected in all but two observations. The pulse profiles are near
sinusoidal with two peaks and the relative intensity of the second peak
decreasing with decreasing luminosity. These findings suggest that the
super-orbital modulation in SMC X-1 is not caused by absorption in precessing
warped accretion disc alone and there are intrinsic changes in X-rays emanating
from the neutron star at different super-orbital states. We also note a
putative cyclotron line at $\sim$ 50 keV in the \nustar~spectra of three bright
states indicating a possible magnetic field of $\sim$ 4.2 $\times$ 10$^{12}$ G.
Finally, with the new pulse period measurements reported here, the time base
for the secular spin-up of SMC X-1 is increased by thirteen years and the
complete pulse period history shows a sudden change in the spin-up trend around
1995.


  Deep learning models for automatic readability assessment generally discard
linguistic features traditionally used in machine learning models for the task.
We propose to incorporate linguistic features into neural network models by
learning syntactic dense embeddings based on linguistic features. To cope with
the relationships between the features, we form a correlation graph among
features and use it to learn their embeddings so that similar features will be
represented by similar embeddings. Experiments with six data sets of two
proficiency levels demonstrate that our proposed methodology can complement
BERT-only model to achieve significantly better performances for automatic
readability assessment.


  We study the problem of network regression, where one is interested in how
the topology of a network changes as a function of Euclidean covariates. We
build upon recent developments in generalized regression models on metric
spaces based on Fr\'echet means and propose a network regression method using
the Wasserstein metric. We show that when representing graphs as multivariate
Gaussian distributions, the network regression problem requires the computation
of a Riemannian center of mass (i.e., Fr\'echet means). Fr\'echet means with
non-negative weights translates into a barycenter problem and can be
efficiently computed using fixed point iterations. Although the convergence
guarantees of fixed-point iterations for the computation of Wasserstein affine
averages remain an open problem, we provide evidence of convergence in a large
number of synthetic and real-data scenarios. Extensive numerical results show
that the proposed approach improves existing procedures by accurately
accounting for graph size, topology, and sparsity in synthetic experiments.
Additionally, real-world experiments using the proposed approach result in
higher Coefficient of Determination ($R^{2}$) values and lower mean squared
prediction error (MSPE), cementing improved prediction capabilities in
practice.


  We study the direct radiation excited by oscillating axion (or axion-like
particle) dark matter in a homogenous magnetic field and its detection scheme.
We concretely derive the analytical expression of the axion-induced radiated
power for a cylindrical uniform magnetic field. In the long wave limit, the
radiation power is proportional to the square of the B-field volume and the
axion mass $m_a$, whereas it oscillate as approaching the short wave limit and
the peak powers are proportional to the side area of the cylindrical magnetic
field and $m_a^{-2}$. The maximum power locates at mass
$m_a\sim\frac{3\pi}{4R}$ for fixed radius $R$. Based on this characteristic of
the power, we discuss a scheme to detect the axions in the mass range
$1-10^4$\,neV, where four detectors of different bandwidths surround the
B-field. The expected sensitivity for $m_a\lesssim1\,\mu$eV under
typical-parameter values can far exceed the existing constraints.


  Motivated by recent studies of record statistics in relation to strongly
correlated time series, we consider explicitly the drawdown time of a Levy
process, which is defined as the time since it last achieved its running
maximum when observed over a fixed time period [0,T]. We show that the density
function of this drawdown time, in the case of a completely asymmetric jump
process, may be factored as a function of $t$ multiplied by a function of T-t.
This extends a known result for the case of pure Brownian motion. We state the
factors explicitly for the cases of exponential down-jumps with drift, and for
the downward Inverse Gaussian Levy process with drift.


