  The nonlinear dynamo effect of tearing modes is derived with the resistive
MHD equations. The dynamo effect is divided into two parts, parallel and
perpendicular to the magnetic field. Firstly, the force-free plasma is
considered. It is found that the parallel dynamo effect drives opposite current
densities at the different sides of the rational surface, making the $\lambda
=\boldsymbol{j}\cdot\boldsymbol{B}/|\boldsymbol{B}|^2$ profile completely
flattened near the rational surface. There are many rational surfaces for the
turbulent plasma, which means the plasma is tending to relax into the Taylor
state. In contrast, a bit far from the rational surface, the parallel dynamo
effect is much smaller, and the nonlinear dynamo form approximates the
quasilinear form. Secondly, the pressure gradient is included. It is found that
rather than the $\lambda$ profile, the $\boldsymbol{j}\cdot\boldsymbol{B}$
profile is flattened by the parallel dynamo effect. Besides, the perpendicular
dynamo effect of tearing modes is found to eliminate the pressure gradient near
the rational surface. In addition, our result also provides another basis for
the assumption that current density is flat in the magnetic island for the
tearing modes theory.


  We prove two multivariate $q$-binomial identities conjectured by Bousseau,
Brini and van Garrel [Stable maps to Looijenga pairs, arXiv:2011.08830] which
give generating series for Gromov-Witten invariants of two specific log
Calabi-Yau surfaces. The key identity in all the proofs is Jackson's
$q$-analogue of the Pfaff-Saalsch\"utz summation formula from the theory of
basic hypergeometric series.


  To test incomplete search algorithms for constraint satisfaction problems
such as 3-SAT, we need a source of hard, but satisfiable, benchmark instances.
A simple way to do this is to choose a random truth assignment A, and then
choose clauses randomly from among those satisfied by A. However, this method
tends to produce easy problems, since the majority of literals point toward the
``hidden'' assignment A. Last year, Achlioptas, Jia and Moore proposed a
problem generator that cancels this effect by hiding both A and its complement.
While the resulting formulas appear to be just as hard for DPLL algorithms as
random 3-SAT formulas with no hidden assignment, they can be solved by WalkSAT
in only polynomial time. Here we propose a new method to cancel the attraction
to A, by choosing a clause with t > 0 literals satisfied by A with probability
proportional to q^t for some q < 1. By varying q, we can generate formulas
whose variables have no bias, i.e., which are equally likely to be true or
false; we can even cause the formula to ``deceptively'' point away from A. We
present theoretical and experimental results suggesting that these formulas are
exponentially hard both for DPLL algorithms and for incomplete algorithms such
as WalkSAT.


  Vector fields with components which are generalized zero-forms are
constructed. Inner products with generalized forms, Lie derivatives and Lie
brackets are computed. The results are shown to generalize previously reported
results for generalized vector fields. Hamiltonian vector fields are discussed.
Generalized affine connections and metrics are defined and the fundamental
theorem of metric differential geometry is extended. The global structure of
the exterior derivative of generalized forms is investigated.


  Our knowledge of main-belt comets (MBCs), which exhibit comet-like activity
likely due to the sublimation of volatile ices, yet orbit in the main asteroid
belt, has increased greatly since the discovery of the first known MBC,
133P/Elst-Pizarro, in 1996, and their recognition as a new class of solar
system objects after the discovery of two more MBCs in 2005. I review work that
has been done over the last 10 years to improve our understanding of these
enigmatic objects, including the development of systematic discovery methods
and diagnostics for distinguishing MBCs from disrupted asteroids (which exhibit
comet-like activity due to physical disruptions such as impacts or rotational
destabilization). I also discuss efforts to understand the dynamical and
thermal properties of these objects.


  We obtained optical/near-IR rest-frame Magellan FIRE spectra (including
Pa$\beta$ and Pa$\gamma$) of 25 starburst galaxies at 0.5<z<0.9, with average
star formation rates (SFR) x7 above the Main Sequence (MS). We find that
Paschen-to-Balmer line ratios saturate around a constant value corresponding to
$A_{\rm V}\sim$2-3 mag, while line to IR luminosity ratios suggest a large
range of more extreme obscurations and appear to be uncorrelated to the former.
This behavior is not consistent with standard attenuation laws derived for
local and distant galaxies, while being remarkably consistent with observations
of starburst cores in which young stars and dust are homogeneously mixed. This
model implies $A_{\rm V}=$2-30 mag attenuation to the center of starburst
cores, with a median of ~9 mag (a factor of 4000). X-ray hardness ratios for 6
AGNs in our sample and column densities derived from observed dust masses and
radio sizes independently confirm this level of attenuation. In these
conditions observed optical/near-IR emission comes from surface regions, while
inner starburst cores are invisible. We thus attribute the high [NII]/H$\alpha$
ratios to widespread shocks from accretion, turbulence and dynamic disturbances
rather than to AGNs. The large range of optical depths demonstrates that
substantial diversity is present within the starburst population, possibly
connected to different merger phases or progenitor properties. The majority of
our targets are, in fact, morphologically classified as mergers. We argue that
the extreme obscuration provides in itself smoking gun evidence of their merger
origin, and a powerful tool for identifying mergers at even higher redshifts.


  With the turmoil in cybersecurity and the mind-blowing advances in AI, it is
only natural that cybersecurity practitioners consider further employing
learning techniques to help secure their organizations and improve the
efficiency of their security operation centers. But with great fears come great
opportunities for both the good and the evil, and a myriad of bad deals. This
paper discusses ten issues in cybersecurity that hopefully will make it easier
for practitioners to ask detailed questions about what they want from an AI
system in their cybersecurity operations. We draw on the state of the art to
provide factual arguments for a discussion on well-established AI in
cybersecurity issues, including the current scope of AI and its application to
cybersecurity, the impact of privacy concerns on the cybersecurity data that
can be collected and shared externally to the organization, how an AI decision
can be explained to the person running the operations center, and the
implications of the adversarial nature of cybersecurity in the learning
techniques. We then discuss the use of AI by attackers on a level playing field
including several issues in an AI battlefield, and an AI perspective on the old
cat-and-mouse game including how the adversary may assess your AI power.


  We consider dynamo action under the combined influence of turbulence and
large-scale shear in sheared jets. Shear can stretch turbulent magnetic field
lines in such a way that even turbulent motions showing mirror symmetry become
suitable for generation of a large-scale magnetic field. We derive the integral
induction equation governing the behaviour of the mean field in jets. The main
result is that sheared jets may generate a large-scale magnetic field if shear
is sufficiently strong. The generated mean field is mainly concentrated in a
magnetic sheath surrounding the central region of a jet, and it exhibits sign
reversals in the direction of the jet axis. Typically, the magnetic field in a
sheath is dominated by the component along the jet that can reach equipartition
with the kinetic energy of particles, The field in the central region of jets
has a more disordered structure.


  Wavefront sampling experiments record arrival times of \v{C}erenkov photons
with high precision at various locations in \v{C}erenkov pool using a
distributed array of telescopes. It was shown earlier that this photon front
can be fitted with a spherical surface traveling at a speed of light and
originating from a single point on the shower axis. Radius of curvature of the
spherical shower front ($R$) is approximately equal to the height of shower
maximum from observation level. For a given primary species, it is also found
that $R$ varies with the primary energy ($E$) and this provides a method of
estimating the primary energy. In general, one can estimate the arrival times
at each telescope using the radius of curvature, arrival direction of the
primary and the core location. This, when compared with the data enables us to
estimate the above parameters for each shower. This method of obtaining the
arrival direction alleviates the difficulty in the form of systematics arising
out of the plane wavefront approximation for the \v{C}erenkov front. Another
outstanding problem in the field of atmospheric \v{C}erenkov technique is the
difficulty in locating the shower core. This method seems to solve both these
problems and provides an elegant method to determine the arrival direction as
well as the core location from timing information alone. In addition, using the
\v{C}erenkov photon density information and the core position we can estimate
the energy of the primary if the nature of the primary is known. Combining
these two independent estimates of the primary energy, the energy resolution
can be further improved. Application of this methodology to simulated data and
the results will be presented. The intrinsic uncertainties on the various
estimated parameters also will be discussed.


  I review the effects of dust obscuration in galaxies at both low and high
redshifts, and briefly discuss a method to remove dust reddening from the
emerging light of star-forming galaxies. I also analyze the evolution of the
dust opacity in galaxies as a function of redshift, and discuss its effect on
the observed UV-optical light. The quantitative corrections for dust
obscuration given here allow one to recover the intrinsic value of the global
star formation at different cosmological times.


  In this article we discuss a procedure to solve the one dimensional (1D)
Schroedinger Equation for a periodic potential, which may be well suited to
teach band structure theory. The procedure is conceptually very simple, so that
it may be used to teach band theory at the undergraduate level; at the same
time the point of view is practical, so that the students may experiment
computing band gaps, and other features of band structure. Another advantage of
the procedure lies in that it does not use specific symmetry properties of the
potential, so that the results are generally valid.


  Recent LHC data showed excesses of Higgs-like signals at the Higgs mass of
around 125GeV. This may indicate supersymmetric models with relatively heavy
scalar fermions to enhance the Higgs mass. The desired mass spectrum is
realized in the anomaly-mediated supersymmetry breaking model, in which the
Wino can naturally be the lightest superparticle (LSP). We discuss
possibilities for confirming such a scenario, particularly detecting signals
from Wino LSP at direct detection experiments, indirect searches at neutrino
telescopes and at the LHC.


  The construction of an averaged theory of gravity based on Einstein's General
Relativity is very difficult due to the non-linear nature of the gravitational
field equations. This problem is further exacerbated by the difficulty in
defining a mathematically precise covariant averaging procedure for tensor
fields over differentiable manifolds. Together, these two ideas have been
called the averaging problem for General Relativity. In the first part of the
talk, an attempt to review some the various approaches to this problem will be
given, highlighting strengths, weaknesses, and commonalities between them. In
the second part of the talk, an argument will be made, that if one wishes to
develop a well-defined averaging procedure, one may choose to parallel
transport along geodesics with respect to the Levi-Cevita connection or, use
the Weitzenb\"ock connection and ensure the transportation is independent of
path. The talk concludes with some open questions to generate further
discussion.


