  In recent years, deep learning has shown promise in predicting hypertension
(HTN) from fundus images. However, most prior research has primarily focused on
analyzing a single type of data, which may not capture the full complexity of
HTN risk. To address this limitation, this study introduces a multimodal deep
learning (MMDL) system, dubbed HyMNet, which combines fundus images and
cardiometabolic risk factors, specifically age and gender, to improve
hypertension detection capabilities. Our MMDL system uses RETFound, a
foundation model pre-trained on 1.6 million retinal images, for the fundus path
and a fully connected neural network for the age and gender path. The two paths
are jointly trained by concatenating the feature vectors from each path that
are then fed into a fusion network. The system was trained on 5,016 retinal
images from 1,243 individuals collected from the Saudi Ministry of National
Guard Health Affairs. The results show that the multimodal model that
integrates fundus images along with age and gender outperforms the unimodal
system trained solely on fundus photographs, with an F1 score of 0.771 [0.747,
0.796], and 0.745 [0.719, 0.772] for hypertension detection, respectively.
Additionally, we studied the effect underlying diabetes mellitus has on the
model's predictive ability, concluding that diabetes is used as a confounding
variable for distinguishing hypertensive cases. Our code and model weights are
publicly available at https://github.com/MohammedSB/HyMNet.


  Two similar Minkowskian diffusions have been considered, on one hand by
Barbachoux, Debbasch, Malik and Rivet ([BDR1], [BDR2], [BDR3], [DMR], [DR]),
and on the other hand by Dunkel and H\"anggi ([DH1], [DH2]). We address here
two questions, asked in [DR] and in ([DH1], [DH2]) respectively, about the
asymptotic behaviour of such diffusions. More generally, we establish a central
limit theorem for a class of Minkowskian diffusions, to which the two above
ones belong. As a consequence, we correct a partially wrong guess in [DH1].


  Despite the growing popularity of 802.11 wireless networks, users often
suffer from connectivity problems and performance issues due to unstable radio
conditions and dynamic user behavior among other reasons. Anomaly detection and
distinction are in the thick of major challenges that network managers
encounter. Complication of monitoring the broaden and complex WLANs, that often
requires heavy instrumentation of the user devices, makes the anomaly detection
analysis even harder. In this paper we exploit 802.11 access point usage data
and propose an anomaly detection technique based on Hidden Markov Model (HMM)
and Universal Background Model (UBM) on data that is inexpensive to obtain. We
then generate a number of network anomalous scenarios in OMNeT++/INET network
simulator and compare the detection outcomes with those in baseline approaches
(RawData and PCA). The experimental results show the superiority of HMM and
HMM-UBM models in detection precision and sensitivity.


  We introduce a lightweight structure optimization approach for problems in
which there is uncertainty in the force locations. Such uncertainty may arise
due to force contact locations that change during use or are simply unknown a
priori. Given an input 3D model, regions on its boundary where arbitrary normal
forces may make contact, and a total force-magnitude budget, our algorithm
generates a minimum weight 3D structure that withstands any force configuration
capped by the budget. Our approach works by repeatedly finding the most
critical force configuration and altering the internal structure accordingly. A
key issue, however, is that the critical force configuration changes as the
structure evolves, resulting in a significant computational challenge. To
address this, we propose an efficient critical instant analysis approach.
Combined with a reduced order formulation, our method provides a practical
solution to the structural optimization problem. We demonstrate our method on a
variety of models and validate it with mechanical tests.


  We show how the relation between Poisson brackets and symplectic forms can be
extended to the case of inhomogeneous multivector fields and inhomogeneous
differential forms (or pseudodifferential forms). In particular we arrive at a
notion which is a generalization of a symplectic structure and gives rise to
higher Poisson brackets. We also obtain a construction of Koszul type brackets
in this setting.


  Determining whether inputs are out-of-distribution (OOD) is an essential
building block for safely deploying machine learning models in the open world.
However, previous methods relying on the softmax confidence score suffer from
overconfident posterior distributions for OOD data. We propose a unified
framework for OOD detection that uses an energy score. We show that energy
scores better distinguish in- and out-of-distribution samples than the
traditional approach using the softmax scores. Unlike softmax confidence
scores, energy scores are theoretically aligned with the probability density of
the inputs and are less susceptible to the overconfidence issue. Within this
framework, energy can be flexibly used as a scoring function for any
pre-trained neural classifier as well as a trainable cost function to shape the
energy surface explicitly for OOD detection. On a CIFAR-10 pre-trained
WideResNet, using the energy score reduces the average FPR (at TPR 95%) by
18.03% compared to the softmax confidence score. With energy-based training,
our method outperforms the state-of-the-art on common benchmarks.


  A superconducting quantum magnetometer based on magnetic flux-driven
modulation of the density of states of a proximized metallic nanowire is
theoretically analyzed. With optimized geometrical and material parameters
transfer functions up to a few mV/Phi_0 and intrinsic flux noise ~10^{-9}Phi_0
Hz^{-1/2} below 1 K are achievable. The opportunity to access single-spin
detection joined with limited dissipation (of the order of ~ 10^{-14} W) make
this magnetometer interesting for the investigation of the switching dynamics
of molecules or individual magnetic nanoparticles.


  Simulation frameworks are important tools for the analysis and design of
communication networks and protocols, but they can result extremely costly
and/or complex (for the case of very specialized tools), or too naive and
lacking proper features and support (for the case of ad-hoc tools). In this
paper, we present an analysis of three 5G scenarios using 'simmer', a recent R
package for discrete-event simulation that sits between the above two
paradigms. As our results show, it provides a simple yet very powerful syntax,
supporting the efficient simulation of relatively complex scenarios at a low
implementation cost.


  We continue the study of the homogenization of coercive non-convex
Hamilton-Jacobi equations in random media identifying two general classes of
Hamiltonians with very distinct behavior. For the first class there is no
homogenization in a particular environment while for the second homogenization
takes place in environments with finite range dependence. Motivated by the
recent counter-example of Ziliotto, who constructed a coercive but non-convex
Hamilton-Jacobi equation with stationary ergodic random potential field for
which homogenization does not hold, we show that same happens for coercive
Hamiltonians which have a strict saddle-point, a very local property. We also
identify, based on the recent work of Armstrong and Cardaliaguet on the
homogenization of positively homogeneous random Hamiltonians in environments
with finite range dependence, a new general class Hamiltonians, namely
equations with uniformly strictly star-shaped sub-level sets, which homogenize.


  The notion of density of a finite set is discussed. We proof a general
theorem of set theory which refines Bose-Einstein distribution.


  The past years have witnessed great progress on remote sensing (RS) image
interpretation and its wide applications. With RS images becoming more
accessible than ever before, there is an increasing demand for the automatic
interpretation of these images. In this context, the benchmark datasets serve
as essential prerequisites for developing and testing intelligent
interpretation algorithms. After reviewing existing benchmark datasets in the
research community of RS image interpretation, this article discusses the
problem of how to efficiently prepare a suitable benchmark dataset for RS image
interpretation. Specifically, we first analyze the current challenges of
developing intelligent algorithms for RS image interpretation with bibliometric
investigations. We then present the general guidances on creating benchmark
datasets in efficient manners. Following the presented guidances, we also
provide an example on building RS image dataset, i.e., Million-AID, a new
large-scale benchmark dataset containing a million instances for RS image scene
classification. Several challenges and perspectives in RS image annotation are
finally discussed to facilitate the research in benchmark dataset construction.
We do hope this paper will provide the RS community an overall perspective on
constructing large-scale and practical image datasets for further research,
especially data-driven ones.


  We have developed models of metallic alloy glass forming ability based on
newly computationally accessible features obtained from molecular dynamics
simulations. In this work we showed that it is possible to increase the
predictive value of GFA models by using input features obtained from molecular
dynamics simulations. Such features require only relatively straightforward and
scalable simulations, making them significantly easier and less expensive to
obtain than experimental measurements. We generated a database of molecular
dynamics critical cooling rates along with associated candidate features that
are inspired from previous research on GFA. Out of the list of 9 proposed GFA
features, we identify two as being the most important to performance through a
LASSO model. Enthalpy of crystallization and icosahedral-like fraction at 100 K
showed promise because they enable a significant improvement to model
performance and because they are accessible to flexible ab initio quantum
mechanical methods readily applicable to almost all systems. This advancement
in computationally accessible features for machine learning predictions GFA
will enable future models to more accurately predict new glass forming alloys.


  The convergence rates of iterative methods for solving a linear system
$\mathbf{A} x = b$ typically depend on the condition number of the matrix
$\mathbf{A}$. Preconditioning is a common way of speeding up these methods by
reducing that condition number in a computationally inexpensive way. In this
paper, we revisit the decades-old problem of how to best improve $\mathbf{A}$'s
condition number by left or right diagonal rescaling. We make progress on this
problem in several directions.
  First, we provide new bounds for the classic heuristic of scaling
$\mathbf{A}$ by its diagonal values (a.k.a. Jacobi preconditioning). We prove
that this approach reduces $\mathbf{A}$'s condition number to within a
quadratic factor of the best possible scaling. Second, we give a solver for
structured mixed packing and covering semidefinite programs (MPC SDPs) which
computes a constant-factor optimal scaling for $\mathbf{A}$ in
$\widetilde{O}(\text{nnz}(\mathbf{A}) \cdot \text{poly}(\kappa^\star))$ time;
this matches the cost of solving the linear system after scaling up to a
$\widetilde{O}(\text{poly}(\kappa^\star))$ factor. Third, we demonstrate that a
sufficiently general width-independent MPC SDP solver would imply near-optimal
runtimes for the scaling problems we consider, and natural variants concerned
with measures of average conditioning.
  Finally, we highlight connections of our preconditioning techniques to
semi-random noise models, as well as applications in reducing risk in several
statistical regression models.


  In value-based deep reinforcement learning with replay memories, the batch
size parameter specifies how many transitions to sample for each gradient
update. Although critical to the learning process, this value is typically not
adjusted when proposing new algorithms. In this work we present a broad
empirical study that suggests {\em reducing} the batch size can result in a
number of significant performance gains; this is surprising, as the general
tendency when training neural networks is towards larger batch sizes for
improved performance. We complement our experimental findings with a set of
empirical analyses towards better understanding this phenomenon.