  Mathematical optimization is one of the cornerstones of modern engineering
research and practice. Yet, throughout all application domains, mathematical
optimization is, for the most part, considered to be a numerical discipline.
Optimization problems are formulated to be solved numerically with specific
algorithms running on microprocessors. An emerging alternative is to view
optimization algorithms as dynamical systems. Besides being insightful in
itself, this perspective liberates optimization methods from specific numerical
and algorithmic aspects and opens up new possibilities to endow complex
real-world systems with sophisticated self-optimizing behavior. Towards this
goal, it is necessary to understand how numerical optimization algorithms can
be converted into feedback controllers to enable robust "closed-loop
optimization". In this article, we focus on recent control designs under the
name of "feedback-based optimization" which implement optimization algorithms
directly in closed loop with physical systems. In addition to a brief overview
of selected continuous-time dynamical systems for optimization, our particular
emphasis in this survey lies on closed-loop stability as well as the robust
enforcement of physical and operational constraints in closed-loop
implementations. To bypass accessing partial model information of physical
systems, we further elaborate on fully data-driven and model-free operations.
We highlight an emerging application in autonomous reserve dispatch in power
systems, where the theory has transitioned to practice by now. We also provide
short expository reviews of pioneering applications in communication networks
and electricity grids, as well as related research streams, including extremum
seeking and pertinent methods from model predictive and process control, to
facilitate high-level comparisons with the main topic of this survey.


  Using gauge/gravity duality techniques, we discuss the sound-channel retarded
correlators of vector and tensor conserved currents in a class of
$(2+1)$-dimensional strongly-coupled field theories at zero temperature and
finite charge density, assumed to be holographically dual to the extremal
Reissner-Nordstr\"{o}m AdS$_4$ black hole. Using a combination of analytical
and numerical methods, we determine the quasinormal mode spectrum at finite
momentum for the coupled gravitational and electromagnetic perturbations, and
discuss the appropriate choice of gauge-invariant variables (master fields) in
order for the black hole quasinormal frequencies to reproduce the field theory
spectrum. We discuss the role of the near horizon AdS$_{2}$ geometry in
determining the low-frequency behavior of retarded correlators in the boundary
theory, and comment on the emergence of criticality in the IR. In addition, we
establish the existence of a sound mode at zero temperature and compute the
speed of sound and sound attenuation constant numerically, obtaining a result
consistent with the expectations from the zero temperature limit of
hydrodynamics. The dispersion relation of higher resonances is also
investigated.


  Quantum manipulation based on geometric phases provides a promising way
towards robust quantum gates. However, in the current implementation of
nonadiabatic geometric phases, operational and/or random errors tend to
destruct the conditions that induce geometric phases, thereby smearing their
noise-resilient feature. In a recent experiment [Y. Xu et al., Phys. Rev. Lett.
124, 230503 (2020)], high-fidelity universal geometric quantum gates have been
implemented in a superconducting circuit, which are robust to different types
of errors under different configurations of the geometric evolution paths.
Here, we apply the path-design strategy to explain in detail why both
configurations can realize universal quantum gates in a single-loop way.
Meanwhile, we purposefully induce our geometric manipulation by selecting the
path configuration that is robust against the qubit-frequency-drift induced
error, which is the dominant error source on realistic superconducting circuits
and has not been deliberately addressed. Moreover, our proposal can further
integrate with the composite scheme to enhance the gate robustness, which is
verified by numerical simulations. Therefore, our scheme provides a promising
way towards practical realization of high-fidelity and robust nonadiabatic
geometric quantum gates.


  Current experiments are taking the first steps toward noise-resilient logical
qubits. Crucially, a quantum computer must not merely store information, but
also process it. A fault-tolerant computational procedure ensures that errors
do not multiply and spread. This review compares the leading proposals for
promoting a quantum memory to a quantum processor. We compare magic state
distillation, color code techniques and other alternative ideas, paying
attention to relative resource demands. We discuss the several no-go results
which hold for low-dimensional topological codes and outline the potential
rewards of using high-dimensional quantum (LDPC) codes in modular
architectures.


