  I show how to reintroduce velocity dispersion into perturbation theory (PT)
calculations of structure in the Universe, i.e., how to go beyond the
pressureless fluid approximation, starting from first principles. This
addresses a possible deficiency in uses of PT to compute clustering on the
weakly non-linear scales that will be critical for probing dark energy.
Specifically, I show how to derive a non-negligible value for the (initially
tiny) velocity dispersion of dark matter particles, <\delta v^2>, where \delta
v is the deviation of particle velocities from the local bulk flow. The
calculation is essentially a renormalization of the homogeneous (zero order)
dispersion by fluctuations 1st order in the initial power spectrum. For power
law power spectra with n>-3, the small-scale fluctuations diverge and
significant dispersion can be generated from an arbitrarily small starting
value -- the dispersion level is set by an equilibrium between fluctuations
generating more dispersion and dispersion suppressing fluctuations. For an
n=-1.4 power law normalized to match the present non-linear scale, the
dispersion would be ~100 km/s. This n corresponds roughly to the slope on the
non-linear scale in the real \LambdaCDM Universe, but \LambdaCDM contains much
less initial small-scale power -- not enough to bootstrap the small starting
dispersion up to a significant value within linear theory (viewed very broadly,
structure formation has actually taken place rather suddenly and recently, in
spite of the usual "hierarchical" description). The next order PT calculation,
which I carry out only at an order of magnitude level, should drive the
dispersion up into balance with the growing structure, accounting for small
dispersion effects seen recently in simulations.


  A general review of the crystalline solutions of the generalized Skyrme model
and their application to the study of cold nuclear matter at finite density and
the Equation of State (EOS) of neutron stars is presented. For the relevant
range of densities, the ground state of the Skyrme model in the three torus is
shown to correspond to configurations with different symmetries, with a
sequence of phase transitions between such configurations. The effects of
nonzero finite isospin asymmetry are taken into account by the canonical
quantization of isospin collective coordinates, and some thermodynamical and
nuclear observables (such as the symmetry energy) are computed as a function of
the density. We also explore the extension of the model to accommodate strange
degrees of freedom, and find a first order transition for the condensation of
kaons in the Skyrme crystal background in a thermodynamically consistent,
non-perturbative way. Finally, an approximate EOS of dense matter is
constructed by fitting the free parameters of the model to some nuclear
observables close to saturation density, which are particularly relevant for
the description of nuclear matter. The resulting neutron star mass-radius
curves already reasonably satisfy current astrophysical constraints.


  We consider the magnetohydrodynamics system with Hall effect accompanied with
initial data in supercritical Sobolev space. Via an appropriate randomization
of the supercritical initial data, both local and small data global
well-posedness for the system are obtained almost surely in critical Sobolev
space.


  In this paper we introduce a new method simulating stent graft deployment for
assisting endovascular repair of abdominal aortic aneurysms. The method relies
on intraoperative images coupled with mechanical models. A multi-step algorithm
has been developed to increase the reliability of simulations. The first step
predicts the position of the stent graft within the aorta. The second step is
an axisymmetric geometric reconstruction of each individual stent. The third
step minimizes the rotation of each stent around its main axis. Finally, the
last step virtually deploys each stent within a deployment box extracted from
the preoperative CT scan. A proof of concept is performed on a patient. The
accuracy is compatible with the clinical threshold of 3 mm: the average
distance between target and simulated stents is 1.73 $\pm$ 0.37 mm.
Fenestrations of the stent-graft are reconstructed with a maximum error of less
than 2.5 mm, which is enables a secure catheterization of secondary arteries.
In summary, the method is able to assist EVAR practitioners by providing all
necessary information for a fast and accurate stent graft positioning,
combining intraoperative data and a mechanical model in a very low cost
framework.


  Using an effective vertex method we explicitly derive the two-loop dilatation
generator of ABJM theory in its SU(2)xSU(2) sector, including all non-planar
corrections. Subsequently, we apply this generator to a series of finite length
operators as well as to two different types of BMN operators. As in N=4 SYM, at
the planar level the finite length operators are found to exhibit a degeneracy
between certain pairs of operators with opposite parity - a degeneracy which
can be attributed to the existence of an extra conserved charge and thus to the
integrability of the planar theory. When non-planar corrections are taken into
account the degeneracies between parity pairs disappear hinting the absence of
higher conserved charges. The analysis of the BMN operators resembles that of
N=4 SYM. Additional non-planar terms appear for BMN operators of finite length
but once the strict BMN limit is taken these terms disappear.


  We apply a recently developed unsupervised machine learning scheme for local
atomic environments to characterize large-scale, disordered aggregates formed
by sequence-defined macromolecules. This method provides new insight into the
structure of these disordered, dilute aggregates, which has proven difficult to
understand using collective variables manually derived from expert knowledge.
In contrast to such conventional order parameters, we are able to classify the
global aggregate structure directly using descriptions of the local
environments. The resulting characterization provides a deeper understanding of
the range of possible self-assembled structures and their relationships to each
other. We also provide a detailed analysis of the effects of finite system
size, stochasticity, and kinetics of these aggregates based on the learned
collective variables. Interestingly, we find that the spatiotemporal evolution
of systems in the learned latent space is smooth and continuous, despite being
derived from only a single snapshot from each of about 1000 monomer sequences.
These results demonstrate the insight which can be gained by applying
unsupervised machine learning to soft matter systems, especially when suitable
order parameters are not known.


  We determine the algebra of holonomy symmetries of sigma models propagating
on supersymmetric heterotic backgrounds with a non-compact holonomy group. We
demonstrate that these close as a W-algebra, which in turn is specified by a
Lie algebra structure on the space of covariantly constant forms that generate
the holonomy symmetries. In addition, we identify the chiral anomalies
associated with these symmetries. We argue that these anomalies are consistent
and can be cancelled up to two loops in the sigma model perturbation theory.