  We find that significant incompleteness in stellar number counts results in a
significant overestimate of the microlensing optical depth $\tau$ and event
rate per star per year $\Gamma$ toward the Galactic bulge from the first two
years of the MOA-II survey. We find that the completeness in Red Clump Giant
(RCG) counts $f_{\rm RC}$ decreases proportional to the galactic latitude $b$,
as $f_{\rm RC}=(0.63\pm0.11)-(0.052\pm0.028)\times b$, ranging between 1 and
0.7 at $b=-6^\circ\sim-1.5^\circ$. The previous measurements using all sources
by Difference Image Analysis (DIA) by MACHO and MOA-I suffer the same bias. On
the other hand, the measurements using a RCG sample by OGLE-II, MACHO and EROS
were free from this bias because they selected only the events associated with
the resolved stars. Thus, the incompleteness both in the number of events and
stellar number count cancel out. We estimate $\tau$ and $\Gamma$ by correcting
this incompleteness. In the central fields with $|l|<5^\circ$, we find
$\Gamma=[18.74\pm0.91]\times10^{-6}\exp[(0.53\pm0.05)(3-|b|)]$ star$^{-1}$
yr$^{-1}$ and $\tau_{200}=[1.84\pm0.14]\times10^{-6}\exp[(0.44\pm0.07)(3-|b|)]$
for the 427 events with $t_{\rm E}\leq200\,$days using all sources brighter
than $I_s\leq20$ mag. Our revised all-source $\tau$ measurements are about
2-$\sigma$ smaller than the other all-source measurements and are consistent
with the RCG measurements within 1-$\sigma$. We conclude that the long-standing
problem on discrepancy between the high $\tau$ with all-source samples by DIA
and low $\tau$ with RCG samples can probably be explained by the incompleteness
of the stellar number count. A model fit to these measurements predicts
$\Gamma=4.60\pm0.25\times10^{-5}$ star$^{-1}$ yr$^{-1}$ at $|b|\sim-1^\circ.4$
and $-2^\circ.25<l<3^\circ.75$ for sources with $I<20$, where the future space
mission WFIRST will observe.


  We consider the simplest representative of the class of multiply branched
polymer macromolecules, known as a pom-pom structure. The molecule consists of
a backbone linear chain terminated by two branching points with functionalities
(numbers of side chains) of $f_1$ and $f_2$, respectively. In a symmetrical
case, considered in the present study, one has $f_1=f_2=f$ with the total
number of chains $F=2f+1$. Whereas rheological behaviour of melts of pom-pom
molecules are intensively studied so far, we turn our attention towards
conformational properties of such polymers in a regime of dilute solution. The
universality concept, originated in the critical phenomena and in scaling
properties of polymers, is used in this study. To be able to compare the
outcome of the direct polymer renormalization approach with that obtained via
dissipative particle dynamics simulations, we concentrated on the universal
ratios of the shape characteristics. In this way the differences in the energy
and length scales are eliminated and the universal ratios depend only on space
dimension, solvent quality and a type of molecular branching. Such universal
ratios were evaluated both for a whole molecule and for its individual
branches. For some shape properties, theory and simulations are in excellent
agreement, for other we found the interval of $F$ where both agree reasonably
well. Combination of theoretical and simulation approaches provide thorough
quantitative description of the peculiarities of swelling effects and spatial
extension of pom-pom molecules and are compared with the known results for
simpler molecular topologies.


  We give a description of the Picard group of a reductive group over a number
field as an abelianized Galois cohomology group. It gives another approach of a
result due to Labesse.


  A unified field theory in ten dimensions, of all interactions, can describe
high energy processes occuring in the early universe. In such a theory
transitions that give properties of the universe can occur due to the presence
of algebraic and geometric structures. A correspondence between theory and
observations of the universe is made, to obtain a new interpretation and
properties. This paper consists of a field theory and cosmological model of
dark and normal energy and matter, cosmological constant, acceleration and
inflation in the early universe.


  Vertical integration of two-dimensional materials has recently emerged as an
exciting method for the design of novel electronic and optoelectronic devices.
Using density functional theory, we investigatethe structural and electronic
properties of two heterostruc-tures, graphene/phosphorene (G/BP) and hexagonal
boron nitride/phosphorene (BN/BP). We found that the interlayer distance,
binding energy, and charge transfer in G/BP and BN/BP are similar. Interlayer
noncovalentbonding is predicted due to the weak coupling between the pz orbital
of BP and the {\pi} orbital of graphene and BN. A small amount of electron
transfer from graphene and BN, scaling with the vertical strain, renders BP
slightly n-doped for both heterostructures. Several attractive characteristics
of BP, including direct band gap and linear dichroism, are preserved. However,
a large redistribution of electrostatic potential across the interface is
observed, which may significantly renormalize the carrier dynamics and affect
the excitonic behavior of BP. Our work suggests that graphene and BN can be
used not only as an effective capping layer to protect BP from its structural
and chemical degradation while still maintain its major electronic
characteristics, but also as an active layer to tune the carrier dynamics and
optical properties of BP.


  In this paper, we consider two types of problems that have some similarity in
their structure, namely, min-min problems and min-max saddle-point problems.
Our approach is based on considering the outer minimization problem as a
minimization problem with inexact oracle. This inexact oracle is calculated via
inexact solution of the inner problem, which is either minimization or a
maximization problem. Our main assumptions are that the problem is smooth and
the available oracle is mixed: it is only possible to evaluate the gradient
w.r.t. the outer block of variables which corresponds to the outer minimization
problem, whereas for the inner problem only zeroth-order oracle is available.
To solve the inner problem we use accelerated gradient-free method with
zeroth-order oracle. To solve the outer problem we use either inexact variant
of Vaydya's cutting-plane method or a variant of accelerated gradient method.
As a result, we propose a framework that leads to non-asymptotic complexity
bounds for both min-min and min-max problems. Moreover, we estimate separately
the number of first- and zeroth-order oracle calls which are sufficient to
reach any desired accuracy.


  Spotlight is a proprietary desktop search technology released by Apple in
2004 for its Macintosh operating system Mac OS X 10.4 (Tiger) and remains as a
feature in current releases of macOS. Spotlight allows users to search for
files or information by querying databases populated with filesystem
attributes, metadata, and indexed textual content. Existing forensic research
into Spotlight has provided an understanding of the metadata attributes stored
within the metadata store database. Current approaches in the literature have
also enabled the extraction of metadata records for extant files, but not for
deleted files. The objective of this paper is to research the persistence of
records for deleted files within Spotlight's metadata store, identify if
deleted database pages are recoverable from unallocated space on the volume,
and to present a strategy for the processing of discovered records. In this
paper, the structure of the metadata store database is outlined, and
experimentation reveals that records persist for a period of time within the
database but once deleted, are no longer recoverable. The experimentation also
demonstrates that deleted pages from the database (containing metadata records)
are recoverable from unused space on the filesystem.


  The ion-sensitive field-effect transistor (ISFET) is an emerging technology
that has received much attention in numerous research areas, including
biochemistry, medicine, and security applications. However, compared to other
types of sensors, the complexity of ISFETs make it more challenging to achieve
a sensitive, fast and repeatable response. Therefore, various readout circuits
have been developed to improve the performance of ISFETs, especially to
eliminate the temperature effect. This paper presents a new approach for a
temperature-independent readout circuit that uses the threshold voltage
differences of an ISFET-MOSFET pair. The Linear Technology Simulation Program
with Integrated Circuit Emphasis (LTspice) is used to analyze the ISFET
performance based on the proposed readout circuit characteristics. A
macro-model is used to model ISFET behavior, including the first-level Spice
model for the MOSFET part and Verilog-A to model the surface potential,
reference electrode, and electrolyte of the ISFET to determine the
relationships between variables.In this way, the behavior of the ISFET is
monitored by the output voltage of the readout circuit based on a change in the
electrolyte's hydrogen potential (pH), determined by the simulation. The
proposed readout circuit has a temperature coefficient of 11.9 $ppm/{\deg}C$
for a temperature range of 0-100 ${\deg}C$ and pH between 1 and 13. The
proposed ISFET readout circuit outperforms other designs in terms of simplicity
and not requiring an additional sensor.


  Deep neural networks are powerful tools to model observations over time with
non-linear patterns. Despite the widespread use of neural networks in such
settings, most theoretical developments of deep neural networks are under the
assumption of independent observations, and theoretical results for temporally
dependent observations are scarce. To bridge this gap, we study theoretical
properties of deep neural networks on modeling non-linear time series data.
Specifically, non-asymptotic bounds for prediction error of (sparse)
feed-forward neural network with ReLU activation function is established under
mixing-type assumptions. These assumptions are mild such that they include a
wide range of time series models including auto-regressive models. Compared to
independent observations, established convergence rates have additional
logarithmic factors to compensate for additional complexity due to dependence
among data points. The theoretical results are supported via various numerical
simulation settings as well as an application to a macroeconomic data set.


  Monocular 3D object detection is an essential task in autonomous driving.
However, most current methods consider each 3D object in the scene as an
independent training sample, while ignoring their inherent geometric relations,
thus inevitably resulting in a lack of leveraging spatial constraints. In this
paper, we propose a novel method that takes all the objects into consideration
and explores their mutual relationships to help better estimate the 3D boxes.
Moreover, since 2D detection is more reliable currently, we also investigate
how to use the detected 2D boxes as guidance to globally constrain the
optimization of the corresponding predicted 3D boxes. To this end, a
differentiable loss function, termed as Homography Loss, is proposed to achieve
the goal, which exploits both 2D and 3D information, aiming at balancing the
positional relationships between different objects by global constraints, so as
to obtain more accurately predicted 3D boxes. Thanks to the concise design, our
loss function is universal and can be plugged into any mature monocular 3D
detector, while significantly boosting the performance over their baseline.
Experiments demonstrate that our method yields the best performance (Nov. 2021)
compared with the other state-of-the-arts by a large margin on KITTI 3D
datasets.