  Synthetic image generation has opened up new opportunities but has also
created threats in regard to privacy, authenticity, and security. Detecting
fake images is of paramount importance to prevent illegal activities, and
previous research has shown that generative models leave unique patterns in
their synthetic images that can be exploited to detect them. However, the
fundamental problem of generalization remains, as even state-of-the-art
detectors encounter difficulty when facing generators never seen during
training. To assess the generalizability and robustness of synthetic image
detectors in the face of real-world impairments, this paper presents a
large-scale dataset named ArtiFact, comprising diverse generators, object
categories, and real-world challenges. Moreover, the proposed multi-class
classification scheme, combined with a filter stride reduction strategy
addresses social platform impairments and effectively detects synthetic images
from both seen and unseen generators. The proposed solution significantly
outperforms other top teams by 8.34% on Test 1, 1.26% on Test 2, and 15.08% on
Test 3 in the IEEE VIP Cup challenge at ICIP 2022, as measured by the accuracy
metric.


  Much recent work has been devoted to approximate nearest neighbor queries.
Motivated by applications in recommender systems, we consider approximate
furthest neighbor (AFN) queries and present a simple, fast, and highly
practical data structure for answering AFN queries in high- dimensional
Euclidean space. The method builds on the technique of In- dyk (SODA 2003),
storing random projections to provide sublinear query time for AFN. However, we
introduce a different query algorithm, improving on Indyk's approximation
factor and reducing the running time by a logarithmic factor. We also present a
variation based on a query- independent ordering of the database points; while
this does not have the provable approximation factor of the query-dependent
data structure, it offers significant improvement in time and space complexity.
We give a theoretical analysis, and experimental results. As an application,
the query-dependent approach is used for deriving a data structure for the
approximate annulus query problem, which is defined as follows: given an input
set S and two parameters r > 0 and w >= 1, construct a data structure that
returns for each query point q a point p in S such that the distance between p
and q is at least r/w and at most wr.


  In November, 2011, the Financial Stability Board, in collaboration with the
International Monetary Fund, published a list of 29 "systemically important
financial institutions" (SIFIs). This designation reflects a concern that the
failure of any one of them could have dramatic negative consequences for the
global economy and is based on "their size, complexity, and systemic
interconnectedness". While the characteristics of "size" and "systemic
interconnectedness" have been the subject of a good deal of quantitative
analysis, less attention has been paid to measures of a firm's "complexity." In
this paper we take on the challenges of measuring the complexity of a financial
institution and to that end explore the use of the structure of an individual
firm's control hierarchy as a proxy for institutional complexity. The control
hierarchy is a network representation of the institution and its subsidiaries.
We show that this mathematical representation (and various associated metrics)
provides a consistent way to compare the complexity of firms with often very
disparate business models and as such may provide the foundation for
determining a SIFI designation. By quantifying the level of complexity of a
firm, our approach also may prove useful should firms need to reduce their
level of complexity either in response to business or regulatory needs. Using a
data set containing the control hierarchies of many of the designated SIFIs, we
find that in the past two years, these firms have decreased their level of
complexity, perhaps in response to regulatory requirements.


  The equations of motion describing buoyant fluids are often simplified using
a set of approximations proposed by J. Boussinesq one century ago. To resume,
they consist in assuming constant fluid properties, incompressibility and
conservation of calories during heat transport. Assuming fulfilment of the
first requirement (constant fluid properties), we derive a set of 4 criteria
for assessing the validity of the two other requirements in turbulent
Rayleigh-B\'enard convection. The first criterion $\alpha \Delta \ll 1 $ simply
results from the incompressibility condition in the thermal boundary layer
($\alpha$ and $\Delta$ are the thermal expansion coefficient and the
temperature difference driving the flow). The 3 other criteria are proportional
or quadratic with the density stratification or, equivalently with the
temperature difference resulting from the adiabatic gradient across the cell
$\Delta_{h}$. Numerical evaluations with air, water and cryogenic helium show
that most laboratory experiments are free from such Boussinesq violation as
long as the first criterion is fulfilled. In ultra high Rayleigh numbers
($Ra>10^{16}$) experiments in He, one of the stratification criteria, scaling
with $\alpha \Delta_{h}$, could be violated. This criterion garanties that
pressure fluctuations have a negligible influence both on the density variation
and on the heat transfer equation through compression/expansion cycles.
Extrapolation to higher $Ra$ suggests that strong violation of Boussinesq
approximation could occur in atmospheric convection.


  Brane model of universe is considered for free particle. Conservation laws on
the brane are obtained using the symmetry properties of the brane. Equation of
motion is derived for a particle using variation principle from these
conservation laws. This equation includes terms accounting the variation of
brane radius. Its solution is obtained at some approximations and dispersion
relation for a particle is derived.


  This paper is a continuation and development of our previous articles (Popov
et al., 1997, 1998). We use ``Scenario Machine'' (Lipunov et al., 1996b) -- the
population synthesis simulator (for single binary systems calculations the
program is available in WWW at: http://xray.sai.msu.su/sciwork/scenario.html
(Nazin et al., 1998) -- to calculate evolution of populations of several types
of X-ray sources during the first 20 Myrs after a starformation burst.
  We examined the evolution of 12 types of X-ray sources in close binary
systems (both with neutron stars and with black holes) for different parameters
of the IMF -- slopes: $\alpha=1, \alpha=1.35$ and $\alpha=2.35$ and upper mass
limits: 120 M_{\odot}, 60 M_{\odot} and 40 M_{\odot}. Results, especially for
sources with black holes, are very sensitive to variations of the IMF, and it
should be taken into account when fitting parameters of starformation bursts.
  Results are applied to several regions of recent starformation in different
galaxies: Tol 89, NGC 5253, NGC 3125, He 2-10, NGC 3049. Using known ages and
total masses of starformation bursts (Shaerer at al., 1998) we calculate
expected numbers of X-ray sources in close binaries for different parameters of
the IMF. Usially, X-ray transient sources consisting of a neutron star and a
main sequence star are most abundant, but for very small ages of bursts (less
than $\approx 4$ Myrs) sources with black holes can become more abundant.


  We obtain a closed-form analytical expression for the zero temperature
Fourier transform of the $2k_F$ component of the density-density correlation
function in a Luttinger liquid with different spin and charge velocities. For
frequencies near the spin and charge singularities approximate analytical forms
are given and compared with the exact result. We find power law like
singularities leading to either divergence or cusps, depending on the values of
the Luttinger parameters and compute the corresponding exponents. Exact
integral expressions and numerical results are given for the finite temperature
case as well. We show in particular how the temperature rounds the
singularities in the correlation function.


  This study introduces a progressive neural network (PNN) model for direction
of arrival (DOA) estimation, DOA-PNN, addressing the challenge due to
catastrophic forgetting in adapting dynamic acoustic environments. While
traditional methods such as GCC, MUSIC, and SRP-PHAT are effective in static
settings, they perform worse in noisy, reverberant conditions. Deep learning
models, particularly CNNs, offer improvements but struggle with a mismatch
configuration between the training and inference phases. The proposed DOA-PNN
overcomes these limitations by incorporating task incremental learning of
continual learning, allowing for adaptation across varying acoustic scenarios
with less forgetting of previously learned knowledge. Featuring task-specific
sub-networks and a scaling mechanism, DOA-PNN efficiently manages parameter
growth, ensuring high performance across incremental microphone configurations.
We study DOA-PNN on a simulated data under various mic distance based
microphone settings. The studies reveal its capability to maintain performance
with minimal parameter increase, presenting an efficient solution for DOA
estimation.


  In this article the right nilpotent $\mathbb{F}_p$-braces of cardinality
$p^5$ has been classified. We use the connection between nilpotent
$\mathbb{F}_p$-braces of cardinality $p^5$ and nilpotent pre-Lie algebras of
the same order, building on the known relationship between pre-Lie algebras and
braces. Leveraging insights from the classification of nilpotent pre-Lie
algebras over $\mathbb{F}_p$, we aim to provide a comprehensive classification
of right nilpotent $\mathbb{F}_p$-braces of cardinality $p^5$.


  In this paper, we will study a class of linear integral operators with the
nonnegative kernels on higher-dimensional product spaces, the norms of the
operators can be obtained by integral of the product of the kernel function and
finitely many basic functions. As application, we obtain the sharp constant for
linear Hilbert operator on Higher-dimensional product spaces.


  We report on a multi-wavelength study of the relationship between young star
clusters in the Antennae galaxies (NGC 4038/9) and their interstellar
environment, with the goal of understanding the formation and feedback effects
of star clusters in merging galaxies. This is possible for the first time
because various new observations (from X-rays to radio wavelengths) have become
available in the past several years. Quantitative comparisons are made between
the positions of the star clusters (broken into three age groups) and the
properties of the interstellar medium by calculating the two-point correlation
functions. We find that young star clusters are distributed in a clustered
fashion. The youngest star clusters are associated with molecular cloud
complexes with characteristic radii of about 1 kpc. In addition, there is a
weak tendency for them to be found in regions with higher HI velocity
dispersions. No dominant triggering mechanism is identified for the majority of
the clusters in the Antennae. Feedback from young bright cluster complexes show
large H_alpha bubbles and H_alpha velocity gradients in shells around the
complexes. We estimate the current star formation rate to be 20 solar mass/yr,
and the gas consumption timescale to be 700 Myr. The latter is comparable to
the merging time scale and indicates that star formation has been enchanced by
the merger event. Finally, we find that the Schmidt law, with index N=-1.4, is
also a good description of the cluster formation triggered by merging in the
Antennae. There is some evidence that feedback effects may modify the Schmidt
law at scales below 1 kpc.