  Skeletal muscle is subjected to simultaneous time-varying neural stimuli and
length changes in vivo. Work loops are experimental representations of these in
vivo conditions and exhibit force versus length responses that are not
explainable using either soft matter rheology or the classical isometric and
isotonic characterizations of muscle. These gaps in our understanding have
often prompted the search for new muscle phenomena. However, we presently lack
a framework to explain the mechanical origins of work loops that integrates
multiple facets of current understanding of muscle, as a rheological material
and also a stimulus-responsive actuator. Here we present a new hypothesis that
work loops emerge by splicing together force versus length loops corresponding
to different constant stimuli. Using published muscle datasets and a detailed
sarcomere model, we find that the hypothesis accurately predicts work loops and
helps understand them in terms of rheological behaviors measured at
fixed-stimuli. Importantly, this framework identifies conditions under which a
rheological understanding of muscle fails to explain the emergent work loops,
and new muscle phenomena may be necessary to explain its in vivo function.


  In the non-negative matrix factorization (NMF) problem, the input is an
$m\times n$ matrix $M$ with non-negative entries and the goal is to factorize
it as $M\approx AW$. The $m\times k$ matrix $A$ and the $k\times n$ matrix $W$
are both constrained to have non-negative entries. This is in contrast to
singular value decomposition, where the matrices $A$ and $W$ can have negative
entries but must satisfy the orthogonality constraint: the columns of $A$ are
orthogonal and the rows of $W$ are also orthogonal. The orthogonal non-negative
matrix factorization (ONMF) problem imposes both the non-negativity and the
orthogonality constraints, and previous work showed that it leads to better
performances than NMF on many clustering tasks. We give the first
constant-factor approximation algorithm for ONMF when one or both of $A$ and
$W$ are subject to the orthogonality constraint. We also show an interesting
connection to the correlation clustering problem on bipartite graphs. Our
experiments on synthetic and real-world data show that our algorithm achieves
similar or smaller errors compared to previous ONMF algorithms while ensuring
perfect orthogonality (many previous algorithms do not satisfy the hard
orthogonality constraint).


  Spatiotemporal spin dynamics under spin-orbit interaction is investigated in
a (001) GaAs two-dimensional electron gas using magneto-optical Kerr rotation
microscopy. Spin polarized electrons are diffused away from the excited
position, resulting in spin precession because of the diffusion-induced
spin-orbit field. Near the cancellation between spin-orbit field and external
magnetic field, the induced spin precession frequency depends nonlinearly on
the diffusion velocity, which is unexpected from the conventional linear
relation between the spin-orbit field and the electron velocity.This behavior
originates from an enhancement of the spin relaxation anisotropy by the
electron velocity perpendicular to the diffused direction. We demonstrate that
the spin relaxation anisotropy, which has been regarded as a material constant,
can be controlled via diffusive electron motion.


  We investigate creation, manipulation, and steering of entanglement in spin
chains from the viewpoint of quantum communication between distant parties. We
demonstrate how global parametric driving of the spin-spin coupling and/or
local time-dependent Zeeman fields produce a large amount of entanglement
between the first and the last spin of the chain. This occurs whenever the
driving frequency meets a resonance condition, identified as "entanglement
resonance". Our approach marks a promising step towards an efficient quantum
state transfer or teleportation in solid state system. Following the reasoning
of Zueco et al. [1], we propose generation and routing of multipartite
entangled states by use of symmetric tree-like structures of spin chains.
Furthermore, we study the effect of decoherence on the resulting spin
entanglement between the corresponding terminal spins.


  In clinics, a radiology report is crucial for guiding a patient's treatment.
However, writing radiology reports is a heavy burden for radiologists. To this
end, we present an automatic, multi-modal approach for report generation from a
chest x-ray. Our approach, motivated by the observation that the descriptions
in radiology reports are highly correlated with specific information of the
x-ray images, features two distinct modules: (i) Learned knowledge base: To
absorb the knowledge embedded in the radiology reports, we build a knowledge
base that can automatically distil and restore medical knowledge from textual
embedding without manual labour; (ii) Multi-modal alignment: to promote the
semantic alignment among reports, disease labels, and images, we explicitly
utilize textual embedding to guide the learning of the visual feature space. We
evaluate the performance of the proposed model using metrics from both natural
language generation and clinic efficacy on the public IU-Xray and MIMIC-CXR
datasets. Our ablation study shows that each module contributes to improving
the quality of generated reports. Furthermore, with the assistance of both
modules, our approach outperforms state-of-the-art methods over almost all the
metrics.


  Test Impact Analysis is an approach to obtain a subset of tests impacted by
code changes. This approach is mainly applied to unit testing where the link
between the code and its associated tests is easy to obtain. On the integration
level, however, it is not straightforward to find such a link programmatically,
especially when the integration tests are held into separate repositories. We
propose an approach for selecting integration tests based on the runtime
analysis of code changes to reduce the test execution overhead. We provide a
set of tools and a framework that can be plugged into existing CI/CD pipelines.
We have evaluated the approach on a range of open-source Java programs and
found $\approx$50\% reduction in tests on average, and above 80\% in a few
cases. We have also applied the approach to a large-scale commercial system in
production and found similar results.


  We first introduce the notion of Doi Hom-Hopf modules and find the sufficient
condition for the category of Doi Hom-Hopf modules to be monoidal. Also we
obtain the condition for the monoidal Hom-algebra and monoidal Hom-coalgebra to
be monoidal Hom-bialgebras. Second, we give the maps between the underlying
monoidal Hom-Hopf algebras, Hom-comodule algebras and Hom-module coalgebras
give rise to functors between the category of Doi Hom-Hopf modules and study
tensor identities for monodial categories of Doi Hom-Hopf modules. Furthermore,
we construct a braiding on the category of Doi Hom-Hopf modules.
  Finally, as an application of our theory, we consider the braiding on the
category of Hom-modules, the category of Hom-comodules and the category of
Hom-Yetter-Drinfeld modules respectively.