  Simultaneous interpretation, the translation of speech from one language to
another in real-time, is an inherently difficult and strenuous task. One of the
greatest challenges faced by interpreters is the accurate translation of
difficult terminology like proper names, numbers, or other entities.
Intelligent computer-assisted interpreting (CAI) tools that could analyze the
spoken word and detect terms likely to be untranslated by an interpreter could
reduce translation error and improve interpreter performance. In this paper, we
propose a task of predicting which terminology simultaneous interpreters will
leave untranslated, and examine methods that perform this task using supervised
sequence taggers. We describe a number of task-specific features explicitly
designed to indicate when an interpreter may struggle with translating a word.
Experimental results on a newly-annotated version of the NAIST Simultaneous
Translation Corpus (Shimizu et al., 2014) indicate the promise of our proposed
method.


  We discuss the design and performance of a threshold Cerenkov counter for
identification of charged hadrons. The radiator is pressurized gas, which is
contained in thin-walled cylindrical modules. A mirror system of novel design
transports Cerenkov photons to photomultiplier tubes. This system is compact,
contains relatively little material, and has a large fraction of active volume.
A prototype of a module designed for the proposed CLEO III detector has been
studied using cosmic rays. Results from these studies show good agreement with
a detailed Monte Carlo simulation of the module and indicate that it should
achieve separation of pions and kaons at the 2.5-3.0sigma level in the momentum
range 0.8-2.8 GeV/c. We predict performance for specific physics analyses using
a GEANT-based simulation package.


  We review the progress in atomic structure theory with a focus on superheavy
elements and the aim to predict their ground state configuration and element's
placement in the periodic table. To understand the electronic structure and
correlations in the regime of large atomic numbers, it is important to
correctly solve the Dirac equation in strong Coulomb fields, and also to take
into account quantum electrodynamic effects. We specifically focus on the
fundamental difficulties encountered when dealing with the many-particle Dirac
equation. We further discuss the possibility for future many-electron atomic
structure calculations going beyond the critical nuclear charge \(Z_{\rm
crit}\approx 170\), where levels such as the \(1s\) shell dive into the
negative energy continuum (\(E_{n\kappa}<-m_ec^2\)). The nature of the
resulting Gamow states within a rigged Hilbert space formalism is highlighted.


  Over a non-closed field, it is a common strategy to use separable algebras as
invariants to distinguish algebraic and geometric objects. The most famous
example is the deep connection between Severi-Brauer varieties and central
simple algebras. For more general varieties, one might use endomorphism
algebras of line bundles, of indecomposable vector bundles, or of exceptional
objects in their derived categories.
  Using Galois cohomology, we describe a new invariant of reductive algebraic
groups that captures precisely when this strategy will fail. Our main result
characterizes this invariant in terms of coflasque resolutions of linear
algebraic groups introduced by Colliot-Th\'el\`ene. We determine whether or not
this invariant is trivial for many fields. For number fields, we show it agrees
with the Tate-Shafarevich group of the linear algebraic group, up to behavior
at real places.


  Education in the digital period in which we live, is reaching challenges
never before seen, preceded by phenomena that involve not only traditional
social units, but also new virtual communities; Innovating is difficult, it is
a challenge, however, we must think of new teaching methods that impact the
current generation of students, who arrive with new needs and expectations. The
construction of knowledge from the subject and the virtual world that surrounds
it, establishes the basis for the development of a new model of teaching, where
the classroom is the particular representation of a new physical-cybernetic
ecosystem composed of the three large dimensions. Which are part of this new
techno-social convergence (human - information - machine); Allowing an
interrelation between the student, information, machine and the teacher; Using
Biocybernetic methods of influence, control and replication, by means of the
massive impact vector (i); In addition, the development of new strategies
assisted by cybernetics and the updating of academic content according to the
new teaching environment. Hence the importance of this study, which leads us to
the need for a new model of transforming academic instruction, which is not
based on a conglomerate of technological tools, but establishes a new
educational and transformative model, based on "Collaborative Thinking" and the
ubiquity of information, thus establishing the relationship between the subject
and object of study, thus allowing us to establish the new Biocybernetic
educational paradigm in the digital period.


  We prove that a Latt' es map admits an eventually simply-connected wandering
continuum precisely when it is flexible. The simply-connected wandering
continuum is a line segment in a bi-infinite geodesic under the flat metric.


  This paper has two main goals. First, we prove nonabelian refinements of
basechange theorems in \'etale cohomology (i.e., prove analogues of the
classical statements for sheaves of spaces). Second, we apply these theorems to
prove a number of results about the \'etale homotopy type. Specifically, we
prove nonabelian refinements of the smooth basechange theorem, Huber-Gabber
affine analogue of the proper basechange theorem, and Fujiwara-Gabber rigidity
theorem. Our methods also recover Chough's nonabelian refinement of the proper
basechange theorem. Transporting an argument of Bhatt-Mathew to the nonabelian
setting, we apply nonabelian proper basechange to show that the profinite
\'etale homotopy type satisfies arc-descent. Using nonabelian smooth and proper
basechange and descent, we give rather soft proofs of a number of K\"unneth
formulas for the \'etale homotopy type.


  We present a method to compressed the 2D transverse phase space projections
from a hadron accelerator and use that information to predict the beam
transmission. This method assumes that it is possible to obtain at least three
projections of the 4D transverse phase space and that an accurate simulation
model is available for the beamline. Using a simulated model we show that, a
procedure using a convolutional autoencoder can be trained to reduce
phase-space information which can later be used to predict the beam
transmission. Finally, we argue that although using projections from a
realistic non-linear distribution produces less accurate results, the method
still generalizes well.


  Developments in deeply virtual Compton scattering allow the direct
measurements of scattering amplitudes for exchange of a highly virtual photon
with fine spatial resolution. Real-space images of the target can be obtained
from this information. Spatial resolution is determined by the momentum
transfer rather than the wavelength of the detected photon. Quantum photographs
of the proton, nuclei, and other elementary particles with resolution on the
scale of a fraction of a femtometer is feasible with existing experimental
technology.


  We revisit the three-body problem in the framework of general relativity. The
Newtonian N-body problem admits choreographic solutions, where a solution is
called choreographic if every massive particles move periodically in a single
closed orbit. One is a stable figure-eight orbit for a three-body system, which
was found first by Moore (1993) and re-discovered with its existence proof by
Chenciner and Montgomery (2000). In general relativity, however, the periastron
shift prohibits a binary system from orbiting in a single closed curve.
Therefore, it is unclear whether general relativistic effects admit a
choreographic solution such as the figure eight. We carefully examine general
relativistic corrections to initial conditions so that an orbit for a
three-body system can be closed and a figure eight. This solution is still
choreographic. This illustration suggests that the general relativistic N-body
problem also may admit a certain class of choreographic solutions.


  In this paper, we define locally convex vector spaces of weighted vector
fields and use them as model spaces for Lie groups of weighted diffeomorphisms
on Riemannian manifolds. We prove an easy condition on the weights that ensures
that these groups contain the compactly supported diffeomorphisms. We finally
show that for the special case where the manifold is the euclidean space, these
Lie groups coincide with the ones constructed in the author's earlier work
["Weighted diffeomorphism groups of Banach spaces and weighted mapping groups".
In: Dissertationes Math. 484 (2012), p. 128. DOI: 10.4064/dm484-0-1].


  Computer simulations show that liquids of molecules with harmonic
intramolecular bonds may have "pseudoisomorphic" lines of approximately
invariant dynamics in the thermodynamic phase diagram. We demonstrate that
these lines can be identified by requiring scale invariance of the
inherent-structure reduced-unit low-frequency vibrational spectrum evaluated
for a single equilibrium configuration. This rationalizes why excess-entropy
scaling, density scaling, and isochronal superposition apply for many liquids
with internal degrees of freedom.


  We define the Simons-Sullivan differential analytic index by translating the
Freed-Lott differential analytic index via explicit ring isomorphisms between
Freed-Lott differential K-theory and Simons-Sullivan differential K-theory. We
prove the differential Grothendieck-Riemann-Roch theorem in Simons-Sullivan
differential K-theory using a theorem of Bismut.


  Dynamic crack branching in unsaturated porous media holds significant
relevance in various fields, including geotechnical engineering, geosciences,
and petroleum engineering. This article presents a numerical investigation into
dynamic crack branching in unsaturated porous media using a recently developed
coupled micro-periporomechanics paradigm. This paradigm extends the
periporomechanics model by incorporating the micro-rotation of the solid
skeleton. Within this framework, each material point is equipped with three
degrees of freedom: displacement, micro-rotation, and fluid pressure.
Consistent with the Cosserat continuum theory, a length scale associated with
the micro-rotation of material points is inherently integrated into the model.
This study encompasses several key aspects: (1) Validation of the coupled
micro-periporomechanics paradigm for effectively modeling crack branching in
deformable porous media, (2) Examination of the transition from a single branch
to multiple branches in porous media under drained conditions, (3) Simulation
of single crack branching in unsaturated porous media under dynamic loading
conditions, and (4) Investigation of multiple crack branching in unsaturated
porous media under dynamic loading conditions. The numerical results obtained
in this study are systematically analyzed to elucidate the factors that
influence dynamic crack branching in porous media subjected to dynamic loading.
Furthermore, the comprehensive numerical findings underscore the efficacy and
robustness of the coupled micro-periporomechanics paradigm in accurately
modeling dynamic crack branching in variably saturated porous media.


  This is an extended, renovated and updated report on a joint work which the
second named author presented at the Conference on Algebraic Geometry held at
Saitama University, 15-17 of March, 1995. The main result is an inequality for
the numerical type of singularities of a plane curve, which involves the degree
of the curve, the multiplicities and the Milnor numbers of its singular points.
It is a corollary of the logarithmic Bogomolov-Miyaoka-Yau's type inequality
due to Miyaoka. It was first proven by F. Sakai at 1990 and rediscovered by the
authors independently in the particular case of an irreducible cuspidal curve
at 1992. Our proof is based on the localization, the local Zariski--Fujita
decomposition and uses a graph discriminant calculus. The key point is a local
analog of the BMY-inequality for a plane curve germ. As a corollary, a
boundedness criterium for a family of plane curves has been obtained. Another
application of our methods is the following fact: a rigid rational cuspidal
plane curve cannot have more than 9 cusps.