  Simple airtrack simulations, like those now possible with web-based 3D
environments, can provide explorers of any age with experiential data
sufficient to formulate their own models of motion. In particular, with a
compressable spring, two gliders, a moving clock and two gate pairs with timers
(pre and post collision), Newton's laws (or one's own version thereof) may
emerge (or be tested) in the lab. With a high speed simulation, one may find
instead Minkowski's spacetime version of Pythagoras' theorem (the metric
equation) in the data, along with ``anyspeed'' expressions for momentum and
kinetic energy.


  This dataset encompasses fluorescence spectra and chemical parameters of 24
olive oil samples from the 2019-2020 harvest provided by the producer Conde de
Benalua, Granada, Spain. The oils are characterized by different qualities: 10
extra virgin olive oil (EVOO), 8 virgin olive oil (VOO), and 6 lampante olive
oil (LOO) samples. For each sample, the dataset includes fluorescence spectra
obtained with two excitation wavelengths, oil quality, and five chemical
parameters necessary for the quality assessment of olive oil. The fluorescence
spectra were obtained by exciting the samples at 365 nm and 395 nm under
identical conditions. The dataset includes the values of the following chemical
parameters for each olive oil sample: acidity, peroxide value, K270, K232,
ethyl esters, and the quality of the samples (EVOO, VOO, or LOO). The dataset
offers a unique possibility for researchers in food technology to develop
machine learning models based on fluorescence data for the quality assessment
of olive oil due to the availability of both spectroscopic and chemical data.
The dataset can be used, for example, to predict one or multiple chemical
parameters or to classify samples based on their quality from fluorescence
spectra.


  The use of Convolutional Neural Networks (CNNs) is widespread in Deep
Learning due to a range of desirable model properties which result in an
efficient and effective machine learning framework. However, performant CNN
architectures must be tailored to specific tasks in order to incorporate
considerations such as the input length, resolution, and dimentionality. In
this work, we overcome the need for problem-specific CNN architectures with our
Continuous Convolutional Neural Network (CCNN): a single CNN architecture
equipped with continuous convolutional kernels that can be used for tasks on
data of arbitrary resolution, dimensionality and length without structural
changes. Continuous convolutional kernels model long range dependencies at
every layer, and remove the need for downsampling layers and task-dependent
depths needed in current CNN architectures. We show the generality of our
approach by applying the same CCNN to a wide set of tasks on sequential
(1$\mathrm{D}$) and visual data (2$\mathrm{D}$). Our CCNN performs
competitively and often outperforms the current state-of-the-art across all
tasks considered.


  To address the lack of comparative evaluation of Human-in-the-Loop Topic
Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM
modeling approaches using simulation experiments. These approaches extend
previously proposed frameworks, including constraints and informed prior-based
methods. Users should have a sense of control in HLTM systems, so we propose a
control metric to measure whether refinement operations' results match users'
expectations. Informed prior-based methods provide better control than
constraints, but constraints yield higher quality topics.


  Stable mixtures of cooperators and defectors are often seen in nature. This
fact is at odds with predictions based on linear public goods games under weak
selection. That model implies fixation either of cooperators or of defectors,
and the former scenario requires a level of group relatedness larger than the
cost/benefit ratio, being therefore expected only if there is either kin
recognition or a very low cost/benefit ratio, or else under stringent
conditions with low gene flow. This motivated us to study here social evolution
in a large class of group structured populations, with arbitrary
multi-individual interactions among group members and random migration among
groups. Under the assumption of weak selection, we analyze the equilibria and
their stability. For some significant models of social evolution with
non-linear fitness functions, including contingent behavior in iterated public
goods games and threshold models, we show that three regimes occur, depending
on the migration rate among groups. For sufficiently high migration rates, a
rare cooperative allele A cannot invade a monomorphic population of asocial
alleles N. For sufficiently low values of the migration rate, allele A can
invade the population, when rare, and then fixate, eliminating N. For
intermediate values of the migration rate, allele A can invade the population,
when rare, producing a polymorphic equilibrium, in which it coexists with N.
The equilibria and their stability do not depend on the details of the
population structure. The levels of migration (gene flow) and group relatedness
that allow for invasion of the cooperative allele leading to polymorphic
equilibria with the non-cooperative allele are common in nature.


  In this paper, we study the distributed adaptive estimation problem of
continuous-time stochastic dynamic systems over sensor networks where each
agent can only communicate with its local neighbors. A distributed least
squares (LS) algorithm based on diffusion strategy is proposed such that the
sensors can cooperatively estimate the unknown time-invariant parameter vector
from continuous-time noisy signals. By using the martingal estimation theory
and Ito formula, we provide upper bounds for the estimation error of the
proposed distributed LS algorithm, and further obtain the convergence results
under a cooperative excitation condition. Compared with the existing results,
our results are established without using the boundedness or persistent
excitation (PE) conditions of regression signals. We provide simulation
examples to show that multiple sensors can cooperatively accomplish the
estimation task even if any individual can not.


  We derive a hybrid feedback control law for the lateral leg spring (LLS)
model so that the center of mass of a legged runner follows a curved path in
horizontal plane. The control law enables the runner to change the placement
and the elasticity of its legs to move in a desired direction. Stable motion
along a curved path is achieved using curvature, bearing and relative distance
between the runner and the curve as feedback. Constraints on leg parameters
determine the class of curves that can be followed. We also derive an optimal
control law that stabilizes the orientation of the runner's body relative to
the velocity of the runner's center of mass.


  The wave function of the universe is usually taken to be a functional of the
three-metric on a spacelike section, Sigma, which is measured. It is sometimes
better, however, to work in the conjugate representation, where the wave
function depends on a quantity related to the second fundamental form of Sigma.
This makes it possible to ensure that Sigma is part of a Lorentzian universe by
requiring that the argument of the wave function be purely imaginary. We
demonstrate the advantages of this formalism first in the well-known examples
of the nucleation of a de Sitter or a Nariai universe. We then use it to
calculate the pair creation rate for sub-maximal black holes in de Sitter
space, which had been thought to vanish semi-classically.


  The sample frequency spectrum (SFS), which describes the distribution of
mutant alleles in a sample of DNA sequences, is a widely used summary statistic
in population genetics. The expected SFS has a strong dependence on the
historical population demography and this property is exploited by popular
statistical methods to infer complex demographic histories from DNA sequence
data. Most, if not all, of these inference methods exhibit pathological
behavior, however. Specifically, they often display runaway behavior in
optimization, where the inferred population sizes and epoch durations can
degenerate to 0 or diverge to infinity, and show undesirable sensitivity of the
inferred demography to perturbations in the data. The goal of this paper is to
provide theoretical insights into why such problems arise. To this end, we
characterize the geometry of the expected SFS for piecewise-constant
demographic histories and use our results to show that the aforementioned
pathological behavior of popular inference methods is intrinsic to the geometry
of the expected SFS. We provide explicit descriptions and visualizations for a
toy model with sample size 4, and generalize our intuition to arbitrary sample
sizes n using tools from convex and algebraic geometry. We also develop a
universal characterization result which shows that the expected SFS of a sample
of size n under an arbitrary population history can be recapitulated by a
piecewise-constant demography with only k(n) epochs, where k(n) is between n/2
and 2n-1. The set of expected SFS for piecewise-constant demographies with
fewer than k(n) epochs is open and non-convex, which causes the above phenomena
for inference from data.


  We study the problem of allocating a set of indivisible goods among a set of
agents with \emph{2-value additive valuations}. In this setting, each good is
valued either $1$ or $\sfrac{p}{q}$, for some fixed co-prime numbers $p,q\in
\NN$ such that $1\leq q < p$. Our goal is to find an allocation maximizing the
\emph{Nash social welfare} (\NSW), i.e., the geometric mean of the valuations
of the agents. In this work, we give a complete characterization of
polynomial-time tractability of \NSW\ maximization that solely depends on the
values of $q$.
  We start by providing a rather simple polynomial-time algorithm to find a
maximum \NSW\ allocation when the valuation functions are \emph{integral}, that
is, $q=1$. We then exploit more involved techniques to get an algorithm
producing a maximum \NSW\ allocation for the \emph{half-integral} case, that
is, $q=2$. Finally, we show it is \classNP-hard to compute an allocation with
maximum \NSW\ whenever $q\geq3$.


  For a fixed countably infinite structure \Gamma\ with finite relational
signature \tau, we study the following computational problem: input are
quantifier-free \tau-formulas \phi_0,\phi_1,...,\phi_n that define relations
R_0,R_1,...,R_n over \Gamma. The question is whether the relation R_0 is
primitive positive definable from R_1,...,R_n, i.e., definable by a first-order
formula that uses only relation symbols for R_1,..., R_n, equality,
conjunctions, and existential quantification (disjunction, negation, and
universal quantification are forbidden).
  We show decidability of this problem for all structures \Gamma\ that have a
first-order definition in an ordered homogeneous structure \Delta\ with a
finite relational signature whose age is a Ramsey class and determined by
finitely many forbidden substructures. Examples of structures \Gamma\ with this
property are the order of the rationals, the random graph, the homogeneous
universal poset, the random tournament, all homogeneous universal C-relations,
and many more. We also obtain decidability of the problem when we replace
primitive positive definability by existential positive, or existential
definability. Our proof makes use of universal algebraic and model theoretic
concepts, Ramsey theory, and a recent characterization of Ramsey classes in
topological dynamics.


  We consider Schr\"odinger operators in $L^2(\mathrm{R}^\nu),\, \nu=2,3$, with
the interaction in the form on an array of potential wells, each on them having
rotational symmetry, arranged along a curve $\Gamma$. We prove that if $\Gamma$
is a bend or deformation of a line, being straight outside a compact, and the
wells have the same arcwise distances, such an operator has a nonempty discrete
spectrum. It is also shown that if $\Gamma$ is a circle, the principal
eigenvalue is maximized by the arrangement in which the wells have the same
angular distances. Some conjectures and open problems are also mentioned.


  We establish the eventual periodicity of the spectrum of any monadic
second-order formula where:
  (i) all relation symbols, except equality, are unary, and
  (ii) there is only one function symbol and that symbol is unary.


