  There is an increase in demand for organs as transplantation is becoming a
common practice to elongate human life. To reach this demand, three-dimensional
bioprinting is developing from prior knowledge of scaffolds, growth factors,
etc. This review paper aims to determine the current status and future
possibilities of three-dimensional bioprinting of organs and evaluate the
benefits and challenges, along with the history of its development. Prior
research has viewed three-dimensional bioprinting as a technology that will
enable safer transplantation without graft rejection and provide demand-based
production. However, it faces challenges such as the need to improve
biocompatibility and biofunctionality, legal and ethical issues, and the need
to improve the technology itself. While the development of three-dimensional
printing organs is not yet completed, we are seeing improvements and expecting
it to be clinically applied soon.


  This presentation discusses some of the signals for Lorentz violation
potentially observable in atomic spectroscopy and clock-comparison experiments.
The emphasis of the discussion is on how the angular-momentum quantum numbers
of the states involved in the transition determine the properties of the
possible sidereal variation of the transition frequency.


  We consider the problem of estimating the $L_1$ distance between two discrete
probability measures $P$ and $Q$ from empirical data in a nonasymptotic and
large alphabet setting. When $Q$ is known and one obtains $n$ samples from $P$,
we show that for every $Q$, the minimax rate-optimal estimator with $n$ samples
achieves performance comparable to that of the maximum likelihood estimator
(MLE) with $n\ln n$ samples. When both $P$ and $Q$ are unknown, we construct
minimax rate-optimal estimators whose worst case performance is essentially
that of the known $Q$ case with $Q$ being uniform, implying that $Q$ being
uniform is essentially the most difficult case. The \emph{effective sample size
enlargement} phenomenon, identified in Jiao \emph{et al.} (2015), holds both in
the known $Q$ case for every $Q$ and the $Q$ unknown case. However, the
construction of optimal estimators for $\|P-Q\|_1$ requires new techniques and
insights beyond the approximation-based method of functional estimation in Jiao
\emph{et al.} (2015).


  The definitions and main properties of the Ihara and Bartholdi zeta functions
for infinite graphs are reviewed. The general question of the validity of a
functional equation is discussed, and various possible solutions are proposed.


  We use a Green's function method with Random Phase Approximation to show how
magnetic correlations may affect electric polarization in multiferroic
materials with magnetic-exchange-type magnetoelectric coupling. We use a model
spin 1/2 ferromagnetic ferroelectric system but our results are expected to
apply to multiferroic materials with more complex magnetic structures. In
particular, we find that transverse magnetic correlations result in a change in
the free energy of the ferroelectric solutions leading to the possibility for
thermal hysteresis of the electric polarization above the magnetic Curie
temperature. Although we are motivated by multiferroic materials, this problem
represents a more general calculation of the effect of fluctuations on coupled
order parameters.


  This paper presents a vehicle-independent, non-intrusive, and light
monitoring system for accurately measuring fuel consumption in road vehicles
from longitudinal speed and acceleration derived continuously in time from GNSS
and IMU sensors mounted inside the vehicle. In parallel to boosting the
transition to zero-carbon cars, there is an increasing interest in low-cost
instruments for precise measurement of the environmental impact of the many
internal combustion engine vehicles still in circulation. The main contribution
of this work is the design and comparison of two innovative black-box
algorithms, one based on a reduced complexity physics modeling while the other
relying on a feedforward neural network for black-box fuel consumption
estimation using only velocity and acceleration measurements. Based on suitable
metrics, the developed algorithms outperform the state of the art best
approach, both in the instantaneous and in the integral fuel consumption
estimation, with errors smaller than 1\% with respect to the fuel flow ground
truth. The data used for model identification, testing, and experimental
validation is composed of GNSS velocity and IMU acceleration measurements
collected during several trips using a diesel fuel vehicle on different roads,
in different seasons, and with varying numbers of passengers. Compared to
built-in vehicle monitoring systems, this methodology is not customized, uses
off-the-shelf sensors, and is based on two simple algorithms that have been
validated offline and could be easily implemented in a real-time environment.


  In his Nobel Prize lecture Victor Hess urged that different instruments,
working together, should be used to solve the problem of the origin of cosmic
rays. I review some of the key developments that have opened up the new fields
of direct and indirect multi-messenger astronomy and that are guiding us to the
solution of this riddle. I then discuss, very briefly, some of the new
instruments that are shortly to come on line and give examples to show the long
lead-times from conception to implementation that occur in this field. I
conclude with some remarks about very ambitious future projects. The paper is
not intended as a review: rather it is an attempt to set down issues discussed
in the Hess Memorial Public Lecture given at the 2019 ICRC in Madison,
Wisconsin and accessible at www.icrc2019.org.


  We report an example that demonstrates the clear interdependence between
surface-supported reactions and molecular adsorption configurations. Two
biphenyl-based molecules with two and four bromine substituents, i.e.
2,2-dibromo-biphenyl (DBBP) and 2,2,6,6-tetrabromo-1,1-biphenyl (TBBP), show
completely different reaction pathways on a Ag(111) surface, leading to the
selective formation of dibenzo[e,l]pyrene and biphenylene dimer, respectively.
By combining low-temperature scanning tunneling microscopy, synchrotron
radiation photoemission spectroscopy, and density functional theory
calculations, we unravel the underlying reaction mechanism. After
debromination, a bi-radical biphenyl can be stabilized by surface Ag adatoms,
while a four-radical biphenyl undergoes spontaneous intramolecular annulation
due to its extreme instability on Ag(111). Such different chemisorption-induced
precursor states between DBBP and TBBP consequently lead to different reaction
pathways after further annealing. In addition, using bond-resolving scanning
tunneling microscopy and scanning tunneling spectroscopy, we determine the bond
length alternation of biphenylene dimer product with atomic precision, which
contains four-, six-, and eight-membered rings. The four-membered ring units
turn out to be radialene structures.


  Strongly coupled gauge theories provide an ultra-violet realization of new
physics models for physics beyond the Standard Model of particle physics
arising from composite dynamics. Depending on the gauge group and matter
content, they are expected to exhibit interesting features and rich
phenomenology, similar or dissimilar to QCD, of which model builders and
phenomenologists can take advantage. Due to the non-perturbative nature of
these theories, first principles lattice calculations are essential to test the
validity of composite models and provide theoretical inputs, that are otherwise
unattainable, to the experimental searches for new physics. In this
contribution, we will review recent efforts in the non-perturbative lattice
studies of strongly coupled gauge theories other than QCD in the context of
physics beyond the Standard Model by focusing on technical developments and new
results.


  Orientational field-dependence of the zero energy density of states (ZEDOS)
is calculated for superconductors with the polar state (line node), axial state
(point node) and 3D d-wave state. Depending on the gap topology and relative
field direction the field dependencies of ZEDOS sensitively differ, providing
us a useful and practical method to identify the gap topology. It is also
demonstrated that for d-wave state the field rotation in the basal plane shows
a sizable oscillation ~3% of ZEDOS. This is directly measurable in low-T
specific heat experiment in the mixed state.


  Recent advances in Big Data has prompted health care practitioners to utilize
the data available on social media to discern sentiment and emotions
expression. Health Informatics and Clinical Analytics depend heavily on
information gathered from diverse sources. Traditionally, a healthcare
practitioner will ask a patient to fill out a questionnaire that will form the
basis of diagnosing the medical condition. However, medical practitioners have
access to many sources of data including the patients writings on various
media. Natural Language Processing (NLP) allows researchers to gather such data
and analyze it to glean the underlying meaning of such writings. The field of
sentiment analysis (applied to many other domains) depend heavily on techniques
utilized by NLP. This work will look into various prevalent theories underlying
the NLP field and how they can be leveraged to gather users sentiments on
social media. Such sentiments can be culled over a period of time thus
minimizing the errors introduced by data input and other stressors.
Furthermore, we look at some applications of sentiment analysis and application
of NLP to mental health. The reader will also learn about the NLTK toolkit that
implements various NLP theories and how they can make the data scavenging
process a lot easier.


  In 1935, Einstein, Podolsky and Rosen (EPR) conceived a Gedankenexperiment
which became a cornerstone of quantum technology and still challenges our
understanding of reality and locality today. While the experiment has been
realized with small quantum systems, a demonstration of the EPR paradox with
spatially separated, massive many-particle systems has so far remained elusive.
We observe the EPR paradox in an experiment with two spatially separated
Bose-Einstein condensates containing about 700 Rubidium atoms each. EPR
entanglement in conjunction with individual manipulation of the two condensates
on the quantum level, as demonstrated here, constitutes an important resource
for quantum metrology and information processing with many-particle systems.
Our results show that the conflict between quantum mechanics and local realism
does not disappear as the system size is increased to over a thousand massive
particles.


  Our purpose is to release a chemical network, and the associated rate
coefficients, developed for the temperature and pressure range relevant to hot
Jupiters atmospheres. Using this network, we study the vertical atmospheric
composition of the two hot Jupiters (HD209458b, HD189733b) with a model that
includes photolyses and vertical mixing and we produce synthetic spectra. The
chemical scheme is derived from applied combustion models that have been
methodically validated over a range of temperatures and pressures typical of
the atmospheric layers influencing the observations of hot Jupiters. We compare
the predictions obtained from this scheme with equilibrium calculations, with
different schemes available in the literature that contain N-bearing species
and with previously published photochemical models. Compared to other chemical
schemes that were not subjected to the same systematic validation, we find
significant differences whenever non-equilibrium processes take place. The
deviations from the equilibrium, and thus the sensitivity to the network, are
more important for HD189733b, as we assume a cooler atmosphere than for
HD209458b. We found that the abundances of NH3 and HCN can vary by two orders
of magnitude depending on the network, demonstrating the importance of
comprehensive experimental validation. A spectral feature of NH3 at 10.5$\mu$m
is sensitive to these abundance variations and thus to the chemical scheme. Due
to the influence of the kinetics, we recommend the use of a validated scheme to
model the chemistry of exoplanet atmospheres. Our network is robust for
temperatures within 300-2500K and pressures from 10mbar up to a few hundreds of
bars, for species made of C,H,O,N. It is validated for species up to 2 carbon
atoms and for the main nitrogen species.