  This paper introduces InstUPR, an unsupervised passage reranking method based
on large language models (LLMs). Different from existing approaches that rely
on extensive training with query-document pairs or retrieval-specific
instructions, our method leverages the instruction-following capabilities of
instruction-tuned LLMs for passage reranking without any additional
fine-tuning. To achieve this, we introduce a soft score aggregation technique
and employ pairwise reranking for unsupervised passage reranking. Experiments
on the BEIR benchmark demonstrate that InstUPR outperforms unsupervised
baselines as well as an instruction-tuned reranker, highlighting its
effectiveness and superiority. Source code to reproduce all experiments is
open-sourced at https://github.com/MiuLab/InstUPR


  The veterinary field lacks automation in contrast to the tremendous
technological advances made in the human medical field. Implementation of
machine learning technology can shorten any step of the automation process.
This paper explores these core concepts and starts with automation in sorting
radiographs for canines by view and anatomy. This is achieved by developing a
new lightweight algorithm inspired by AlexNet, Inception, and SqueezeNet. The
proposed module proves to be lighter than SqueezeNet while maintaining accuracy
higher than that of AlexNet, ResNet, DenseNet, and SqueezeNet.


  Over the past 15 years models with large extra space-time dimensions have
been extensively studied. We have learned from these models that the energy
scale of quantum gravity may be many orders of magnitude smaller than the
conventional value of 10^19 GeV. This raises the tantalizing prospect of
probing quantum gravity effects at the LHC. Of the possible quantum gravity
processes at the LHC, the formation and subsequent evaporation of microscopic
black holes is one of the most spectacular. We give an overview of some of the
fundamental ideas of the large extra dimensions scenarios and the resulting
black hole processes at the LHC.


  Based on Newton's laws reformulated in the Hamiltonian dynamics combined with
statistical mechanics, we formulate a statistical mechanical theory supporting
the hypothesis of a closed oscillating universe. We find that the behaviour of
the universe as a whole can be represented by a free entropic oscillator whose
lifespan is nonhomogeneous, thus implying that time is shorter or longer
according to the state of the universe itself given through its entropy. We
conclude that time reduces to the entropy production of the universe and that a
nonzero entropy production means that local fluctuations could exist giving
rise to the appearance of masses and to the curvature of the space.


  It is of keen interest to researchers understanding different approaches to
confine massless Dirac fermions in graphene, which is also a central problem in
making electronic devices based on graphene. Here, we studied spatial
confinement, magnetic localization and their interactions on massless Dirac
fermions in an angled graphene wedge formed by two linear graphene p-n
boundaries with an angle 34. Using scanning tunneling microscopy, we visualized
quasibound states temporarily confined in the studied graphene wedge. Large
perpendicular magnetic fields condensed the massless Dirac fermions in the
graphene wedge into Landau levels (LLs). The spatial confinement of the wedge
affects the Landau quantization, which enables us to experimentally measure the
spatial extent of the wave functions of the LLs. The magnetic fields induce a
sudden and large increase in energy of the quasibound states because of a pi
Berry phase jump of the massless Dirac fermions in graphene. Such a behavior is
the hallmark of the Klein tunneling in graphene. Our experiment demonstrated
that the angled wedge is a unique system with the critical magnetic fields for
the pi Berry phase jump depending on distance from summit of the wedge.


  We show that the reduced point variety of a quantum polynomial algebra is the
union of specific linear subspaces in $\mathbb{P}^n$, we describe its
irreducible components and give a combinatorial description of the possible
configurations in small dimensions.


  A full understanding of the origin of the hard X-ray background requires a
complete and accurate census of the distant galaxies that produce it.
Unfortunately, distant X-ray galaxies tend to be very faint at all wavelengths,
which hinders efforts to perform this census. This chapter discusses the
insights that can be obtained through comparison of the distant population to
local X-ray galaxies, whose properties are well characterized. Such comparisons
will ultimately aid investigations into the cosmic evolution of supermassive
black holes and their environments.


  The topological entropy of a braid is the infimum of the entropies of all
homeomorphisms of the disc which have a finite invariant set represented by the
braid. When the isotopy class represented by the braid is pseudo-Anosov or is
reducible with a pseudo-Anosov component, this entropy is positive. Fried and
Kolev proved that the entropy is bounded below by the logarithm of the spectral
radius of the braid's Burau matrix, $B(t)$, after substituting a complex number
of modulus~1 in place of $t$. In this paper we show that for a pseudo-Anosov
braid the estimate is sharp for the substitution of a root of unity if and only
if it is sharp for $t=-1$. Further, this happens if and only if the invariant
foliations of the pseudo-Anosov map have odd order singularities at the strings
of the braid and all interior singularities have even order. An analogous
theorem for reducible braids is also proved.


  In this paper, we first show that for an acyclic gentle algebra A, the
irreducible components of any moduli space of A-modules are products of
projective spaces. Next, we show that the nice geometry of the moduli spaces of
modules of an algebra does not imply the tameness of the representation type of
the algebra in question. Finally, we place these results in the general context
of moduli spaces of modules of Schur-tame algebras. More specifically, we show
that for an arbitrary Schur-tame algebra A and theta-stable irreducible
component C of a module variety of A-modules, the moduli space of
theta-semi-stable points of C is either a point or a rational projective curve.


  We show that all cosmological models with an accelerated stationary global
attractor reduce asymptotically to a dark energy field with an exponential
potential coupled linearly to a perfect fluid dark matter. In such models the
abundance of the dark components reaches a stationary value and therefore the
problem of their present coincidence is solved. The requirement of a vanishing
coupling of the baryons in order to pass local gravity experiments induces the
existence of an intermediate baryon-dominated era. We discuss in detail the
properties of these models and show that to accomodate standard nucleosynthesis
they cannot produce a microwave background consistent with observations. We
conclude that, among stationary models, only a time-dependent coupling or
equation of state might provide a realistic cosmology.


  In this paper, we propose a mixed-precision convolution unit architecture
which supports different integer and floating point (FP) precisions. The
proposed architecture is based on low-bit inner product units and realizes
higher precision based on temporal decomposition. We illustrate how to
integrate FP computations on integer-based architecture and evaluate overheads
incurred by FP arithmetic support. We argue that alignment and addition
overhead for FP inner product can be significant since the maximum exponent
difference could be up to 58 bits, which results into a large alignment logic.
To address this issue, we illustrate empirically that no more than
26-bitproduct bits are required and up to 8-bit of alignment is sufficient in
most inference cases. We present novel optimizations based on the above
observations to reduce the FP arithmetic hardware overheads. Our empirical
results, based on simulation and hardware implementation, show significant
reduction in FP16 overhead. Over typical mixed precision implementation, the
proposed architecture achieves area improvements of up to 25% in TFLOPS/mm2and
up to 46% in TOPS/mm2with power efficiency improvements of up to 40% in
TFLOPS/Wand up to 63% in TOPS/W.