  This paper presents the OPUS ecosystem with a focus on the development of
open machine translation models and tools, and their integration into end-user
applications, development platforms and professional workflows. We discuss our
on-going mission of increasing language coverage and translation quality, and
also describe on-going work on the development of modular translation models
and speed-optimized compact solutions for real-time translation on regular
desktops and small devices.


  We construct discrete symmetry transformations for deformed relativistic
kinematics based on group valued momenta. We focus on the specific example of
kappa-deformations of the Poincare algebra with associated momenta living on (a
sub-manifold of) de Sitter space. Our approach relies on the description of
quantum states constructed from deformed kinematics and the observable charges
associated with them. The results we present provide the first step towards the
analysis of experimental bounds on the deformation parameter kappa to be
derived via precision measurements of discrete symmetries and CPT.


  The coronavirus disease (COVID-19) has spread worldwide with an unprecedented
impact on society. In the Philippines, several interventions such as mobility
restrictions for different age groups and vaccination prioritization programs
have been implemented to reduce the risks of infections and mortality. This
study aimed to identify age-sex composition with greater susceptibility, longer
hospitalization and higher fatality. The COVID-19 cases from March 2020 to
April 2021 provided by the Department of Health Davao Region in the Philippines
were analyzed. A Chi-square test was used to determine the difference in
proportions of COVID-19 cases among age-sex compositions. A correlation plot of
\c{hi}2 test residual was employed to investigate the differences in
susceptibility. Boxplots and Kruskal-Wallis tests were utilized to compare the
length of hospitalizations. The study found a significant difference in the
COVID-19 susceptibility among age-sex compositions (p < 0.01). Male children
and female senior citizens were the most susceptible age-sex compositions.
Furthermore, senior citizens had the longest hospital days wherein the median
and IQR days were 19 (15-27) for men and 18 (16-29) for women. Male senior
citizen was the subgroup with the highest case fatality (21.4%, p < 0.01). It
is recommended that the number of cases among senior citizens be used as an
input in the planning and allocation of medical resources at the provincial and
regional levels. The local government unit executives in the region can also
take advantage of the availability of age-sex composition data in stratifying
localities, planning, allocating COVID-19-related resources and imposing
mobility restrictions.


  The traffic in wireless networks has become diverse and fluctuating both
spatially and temporally due to the emergence of new wireless applications and
the complexity of scenarios. The purpose of this paper is to quantitatively
analyze the impact of the wireless traffic, which fluctuates both spatially and
temporally, on the performance of the wireless networks. Specially, we propose
to combine the tools from stochastic geometry and queueing theory to model the
spatial and temporal fluctuation of traffic, which to our best knowledge has
seldom been evaluated analytically. We derive the spatial and temporal
statistics, the total arrival rate, the stability of queues and the delay of
users by considering two different spatial properties of traffic, i.e., the
uniformly and non-uniformly distributed cases. The numerical results indicate
that although the fluctuation of traffic (reflected by the variance of total
arrival rate) when the users are clustered is much fiercer than that when the
users are uniformly distributed, the unstable probability is smaller. Our work
provides a useful reference for the design of wireless networks when the
complex spatio-temporal fluctuation of the traffic is considered.


  In 1971, McMullen and Walkup posed the following conjecture, which is called
the generalized lower bound conjecture: If $P$ is a simplicial $d$-polytope
then its $h$-vector $(h_0,h_1,...,h_d)$ satisfies $h_0 \leq h_1 \leq ... \leq
h_{\lfloor \frac d 2 \rfloor}$. Moreover, if $h_{r-1}=h_r$ for some $r \leq
\frac d 2$ then $P$ can be triangulated without introducing simplices of
dimension $\leq d-r$.
  The first part of the conjecture was solved by Stanley in 1980 using the hard
Lefschetz theorem for projective toric varieties. In this paper, we give a
proof of the remaining part of the conjecture. In addition, we generalize this
property to a certain class of simplicial spheres, namely those admitting the
weak Lefschetz property.


  Patient datasets contain confidential information which is protected by laws
and regulations such as HIPAA and GDPR. Ensuring comprehensive patient
information necessitates privacy-preserving entity resolution (PPER), which
identifies identical patient entities across multiple databases from different
healthcare organizations while maintaining data privacy. Existing methods often
lack cryptographic security or are computationally impractical for real-world
datasets. We introduce a PPER pipeline based on AMPPERE, a secure abstract
computation model utilizing cryptographic tools like homomorphic encryption.
Our tailored approach incorporates extensive parallelization techniques and
optimal parameters specifically for patient datasets. Experimental results
demonstrate the proposed method's effectiveness in terms of accuracy and
efficiency compared to various baselines.


  The implementation of global non-analytical fit is exemplified by its
application to the spectral analysis of the complex lineshape of
Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ at $(\pi,0)$. It deals with properly a
multi-level globality in the fitting parameters and efficiently the
non-analytical evaluation of the fitting function, thus exhibiting a potential
applicability to a wide range of systematic analysis task.


  Doping is one of the most prominent techniques to alter properties of a given
material. Herein, the influence of the electron- and hole-doping on the
selected superconducting properties of graphene are considered. In details, the
Migdal-Eliashberg formalism is employed to analyze the specific heat and the
critical magnetic field in the representative case of graphene doped with
nitrogen or boron, respectively. It is found that the electron doping is much
more favorable in terms of enhancing the aforementioned properties than its
hole counterpart. These findings are appropriately summarized by the means of
the dimensionless thermodynamic ratios, familiar in the
Bardeen-Cooper-Schrieffer theory. To this end, the perspectives for future
research on superconductivity in graphene are drawn.


  The energy calibration and resolution of the electromagnetic calorimeter
(ECAL) of the CMS detector have been determined using proton-proton collision
data from LHC operation in 2010 and 2011 at a centre-of-mass energy of
sqrt(s)=7 TeV with integrated luminosities of about 5 inverse femtobarns.
Crucial aspects of detector operation, such as the environmental stability,
alignment, and synchronization, are presented. The in-situ calibration
procedures are discussed in detail and include the maintenance of the
calibration in the challenging radiation environment inside the CMS detector.
The energy resolution for electrons from Z-boson decays is better than 2% in
the central region of the ECAL barrel (for pseudorapidity abs(eta)<0.8) and is
2-5% elsewhere. The derived energy resolution for photons from 125 GeV Higgs
boson decays varies across the barrel from 1.1% to 2.6% and from 2.2% to 5% in
the endcaps. The calibration of the absolute energy is determined from Z to
e+e- decays to a precision of 0.4% in the barrel and 0.8% in the endcaps.


  We investigate which free constructions (amalgamated products and
HNN-extensions) over word hyperbolic groups produce groups that are again word
hyperbolic. A complete answer is obtained for the case when the amalgamated
subgroups are virtually cyclic. The results are applied, in particular, to show
that a ${\bf Q}$-completion of a torsion-free hyperbolic group has solvable
word problem and conjugacy problem.


  Discovering the low-energy conformations of a molecule is of great interest
to computational chemists, with applications in {\em in silico} materials
design and drug discovery. In this paper, we propose a variable neighbourhood
search heuristic for the conformational search problem. Using the structure of
a molecule, neighbourhoods are chosen to allow for the efficient use of a
binary quadratic optimizer for conformational search. The method is flexible
with respect to the choice of molecular force field and the number of
discretization levels in the search space, and can be further generalized to
take advantage of higher-order binary polynomial optimizers. It is well-suited
for the use of devices such as quantum annealers. After carefully defining
neighbourhoods, the method easily adapts to the size and topology of these
devices, allowing for seamless scaling alongside their future improvements.


  I develop the decision-theoretic approach to quantum probability, originally
proposed by David Deutsch, into a mathematically rigorous proof of the Born
rule in (Everett-interpreted) quantum mechanics. I sketch the argument
informally, then prove it formally, and lastly consider a number of proposed
``counter-examples'' to show exactly which premises of the argument they
violate.


  The Pan-Andromeda Archaeological Survey is a survey of $>400$ square degrees
centered on the Andromeda (M31) and Triangulum (M33) galaxies that has provided
the most extensive panorama of a $L_\star$ galaxy group to large projected
galactocentric radii. Here, we collate and summarise the current status of our
knowledge of the substructures in the stellar halo of M31, and discuss
connections between these features. We estimate that the 13 most distinctive
substructures were produced by at least 5 different accretion events, all in
the last 3 or 4 Gyrs. We suggest that a few of the substructures furthest from
M31 may be shells from a single accretion event. We calculate the luminosities
of some prominent substructures for which previous estimates were not
available, and we estimate the stellar mass budget of the outer halo of M31. We
revisit the problem of quantifying the properties of a highly structured
dataset; specifically, we use the OPTICS clustering algorithm to quantify the
hierarchical structure of M31's stellar halo, and identify three new faint
structures. M31's halo, in projection, appears to be dominated by two
`mega-structures', that can be considered as the two most significant branches
of a merger tree produced by breaking M31's stellar halo into smaller and
smaller structures based on the stellar spatial clustering. We conclude that
OPTICS is a powerful algorithm that could be used in any astronomical
application involving the hierarchical clustering of points. The publication of
this article coincides with the public release of all PAndAS data products.


  Close encounters between neutron stars and main-sequence stars occur in
globular clusters and may lead to various outcomes. Here we study encounters
resulting in tidal disruption of the star. Using $N$-body models, we predict
the typical stellar masses in these disruptions and the dependence of the event
rate on host cluster properties. We find that tidal disruption events occur
most frequently in core-collapsed globular clusters and that roughly $25\%$ of
the disrupted stars are merger products (i.e., blue straggler stars). Using
hydrodynamic simulations, we model the tidal disruptions themselves (over
timescales of days) to determine the mass bound to the neutron star and the
properties of the accretion disks formed. In general, we find that roughly
$80-90\%$ of the initial stellar mass becomes bound to the neutron star
following disruption. Additionally, we find that neutron stars receive
impulsive kicks of up to about $20\,$km/s as a result of the asymmetry of
unbound ejecta; these kicks place these neutron stars on elongated orbits
within their host cluster, with apocenter distances well outside the cluster
core. Finally, we model the evolution of the (hypercritical) accretion disks on
longer timescales (days to years after disruption) to estimate the accretion
rate onto the neutron stars and accompanying spin-up. As long as $\gtrsim1\%$
of the bound mass accretes onto the neutron star, millisecond spin periods can
be attained. We argue the growing numbers of isolated millisecond pulsars
observed in globular clusters may have formed, at least in part, through this
mechanism. In the case of significant mass growth, some of these neutron stars
may collapse to form low-mass ($\lesssim3\,M_{\odot}$) black holes.