  Everett's interpretation of quantum mechanics was proposed to avoid problems
inherent in the prevailing interpretational frame. It assumes that quantum
mechanics can be applied to any system and that the state vector always evolves
unitarily. It then claims that whenever an observable is measured, all possible
results of the measurement exist. This notion of multiplicity has been
understood in different ways by proponents of Everett's theory. In fact the
spectrum of opinions on various ontological questions raised by Everett's
approach is rather large, as we attempt to document in this critical review. We
conclude that much remains to be done to clarify and specify Everett's
approach.


  We present a microscopic theory of a lateral quantum dot molecule in a radial
magnetic field with a Greenberger- Horne- Zeilinger (GHZ) maximally entangled
three particle groundstate. The quantum dot molecule consists of three quantum
dots with one electron spin each forming a central equilateral triangle. The
anti-ferromagnetic spin-spin interaction is changed to the ferromagnetic
interaction by additional doubly occupied quantum dots, one dot near each side
of a triangle. The magnetic field is provided by micro-magnets. The interaction
among the electrons is described within an extended Hubbard Hamiltonian and
electronic states are obtained using configuration interaction approach. The
set of parameters is established for which the ground state of the molecule in
a radial magnetic field is well approximated by a GHZ state.


  We describe four classical undergraduate physics experiments that were done
with everyday objects and low-cost sensors: mechanical oscillations,
transmittance of light through a slab of matter, beam deformation under load,
and thermal relaxation due to heat loss. We used these experiments to train
students for experimental homework projects but they could be used and expanded
in a variety of contexts: lecture demonstrations, low cost students' labs,
science projects, distance learning courses...


  Reward-based crowdfunding platforms are becoming increasingly popular to
finance projects proposing innovative products, e.g., Kickstarter. One
important challenge of this form of financing is the uncertainty in the quality
of projects. To mitigate the negative effects of this uncertainty for backers,
platforms share information regarding the decisions of earlier backers visiting
the project campaign pages. This allows backers not only to rely on their
expertise to identify project qualities but also to learn from the decisions of
their fellow backers who might be more informed. Current studies on
observational learning (OL) in crowdfunding mainly focus on predicting the
success chances of projects, and there is a lack of understanding of how OL
affects crowdfunding dynamics for backers, project creators and platforms. This
paper aims to fill this gap by using a theoretical OL model involving two
projects competing for funding from backers who may have differentiated
expertness in identifying project quality. By introducing various performance
measures for backers, creators and platforms and comparing these measures under
OL to the case without learning, we provide a thorough analysis of how OL
impacts crowdfunding outcomes. We find that information sharing and OL always
benefit backers, especially when the early backers are experts. Regarding the
impact of OL on creators and platforms, our analysis reveals two understudied
but important aspects: the tightness of the competition for projects according
to the availability of funding, and the quality difference among the proposed
projects. Additionally, we investigate how OL affects the quality decisions of
creators and show that OL increases the incentive for high-quality products,
especially in situations where funding is scarce.


  We attach a certain $n \times n$ matrix $A_n$ to the Dirichlet series
$L(s)=\sum_{k=1}^{\infty}a_k k^{-s}$. We study the determinant, characteristic
polynomial, eigenvalues, and eigenvectors of these matrices. The determinant of
$A_n$ can be understood as a weighted sum of the first $n$ coefficients of the
Dirichlet series $L(s)^{-1}$. We give an interpretation of the partial sum of a
Dirichlet series as a product of eigenvalues. In a special case, the
determinant of $A_n$ is the sum of the M\"obius function. We disprove a
conjecture of Barrett and Jarvis regarding the eigenvalues of $A_n$.


  Inspired by the AdS/CFT correspondence, we show that any G/H symmetry
breaking pattern can be described by a simple two-site moose diagram. This
construction trivially reproduces the CCWZ prescription in the context of
Hidden Local Symmetry. We interpret this moose in a novel way to show that many
little Higgs theories can emerge from ordinary chiral symmetry breaking in
scaled-up QCD. We apply this reasoning to the simple group little Higgs to see
that the same low energy degrees of freedom can arise from a variety of UV
complete theories. We also show how models of holographic composite Higgs
bosons can turn into brane-localized little technicolor theories by
"integrating in" the IR brane.


  We study the Levenberg-Marquardt (L-M) method for solving the highly
nonlinear and ill-posed inverse problem of identifying the Robin coefficients
in elliptic and parabolic systems. The L-M method transforms the Tikhonov
regularized nonlinear non-convex minimizations into convex minimizations. And
the quadratic convergence of the L-M method is rigorously established for the
nonlinear elliptic and parabolic inverse problems for the first time, under a
simple novel adaptive strategy for selecting regularization parameters during
the L-M iteration. Then the surrogate functional approach is adopted to solve
the strongly ill-conditioned convex minimizations, resulting in an explicit
solution of the minimisation at each L-M iteration for both the elliptic and
parabolic cases. Numerical experiments are provided to demonstrate the accuracy
and efficiency of the methods.


  An n-ary operation q:A^n->A is called an n-ary quasigroup of order |A| if in
x_0=q(x_1,...,x_n) knowledge of any n elements of x_0,...,x_n uniquely
specifies the remaining one. An n-ary quasigroup q is permutably reducible if
q(x_1,...,x_n)=p(r(x_{s(1)},...,x_{s(k)}),x_{s(k+1)},...,x_{s(n)}) where p and
r are (n-k+1)-ary and k-ary quasigroups, s is a permutation, and 1<k<n. For
even n we construct a permutably irreducible n-ary quasigroup of order 4r such
that all its retracts obtained by fixing one variable are permutably reducible.
We use a partial Boolean function that satisfies similar properties. For odd n
the existence of a permutably irreducible n-ary quasigroup such that all its
(n-1)-ary retracts are permutably reducible is an open question; however, there
are nonexistence results for 5-ary and 7-ary quasigroups of order 4.
Keywords:n-ary quasigroups, n-quasigroups, reducibility, Seidel switching,
two-graphs


  We consider the antiferromagnetic Heisenberg model on a distorted diamond
chain and use the localized-magnon picture adapted to a distorted geometry to
discuss some of its high-field low-temperature properties. More specifically,
in our study we assume that the partition function for a slightly distorted
geometry has the same form as for ideal geometry, though with slightly
dispersive one-magnon energies. We also discuss the relevance of such a
description to azurite.


  Let $(K, \nu)$ be a valued field, the notions of \emph{augmented valuation},
of \emph{limit augmented valuation} and of \emph{admissible family} of
valuations enable to give a description of any valuation $\mu$ of $K [x]$
extending $\nu$. In the case where the field $K$ is algebraically closed, this
description is particularly simple and we can reduce it to the notions of
\emph{minimal pair} and \emph{pseudo-convergent family}. Let $(K, \nu )$ be a
henselian valued field and $\bar\nu$ the unique extension of $\nu$ to the
algebraic closure $\bar K$ of $K$ and let $\mu$ be a valuation of $ K [x]$
extending $\nu$, we study the extensions $\bar\mu$ from $\mu$ to $\bar K [x]$
and we give a description of the valuations $\bar\mu_i$ of $\bar K [x]$ which
are the extensions of the valuations $\mu_i$ belonging to the admissible family
associated with $\mu$.


  The $^{58}$Ni $(n,\gamma)$ cross section has been measured at the neutron
time of flight facility n_TOF at CERN, in the energy range from 27 meV up to
400 keV. In total, 51 resonances have been analyzed up to 122 keV. Maxwellian
averaged cross sections (MACS) have been calculated for stellar temperatures of
kT$=$5-100 keV with uncertainties of less than 6%, showing fair agreement with
recent experimental and evaluated data up to kT = 50 keV. The MACS extracted in
the present work at 30 keV is 34.2$\pm$0.6$_\mathrm{stat}\pm$1.8$_\mathrm{sys}$
mb, in agreement with latest results and evaluations, but 12% lower relative to
the recent KADoNIS compilation of astrophysical cross sections. When included
in models of the s-process nucleosynthesis in massive stars, this change
results in a 60% increase of the abundance of $^{58}$Ni, with a negligible
propagation on heavier isotopes. The reason is that, using both the old or the
new MACS, 58Ni is efficiently depleted by neutron captures.


  We study the degree of reliability of extrapolation of complex
electromagnetic permittivity functions based on their analyticity properties.
Given two analytic functions, representing extrapolants of the same
experimental data, we examine how much they can differ at an extrapolation
point outside of the experimentally accessible frequency band. We give a sharp
upper bound on the worst case extrapolation error, in terms of a solution of an
integral equation of Fredholm type. We conjecture and give numerical evidence
that this bound exhibits a power law precision deterioration as one moves
further away from the frequency band containing measurement data.


  We propose a non-deterministic CNOT gate based on a quantum cloner, a quantum
switch based on all optical routing of single photon by single photon, a
quantum-dot spin in a double-sided optical microcavity with two photonic
qubits, delay lines and other linear optical photonic devices. Our CNOT
provides a fidelity of 78% with directly useful outputs for a quantum computing
circuit and requires no ancillary qubits or electron spin measurements.


  An integrable Hamiltonian system presents monodromy if the action-angle
variables cannot be defined globally. As a prototype of classical monodromy
with azimuthal symmetry, we consider a linear molecule interacting with
external fields and explore the topology structure of its phase space. Based on
the behavior of closed orbits around singular points or regions of the
energy-momentum plane, a semi-theoretical method is derived to detect classical
monodromy. The validity of the monodromy test is numerically illustrated for
several systems with azimuthal symmetry.