  We study the structural properties of self-attracting walks in d dimensions
using scaling arguments and Monte Carlo simulations. We find evidence for a
transition analogous to the \Theta transition of polymers. Above a critical
attractive interaction u_c, the walk collapses and the exponents \nu and k,
characterising the scaling with time t of the mean square end-to-end distance
<R^2> ~ t^{2 \nu} and the average number of visited sites <S> ~ t^k, are
universal and given by \nu=1/(d+1) and k=d/(d+1). Below u_c, the walk swells
and the exponents are as with no interaction, i.e. \nu=1/2 for all d, k=1/2 for
d=1 and k=1 for d >= 2. At u_c, the exponents are found to be in a different
universality class.


  Optical flow estimation with occlusion or large displacement is a problematic
challenge due to the lost of corresponding pixels between consecutive frames.
In this paper, we discover that the lost information is related to a large
quantity of motion features (more than 40%) computed from the popular
discriminative cost-volume feature would completely vanish due to invalid
sampling, leading to the low efficiency of optical flow learning. We call this
phenomenon the Vanishing Cost Volume Problem. Inspired by the fact that local
motion tends to be highly consistent within a short temporal window, we propose
a novel iterative Motion Feature Recovery (MFR) method to address the vanishing
cost volume via modeling motion consistency across multiple frames. In each MFR
iteration, invalid entries from original motion features are first determined
based on the current flow. Then, an efficient network is designed to adaptively
learn the motion correlation to recover invalid features for lost-information
restoration. The final optical flow is then decoded from the recovered motion
features. Experimental results on Sintel and KITTI show that our method
achieves state-of-the-art performances. In fact, MFR currently ranks second on
Sintel public website.


  It has long been known that the "detection loophole", present when detector
efficiencies are below a critical figure, could open the way for alternative
"local realist" explanations for the violation of Bell tests. It has in recent
years become common to assume the loophole can be ignored, regardless of which
version of the Bell test is employed. A simple model is presented that
illustrates that this may not be justified. Two of the versions -- the standard
test of form -2 <= S <= 2 and the currently-popular "visibility" test -- are at
grave risk of bias. Statements implying that experimental evidence "refutes
local realism" or shows that the quantum world really is "weird" should be
reviewed. The detection loophole is on its own unlikely to account for more
than one or two test violations, but when taken in conjunction with other
loopholes (briefly discussed) it is seen that the experiments refute only a
narrow class of "local hidden variable" models, applicable to idealised
situations, not to the real world. The full class of local realist models
provides straightforward explanations not only for the publicised Bell-test
violations but also for some lesser-known "anomalies".


  We present some consequences of non-anomalous propagation requirements on
various massless fields. Among the models of nonlinear electrodynamics we show
that only Maxwell and Born-Infeld also obey duality invariance. Separately we
show that, for actions depending only on the $F_\mn^2$ invariant, the permitted
models have $L \sim \sqrt{1 + F^2}$. We also characterize acceptable
vector-scalar systems. Finally we find that wide classes of gravity models
share with Einstein the null nature of their characteristic surfaces.


  We study the interaction of $\Lambda-$hyperon with proton and neutron inside
a nucleus within the framework of relativistic mean field formalism. The single
particle energy levels for some of the specific proton and neutron orbits are
analyzed with the addition of $\Lambda-$successively. We found that the
interaction of $\Lambda$ with neutron is more stronger than proton.


  The times of minima of the bright, eccentric eclipsing binary RR Lyncis are
re-investigated. From the TESS data it is found that significant differences in
the eclipse timings, particularly for the primary minima, are probably due to
small, irregular changes in the eclipse profile due to pulsations, rather than
light-curve asymmetries.


  In order to explore CP asymmetry in the lepton sector, a power upgrade to the
neutrino experimental facility at J-PARC is a key requirement for both the
Tokai to Kamioka (T2K) long-baseline neutrino oscillation experiment and a
future project with Hyper-Kamiokande. Based on five years of operational
experience, the facility has achieved stable operation with 230 kW beam power
without significant problems on the beam-line apparatus. After successful
maintenance works in 2013-2014 to replace all electromagnetic horns and a
production target, the facility is now ready to accomodate a 750-kW-rated beam.
Also, the possibility of achieving a few to multi-MW beam operation is
discussed in detail.


