  This paper proposes a joint model for longitudinal binary and count outcomes.
We apply the model to a unique longitudinal study of teen driving where risky
driving behavior and the occurrence of crashes or near crashes are measured
prospectively over the first 18 months of licensure. Of scientific interest is
relating the two processes and predicting crash and near crash outcomes. We
propose a two-state mixed hidden Markov model whereby the hidden state
characterizes the mean for the joint longitudinal crash/near crash outcomes and
elevated g-force events which are a proxy for risky driving. Heterogeneity is
introduced in both the conditional model for the count outcomes and the hidden
process using a shared random effect. An estimation procedure is presented
using the forward-backward algorithm along with adaptive Gaussian quadrature to
perform numerical integration. The estimation procedure readily yields hidden
state probabilities as well as providing for a broad class of predictors.


  Blue metal-poor stars (BMPs) are main sequence stars that appear bluer and
more luminous than normal turnoff stars. They were originally singled out by
using B-V and U-B colour cuts. Early studies found that a larger fraction of
field BMP stars were binaries compared to normal halo stars. Thus, BMP stars
are ideal field blue straggler candidates for investigating internal stellar
evolution processes and binary interaction. In particular, the presence or
depletion in lithium in their spectra is a powerful indicator as to their
origin. They are either old, halo blue stragglers experiencing internal mixing
processes or mass transfer (Li-depletion), or intermediate-age, single stars of
possibly extragalactic origin (2.2dex halo plateau Li). However, we note that
internal mixing processes can lead to an increased level of Li. Hence, this
study combines photometry and spectroscopy to unveil the origin of various BMP
stars. We first show how to separate binaries from young blue stars using
photometry, metallicity, and lithium. Using a sample of 80 BMP stars (T>6300K),
we find that 97% of the BMP binaries have V-Ks_0 < 1.08$\pm0.03$, while BMP
stars that are not binaries lie above this cut in 2/3 of the cases. This cut
can help classify stars which lack radial velocities from follow-up
observations. We trace the origin of two BMP stars from the photometric sample
by conducting a full chemical analysis using new high-resolution and high SN
spectra. Based on their radial velocities, Li, alpha, and s- and r-process
abundances we show that BPS CS22874-042 is a single star
(A(Li)$=2.38\pm0.10$dex) while with A(Li)$=2.23\pm0.07$dex CD-48 2445 is a
binary, contrary to earlier findings. Our analysis emphasises that field blue
stragglers can be segregated from single metal-poor stars, using V-Ks colours
with a fraction of single stars polluting the binary sample, but not vice
versa.(Abridged)


  Rates of coherent neutrino-nucleus elastic scattering at a high-intensity
stopped-pion neutrino source in various detector materials (relevant for novel
low-threshold detectors) are calculated. Sensitivity of a coherent
neutrino-nucleus elastic scattering experiment to new physics is also explored.


  Let M be a smooth manifold, A a local algebra in sense of Andr\'e Weil, M^{A}
the manifold of near points on M of kind A and X(M^{A}) the module of vector
fields on M^{A}. We give a new definition of vector fields on M^{A} and we show
that X(M^{A}) is a Lie algebra over A. We study the cohomology of
A-differential forms.


  Phylogenetic networks are a generalization of phylogenetic trees that are
used to represent reticulate evolution. Unrooted phylogenetic networks form a
special class of such networks, which naturally generalize unrooted
phylogenetic trees. In this paper we define two operations on unrooted
phylogenetic networks, one of which is a generalization of the well-known
nearest-neighbor interchange (NNI) operation on phylogenetic trees. We show
that any unrooted phylogenetic network can be transformed into any other such
network using only these operations. This generalizes the well-known fact that
any phylogenetic tree can be transformed into any other such tree using only
NNI operations. It also allows us to define a generalization of tree space and
to define some new metrics on unrooted phylogenetic networks. To prove our main
results, we employ some fascinating new connections between phylogenetic
networks and cubic graphs that we have recently discovered. Our results should
be useful in developing new strategies to search for optimal phylogenetic
networks, a topic that has recently generated some interest in the literature,
as well as for providing new ways to compare networks.


  Interactions between an edge dislocation and a void in copper are
investigated by means of molecular dynamics simulation. The depinning stresses
of the leading partial and of the trailing partial show qualitatively different
behaviors. The depinning stress of the trailing partial increases
logarithmically with the void radius, while that of the leading partial shows a
crossover at 1 nm above which two partials are simultaneously trapped by the
void. The pinning angle, which characterizes the obstacle strength, approaches
zero when the void radius exceeds 3 nm. No temperature dependence is found in
the critical stress and the critical angle. This is attributed to an absence of
climb motion. The distance between the void center and a glide plane
asymmetrically affects the pinning strength.


  Guarded Kleene Algebra with Tests (GKAT) is a variation on Kleene Algebra
with Tests (KAT) that arises by restricting the union ($+$) and iteration ($*$)
operations from KAT to predicate-guarded versions. We develop the (co)algebraic
theory of GKAT and show how it can be efficiently used to reason about
imperative programs. In contrast to KAT, whose equational theory is
PSPACE-complete, we show that the equational theory of GKAT is (almost) linear
time. We also provide a full Kleene theorem and prove completeness for an
analogue of Salomaa's axiomatization of Kleene Algebra.


  This article deals with the interpolating sesqui-harmonicity of a vector
field $X$ viewed as a map from a Riemannian manifold $(M,g)$ to its tangent
bundle $TM$ endowed with the Sasaki metric $g_{S}$. We show characterization
theorem for $X$ to be interpolating sesqui-harmonic map. We give also the
critical point condition which characterizes interpolating sesqui-harmonic
vector fields. When $(M,g)$ is compact and oriented and under some conditions,
we prove that $X$ is an interpolating sesqui-harmonic vector field (resp.
interpolating sesqui-harmonic map) if and only if $X$ is parallel. Moreover, we
extend this result for a left-invariant vector field on a Lie group $G$ having
a discrete subgroup $\Gamma$ such that the quotient $\Gamma\backslash G$ is
compact.


  It is established that both radiative transfer and magnetic field have a
strong impact on the collapse and the fragmentation of prestellar dense cores,
but no consistent calculation exists yet at such scales. We present original
AMR calculations including magnetic field (in the ideal MHD limit) and
radiative transfer, within the Flux Limited Diffusion approximation, of the
collapse of a 1 solar mass dense core. We compare the results with calculations
performed with a barotropic EOS. We show that radiative transfer has an
important impact on the collapse and the fragmentation, through the cooling or
heating of the gas, and is complementary of the magnetic field. A larger field
yields a stronger magnetic braking, increasing the accretion rate and thus the
effect of the radiative feedback. Even for a strongly magnetized core, where
the dynamics of the collapse is dominated by the magnetic field, radiative
transfer is crucial to determine the temperature and optical depth
distributions, two potentially accessible observational diagnostics. A
barotropic EOS cannot account for realistic fragmentation. The diffusivity of
the numerical scheme, however, is found to strongly affect the output of the
collapse, leading eventually to spurious fragmentation. Both radiative transfer
and magnetic field must be included in numerical calculations of star formation
to obtain realistic collapse configurations and observable signatures.
Nevertheless, the numerical resolution and the robustness of the solver are of
prime importance to obtain reliable results. When using an accurate solver, the
fragmentation is found to always remain inhibited by the magnetic field, at
least in the ideal MHD limit, even when radiative transfer is included.


  In this paper, we report on 606 S-type stars identified from Data Release 9
of the LAMOST medium-resolution spectroscopic (MRS) survey, and 539 of them are
reported for the first time. The discovery of these stars is a three-step
process, i.e., selecting with the ZrO band indices greater than 0.25, excluding
non-S-type stars with the iterative Support Vector Machine method, and finally
retaining stars with absolute bolometric magnitude larger than -7.1. The 606
stars are consistent with the distribution of known S-type stars in the
color-magnitude diagram. We estimated the C/Os using the [C/Fe] and [O/Fe]
provided by APOGEE and the MARCS model for S-type stars, respectively, and the
results of the two methods show that C/Os of all stars are larger than 0.5.
Both the locations on the color-magnitude diagram and C/Os further verify the
nature of our S-type sample. Investigating the effect of TiO and atmospheric
parameters on ZrO with the sample, we found that log g has a more significant
impact on ZrO than Teff and [Fe/H], and both TiO and log g may negatively
correlate with ZrO. According to the criterion of Tian et al. (2020), a total
of 238 binary candidates were found by the zero-point-calibrated radial
velocities from the officially released catalog of LAMOST MRS and the catalog
of Zhang et al. (2021). A catalog of these 606 S-type stars is available from
the following link https://doi.org/10.12149/101097.


  In the background effective field theory of heterotic string theory, the
Green-Schwarz anomaly cancellation mechanism plays a key role. Here we
reinterpret it and its magnetic dual version in terms of differential twisted
String- and differential twisted Fivebrane-structures that generalize the
notion of Spin-structures and Spin-lifting gerbes and their differential
refinement to smooth Spin-connections. We show that all these structures can be
encoded in terms of nonabelian cohomology, twisted nonabelian cohomology, and
differential twisted nonabelian cohomology, extending the differential
generalized abelian cohomology as developed by Hopkins and Singer and shown by
Freed to formalize the global description of anomaly cancellation problems in
higher gauge theories arising in string theory. We demonstrate that the
Green-Schwarz mechanism for the H_3-field, as well as its magnetic dual version
for the H_7-field define cocycles in differential twisted nonabelian cohomology
that may be called, respectively, differential twisted Spin(n)-, String(n)- and
Fivebrane(n)-structures on target space, where the twist in each case is
provided by the obstruction to lifting the classifying map of the gauge bundle
through a higher connected cover of U(n) or O(n). We show that the twisted
Bianchi identities in string theory can be captured by the (nonabelian)
L-infinity-algebra valued differential form data provided by the differential
refinements of these twisted cocycles.