  Recently, the deep learning community has given growing attention to neural
architectures engineered to learn problems in relational domains. Convolutional
Neural Networks employ parameter sharing over the image domain, tying the
weights of neural connections on a grid topology and thus enforcing the
learning of a number of convolutional kernels. By instantiating trainable
neural modules and assembling them in varied configurations (apart from grids),
one can enforce parameter sharing over graphs, yielding models which can
effectively be fed with relational data. In this context, vertices in a graph
can be projected into a hyperdimensional real space and iteratively refined
over many message-passing iterations in an end-to-end differentiable
architecture. Architectures of this family have been referred to with several
definitions in the literature, such as Graph Neural Networks, Message-passing
Neural Networks, Relational Networks and Graph Networks. In this paper, we
revisit the original Graph Neural Network model and show that it generalises
many of the recent models, which in turn benefit from the insight of thinking
about vertex \textbf{types}. To illustrate the generality of the original
model, we present a Graph Neural Network formalisation, which partitions the
vertices of a graph into a number of types. Each type represents an entity in
the ontology of the problem one wants to learn. This allows - for instance -
one to assign embeddings to edges, hyperedges, and any number of global
attributes of the graph. As a companion to this paper we provide a
Python/Tensorflow library to facilitate the development of such architectures,
with which we instantiate the formalisation to reproduce a number of models
proposed in the current literature.


  Asymptotically flat static causal fermion systems are introduced. Their total
mass is defined as a limit of surface layer integrals which compare the
measures describing the asymptotically flat spacetime and a vacuum spacetime
near spatial infinity. Our definition does not involve any regularity
assumptions; it even applies to singular or generalized "quantum" spacetimes. A
positive mass theorem is proven. Our methods and results explain why and how
the causal action principle incorporates the nonlinear effects of gravity for
static systems.


  We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval.
Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank,
Query Bank and Relevance Set. The FAQ Bank contains ~16K FAQ items scraped from
55 credible websites (e.g., CDC and WHO). For evaluation, we introduce Query
Bank and Relevance Set, where the former contains 1,236 human-paraphrased
queries while the latter contains ~32 human-annotated FAQ items for each query.
We analyze COUGH by testing different FAQ retrieval models built on top of BM25
and BERT, among which the best model achieves 48.8 under P@5, indicating a
great challenge presented by COUGH and encouraging future research for further
improvement. Our COUGH dataset is available at
https://github.com/sunlab-osu/covid-faq.


  For multimodal tasks, a good feature extraction network should extract
information as much as possible and ensure that the extracted feature embedding
and other modal feature embedding have an excellent mutual understanding. The
latter is often more critical in feature fusion than the former. Therefore,
selecting the optimal feature extraction network collocation is a very
important subproblem in multimodal tasks. Most of the existing studies ignore
this problem or adopt an ergodic approach. This problem is modeled as an
optimization problem in this paper. A novel method is proposed to convert the
optimization problem into an issue of comparative upper bounds by referring to
the general practice of extreme value conversion in mathematics. Compared with
the traditional method, it reduces the time cost.
  Meanwhile, aiming at the common problem that the feature similarity and the
feature semantic similarity are not aligned in the multimodal time-series
problem, we refer to the idea of contrast learning and propose a multimodal
time-series contrastive loss(MTSC).
  Based on the above issues, We demonstrated the feasibility of our approach in
the audio-visual video parsing task. Substantial analyses verify that our
methods promote the fusion of different modal features.


  In this article, we develop methods for sample size and power calculations in
four-level intervention studies when intervention assignment is carried out at
any level, with a particular focus on cluster randomized trials (CRTs). CRTs
involving four levels are becoming popular in health care research, where the
effects are measured, for example, from evaluations (level 1) within
participants (level 2) in divisions (level 3) that are nested in clusters
(level 4). In such multi-level CRTs, we consider three types of intraclass
correlations between different evaluations to account for such clustering: that
of the same participant, that of different participants from the same division,
and that of different participants from different divisions in the same
cluster. Assuming arbitrary link and variance functions, with the proposed
correlation structure as the true correlation structure, closed-form sample
size formulas for randomization carried out at any level (including
individually randomized trials within a four-level clustered structure) are
derived based on the generalized estimating equations approach using the
model-based variance and using the sandwich variance with an independence
working correlation matrix. We demonstrate that empirical power corresponds
well with that predicted by the proposed method for as few as 8 clusters, when
data are analyzed using the matrix-adjusted estimating equations for the
correlation parameters with a bias-corrected sandwich variance estimator, under
both balanced and unbalanced designs.


  Many tasks can be composed from a few independent components. This gives rise
to a combinatorial explosion of possible tasks, only some of which might be
encountered during training. Under what circumstances can transformers
compositionally generalize from a subset of tasks to all possible combinations
of tasks that share similar components? Here we study a modular multitask
setting that allows us to precisely control compositional structure in the data
generation process. We present evidence that transformers learning in-context
struggle to generalize compositionally on this task despite being in principle
expressive enough to do so. Compositional generalization becomes possible only
when introducing a bottleneck that enforces an explicit separation between task
inference and task execution.


  We examine the response of an Unruh-DeWitt particle detector coupled to a
massless scalar field on the (3+1)-dimensional Schwarzschild spacetime, in the
Boulware, Hartle-Hawking and Unruh states, for static detectors and detectors
on circular geodesics, by primarily numerical methods. For the static detector,
the response in the Hartle-Hawking state exhibits the known thermality at the
local Hawking temperature, and the response in the Unruh state is thermal at
the local Hawking temperature in the limit of a large detector energy gap. For
the circular-geodesic detector, we find evidence of thermality in the limit of
a large energy gap for the Hartle-Hawking and Unruh states, at a temperature
that exceeds the Doppler-shifted local Hawing temperature. Detailed
quantitative comparisons between the three states are given. The response in
the Hartle-Hawking state is compared with the response in the Minkowski vacuum
and in the Minkowski thermal state for the corresponding Rindler, drifted
Rindler, and circularly accelerated trajectories. The analysis takes place
within first-order perturbation theory and relies in an essential way on
stationarity.


  Stellar distance is an important basic parameter in stellar astrophysics.
Stars in a cluster are thought to be formed coevally from the same interstellar
cloud of gas and dust. They are therefore expected to have common properties.
These common properties strengthen our ability to constrain theoretical models
and/or to determine fundamental parameters, such as stellar mass, metal
fraction, and distance when tested against an ensemble of cluster stars. Here
we derive a new relation based on solar-like oscillations, photometric
observations, and the theory of stellar structure and evolution of red giant
branch stars to determine cluster distance moduli through the global
oscillation parameters $\Delta\nu$ and $\nu_{\rm max}$, and photometric data
\textit{V}. The values of $\Delta\nu$ and $\nu_{\rm max}$ are derived from
\textit{kepler} observations. At the same time, it is used to interpret the
trends between \textit{V} and $\Delta\nu$. From the analyses of this newly
derived relation and observational data of NGC 6791 and NGC 6819 we devise a
method in which all stars in a cluster are regarded as one entity to determine
the cluster distance modulus. This approach fully reflects the characteristic
of member stars in a cluster as a natural sample. From this method we derive
true distance moduli of $13.09\pm0.10$ mag for NGC 6791 and $11.88\pm0.14$ mag
for NGC 6819. Additionally, we find that the distance modulus only slightly
depends on the metallicity [Fe/H] in the new relation. A change of 0.1 dex in
[Fe/H] will lead to a change of 0.06 mag in the distance modulus.


  Bose-Einstein condensation is usually modeled by nonlinear Schroedinger
equations with harmonic potential. We study the Cauchy problem for these
equations. We show that the local problem can be treated as in the case with no
potential. For the global problem, we establish an evolution law, which is the
analogue of the pseudo-conformal conservation law for the nonlinear
Schroedinger equation. With this evolution law, we give wave collapse criteria,
as well as an upper bound for the blow up time. Taking the physical scales into
account, we finally give a lower bound for the blow up time.


  I present photometric and radial velocity data for Galactic Cepheids, most of
them being in the southern hemisphere. There are 1250 Geneva 7-color
photometric measurements for 62 Cepheids, the average uncertainty per
measurement is better than 0.01 mag. A total of 832 velocity measurements have
been obtained with the CORAVEL radial velocity spectrograph for 46 Cepheids.
The average accuracy of the radial velocity data is 0.38 km/s. There are 33
stars with both photometry and radial velocity data. I discuss the possible
binarity or period change that these new data reveal. I also present reddenings
for all Cepheids with photometry. The data are available electronically.


  Parity binomial edge ideals of simple undirected graphs are introduced.
Unlike binomial edge ideals, they do not have square-free Gr\"obner bases and
are radical if only if the graph is bipartite or the characteristic of the
ground field is not two. The minimal primes are determined and shown to encode
combinatorics of even and odd walks in the graph. A mesoprimary decomposition
is determined and shown to be a primary decomposition in characteristic two.


  Boolean functions can be used to express the groundness of, and trace
grounding dependencies between, program variables in (constraint) logic
programs. In this paper, a variety of issues pertaining to the efficient Prolog
implementation of groundness analysis are investigated, focusing on the domain
of definite Boolean functions, Def. The systematic design of the representation
of an abstract domain is discussed in relation to its impact on the algorithmic
complexity of the domain operations; the most frequently called operations
should be the most lightweight. This methodology is applied to Def, resulting
in a new representation, together with new algorithms for its domain operations
utilising previously unexploited properties of Def -- for instance,
quadratic-time entailment checking. The iteration strategy driving the analysis
is also discussed and a simple, but very effective, optimisation of induced
magic is described. The analysis can be implemented straightforwardly in Prolog
and the use of a non-ground representation results in an efficient, scalable
tool which does not require widening to be invoked, even on the largest
benchmarks. An extensive experimental evaluation is given


  We study the effects of short-range interactions on a generalized
three-dimensional Weyl semimetal, where the band touching points act as the
(anti)monopoles of Abelian Berry curvature of strength $n$. We show that any
local interaction has a \emph{negative} scaling dimension $-2/n$. Consequently
all Weyl semimetals are stable against weak short-range interactions. For
sufficiently strong interactions, we demonstrate that the Weyl semimetal either
undergoes a first order transition into a band insulator or a continuous
transition into a symmetry breaking phase. A translational symmetry breaking
axion insulator and a rotational symmetry breaking semimetal are two prominent
candidates for the broken symmetry phase. At the one-loop order, the
correlation length exponent for continuous transitions is $\nu=n/2$, indicating
their non-Gaussian nature for any $n>1$. We also discuss the scaling of the
thermodynamic and transport quantities in general Weyl semimetals as well as
inside the broken symmetry phases.