  We present an efficient implemention of a non-equilibrium Green function
(NEGF) method for self-consistent calculations of electron transport and forces
in nanostructured materials. The electronic structure is described at the level
of density functional theory (DFT) using the projector augmented wave method
(PAW) to describe the ionic cores and an atomic orbital basis set for the
valence electrons. External bias and gate voltages are treated in a
self-consistent manner and the Poisson equation with appropriate boundary
conditions is solved in real space. Contour integration of the Green function
and parallelization over k-points and real space makes the code highly
efficient and applicable to systems containing several hundreds of atoms. The
method is applied to a number of different systems demonstrating the effects of
bias and gate voltages, multiterminal setups, non-equilibrium forces, and spin
transport.


  The family of edge-sharing tri-coordinated iridates and ruthenates has
emerged in recent years as a major platform for Kitaev spin liquid physics,
where spins fractionalize into emergent magnetic fluxes and Majorana fermions
with Dirac-like dispersions. While such exotic states are usually pre-empted by
long-range magnetic order at low temperatures, signatures of Majorana fermions
with long coherent times have been predicted to manifest at intermediate and
higher energy scales, similar to the observation of spinons in quasi-1D spin
chains. Here we present a Resonant Inelastic X-ray Scattering study of the
magnetic excitations of the hyperhoneycomb iridate $\beta$-Li$_2$IrO$_3$ under
a magnetic field with a record-high-resolution spectrometer. At
low-temperatures, dispersing spin waves can be resolved around the predicted
intertwined incommensurate spiral and field-induced zigzag orders, whose
excitation energy reaches a maximum of 16meV. A 2T magnetic field softens the
dispersion around ${\bf Q}=0$. The behavior of the spin waves under magnetic
field is consistent with our semiclassical calculations for the ground state
and the dynamical spin structure factor, which further predicts that the ensued
intertwined uniform states remain robust up to very high fields (100 T). Most
saliently, the low-energy magnon-like mode is superimposed by a broad continuum
of excitations, centered around 35meV and extending up to 100meV. This
high-energy continuum survives up to at least 300K -- well above the ordering
temperature of 38K -- and gives evidence for pairs of long-lived Majorana
fermions of the proximate Kitaev spin liquid.


  Mining genuine mechanisms underlying the complex data generation process in
real-world systems is a fundamental step in promoting interpretability of, and
thus trust in, data-driven models. Therefore, we propose a variation-based
cause effect identification (VCEI) framework for causal discovery in bivariate
systems from a single observational setting. Our framework relies on the
principle of independence of cause and mechanism (ICM) under the assumption of
an existing acyclic causal link, and offers a practical realization of this
principle. Principally, we artificially construct two settings in which the
marginal distributions of one covariate, claimed to be the cause, are
guaranteed to have non-negligible variations. This is achieved by re-weighting
samples of the marginal so that the resultant distribution is notably distinct
from this marginal according to some discrepancy measure. In the causal
direction, such variations are expected to have no impact on the effect
generation mechanism. Therefore, quantifying the impact of these variations on
the conditionals reveals the genuine causal direction. Moreover, we formulate
our approach in the kernel-based maximum mean discrepancy, lifting all
constraints on the data types of cause-and-effect covariates, and rendering
such artificial interventions a convex optimization problem. We provide a
series of experiments on real and synthetic data showing that VCEI is, in
principle, competitive to other cause effect identification frameworks.