  Automatic emotion recognition in speech is a research area with a wide range
of applications in human interactions. The basic mathematical tool used for
emotion recognition is Pattern recognition which involves three operations,
namely, pre-processing, feature extraction and classification. This paper
introduces a procedure for emotion recognition using Hidden Markov Models
(HMM), which is used to divide five emotional states: anger, surprise,
happiness, sadness and neutral state. The approach is based on standard speech
recognition technology using hidden continuous markov model by selection of low
level features and the design of the recognition system. Emotional Speech
Database from Telugu Rural Dialects of Andhra Pradesh (TRDAP) was designed
using several speaker's voices comprising the emotional states. The accuracy of
recognizing five different emotions for both genders of classification is 80%
for anger-emotion which is achieved by using the best combination of
39-dimensioanl feature vector for every frame (13 MFCCs, 13 Delta Coefficients
and 13 Acceleration Coefficients) and a classifier using HMM. This outcome very
much matches with that acquired with the same database with subjective
evaluation by human judges. Both gender-dependent and gender-independent
experiments are conducted on TRDAP emotional speech database.


  We report results from a systematic strong-coupling expansion of a spin-1/2
Heisenberg chain coupled to Einstein phonons. In the non-adiabatic regime this
model is used to describe zero temperature properties of CuGeO3. The linked
cluster expansion allows the determination of observables in the thermodynamic
limit preserving the full lattice dynamics without a truncation of the phononic
Hilbert space. In particular, the spin gap and the dispersion of the elementary
triplet excitation are calculated up to 10th order in a dimer expansion. The
magnetic structure factor of the ground state is evaluated up to 6th order. We
show that the spin-phonon coupling leads to a renormalization of the elementary
triplet dispersion. Surprisingly in the non-adiabatic regime a substantial
renormalization of the spin gap only sets in at much larger couplings than
those proposed for CuGeO3. The ground state magnetic correlations are found to
be hardly effected by the spin-phonon coupling, but dominated by the
frustrating magnetic interaction in the parameter regime relevant for CuGeO3.


  This paper investigates a natural generalization of the kappa-mu fading
channel in which the line-of-sight (LOS) component is subject to shadowing.
This fading distribution has a clear physical interpretation, good analytical
properties and unifies the one-side Gaussian, Rayleigh, Nakagami-m, Ricean,
kappa-mu and Ricean shadowed fading distributions. The three basic statistical
characterizations, i.e. probability density function (PDF), cumulative
distribution function (CDF) and moment generating function (MGF), of the
kappa-mu shadowed distribution are obtained in closed-form. Then, it is also
shown that the sum and maximum distributions of independent but arbitrarily
distributed kappa-mu shadowed variates can be expressed in closed-form. This
set of new statistical results is finally applied to the performance analysis
of several wireless communication systems.


  Motivation: Array-based comparative genomic hybridization (arrayCGH) has
recently become a popular tool to identify DNA copy number variations along the
genome. These profiles are starting to be used as markers to improve prognosis
or diagnosis of cancer, which implies that methods for automated supervised
classification of arrayCGH data are needed. Like gene expression profiles,
arrayCGH profiles are characterized by a large number of variables usually
measured on a limited number of samples. However, arrayCGH profiles have a
particular structure of correlations between variables, due to the spatial
organization of BACs along the genome. This suggests that classical
classification methods, often based on the selection of a small number of
discriminative features, may not be the most accurate methods and may not
produce easily interpretable prediction rules.
  Results: We propose a new method for supervised classification of arrayCGH
data. The method is a variant of support vector machine (SVM) that incorporates
the biological specificities of DNA copy number variations along the genome as
prior knowledge. The resulting classifier is a sparse linear classifier based
on a limited number of regions automatically selected on the chromosomes,
leading to easy interpretation and identification of discriminative regions of
the genome. We test this method on three classification problems for bladder
and uveal cancer, involving both diagnosis and prognosis. We demonstrate that
the introduction of the new prior on the classifier leads not only to more
accurate predictions, but also to the identification of known and new regions
of interest in the genome.
  Availability: All data and algorithms are publicly available.


  We present a program that manages a database of temporally scoped beliefs.
The basic functionality of the system includes maintaining a network of
constraints among time points, supporting a variety of fetches, mediating the
application of causal rules, monitoring intervals of time for the addition of
new facts, and managing data dependencies that keep the database consistent. At
this level the system operates independent of any measure of belief or belief
calculus. We provide an example of how an application program mi9ght use this
functionality to implement a belief calculus.


  We examine the unitarity properties of spontaneously broken non-commutative
gauge theories. We find that the symmetry breaking mechanism in the
non-commutative Standard Model of Chaichian et al. leads to an unavoidable
violation of tree-level unitarity in gauge boson scattering at high energies.
We then study a variety of simplified spontaneously broken non-commutative
theories and isolate the source of this unitarity violation. Given the group
theoretic restrictions endemic to non-commutative model building, we conclude
that it is difficult to build a non-commutative Standard Model under the
Weyl-Moyal approach that preserves unitarity.


  In highly dissipative collisions between heavy ions, the optimal conditions
to investigate different de-excitation channels of hot nuclei such as
evaporation, fission or multifragmentation are well known. One crucial issue
remains the excitation energy region where fission gives way to
multifragmentation. In this paper, the onset of multi-fragment exit channels is
investigated in terms of sequential fission. For the first time, the dynamical
approach based on solving Langevin transport equations in multidimensional
collective coordinate space is used to follow the de-excitation of highly
excited (up to E* =223-656 MeV) 248Rf compound nuclei. The sequential fission
model we propose contains two steps: (1) time evolution of the compound nucleus
up to either scission or residue formation, followed by (2) dynamical
calculations of each primary fragment separately. This procedure allows to
obtain from one to four cold fragments correlated with the light particles
emitted during the de-excitation process. Experimental data measured with the
INDRA detector for the 129Xe+ natSn reaction at beam energies 8, 12 and 15
MeV/nucleon provide strong constraints for this sequential fission scenario.


  The identification and characterisation of low-mass binaries is of importance
for a range of astrophysical investigations. Low-mass binaries in young
($\sim10 - 100 $ Myr) moving groups (YMGs) are of particular significance as
they provide unique opportunities to calibrate stellar models and evaluate the
ages and coevality of the groups themselves. Low-mass M-dwarfs have pre-main
sequence life times on the order of $\sim100$ Myr and therefore are continually
evolving along a mass-luminosity track throughout the YMG phase, providing
ideal laboratories for precise isochronal dating, if a model-independent
dynamical mass can be measured. AstraLux lucky imaging multiplicity surveys
have recently identified hundreds of new YMG low-mass binaries, where a
subsample of M-dwarf multiples have estimated orbital periods less than 50
years. We have conducted a radial velocity survey of a sample of 29 such
targets to complement the astrometric data. This will allow enhanced orbital
determinations and precise dynamical masses to be derived in a shorter
timeframe than possible with astrometric monitoring alone, and allow for a more
reliable isochronal analysis. Here we present radial velocity measurements for
our sample over several epochs. We report the detection of the three-component
spectroscopic multiple 2MASS J05301858-5358483, for which the C component is a
new discovery, and forms a tight pair with the B component. Originally
identified as a YMG member, we find that this system is a likely old field
interloper, whose high chromospheric activity level is caused by tidal spin-up
of the tight BC pair. Two other triple systems with a tight pair exist in the
sample, but for the rest of the targets we find that additional tidally
synchronized companions are highly unlikely, providing further evidence that
their high chromospheric activity levels are generally signatures of youth.


  The simulation of quantum dynamics calls for quantum algorithms working in
first quantized grid encodings. Here, we propose a variational quantum
algorithm for performing quantum dynamics in first quantization. In addition to
the usual reduction in circuit depth conferred by variational approaches, this
algorithm also enjoys several advantages compared to previously proposed ones.
For instance, variational approaches suffer from the need for a large number of
measurements. However, the grid encoding of first quantized Hamiltonians only
requires measuring in position and momentum bases, irrespective of the system
size. Their combination with variational approaches is therefore particularly
attractive. Moreover, heuristic variational forms can be employed to overcome
the limitation of the hard decomposition of Trotterized first quantized
Hamiltonians into quantum gates. We apply this quantum algorithm to the
dynamics of several systems in one and two dimensions. Our simulations exhibit
the previously observed numerical instabilities of variational time propagation
approaches. We show how they can be significantly attenuated through subspace
diagonalization at a cost of an additional $\mathcal{O}(MN^2)$ 2-qubit gates
where $M$ is the number of dimensions and $N^M$ is the total number of grid
points.


  A ubiquitous learning problem in today's digital market is, during repeated
interactions between a seller and a buyer, how a seller can gradually learn
optimal pricing decisions based on the buyer's past purchase responses. A
fundamental challenge of learning in such a strategic setup is that the buyer
will naturally have incentives to manipulate his responses in order to induce
more favorable learning outcomes for him. To understand the limits of the
seller's learning when facing such a strategic and possibly manipulative buyer,
we study a natural yet powerful buyer manipulation strategy. That is, before
the pricing game starts, the buyer simply commits to "imitate" a different
value function by pretending to always react optimally according to this
imitative value function.
  We fully characterize the optimal imitative value function that the buyer
should imitate as well as the resultant seller revenue and buyer surplus under
this optimal buyer manipulation. Our characterizations reveal many useful
insights about what happens at equilibrium. For example, a seller with concave
production cost will obtain essentially 0 revenue at equilibrium whereas the
revenue for a seller with convex production cost is the Bregman divergence of
her cost function between no production and certain production. Finally, and
importantly, we show that a more powerful class of pricing schemes does not
necessarily increase, in fact, may be harmful to, the seller's revenue. Our
results not only lead to an effective prescriptive way for buyers to manipulate
learning algorithms but also shed lights on the limits of what a seller can
really achieve when pricing in the dark.


  Low-rank matrix factorization (MF) is an important technique in data science.
The key idea of MF is that there exists latent structures in the data, by
uncovering which we could obtain a compressed representation of the data. By
factorizing an original matrix to low-rank matrices, MF provides a unified
method for dimension reduction, clustering, and matrix completion. In this
article we review several important variants of MF, including: Basic MF,
Non-negative MF, Orthogonal non-negative MF. As can be told from their names,
non-negative MF and orthogonal non-negative MF are variants of basic MF with
non-negativity and/or orthogonality constraints. Such constraints are useful in
specific senarios. In the first part of this article, we introduce, for each of
these models, the application scenarios, the distinctive properties, and the
optimizing method. By properly adapting MF, we can go beyond the problem of
clustering and matrix completion. In the second part of this article, we will
extend MF to sparse matrix compeletion, enhance matrix compeletion using
various regularization methods, and make use of MF for (semi-)supervised
learning by introducing latent space reinforcement and transformation. We will
see that MF is not only a useful model but also as a flexible framework that is
applicable for various prediction problems.