  Spontaneous localisation is a falsifiable dynamical mechanism which modifies
quantum mechanics, and explains the absence of position superpositions in the
macroscopic world. However, this is an ad hoc phenomenological proposal.
Adler's theory of trace dynamics, working on a flat Minkowski space-time,
derives quantum (field) theory, and spontaneous localisation, as a
thermodynamic approximation to an underlying non-commutative matrix dynamics.
We describe how to incorporate gravity into trace dynamics, by using ideas from
Connes' non-commutative geometry programme. This leads us to a new quantum
theory of gravity, from which we can predict spontaneous localisation, and give
an estimate of the Bekenstein-Hawking entropy of a Schwarzschild black hole.


  This paper investigates the linear precoder design that maximizes the average
mutual information of multiple-input multiple-output channels with
finite-alphabet inputs and statistical channel state information known at the
transmitter. This linear precoder design is an important open problem and is
extremely difficult to solve: First, average mutual information lacks
closed-form expression and involves complicated computations; Second, the
optimization problem over precoder is nonconcave. This study explores the
solution to this problem and provides the following contributions: 1) A
closed-form lower bound of average mutual information is derived. It achieves
asymptotic optimality at low and high signal-to-noise ratio regions and, with a
constant shift, offers an accurate approximation to the average mutual
information; 2) The optimal structure of the precoder is revealed, and a
unified two-step iterative algorithm is proposed to solve this problem.
Numerical examples show the convergence and the efficacy of the proposed
algorithm. Compared to its conventional counterparts, the proposed linear
precoding method provides a significant performance gain.


  Mathematics can serve many functions in physics. It can provide a
computational system, reflect a physical idea, conveniently encode a rule, and
so forth. A physics student thus has many different options for using
mathematics in his physics problem solving. We present a short example from the
problem solving work of upper level physics students and use it to illustrate
the epistemic framing process: framing because these students are focusing on a
subset of their total math knowledge, epistemic because their choice of subset
relates to what they see (at that particular time) as the nature of the math
knowledge in play. We illustrate how looking for the warrants students use, the
often unspoken reasons they think their evidence supports their mathematical
claims, serves as a window to their epistemic framing. These warrants provide a
powerful, concise piece of evidence of their epistemic framing.


  In inhomogeneous bulk ferromagnets, the dominating sources of spin disorder
are related to spatial variations of (i) the magnitude of the local saturation
magnetization and of (ii) the magnitude and/or direction of the magnetic
anisotropy field. For the particular example of a porous ferromagnet, where the
magnetization inhomogeneity is at maximum, we demonstrate, by means of
experimental neutron scattering data and micromagnetic simulations, the
anisotropic character of magnetization fluctuations induced by the dipolar
interaction.


  The product of any finite number of factorial Schur functions can be expanded
as a $Z[y]$-linear combination of Schur functions. We give a rule for computing
the coefficients in such an expansion which generalizes a specialization of the
Molev-Sagan rule, which in turn generalizes the classical Littlewood-Richardson
rule.


  This paper examines the link between conversational communities on Twitter
and their members' expressions of social identity. It specifically tests the
presence of community prototypes, or collections of attributes which define a
group through meta-contrast: high in-group cohesiveness and high out-group
distinctiveness. Analyzing four datasets of political discussions ranging from
roughly 4 to 30 million tweets, we find strong evidence for the presence of
distinctive community prototypes. We observe that community prototypes are
constructed through hashtags, mentions, emojis, and identity-phrases. This
finding situates prior work on the identity signaling of individual users
within a larger group process playing out within communication communities.
Community prototypes are then constructed for specific communities by measuring
the salience of identity signals for each community. Observed community
prototypes tend to be based on political ideology, location and language, or
general interests. While the presence of community prototypes may be a natural
group behavior, the high levels of contrast observed between communities
displaying ideologically opposed prototypes indicate the presence of
identity-related polarization.


  A story premise succinctly defines a story's main idea, foundation, and
trajectory. It serves as the initial trigger in automatic story generation.
Existing sources of story premises are limited by a lack of diversity, uneven
quality, and high costs that make them difficult to scale. In response, we
introduce Modular Story Premise Synthesis (MoPS) which breaks down story
premises into modules like background and persona for automated design and
generation. MoPS consists of three phases: (1) Precollect a consistent set of
candidates for each module to form a nested dictionary. (2) Extract a key path
from the nested dictionary as the premise design. (3) Instruct an LLM to
integrate the design into a coherent premise sentence. Thorough evaluations
demonstrate that our synthesized premises excel in diversity, fascination,
completeness, and originality compared to those induced from large language
models and captured from public story datasets. Similarly, the extended novels
and scripts generated from our premises also exhibit higher quality. In
supplementary materials, we provide the MoPS code suite, along with 7.6k
generated premises and 1k extended stories. Code:
https://github.com/GAIR-NLP/MoPS.


  Nonlinear evolution of three-dimensional strained islands or quantum dots in
heteroepitaxial thin films is studied via a continuum elasticity model and the
development of a nonlinear dynamic equation governing the film morphological
profile. All three regimes of island array evolution are identified and
examined, including a film instability regime at early stage, a nonlinear
coarsening regime at intermediate times, and the crossover to a saturated
asymptotic state, with detailed behavior depending on film-substrate misfit
strains but not qualitatively on finite system sizes. The phenomenon of island
stabilization and saturation, which corresponds to the formation of steady but
non-ordered arrays of strained quantum dots, occurs at later time for smaller
misfit strain. It is found to be controlled by the strength of film-substrate
wetting interaction which would constrain the valley-to-peak mass transport and
hence the growth of island height, and also determined by the effect of elastic
interaction between surface islands and the high-order strain energy of
individual islands at late evolution stage. The results are compared to
previous experimental and theoretical studies on quantum dots coarsening and
saturation.


  We introduce Tangent Attention Fine-Tuning (TAFT), a method for fine-tuning
linearized transformers obtained by computing a First-order Taylor Expansion
around a pre-trained initialization. We show that the Jacobian-Vector Product
resulting from linearization can be computed efficiently in a single forward
pass, reducing training and inference cost to the same order of magnitude as
its original non-linear counterpart, while using the same number of parameters.
Furthermore, we show that, when applied to various downstream visual
classification tasks, the resulting Tangent Transformer fine-tuned with TAFT
can perform comparably with fine-tuning the original non-linear network. Since
Tangent Transformers are linear with respect to the new set of weights, and the
resulting fine-tuning loss is convex, we show that TAFT enjoys several
advantages compared to non-linear fine-tuning when it comes to model
composition, parallel training, machine unlearning, and differential privacy.
Our code is available at:
https://github.com/tianyu139/tangent-model-composition


  Quantum cloud computing is an emerging computing paradigm that allows
seamless access to quantum hardware as cloud-based services. However, effective
use of quantum resources is challenging and necessitates robust simulation
frameworks for effective resource management design and evaluation. To address
this need, we proposed QSimPy, a novel discrete-event simulation framework
designed with the main focus of facilitating learning-centric approaches for
quantum resource management problems in cloud environments. Underpinned by
extensibility, compatibility, and reusability principles, QSimPy provides a
lightweight simulation environment based on SimPy, a well-known Python-based
simulation engine for modeling dynamics of quantum cloud resources and task
operations. We integrate the Gymnasium environment into our framework to
support the creation of simulated environments for developing and evaluating
reinforcement learning-based techniques for optimizing quantum cloud resource
management. The QSimPy framework encapsulates the operational intricacies of
quantum cloud environments, supporting research in dynamic task allocation and
optimization through DRL approaches. We also demonstrate the use of QSimPy in
developing reinforcement learning policies for quantum task placement problems,
demonstrating its potential as a useful framework for future quantum cloud
research.


  Future weak lensing surveys will directly probe the density fluctuation in
the universe. Recent studies have shown how the statistics of the weak lensing
convergence field is related to the statistics of collapsed objects. Extending
earlier analytical results on the probability distribution function of the
convergence field we show that the bias associated with the convergence field
can directly be related to the bias associated with the statistics of
underlying over-dense objects. This will provide us a direct method to study
the gravity induced bias in galaxy clustering. Based on our analytical results
which use the hierarchical {\em ansatz} for non-linear clustering, we study how
such a bias depends on the smoothing angle and the source red-shift. We compare
our analytical results against ray tracing experiments through N-body
simulations of four different realistic cosmological scenarios and found a very
good match. Our study shows that the bias in the convergence map strongly
depends on the background geometry and hence can help us in distinguishing
different cosmological models in addition to improving our understanding of the
gravity induced bias in galaxy clustering.


  In scenarios with long-tailed distributions, the model's ability to identify
tail classes is limited due to the under-representation of tail samples. Class
rebalancing, information augmentation, and other techniques have been proposed
to facilitate models to learn the potential distribution of tail classes. The
disadvantage is that these methods generally pursue models with balanced class
accuracy on the data manifold, while ignoring the ability of the model to
resist interference. By constructing noisy data manifold, we found that the
robustness of models trained on unbalanced data has a long-tail phenomenon.
That is, even if the class accuracy is balanced on the data domain, it still
has bias on the noisy data manifold. However, existing methods cannot
effectively mitigate the above phenomenon, which makes the model vulnerable in
long-tailed scenarios. In this work, we propose an Orthogonal Uncertainty
Representation (OUR) of feature embedding and an end-to-end training strategy
to improve the long-tail phenomenon of model robustness. As a general
enhancement tool, OUR has excellent compatibility with other methods and does
not require additional data generation, ensuring fast and efficient training.
Comprehensive evaluations on long-tailed datasets show that our method
significantly improves the long-tail phenomenon of robustness, bringing
consistent performance gains to other long-tailed learning methods.


  We observe a broadband chaotic signal of Terahertz frequency emitted from a
superconducting junction. The generated radiation has a wide spectrum reaching
0.7 THz and power sufficient to drive on-chip circuit elements. To our
knowledge, this is the first experimental observation of a high-frequency
chaotic signal emitted by a superconducting system which lies inside the
Terahertz gap. Our experimental finding is fully confirmed by the numerical
modeling based on the microscopic theory and reveals the unrealized potential
of superconducting systems in chaos-based Terahertz communication, fast
generation of true random numbers and non-invasive Terahertz spectroscopy
applicable to physical, chemical and biological systems.