  On-orbit service is important for maintaining the sustainability of space
environment. Space-based visible camera is an economical and lightweight sensor
for situation awareness during on-orbit service. However, it can be easily
affected by the low illumination environment. Recently, deep learning has
achieved remarkable success in image enhancement of natural images, but seldom
applied in space due to the data bottleneck. In this article, we first propose
a dataset of the Beidou Navigation Satellite for on-orbit low-light image
enhancement (LLIE). In the automatic data collection scheme, we focus on
reducing domain gap and improving the diversity of the dataset. we collect
hardware in-the-loop images based on a robotic simulation testbed imitating
space lighting conditions. To evenly sample poses of different orientation and
distance without collision, a collision-free working space and pose stratified
sampling is proposed. Afterwards, a novel diffusion model is proposed. To
enhance the image contrast without over-exposure and blurring details, we
design a fused attention to highlight the structure and dark region. Finally,
we compare our method with previous methods using our dataset, which indicates
that our method has a better capacity in on-orbit LLIE.


  We present measurements of resonant tunneling through discrete energy levels
of a silicon double quantum dot formed in a thin silicon-on-insulator layer. In
the absence of piezoelectric phonon coupling, spontaneous phonon emission with
deformation-potential coupling accounts for inelastic tunneling through the
ground states of the two dots. Such transport measurements enable us to observe
a Pauli spin blockade due to effective two-electron spin-triplet correlations,
evident in a distinct bias-polarity dependence of resonant tunneling through
the ground states. The blockade is lifted by the excited-state resonance by
virtue of efficient phonon emission between the ground states. Our experiment
demonstrates considerable potential for investigating silicon-based spin
dynamics and spin-based quantum information processing.


  A low-energy fixed-point Hamiltonian is constructed for the s-wave
odd-frequency pairing state with staggered ordering vector in the two-channel
Kondo lattice. The effective model is justified because it reproduces
low-energy behaviors of self energy obtained by the dynamical mean-field
theory. The retardation effect is essential for the odd-frequency pairing,
which comes from the hybridization process between conduction electrons and
pseudofermions originating from localized spins at low energies. Using the
effective Hamiltonian, the electromagnetic response functions are
microscopically calculated. The present system shows the "weak" Meissner
effect, where both paramagnetic and diamagnetic parts contribute to the
Meissner kernel to give a small total diamagnetic response in the
superconducting state. This feature is in contrast to the ordinary s-wave BCS
pairing where only the diamagnetic kernel is finite in the ground state. The
staggered nature of the odd-frequency order parameter plays an important role
for the sign of the Meissner kernel.


  In the framework of the Jaynes-Cummings model, we investigate how atomic
lineshapes are affected by coherently driving the atom-field interaction. We
pay particular attention to the two-level atom interaction with a thermal
cavity field, when both are influenced by external classical fields. Adopting a
density matrix formalism, we calculate the average atomic inversion and
demonstrate how the corresponding lineshapes vary as a function of the average
number of thermal photons, and the atom-field classical coupling. Furthermore,
we compare these results with those obtained from the standard Jaynes-Cummings
model and validate our findings through numerical calculations.


  This article treats optimal sparse control problems with multiple constraints
defined at intermediate points of the time domain. For such problems with
intermediate constraints, we first establish a new Pontryagin maximum principle
that provides first order necessary conditions for optimality in such problems.
Then we announce and employ a new numerical algorithm to arrive at, in a
computationally tractable fashion, optimal state-action trajectories from the
necessary conditions given by our maximum principle. Several detailed
illustrative examples are included.


  In emergency egress crowd behavior critically affects egress efficiency and
public safety. By integrating psychological principles to Newtonian motion of
crowd, a fluid-based equation is derived in this paper to explore how energy in
different forms is balanced when pedestrian crowd pass through a bottleneck.
Such fluid-based analysis helps to bridge a gap among psychological findings,
pedestrian models and simulation results, and it further provides a new
perspective to understand how the faster-is-slower effect is caused and how
disastrous events (e.g., jamming and stampede) occur at a bottleneck passage.


  The paper concerns the use of satellites of the Galileo constellation for
relativistic positioning and for measurements of the gravito-magnetic effects
induced by the angular momentum both of the Earth and of the dark halo of the
Milky Way. The experimental approach is based on the generalized Sagnac effect,
induced both by the rotation of the device and the fact that the observer is
located within the gravitational field of a spinning mass. Among the possible
sources there is also the angular momentum of the dark halo of the Milky Way.
Time modulation of the expected signal would facilitate its disentanglement
from the other contributions. The modulation could be obtained using satellites
located on different orbital planes.


  We examine the effectiveness of the Generalised Quasilinear (GQL)
Approximation introduced by Marston et al (2016). This approximation splits the
variables into large and small scales in directions where there is a
translational symmetry and removes nonlinear interactions involving only small
scales. We utilise as a paradigm problem three-dimensional, turbulent, rotating
Couette flow. We compare the results obtained from Direct Numerical Solution of
the equations with those from Quasilinear (QL) and GQL calculations. In this
three-dimensional setting, there is a choice of cut-off wavenumber for the GQL
approximation both in the streamwise and in the spanwise directions. We
demonstrate that the GQL approximation significantly improves the accuracy of
mean flows, spectra and two-point correlation functions over models that are
quasilinear in any of the translationally invariant directions, even if only a
few streamwise and spanwise modes are included. We argue that this provides
significant support for a programme of Direct Statistical Simulation utilising
the GQL approximation.


  We calculate the two loop correction to the quark 2-point function with the
non-zero momentum insertion of the flavour singlet axial vector current at the
fully symmetric subtraction point for massless quarks in the modified minimal
subtraction (MSbar) scheme. The Larin method is used to handle $\gamma^5$
within dimensional regularization at this loop order ensuring that the effect
of the chiral anomaly is properly included within the construction.


  Do scientists follow hot topics in their scientific investigations? In this
paper, by performing analysis to papers published in the American Physical
Society (APS) Physical Review journals, it is found that papers are more likely
to be attracted by hot fields, where the hotness of a field is measured by the
number of papers belonging to the field. This indicates that scientists
generally do follow hot topics. However, there are qualitative differences
among scientists from various countries, among research works regarding
different number of authors, different number of affiliations and different
number of references. These observations could be valuable for policy makers
when deciding research funding and also for individual researchers when
searching for scientific projects.


  We discuss prospects for probing short-range sterile neutrino oscillation
using neutrino-nucleus coherent scattering with ultra-low energy ($\sim 10$ eV
- 100 eV) recoil threshold cryogenic Ge detectors. The analysis is performed in
the context of a specific and contemporary reactor-based experimental proposal,
developed in cooperation with the Nuclear Science Center at Texas A\&M
University, and references developing technology based upon economical and
scalable detector arrays. The baseline of the experiment is substantially
shorter than existing measurements, as near as about 2 meters from the reactor
core, and is moreover variable, extending continuously up to a range of about
10 meters. This proximity and variety combine to provide extraordinary
sensitivity to a wide spectrum of oscillation scales, while facilitating the
tidy cancellation of leading systematic uncertainties in the reactor source and
environment. With 100~eV sensitivity, for exposures on the order of 200
kg$\cdot$y, we project an estimated sensitivity to first/fourth neutrino
oscillation with a mass gap $\Delta m^2 \sim 1 \, {\rm eV}^2$ at an amplitude
$\sin^2 2\theta \sim 10^{-1}$, or $\Delta m^2 \sim 0.2 \, {\rm eV}^2$ at unit
amplitude. Larger exposures, around 5,000 kg$\cdot$y, together with 10 eV
sensitivity are capable of probing more than an additional order of magnitude
in amplitude.


  In a class of spatially homogeneous cosmologies including those of Bianchi
type I--VIII mathematical results are presented which show that a scalar field
non-minimally coupled to the scalar curvature of spacetime can dynamically
yield a positive cosmological constant without the potential being required to
include one. More precisely, it is shown that in an exponential potential any
positive coupling constant leads eventually to late-time de Sitter expansion
and isotropization corresponding to a positive cosmological constant and that
this behaviour is independent of the steepness of the potential. This is in
marked contrast to the minimally coupled case where power-law inflation occurs
at most, provided the potential is sufficiently shallow.


  Digital forensic science is very much still in its infancy, but is becoming
increasingly invaluable to investigators. A popular area for research is
seeking a standard methodology to make the digital forensic process accurate,
robust, and efficient. The first digital forensic process model proposed
contains four steps: Acquisition, Identification, Evaluation and Admission.
Since then, numerous process models have been proposed to explain the steps of
identifying, acquiring, analysing, storage, and reporting on the evidence
obtained from various digital devices. In recent years, an increasing number of
more sophisticated process models have been proposed. These models attempt to
speed up the entire investigative process or solve various of problems commonly
encountered in the forensic investigation. In the last decade, cloud computing
has emerged as a disruptive technological concept, and most leading enterprises
such as IBM, Amazon, Google, and Microsoft have set up their own cloud-based
services. In the field of digital forensic investigation, moving to a
cloud-based evidence processing model would be extremely beneficial and
preliminary attempts have been made in its implementation. Moving towards a
Digital Forensics as a Service model would not only expedite the investigative
process, but can also result in significant cost savings - freeing up digital
forensic experts and law enforcement personnel to progress their caseload. This
paper aims to evaluate the applicability of existing digital forensic process
models and analyse how each of these might apply to a cloud-based evidence
processing paradigm.


  We characterize digital cash as the digital equivalent of physical cash:
secure, fungible, decentralized, directly controlled, privacy-preserving; but
enhanced with qualitatively new functionality. It is extremely efficiently
transferable and, most importantly, transactional or, more generally,
contract-backed. This facilitates fully automated, guaranteed transactional
execution of atomic resource exchanges and more complex contracts, without a
multitude of intermediaries and expensive or slow semi-manual processes.
  A didactic objective is separating money characteristics from technology
aspects such as specific blockchain and distributed ledger systems to help
disentangle discussions of digital money design from implementation techniques.
  We finally discuss the power and role of programmable (contract-backed)
digital money in case studies: tokenization of invoice debt using smart
contracts on Ethereum, with stablecoins serving as digital money; smart
contracts for disbursing payments transparently and reliably in accordance with
social legislation; and a Danish e-krone for crowdfunding public and private
community projets.
  These contributions are made in independent chapters by participants of the
Working Group on Digital Cash at Copenhagen FinTech in 2018 and 2019, which
have not been published before.
  Collectively, the contributions illustrate the design space and potential of
digital money when powered by smart digital contracts that effectively
eliminate both counterparty risk (somebody does not pay or does not deliver)
and settlement risk (a trade fails and needs to be aborted) orders of magnitude
faster than in current financial practice.