  Greenhouse segmentation has pivotal importance for climate-smart agricultural
land-use planning. Deep learning-based approaches provide state-of-the-art
performance in natural image segmentation. However, semantic segmentation on
high-resolution optical satellite imagery is a challenging task because of the
complex environment. In this paper, a sound methodology is proposed for
pixel-wise classification on images acquired by the Azersky (SPOT-7) optical
satellite. In particular, customized variations of U-Net-like architectures are
employed to identify greenhouses. Two models are proposed which uniquely
incorporate dilated convolutions and skip connections, and the results are
compared to that of the baseline U-Net model. The dataset used consists of
pan-sharpened orthorectified Azersky images (red, green, blue,and near infrared
channels) with 1.5-meter resolution and annotation masks, collected from 15
regions in Azerbaijan where the greenhouses are densely congested. The images
cover the cumulative area of 1008 $km^2$ and annotation masks contain 47559
polygons in total. The $F_1, Kappa, AUC$, and $IOU$ scores are used for
performance evaluation. It is observed that the use of the deconvolutional
layers alone throughout the expansive path does not yield satisfactory results;
therefore, they are either replaced or coupled with bilinear interpolation. All
models benefit from the hard example mining (HEM) strategy. It is also reported
that the best accuracy of $93.29\%$ ($F_1\,score$) is recorded when the
weighted binary cross-entropy loss is coupled with the dice loss. Experimental
results showed that both of the proposed models outperformed the baseline U-Net
architecture such that the best model proposed scored $4.48\%$ higher in
comparison to the baseline architecture.


  An accurate simulation of the propagation of muons through matter is needed
for the analysis of data produced by muon/neutrino underground experiments. A
muon may sustain hundreds of interactions before it is detected by the
experiment. Since a small systematic uncertainty repeated hundreds of times may
lead to sizable errors, requirements on the precision of the muon propagation
code are very stringent. A new tool for propagating muon and tau charged
leptons through matter that is believed to meet these requirements is presented
here. An overview of the program is given and some results of its application
are discussed.


  Upon introducing a one-parameter quadratic deformation of the q-boson algebra
and a diagonal perturbation at the end point, we arrive at a semi-infinite
q-boson system with a two-parameter boundary interaction. The eigenfunctions
are shown to be given by Macdonald's hyperoctahedral Hall-Littlewood functions
of type BC. It follows that the n-particle spectrum is bounded and absolutely
continuous and that the corresponding scattering matrix factorizes as a product
of two-particle bulk and one-particle boundary scattering matrices.


  We investigate here populations of cataclysmic variables (CVs) in a set of
288 globular cluster (GC) models evolved with the MOCCA code. This is by far
the largest sample of GC models ever analysed with respect to CVs. Contrary to
what has been argued for a long time, we found that dynamical destruction of
primordial CV progenitors is much stronger in GCs than dynamical formation of
CVs, and that dynamically formed CVs and CVs formed under no/weak influence of
dynamics have similar white dwarf mass distributions. In addition, we found
that, on average, the detectable CV population is predominantly composed of CVs
formed via typical common envelope phase (CEP) ($\gtrsim70$ per cent), that
only $\approx2-4$ per cent of all CVs in a GC is likely to be detectable, and
that core-collapsed models tend to have higher fractions of bright CVs than
non-core-collapsed ones. We also consistently show, for the first time, that
the properties of bright and faint CVs can be understood by means of the pre-CV
and CV formation rates, their properties at their formation times and cluster
half-mass relaxation times. Finally, we show that models following the initial
binary population proposed by Kroupa and set with low CEP efficiency better
reproduce the observed amount of CVs and CV candidates in NGC 6397, NGC 6752
and 47 Tuc. To progress with comparisons, the essential next step is to
properly characterize the candidates as CVs (e.g. by obtaining orbital periods
and mass ratios).