  We investigate the problem of estimating a function $f$ based on observations
from its noisy convolution when the noise exhibits long-range dependence. We
construct an adaptive estimator based on the kernel method, derive minimax
lower bound for the $L^2$-risk when $f$ belongs to Sobolev space and show that
such estimator attains optimal rates that deteriorate as the LRD worsens.


  We present a non-commutative algorithm for multiplying 5x5 matrices using 99
multiplications. This algorithm is a minor modification of Makarov's algorithm
which exhibit the previous best known bound with 100 multiplications.


  We describe the necessary and sufficient conditions for two sequences
{\mu_n}^\infty_n=0 and {a_n}^\infty_n=0 to be correspondingly the set of
eigenvalues and the set of norming constants of a Sturm-Liouville problem with
real summable potential q and in advance fixed separated boundary conditions.


  A static sphere of incompressible fluid with uniform proper energy density is
considered as an example of exact star-like solution with weakened central
regularity conditions characteristic of a nakedly singular spherical vaccuum
solution. The solution is a singular counterpart of the Schwarzschild's
interior solution. The initial condition in the center for general barotropic
equations of state is established.


  We study the quantum capacity of continuous variable dephasing channel, which
is a notable example of non-Gaussian quantum channel. We prove that a single
letter formula applies. We then consider input energy restriction and show that
by increasing it, the capacity saturates to a finite value. The optimal input
state is found to be diagonal in the Fock basis and with a distribution that is
a discrete version of a Gaussian. Relations between its mean/variance and
dephasing rate/input energy are put forward. We also show that quantum capacity
decays exponentially for large values of dephasing rates.


  The past decade has seen major progress in our understanding of terrestrial
planet formation. Yet key questions remain. In this review we first address the
growth of 100 km-scale planetesimals as a consequence of dust coagulation and
concentration, with current models favoring the streaming instability.
Planetesimals grow into Mars-sized (or larger) planetary embryos by a
combination of pebble- and planetesimal accretion. Models for the final
assembly of the inner Solar System must match constraints related to the
terrestrial planets and asteroids including their orbital and compositional
distributions and inferred growth timescales. Two current models -- the
Grand-Tack and low-mass (or empty) primordial asteroid belt scenarios -- can
each match the empirical constraints but both have key uncertainties that
require further study. We present formation models for close-in super-Earths --
the closest current analogs to our own terrestrial planets despite their very
different formation histories -- and for terrestrial exoplanets in gas giant
systems. We explain why super-Earth systems cannot form in-situ but rather may
be the result of inward gas-driven migration followed by the disruption of
compact resonant chains. The Solar System is unlikely to have harbored an early
system of super-Earths; rather, Jupiter's early formation may have blocked the
ice giants' inward migration. Finally, we present a chain of events that may
explain why our Solar System looks different than more than 99\% of exoplanet
systems.


  Invited discussion for Biometrika of 'Multivariate Fisher's independence test
for multivariate dependence' by Gorsky and Ma (2022).


  Fairness for Machine Learning has received considerable attention, recently.
Various mathematical formulations of fairness have been proposed, and it has
been shown that it is impossible to satisfy all of them simultaneously. The
literature so far has dealt with these impossibility results by quantifying the
tradeoffs between different formulations of fairness. Our work takes a
different perspective on this issue. Rather than requiring all notions of
fairness to (partially) hold at the same time, we ask which one of them is the
most appropriate given the societal domain in which the decision-making model
is to be deployed. We take a descriptive approach and set out to identify the
notion of fairness that best captures \emph{lay people's perception of
fairness}. We run adaptive experiments designed to pinpoint the most compatible
notion of fairness with each participant's choices through a small number of
tests. Perhaps surprisingly, we find that the most simplistic mathematical
definition of fairness---namely, demographic parity---most closely matches
people's idea of fairness in two distinct application scenarios. This
conclusion remains intact even when we explicitly tell the participants about
the alternative, more complicated definitions of fairness, and we reduce the
cognitive burden of evaluating those notions for them. Our findings have
important implications for the Fair ML literature and the discourse on
formalizing algorithmic fairness.


  Upon arrival to a new country, many immigrants face job downgrading, a
phenomenon describing workers being in jobs below the ones they have based on
the skills they possess. Moreover, in the presence of downgrading immigrants
receiving lower wage returns to the same skills compared to natives. The level
of downgrading could depend on the immigrant type and numerous other factors.
This study examines the determinants of skill downgrading among two types of
immigrants - refugees and economic immigrants - in the German labor markets
between 1984 and 2018. We find that refugees downgrade more than economic
immigrants, and this discrepancy between the two groups persists over time. We
show that language skill improvements exert a strong influence on subsequent
labor market outcomes of both groups.


  We report the detection of the hot spot in light curves of the eclipsing
dwarf nova HT Cassiopeiae during its superoutburst in 2010 November. Analysis
of eight reconstructed light curves of the hot spot eclipses showed directly
that the brightness of the hot spot was changing significantly during the
superoutburst. Thereby, detected hot spot manifestation in HT Cas is the newest
observational evidence for the EMT model for dwarf novae.


  3D LiDAR-based single object tracking (SOT) has gained increasing attention
as it plays a crucial role in 3D applications such as autonomous driving. The
central problem is how to learn a target-aware representation from the sparse
and incomplete point clouds. In this paper, we propose a novel Correlation
Pyramid Network (CorpNet) with a unified encoder and a motion-factorized
decoder. Specifically, the encoder introduces multi-level self attentions and
cross attentions in its main branch to enrich the template and search region
features and realize their fusion and interaction, respectively. Additionally,
considering the sparsity characteristics of the point clouds, we design a
lateral correlation pyramid structure for the encoder to keep as many points as
possible by integrating hierarchical correlated features. The output features
of the search region from the encoder can be directly fed into the decoder for
predicting target locations without any extra matcher. Moreover, in the decoder
of CorpNet, we design a motion-factorized head to explicitly learn the
different movement patterns of the up axis and the x-y plane together.
Extensive experiments on two commonly-used datasets show our CorpNet achieves
state-of-the-art results while running in real-time.