  A famous conjecture of Tuza states that the minimum number of edges needed to
cover all the triangles in a graph is at most twice the maximum number of
edge-disjoint triangles. This conjecture was couched in a broader setting by
Aharoni and Zerbib who proposed a hypergraph version of this conjecture, and
also studied its implied fractional versions. We establish the fractional
version of the Aharoni-Zerbib conjecture up to lower order terms. Specifically,
we give a factor $t/2+ O(\sqrt{t \log t})$ approximation based on LP rounding
for an algorithmic version of the hypergraph Tur\'{a}n problem (AHTP). The
objective in AHTP is to pick the smallest collection of $(t-1)$-sized subsets
of vertices of an input $t$-uniform hypergraph such that every hyperedge
contains one of these subsets.
  Aharoni and Zerbib also posed whether Tuza's conjecture and its hypergraph
versions could follow from non-trivial duality gaps between vertex covers and
matchings on hypergraphs that exclude certain sub-hypergraphs, for instance, a
"tent" structure that cannot occur in the incidence of triangles and edges. We
give a strong negative answer to this question, by exhibiting tent-free
hypergraphs, and indeed $\mathcal{F}$-free hypergraphs for any finite family
$\mathcal{F}$ of excluded sub-hypergraphs, whose vertex covers must include
almost all the vertices.
  The algorithmic questions arising in the above study can be phrased as
instances of vertex cover on simple hypergraphs, whose hyperedges can pairwise
share at most one vertex. We prove that the trivial factor $t$ approximation
for vertex cover is hard to improve for simple $t$-uniform hypergraphs.
However, for set cover on simple $n$-vertex hypergraphs, the greedy algorithm
achieves a factor $(\ln n)/2$, better than the optimal $\ln n$ factor for
general hypergraphs.


  A method is presented for obtaining the $\epsilon$-expansion for on-shell
massless scalar double-box diagram.


  With the ultimate aim of using the fundamental or $f$-mode to study
helioseismic aspects of turbulence-generated magnetic flux concentrations, we
use randomly forced hydromagnetic simulations of a piecewise isothermal layer
in two dimensions with reflecting boundaries at top and bottom. We compute
numerically diagnostic wavenumber-frequency diagrams of the vertical velocity
at the interface between the denser gas below and the less dense gas above. For
an Alfv\'en-to-sound speed ratio of about 0.1, a 5% frequency increase of the
$f$-mode can be measured when $k_xH_{\rm p}=3$-$4$, where $k_x$ is the
horizontal wavenumber and $H_{\rm p}$ is the pressure scale height at the
surface. Since the solar radius is about 2000 times larger than $H_{\rm p}$,
the corresponding spherical harmonic degree would be 6000-8000. For weaker
fields, a $k_x$-dependent frequency decrease by the turbulent motions becomes
dominant. For vertical magnetic fields, the frequency is enhanced for
$k_xH_{\rm p}\approx4$, but decreased relative to its nonmagnetic value for
$k_xH_{\rm p}\approx9$.


  By exploring a phase space hydrodynamics description of one-dimensional free
Fermi gas, we discuss how systems settle down to steady states described by the
generalized Gibbs ensembles through quantum quenches. We investigate time
evolutions of the Fermions which are trapped in external potentials or a circle
for a variety of initial conditions and quench protocols. We analytically
compute local observables such as particle density and show that they always
exhibit power law relaxation at late times. We find a simple rule which
determines the power law exponent. Our findings are, in principle, observable
in experiments in an one dimensional free Fermi gas or Tonk's gas (Bose gas
with infinite repulsion).


  We generalize Greenberger-Horne-Zeilinger (GHZ) nonlocality to every
even-dimensional and odd-partite system. For the purpose we employ concurrent
observables that are incompatible and nevertheless have a common eigenstate. It
is remarkable that a tripartite system can exhibit the genuinely
high-dimensional GHZ nonlocality.


  The basic notions and the main historical facts on the Bose-Einstein
condensation are surveyed.


  Spatially localized one-electron orbitals, orthogonal and nonorthogonal, are
widely used in electronic structure theory to describe chemical bonding and
speed up calculations. In order to avoid linear dependencies of localized
orbitals, the existing localization methods either constrain orbital
transformations to be unitary, that is, metric preserving, or, in the case of
variable-metric methods, fix the centers of nonorthogonal localized orbitals.
Here, we developed a different approach to orbital localization, in which these
constraints are replaced with a single restriction that specifies the maximum
allowed deviation from the orthogonality for the final set of localized
orbitals. This reformulation, which can be viewed as a generalization of
existing localization methods, enables one to choose the desired balance
between the orthogonality and locality of the orbitals. Furthermore, the
approach is conceptually and practically simple as it obviates the necessity in
unitary transformations and allows to determine optimal positions of the
centers of nonorthogonal orbitals in a unconstrained and straightforward
minimization procedure. It is demonstrated to produce well-localized orthogonal
and nonorthogonal orbitals with the Berghold and Pipek-Mezey localization
functions for a variety of molecules and periodic materials including large
systems with non-trivial bonding.


  We study the effect of isospin degree of freedom on the balance energy
(E$_{bal}$) as well as its mass dependence throughout the mass range for two
different sets of isobaric systems with N/Z = 1 and 1.4 at different colliding
geometries ranging from the central to peripheral ones. Our findings reveal the
dominance of Coulomb repulsion in isospin effects on E$_{bal}$ as well as its
mass dependence throughout the range of the colliding geometry. Our results
also indicate that the effect of symmetry energy on the energy of vanishing
flow is uniform throughout the mass range and throughout the colliding
geometry.


  This work inspects the parameters that affect the conversion rate of a
ramp-counter ADC and propose two small systematical changes to reduce the time
needed to complete a single analog to digital conversion by use of the locality
principle point of view. System full architecture is modeled and simulated by
Matlab script and the results show up to 97% reduction in conversion time for
an electro-cardiograph record.


  We investigate wave propagation in curved, thin elastic waveguides, where
curvature is shown to be equivalent to a spatially modulated refractive index.
We establish this relationship within a theoretical framework that leverages
non-Euclidean transformations. Theoretical predictions illustrate how curved
profiles can be designed to manipulate wave trajectories in analogy with
geometrical optics principles. Experiments on 3D printed curved membranes
confirm the theoretical predictions, and suggest curvature as a viable approach
for guiding, focusing and steering of elastic waves.


  We have grown VO2 thin films by laser ablation for electronic device
applications. In obtaining the thin films of the pure VO2 phase, oxygen partial
pressure is a critical parameter because vanadium oxides have several phases
with the oxygen concentration. It is found that the pure VO2 films are
epitaxially grown on Al2O3 substrate in the narrow ranges of 55-60 mTorr in an
Ar+10% O2 ambient, and that the mixed phase films are synthesized when the
deposition pressure slightly deviates from the optimum pressure. The (100)
oriented VO2 films undergo an abrupt metal-insulator transition (MIT) with
resistance change of an order of 104 at 338K. In the films of mixed phases, the
small change of the resistance is observed at the same temperature. The
polycrystalline films grown on SiO2/Si substrate undergo a broaden MIT of the
resistance. Furthermore, the abrupt MIT and collective current motion appearing
in metal are observed when the electric field is applied to the film.


  In the paper, we establish commutator estimates for the Dirichlet-to-Neumann
map of Stokes systems in Lipschitz domains. The approach is based on Dahlberg's
bilinear estimates, and the results may be regarded as an extension of
[Dahlberg, Poisson semigroups and singular integrals, Proc. Amer. Math. Soc.,
97(1986), no.1, 41-48.] and [Shen, Commutator estimates for the
Dirichlet-to-Neumann map in Lipschitz domains, Some topics in harmonic analysis
and applications, 369C384, Adv. Lect. Math. (ALM)34, Int. Press, Somerville,
MA, 2016.] to Stokes systems.


  Recent observations have revealed that high surface-brightness, metal-rich
debris is present over large regions of the Andromeda (M31) stellar halo. We
use a set of numerical models to determine whether extended metal-rich debris
is expected to exist in galaxy halos formed in a hierarchical LambdaCDM
universe. We identify tidal debris in the simulations according to the current
best surface brightness detection limits in M31 and demonstrate that bright
features in the outer halo regions usually originate from single satellites,
with very little contamination from other sources due to the low probability of
tidal streams from two overlapping accretion events. In contrast, high-surface
brightness features in the inner halo often originate from multiple
progenitors. We also compare the age and metallicity distribution of the debris
with the well-mixed stellar halos in which they reside. We find that
high-surface brightness tidal debris is produced almost exclusively by
relatively high mass progenitors (Mstar ~ 10^7- 10^9 Msun) and thus is expected
to be of moderate- to high-metallicity. Similarly, in our models the smooth
inner halo is expected to be metal-rich as this region has been built up mainly
from massive satellites. Our results imply that the stellar populations of
substructure observed around external galaxies with current techniques should
not resemble old and metal-poor dwarf spheroidal satellites, nor the underlying
component of the stellar halo.


  In this paper, we give a characterization of the normal subgroups of index
$2^{s}(2n+1),\ s\in\{1,2\},\ n\in \mathbb{N}$ and of the subgroups of index
three of the group representation of the Cayley tree.


  Whether it is object detection, model reconstruction, laser odometry, or
point cloud registration: Plane extraction is a vital component of many robotic
systems. In this paper, we propose a strictly probabilistic method to detect
finite planes in organized 3-D laser range scans. An agglomerative hierarchical
clustering technique, our algorithm builds planes from bottom up, always
extending a plane by the point that decreases the measurement likelihood of the
scan the least. In contrast to most related methods, which rely on heuristics
like orthogonal point-to-plane distance, we leverage the ray path information
to compute the measurement likelihood. We evaluate our approach not only on the
popular SegComp benchmark, but also provide a challenging synthetic dataset
that overcomes SegComp's deficiencies. Both our implementation and the
suggested dataset are available at www.github.com/acschaefer/ppe.