  The linear k_\perp-factorization is part and parcel of the pQCD description
of high energy hard processes off free nucleons.
  In the case of heavy nuclear targets the very concept of nuclear parton
density becomes ill-defined as exemplified by the recent derivation [2] of
nonlinear nuclear k_\perp-factorization for forward dijet production in DIS off
nuclei. Here we report a derivation of the related breaking of
k_\perp-factorization for single-jet processes. We present a general formalism
and apply it to several cases of practical interest: open charm and quark and
gluon jet production in the central to beam fragmentation region of
\gamma^*p,\gamma^*A, pp and pA collisions. We show how the pattern of
k_\perp-factorization breaking and the nature and number of exchanged nuclear
pomerons do change within the phase space of produced quark and gluon jets. As
an application of the nonlinear k_\perp-factorization we discuss the Cronin
effect. Our results are also applicable to the p_\perp-dependence of the
Landau-Pomeranchuk-Migdal effect for, and nuclear quenching of, jets produced
in the proton hemisphere of pA collisions.


  A coloring of a digraph is a partition of its vertex set such that each class
induces a digraph with no directed cycles. A digraph is $k$-chromatic if $k$ is
the minimum number of classes in such partition, and a digraph is oriented if
there is at most one arc between each pair of vertices. Clearly, the smallest
$k$-chromatic digraph is the complete digraph on $k$ vertices, but determining
the order of the smallest $k$-chromatic oriented graphs is a challenging
problem. It is known that the smallest $2$-, $3$- and $4$-chromatic oriented
graphs have $3$, $7$ and $11$ vertices, respectively. In 1994, Neumann-Lara
conjectured that a smallest $5$-chromatic oriented graph has $17$ vertices. We
solve this conjecture and show that the correct order is $19$.


  The equivariant with respect to a finite group action Poincar\'e series of a
collection of $r$ valuations was defined earlier as a power series in $r$
variables with the coefficients from a modification of the Burnside ring of the
group. Here we show that (modulo simple exceptions) the equivariant Poincar\'e
series determines the equivariant topology of the collection of valuations.


  Given a finite associative ring with unity, $R$, any free (left) cyclic
submodule (FCS) generated by a $uni$modular ($n+1$)-tuple of elements of $R$
represents a point of the $n$-dimensional projective space over $R$. Suppose
that $R$ also features FCSs generated by ($n+1$)-tuples that are $not$
unimodular: what kind of geometry can be ascribed to such FCSs? Here, we
(partially) answer this question for $n=2$ when $R$ is the (unique)
non-commutative ring of order eight. The corresponding geometry is dubbed a
"Fano-Snowflake" due to its diagrammatic appearance and the fact that it
contains the Fano plane in its center. There exist, in fact, two such
configurations -- each being tied to either of the two maximal ideals of the
ring -- which have the Fano plane in common and can, therefore, be viewed as
twins. Potential relevance of these noteworthy configurations to quantum
information theory and stringy black holes is also outlined.


  We discuss the possibility that the detection of gravitational waves emitted
by compact stars may allow to constrain the MIT bag model of quark matter
equation of state. Our results show that the combined knowledge of the
frequency of the emitted gravitational wave and of the mass, or the radiation
radius, of the source allows one to discriminate between strange stars and
neutron stars and set stringent bounds on the bag constants.


  This article surveys the research presented by the author at the MATRIX
Institute workshop "Hyperbolic Differential Equations in Geometry and Physics"
in April 2022. The work is centered about establishing rigorous mathematical
statements toward the AdS/CFT correspondence in theoretical physics, in
particular in dynamical settings. The contents are mainly based on the recent
paper with G. Holzegel that proved a unique continuation result for the
Einstein-vacuum equations from asymptotically Anti-de Sitter (aAdS) conformal
boundaries. We also discuss some preceding results, in particular novel
Carleman estimates for wave equations on aAdS spacetimes, which laid the
foundations toward the main correspondence theorems.


  We study the possibility of the vortices formation in axion condensate on the
galactic scale. Such vortices can occur as a result of global rotation of the
early universe. We study analytical models of vortices and calculate exemplary
galaxy rotation curves. Depending on the setup it is possible to obtain a
variety of shapes which give a good qualitative agreement with observational
results. However, as we show, the extremely low velocity dispersions of the
axions velocity are required to form the single vortex on the galactic scales.
We find that the required velocity dispersion is of the order of $\sigma
\approx 10^{-12} \frac{m}{s}$. This is much smaller that predicted within the
present understanding of the axion physics. The vortices in axion condensate
can be however formed on the much smaller scales and give seeds to the galaxy
formation and to their angular momenta. On the other hand, the vortices can be
formed on the galactic scales, but only if the mass of the axion-like particles
is of the order of $10^{-30}$eV. In this case, the particle de Broglie
wavelength is comparable with the galactic diameter. This condition must be
fulfilled in order to keep the coherence of the quantum condensate on the
galactic scales.