  This paper is devoted to the analysis of the distribution of the total
magnetic quantum number $M$ in a relativistic subshell with $N$ equivalent
electrons of momentum $j$. This distribution is analyzed through its cumulants
and through their generating function, for which an analytical expression is
provided. This function also allows us to get the values of the cumulants at
any order. Such values are useful to obtain the moments at various orders.
Since the cumulants of the distinct subshells are additive this study directly
applies to any relativistic configuration. Recursion relations on the
generating function are given. It is shown that the generating function of the
magnetic quantum number distribution may be expressed as a n-th derivative of a
polynomial. This leads to recurrence relations for this distribution which are
very efficient even in the case of large $j$ or $N$. The magnetic quantum
number distribution is numerically studied using the Gram-Charlier and
Edgeworth expansions. The inclusion of high-order terms may improve the
accuracy of the Gram-Charlier representation for instance when a small and a
large angular momenta coexist in the same configuration. However such series
does not exhibit convergence when high orders are considered and the account
for the first two terms often provides a fair approximation of the magnetic
quantum number distribution. The Edgeworth series offers an interesting
alternative though this expansion is also divergent and of asymptotic nature.


  We give a full classification of vertex-transitive zonotopes.
  We prove that a vertex-transitive zonotope is a $\Gamma$-permutahedron for
some finite reflection group $\Gamma\subset\mathrm{O}(\mathbb R^d)$. The same
holds true for zonotopes in which all vertices are on a common sphere, and all
edges are of the same length (which we call homogeneous zonotopes). The
classification of these then follows from the classification of finite
reflection groups.
  We proof that root systems can be characterized as those centrally symmetric
sets of vectors, for which all intersections with half-spaces, that contain
exactly half the vectors, are congruent. We provide a further sufficient
condition for a centrally symmetric set being a root system.


  This paper adds to the discussion about theoretical aspects of particle swarm
stability by proposing to employ stochastic Lyapunov functions and to determine
the convergence set by quantifier elimination. We present a computational
procedure and show that this approach leads to reevaluation and extension of
previously know stability regions for PSO using a Lyapunov approach under
stagnation assumptions.


  Momentum and doping dependence of the static charge susceptibility \chi(q) in
the t-t'-J model is investigated. Correlations lead to a strongly momentum
dependent renormalization of \chi(q). The charge susceptibility near (\pi,\pi)
region of the Brillouin zone is strongly suppressed as the hole density \delta
is decreased. However, contrary to naive expectations, \chi(q) around q =
(\pi,0) and (0,\pi) remains large and practically unchanged at \delta \sim
0.1-0.5. This effect is consistent with a tendency towards low-energy charge
fluctuations with the wave vectors along the \Gamma-X direction, reported in
earlier studies. Our main finding is that the above trends are amplified by
J-driven pairing effects, indicating that the pseudogap formation may promote
the charge inhomogeneity. The next-nearest hopping t' leads to weakening of the
above momentum-selective renormalizations of \chi(q). We analyze the effects of
long-range Coulomb interaction, taking into account a layered structure of
cuprates. As an application, the results are discussed in the context of
bond-stretching phonon softening in hole-doped cuprates. In particular, a
peculiar doping and momentum dependence of the electron-phonon coupling
constant is found.


  We consider the "soliton" interference model that complements the usual wave
and corpuscular models of two-slit interference. The scheme of the experiment
to verify such "soliton" interference model has been suggested.


  We investigate the use of spatial interpolation methods for reconstructing
the horizontal near-surface wind field given a sparse set of measurements. In
particular, random Fourier features is compared to a set of benchmark methods
including Kriging and Inverse distance weighting. Random Fourier features is a
linear model $\beta(\pmb x) = \sum_{k=1}^K \beta_k e^{i\omega_k \pmb x}$
approximating the velocity field, with frequencies $\omega_k$ randomly sampled
and amplitudes $\beta_k$ trained to minimize a loss function. We include a
physically motivated divergence penalty term $|\nabla \cdot \beta(\pmb x)|^2$,
as well as a penalty on the Sobolev norm. We derive a bound on the
generalization error and derive a sampling density that minimizes the bound.
Following (arXiv:2007.10683 [math.NA]), we devise an adaptive
Metropolis-Hastings algorithm for sampling the frequencies of the optimal
distribution. In our experiments, our random Fourier features model outperforms
the benchmark models.


  Conditional GANs are widely used in translating an image from one category to
another. Meaningful conditions to GANs provide greater flexibility and control
over the nature of the target domain synthetic data. Existing conditional GANs
commonly encode target domain label information as hard-coded categorical
vectors in the form of 0s and 1s. The major drawbacks of such representations
are inability to encode the high-order semantic information of target
categories and their relative dependencies. We propose a novel end-to-end
learning framework with Graph Convolutional Networks to learn the attribute
representations to condition on the generator. The GAN losses, i.e. the
discriminator and attribute classification losses, are fed back to the Graph
resulting in the synthetic images that are more natural and clearer in
attributes. Moreover, prior-arts are given priorities to condition on the
generator side, not on the discriminator side of GANs. We apply the conditions
to the discriminator side as well via multi-task learning. We enhanced the four
state-of-the art cGANs architectures: Stargan, Stargan-JNT, AttGAN and STGAN.
Our extensive qualitative and quantitative evaluations on challenging face
attributes manipulation data set, CelebA, LFWA, and RaFD, show that the cGANs
enhanced by our methods outperform by a large margin, compared to their
counter-parts and other conditioning methods, in terms of both target
attributes recognition rates and quality measures such as PSNR and SSIM.