  We address several issues regarding the derivation and implementation of the
Cauchy-Born approximation of the stress at finite temperature. In particular,
an asymptotic expansion is employed to derive a closed form expression for the
first Piola-Kirchhoff stress. For systems under periodic boundary conditions, a
derivation is presented, which takes into account the translational invariance
and clarifies the removal of the zero phonon modes. Also revealed by the
asymptotic approach is the role of the smoothness of the interatomic potential.
Several numerical examples are provided to validate this approach.


  Phase I dose-finding studies aim at identifying the maximal tolerated dose
(MTD). It is not uncommon that several dose-finding studies are conducted,
although often with some variation in the administration mode or dose panel.
For instance, sorafenib (BAY 43-900) was used as monotherapy in at least 29
phase I trials according to a recent search in clinicaltrials.gov. Since the
toxicity may not be directly related to the specific indication, synthesizing
the information from several studies might be worthwhile. However, this is
rarely done in practice and only a fixed-effect meta-analysis framework was
proposed to date. We developed a Bayesian random-effects meta-analysis
methodology to pool several phase I trials and suggest the MTD. A curve free
hierarchical model on the logistic scale with random effects, accounting for
between-trial heterogeneity, is used to model the probability of toxicity
across the investigated doses. An Ornstein-Uhlenbeck Gaussian process is
adopted for the random effects structure. Prior distributions for the curve
free model are based on a latent Gamma process. An extensive simulation study
showed good performance of the proposed method also under model deviations.
Sharing information between phase I studies can improve the precision of MTD
selection, at least when the number of trials is reasonably large.


  We generalize the classical semi-continuity theorem for GIT (semi)stable loci
under variations of linearizations to a relative situation of an equivariant
projective morphism from X to an affine base S. As an application to moduli
problems, we consider degenerations of Hilbert schemes, and give a conceptual
interpretation of the (semi)stable loci of the degeneration families
constructed by Gulbrandsen-Halle-Hulek.


  Macroscopic concepts pertaining to the Unruh effect are elaborated and used
to clarify its physical manifestations. Based on a description of the motion of
accelerated, spatially extended laboratories in Minkowski space in terms of
Poincar\'e transformations, it is shown that, from a macroscopic perspective,
an accelerated observer will not register with his measuring instruments any
global thermal effects of acceleration in the inertial (Minkowskian) vacuum
state. As is explained, this result is not in conflict with the well-known fact
that microscopic probes respond non-trivially to acceleration if coupled to the
vacuum. But this response cannot be interpreted as the effect of some heat bath
surrounding the observer. It is also shown that genuine equilibrium states in a
uniformly accelerated laboratory cannot be spatially homogeneous. In
particular, these states coincide with the homogeneous inertial vacuum at
sufficiently large distances from the horizon of the observer and consequently
have the same (zero) temperature there. The analysis is carried out in the
theory of a free massless scalar field; however the conclusion that the Unruh
effect is not of a thermal nature, viz. induced by heat transfer, is generally
valid.


  We found, within the plane-wave first Born approximation (PWFBA), that the
proton-helium fully differential cross section (FDCS) for transfer excitation
agrees well with the experimental one at the proton energy Ep = 300 keV and
small scattering angles both in shape and in magnitude. This result is in a
contradiction with that obtained in [1].


  Having multi-output logic gates saves much energy because the same structure
can be used to feed multiple inputs of next stage gates simultaneously. This
paper proposes novel triangle shape fanout of 2 spin wave Majority and XOR
gates; the Majority gate is achieved by phase detection, whereas the XOR gate
is achieved by threshold detection. The proposed logic gates are validated by
means of micromagnetic simulations. Furthermore, the energy and delay are
estimated for the proposed structures and compared with the state-of-the-art
spin wave logic gates, and 16nm and 7nm CMOS. The results demonstrate that the
proposed structures provide energy reduction of 25%-50% in comparison to the
other 2-output spin-wave devices while having the same delay, and energy
reduction between 43x and 0.8x when compared to the 16nm and 7nm CMOS while
having delay overhead between 11x and 40x.


  The charge redistribution at grain boundaries determines the applicability of
high-Tc superconductors in electronic devices, because the transport across the
grains can be hindered considerably. We investigate the local charge transfer
and the modification of the electronic states in the vicinity of the
grain-grain interface by first principles calculations for a (normal-state)
45^o tilted [001] grain boundary in YBa2Cu3O7. Our results explain the
suppressed interface transport and the influence of grain boundary doping in a
quantitative manner, in accordance with the experimental situation. The charge
redistribution is found to be strongly inhomogeneous, which has a substantial
effect on transport properties since it gives rise to a self-doping of 0.10
(+/- 0.02) holes per Cu atom.


  The structure of a star composed of locally non-electroneutral incompressible
three-component matter is considered within the framework of general
relativity. For thermodynamic quantities like the pressure, the solution can be
represented as a series in the small parameter $1/\Lambda_{\mathrm G}\sim
10^{-36}$, where the first approximation is the well-known electroneutral
solution. However, the equilibrium equations for the chemical potentials of the
matter components, as it turns out, contain finite contributions from
non-electroneutrality effects even in the zeroth order. Analytical solutions
have been obtained for all of the parameters of the problem under
consideration, which are illustrated by numerical examples.


  Point sets in 2D with multiple classes are a common type of data. A canonical
visualization design for them are scatterplots, which do not scale to large
collections of points. For these larger data sets, binned aggregation (or
binning) is often used to summarize the data, with many possible design
alternatives for creating effective visual representations of these summaries.
There are a wide range of designs to show summaries of 2D multi-class point
data, each capable of supporting different analysis tasks. In this paper, we
explore the space of visual designs for such data, and provide design
guidelines for different analysis scenarios. To support these guidelines, we
compile a set of abstract tasks and ground them in concrete examples using
multiple sample datasets. We then assess designs, and survey a range of design
decisions, considering their appropriateness to the tasks. In addition, we
provide a web-based implementation to experiment with design choices,
supporting the validation of designs based on task needs.


  In this paper, we study neural cognition in social network. A stochastic
model is introduced and shown to incorporate two well-known models in Pavlovian
conditioning and social networks as special case, namely Rescorla-Wagner model
and Friedkin-Johnsen model. The interpretation and comparison of these model
are discussed. We consider two cases when the disturbance is independent
identical distributed for all time and when the distribution of the random
variable evolves according to a markov chain. We show that the systems for both
cases are mean square stable and the expectation of the states converges to
consensus.


  We extend the model-free Data-Driven computing paradigm to solids and
structures that are stochastic due to intrinsic randomness in the material
behavior. The behavior of such materials is characterized by a likelihood
measure instead of a constitutive relation. We specifically assume that the
material likelihood measure is known only through an empirical point-data set
in material or phase space. The state of the solid or structure is additionally
subject to compatibility and equilibrium constraints. The problem is then to
infer the likelihood of a given structural outcome of interest. In this work,
we present a Data-Driven method of inference that determines likelihoods of
outcomes from the empirical material data and that requires no material or
prior modeling. In particular, the computation of expectations is reduced to
explicit sums over local material data sets and to quadratures over admissible
states, i. e., states satisfying compatibility and equilibrium. The complexity
of the material data-set sums is linear in the number of data points and in the
number of members in the structure. Efficient population annealing procedures
and fast search algorithms for accelerating the calculations are presented. The
scope, cost and convergence properties of the method are assessed with the aid
selected applications and benchmark tests.


  Multiplexed operations and extended coherent control over multiple trapping
sites are fundamental requirements for a trapped-ion processor in a large scale
architecture. Here we demonstrate these building blocks using a surface
electrode trap with integrated photonic components which are scalable to larger
numbers of zones. We implement a Ramsey sequence using the integrated light in
two zones, separated by 375 $\mu$m, performing transport of the ion from one
zone to the other in 200 $\mu$s between pulses. In order to achieve low
motional excitation during transport we developed techniques to measure and
mitigate the effect of the exposed dielectric surfaces used to deliver the
integrated light to the ion. We also demonstrate simultaneous control of two
ions in separate zones with low optical crosstalk, and use this to perform
simultaneous spectroscopy to correlate field noise between the two sites. Our
work demonstrates the first transport and coherent multi-zone operations in
integrated photonic ion trap systems, forming the basis for further scaling in
the trapped-ion QCCD architecture.


  In this paper we investigate the validity of first and second order $L^{p}$
estimates for the solutions of the Poisson equation depending on the geometry
of the underlying manifold. We first present $L^{p}$ estimates of the gradient
under the assumption that the Ricci tensor is lower bounded in a local integral
sense and construct the first counterexample showing that they are false, in
general, without curvature restrictions. Next, we obtain $L^p$ estimates for
the second order Riesz transform (or, equivalently, the validity of $L^{p}$
Calder\'on--Zygmund inequalities) on the whole scale $1<p<+\infty$ by assuming
that the injectivity radius is positive and that the Ricci tensor is either
pointwise lower bounded or non-negative in a global integral sense. When $1<p
\leq 2$, analogous $L^p$ bounds on even higher order Riesz transforms are
obtained provided that also the derivatives of Ricci are controlled up to a
suitable order. In the same range of values of $p$, for manifolds with lower
Ricci bounds and positive bottom of the spectrum, we show that the $L^{p}$ norm
of the Laplacian controls the whole $W^{2,p}$-norm on compactly supported
functions.