  We show that the $L^2$-Alexander torsion of a 3-manifold is symmetric. This
can be viewed as a generalization of the symmetry of the Alexander polynomial
of a knot.


  Content distribution over networks is often achieved by using mirror sites
that hold copies of files or portions thereof to avoid congestion and delay
issues arising from excessive demands to a single location. Accordingly, there
are distributed storage solutions that divide the file into pieces and place
copies of the pieces (replication) or coded versions of the pieces (coding) at
multiple source nodes. We consider a network which uses network coding for
multicasting the file. There is a set of source nodes that contains either
subsets or coded versions of the pieces of the file. The cost of a given
storage solution is defined as the sum of the storage cost and the cost of the
flows required to support the multicast. Our interest is in finding the storage
capacities and flows at minimum combined cost. We formulate the corresponding
optimization problems by using the theory of information measures. In
particular, we show that when there are two source nodes, there is no loss in
considering subset sources. For three source nodes, we derive a tight upper
bound on the cost gap between the coded and uncoded cases. We also present
algorithms for determining the content of the source nodes.


  We studied possible evolution of the rotational period and the magnetic field
of the X-ray source RX J0720.4-3125 assuming this source to be an isolated
neutron star accreting from the interstellar medium. Magnetic field of the
source is estimated to be $10^6 - 10^9$ Gs (most probably $\approx 2\cdot 10^8$
Gs), and it is difficult to explain the observable rotational period 8.38 s
without invoking hypothesis of the magnetic field decay. We used the model of
ohmic decay of the crustal magnetic field. The estimates of the accretion rate
($10^{-14} - 10^{-16} M_\odot/yr$), velocity of the source relative to the
interstellar medium ($10 - 50 $ km/s), the neutron star age ($2\cdot 10^9 -
10^{10}$ yrs) are obtained.


  We define a very general class of CHL-models associated with any string
theory (bosonic or supersymmetric) compactified on an internal CFT C x T^d. We
take the orbifold by a pair (g,\delta), where g is a (possibly non-geometric)
symmetry of C and \delta is a translation along T^d. We analyze the T-dualities
of these models and show that in general they contain Atkin-Lehner type
symmetries. This generalizes our previous work on N=4 CHL-models based on
heterotic string theory on T^6 or type II on K3 x T^2, as well as the
`monstrous' CHL-models based on a compactification of heterotic string theory
on the Frenkel-Lepowsky-Meurman CFT V^{\natural}.


  Quantum key agreement enables remote participants to fairly establish a
secure shared key based on their private inputs. In the circular-type
multiparty quantum key agreement mode, two or more malicious participants can
collude together to steal private inputs of honest participants or to generate
the final key alone. In this work, we focus on a powerful collusive attack
strategy in which two or more malicious participants in particular positions,
can learn sensitive information or generate the final key alone without
revealing their malicious behaviour. Many of the current circular-type
multiparty quantum key agreement protocols are not secure against this
collusive attack strategy. As an example, we analyze the security of a recently
proposed multiparty key agreement protocol to show the vulnerability of
existing circular-type multiparty quantum key agreement protocols against this
collusive attack. Moreover, we design a general secure multiparty key agreement
model that would remove this vulnerability from such circular-type key
agreement protocols and describe the necessary steps to implement this model.
The proposed model is general and does not depend on the specific physical
implementation of the quantum key agreement.


  We present a new simulated galaxy cluster catalog based on the IllustrisTNG
simulation. We use the Mulguisin (MGS) algorithm to identify galaxy
overdensities. Our cluster identification differs from the previous FoF cluster
identification in two aspects; 1) we identify cluster halos based on the galaxy
subhalos instead of unobservable dark matter particles, and 2) we use the MGS
algorithm that separates galaxy overdensities hosted by massive galaxies. Our
approach provides a cluster catalog constructed similar to the observed cluster
catalogs using spectroscopic surveys. The MGS cluster catalog lists 303 halos
with M$_{200} > 10^{14}$ M$_{\odot}$, including $\sim 10\%$ more than the FoF.
The MGS catalog includes more systems because we separate some independent
massive MGS cluster halos that are bundled into a single FoF algorithm. These
independent MGS halos are apparently distinguishable in galaxy spatial
distribution and the phase-space diagram. Because we constructed a refined
cluster catalog that identifies local galaxy overdensities, we evaluate the
effect of MGS clusters on the evolution of galaxies better than using the FoF
cluster catalog. The MGS halo identification also enables effective
identifications of merging clusters by selecting systems with neighboring
galaxy overdensities. We thus highlight that the MGS cluster catalog is a
useful tool for studying clusters in cosmological simulations and for comparing
with the observed cluster samples.


  A joint sparse-regression-code (SPARC) and low-density-parity-check (LDPC)
coding scheme for multiple-input multiple-output (MIMO) massive unsourced
random access (URA) is proposed in this paper. Different from the
state-of-the-art covariance-based maximum likelihood (CB-ML) detection scheme,
we first split users' messages into two parts. The former part is encoded by
SPARCs and tasked to recover part of the messages, the corresponding channel
coefficients as well as the interleaving patterns by compressed sensing. The
latter part is coded by LDPC codes and then interleaved by the
interleave-division multiple access (IDMA) scheme. The decoding of the latter
part is based on belief propagation (BP) joint with successive interference
cancellation (SIC). Numerical results show our scheme outperforms the CB-ML
scheme when the number of antennas at the base station is smaller than that of
active users. The complexity of our scheme is with the order
$\mathcal{O}\left(2^{B_p}ML+\widehat{K}ML\right)$ and lower than the CB-ML
scheme. Moreover, our scheme has higher spectral efficiency (nearly $15$ times
larger) than CB-ML as we only split messages into two parts.


  We devise a new regularization, called self-verification, for image
denoising. This regularization is formulated using a deep image prior learned
by the network, rather than a traditional predefined prior. Specifically, we
treat the output of the network as a ``prior'' that we denoise again after
``re-noising''. The comparison between the again denoised image and its prior
can be interpreted as a self-verification of the network's denoising ability.
We demonstrate that self-verification encourages the network to capture
low-level image statistics needed to restore the image. Based on this
self-verification regularization, we further show that the network can learn to
denoise even if it has not seen any clean images. This learning strategy is
self-supervised, and we refer to it as Self-Verification Image Denoising
(SVID). SVID can be seen as a mixture of learning-based methods and traditional
model-based denoising methods, in which regularization is adaptively formulated
using the output of the network. We show the application of SVID to various
denoising tasks using only observed corrupted data. It can achieve the
denoising performance close to supervised CNNs.


  The maximum-entropy method is applied to the problem of reconstructing the
projected mass density of a galaxy cluster using its gravitational lensing
effects on background galaxies. We demonstrate the method by reconstructing the
mass distribution in a model cluster using simulated shear and magnification
data to which Gaussian noise is added. The mass distribution is reconstructed
directly and the inversion is regularised using the entropic prior for this
positive additive distribution. For realistic noise levels, we find that the
method faithfully reproduces the main features of the cluster mass distribution
not only within the observed field but also slightly beyond it. We estimate the
uncertainties on the reconstruction by calculating an analytic approximation to
the covariance matrix of the reconstruction values of each pixel. This result
is compared with error estimates derived from Monte-Carlo simulations for
different noise realisations and found to be in good agreement.


  Coherent scattering of an elliptically polarised tweezer into a cavity mode
provides a promising platform for cooling levitated nanoparticles into their
combined rotational and translational quantum regime [Phys. Rev. Lett. 126,
163603 (2021)]. This article presents the theory of how aspherical
nanoparticles are affected by elliptically polarised laser beams, how two
orthogonal cavity modes enable rotational and translational cooling, and how
the resulting power spectra contain signatures of rotational non-linearities.
We provide analytic expressions for the resulting trapping frequencies,
opto-mechanical coupling strengths, cooling rates, and steady-state occupations
and we study their dependence on the tweezer ellipticity.


  Removing the undesired reflections from images taken through the glass is of
broad application to various computer vision tasks. Non-learning based methods
utilize different handcrafted priors such as the separable sparse gradients
caused by different levels of blurs, which often fail due to their limited
description capability to the properties of real-world reflections. In this
paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle
this problem in a unified framework. Our proposed network integrates image
appearance information and multi-scale gradient information with human
perception inspired loss function, and is trained on a new dataset with 3250
reflection images taken under diverse real-world scenes. Extensive experiments
on a public benchmark dataset show that the proposed method performs favorably
against state-of-the-art methods.


  We introduce the concept of non-uniform metamirrors (full-reflection
metasurfaces) providing full control of reflected wave fronts independently
from the two sides of the mirror. Metamirror is a single planar array of
electrically small bianisotropic inclusions. The electric and magnetic
responses of the inclusions enable creating controlled gradient of phase
discontinuities over the surface. Furthermore, presence of electromagnetic
coupling in the inclusions allows independent control of reflection phase from
the opposite sides of the mirror. Based on the proposed concept, we design and
simulate metamirrors for highly efficient light bending and
near-diffraction-limit focusing with a sub-wavelength focal distance.