  In 1983, Burr and Erd\H{o}s initiated the study of Ramsey goodness
problems.Nikiforov and Rousseau (2009) resolved almost all goodness questions
raised by Burr and Erd\H{o}s, in which the bounds on the parameters are of
tower type since their proofs rely on the regularity lemma. Let $B_{k,n}$ be
the book graph on $n$ vertices which consists of $n-k$ copies of $K_{k+1}$ all
sharing a common $K_k$, and let $H=K_p(a_1,\dots,a_{p})$ be the complete
$p$-partite graph with parts of sizes $a_1,\dots,a_{p}$.
  Recently, avoiding use of the regularity lemma, Fox, He and Wigderson (2021)
revisit several Ramsey goodness results involving books. They comment that it
would be very interesting to see how far one can push these ideas. In
particular, they conjecture that for all integers $k, p, t\ge 2$, there exists
some $\delta>0$ such that for all $n\ge 1$, $1\leq a_1\le\cdots\le a_{p-1}\le
t$ and $a_p \le \delta n$, we have $r(H, B_{k,n})=
(p-1)(n-1)+d_k(n,K_{a_1,a_2})+1,$ where $d_k(n,K_{a_1,a_2})$ is the maximum $d$
for which there is an $(n+d-1)$-vertex $K_{a_1,a_2}$-free graph in which at
most $k-1$ vertices have degree less than $d$.They verify the conjecture when
$a_1=a_2=1$.
  Building upon the work of Fox et al. (2021), we make a substantial step by
showing that the conjecture "roughly" holds if $a_1=1$ and $a_2|(n-1-k)$, i.e.
$a_2$ divides $n-1-k$. Moreover, avoiding use of the regularity lemma, we prove
that for every $k, a\geq 1$ and $p\ge2$, there exists $\delta>0$ such that for
all large $n$ and $b\le \delta\ln n$, $r(K_p(1,a,b,\dots,b), B_{k,n})=
(p-1)(n-1)+k(p-1)(a-1)+1$ if $a|(n-1-k)$, where the case when $a=1$ has been
proved by Nikiforov and Rousseau (2009) using the regularity lemma. The bounds
on $1/\delta$ we obtain are not of tower-type since our proofs do not rely on
the regularity lemma.


  A backdoor set is a set of variables of a propositional formula such that
fixing the truth values of the variables in the backdoor set moves the formula
into some polynomial-time decidable class. If we know a small backdoor set we
can reduce the question of whether the given formula is satisfiable to the same
question for one or several easy formulas that belong to the tractable class
under consideration. In this survey we review parameterized complexity results
for problems that arise in the context of backdoor sets, such as the problem of
finding a backdoor set of size at most k, parameterized by k. We also discuss
recent results on backdoor sets for problems that are beyond NP.


  Far-Infrared (FIR) photometry from the the Photodetector Array Camera and
Spectrometer (PACS) on the Herschel Space Observatory is presented for 313
nearby, hard X-ray selected galaxies from the 58-month Swift Burst Alert
Telescope (BAT) Active Galactic catalog. The present data do not distinguish
between the FIR luminosity distributions at 70 and 160um for Seyfert 1 and
Seyfert 2 galaxies. This result suggests that if the FIR emission is from the
nuclear obscuring material surrounding the accretion disk, then it emits
isotropically, independent of orientation. Alternatively, a significant
fraction of the 70 and 160um could be from star formation, independent of AGN
type. Using a non-parametric test for partial correlation with censored data,
we find a statistically significant correlation between the AGN intrinsic power
(in the 14-195 keV band ) and the FIR emission at 70 and 160um for Seyfert 1
galaxies. We find no correlation between the 14-195 keV and FIR luminosities in
Seyfert 2 galaxies. The observed correlations suggest two possible scenarios:
(i) if we assume that the FIR luminosity is a good tracer of star formation,
then there is a connection between star formation and the AGN at sub-kiloparsec
scales, or (ii) dust heated by the AGN has a statistically significant
contribution to the FIR emission. Using a Spearman rank-order analysis, the
14-195 keV luminosities for the Seyfert 1 and 2 galaxies are weakly
statistically correlated with the F70/F160 ratios.


  In supersymmetric models with gauge-mediated supersymmetry breaking, charged
sleptons are the next lightest supersymmetric particles and decay outside the
detector for large regions of parameter space. In such scenarios, supersymmetry
may be discovered by searches for a number of novel signals, including highly
ionizing tracks from long-lived slow charged particles and excesses of
multi-lepton signals. We consider this scenario in detail and find that the
currently available Tevatron data probes regions of parameter space beyond the
kinematic reach of LEP II. Future Tevatron runs with integrated luminosities of
2, 10, and 30 fb-1 probe right-handed slepton masses of 110, 180, and 230 GeV
and Wino masses of 310, 370, and 420 GeV, respectively, greatly extending
current search limits.


  In this paper, we compute constant terms of Eisenstein series defined over a
totally real field, at various cusps. In his paper published in 2003, M. Ohta
computed the constant terms of Eisenstein series of weight two over the field
of rational numbers, at all equivalence classes of cusps. As for Eisenstein
series defined over a totally real field, S. Dasgupta, H. Darmon and R. Pollack
calculated the constant terms at particular (not all) equivalence classes of
cusps in 2011. We compute constant terms of Eisenstein series defined over a
general totally real field at all equivalence classes of cusps, and describe
them explicitly in terms of Hecke $L$-functions. This investigation is
motivated by M. Ohta's work on congruence modules related to Eisenstein series
defined over the field of rational numbers.


  K-theory allows us to define an analytical condition for the existence of
`false' gauge field copies through the use of the Atiyah-Singer index theorem.
After establishing that result we discuss a possible extension of the same
result without the help of the index theorem and suggest possible related lines
of work.


  We present a deep (50 hours exposed) image of the nearby spiral galaxy M 63
(NGC 5055), taken with a 0.14-m aperture telescope. The galaxy halo exhibits
the known very faint system of stellar streams extending across 110 kpc. We
found 5 very low-surface-brightness dwarf galaxies around M 63. Assuming they
are satellites of M 63, their median parameters are: absolute $B$-magnitude
-8.8 mag, linear diameter 1.3 kpc, surface brightness $\sim$ 27.8 mag/sq.
arcsec and linear projected separation 93 kpc. Based on four brighter
satellites with measured radial velocities, we derived a low orbital mass
estimate of M 63 to be (5.1$\pm$1.8) 10$^{11} M_{\odot}$ on a scale of
$\sim$216 kpc. The specific property of M 63 is its declining rotation curve.
Taking into account the declining rotation curves of the M 63 and three nearby
massive galaxies NGC 2683, NGC 2903, NGC 3521, we recognize their low mean
orbital mass-to-K-band luminosity ratio, (4.8$\pm$1.1) $M_{\odot}/L_{\odot}$,
that is only $\sim$1/6 of the corresponding ratio for the Milky Way and M 31.