  Today, we know ~4330 exoplanets orbiting their host stars in ~3200 planetary
systems. The diversity of these exoplanets is large, and none of the known
exoplanets is a twin to any of the solar system planets, nor is any of the
known extrasolar planetary systems a twin of the solar system. Such diversity
on many scales and structural levels requires fundamental theoretical
approaches. Large efforts are underway to develop individual aspects of
exoplanet sciences, like exoplanet atmospheres, cloud formation, disk
chemistry, planet system dynamics, mantle convection, mass loss of planetary
atmospheres.
  The following challenges need to be addressed in tandem with observational
efforts. They provide the opportunity to progress our understanding of
exoplanets and their atmospheres by exploring our models as virtual
laboratories to fill gaps in observational data from different instruments and
missions, and taken at different instances of times: Challenge a) Building
complex models based on theoretical rigour that aim to understand the
interactions of atmospheric processes, to treat cloud formation and its
feedback onto the gas-phase chemistry and the energy budget of the planetary
atmosphere moving away from solar-system inspired parameterisations. Challenge
b) Enabling cloud modelling based on fundamental physio-chemical insights in
order to be applicable to the large and unexplored chemical, radiative and
thermodynamical parameter range of exoplanets in the universe. Challenge b)
will be explored in this chapter of the book ExoFrontiers.


  Owing to data-intensive large-scale applications, distributed computation
systems have gained significant recent interest, due to their ability of
running such tasks over a large number of commodity nodes in a time efficient
manner. One of the major bottlenecks that adversely impacts the time efficiency
is the computational heterogeneity of distributed nodes, often limiting the
task completion time due to the slowest worker.
  In this paper, we first present a lower bound on the expected computation
time based on the work-conservation principle. We then present our approach of
work exchange to combat the latency problem, in which faster workers can be
reassigned additional leftover computations that were originally assigned to
slower workers. We present two variations of the work exchange approach: a)
when the computational heterogeneity knowledge is known a priori; and b) when
heterogeneity is unknown and is estimated in an online manner to assign tasks
to distributed workers. As a baseline, we also present and analyze the use of
an optimized Maximum Distance Separable (MDS) coded distributed computation
scheme over heterogeneous nodes. Simulation results also compare the proposed
approach of work exchange, the baseline MDS coded scheme and the lower bound
obtained via work-conservation principle. We show that the work exchange scheme
achieves time for computation which is very close to the lower bound with
limited coordination and communication overhead even when the knowledge about
heterogeneity levels is not available.


  Lattice simulations have demonstrated that a background (electro)magnetic
field reduces the chiral/deconfinement transition temperature of quantum
chromodynamics for eB < 1 GeV^2. On the level of observables, this reduction
manifests itself in an enhancement of the Polyakov loop and in a suppression of
the light quark condensates (inverse magnetic catalysis) in the transition
region. In this paper, we report on lattice simulations of 1+1+1-flavor QCD at
an unprecedentedly high value of the magnetic field eB = 3.25 GeV^2. Based on
the behavior of various observables, it is shown that even at this extremely
strong field, inverse magnetic catalysis prevails and the transition, albeit
becoming sharper, remains an analytic crossover. In addition, we develop an
algorithm to directly simulate the asymptotically strong magnetic field limit
of QCD. We find strong evidence for a first-order deconfinement phase
transition in this limiting theory, implying the presence of a critical point
in the QCD phase diagram. Based on the available lattice data, we estimate the
location of the critical point.


  Let S be a standard graded Artinian algebra over a field k. We identify
constraints on the Hilbert function of S which are imposed by the hypothesis
that S contains an exact pair of homogeneous zero divisors. As a consequence,
we prove that if S is a compressed level algebra, then S does not contain any
homogeneous zero divisors.


  We show a unified physical picture of single cyclotron electron with
radiation-reaction, which bridges the classical electron models and quantum
mechanical self-consistent field theory. On a classical level, we suggest an
improved electrodynamical action, which build the classical electron models
into a first-principle framework. The link between dynamical defections and
non-physical action configurations emerges naturally. On a quantum level, a
self-consistent description for electron gyro-motion with self-force is
constructed in the Schr\"odinger-Maxwell theory. We derive a class of
asymptotic equations. The leading and next-to-leading orders give a good
analogue of a classical cyclotron electron, and the limit field theory avoids
classical electron induced defections gracefully. Beyond the Hamiltonian
perturbation theory, we use state-of-the-art geometric simulator to observe
single electron gyro-motions at quantum region. The non-linear and
non-perturbative features captured by simulations provide a complete physical
picture in a very wide range. We show an optimal complementary relation between
classical and quantum cyclotron electrons, and find a strange and inexplicable
electron chimera state existing at strong non-linear regions, which may be
observed in astrophysical environments and strong magnetic experiments.


  The relation between shapes of cosmic string and baryon number violation is
investigated. If there exist fermionic zero modes on the string, using
bosonization technique, it is possible to obtain the effective action which
describes the fermion coupled to the arbitrarily shaped cosmic string. The
relation between baryon number and the sum of the writhing number and linking
number of the cosmic strings is rederived. Baryons are created on the strings
as the shapes of the cosmic strings change. Furthermore we discuss implications
of this baryon number violating process to baryogenesis.


  Facial Expression Recognition (FER) holds significant importance in
human-computer interactions. Existing cross-domain FER methods often transfer
knowledge solely from a single labeled source domain to an unlabeled target
domain, neglecting the comprehensive information across multiple sources.
Nevertheless, cross-multidomain FER (CMFER) is very challenging for (i) the
inherent inter-domain shifts across multiple domains and (ii) the intra-domain
shifts stemming from the ambiguous expressions and low inter-class
distinctions. In this paper, we propose a novel Learning with Alignments CMFER
framework, named LA-CMFER, to handle both inter- and intra-domain shifts.
Specifically, LA-CMFER is constructed with a global branch and a local branch
to extract features from the full images and local subtle expressions,
respectively. Based on this, LA-CMFER presents a dual-level inter-domain
alignment method to force the model to prioritize hard-to-align samples in
knowledge transfer at a sample level while gradually generating a
well-clustered feature space with the guidance of class attributes at a cluster
level, thus narrowing the inter-domain shifts. To address the intra-domain
shifts, LA-CMFER introduces a multi-view intra-domain alignment method with a
multi-view clustering consistency constraint where a prediction similarity
matrix is built to pursue consistency between the global and local views, thus
refining pseudo labels and eliminating latent noise. Extensive experiments on
six benchmark datasets have validated the superiority of our LA-CMFER.


  A previously proposed unified field theory of electro-strong interactions
requires two scales of length within hadrons, $\sim10^{-15}m$ and
$\sim10^{-18}m$ respectively, and the onset of new phenomena at the shorter
scale. Studies at the HERA electron-proton collider at the shorter scale have
revealed a possible excess of high-transverse momentum events, as expected,
with $Q^2 \geq 30,000$ $(Gev/c)^2$. The collider is currently being upgraded.
This will permit a clearer test to be carried out.


  Strong bounds - going beyond Sarnak's density hypothesis - are obtained for
the number of automorphic forms for the congruence subgroup Gamma_0(q) of
SL_n(Z) violating the Ramanujan conjecture at any given unramified place. The
proof is based on a relative trace formula of Kuznetsov type and best-possible
bounds for certain Kloosterman sums for GL(n). Further applications are given.


  In this study, we extend the HAL QCD method to a case where a total momentum
of a two-particle system is non-zero and apply it to the $I=2$ S-wave $\pi\pi$
scattering in order to confirm its validity. We derive a fundamental relation
of an energy-independent non-local potential defined in the center of mass
frame with NBS wave functions in a laboratory frame. Based on the relation, we
propose the time-dependent method to extract potentials, often used in practice
for the HALQCD method in the center of mass frame. For numerical simulations in
the $I=2$ $\pi\pi$ system, we employ (2+1)-flavor gauge configurations on a
$32^3 \times 64$ lattice at the lattice spacing $a \approx 0.0907$ fm and
$m_{\pi} \approx 700$ MeV. Both effective leading order (LO) potentials and
corresponding phase shifts obtained in laboratory frames agree with those
obtained in the center-of-mass frame by the conventional HAL QCD method within
somewhat larger statistical errors. In addition, we observe a consistency in
scattering phase shifts between ours and results by the finite-volume method as
well. The HAL QCD method with non-zero total momenta, established in this
study, brings more flexibility to the HAL QCD method, which enables us to
handle systems having the same quantum numbers with a vacuum or to access
energy regions prohibited in the center of mass frame.


  The possibility of an emergent universe solution to Einstein's field
equations allowing for an irreversible creation of matter at the expense of the
gravitational field is shown. With the universe being chosen as spatially flat
FRW spacetime together with equation of state proposed in [17], the solution
exists when the ratio of the phenomenological matter creation rate to the
number density times the Hubble parameter is a number $\beta$ of the order of
unity and independent of time. The thermodynamic behaviour is also determined
for this solution. Interestingly, we also find that an emergent universe
scenario is present with usual equation of state in cosmology when the matter
creation rate is chosen to be a constant. More general class of emergent
universe solutions are also discussed.


  Although the creation of spin polarization in various non-magnetic media via
electrical spin injection from a ferromagnetic tunnel contact has been
demonstrated, much of the basic behavior is heavily debated. It is reported
here for semiconductor/Al2O3/ferromagnet tunnel structures based on Si or GaAs
that local magnetostatic fields arising from interface roughness dramatically
alter and even dominate the accumulation and dynamics of spins in the
semiconductor. Spin precession in the inhomogeneous magnetic fields is shown to
reduce the spin accumulation up to tenfold, and causes it to be inhomogeneous
and non-collinear with the injector magnetization. The inverted Hanle effect
serves as experimental signature. This interaction needs to be taken into
account in the analysis of experimental data, particularly in extracting the
spin lifetime and its variation with different parameters (temperature, doping
concentration). It produces a broadening of the standard Hanle curve and
thereby an apparent reduction of the spin lifetime. For heavily doped n-type Si
at room temperature it is shown that the spin lifetime is larger than
previously determined, and a new lower bound of 0.29 ns is obtained. The
results are expected to be general and occur for spins near a magnetic
interface not only in semiconductors but also in metals, organic and
carbon-based materials including graphene, and in various spintronic device
structures.