  The ability to handle large scale graph data is crucial to an increasing
number of applications. Much work has been dedicated to supporting basic graph
operations such as subgraph matching, reachability, regular expression
matching, etc. In many cases, graph indices are employed to speed up query
processing. Typically, most indices require either super-linear indexing time
or super-linear indexing space. Unfortunately, for very large graphs,
super-linear approaches are almost always infeasible. In this paper, we study
the problem of subgraph matching on billion-node graphs. We present a novel
algorithm that supports efficient subgraph matching for graphs deployed on a
distributed memory store. Instead of relying on super-linear indices, we use
efficient graph exploration and massive parallel computing for query
processing. Our experimental results demonstrate the feasibility of performing
subgraph matching on web-scale graph data.


  We present a rest-frame ultraviolet morphological analysis of 78 resolved,
high S/N z ~ 3.1 Lyman Alpha Emitters (LAEs) in the Extended Chandra Deep Field
South (ECDF-S). Using HST/ACS V -band images taken as part of the GEMS, GOODS,
and HUDF surveys. For each LAE system identified via our ground-based
narrow-band imaging, we have identified those LAE systems with multiple
components. We measure the concentration index and present the results of our
GALFIT fits for ellipticity, Sersic index, and sizes for each resolved
component with S/N > 30 as well as for each LAE system with S/N > 30. The LAEs
show a heterogeneous distribution of morphologies while the ma jority tend to
be highly concentrated and compact in size. We only measure the morphological
properties of resolved LAEs. For systems showing multiple components we also
measured the morphology of the individual components. The resolved LAEs are
highly concentrated (2 < C < 4) and show a similar distribution to that
measured for stars, suggesting that this diagnostic is a poor discriminator
near the resolution limit. The measured ellipticities for components show a
distribution peaked at {\epsilon} ~ 0.55 which is significantly different from
the flat distribution of ellipticities observed for local spiral galaxies and
is similar to the distribution found for Lyman-break galaxies at the same
redshift. There is a wide range of best-fit Sersic indices (1 < n < 10) with
the majority being between 0 < n < 2. The distribution is similar to the
distribution of Sersic indices seen locally. A visual inspection of the images
suggests a qualitative morphological transition at n ~ 2, with small-n LAEs
having extended or multimodal light distributions and relatively little diffuse
emission and large-n LAEs have compact central components surrounded by diffuse
emission.


  Being able to reason in an environment with a large number of discrete
actions is essential to bringing reinforcement learning to a larger class of
problems. Recommender systems, industrial plants and language models are only
some of the many real-world tasks involving large numbers of discrete actions
for which current methods are difficult or even often impossible to apply. An
ability to generalize over the set of actions as well as sub-linear complexity
relative to the size of the set are both necessary to handle such tasks.
Current approaches are not able to provide both of these, which motivates the
work in this paper. Our proposed approach leverages prior information about the
actions to embed them in a continuous space upon which it can generalize.
Additionally, approximate nearest-neighbor methods allow for logarithmic-time
lookup complexity relative to the number of actions, which is necessary for
time-wise tractable training. This combined approach allows reinforcement
learning methods to be applied to large-scale learning problems previously
intractable with current methods. We demonstrate our algorithm's abilities on a
series of tasks having up to one million actions.


  We give a short introduction to cough detection efforts that were undertaken
during the last decade and we describe the solution for automatic cough
detection developed for the AioCare portable spirometry system. In contrast to
more popular analysis of sound and audio recordings, we fully based our
approach on airflow signals only. As the system is intended to be used in a
large variety of environments and different patients, we trained and validated
the algorithm using AioCare-collected data and the large database of spirometry
curves from the NHANES database by the American National Center for Health
Statistics. We trained different classifiers, such as logistic regression,
feed-forward artificial neural network, support vector machine, and random
forest to choose the one with the best performance. The ANN solution was
selected as the final classifier. The classification results on the test set
(AioCare data) are: 0.86 (sensitivity), 0.91 (specificity), 0.91 (accuracy) and
0.88 (F1 score). The classification methodology developed in this study is
robust for detecting cough events during spirometry measurements. As far as we
know, the solution presented in this work is the first fully reproducible
description of the automatic cough detection algorithm based totally on airflow
signals and the first cough detection implemented in a commercial spirometry
system that is to be published.


  The LiDAR fiducial tag, akin to the well-known AprilTag used in camera
applications, serves as a convenient resource to impart artificial features to
the LiDAR sensor, facilitating robotics applications. Unfortunately, the
existing LiDAR fiducial tag localization methods do not apply to 3D LiDAR maps
while resolving this problem is beneficial to LiDAR-based relocalization and
navigation. In this paper, we develop a novel approach to directly localize
fiducial tags on a 3D LiDAR prior map, returning the tag poses (labeled by ID
number) and vertex locations (labeled by index) w.r.t. the global coordinate
system of the map. In particular, considering that fiducial tags are thin sheet
objects indistinguishable from the attached planes, we design a new pipeline
that gradually analyzes the 3D point cloud of the map from the intensity and
geometry perspectives, extracting potential tag-containing point clusters.
Then, we introduce an intermediate-plane-based method to further check if each
potential cluster has a tag and compute the vertex locations and tag pose if
found. We conduct both qualitative and quantitative experiments to demonstrate
that our approach is the first method applicable to localize tags on a 3D LiDAR
map while achieving better accuracy compared to previous methods. The
open-source implementation of this work is available at:
https://github.com/York-SDCNLab/Marker-Detection-General.


  This letter considers a multi-pair decode-and-forward relay network where a
power-splitting (PS) protocol is adopted at the energy-constrained relay to
provide simultaneous wireless information and energy harvesting (EH). To
achieve higher efficiency of EH, we propose a new PS-based EH architecture at
the relay by incorporating an alternating current (AC) computing logic, which
is employed to directly use the wirelessly harvested AC energy for
computational blocks. Under a nonlinear EH circuit, our goal is to maximize the
fairness of end-to-end rate among user pairs subject to power constraints,
resulting in a nonconvex problem. We propose an iterative algorithm to achieve
a suboptimal and efficient solution to this challenging problem by leveraging
the inner approximation framework. Numerical results demonstrate that the
proposed algorithm outperforms the traditional direct current computing and
other baseline schemes.


  We provide analytical results for the $O(\alpha_s)$ corrections to the
double-spin density matrix elements in the reaction $e^+e^-\to t\bar t$. These
concern the elements $ll$, $lt$, $ln$, $tt$, $tn$, and $nn$ of the double-spin
density matrix elements where $l,t,n$ stand for longitudinal, transverse and
normal orientations with respect to the beam frame spanned by the electron and
the top quark momentum.


  Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstand
strong winds, have great potential for oceanic exploration and research. This
paper studies a UAV-aided maritime data collection system with a fixed-wing UAV
dispatched to collect data from marine buoys. We aim to minimize the UAV's
energy consumption in completing the task by jointly optimizing the
communication time scheduling among the buoys and the UAV's flight trajectory
subject to wind effect. The conventional successive convex approximation (SCA)
method can provide efficient sub-optimal solutions for collecting
small/moderate data volume, whereas the solution heavily relies on trajectory
initialization and has not explicitly considered wind effect, while the
computational/trajectory complexity both become prohibitive for the task with
large data volume. To this end, we propose a new cyclical trajectory design
framework with tailored initialization algorithm that can handle arbitrary data
volume efficiently, as well as a hybrid offline-online (HO2) design that
leverages convex stochastic programming (CSP) offline based on wind statistics,
and refines the solution by adapting online to real-time wind velocity.
Numerical results show that our optimized trajectory can better adapt to
various setups with different target data volume and buoys' topology as well as
various wind speed/direction/variance compared with benchmark schemes.


  We introduce nonlinear scalar field models for open and open-closed strings
with spacetime derivatives encoded in the operator valued Riemann zeta
function. The corresponding two Lagrangians are derived in an adelic approach
starting from the exact Lagrangians for effective fields of $p$-adic tachyon
strings. As a result tachyons are absent in these models. These new strings we
propose to call zeta strings. Some basic classical properties of the zeta
strings are obtained and presented in this paper.


  Layered semi-convection is a possible candidate to explain Saturn's
luminosity excess and the abnormally large radius of some hot Jupiters. In
giant planet interiors, it could lead to the creation of density staircases,
which are convective layers separated by thin stably stratified interfaces. We
study the propagation of internal waves in a region of layered semi-convection,
with the aim to predict energy transport by internal waves incident upon a
density staircase. The goal is then to understand the resulting tidal
dissipation when these waves are excited by other bodies such as moons in giant
planets systems. We use a local Cartesian analytical model, taking into account
the complete Coriolis acceleration at any latitude, thus generalizing previous
works. We find transmission of incident internal waves to be strongly affected
by the presence of a density staircase, even if these waves are initially pure
inertial waves (which are restored by the Coriolis acceleration). In
particular, low-frequency waves of all wavelengths are perfectly transmitted
near the critical latitude. Otherwise, short-wavelength waves are only
efficiently transmitted if they are resonant with a free mode (interfacial
gravity wave or short-wavelength inertial mode) of the staircase. In all other
cases, waves are primarily reflected unless their wavelengths are longer than
the vertical extent of the entire staircase (not just a single step). We expect
incident internal waves to be strongly affected by the presence of a density
staircase in a frequency-, latitude- and wavelength-dependent manner. First,
this could lead to new criteria to probe the interior of giant planets by
seismology; and second, this may have important consequences for tidal
dissipation and our understanding of the evolution of giant planet systems.


  The totally asymmetric simple exclusion process (TASEP), which describes the
stochastic dynamics of interacting particles on a lattice, has been actively
studied over the past several decades and applied to model important biological
transport processes. Here we present a software package, called EGGTART
(Extensive GUI gives TASEP-realization in real time), which quantifies and
visualizes the dynamics associated with a generalized version of the TASEP with
an extended particle size and heterogeneous jump rates. This computational tool
is based on analytic formulas obtained from deriving and solving the
hydrodynamic limit of the process. It allows an immediate quantification of the
particle density, flux, and phase diagram, as a function of a few key
parameters associated with the system, which would be difficult to achieve via
conventional stochastic simulations. Our software should therefore be of
interest to biophysicists studying general transport processes, and can in
particular be used in the context of gene expression to model and quantify mRNA
translation of different coding sequences.