  We examine reaction networks (CRNs) through their associated continuous-time
Markov processes. Studying the dynamics of such networks is in general hard,
both analytically and by simulation. In particular, stationary distributions of
stochastic reaction networks are only known in some cases. We analyze class
properties of the underlying continuous-time Markov chain of CRNs under the
operation of join and examine conditions such that the form of the stationary
distributions of a CRN is derived from the parts of the decomposed CRNs. The
conditions can be easily checked in examples and allow recursive application.
The theory developed enables sequential decomposition of the Markov processes
and calculations of stationary distributions. Since the class of processes
expressible through such networks is big and only few assumptions are made, the
principle also applies to other stochastic models. We give examples of interest
from CRN theory to highlight the decomposition.


  This paper proposes a tuning methodology for proportional resonant (PR)
controllers by using the design philosophy of the Ziegler-Nichols forced
oscillation method. Unlike such related methods that are usual for PID design,
and those that have been recently proposed for PR controllers, the method in
this paper is not restricted to plants whose Nyquist diagram crosses the
negative real axis. It involves a feedback experiment with a relay of
adjustable phase, which allows the identification of the most appropriate point
of the frequency response for each class of plants. The validation of the
proposed method, which includes the identification experiment and specific
tuning formulas that provide appropriate stability margins for each class of
plants, is performed by means of numerical examples in a large variety of
plants, showing its wide applicability.


  We associate to a filtration of a graded linear series of a big line bundle a
concave function on the Okounkov body whose law with respect to Lebesgue's
measure describes the asymptotic distribution of the jumps of the filtration.
As a consequence we obtain a Fujita-type approximation theorem in this general
filtered setting. We then specialize these results to filtrations by minima in
the usual context of Arakelov geometry, thereby obtaining in a simple way a
natural construction of an arithmetic Okounkov body, the existence of the
arithmetic volume as a limit and the arithmetic Fujita approximation theorem
for adelically normed graded linear series. We also obtain by a variant of this
construction a short proof of the existence of the sectional capacity.


  This paper will present a model for pedestrian motion by defining a spacetime
metric. This model considers the factors that are effective in the movement of
pedestrians (such as obstacles, walls and other pedestrians) by defining a
proper metric. In fact, the surrounding environment that affects the motion of
pedestrians changes the flat(Euclidean) spacetime metric and, therefore, the
shortest possible path to their destination. This change is such that
pedestrians have a different feeling of time at any point and moment in the
environment. Based on this feeling, they adjust their route to the destination
so that they travel the shortest possible route in curved spacetime, which
follows a geodesic. The contributions are :1. Defining spacetime metric for
pedestrian movement.2. Visualizing the spacetime geometry for several
timesteps.3. Introducing a parameter called the time factor and its physical
meaning in terms of the pedestrian feeling of time. According to this paper
formulation, pedestrians feel time differently at each position and moment, and
the time factor measures this feeling of time. 4. Proposing a simple model with
differential equations suitable for data assimilation methods while preserving
the essential feature of collision avoidance behaviour.


  The recent surge of interest in physics-informed neural network (PINN)
methods has led to a wave of studies that attest to their potential for solving
partial differential equations (PDEs) and predicting the dynamics of physical
systems. However, the predictive limitations of PINNs have not been thoroughly
investigated. We look at the flow around a 2D cylinder and find that data-free
PINNs are unable to predict vortex shedding. Data-driven PINN exhibits vortex
shedding only while the training data (from a traditional CFD solver) is
available, but reverts to the steady state solution when the data flow stops.
We conducted dynamic mode decomposition and analyze the Koopman modes in the
solutions obtained with PINNs versus a traditional fluid solver (PetIBM). The
distribution of the Koopman eigenvalues on the complex plane suggests that PINN
is numerically dispersive and diffusive. The PINN method reverts to the steady
solution possibly as a consequence of spectral bias. This case study reaises
concerns about the ability of PINNs to predict flows with instabilities,
specifically vortex shedding. Our computational study supports the need for
more theoretical work to analyze the numerical properties of PINN methods. The
results in this paper are transparent and reproducible, with all data and code
available in public repositories and persistent archives; links are provided in
the paper repository at \url{https://github.com/barbagroup/jcs_paper_pinn}, and
a Reproducibility Statement within the paper.


  In this paper we study correlation measures introduced in
\cite{emme_asymptotic_2017}. Denote by $\mu_a(d)$ the asymptotic density of the
set $\mathcal{E}_{a,d}=\{n \in \mathbb{N}, \ s_2(n+a)-s_2(n)=d\}$ (where $s_2$
is the sum-of-digits function in base 2). Then, for any point $X$ in
$\{0,1\}^\mathbb{N}$, define the integer sequence $\left(a_X (n)\right)_{n\in
\mathbb{N}}$ such that the binary decomposition of $a_X (n) $ is the prefix of
length $n$ of $X$. We prove that for \textit{any} shift-invariant ergodic
probability measure $\nu$ on $\{0,1\}^\mathbb{N}$, the sequence
$\left(\mu_{a_X(n)}\right)_{n \in \mathbb{N}}$ satisfies a central limit
theorem. This result was proven in the case where $\nu$ is the symmetric
Bernoulli measure in \cite{emme_central_2018}.