  In this paper, the problem of designing network codes that are both
communicationally and computationally efficient over packet line networks with
worst-case schedules is considered. In this context, random linear network
codes (dense codes) are asymptotically capacity-achieving, but require highly
complex coding operations. To reduce the coding complexity, Maymounkov et al.
proposed chunked codes (CC). Chunked codes operate by splitting the message
into non-overlapping chunks and send a randomly chosen chunk at each
transmission time by a dense code. The complexity, that is linear in the chunk
size, is thus reduced compared to dense codes. In this paper, the existing
analysis of CC is revised, and tighter bounds on the performance of CC are
derived. As a result, we prove that (i) CC with sufficiently large chunks are
asymptotically capacity-achieving, but with a slower speed of convergence
compared to dense codes; and (ii) CC with relatively smaller chunks approach
the capacity with an arbitrarily small but non-zero constant gap. To improve
the speed of convergence of CC, while maintaining their advantage in reducing
the computational complexity, we propose and analyze a new CC scheme with
overlapping chunks, referred to as overlapped chunked codes (OCC). We prove
that for smaller chunks, which are advantageous due to lower computational
complexity, OCC with larger overlaps provide a better tradeoff between the
speed of convergence and the message or packet error rate. This implies that
for smaller chunks, and with the same computational complexity, OCC outperform
CC in terms of the speed of approaching the capacity for sufficiently small
target error rate. In fact, we design linear-time OCC with very small chunks
(constant in the message size) that are both computationally and
communicationally efficient, and that outperform linear-time CC.


  Nonlocal Hamiltonians are used widely in first-principles quantum
calculations; the nonlocality stems from eliminating undesired degrees of
freedom, e.g. core electrons. To date, attempts to couple nonlocal systems to
external electromagnetic (EM) fields have been heuristic or limited to weak or
long wavelength fields. Using Feynman path integrals, we derive an exact,
closed-form coupling of arbitrary EM fields to nonlocal systems. Our results
justify and clarify the couplings used to date and are essential for systematic
computation of linear and especially nonlinear response.


  Thin films buckle easily and form wrinkled states in regions of well defined
size. The extent of a wrinkled region is typically assumed to reflect the zone
of in-plane compressive stresses prior to buckling, but recent experiments on
ultrathin sheets have shown that wrinkling patterns are significantly longer
and follow different scaling laws than those predicted by standard buckling
theory. Here we focus on a simple setup to show the striking differences
between near-threshold buckling and the analysis of wrinkle patterns in very
thin films, which are typically far from threshold.


  The ahadith, prophetic traditions for the Muslims around the world, are
narrations originating from the sayings and the deeds of Prophet Muhammad
(pbuh). They are considered one of the fundamental sources of Islamic
legislation along with the Quran. The list of persons involved in the narration
of each hadith is carefully scrutinized by scholars studying the hadith, with
respect to their reputation and authenticity of the hadith. This is due to the
its legislative importance in Islamic principles. There were many narrators who
contributed to this responsibility of preserving prophetic narrations over the
centuries. But to date, no systematic and comprehensive study, based on the
social network, has been adapted to understand the contribution of early hadith
narrators and the propagation of hadith across generations. In this study, we
represented the chain of narrators of the hadith collection from Sahih Bukhari
as a social graph. Based on social network analysis (SNA) on this graph, we
found that the network of narrators is a scale-free network. We identified a
list of influential narrators from the companions as well as the narrators from
the second and third-generation who contribute significantly in the propagation
of hadith collected in Sahih Bukhari. We discovered sixteen communities from
the narrators of Sahih Bukhari. In each of these communities, there are other
narrators who contributed significantly to the propagation of prophetic
narrations. We also found that most narrators were centered in Makkah and
Madinah in the era of companions and, then, gradually the center of hadith
narrators shifted towards Kufa, Baghdad and central Asia over a period of time.
To the best of our knowledge, this the first comprehensive and systematic study
based on SNA, representing the narrators as a social graph to analyze their
contribution to the preservation and propagation of hadith.


  Quantum machine learning (QML) has been identified as one of the key fields
that could reap advantages from near-term quantum devices, next to optimization
and quantum chemistry. Research in this area has focused primarily on
variational quantum algorithms (VQAs), and several proposals to enhance
supervised, unsupervised and reinforcement learning (RL) algorithms with VQAs
have been put forward. Out of the three, RL is the least studied and it is
still an open question whether VQAs can be competitive with state-of-the-art
classical algorithms based on neural networks (NNs) even on simple benchmark
tasks. In this work, we introduce a training method for parametrized quantum
circuits (PQCs) that can be used to solve RL tasks for discrete and continuous
state spaces based on the deep Q-learning algorithm. We investigate which
architectural choices for quantum Q-learning agents are most important for
successfully solving certain types of environments by performing ablation
studies for a number of different data encoding and readout strategies. We
provide insight into why the performance of a VQA-based Q-learning algorithm
crucially depends on the observables of the quantum model and show how to
choose suitable observables based on the learning task at hand. To compare our
model against the classical DQN algorithm, we perform an extensive
hyperparameter search of PQCs and NNs with varying numbers of parameters. We
confirm that similar to results in classical literature, the architectural
choices and hyperparameters contribute more to the agents' success in a RL
setting than the number of parameters used in the model. Finally, we show when
recent separation results between classical and quantum agents for policy
gradient RL can be extended to inferring optimal Q-values in restricted
families of environments.


  In lepton-number-violating supersymmetric models, there is no natural choice
of basis to distinguish the down-type Higgs and lepton superfields. We employ
basis-independent techniques to identify the massless majoron and associated
light scalar in the case of spontaneously-broken lepton number (L). When
explicit L-violation is added, these two scalars can acquire masses of order
the electroweak scale and can be identified as massive sneutrinos.


  Although different architectures of quantum perceptrons have been recently
put forward, the capabilities of such quantum devices versus their classical
counterparts remain debated. Here, we consider random patterns and targets
independently distributed with biased probabilities and investigate the storage
capacity of a continuous quantum perceptron model that admits a classical
limit, thus facilitating the comparison of performances. Such a more general
context extends a previous study of the quantum storage capacity where using
statistical mechanics techniques in the limit of a large number of inputs, it
was proved that no quantum advantages are to be expected concerning the storage
properties. This outcome is due to the fuzziness inevitably introduced by the
intrinsic stochasticity of quantum devices. We strengthen such an indication by
showing that the possibility of indefinitely enhancing the storage capacity for
highly correlated patterns, as it occurs in a classical setting, is instead
prevented at the quantum level.


  The primary breakup of a planar liquid jet is explored via direct numerical
simulation (DNS) of the incompressible Navier-Stokes equation with level-set
and volume-of-fluid interface capturing methods. PDFs of the local radius of
curvature and the local cross-flow displacement of the liquid-gas interface are
evaluated over wide ranges of the Reynolds number ($Re$), Weber number ($We$),
density ratio and viscosity ratio. The temporal cascade of liquid-structure
length scales and the spread rate of the liquid jet during primary atomization
are analyzed. The formation rate of different surface structures, e.g. lobes,
ligaments and droplets, are compared for different flow conditions and are
explained in terms of the vortex dynamics in each atomization domain that we
identified recently. With increasing $We$, the average radius of curvature of
the surface decreases, the number of small droplets increases, and the cascade
and the surface area growth occur at faster rates. The spray angle is mainly
affected by $Re$ and density ratio, and is larger at higher $We$, at higher
density ratios, and also at lower $Re$. The change in the spray spread rate
versus $Re$ is attributed to the angle of ligaments stretching from the jet
core, which increases as $Re$ decreases. Gas viscosity has negligible effect on
both the droplet-size distribution and the spray angle. Increasing the
wavelength-to-sheet-thickness ratio, however, increases the spray angle and the
structure cascade rate, while decreasing the droplet size. The smallest length
scale is determined more by surface tension and liquid inertia than by the
liquid viscosity, while gas inertia and liquid surface tension are the key
parameters in determining the spray angle.


  Uniqueness problem of static axially symmetric black hole in magnetic
Universe filled with {\it dark matter} component has been considered. The {\it
dark matter} model comprises the additional $U(1)$-gauge field ({\it dark
photon}) interacting with Maxwell one by the kinetic mixing term. We show that
all the solutions of Einstein-Maxwell {\it dark photon} gravity subject to the
same boundary and regularity conditions authorize the only static axially
symmetric black hole solutions with non-vanishing time and azimuthal components
of Maxwell and {\it hidden} sector gauge fields, say Schwarzschild-like black
hole immersed in {\it dark matter} Melvin Universe.


  5G mobile networks provide additional benefits in terms of lower latency,
higher data rates, and more coverage, in comparison to 4G networks, and they
are also coming close to standardization. For example, 5G has a new level of
data transfer and processing speed that assures users are not disconnected when
they move from one cell to another; thus, supporting faster connection.
However, it comes with its own technical challenges relating to resource
management, authentication handover and user privacy protection. In 5G, the
frequent displacement of the users among the cells as a result of repeated
authentication handovers often lead to a delay, contradicting the 5G
objectives. In this paper, we propose a new authentication approach that
utilizes blockchain and software defined networking (SDN) techniques to remove
the re-authentication in repeated handover among heterogeneous cells. The
proposed approach is designed to assure the low delay, appropriate for the 5G
network in which users can be replaced with the least delay among heterogeneous
cells using their public and private keys provided by the devised blockchain
component while protecting their privacy. In our comparison between
Proof-of-Work (POW)-based and network-based models, the delay of our
authentication handover was shown to be less than 1ms. Also, our approach
demonstrated less signaling overhead and energy consumption compared to peer
models.