  Dynamics of symmetric and antisymmetric 2-solitons and 3-solitons is studied
in the model of the nonlinear dual-core coupler and its PT-symmetric version.
Regions of the convergence of the injected perturbed symmetric and
antisymmetric N-solitons into symmetric and asymmetric quasi-solitons are
found. In the PT-symmetric system, with the balanced gain and loss acting in
the two cores, borders of the stability against the blowup are identified.
Notably, in all the cases the stability regions are larger for antisymmetric
2-soliton inputs than for their symmetric counterparts, on the contrary to
previously known results for fundamental solitons (N=1). Dynamical regimes
(switching) are also studied for the 2-soliton injected into a single core of
the coupler. In particular, a region of splitting of the input into a pair of
symmetric solitons is found, which is explained as a manifestation of the
resonance between the vibrations of the 2-soliton and oscillations of energy
between the two cores in the coupler.


  Motivated by recent experimental demonstrations of Floquet topological
insulators, there have been several theoretical proposals for using structured
light, either spatial or spectral, to create other properties such as flat band
and vortex states. In particular, the generation of vortex states in a massive
Dirac fermion insulator irradiated by light carrying nonzero orbital angular
momentum (OAM) has been proposed [Kim et al. Phys. Rev. B 105, L081301(2022)].
Here, we evaluate the orbital magnetization and optical conductivity as
physical observables for such a system. We show that the OAM of light induces
nonzero orbital magnetization and current density. The orbital magnetization
density increases linearly as a function of OAM degree. In certain regimes, we
find that orbital magnetization density is independent of the system size,
width, and Rabi frequency of light. It is shown that the orbital magnetization
arising from our Floquet theory is large and can be probed by magnetometry
measurements. Furthermore, we study the optical conductivity for various types
of electron transitions between different states such as vortex, edge, and bulk
that are present in the system. Based on conductance frequency peaks, a scheme
for the detection of vortex states is proposed.


  A dynamic mitigation mechanism of the two-stream instability is discussed
based on a phase control for plasma and fluid instabilities. The basic idea for
the dynamic mitigation mechanism by the phase control was proposed in the paper
[Phys. Plasmas 19, 024503(2012)]. The mitigation method is applied to the
two-stream instability in this paper. In general, instabilities appear from the
perturbations, and normally the perturbation phase is unknown. Therefore, the
instability growth rate is discussed in fluids and plasmas. However, if the
perturbation phase is known, the instability growth can be controlled by a
superimposition of perturbations imposed actively. For instance, a perturbed
driver induces a perturbation to fluids or plasmas; if the perturbation induced
by the perturbed driver is oscillated actively by the driver oscillation, the
perturbation phase is known and the perturbation amplitude can be controlled,
like a feedforward control. The application result shown in this paper
demonstrates that the dynamic mitigation mechanism works well to smooth the
non-uniformities and mitigate the instabilities in plasmas.


  Scaling up language models has been shown to predictably improve performance
and sample efficiency on a wide range of downstream tasks. This paper instead
discusses an unpredictable phenomenon that we refer to as emergent abilities of
large language models. We consider an ability to be emergent if it is not
present in smaller models but is present in larger models. Thus, emergent
abilities cannot be predicted simply by extrapolating the performance of
smaller models. The existence of such emergence implies that additional scaling
could further expand the range of capabilities of language models.


  This paper provides a comprehensive estimation framework via nuclear norm
plus $l_1$ norm penalization for high-dimensional approximate factor models
with a sparse residual covariance. The underlying assumptions allow for
non-pervasive latent eigenvalues and a prominent residual covariance pattern.
In that context, existing approaches based on principal components may lead to
misestimate the latent rank, due to the numerical instability of sample
eigenvalues. On the contrary, the proposed optimization problem retrieves the
latent covariance structure and exactly recovers the latent rank and the
residual sparsity pattern. Conditioning on them, the asymptotic rates of the
subsequent ordinary least squares estimates of loadings and factor scores are
provided, the recovered latent eigenvalues are shown to be maximally
concentrated and the estimates of factor scores via Bartlett's and Thompson's
methods are proved to be the most precise given the data. The validity of
outlined results is highlighted in an exhaustive simulation study and in a real
financial data example.


  Weyl semimetals are a new paradigmatic topological phase of matter featuring
a gapless spectrum. One of its most distinctive features is the presence of
Fermi arc surface states. Here, we report on atomistic simulations of the dc
conductance and quantum Hall response of a minimal Weyl semimetal. By using
scattering theory we show that a quantized Hall conductance with a
non-vanishing longitudinal conductance emerges associated to the Fermi arc
surface states with a remarkable robustness to high concentrations of defects
in the system. Additionally, we predict that a slab of a Weyl semimetal with
broken time-reversal symmetry bears persistent currents fully determined by the
system size and the lattice parameters.


  The Third Family Hypercharge Model predicts a $Z^\prime$ gauge boson with
flavour dependent couplings which has been used to explain anomalies in meson
decay processes which involve the $b\rightarrow s\mu^+\mu^-$ transition. The
model predicts that a TeV-scale $Z^\prime$ should decay to
particle-antiparticle pairs of muons, taus, top quarks and bottom quarks with
appreciable branching ratios. We reinterpret various ATLAS and CMS search
limits for $Z^\prime$ production followed by such decays at the LHC over a
parameter space of the model that results from a successful combined fit to the
$b\rightarrow s l^+ l^-$ data and precision electroweak observables. Current
exclusions in parameter space and expected sensitivities of the various
different channels in the high-luminosity run are compared. We find that the
high-luminosity run of the LHC will significantly increase the sensitivity to
the model beyond the existing observational limits, which we find to be
surprisingly weak.


  Photoionization of atoms immersed in an environment such as an ultracold gas
is investigated. We show that the interference of two ionization pathways, one
passing directly to the continuum and one accounting for scattering processes
between the photoelectron and a neighboring atom, produces a fine structure in
the photoionization cross-section over an energy range less than 1 eV above
threshold. This fine structure includes all the details of the corresponding
three-body system, e.g. the interatomic distance or the scattering information
of the electron-atom subsystem; therefore, photoelectrons produced in a
multi-particle environment can be utilized as structural probes. As an
illustration, for experimentally relevant parameters, we propose a scheme based
on the photoionization of a Rydberg molecule where the low-energy electron-atom
phase shifts are extracted from the fine structure spectra using neural
networks.


  An increase in the use of smartphones has laid to the use of the internet and
social media platforms. The most commonly used social media platforms are
Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal
experiences, reviews, feedbacks on the web. The information which is available
on the web is unstructured and enormous. Hence, there is a huge scope of
research on understanding the sentiment of the data available on the web.
Sentiment Analysis (SA) can be carried out on the reviews, feedbacks,
discussions available on the web. There has been extensive research carried out
on SA in the English language, but data on the web also contains different
other languages which should be analyzed. This paper aims to analyze, review
and discuss the approaches, algorithms, challenges faced by the researchers
while carrying out the SA on Indigenous languages.


  In this note we present a combinatorial link invariant that underlies some
recent stable homotopy refinements of Khovanov homology of links. The invariant
takes the form of a functor between two combinatorial 2-categories, modulo a
notion of stable equivalence. We also develop some general properties of such
functors.


  The present paper is an evolution of the Mengoli's series to the set of
rational numbers, which eventually will allow developing the summation, by
limits, obtaining the value of zeta(2); problem which Mengoli himself was the
first to postulate. More specifically in this paper is postulated and
demonstrated the function which resolves every summation since n=1 to infinite
of the type 1/((n+a/w)(n+b/w)...(n+z/w)) for all a,b,...,z,w belonging to the
set of integers, where a>b>...>z are different; a/w,b/w,...,z/w not equal to
-1,-2,-3,... Finally limits to the past summation is applied to demonstrate
zeta(2).


  We derive expression for energy flux in terms of lattice normal mode
coordinates. Energy transmission across solid junctions from lattice dynamic
point of view is given and its relation with atomic masses, lattice constants,
and group velocities is clarified. A scattering boundary method (SBM) is
proposed for calculating the amplitude transmission across solid junctions. The
phonon transmission coefficients and thermal conductance are calculated for two
kinds of acoustically mismatched junctions: different chirality nanotubes
(11,0) to (8,0), and Si-Ge interface structure. Our calculation shows a
mode-dependent transmission in nanotube junction due to the high symmetry
vibrating motions for nanotube atoms, indicating its possible important role in
nanotube mixture thermal conductance. Energy transmission and Kapitza
conductance across the Si-Ge interface [001] are calculated for the Si-Ge
diamond-type structure. It is shown that the energy transmission across the
Si-Ge interface depends on the incident angle and on the interface mode
conversion. A critical incident angle about 42 degrees numerically found for
waves incident from Ge to Si. Our numerical result of the Kapitza conductance
at temperature T=200K is G_K=4.6x10^8 W/(Km^2) We find numerically scaling law
G_K proportional to T^{2.87} for [001] interface at low temperature.


  We describe an affine transformation on the (CIE) color matching functions
and map the spectral locus as a circle. We then homogenize the right circular
cylinder erected by the circle, with respect to a normalizing plane and develop
an analytical equation to the chromaticity cone, for the spectral colors. In
the interior of the (CIE) chromaticity diagram, by homogenizing elliptic
cylinders with respect to the normalizing planes, analytical equations to
subsets (also cones) of the chromaticity cone are developed. These equations
provide an algebraic method for describing color perception. As an application
of the interior chromaticity cones, we demonstrate that by sectioning
homogenized cones with planes and projecting, analytical equations to the
Macadam ellipses may be derived. Further, the cone equations are used to
propose new types of color order systems.