  Traditional model-based image reconstruction (MBIR) methods combine forward
and noise models with simple object priors. Recent machine learning methods for
image reconstruction typically involve supervised learning or unsupervised
learning, both of which have their advantages and disadvantages. In this work,
we propose a unified supervised-unsupervised (SUPER) learning framework for
X-ray computed tomography (CT) image reconstruction. The proposed learning
formulation combines both unsupervised learning-based priors (or even simple
analytical priors) together with (supervised) deep network-based priors in a
unified MBIR framework based on a fixed point iteration analysis. The proposed
training algorithm is also an approximate scheme for a bilevel supervised
training optimization problem, wherein the network-based regularizer in the
lower-level MBIR problem is optimized using an upper-level reconstruction loss.
The training problem is optimized by alternating between updating the network
weights and iteratively updating the reconstructions based on those weights. We
demonstrate the learned SUPER models' efficacy for low-dose CT image
reconstruction, for which we use the NIH AAPM Mayo Clinic Low Dose CT Grand
Challenge dataset for training and testing. In our experiments, we studied
different combinations of supervised deep network priors and unsupervised
learning-based or analytical priors. Both numerical and visual results show the
superiority of the proposed unified SUPER methods over standalone supervised
learning-based methods, iterative MBIR methods, and variations of SUPER
obtained via ablation studies. We also show that the proposed algorithm
converges rapidly in practice.


  We use $>$9400 $\log(m/M_{\odot})>10$ quiescent and star-forming galaxies at
$z\lesssim2$ in COSMOS/UltraVISTA to study the average size evolution of these
systems, with focus on the rare, ultra-massive population at
$\log(m/M_{\odot})>11.4$. The large 2-square degree survey area delivers a
sample of $\sim400$ such ultra-massive systems. Accurate sizes are derived
using a calibration based on high-resolution images from the Hubble Space
Telescope. We find that, at these very high masses, the size evolution of
star-forming and quiescent galaxies is almost indistinguishable in terms of
normalization and power-law slope. We use this result to investigate possible
pathways of quenching massive $m>M^*$ galaxies at $z<2$. We consistently model
the size evolution of quiescent galaxies from the star-forming population by
assuming different simple models for the suppression of star-formation. These
models include an instantaneous and delayed quenching without altering the
structure of galaxies and a central starburst followed by compaction. We find
that instantaneous quenching reproduces well the observed mass-size relation of
massive galaxies at $z>1$. Our starburst$+$compaction model followed by
individual growth of the galaxies by minor mergers is preferred over other
models without structural change for $\log(m/M_{\odot})>11.0$ galaxies at
$z>0.5$. None of our models is able to meet the observations at $m>M^*$ and
$z<1$ with out significant contribution of post-quenching growth of individual
galaxies via mergers. We conclude that quenching is a fast process in galaxies
with $ m \ge 10^{11} M_\odot$, and that major mergers likely play a major role
in the final steps of their evolution.


  We investigate quantum algorithms for classification, a fundamental problem
in machine learning, with provable guarantees. Given $n$ $d$-dimensional data
points, the state-of-the-art (and optimal) classical algorithm for training
classifiers with constant margin runs in $\tilde{O}(n+d)$ time. We design
sublinear quantum algorithms for the same task running in $\tilde{O}(\sqrt{n}
+\sqrt{d})$ time, a quadratic improvement in both $n$ and $d$. Moreover, our
algorithms use the standard quantization of the classical input and generate
the same classical output, suggesting minimal overheads when used as
subroutines for end-to-end applications. We also demonstrate a tight lower
bound (up to poly-log factors) and discuss the possibility of implementation on
near-term quantum machines. As a side result, we also give sublinear quantum
algorithms for approximating the equilibria of $n$-dimensional matrix zero-sum
games with optimal complexity $\tilde{\Theta}(\sqrt{n})$.


  We assemble the equations of general relativistic magnetohydrodynamics (MHD)
in 3+1 form. These consist of the complete coupled set of Maxwell equations for
the electromagnetic field, Einstein's equations for the gravitational field,
and the equations of relativistic MHD for a perfectly conducting ideal gas. The
adopted form of the equations is suitable for evolving numerically a
relativistic MHD fluid in a dynamical spacetime characterized by a strong
gravitational field.


  This work is concerned with modeling and simulation of the mitral valve, one
of the four valves in the human heart. The valve is composed of leaflets, the
free edges of which are supported by a system of chordae, which themselves are
anchored to the papillary muscles inside the left ventricle. First, we examine
valve anatomy and present the results of original dissections. These display
the gross anatomy and information on fiber structure of the mitral valve. Next,
we build a model valve following a design-based methodology, meaning that we
derive the model geometry and the forces that are needed to support a given
load, and construct the model accordingly. We incorporate information from the
dissections to specify the fiber topology of this model. We assume the valve
achieves mechanical equilibrium while supporting a static pressure load. The
solution to the resulting differential equations determines the pressurized
configuration of the valve model. To complete the model we then specify a
constitutive law based on a stress-strain relation consistent with experimental
data that achieves the necessary forces computed in previous steps. Finally,
using the immersed boundary method, we simulate the model valve in fluid in a
computer test chamber. The model opens easily and closes without leak when
driven by physiological pressures over multiple beats. Further, its closure is
robust to driving pressures that lack atrial systole or are much lower or
higher than normal.


  The Bessel-Gaussian modal spectrum, generated in spontaneous parametric
down-conversion of a Gaussian pump beam, is considered. This is done by first
deriving a general expression for the true probability of detecting specific
transverse spatial modes for the pairs of photons generated in a spontaneous
parametric down-conversion process. These expressions are applied for
Bessel-Gaussian modes in type I phase matching with collinear, degenerate
down-converted beams. The result shows that a broad orbital angular momentum
spectrum can be obtained for Bessel-Gaussian modes under certain conditions.


  Collective tunneling is a ubiquitous phenomenon in finite-size spin clusters
that shows up in systems as diverse as molecular magnets or spin clusters
adsorbed at surfaces. The problem we explore is to understand how small
flipping terms can cooperate to flip a large spin to the opposite direction or
a cluster of interacting elementary Ising spins into the time-reversed state.
These high order processes will involve at least two channels, a single
spin-flip channel due to a transverse field and a two-spin flip channel due to
exchange or due to single-ion anisotropies. In the present paper, we show that
high-order perturbation theory can be formulated and evaluated with the help of
simple recurrence relations, leading to a compact theory of tunnelling in
macroscopic spins, in one-dimensional clusters, as well as in small
higher-dimensional clusters. This is demonstrated explicitly in the case of the
Ising model with a transverse field and transverse exchange, and in the case of
macroscopic spins with uniaxial anisotropy. Our approach provides a transparent
theory of level crossings, where the tunneling between time reversed
configurations vanishes as a function of the external field. The crossings
result from destructive quantum interferences between competing flipping
channels. Our theory consistently predicts N crossings in chains of N Ising
spins, 2S crossings in single spins of magnitude S, and yields explicit
analytical formulae for the level crossings of open chains and macroscopic
spins. Disorder can be easily implemented in this perturbative formalism.
Leading disorder effects can be treated analytically for spin rings. At the
smallest transverse field crossing, the suppression of tunneling is most robust
with respect to disorder. We briefly discuss the implications of our findings
for the use of realistic spin clusters on surfaces to store information.


  The method of using periodic approximations to compute the spectral
decomposition of the Koop- man operator is generalized to the class of
measure-preserving flows on compact metric spaces. It is shown that the
spectral decomposition of the continuous one-parameter unitary group can be
approximated from an intermediate time discretization of the flow. A sufficient
condition is established between the time-discretization of the flow and the
spatial discretization of the periodic approximation, so that weak convergence
of spectra will occur in the limit. This condition effectively translates to
the requirement that the spatial refinements must occur at a faster pace than
the temporal refinements. This result is contrasted with the well-known CLF
condition of finite difference schemes for advection equations. Numerical
results of spectral computations are shown for some benchmark examples of
volume-preserving flows.


  We investigate dynamics and interactions of particles levitated and trapped
by the thermophoretic force in a vacuum cell. Our analysis is based on footage
taken by orthogonal cameras that are able to capture the three dimensional
trajectories of the particles. In contrast to spherical particles, which remain
stationary at the center of the cell, here we report new qualitative features
of the motion of particles with non-spherical geometry. Singly levitated
particles exhibit steady spinning around their body axis and rotation around
the symmetry axis of the cell. When two levitated particles approach each
other, repulsive or attractive interactions between the particles are observed.
Our levitation system offers a wonderful platform to study interaction between
particles in a microgravity environment.


  In this paper, Ramsey theory for discrete hypergroups is introduced with
emphasis on polynomial hypergroups, discrete orbit hypergroups and hypergroup
deformations of semigroups. In this context, new notions of Ramsey principle
for hypergroups and $\alpha$-Ramsey hypergroup, $0 \leq \alpha<1,$ are defined
and studied.


  In this big data era, the use of large dataset in conjunction with machine
learning (ML) has been increasingly popular in both industry and academia. In
recent times, the field of materials science is also undergoing a big data
revolution, with large database and repositories appearing everywhere.
Traditionally, materials science is a trial-and-error field, in both the
computational and experimental departments. With the advent of machine
learning-based techniques, there has been a paradigm shift: materials can now
be screened quickly using ML models and even generated based on materials with
similar properties; ML has also quietly infiltrated many sub-disciplinary under
materials science. However, ML remains relatively new to the field and is
expanding its wing quickly. There are a plethora of readily-available big data
architectures and abundance of ML models and software; The call to integrate
all these elements in a comprehensive research procedure is becoming an
important direction of material science research. In this review, we attempt to
provide an introduction and reference of ML to materials scientists, covering
as much as possible the commonly used methods and applications, and discussing
the future possibilities.


  The online square detection problem is to detect the first occurrence of a
square in a string whose characters are provided as input one at a time. Recall
that a square is a string that is a concatenation of two identical strings. In
this paper we present an algorithm solving this problem in $O(n\log\sigma)$
time and linear space on ordered alphabet, where $\sigma$ is the number of
different letters in the input string. Our solution is relatively simple and
does not require much memory unlike the previously known online algorithm with
the same working time. Also we present an algorithm working in $O(n\log n)$
time and linear space on unordered alphabet, though this solution does not
outperform the previously known result with the same time bound.


  The richness of the data set, collected by the INDRA collaboration during the
last twenty years, enabled us to build a set of caloric curves for nuclei of
various sizes, by using, for the first time, a single experimental set-up and a
single experimental protocol. We will therefore present the different caloric
curves ($E^{\ast}-T$) obtained by a new calorimetry, for Quasi-Projectiles
produced by symmetric or quasi symmetric reactions at different incident
energies (Au+Au, Xe+Sn, Ni+Ni). For all these systems, a clear change of the
de-excitation process of hot nuclei is observed but this one is neither a
plateau nor a back-bending, but a sudden change of slope.