  The acoustic-electric transformation in high-quality FeSe single crystals is
studied. In zero magnetic field we observe an abnormally strong electromagnetic
radiation induced by a transverse elastic wave. Usually a radiation of such
intensity and polarization is observed only in metals subjected to a high
magnetic field (the radiation is caused by the Hall current). We argue that in
FeSe in zero magnetic field it is caused by the piezomagnetic effect which is
most probably of dynamical origin. We find that the piezomagnetism survives
under the transition from the normal to superconducting state. In the
superconducting state the electromagnetic signal decreases with decreasing
temperature that is connected with the change in the London penetration depth.


  We study real-time routing policies in smart transit systems, where the
platform has a combination of cars and high-capacity vehicles (e.g., buses or
shuttles) and seeks to serve a set of incoming trip requests. The platform can
use its fleet of cars as a feeder to connect passengers to its high-capacity
fleet, which operates on fixed routes. Our goal is to find the optimal set of
(bus) routes and corresponding frequencies to maximize the social welfare of
the system in a given time window. This generalizes the Line Planning Problem,
a widely studied topic in the transportation literature, for which existing
solutions are either heuristic (with no performance guarantees), or require
extensive computation time (and hence are impractical for real-time use). To
this end, we develop a $1-\frac{1}{e}-\varepsilon$ approximation algorithm for
the Real-Time Line Planning Problem, using ideas from randomized rounding and
the Generalized Assignment Problem. Our guarantee holds under two assumptions:
$(i)$ no inter-bus transfers and $(ii)$ access to a pre-specified set of
feasible bus lines. We moreover show that these two assumptions are crucial by
proving that, if either assumption is relaxed, the Real-Time Line Planning
Problem does not admit any constant-factor approximation. Finally, we
demonstrate the practicality of our algorithm via numerical experiments on
real-world and synthetic datasets, in which we show that, given a fixed time
budget, our algorithm outperforms Integer Linear Programming-based exact
methods.


  Ferroelectric photovoltaics (FPVs) have drawn much attention owing to their
high stability, environmental safety, anomalously high photovoltages, coupled
with reversibly switchable photovoltaic responses. However, FPVs suffer from
extremely low photocurrents, which is primarily due to their wide band gaps.
Here, we present a new class of FPVs by demonstrating switchable ferroelectric
photovoltaic effects using hexagonal ferrite (h-RFeO3) thin films having narrow
band gaps of ~1.2 eV, where R denotes rare-earth ions. FPVs with narrow band
gaps suggests their potential applicability as photovoltaic and optoelectronic
devices. The h-RFeO3 films further exhibit reasonably large ferroelectric
polarizations, which possibly reduces a rapid recombination rate of the
photo-generated electron-hole pairs. The power conversion efficiency (PCE) of
h-RFeO3 thin-film devices is sensitive on the magnitude of polarization. In the
case of h-TmFeO3 (h-TFO) thin film, the measured PCE is twice as large as that
of the BiFeO3 thin film, a prototypic FPV. We have further shown that the
switchable photovoltaic effect dominates over the unswitchable internal field
effect arising from the net built-in potential. This work thus demonstrates a
new class of FPVs towards high-efficiency solar cell and optoelectronic
applications.


  Self-avoiding walks (SAWs) and loop-erased random walks (LERWs) are two
ensembles of random paths with numerous applications in mathematics,
statistical physics and quantum field theory. While SAWs are described by the
$n \to 0$ limit of $\phi^4$-theory with $O(n)$-symmetry, LERWs have no obvious
field-theoretic description. We analyse two candidates for a field theory of
LERWs, and discover a connection between the corresponding and a priori
unrelated theories. The first such candidate is the $O(n)$-symmetric $\phi^4$
theory at $n=-2$ whose link to LERWs was known in two dimensions due to
conformal field theory. Here it is established in arbitrary dimension via a
perturbation expansion in the coupling constant. The second candidate is a
field theory for charge-density waves pinned by quenched disorder, whose
relation to LERWs had been conjectured earlier using analogies with Abelian
sandpiles. We explicitly show that both theories yield identical results to
4-loop order and give both a perturbative and a non-perturbative proof of their
equivalence. This allows us to compute the fractal dimension of LERWs to order
$\epsilon^5$ where $\epsilon=4-d$. In particular, in $d=3$ our theory gives
$z_{\rm LERW}(d=3)= 1.6243 \pm 0.001$, in excellent agreement with the estimate
$z = 1.624 00 \pm 0.00005$ of numerical simulations.


  We study the estimation of policy gradients for continuous-time systems with
known dynamics. By reframing policy learning in continuous-time, we show that
it is possible construct a more efficient and accurate gradient estimator. The
standard back-propagation through time estimator (BPTT) computes exact
gradients for a crude discretization of the continuous-time system. In
contrast, we approximate continuous-time gradients in the original system. With
the explicit goal of estimating continuous-time gradients, we are able to
discretize adaptively and construct a more efficient policy gradient estimator
which we call the Continuous-Time Policy Gradient (CTPG). We show that
replacing BPTT policy gradients with more efficient CTPG estimates results in
faster and more robust learning in a variety of control tasks and simulators.


  Recent advancements in large vision-language models (LVLMs) have demonstrated
impressive capability in visual information understanding with human language.
Despite these advances, LVLMs still face challenges with multimodal
hallucination, such as generating text descriptions of objects that are not
present in the visual information. However, the underlying fundamental reasons
of multimodal hallucinations remain poorly explored. In this paper, we propose
a new perspective, suggesting that the inherent biases in LVLMs might be a key
factor in hallucinations. Specifically, we systematically identify a semantic
shift bias related to paragraph breaks (\n\n), where the content before and
after '\n\n' in the training data frequently exhibit significant semantic
changes. This pattern leads the model to infer that the contents following
'\n\n' should be obviously different from the preceding contents with less
hallucinatory descriptions, thereby increasing the probability of hallucinatory
descriptions subsequent to the '\n\n'. We have validated this hypothesis on
multiple publicly available LVLMs. Besides, we find that deliberately inserting
'\n\n' at the generated description can induce more hallucinations. A simple
method is proposed to effectively mitigate the hallucination of LVLMs by
skipping the output of '\n'.


  In frame theory literature, there are several generalizations of frame,
K-fusion frame presents a flavour of one such generalization, basically it is
an intertwined replica of K-frame and fusion frame. K-fusion frames come
naturally (having significant applications) when one needs to reconstruct
functions (signals) from a large data in the range of a bounded linear
operator. Getting inspiration from the concept of weaving frames in Hilbert
space, we study the weaving form of K-fusion frames which have significant
applications in wireless sensor networks. This article produces various
characterizations of weaving K-fusion frames in different spaces. Furthermore,
Paley-Wiener type perturbation and conditions on erasure of frame components
have been assembled to scrutinize wovenness of the same.


  Students of Computer Science often wonder when, exactly, one can apply a
greedy algorithm to a problem, and when one must use the more complicated and
time-consuming techniques of dynamic programming. This paper argues that the
existing pedagogical literature does not offer clear guidance on this issue. We
suggest improving computer science pedagogy by importing a concept economists
use in their own implementations of dynamic programming. That economic concept
is "opportunity cost," and we explain how it can aid students in
differentiating "greedy problems" from problems requiring dynamic programming


  We evaluate the significance of the line candidates identified by a visual
search of burst spectra from BATSE's Spectroscopy Detectors. None of the
candidates satisfy our detection criteria: an F-test probability less than
10^-4 for a feature in one detector and consistency among the detectors which
viewed the burst. Most of the candidates are not very significant, and are
likely to be fluctuations. Because of the expectation of finding absorption
lines, the search was biased towards absorption features. We do not have a
quantitative measure of the completeness of the search which would enable a
comparison with previous missions. Therefore a more objective computerized
search has begun.


  Hybrid intelligence aims to enhance decision-making, problem-solving, and
overall system performance by combining the strengths of both, human cognitive
abilities and artificial intelligence. With the rise of Large Language Models
(LLM), progressively participating as smart agents to accelerate machine
learning development, Hybrid Intelligence is becoming an increasingly important
topic for effective interaction between humans and machines. This paper
presents an approach to leverage Hybrid Intelligence towards sustainable and
energy-aware machine learning. When developing machine learning models, final
model performance commonly rules the optimization process while the efficiency
of the process itself is often neglected. Moreover, in recent times, energy
efficiency has become equally crucial due to the significant environmental
impact of complex and large-scale computational processes. The contribution of
this work covers the interactive inclusion of secondary knowledge sources
through Human-in-the-loop (HITL) and LLM agents to stress out and further
resolve inefficiencies in the machine learning development process.


  We generalise the distribution semantics underpinning probabilistic logic
programming by distilling its essential concept, the separation of a free
random component and a deterministic part. This abstracts the core ideas beyond
logic programming as such to encompass frameworks from probabilistic databases,
probabilistic finite model theory and discrete lifted Bayesian networks. To
demonstrate the usefulness of such a general approach, we completely
characterise the projective families of distributions representable in the
generalised distribution semantics and we demonstrate both that large classes
of interesting projective families cannot be represented in a generalised
distribution semantics and that already a very limited fragment of logic
programming (acyclic determinate logic programs) in the determinsitic part
suffices to represent all those projective families that are representable in
the generalised distribution semantics at all.


  Recently, it has been shown that the enumeration of Minimal Correction
Subsets (MCS) of Boolean formulas allows solving Multi-Objective Boolean
Optimization (MOBO) formulations. However, a major drawback of this approach is
that most MCSs do not correspond to Pareto-optimal solutions. In fact, one can
only know that a given MCS corresponds to a Pareto-optimal solution when all
MCSs are enumerated. Moreover, if it is not possible to enumerate all MCSs,
then there is no guarantee of the quality of the approximation of the Pareto
frontier. This paper extends the state of the art for solving MOBO using MCSs.
First, we show that it is possible to use MCS enumeration to solve MOBO
problems such that each MCS necessarily corresponds to a Pareto-optimal
solution. Additionally, we also propose two new algorithms that can find a (1 +
{\varepsilon})-approximation of the Pareto frontier using MCS enumeration.
Experimental results in several benchmark sets show that the newly proposed
algorithms allow finding better approximations of the Pareto frontier than
state-of-the-art algorithms, and with guaranteed approximation ratios.