  An endo-commutative algebra is a nonassociative algebra in which the square
mapping preserves multiplication. In this paper, we give a complete
classification of 2-dimensional endo-commutative straight algebras of rank one
over an arbitrary non-trivial field, where a straight algebra of dimension 2
satisfies the condition that there exists an element $x$ such that $x$ and
$x^2$ are linearly independent. We list all multiplication tables of the
algebras up to isomorphism.


  We elaborate on the recent work of Moritz et. al. where it is argued that
anti-brane uplifting in KKLT models never leads to dS vacua. This is due to
flattening effects in the effective potential found when treating the gaugino
condensate energy momentum in ten-dimensional supergravity. We point out that
the Silverstein-Saltman uplift, which effectively dissolves the anti-brane in
3-form fluxes, is a conceptually simpler setting in which flattening effects
can be present, without requiring assumptions about UV/IR mixings. Along the
way we revise and improve on the arguments of Moritz et. al. and discuss
various subleties when studying gaugino condensates from a ten-dimensional
point-of-view. In particular, the "no dS" argument is slightly weaker and we
emphasize a technical loophole that might allow dS vacua. Finally we suggest
that AdS vacua which are parametrically controlled from these flattening
effects belong to the Swampland. We comment on the relation with the recent
refined dS Swampland inequalities.


  User reviews reflect significant value of product in the world of e-market.
Many firms or product providers hire spammers for misleading new customers by
posting spam reviews. There are three types of fake reviews, untruthful
reviews, brand reviews and non-reviews. All three types mislead the new
customers. A multinomial organization "Yelp" is separating fake reviews from
non-fake reviews since last decade. However, there are many e-commerce sites
which do not filter fake and non-fake reviews separately. Automatic fake review
detection is focused by researcher for last ten years. Many approaches and
feature set are proposed for improving classification model of fake review
detection. There are two types of dataset commonly used in this research area:
psuedo fake and real life reviews. Literature reports low performance of
classification model real life dataset if compared with pseudo fake reviews.
After investigation behavioral and contextual features are proved important for
fake review detection Our research has exploited important behavioral feature
of reviewer named as "reviewer deviation". Our study comprises of investigating
reviewer deviation with other contextual and behavioral features. We
empirically proved importance of selected feature set for classification model
to identify fake reviews. We ranked features in selected feature set where
reviewer deviation achieved ninth rank. To assess the viability of selected
feature set we scaled dataset and concluded that scaling dataset can improve
recall as well as accuracy. Our selected feature set contains a contextual
feature which capture text similarity between reviews of a reviewer. We
experimented on NNC, LTC and BM25 term weighting schemes for calculating text
similarity of reviews. We report that BM25 outperformed other term weighting
scheme.


  We give an explicit version of Shimura's reciprocity law for singular values
of Siegel modular functions. We use this to construct the first examples of
class invariants of quartic CM fields that are smaller than Igusa invariants.
Our statement also enables a new proof of Shimura's reciprocity law by Tonghai
Yang.


  We propose a simple model, supported by contact-dynamics simulations as well
as rheology and friction measurements, that links the transition from
continuous to discontinuous shear-thickening in dense granular pastes to
distinct lubrication regimes in the particle contacts. We identify a local
Sommerfeld number that determines the transition from Newtonian to
shear-thickening flows, and then show that the suspension's volume fraction and
the boundary lubrication friction coefficient control the nature of the
shear-thickening transition, both in simulations and experiments.


  We introduce a mixed density fitting scheme that uses both a Gaussian and a
plane-wave fitting basis to accurately evaluate electron repulsion integrals in
crystalline systems. We use this scheme to enable efficient all-electron
Gaussian based periodic density functional and Hartree-Fock calculations.


  Over the past fifteen years or so, Minhyong Kim has developed a framework for
making effective use of the fundamental group to bound (or even compute)
integral points on hyperbolic curves. This is the third installment in a series
whose goal is to realize the potential effectivity of Kim's approach in the
case of the thrice punctured line. As envisioned in the last installment, we
construct an algorithm whose output upon halting is provably the set of
integral points, and whose halting would follow from conjectures. Our results
go a long way towards achieving our goals over the rationals, while broaching
the topic of higher number fields.


  The structure of light quark star is studied within a new two-flavor NJL
model. By retaining the contribution from the vector term in the
Fierz-transformed Lagrangian, a two-solar-mass pure quark star is achieved. To
overcome the disadvantage of three-momentum truncation in the regularisation
procedure, we introduce the proper-time regularisation. We also employ the
newly proposed definition of vacuum pressure, in which the quasi-Wigner vacuum
(corresponding to the quasi-Winger solution of the gap equation) is used as the
reference ground state. Free parameter includes only a mixing constant $\alpha$
which weighs contribution from Fierz-transformed Lagrangian. We constrain
$\alpha$ to be around $0.9$ by the observed mass of pulsars $PSR J0348+0432$
and $PSR J1614-2230$. We find the calculated surface energy density meets the
requirement ($> 2.80\times10^{14}$g/cm$^3 $). Besides, for a 1.4 solar mass
star, the tidal Love number $k_2$ and deformability $\Lambda$ are calculated
which satisfies the constrain $200 < \Lambda < 800$.


  We investigate the synchronization between two neurons using the stochastic
version of the map-based Chialvo model. To simulate non-identical neurons, a
mismatch is introduced in one of the main parameters of the model.
Subsequently, the synchronization of the neurons is studied as a function of
this mismatch, the noise introduced in the stochastic model, and the coupling
strength between the neurons. We propose the simplest neuron network for study,
as its analysis is more straightforward and does not compromise generality.
Within this network, two nonidentical neuron maps are electrically coupled. In
order to understand if specific behaviors affect the global behavior of the
system, we consider different cases related to the behavior of the neurons
(chaotic or periodic). Furthermore, we study how variations in model parameters
affect the firing frequency in all cases. Additionally, we consider that the
two neurons have both excitatory and inhibitory couplings. Consequently, we
identify critical values of noise and mismatch for achieving satisfactory
synchronization between the neurons in both cases. Finally, we conjecture that
the results are of a general nature and are applicable to different neuron
models.


  We expand the recently discussed continuous-variable quantum key distribution
scheme of Heid and Luetkenhaus (2006) to qudits with a lossy but noiseless
quantum channel. Postselection methods are used. Secret key rates are
calculated in the presence of loss for the direct and the reverse
reconciliation method. In the case of reverse reconciliation the secret key
rate increases with higher dimensions, in the case of direct reconciliation the
result is reverse. Several ways of optimising the amplitude are discussed. In a
next step the used states are generalised to squeezed states. Again secret key
rates are calculated. The last part deals with the dual-homodyne detection
which replaces the single-homodyne measurement. Most results are analytical and
allow further treatments if the preparation symmetry stays unchanged.


  Characterising the correlations that arise from locally measuring a single
part of a joint quantum system is one of the main problems of quantum
information theory. The seminal work [M. Navascu\'es et al, NJP 10,7,073013
(2008)], known as the NPA hierarchy, reformulated this question as a polynomial
optimisation problem over noncommutative variables and proposed a convergent
hierarchy of necessary conditions, each testable using semidefinite
programming. More recently, the problem of characterising the quantum network
correlations, which arise when locally measuring several independent quantum
systems distributed in a network, has received considerable interest. Several
generalisations of the NPA hierarchy, such as the Scalar Extension
[Pozas-Kerstjens et al, Phys. Rev. Lett. 123, 140503 (2019)], were introduced
while their converging sets remain unknown. In this work, we introduce a new
hierarchy, prove its equivalence to the Scalar Extension, and characterise its
convergence in the case of the simplest network, the bilocal scenario, and
explore its relations with the known generalisations.


  Gravitational wave echoes may imply physics beyond general relativity;
however, several approaches to searching for echoes require fitting the data to
toy templates which are not physically motivated. Here, I create a procedure
which outputs a more realistic form for echoes, starting from initial
conditions, by calculating the modulated transfer function.


  We have studied a massive U(1) gauge holographic model with pure gauge and
mixed gauge-gravitational Chern-Simons terms. The full backreaction of the
gauge field on the metric tensor has been considered in order to explore the
vortical and energy transport sector. The background solution has been computed
numerically. On this background, we have considered the fluctuation of the
fields and evaluated the different correlators. We have found that all the
correlators depend on the mass of the gauge field. Correlators such as the
current-current one, $\langle J_xJ_x\rangle$, which were completely absent in
the massless case, in the presence of a finite gauge boson mass start picking
up some finite value even at zero chemical potential. Similarly, the
energy-current correlator, $\langle T_{0x}J_x\rangle$, which was also absent in
the massless theory, has now a non-vanishing value but for finite values of the
chemical potential. Our findings for the chiral vortical conductivity,
$\sigma_V$, and the chiral magnetic/vortical conductivity of energy current,
$\sigma^\varepsilon_B = \sigma^\varepsilon_V$, are completely new results. In
addition to this, we have found that these anomalous transport coefficients
depend linearly both on the pure Chern-Simon coupling, $\kappa$, and on the
mixed gauge-gravity Chern-Simon coupling, $\lambda$. One of the results that we
would like to highlight is that its not just $\kappa$ that contributes to the
$\sigma_B$ but there is an additional contribution from $\lambda$ as well
unlike the previous studies.


  We present a novel concept of universal text preprocessing and text-embedded
programming (PTEP). Preprocessing and text-embedded programming has been widely
used in programming languages and frameworks in a fragmented and mutually
isolated way. The PTEP ideas can be found in the implementation of the \TeX\
typesetting system; they are prominent in PHP and similar web languages, and
finally they are used in the Jupyter data science framework. This paper
presents this area of research and related work in a more unified framework,
and we describe the implemented system Starfish that satisfies the following
novel principles of PTEP: universality, update and replace modes, flexiblity,
configurability, and transparency. We describe the operating model and design
of Starfish, which is an open-source system implementing universal
preprocessing and text-embedded programming in Perl. The system is transparent
and its design allows direct implementation in other programming languages as
well.