  Large Language Models (LLMs) often generate erroneous outputs, known as
hallucinations, due to their limitations in discerning questions beyond their
knowledge scope. While addressing hallucination has been a focal point in
research, previous efforts primarily concentrate on enhancing correctness
without giving due consideration to the significance of rejection mechanisms.
In this paper, we conduct a comprehensive examination of the role of rejection,
introducing the notion of model reliability along with corresponding metrics.
These metrics measure the model's ability to provide accurate responses while
adeptly rejecting questions exceeding its knowledge boundaries, thereby
minimizing hallucinations. To improve the inherent reliability of LLMs, we
present a novel alignment framework called Reinforcement Learning from
Knowledge Feedback (RLKF). RLKF leverages knowledge feedback to dynamically
determine the model's knowledge boundary and trains a reliable reward model to
encourage the refusal of out-of-knowledge questions. Experimental results on
mathematical questions affirm the substantial efficacy of RLKF in significantly
enhancing LLM reliability.


  In this work, we develop a pipeline that associates Persistence Diagrams to
digital data via the most appropriate filtration for the type of data
considered. Using a grid search approach, this pipeline determines optimal
representation methods and parameters. The development of such a topological
pipeline for Machine Learning involves two crucial steps that strongly affect
its performance: firstly, digital data must be represented as an algebraic
object with a proper associated filtration in order to compute its topological
summary, the Persistence Diagram. Secondly, the persistence diagram must be
transformed with suitable representation methods in order to be introduced in a
Machine Learning algorithm. We assess the performance of our pipeline, and in
parallel, we compare the different representation methods on popular benchmark
datasets. This work is a first step toward both an easy and ready-to-use
pipeline for data classification using persistent homology and Machine
Learning, and to understand the theoretical reasons why, given a dataset and a
task to be performed, a pair (filtration, topological representation) is better
than another.


  Using magnetic field and plasma data acquired during "Phobos-2" mission in
regions which have not been explored before, we study the solar wind
interaction with Phobos. The draping magnetic field of the solar wind around
Phobos appears at distances of 200-300 km from the Phobos day-side due to a
density and magnetic field pile up in front of the Phobos obstacle. The nature
of the interaction and the magnetic field signatures observed are consistent
with the ratio of the proton skin depth to the actual size of the Phobos
obstacle to the solar wind. Phobos deflects the flow of the solar wind and the
subsolar stand-off distance of the deflection is about 16-17 Phobos radii.
Source with equivalent magnetic moment M'=10^{15} A m^2 in Phobos leads to the
development of such an obstacle to solar wind flow around Phobos. These data
give a lesson for the study of the interaction of the small, magnetized object
with solar wind.


  We study 3D pure Einstein quantum gravity with negative cosmological
constant, in the regime where the AdS radius $l$ is of the order of the Planck
scale. Specifically, when the Brown-Henneaux central charge $c=3l/2G_N$ ($G_N$
is the 3D Newton constant) equals $c=1/2$, we establish duality between 3D
gravity and 2D Ising conformal field theory by matching gravity and conformal
field theory partition functions for AdS spacetimes with general asymptotic
boundaries. This duality was suggested by a genus-one calculation of Castro et
al. [Phys. Rev. D {\bf 85}, 024032 (2012)]. Extension beyond genus-one requires
new mathematical results based on 3D Topological Quantum Field Theory; these
turn out to uniquely select the $c=1/2$ theory among all those with $c<1$,
extending the previous results of Castro et al..
  Previous work suggests the reduction of the calculation of the gravity
partition function to a problem of summation over the orbits of the mapping
class group action on a "vacuum seed". But whether or not the summation is
well-defined for the general case was unknown before this work. Amongst all
theories with Brown-Henneaux central charge $c<1$, the sum is finite and unique
{\it only} when $c=1/2$, corresponding to a dual Ising conformal field theory
on the asymptotic boundary.


  Let $p\ge 5$ be a prime and let $n$ be a natural number. In this article we
describe the irreducible constituents of the induced characters
$\phi\big\uparrow^{\mathfrak{S}_n}$ for arbitrary linear characters $\phi$ of a
Sylow $p$-subgroup of the symmetric group $\mathfrak{S}_n$, generalising
earlier results of the authors. By doing so, we introduce Sylow branching
coefficients for symmetric groups.


  Just as $^3\roarrow{\rm He}$ can be approximately characterized as a
polarized neutron target, polarized \Li6D has been advocated as a good {\em
isoscalar} nuclear target for the extraction of the polarized gluon content of
the nucleon. The original argument rests upon a presumed ``alpha + deuteron''
picture of \Li6, with the polarization of the nucleus carried by the
polarization of the deuteron. We have calculated the polarization of the
constituents of \Li6 as a three-body bound state of $\alpha + n + p$
interacting with local potentials fitted to the scattering data. It is
necessary to include partial waves up to $j=17/2$ (75 channels, or, when
including the $T=1$ state, 150 channels) in the Faddeev equations before the
energy eigenvalue converges. The longitudinal formfactors are then described
well by the wave function. Various combinations of $\alpha$N and NN strong and
Coulomb potentials yield a straight line in the charge radius {\em vs.} energy
plane which, unlike those of previous calculations, passes through the
experimental datum. We find for all cases a polarization of the valence neutron
in excess of 90\%. This may make polarized \Li6D an attractive target for many
nuclear physics purposes, since its neutrons are effectively 45\% polarized.


  We study the best M\"obius approximations (BMA) to convex and concave
conformal mappings of the disk, including the special case of mappings onto
convex polygons. The crucial factor is the location of the poles of the BMAs.
Finer details are possible in the case of polygons through special properties
of Blaschke products and the prevertices of the mapping function.


  The Dirac structure of confinement is shown to be of timelike-vector nature
in the heavy quark limit of QCD. This stands in contradiction with the
phenomenological success of the Dirac scalar confining potential. A resolution
is achieved through the demonstration that an effective scalar interaction is
dynamically generated by nonperturbative mixing between ordinary and hybrid $Q
\bar Q$ states. The resolution depends crucially on the collective nature of
the gluonic degrees of freedom. This implies that dynamical gluonic effects are
vital when attempting to incorporate fine structure in models of the $Q \bar Q$
interaction.


  We introduce C-Algebras of compact Riemann surfaces $\Sigma$ as
non-commutative analogues of the Poisson algebra of smooth functions on
$\Sigma$. Representations of these algebras give rise to sequences of
matrix-algebras for which matrix-commutators converge to Poisson-brackets as
$N\to\infty$. For a particular class of surfaces, nicely interpolating between
spheres and tori, we completely characterize (even for the intermediate
singular surface) all finite dimensional representations of the corresponding
C-algebras.


  In 1996 Jespers and Wang classified finite semigroups whose integral
semigroup ring has finitely many units. In a recent paper, Iwaki-Juriaans-Souza
Filho continued this line of research by partially classifying the finite
semigroups whose rational semigroup algebra %over a field of characteristic
zero, contains a ${\mathbb{Z}}$-order with hyperbolic unit group. In this paper
we complete this classification by handling the case in which the semigroup is
semi-simple.


  Recent jet quenching measurements in Pb+Pb collisions at the LHC report a
significant energy imbalance of di-jets. The imbalance is found to be
compensated by a large amount of soft particles produced at large angles with
respect to the di-jet axis. This observation questions the conventional picture
of parton energy loss models, established at RHIC, which typically expect that
the radiated gluons are emitted at moderate angles close to the outgoing
parton. In this letter, we qualitatively discuss two possible contributions of
the underlying heavy-ion background that may have to be taken into account when
interpreting the recent data. We show that a large jet v_3, potentially caused
by a pathlength dependent energy loss in the presence of fluctuating initial
conditions, could contribute to the observed excess of soft particles
apparently originating from large angle in-medium radiation. In addition, the
observed excess could also be induced by multiple jets produced in the vicinity
of the leading jet, caused by a potential selection bias imposed on the di-jet
momentum imbalance.


  Optical properties in van der Waals heterostructures based on monolayer
transition-metal dichalcogenides (TMDs), are often dominated by excitonic
transitions. While intrinsic spin-orbit coupling (SOC) and an isotropic band
structure are typically studied in TMDs, in their heterostructures Rashba SOC
and trigonal warping (TW), resulting in bands with threefold anisotropy, are
also present. By considering a low-energy effective Hamiltonian and
Bethe-Salpeter equation, we study the effect of Rashba SOC and TW on the band
structure and absorption spectra. Rashba SOC is predicted to lead to emergent
excitons, which are identified as an admixture between 1s and 2p symmetries. In
contrast, for experimentally relevant values, TW has only a negligible effect
on the absorption spectrum. These findings could guide experimental
demonstrations of emergent bright excitons and further studies of the proximity
effects in van der Waals heterostructure.


  Using theory and simulations, we carried out a first systematic
characterization of DNA unzipping via nanopore translocation. Starting from
partially unzipped states, we found three dynamical regimes depending on the
applied force, f: (i) heterogeneous DNA retraction and rezipping (f < 17pN),
(ii) normal (17pN < f < 60pN) and (iii) anomalous (f > 60pN) drift-diffusive
behavior. We show that the normal drift-diffusion regime can be effectively
modelled as a one-dimensional stochastic process in a tilted periodic
potential. We use the theory of stochastic processes to recover the potential
from nonequilibrium unzipping trajectories and show that it corresponds to the
free-energy landscape for single base-pairs unzipping. Applying this general
approach to other single-molecule systems with periodic potentials ought to
yield detailed free-energy landscapes from out-of-equilibrium trajectories.


  We establish existence of Markov chains of mean-field type with unbounded
jump intensities by means of a fixed point argument using the Total Variation
distance. We further show existence of nearly-optimal controls and, using a
Markov chain backward SDE approach, we suggest conditions for existence of an
optimal control and a saddle-point for respectively a control problem and a
zero-sum differential game associated with payoff functionals of mean-field
type, under dynamics driven by such Markov chains of mean-field type.