  We calculate properties of neutron drops in external potentials using both
quantum Monte Carlo and no-core full configuration techniques. The properties
of the external wells are varied to examine different density profiles. We
compare neutron drop results given by a selection of nuclear Hamiltonians,
including realistic two-body interactions as well as several three-body forces.
We compute a range of properties for the neutron drops: ground-state energies,
spin-orbit splittings, excitation energies, radial densities and rms radii. We
compare the equations of state for neutron matter for several of these
Hamiltonians. Our results can be used as benchmarks to test other many-body
techniques, and to constrain properties of energy-density functionals.


  We consider the asymptotics of the discrete heat kernel on isoradial graphs
for the case where the time and the edge lengths tend to zero simultaneously.
Depending on the asymptotic ratio between time and edge lengths, we show that
two different regimes arise: (i) a Gaussian regime and (ii) a Poissonian
regime, which resemble the short-time asymptotics of the heat kernel on (i)
Euclidean spaces and (ii) graphs, respectively.


  A crowdsourced stream processing system (CSP) is a system that incorporates
crowdsourced tasks in the processing of a data stream. This can be seen as
enabling crowdsourcing work to be applied on a sample of large-scale data at
high speed, or equivalently, enabling stream processing to employ human
intelligence. It also leads to a substantial expansion of the capabilities of
data processing systems. Engineering a CSP system requires the combination of
human and machine computation elements. From a general systems theory
perspective, this means taking into account inherited as well as emerging
properties from both these elements. In this paper, we position CSP systems
within a broader taxonomy, outline a series of design principles and evaluation
metrics, present an extensible framework for their design, and describe several
design patterns. We showcase the capabilities of CSP systems by performing a
case study that applies our proposed framework to the design and analysis of a
real system (AIDR) that classifies social media messages during time-critical
crisis events. Results show that compared to a pure stream processing system,
AIDR can achieve a higher data classification accuracy, while compared to a
pure crowdsourcing solution, the system makes better use of human workers by
requiring much less manual work effort.


  In this paper, we study the Cauchy problem of the nonlinear wave equation
with fractional Laplacian and time-dependent damping. Firstly, we derive the
weighted Sobolev estimate of the solution operators for the linear wave
equation with the damping of constant coefficient, and prove the local
existence and uniqueness in the weighted Sobolev space for the power-type
nonlinearity and $b(t)\in L^\infty$, by the contraction mapping principle.
Secondly, we consider the case of the source nonlinearity $f(u)\approx |u|^p$.
In the subcritical and critical cases $1<p\leq p_c=1+\frac \sigma N$, based on
the blow-up result on the ordinary differential inequality, we could prove the
blow-up of the solution and obtain the upper bound of the lifespan. And the
upper bound of the lifespan in the critical case is independent on the
coefficient of the time-dependent damping and is completely new even if the
classical case $b(t)=1$.


  A formulation of the one-way speed of light in three-dimensional Euclidean
space is derived by a constructive approach. This formulation is consistent
with the result of the Michelson-Morley experiment in that the harmonic mean of
the outward and return speeds is equal to c, the standard value for the speed
of electromagnetic radiation in vacuum. It is also shown that a shift in
synchronization, proportional to the distance along the line of motion, renders
this speed a constant along all directions.


  We establish a C^1,alpha Schauder estimate of a non-standard degenerate
elliptic equation and use it to give another proof of the higher order boundary
Harnack inequality. As an application, we obtain the analyticity of the free
boundary in the classical obstacle problem based on iterating the boundary
Harnack principle.


  In aerial visual area coverage missions, the camera footprint changes over
time based on the camera position and orientation -- a fact that complicates
the whole process of coverage and path planning. This article proposes a
solution to the problem of visual coverage by filling the target area with a
set of randomly distributed particles and harvesting them by camera footprints.
This way, high coverage is obtained at a low computational cost. In this
approach, the path planner considers six degrees of freedom (DoF) for the
camera movement and commands thrust and attitude references to a lower layer
controller, while maximizing the covered area and coverage quality. The
proposed method requires a priori information of the boundaries of the target
area and can handle areas of very complex and highly non-convex geometry. The
effectiveness of the approach is demonstrated in multiple simulations in terms
of computational efficiency and coverage.


  Supermassive black hole accretion and star formation appear intimately
connected. I review the observational and theoretical evidence for this
statement. I then discuss how focussed studies of two systems, our Galactic
Center and the nucleus of M87, can help to improve our understanding of these
processes.


  Let G be a connected reductive complex algebraic group and B a Borel subgroup
of G. We consider a subgroup H of B acting with finitely many orbits on the
flag variety G/B, and we classify the H-orbits in G/B in terms of suitable
combinatorial invariants. As well, we study the Weyl group action defined by
Knop on the set of H-orbits in G/B, and we give a combinatorial model for this
action in terms of weight polytopes.


  During the last decade, intracellular actin waves have attracted much
attention due to their essential role in various cellular functions, ranging
from motility to cytokinesis. Experimental methods have advanced significantly
and can capture the dynamics of actin waves over a large range of
spatio-temporal scales. However, the corresponding coarse-grained theory mostly
avoids the full complexity of this multi-scale phenomenon. In this perspective,
we focus on a minimal continuum model of activator-inhibitor type and highlight
the qualitative role of mass-conservation, which is typically overlooked.
Specifically, our interest is to connect between the mathematical mechanisms of
pattern formation in the presence of a large-scale mode, due to
mass-conservation, and distinct behaviors of actin waves.


  Subgroup identification (clustering) is an important problem in biomedical
research. Gene expression profiles are commonly utilized to define subgroups.
Longitudinal gene expression profiles might provide additional information on
disease progression than what is captured by baseline profiles alone. Moreover,
the longitudinal gene expression data allows for intra-individual variability
to be accounted for when grouping patients. Therefore, subgroup identification
could be more accurate and effective with the aid of longitudinal gene
expression data. However, existing statistical methods are unable to fully
utilize these data for patient clustering. In this article, we introduce a
novel subgroup identification method in the Bayesian setting based on
longitudinal gene expression profiles. This method, called BClustLonG, adopts a
linear mixed-effects framework to model the trajectory of genes over time while
clustering is jointly conducted based on the regression coefficients obtained
from all genes. In order to account for the correlations among genes and
alleviate the high dimensionality challenges, we adopt a factor analysis model
for the regression coefficients. The Dirichlet process prior distribution is
utilized for the means of the regression coefficients to induce clustering.
Through extensive simulation studies, we show that BClustLonG has improved
performance over other clustering methods. When applied to a dataset of
severely injured (burn or trauma) patients, our model is able to distinguish
burn patients from trauma patients and identify interesting subgroups in trauma
patients.


  The purpose of this article is to describe the singularities of
one-dimensional oscillatory integrals, whose phases have a certain singularity,
in the form of an asymptotic expansion. In the case of the Laplace integral, an
analogous result is also given.


  We are developing a new method for the analysis of queuing systems with
heterogeneous in time and space compound (marked) Poisson input flow. The state
space of the input flow is embedded in a higher-dimensional space with a
homogeneous marked Poisson field on it. We prove limit theorems for partial
sums of marks under the ordering of field points by coordinates.


  Automated algorithm configuration relieves users from tedious,
trial-and-error tuning tasks. A popular algorithm configuration tuning paradigm
is dynamic algorithm configuration (DAC), in which an agent learns dynamic
configuration policies across instances by reinforcement learning (RL).
However, in many complex algorithms, there may exist different types of
configuration hyperparameters, and such heterogeneity may bring difficulties
for classic DAC which uses a single-agent RL policy. In this paper, we aim to
address this issue and propose multi-agent DAC (MA-DAC), with one agent working
for one type of configuration hyperparameter. MA-DAC formulates the dynamic
configuration of a complex algorithm with multiple types of hyperparameters as
a contextual multi-agent Markov decision process and solves it by a cooperative
multi-agent RL (MARL) algorithm. To instantiate, we apply MA-DAC to a
well-known optimization algorithm for multi-objective optimization problems.
Experimental results show the effectiveness of MA-DAC in not only achieving
superior performance compared with other configuration tuning approaches based
on heuristic rules, multi-armed bandits, and single-agent RL, but also being
capable of generalizing to different problem classes. Furthermore, we release
the environments in this paper as a benchmark for testing MARL algorithms, with
the hope of facilitating the application of MARL.


  The next-to-next-to-leading order (NNLO) QCD analysis is performed for the
virtual photon structure functions which can be measured in the double-tag
events in two-photon processes in $e^+e^-$ collisions. We investigate the
perturbative QCD evaluation of $F_2^\gamma(x,Q^2,P^2)$ to NNLO and
$F_L^\gamma(x,Q^2,P^2)$ to NLO with and without taking into account the target
mass effects, which are relevant for the large $x$ region. We also carry out
the phenomenological analysis for the experimentally accessible effective
structure function $F_{\rm eff}^\gamma=F_2^\gamma+(3/2)F_L^\gamma$.


  Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial
technologies in the field of medical imaging. Score-based models have proven to
be effective in addressing different inverse problems encountered in CT and
MRI, such as sparse-view CT and fast MRI reconstruction. However, these models
face challenges in achieving accurate three dimensional (3D) volumetric
reconstruction. The existing score-based models primarily focus on
reconstructing two dimensional (2D) data distribution, leading to
inconsistencies between adjacent slices in the reconstructed 3D volumetric
images. To overcome this limitation, we propose a novel two-and-a-half order
score-based model (TOSM). During the training phase, our TOSM learns data
distributions in 2D space, which reduces the complexity of training compared to
directly working on 3D volumes. However, in the reconstruction phase, the TOSM
updates the data distribution in 3D space, utilizing complementary scores along
three directions (sagittal, coronal, and transaxial) to achieve a more precise
reconstruction. The development of TOSM is built on robust theoretical
principles, ensuring its reliability and efficacy. Through extensive
experimentation on large-scale sparse-view CT and fast MRI datasets, our method
demonstrates remarkable advancements and attains state-of-the-art results in
solving 3D ill-posed inverse problems. Notably, the proposed TOSM effectively
addresses the inter-slice inconsistency issue, resulting in high-quality 3D
volumetric reconstruction.