  Hyperproperties, like observational determinism or symmetry, cannot be
expressed as properties of individual computation traces, because they describe
a relation between multiple computation traces. HyperLTL is a temporal logic
that captures such relations through trace variables, which are introduced
through existential and universal trace quantifiers and can be used to refer to
multiple computations at the same time. In this paper, we study the
satisfiability problem of HyperLTL. We show that the problem is PSPACE-complete
for alternation-free formulas (and, hence, no more expensive than LTL
satisfiability), EXPSPACE-complete for exists-forall-formulas, and undecidable
for forall-exists-formulas. Many practical hyperproperties can be expressed as
alternation-free formulas. Our results show that both satisfiability and
implication are decidable for such properties.


  There are many similarities between the theories of matroids and
$q$-matroids. However, when dealing with the direct sum of $q$-matroids many
differences arise. Most notably, it has recently been shown that the direct sum
of representable $q$-matroids is not necessarily representable. In this work,
we focus on the direct sum of uniform $q$-matroids. Using algebraic and
geometric tools, together with the notion of cyclic flats of $q$-matroids, we
show that this is always representable, by providing a representation over a
sufficiently large field.


  3D object detection is vital as it would enable us to capture objects' sizes,
orientation, and position in the world. As a result, we would be able to use
this 3D detection in real-world applications such as Augmented Reality (AR),
self-driving cars, and robotics which perceive the world the same way we do as
humans. Monocular 3D Object Detection is the task to draw 3D bounding box
around objects in a single 2D RGB image. It is localization task but without
any extra information like depth or other sensors or multiple images. Monocular
3D object detection is an important yet challenging task. Beyond the
significant progress in image-based 2D object detection, 3D understanding of
real-world objects is an open challenge that has not been explored extensively
thus far. In addition to the most closely related studies.


  Let g be a complex semisimple Lie algebra and let G' be the Langlands dual
group. We give a description of the cohomology algebra of an arbitrary
spherical Schubert variety in the loop Grassmannian for G' as a quotient of the
form Sym(g^e)/J. Here, J is an appropriate ideal in the symmetric algebra of
g^e, the centralizer of a principal nilpotent in g.
  We also discuss a `topological' proof of Kostant's famous result on the
structure of the polynomial algebra on g.


  Heavy flavor supplies a chance to constrain and improve the hadronization
mechanism. We have established a sequential coalescence model with charm
conservation and applied it to the charmed hadron production in heavy ion
collisions. The charm conservation enhances the earlier hadron production and
suppresses the later production. This relative enhancement (suppression)
changes significantly the ratios between charmed hadrons in heavy ion
collisions.


  In this work, we study the global well-posedeness of the heat equation with
variable time-dependent nonlinearity of the form $\varphi(t)f(u)$ on unimodular
Lie groups when the differential operator arises as the sum of squares of
H\"ormander vector fields. For general unimodular Lie groups, we derive the
necessary conditions for the nonexistence of global positive solutions. This
gives different conditions in the cases of compact, polynomial, and exponential
volume growth groups. In the case of the Heisenberg groups $\mathbb{H}^{n}$, we
also derive sufficient conditions, which coincide with the necessary ones in
the case of $\mathbb{H}^{1}$ (and this is also true for $\mathbb{R}^{n}$). In
particular, in the case of the Heisenberg group $\mathbb{H}^{1}$ we obtain the
necessary and sufficient conditions under which the aforesaid initial value
problem with variable nonlinearity has a global positive solution.