  The chemical and physical properties of interstellar dust in the densest
regions of the Galaxy are still not well understood. X-rays provide a powerful
probe since they can penetrate gas and dust over a wide range of column
densities (up to $10^{24}\ \rm{cm}^{-2}$). The interaction (scattering and
absorption) with the medium imprints spectral signatures that reflect the
individual atoms which constitute the gas, molecule, or solid. In this work we
investigate the ability of high resolution X-ray spectroscopy to probe the
properties of cosmic grains containing iron. Although iron is heavily depleted
into interstellar dust, the nature of the Fe-bearing grains is still largely
uncertain. In our analysis we use iron K-edge synchrotron data of minerals
likely present in the ISM dust taken at the European Synchrotron Radiation
Facility. We explore the prospects of determining the chemical composition and
the size of astrophysical dust in the Galactic centre and in molecular clouds
with future X-ray missions. The energy resolution and the effective area of the
present X-ray telescopes are not sufficient to detect and study the Fe K-edge,
even for bright X-ray sources. From the analysis of the extinction cross
sections of our dust models implemented in the spectral fitting program SPEX,
the Fe K-edge is promising for investigating both the chemistry and the size
distribution of the interstellar dust. We find that the chemical composition
regulates the X-ray absorption fine structures in the post edge region, whereas
the scattering feature in the pre-edge is sensitive to the mean grain size.
Finally, we note that the Fe K-edge is insensitive to other dust properties,
such as the porosity and the geometry of the dust.


  We consider covert communication, i.e., hiding the presence of communication
from an adversary for multiple-input multiple-output (MIMO) additive white
Gaussian noise (AWGN) channels. We characterize the maximum covert coding rate
under a variety of settings, including different regimes where either the
number of transmit antennas or the blocklength is scaled up. We show that a
non-zero covert capacity can be achieved in the massive MIMO regime in which
the number of transmit antennas scales up but under specific conditions. Under
such conditions, we show that the covert capacity of MIMO AWGN channels
converges the capacity of MIMO AWGN channels. Furthermore, we derive the
order-optimal scaling of the number of covert bits in the regime where the
covert capacity is zero. We provide an insightful comparative analysis of
different cases in which secrecy and energy-undetectability constraints are
imposed separately or jointly.


  We suggest the possibility that the anomalies observed in the LSND experiment
and the Gallium radioactive source experiments may be due to neutrino
oscillations generated by a large squared-mass difference of about 20 - 30
eV^2. We consider the simplest 3+1 four-neutrino scheme that can accommodate
also the observed solar and atmospheric neutrino oscillations. We show that, in
this framework, the disappearance of nu_e and nu_mu in short-baseline neutrino
oscillation experiments is mainly due to active-sterile transitions. The
implications of the first MiniBooNE results, appeared after the completion of
this paper, are discussed in an addendum.


  Quantization problem in digital control system have attracted more and more
attention in these years. Normally, a quantized variable is regarded as a
perturbed copy of the unquantized variable in the research of quantization
effect, but this model has shown many obvious disadvantages in control system
analysis and design process. In this paper, we give a new model for
quantization based 'cell mapping' concept. This cell model could clearly
describe the global dynamics of quantized digital system. Then some important
characteristics of control system like controllability are analyzed by this
model. The finite precision control design method based on cell concept is also
presented.


  I give my view of the early history of the discovery of hyperbolic structures
on knot complements from my early work on representations of knot groups into
matrix groups to my meeting with William Thurston in 1976. (This article was
written by Robert Riley about ten years before his death in 2000 and never
submitted for publication. An explanation of why it is being published now and
some information about Riley and this article is given in the article by Brin,
Jones and Singerman which accompanies this article in this issue of the
[journal | arxiv].)


  Numerical solutions for Sommerfeld model in nonrelativistic case are
presented for the scattering of a spinless extended charged body in a static
Coulomb field of a fixed point charge. It is shown that differential cross
section for extended body preserves the form of the Rutherford result with
multiplier, not equal to one (as in classical case), but depending on the size
of Sommerfeld particle. Also the effect of capture by attractive center is
found out for Sommerfeld particle. The origin of this effect lies in radiation
damping.


  Let A be a collection of n linear hyperplanes in k^l, where k is an
algebraically closed field. The Orlik-Terao algebra of A is the subalgebra R(A)
of the rational functions generated by reciprocals of linear forms vanishing on
hyperplanes of A. It determines an irreducible subvariety of projective space.
We show that a flat X of A is modular if and only if R(A) is a split extension
of the Orlik-Terao algebra of the subarrangement A_X. This provides another
refinement of Stanley's modular factorization theorem and a new
characterization of modularity, similar in spirit to the modular fibration
theorem of Paris.
  We deduce that if A is supersolvable, then its Orlik-Terao algebra is Koszul.
In certain cases, the algebra is also a complete intersection, and we
characterize when this happens.


  We performed non-LTE calculations for O I with the plane-parallel model
atmospheres for a set of stellar parameters corresponding to A-K type stars.
The model atom of Przybilla et al. (2000) was updated using the best
theoretical and experimental atomic data available so far. Non-LTE leads to
strengthening the O I lines, and the difference between the non-LTE and LTE
abundances (non-LTE correction) is negative. The departures from LTE grow
toward higher effective temperature and lower surface gravity. In the entire
temperature range and log g = 4, the non-LTE correction does not exceed 0.05
dex in absolute value for O I lines in the visible spectral range. The non-LTE
corrections are significantly larger for the infrared O I 7771-5 A lines and
reach -1.9 dex in the model atmosphere with Teff = 10000 K and log g = 2. To
differentiate the effects of inelastic collisions with electrons and neutral
hydrogen atoms on the statistical equilibrium (SE) of O I, we derived the
oxygen abundance for the three well studied A-type stars Vega, Sirius, and HD
32115. For each star, non-LTE leads to smaller difference between the infrared
and visible lines. For example, for Vega, this difference reduces from 1.17 dex
in LTE down to 0.14 dex when ignoring LTE. To remove the difference between the
infrared and visible lines in A-type stars completely, one needs to reduce the
used electron-impact excitation rates by Barklem (2007) by a factor of 4. In
the case of Procyon and the Sun, inelastic collisions with H I affect the SE of
O I, and agreement between the abundances from different lines is achieved when
using the Drawin's formalism to compute collisional rates. The solar mean
oxygen abundance from the O I 6300, 6158, 7771-5, and 8446 A lines is log
epsilon = 8.74+-0.05, when using the MAFAGS-OS solar model atmosphere and log
epsilon = 8.78+-0.03, when applying the 3D corrections from the literature.


  We prove pinching estimates for solutions of the linearized Ricci flow system
on a closed manifold of dimension $n\geq 4$ with positive scalar curvature and
vanishing Weyl tensor. If the vanishing Weyl tensor condition is removed, we
only give a rough pinching estimate controlled by some blow-up function in a
short time. These results extend the $3$-dimensional case due to Anderson and
Chow (2005) \cite{[AC]}.


  Cardiovascular diseases are the leading cause of mortality globally,
necessitating advancements in diagnostic techniques. This study explores the
application of wavelet transformation for classifying electrocardiogram (ECG)
signals to identify various cardiovascular conditions. Utilizing the MIT-BIH
Arrhythmia Database, we employed both continuous and discrete wavelet
transforms to decompose ECG signals into frequency sub-bands, from which we
extracted eight statistical features per band. These features were then used to
train and test various classifiers, including K-Nearest Neighbors and Support
Vector Machines, among others. The classifiers demonstrated high efficacy, with
some achieving an accuracy of up to 96% on test data, suggesting that
wavelet-based feature extraction significantly enhances the prediction of
cardiovascular abnormalities in ECG data. The findings advocate for further
exploration of wavelet transforms in medical diagnostics to improve automation
and accuracy in disease detection. Future work will focus on optimizing feature
selection and classifier parameters to refine predictive performance further.


  A popular technique for tolerating malicious faults in open distributed
systems is to establish small groups of participants, each of which has a
non-faulty majority. These groups are used as building blocks to design
attack-resistant algorithms.
  Despite over a decade of active research, current constructions require group
sizes of $O(\log n)$, where $n$ is the number of participants in the system.
This group size is important since communication and state costs scale
polynomially with this parameter. Given the stubbornness of this logarithmic
barrier, a natural question is whether better bounds are possible.
  Here, we consider an attacker that controls a constant fraction of the total
computational resources in the system. By leveraging proof-of-work (PoW), we
demonstrate how to reduce the group size exponentially to $O(\log\log n)$ while
maintaining strong security guarantees. This reduction in group size yields a
significant improvement in communication and state costs.


  The master formulae of the CB model of long durations gamma ray bursts (GRBs)
which reproduce very well the light curves and spectral evolution of their
prompt emission pulses and their smooth afterglows, also reproduce very well
the lightcurves and spectral evolution of their X-ray and optical flares. Here
we demonstrate that for GRB090812


  Sparse regression codes (SPARC) connect the sparse signal recovery framework
of compressive sensing with error control coding techniques. SPARC encoding
produces codewords which are \emph{sparse} linear combinations of columns of a
dictionary matrix. SPARC decoding is accomplished using sparse signal recovery
algorithms. We construct dictionary matrices using Gold codes and mutually
unbiased bases and develop suitable generalizations of SPARC (GSPARC). We
develop a greedy decoder, referred as match and decode (MAD) algorithm and
provide its analytical noiseless recovery guarantees. We propose a parallel
greedy search technique, referred as parallel MAD (PMAD), to improve the
performance. We describe the applicability of GSPARC with PMAD decoder for
multi-user channels, providing a non-orthogonal multiple access scheme. We
present numerical results comparing the block error rate (BLER) performance of
the proposed algorithms for GSPARC in AWGN channels, in the short block length
regime. The PMAD decoder gives better BLER than the approximate message passing
decoder for SPARC. GSPARC with PMAD gives comparable and competitive BLER
performance, when compared to other existing codes. In multi-user channels,
GSPARC with PMAD decoder outperforms the sphere packing lower bounds of an
orthogonal multiple access scheme, which has the same spectral efficiency.