  The average thermodynamic power of a time-dependent external potential in the
white-noise Langevin model is derived using a Green's function solution. The
power appears as a driving term in the differential equation for the average
energy and determines whether the solution is stationary or non-stationary.
Different dynamics are illustrated with explicit models: a linear potential
with a static magnetic field, a linear potential perturbed with an oscillating
component and a magnetic field switch modeled using a $\tanh$ protocol.


  Recently, Lin introduced two new partition functions PD$_t(n)$ and
PDO$_t(n)$, which count the total number of tagged parts over all partitions of
$n$ with designated summands and the total number of tagged parts over all
partitions of $n$ with designated summands in which all parts are odd. Lin also
proved some congruences modulo 3 and 9 for PD$_t(n)$ and PDO$_t(n)$, and
conjectured some congruences modulo 8. Very recently, Adansie, Chern, and Xia
found two new infinite families of congruences modulo 9 for PD$_t(n)$. In this
paper, we prove the congruences modulo 8 conjectured by Lin and also find many
new congruences and infinite families of congruences modulo some small powers
of 2.


  A measurement is presented of the phi to K+K- production cross section at
sqrt(s) = 7 TeV using pp collision data corresponding to an integrated
luminosity of 383 mub-1, collected with the ATLAS experiment at the LHC.
Selection of phi(1020) mesons is based on the identification of charged kaons
by their energy loss in the pixel detector. The differential cross section is
measured as a function of the transverse momentum, pTphi, and rapidity, |yphi|,
of the phi(1020) meson in the fiducial region 500 < pTphi< 1200 MeV, |yphi| <
0.8, kaon pTK> 230 MeV and kaon momentum pK< 800 MeV.The integrated
phi(1020)-meson production cross section in this fiducial range is measured to
be s(phi K+K-) = 570 pm 8 (stat) pm 66 (syst) pm 20 (lumi) mub.


  In the present work, we study four-dimensional black strings in Horndeski
models with translation invariance. Imposing that the scalar field depends on
the string-generator coordinate, the Klein-Gordon equation admits a linear
profile as a solution. This relaxation allows finding rotating, asymptotically
AdS$_3\times \mathbb{R}$ black strings, dressed with an effective cosmological
constant. In this regard, we show that in the full spectrum of shift-symmetric
Horndeski theories with Einstein limit, the scalar charge needs to be fixed in
terms of the parameter space. This method is employed to concrete examples to
illustrate the scheme we go along with. Regarding the conserved charges, we
compute them via the Euclidean method and show the fulfillment of the
associated Smarr Law. Finally, we exhibit that our AdS strings are locally and
globally stable under small fluctuations around the equilibrium.


  A propagation of dipolar radiation in a finite length linear chain of
identical dielectric spheres is investigated using the multisphere Mie
scattering formalism (MSMS). A frequency pass band is shown to be formed near
every Mie resonances inherent in the spheres. The manifestation of the pass
band depends on the polarization of the travelling radiation. To prove this
effect, a point dipole placed by the end of the chain is used as an external
source of radiation. It is found that, if this dipole is directed parallel to
the to the chain axis, the frequency pass bands exist if the refractive index
of dielectric spheres is sufficiently large. For the dipole normal to the chain
axis, the pass band can always be formed if the chain is sufficiently long.
Such a distinction is due to different behavior of the far-field dipolar
interaction between the spheres induced by the external source. The edges of
the pass bands are defined by the guiding wave criterion based on the
light-cone constraint. The criterion of creation of the pass bands correlate
with condition of formation of high quality factor modes in these systems found
in our previous papers. A comparison with the results available for infinite
chains is made. In particular, we clarify the nature of braking down the band
structure for small enough wavevectors.


  We give a short proof of a result due to Katz and West: Let $R$ be a
Noetherian ring and $I_1,\ldots,I_t$ ideals of $R$. Let $M$ and $N$ be finitely
generated $R$-modules and $N' \subseteq N$ a submodule. For every fixed $i \ge
0$, the sets $\mathrm{Ass}_R\left( \mathrm{Ext}_R^i(M, N/I_1^{n_1}\cdots
I_t^{n_t} N') \right)$ and $\mathrm{Ass}_R\left( \mathrm{Tor}_i^R(M,
N/I_1^{n_1}\cdots I_t^{n_t} N') \right)$ are independent of $(n_1,\ldots,n_t)$
for all sufficiently large $n_1,\ldots,n_t$.


  Ultra-cool brown dwarfs offer a unique window into understanding substellar
atmospheric physics and chemistry. Their strong molecular absorption bands at
infrared wavelengths, Jupiter-like radii, cool temperatures, and lack of
complicating stellar irradiation, make them ideal test-beds for understanding
Jovian-like atmospheres. Here we report the findings of a uniform atmospheric
retrieval analysis on a set of 14 Y and T-dwarfs observed with the Hubble Space
Telescope Wide Field Camera 3 instrument. From our retrieval analysis, we find
the temperature-structures to be largely consistent with radiative-convective
equilibrium in most objects. We also determine the abundances of water,
methane, and ammonia and upper limits on the alkali metals sodium and
potassium. The constraints on water and methane are consistent with predictions
from chemical equilibrium models, while those of ammonia may be affected by
vertical disequilibrium mixing, consistent with previous works. Our key result
stems from the constraints on the alkali metal abundances where we find their
continued depletion with decreasing effective temperature, consistent with the
trend identified in a previous retrieval analysis on a sample of slightly
warmer late T-dwarfs in Line et al. (2017). These constraints show that the
previously observed Y-J color trend across the T/Y transition is most likely
due to the depletion of these metals in accordance with predictions from
equilibrium condensate rainout chemistry. Finally, we simulate future James
Webb Space Telescope observations of ultra-cool dwarfs and find that the
NIRSpec PRISM offers the best chance at developing high-precision constraints
on fundamental atmospheric characteristics.


  We consider imaging the reflectivity of scatterers from intensity-only data
recorded by a single moving transducer that both emits and receives signals,
forming a synthetic aperture. By exploiting frequency illumination diversity,
we obtain multiple intensity measurements at each location, from which we
determine field cross-correlations using an appropriate phase controlled
illumination strategy and the inner product polarization identity. The field
cross-correlations obtained this way do not, however, provide all the missing
phase information because they are determined up to a phase that depends on the
receiver's location. The main result of this paper is an algorithm with which
we recover the field cross-correlations up to a single phase that is common to
all the data measured over the synthetic aperture, so all the data are
synchronized. Thus, we can image coherently with data over all frequencies and
measurement locations as if full phase information was recorded.


  Quantization becomes a new way to study classical game theory since quantum
strategies and quantum games have been proposed. In previous studies, many
typical game models, such as prisoner's dilemma, battle of the sexes, Hawk-Dove
game, have been investigated by using quantization approaches. In this paper,
several game models of opinion formations have been quantized based on the
Marinatto-Weber quantum game scheme, a frequently used scheme to convert
classical games to quantum versions. Our results show that the quantization can
change fascinatingly the properties of some classical opinion formation game
models so as to generate win-win outcomes.


  Dial-a-ride problem (DARP) deals with the transportation of users between
pickup and drop-off locations associated with specified time windows. This
paper proposes a novel algorithm called multi-atomic annealing (MATA) to solve
static dial-a-ride problem. Two new local search operators (burn and reform), a
new construction heuristic and two request sequencing mechanisms (Sorted List
and Random List) are developed. Computational experiments conducted on various
standard DARP test instances prove that MATA is an expeditious meta-heuristic
in contrast to other existing methods. In all experiments, MATA demonstrates
the capability to obtain high quality solutions, faster convergence, and
quicker attainment of a first feasible solution. It is observed that MATA
attains a first feasible solution 29.8 to 65.1% faster, and obtains a final
solution that is 3.9 to 5.2% better, when compared to other algorithms within
60 sec.


  Nowadays, studying introns is becoming a very promising field in the
genomics. Even though they play a role in the dynamic regulation of gene and in
the organism's evolution, introns have not attracted enough attention like
exons did; especially of digital signal processing researchers. Thus, we focus
on analysis of the C.elegans introns. In this paper, we propose the complex
Morlet wavelet analysis to investigate introns' characterization in the
C.elegans genes. However, catching the change in frequency response with
respect to time of the gene sequences is hindered by their presence in the form
of strings of characters. This can only be counteracted by assigning numerical
values to each of the DNA characters. This operation defines the so called "DNA
coding approach". In this context, we propose a new coding technique based on
the Frequency Chaos Game Representation (FCGR) that we name the "Frequency
Chaos Game Signal" (FCGS). Results of the complex Morlet wavelet Analysis
applied to the Celegans FCGS are showing a very distinguished texture. The
visual interpretation of the colour scalograms is proved to be an efficient
tool for revealing significant information about intronic sequences.


  Given a 2-stranded tangle in a $\ZZ/2$ homology ball, $T\subset Y$, we
investigate the character variety $R(Y,T)$ of conjugacy classes of traceless
SU(2) representations of $\pi_1(Y\setminus T)$. In particular we completely
determine the subspace of binary dihedral representations, and identify all of
$R(Y,T)$ for many tangles naturally associated to knots in S^3. Moreover, we
determine the image of the restriction map from $R(T,Y)$ to the traceless SU(2)
character variety of the 4-punctured 2-sphere (the {\it pillowcase}). We give
examples to show this image can be non-linear in general, and show it is linear
for tangles associated to pretzel knots.


  We consider a harmonically bound Brownian particle coupled to two distinct
heat reservoirs at different temperatures. We show that the presence of a
harmonic trap does not change the large deviation function from the case of a
free Brownian particle discussed by Derrida and Brunet and Visco. Likewise, the
Gallavotti-Cohen fluctuation theorem related to the entropy production at the
heat sources remains in force. We support the analytical results with numerical
simulations.