  Dynamical properties of gauge theories with light flavor quarks are studied
in a dual supergravity by adding a D7-brane probe into the AdS background
deformed by the dilaton. By estimating the vev of flavor quark-bilinear in both
the supersymmetric and non-supersymmetric gravity duals, we find spontaneous
chiral symmetry breaking in the case of the non-supersymmetric background. We
also study quark-antiquark potential for light quarks to see the quark
confinement in the models considered here.


  Chinese word segmentation is a fundamental task for Chinese language
processing. The granularity mismatch problem is the main cause of the errors.
This paper showed that the binary tree representation can store outputs with
different granularity. A binary tree based framework is also designed to
overcome the granularity mismatch problem. There are two steps in this
framework, namely tree building and tree pruning. The tree pruning step is
specially designed to focus on the granularity problem. Previous work for
Chinese word segmentation such as the sequence tagging can be easily employed
in this framework. This framework can also provide quantitative error analysis
methods. The experiments showed that after using a more sophisticated tree
pruning function for a state-of-the-art conditional random field based
baseline, the error reduction can be up to 20%.


  Non-Hermitian wave engineering has attracted a surge of interest in photonics
in recent years. One of the prominent phenomena is coherent perfect absorption
(CPA), in which the annihilation of electromagnetic scattering occurs by
destructive interference of multiple incident waves. This concept has been
implemented in various platforms to demonstrate real-time control of
absorption, scattering and radiation by varying the relative phase of the
excitation signals. However, so far these studies have been limited to simple
photonic systems involving single or few modes at well-defined resonant
frequencies. Realizing CPA in more complex photonic systems is challenging
because it typically requires engineering the interplay of a large number of
resonances featuring large spatial complexity within a narrow frequency range.
Here, we extend the paradigm of coherent control of light to a complex photonic
system involving more than 1,000 optical modes in a chaotic microresonator. We
efficiently model the optical fields within a quasi-normal mode (QNM)
expansion, and experimentally demonstrate chaotic CPA states, as well as their
non-Hermitian degeneracies, which we leverage to efficiently control the cavity
excitation through the input phases of multiple excitation channels. Our
results shed light on the universality of non-Hermitian physics beyond simple
resonant systems, paving the way for new opportunities in the science and
technology of complex nanophotonic systems by chaotic wave interference.


  The composition, including the ionic charge states and elemental abundances
of heavy elements, within interplanetary coronal mass ejections (ICMEs) has
tight correlations with their source regions and eruption processes. This can
help analyze the eruption mechanisms and plasma origins of CMEs, and deepen our
understanding of energetic solar activities. The active regions and quiet-Sun
regions have different physical properties, thus from a statistical point of
view, ICMEs originating from the two types of regions should exhibit different
compositional characteristics. To demonstrate the differences comprehensively,
we conduct survey studies on the ionic charge states of five elements (Mg, Fe,
Si, C, and O) and the relative abundances of six elements (Mg/O, Fe/O, Si/O,
C/O, Ne/O, and He/O) within ICMEs from 1998 February to 2011 August through the
data of advanced composition explorer. The results show that ICMEs from active
regions have higher ionic charge states and relative abundances than those from
quiet-Sun regions. For the active-region ICMEs, we further analyze the
relations between their composition and flare class, and find a positive
relationship between them, i.e., the higher classes of the associated flares,
the larger means of ionic charge states and relative abundances (except the
C/O) within ICMEs. As more (less) fractions of ICMEs originate from active
regions around solar maximum (minimum), and active-region ICMEs usually are
associated with higher-class flares, our studies might answer why ICME
composition measured near 1 au exhibits the solar cycle dependence.


  The discussion of our recent work concerning the vector solution of
boundary-value problems in electromagnetism is extended to the case of no
azimuthal symmetry by means of the spin-weighted spherical harmonics.


  We present a novel incremental learning approach for unsupervised word
segmentation that combines features from probabilistic modeling and model
selection. This includes super-additive penalties for addressing the cognitive
burden imposed by long word formation, and new model selection criteria based
on higher-order generative assumptions. Our approach is fully unsupervised; it
relies on a small number of parameters that permits flexible modeling and a
mechanism that automatically learns parameters from the data. Through
experimentation, we show that this intricate design has led to top-tier
performance in both phonemic and orthographic word segmentation.


  Quantum plasmonics of few electrons that was reported a few years ago in
photodoping experiments has received little or no attention in doped
nanomaterials both theoretically and experimentally. There are no studies of
quantum plasmonics of large quantum dots of the size of Bohr excitonic radius
or less that take electronic and geometric structure of the quantum dots. In
this work, we studied extensively the absorption spectra of a protype quantum
dot of sizeable ZnO of approximately 3.0 nm in diameter doped with Ga, and Al
in diluted limit with density functional theory (DFT) plus Hubbard corrections
(U). The localized surface plasmon resonances (LSPRs) were then determined from
the real part of the dielectric function by correlating the negative portion of
it to resonant spectral lines. Our results show the sensitivity of the spectral
lines to distribution of the dopants, the electronic structure of the dopants,
the polarization of the electric field, and size of the quantum dots. Even
though our findings are based on stoichiometrically simple ZnO, it shows that
the DFT + U method with numerical atomic basis can be used at reasonable
computational cost to study quantum plasmonics of few electrons taking into
account electronic and geometric structures, which is missing currently. The
method can be extended to study magneto-optics of diluted magnetic
semiconductor QDs.


  We formulate and analyze a new model of vector hysteresis for the case of
two-dimensional input signals. We prove certain properties of this model and we
present the solutions to two identification problems connected with our model.


  Software engineers working in large projects must navigate complex
information landscapes. Change Impact Analysis (CIA) is a task that relies on
engineers' successful information seeking in databases storing, e.g., source
code, requirements, design descriptions, and test case specifications. Several
previous approaches to support information seeking are task-specific, thus
understanding engineers' seeking behavior in specific tasks is fundamental. We
present an industrial case study on how engineers seek information in CIA, with
a particular focus on traceability and development artifacts that are not
source code. We show that engineers have different information seeking
behavior, and that some do not consider traceability particularly useful when
conducting CIA. Furthermore, we observe a tendency for engineers to prefer less
rigid types of support rather than formal approaches, i.e., engineers value
support that allows flexibility in how to practically conduct CIA. Finally, due
to diverse information seeking behavior, we argue that future CIA support
should embrace individual preferences to identify change impact by empowering
several seeking alternatives, including searching, browsing, and tracing.


  Several thin Low Gain Avalanche Detectors from Hamamatsu Photonics were
irradiated with neutrons to different equivalent fluences up to
$\Phi_{eq}=3\cdot10^{15}$ cm$^{-2}$. After the irradiation they were annealed
at 60$^\circ$C in steps to times $>20000$ minutes. Their properties, mainly
full depletion voltage, gain layer depletion voltage, generation and leakage
current, as well as their performance in terms of collected charge and time
resolution, were determined between the steps.
  It was found that the effect of annealing on timing resolution and collected
charge is not very large and mainly occurs within the first few tens of
minutes. It is a consequence of active initial acceptor concentration decrease
in the
  gain layer with time, where changes of around 10\% were observed. For any
relevant annealing times for detector operation the changes of effective doping
concentration in the bulk negligibly influences the performance of the device,
due to their small thickness and required high bias voltage operation. At very
long annealing times the increase of the effective doping concentration in the
bulk leads to a significant increase of the electric field in the gain layer
and, by that, to the increase of gain at given voltage. The leakage current
decreases in accordance with generation current annealing.


  Achieving precise preparation of quantum many-body states is crucial for the
practical implementation of quantum computation and quantum simulation.
However, the inherent challenges posed by unavoidable excitations at critical
points during quench processes necessitate careful design of control fields. In
this work, we introduce a promising and versatile dynamic control neural
network tailored to optimize control fields. We address the problem of
suppressing defect density and enhancing cat-state fidelity during the passage
across the critical point in the quantum Ising model. Our method facilitates
seamless transitions between different objective functions by adjusting the
{optimization strategy}. In comparison to gradient-based power-law quench
methods, our approach demonstrates significant advantages for both small system
sizes and long-term evolutions. We provide a detailed analysis of the specific
forms of control fields and summarize common features for experimental
implementation. Furthermore, numerical simulations demonstrate the robustness
of our proposal against random noise and spin number fluctuations. The
optimized defect density and cat-state fidelity exhibit a transition at a
critical ratio of the quench duration to the system size, coinciding with the
quantum speed limit for quantum evolution.


  In this paper we describe the dynamics of certain rational maps of the form
$k \cdot (x+x^{-1})$ over finite fields of odd characteristic.


  We study the effect of uniaxial strain on the band structure of ZrSe$_3$, a
semiconducting material with a marked in-plane structural anisotropy. By using
a modified 3-point bending test apparatus, thin ZrSe$_3$ flakes were subjected
to uniaxial strain along different crystalline orientations monitoring the
effect of strain on their optical properties through micro-reflectance
spectroscopy. The obtained spectra showed excitonic features that blueshift
upon uniaxial tension. This shift is strongly dependent on the direction along
which the strain is being applied. When the flakes are strained along the
b-axis, the exciton peak shifts at ~ 60-95 meV/%, while along the a-axis, the
shift only reaches ~ 0-15 meV/%. Ab initio calculations were conducted to study
the influence of uniaxial strain, applied along different crystal directions,
on the band structure and reflectance spectra of ZrSe$_3$, exhibiting a
remarkable agreement with the experimental results.


  We demonstrate a possibility to stabilize three-dimensional spatiotemporal
solitons ("light bullets") in self--focusing Kerr media by means of a
combination of dispersion management in the longitudinal direction (with the
group-velocity dispersion alternating between positive and negative values) and
periodic modulation of the refractive index in one transverse direction, out of
the two. The analysis is based on the variational approximation (results of
direct three-dimensional simulations will be reported in a follow-up work). A
predicted stability area is identified in the model's parameter space. It
features a minimum of the necessary strength of the transverse modulation of
the refractive index, and finite minimum and maximum values of the soliton's
energy. The former feature is also explained analytically.


  We prove that the Hamiltonian of the model describing a spin which is
linearly coupled to a field of relativistic and massless bosons, also known as
the spin-boson model, admits a ground state for small values of the coupling
constant lambda. We show that the ground state energy is an analytic function
of lambda and that the corresponding ground state can also be chosen to be an
analytic function of lambda. No infrared regularization is imposed. Our proof
is based on a modified version of the BFS operator theoretic renormalization
analysis. Moreover, using a positivity argument we prove that the ground state
of the spin-boson model is unique. We show that the expansion coefficients of
the ground state and the ground state energy can be calculated using regular
analytic perturbation theory.


  The magnetograms of sixteen Active Regions, observed during June - November
2015, were obtained from the Joint Science Operations Center and used to
determine the fractal dimensions of various magnetic field groups. The fields
were divided into strong (positive and negative) and weak (positive and
negative) groupings. The area-perimeter method was used to determine the
fractal dimensions for the umbral and penumbral regions of the magnetograms.
The fractal dimensions were found to be 1.79 \pm 0.49 and 1.96 \pm 0.29 for
strong and weak magnetic fields respectively. When compared to the umbral and
penumbral fractal dimensions determined by (Rajkumar, Haque and Hrudey, 2017)
using white light images, an inverse relationship was found, with the umbra
showing lower fractal dimensions than the penumbra for the magnetic field
groups compared to the intensity images. This directly implies that there is
greater complexity in the penumbral magnetic field groupings than the umbral
region. This has implications for models that constrain how the magnetic field
groups are contained in the cooler umbral regions.


  In this paper, we present results from a multi-stage numerical campaign to
begin to explain and determine why extreme debris disk detections are rare,
what types of impacts will result in extreme debris disks and what we can learn
about the parameters of the collision from the extreme debris disks. We begin
by simulating many giant impacts using a smoothed particle hydrodynamical code
with tabulated equations of state and track the escaping vapour from the
collision. Using an $N$-body code, we simulate the spatial evolution of the
vapour generated dust post-impact.
  We show that impacts release vapour anisotropically not isotropically as has
been assumed previously and that the distribution of the resulting generated
dust is dependent on the mass ratio and impact angle of the collision. In
addition, we show that the anisotropic distribution of post-collision dust can
cause the formation or lack of formation of the short-term variation in flux
depending on the orientation of the collision with respect to the orbit around
the central star. Finally, our results suggest that there is a narrow region of
semi-major axis where a vapour generated disk would be observable for any
significant amount of time implying that giant impacts where most of the
escaping mass is in vapour would not be observed often but this does not mean
that the collisions are not occurring.


  We consider a spatial stochastic model of wireless cellular networks, where
the base stations (BSs) are deployed according to a simple and stationary point
process on $\mathbb{R}^d$, $d\ge2$. In this model, we investigate tail
asymptotics of the distribution of signal-to-interference ratio (SIR), which is
a key quantity in wireless communications. In the case where the path-loss
function representing signal attenuation is unbounded at the origin, we derive
the exact tail asymptotics of the SIR distribution under an appropriate
sufficient condition. While we show that widely-used models based on a Poisson
point process and on a determinantal point process meet the sufficient
condition, we also give a counterexample violating it. In the case of bounded
path-loss functions, we derive a logarithmically asymptotic upper bound on the
SIR tail distribution for the Poisson-based and $\alpha$-Ginibre-based models.
A logarithmically asymptotic lower bound with the same order as the upper bound
is also obtained for the Poisson-based model.


  We present arguments that reinforce the hybrid interpretation of pi(1800) and
we establish that the rho(1450) and the omega(1420) can be interpreted as
radial-hybrid mixtures. Some questions for future experiments are raised.


  We present a set of inequalities for detecting quantum entanglement of
$2\otimes d$ quantum states. For $2\otimes 2$ and $2\otimes 3$ systems, the
inequalities give rise to sufficient and necessary separability conditions for
both pure and mixed states. For the case of $d>3$, these inequalities are
necessary conditions for separability, which detect all entangled states that
are not positive under partial transposition and even some entangled states
with positive partial transposition. These inequalities are given by mean
values of local observables and present an experimental way of detecting the
quantum entanglement of $2\otimes d$ quantum states and even multi-qubit pure
states.


  An overview of the sources for heavy elements in the early Galaxy is given.
It is shown that observations of abundances in metal-poor stars can be used
along with a basic understanding of stellar models to guide the search for the
source of the heavy r-process nuclei (r-nuclei). Observations show that this
source produces very little of the elements from C through Zn including Fe.
This strongly suggests that O-Ne-Mg core-collapse supernovae (SNe) from
progenitors of ~8-11M_sun are the source for the heavy r-nuclei. It is shown
that a two-component model based on the abundances of Fe (from Fe core-collapse
SNe) and Eu (from O-Ne-Mg core-collapse SNe) gives very good quantitative
predictions for the abundances of all the other elements in metal-poor stars.


  We investigate the electric polarizabilities of neutral baryons using the
method of QCD sum rules. The diagrams in the operator product expansion are
taken into account up to dimension 6. We obtain different values of the
polarizabilities among hyperons, the ordering of which is given
$\bar\alpha_n>\bar\alpha_{\Xi^0}>\bar\alpha_{\Lambda}$. The polarizability of
${\Sigma^0}$, $\bar\alpha_{\Sigma^0}$, has possibly a very small value.


  The paper focuses on a new class of combinatorial problems which consists in
restructuring of solutions (as sets/structures) in combinatorial optimization.
Two main features of the restructuring process are examined: (i) a cost of the
restructuring, (ii) a closeness to a goal solution. Three types of the
restructuring problems are under study: (a) one-stage structuring, (b)
multi-stage structuring, and (c) structuring over changed element set.
One-criterion and multicriteria problem formulations can be considered. The
restructuring problems correspond to redesign (improvement, upgrade) of modular
systems or solutions. The restructuring approach is described and illustrated
(problem statements, solving schemes, examples) for the following combinatorial
optimization problems: knapsack problem, multiple choice problem, assignment
problem, spanning tree problems, clustering problem, multicriteria ranking
(sorting) problem, morphological clique problem. Numerical examples illustrate
the restructuring problems and solving schemes.


  Utilizing nonlinear elements, SU(1,1) interferometers demonstrate superior
phase sensitivity compared to passive interferometers. However, the precision
is significantly impacted by photon losses, particularly internal losses. We
propose a theoretical scheme to improve the precision of phase measurement
using homodyne detection by implementing number-conserving operations
(PA-then-PS and PS-then-PA) within the SU(1,1) interferometer, with the
coherent state and the vacuum state as the input states. We analyze the effects
of number-conserving operations on the phase sensitivity, the quantum Fisher
information, and the quantum Cramer-Rao bound under both ideal and photon
losses scenarios. Our findings reveal that the internal non-Gaussian operations
can enhance the phase sensitivity and the quantum Fisher information, and
effectively improve the robustness of the SU(1,1) interferometer against
internal photon losses. Notably, the PS-then-PA scheme exhibits superior
improvement in both ideal and photon losses cases in terms of phase
sensitivity. Moreover, in the ideal case, PA-then-PS scheme slightly
outperforms PS-then-PA scheme in terms of the quantum Fisher information and
the Quantum Cramer-Rao. However, in the presence of photon losses, PS-then-PA
scheme demonstrates a greater advantage.


  Numerical solution of reaction-diffusion equations in three dimensions is one
of the most challenging applied mathematical problems. Since these simulations
are very time consuming, any ideas and strategies aiming at the reduction of
CPU time are important topics of research. A general and robust idea is the
parallelization of source codes/programs. Recently, the technological
development of graphics hardware created a possibility to use desktop video
cards to solve numerically intensive problems. We present a powerful parallel
computing framework to solve reaction-diffusion equations numerically using the
Graphics Processing Units (GPUs) with CUDA. Four different reaction-diffusion
problems, (i) diffusion of chemically inert compound, (ii) Turing pattern
formation, (iii) phase separation in the wake of a moving diffusion front and
(iv) air pollution dispersion were solved, and additionally both the Shared
method and the Moving Tiles method were tested. Our results show that parallel
implementation achieves typical acceleration values in the order of 5-40 times
compared to CPU using a single-threaded implementation on a 2.8 GHz desktop
computer.


  The generalization of the BFKL equation for the case of non-forward
scattering is considered. The kernel of the generalized equation in the
next-to-leading approximation is expressed in terms of the gluon Regge
trajectory and the effective vertices for particle production in Reggeon
collisions. The ``bootstrap'' equations for the gluon Reggeization are
presented.


  Sentiment analysis involves using WordNets enriched with emotional metadata,
which are valuable resources. However, manual annotation is time-consuming and
expensive, resulting in only a few WordNet Lexical Units being annotated. This
paper introduces two new techniques for automatically propagating sentiment
annotations from a partially annotated WordNet to its entirety and to a WordNet
in a different language: Multilingual Structured Synset Embeddings (MSSE) and
Cross-Lingual Deep Neural Sentiment Propagation (CLDNS). We evaluated the
proposed MSSE+CLDNS method extensively using Princeton WordNet and Polish
WordNet, which have many inter-lingual relations. Our results show that the
MSSE+CLDNS method outperforms existing propagation methods, indicating its
effectiveness in enriching WordNets with emotional metadata across multiple
languages. This work provides a solid foundation for large-scale, multilingual
sentiment analysis and is valuable for academic research and practical
applications.


  Although a lot of attentions have been paid to human mobility, the
relationship between travel pattern with city structure is still unclear. Here
we probe into this relationship by analyzing the metro passenger trip
data.There are two unprecedented findings. One, from the average view a linear
law exists between the individual's travel distance with his original distance
to city center. The mechanism underlying is a travel pattern we called "ring
aggregation", i.e., the daily movement of city passengers is just aggregating
to a ring with roughly equal distance to city center.Interestingly, for the
round trips the daily travel pattern can be regarded as a switching between the
home ring at outer area with the office ring at the inner area. Second, this
linear law and ring aggregation pattern seems to be an exclusive characteristic
of the metro system. It can not be found in short distance transportation
modes, such as bicycle and taxi, neither as multiple transportation modes. This
means the ring aggregation pattern is a token of the relationship between
travel pattern with city structure in the large scale space.