  There has been an explosion in interest in machine learning (ML) in recent
years due to its applications to science and engineering. However, as ML
techniques have advanced, tools for explaining and visualizing novel ML
algorithms have lagged behind. Animation has been shown to be a powerful tool
for making engaging visualizations of systems that dynamically change over
time, which makes it well suited to the task of communicating ML algorithms.
However, the current approach to animating ML algorithms is to handcraft
applications that highlight specific algorithms or use complex generalized
animation software. We developed ManimML, an open-source Python library for
easily generating animations of ML algorithms directly from code. We sought to
leverage ML practitioners' preexisting knowledge of programming rather than
requiring them to learn complex animation software. ManimML has a familiar
syntax for specifying neural networks that mimics popular deep learning
frameworks like Pytorch. A user can take a preexisting neural network
architecture and easily write a specification for an animation in ManimML,
which will then automatically compose animations for different components of
the system into a final animation of the entire neural network. ManimML is open
source and available at https://github.com/helblazer811/ManimML.


  This paper considers the problem of adaptive estimation of a mean pattern in
a randomly shifted curve model. We show that this problem can be transformed
into a linear inverse problem, where the density of the random shifts plays the
role of a convolution operator. An adaptive estimator of the mean pattern,
based on wavelet thresholding is proposed. We study its consistency for the
quadratic risk as the number of observed curves tends to infinity, and this
estimator is shown to achieve a near-minimax rate of convergence over a large
class of Besov balls. This rate depends both on the smoothness of the common
shape of the curves and on the decay of the Fourier coefficients of the density
of the random shifts. Hence, this paper makes a connection between mean pattern
estimation and the statistical analysis of linear inverse problems, which is a
new point of view on curve registration and image warping problems. We also
provide a new method to estimate the unknown random shifts between curves. Some
numerical experiments are given to illustrate the performances of our approach
and to compare them with another algorithm existing in the literature.


  The success of the application of machine-learning techniques to compilation
tasks can be largely attributed to the recent development and advancement of
program characterization, a process that numerically or structurally quantifies
a target program. While great achievements have been made in identifying key
features to characterize programs, choosing a correct set of features for a
specific compiler task remains an ad hoc procedure. In order to guarantee a
comprehensive coverage of features, compiler engineers usually need to select
excessive number of features. This, unfortunately, would potentially lead to a
selection of multiple similar features, which in turn could create a new
problem of bias that emphasizes certain aspects of a program's characteristics,
hence reducing the accuracy and performance of the target compiler task. In
this paper, we propose FEAture Selection for compilation Tasks (FEAST), an
efficient and automated framework for determining the most relevant and
representative features from a feature pool. Specifically, FEAST utilizes
widely used statistics and machine-learning tools, including LASSO, sequential
forward and backward selection, for automatic feature selection, and can in
general be applied to any numerical feature set. This paper further proposes an
automated approach to compiler parameter assignment for assessing the
performance of FEAST. Intensive experimental results demonstrate that, under
the compiler parameter assignment task, FEAST can achieve comparable results
with about 18% of features that are automatically selected from the entire
feature pool. We also inspect these selected features and discuss their roles
in program execution.


  The Sole-Manrubia model of ``macro''-evolution describes the origination,
evolution and extinction of species on geological time scales. We report some
properties of this model which provide deep insight into this simple model
which captures several realistic features.


  A new way to run nested sampling, combined with realistic MCMC proposals to
generate new live points, is presented. Nested sampling is run with a fixed
number of MCMC steps. Subsequently, snowballing nested sampling extends the run
to more and more live points. This stabilizes MCMC proposals over time, and
leads to pleasant properties, including that the number of live points and
number of MCMC steps do not have to be calibrated, that the evidence and
posterior approximation improves as more compute is added and can be diagnosed
with convergence diagnostics from the MCMC literature. Snowballing nested
sampling converges to a ``perfect'' nested sampling run with infinite number of
MCMC steps.


  Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) can directly
visualize nonviable myocardium with hyperenhanced intensities with respect to
normal myocardium. For heart attack patients, it is crucial to facilitate the
decision of appropriate therapy by analyzing and quantifying their LGE CMR
images. To achieve accurate quantification, LGE CMR images need to be processed
in two steps: segmentation of the myocardium followed by classification of
infarcts within the segmented myocardium. However, automatic segmentation is
difficult usually due to the intensity heterogeneity of the myocardium and
intensity similarity between the infarcts and blood pool. Besides, the slices
of an LGE CMR dataset often suffer from spatial and intensity distortions,
causing further difficulties in segmentation and classification. In this paper,
we present a comprehensive 3-D framework for automatic quantification of LGE
CMR images. In this framework, myocardium is segmented with a novel method that
deforms coupled endocardial and epicardial meshes and combines information in
both short- and long-axis slices, while infarcts are classified with a
graph-cut algorithm incorporating intensity and spatial information. Moreover,
both spatial and intensity distortions are effectively corrected with specially
designed countermeasures. Experiments with 20 sets of real patient data show
visually good segmentation and classification results that are quantitatively
in strong agreement with those manually obtained by experts.


  We use exact four-boson scattering equations in the momentum-space framework
to study the universal properties of shallow Efimov tetramers and their
dependence on the two-boson scattering length. We demonstrate that, in contrast
to previous predictions, the shallow tetramer in a particular experimentally
unexplored regime is not an unstable bound state but an inelastic virtual
state. This leads to a resonant behaviour of the atom-trimer scattering length
and thereby to a resonant enhancement of the trimer relaxation in ultracold
atom-trimer mixtures.


  We report optical, radio and X-ray observations of a new distant blazar, PMN
J0525-3343, at a redshift of 4.4. The X-ray spectrum measured from ASCA and
BeppoSAX flattens below a few keV, in a manner similar to the spectra of two
other z>4 blazars, GB 1428+4217 (z=4.72) reported by Boller et al and RXJ
1028.6-0844 (z=4.28) by Yuan et al. The spectrum is well fitted by a power-law
continuum which is either absorbed or breaks at a few keV. An intrinsic column
density corresponding to 2 x 10^23 H-atoms cm-2 at solar abundance is required
by the absorption model. This is however a million times greater than the
neutral hydrogen, or dust, column density implied by the optical spectrum,
which covers the rest-frame UV emission of the blazar nucleus. We discuss the
problems raised and suggest that, unless there is intrinsic flattening in the
spectral distribution of the particles/seed photons producing X-rays via
inverse Compton scattering, the most plausible solution is a warm absorber
close to the active nucleus.


  Based on astrophysical constraints derived from Chandrasekhar's mass limit
for white-dwarfs, we study the effects of the model on the parameters of
unparticle-inspired gravity, on scales $\Lambda_U > 1 \; TeV$ and $d_U \approx
1$.


  A field in the vacuum state, which is in principle separable, can evolve to
an entangled state in a dynamical gravitational collapse. We will study,
quantify, and discuss the origin of this entanglement, showing that it could
even reach the maximal entanglement limit for low frequencies or very small
black holes, with consequences in micro-black hole formation and the final
stages of evaporating black holes. This entanglement provides quantum
information resources between the modes in the asymptotic future (thermal
Hawking radiation) and those which fall to the event horizon. We will also show
that fermions are more sensitive than bosons to this quantum entanglement
generation. This fact could be helpful in finding experimental evidence of the
genuine quantum Hawking effect in analog models.


  Multiple shapes must be obtained in the mechanical design process to satisfy
the required design specifications. The inverse design problem has been
analyzed in previous studies to obtain such shapes. However, finding multiple
shapes in a short computation period is difficult while using the conventional
methods. This paper proposes the use of the conditional variational
autoencoders (CVAE) with normal distribution, denoted by N-CVAE, along with the
von Mises-Fischer distribution, denoted by S-CVAE, to find multiple solutions
for the inverse design problems. Both the CVAE models embed shapes into a
latent space. The S-CVAE enables the separation of data in the latent space,
whereas the N-CVAE embeds the data in a narrow space. These different features
are used for various tasks in this study. In one of the tasks, the dataset
consists of only one type of data and generates similar airfoils. Here, S-CVAE
outperforms N-CVAE because it can separate the data. Another task involves
combining different types of airfoils and generating new types of data. N-CVAE
is useful in this instance since it embeds different shapes in the same latent
area, due to which, the model outputs intermediate shapes of different types.
The shape-generation capability of S-CVAE and N-CVAE are experimentally
compared in this study.


  The variational quantum eigensolver (VQE) is one of the most appealing
quantum algorithms to simulate electronic structure properties of molecules on
near-term noisy intermediate-scale quantum devices. In this work, we generalize
the VQE algorithm for simulating extended systems. However, the numerical study
of an one-dimensional (1D) infinite hydrogen chain using existing VQE
algorithms shows a remarkable deviation of the ground state energy with respect
to the exact full configuration interaction (FCI) result. Here, we present two
schemes to improve the accuracy of quantum simulations for extended systems.
The first one is a modified VQE algorithm, which introduces an unitary
transformation of Hartree-Fock orbitals to avoid the complex Hamiltonian. The
second one is a Post-VQE approach combining VQE with the quantum subspace
expansion approach (VQE/QSE). Numerical benchmark calculations demonstrate that
both of two schemes provide an accurate enough description of the potential
energy curve of the 1D hydrogen chain. In addition, excited states computed
with the VQE/QSE approach also agree very well with FCI results.


  Howes et al. Reply to Comment on "Kinetic Simulations of Magnetized
Turbulence in Astrophysical Plasmas" arXiv:0711.4355


  Meshing of geometric domains having curved boundaries by affine simplices
produces a polytopial approximation of those domains. The resulting error in
the representation of the domain limits the accuracy of finite element methods
based on such meshes. On the other hand, the simplicity of affine meshes makes
them a desirable modeling tool in many applications. In this paper, we develop
and analyze higher-order accurate finite element methods that remain stable and
optimally accurate on polytopial approximations of domains with smooth
boundaries. This is achieved by constraining a judiciously chosen extension of
the finite element solution on the polytopial domain to weakly match the
prescribed boundary condition on the true geometric boundary. We provide
numerical examples that highlight key properties of the new method and that
illustrate the optimal $H^1$ and $L^2$-norm convergence rates.


  Mokka is a partial-synchronous, strong consistent BFT consensus algorithm for
reaching the consensus about a certain value in open networks. This algorithm
has some common approaches nested from RAFT, but its nature and design make
Mokka a better solution for DLT (distributed ledger).


  We investigate the mean-field energy spectrum and dynamics in a Bose-Einstein
condensate in a double-well potential with non-Hermiticity from the
nonreciprocal hopping, and show that the interplay of nonreciprocity and
nonlinearity leads to exotic properties. Under the two-mode and mean-field
approximations, the nonreciprocal generalization of the nonlinear
Schr\"{o}dinger equation and Bloch equations of motion for this system are
obtained. We analyze the PT phase diagram and the dynamical stability of fixed
points. The reentrance of PT-symmetric phase and the reformation of stable
fixed points with increasing the nonreciprocity parameter are found. Besides,
we uncover a linear self-trapping effect induced by the nonreciprocity. In the
nonlinear case, the self-trapping oscillation is enhanced by the nonreciprocity
and then collapses in the PT-broken phase, and can finally be recovered in the
reentrant PT-symmetric phase.


  We consider ternary disc packings of the plane, i.e. the packings using discs
of three different radii. Packings in which each ''hole'' is bounded by three
pairwise tangent discs are called triangulated. There are 164 pairs $(r,s)$,
$1{>}r{>}s$, allowing triangulated packings by discs of radii 1, $r$ and $s$.
In this paper, we enhance existing methods of dealing with maximal-density
packings in order to find ternary triangulated packings which maximize the
density among all the packings with the same disc radii. We showed for 16 pairs
that the density is maximized by a triangulated ternary packing; for 15 other
pairs, we proved the density to be maximized by a triangulated packing using
only two sizes of discs; for 40 pairs, we found non-triangulated packings
strictly denser than any triangulated one; finally, we classified the remaining
cases where our methods are not applicable.


  The field of frustrated magnetism has been enriched significantly by the
discovery of various kagome lattice compounds. These materials exhibit a great
variety of macroscopic behaviours ranging from magnetic orders to quantum spin
liquids. Using large-scale exact diagonalization, we construct the phase
diagram of the $S=1/2$ $J_1$-$J_2$ kagome Heisenberg model with $z$-axis
Dzyaloshinskii-Moriya interaction $D_z$. We show that this model can
systematically account for many of the experimentally observed phases. Small
$J_2$ and $D_z$ can stabilize respectively a gapped and a gapless spin liquid.
When $J_2$ or $D_z$ is substantial, the ground state develops a $\mathbf{Q}=0$,
$120^\textrm{o}$ antiferromagnetic order. The critical strengths for inducing
magnetic transition are $D^c_z \sim 0.1\, J_1$ at $J_2=0$, and $J^c_2\sim 0.4\,
J_1$ at $D_z=0$. The previously reported values of $D_z$ and $J_2$ for
herbertsmithite [ZnCu$_3$(OH)$_6$Cl$_2$] place the compound in close proximity
to a quantum critical point.


  Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad
range of applications requiring extensive communications, either to
interconnect the UAVs with each other or with ground resources. Focusing either
on the modeling of UAV operations or communication and network dynamics,
available simulation tools fail to capture the complex interdependencies
between these two aspects of the problem. The main contribution of this paper
is a flexible and scalable open source simulator -- FlyNetSim -- bridging the
two domains. The overall objective is to enable simulation and evaluation of
UAV swarms operating within articulated multi-layered technological ecosystems,
such as the Urban Internet of Things (IoT). To this aim, FlyNetSim interfaces
two open source tools, ArduPilot and ns-3, creating individual data paths
between the devices operating in the system using a publish and subscribe-based
middleware. The capabilities of FlyNetSim are illustrated through several
case-study scenarios including UAVs interconnecting with a multi-technology
communication infrastructure and intra-swarm ad-hoc communications.


  Mobile users (or UEs, to use 3GPP terminology) served by small cells in dense
urban settings may abruptly experience a significant deterioration in their
channel to their serving base stations (BSs) in several scenarios, such as
after turning a corner around a tall building, or a sudden knot of traffic
blocking the direct path between the UE and its serving BS. In this work, we
propose a scheme to temporarily increase the data rate to/from this UE with
additional bandwidth from the nearest Coordinated Multi-Point (CoMP) cluster of
BSs, while the slower process of handover of the UE to a new serving BS is
ongoing. We emphasize that this additional bandwidth is additional to the data
rates the UE is getting over its primary connection to the current serving BS
and, after the handover, to the new serving BS. The key novelty of the present
work is the proposal of a decentralized market-based resource allocation method
to perform resource allocation to support Coordinated Beamforming (CB) CoMP. It
is scalable to large numbers of UEs and BSs, and it is fast because resource
allocations are made bilaterally, between BSs and UEs. Once the resource
allocation to the UE has been made, the coordinated of transmissions occurs as
per the usual CB methods. Thus the proposed method has the benefit of giving
the UE access to its desired amount of resources fast, without waiting for
handover to complete, or reporting channel state information before it knows
the resources it will be allocated for receiving transmissions from the serving
BS.


  The famous Jacobian conjecture asks if a morphism $f:K[x,y]\to K[x,y]$ having
an invertible Jacobian is invertible ($K$ is a characteristic zero field). We
show that if one of the following three equivalent conditions is satisfied,
then $f$ is invertible: $K[f(x),f(y)][x+y]$ is normal; $K[x,y]$ is flat over
$K[f(x),f(y)][x+y]$; $K[f(x),f(y)][x+y]$ is separable over $K[f(x),f(y)]$.


  We show that, by considering physics in dS_4 or AdS_4 spacetime, one can
circumvent the van Dam - Veltman - Zakharov theorem which requires that the
extra polarization states of a massive graviton do not decouple in the massless
limit. It is shown that the smoothness of the m->0 limit is ensured if the H
(``Hubble'') parameter, associated with the horizon of the dS_4 or AdS_4 space,
tends to zero slower than the mass of the graviton m.


  In this paper, a (3\times3)-matrix representation of the
Birman-Wenzl-Murakami(BWM) algebra has been presented. Based on which, unitary
matrices (A(\theta,\phi_1,\phi_2), B(\theta,\phi_1,\phi_2)) are generated via
the Yang-Baxterization approach. A hamiltonian is constructed from the unitary
(B(\theta,\phi)) matrix. We then study the Berry phase of the Yang-Baxter
system and find the topological parameter d has relationship with berry phase.


  The accretion process onto spinning objects in Kerr spacetimes is studied
with numerical simulations. Our results show that accretion onto compact
objects with Kerr parameter (characterizing the spin) $|a| < M$ and $|a| > M$
is very different. In the super-spinning case, for $|a|$ moderately larger than
$M$, the accretion onto the central object is extremely suppressed due to a
repulsive force at short distance. The accreting matter cannot reach the
central object, but instead is accumulated around it, forming a high density
cloud that continues to grow. The radiation emitted in the accretion process
will be harder and more intense than the one coming from standard black holes;
e.g. $\gamma$-rays could be produced as seen in some observations.
Gravitational collapse of this cloud might even give rise to violent bursts. As
$|a|$ increases, a larger amount of accreting matter reaches the central object
and the growth of the cloud becomes less efficient. Our simulations find that a
quasi-steady state of the accretion process exists for $|a|/M \gtrsim 1.4$,
independently of the mass accretion rate at large radii. For such high values
of the Kerr parameter, the accreting matter forms a thin disk at very small
radii. We provide some analytical arguments to strengthen the numerical
results; in particular, we estimate the radius where the gravitational force
changes from attractive to repulsive and the critical value $|a|/M \approx 1.4$
separating the two qualitatively different regimes of accretion. We briefly
discuss the observational signatures which could be used to look for such
exotic objects in the Galaxy and/or in the Universe.


  The reliability and success of any organization such as academic institution
rely on its ability to provide secure, accurate and timely data about its
operations. Erstwhile managing student information in academic institution was
done through paper-based information system, where academic records are
documented in several files that are kept in shelves. Several problems are
associated with paper-based information system. Managing information through
the manual approach require physical exertion to retrieve, alter, and re-file
the paper records. These are nonvalue added services results in data
inconsistency and redundancy, currently institutions have migrated to web-based
student information management system without considering the security
architecture of the web portal. This project seeks to ameliorates and secure
how information is being managed in Nigeria Police Academy through the
development of a secured web-based student information management system, which
has a friendly user interface that provides an easy and secure way to manage
academic information such as students information, staff information, course
registration, course materials and results. This project was developed using
Laravel 5.5 PHP Framework to provide a robust secure web-based student
information system that is not vulnerable to 2018 OWASP TOP 10 web
vulnerabilities.


  We use results from communication complexity, both new and old ones, to prove
lower bounds for unambiguous finite automata (UFAs). We show three results.
  $\textit{Complement:}$ There is a language $L$ recognised by an $n$-state UFA
such that the complement language $\overline{L}$ requires NFAs with
$n^{\tilde{\Omega}(\log n)}$ states. This improves on a lower bound by Raskin.
  $\textit{Union:}$ There are languages $L_1$, $L_2$ recognised by $n$-state
UFAs such that the union $L_1\cup L_2$ requires UFAs with
$n^{\tilde{\Omega}(\log n)}$ states.
  $\textit{Separation:}$ There is a language $L$ such that both $L$ and
$\overline{L}$ are recognised by $n$-state NFAs but such that $L$ requires UFAs
with $n^{\Omega(\log n)}$ states. This refutes a conjecture by Colcombet.


  We derive a model for the optimization of the bending and torsional
rigidities of non-homogeneous elastic rods. This is achieved by studying a
sharp interface shape optimization problem with perimeter penalization, that
treats both rigidities as objectives. We then formulate a phase field
approximation of the optimization problem and show the convergence to the
aforementioned sharp interface model via $\Gamma$-convergence. In the final
part of this work we numerically approximate minimizers of the phase field
problem by using a steepest descent approach and relate the resulting optimal
shapes to the development of the morphology of plant stems.


  We develop Chebyshev symplectic methods based on Chebyshev orthogonal
polynomials of the first and second kind separately in this paper. Such type of
symplectic methods can be conveniently constructed with the newly-built theory
of weighted continuous-stage Runge-Kutta methods. A few numerical experiments
are well performed to verify the efficiency of our new methods.