  We study the performance of three pairs of tilted $\Lambda$CDM cosmological
models, two pairs allowing for non-flat spatial hypersurfaces with CMB
temperature and polarization power spectrum data (P18) from Planck, P18 lensing
(lensing), and non-CMB data (non-CMB). For the six models, we measure
cosmological parameters and study whether or not pairs of the data sets are
mutually consistent in these models. Half of these models allow the lensing
consistency parameter $A_L$ to be an additional free parameter, while the other
three have $A_L = 1$. The tilted spatially-flat models assume the usual
primordial spatial inhomogeneity power spectrum. The tilted non-flat models
assume either the primordial power spectrum used in the Planck group analyses
[Planck $P(q)$] or a recently computed power spectrum [new $P(q)$]. In the
tilted non-flat models with $A_L=1$ we find differences between P18 data and
non-CMB data cosmological parameter constraints, which are large enough to rule
out the Planck $P(q)$ model at 3$\sigma$ but not the new $P(q)$ model. While
both P18 data and non-CMB data separately favor a closed geometry when
P18+non-CMB data are jointly analyzed the evidence in favor of non-flat
hypersurfaces subsides. Differences between P18 data and non-CMB data
cosmological constraints subside when $A_L$ is allowed to vary. From the most
restrictive P18+lensing+non-CMB data combination we get almost
model-independent constraints and find that the $A_L>1$ option is preferred
over the $\Omega_k<0$ one, with the $A_L$ parameter, for all models, being
larger than unity by $\sim 2.5\sigma$. According to the deviance information
criterion, in the P18+lensing+non-CMB analysis, the varying $A_L$ option is on
the verge of being strongly favored over the $A_L=1$ one, which could indicate
a problem for the standard tilted flat $\Lambda$CDM model (Abridged abstract).


  Reusable data/code and reproducible analyses are foundational to quality
research. This aspect, however, is often overlooked when designing interactive
stream analysis workflows for time-series data (e.g., eye-tracking data). A
mechanism to transmit informative metadata alongside data may allow such
workflows to intelligently consume data, propagate metadata to downstream
tasks, and thereby auto-generate reusable, reproducible analytic outputs with
zero supervision. Moreover, a visual programming interface to design, develop,
and execute such workflows may allow rapid prototyping for interdisciplinary
research. Capitalizing on these ideas, we propose StreamingHub, a framework to
build metadata propagating, interactive stream analysis workflows using visual
programming. We conduct two case studies to evaluate the generalizability of
our framework. Simultaneously, we use two heuristics to evaluate their
computational fluidity and data growth. Results show that our framework
generalizes to multiple tasks with a minimal performance overhead.


  Standard Big Bang Nucleosynthesis (BBN) predicts the abundances of the light
elements in the early universe. Even if the overall agreement with the
experimental data is good, still some discrepancies exist on the relic
abundances of ${}^7$Li and ${}^6$Li. In order to exclude or confirm these
scenarios, the BBN model needs precise input parameters, in particular the
cross-sections of the BBN nuclear reaction network. However, the suppression of
the cross-sections due to the Coulomb barrier makes the measurement very
difficult and so affected by large systematic errors. Therefore, reliable
theoretical calculations result fundamental in order to reduce the
uncertainties. In this work we present a theoretical study of two nuclear
reactions connected to ${}^6$Li abundance and recently the
$\alpha$+d$\rightarrow$ ${}^6$Li + $\gamma$ and the
p+${}^6$Li$\rightarrow$${}^7$Be+$\gamma$ radiative captures. For the first
reaction we use a so-called ab-initio approach in which we solve the full
six-body problem by using realistic nuclear potentials to describe the nucleon
interactions. In particular we concentrate on the calculation and
characterization of the final state of the reaction, the ${}^6$Li ground state,
focusing on the electromagnetic static structure and the quantities relevant
from the astrophysical point of view such as the asymptotic normalization
coefficient. For doing this we use the Hyperspherical Harmonic approach
developed by the Pisa group providing for the first time the possibility of
using this approach beyond A = 4 nuclear systems. The second reaction is
instead studied by using a two-body cluster approach where the proton and
${}^6$Li are considered as structureless particles. The angular distribution of
the emitted photon obtained in this work were used by the LUNA Collaboration to
determine the efficiency of the detector used in the measurement of the
reaction.


  10 Hibonite-pyroxene/glass spherules discovered hitherto are a rare suite of
refractory inclusions that show the largest range of exotic isotopic properties
(anomalies in neutron rich isotopes (e.g., $^{48}$Ca, $^{50}$Ti), abundance of
$^{26}$Al) despite their defining simple spherical morphology and mineralogy
consisting predominantly of few hibonites nestled within/with glassy or
crystallised calcium, aluminium-rich pyroxene. $^{26}$Al-$^{26}$Mg
chronological studies along with petrography and mineralogy of a relatively
large (~120 micron diameter), found in Allan Hills 77307 (CO3.03) has been
performed. Uniquely, both hibonite and pyroxene show discordant abundance of
short-lived now-extinct radionuclide $^{26}$Al that suggest disparate and
distinct regions of origin of hibonite and pyroxene. The pristine petrography
and mineralogy of this inclusion allow discernment of their genesis and trend
of alteration in hibonite-pyroxene/glass spherules.


  Graphene, a single infinite, planar, sheet of graphite, has the same
dielectric resonances as bulk graphite, but solid state theory indicates that
its features are about half as wide. Based on this theory, the dielectric
functions of mono- and multi-layer graphenes are deduced and compared with
those of terrestrial graphite. The resonance width of an ordered stack of
graphenes is found to increase with the number of layers while the central
frequency stays constant. This is the basis of the polycrystalline model of the
carrier of the 2175 Angstrom interstellar extinction band. In this model, the
carrier dust grains derive from parent hydrocarbon grains. As a grain ages in
the IS medium, the light atoms are expelled, hexagonal carbon rings lump
together into compact planar clusters, which then assemble into stacks of
parallel, equidistant, graphene-like layers. This so-called graphitization is
well known to occur in the earth or under strong heating. As the number of
layers in each stack increases and their relative orientational order improves,
the pi resonance width increases asymptotically towards that of terrestrial
graphite. Because of the initial random structure of the parent grains, many
randomly oriented stacks may coexist in the same grain. Calculations of the
dielectric response of this composite medium show that, for such a grain, the
width of the extinction efficiency peak follows the same trend as the pi
resonance of the average stack, and thus covers the observed range of IS
feature widths, at very nearly constant peak frequency.


  In a systematic study we investigate the effect of dopant level and hydration
on the short-range structure of the proton conducting perovskite-type oxide
BaIn_{x}Zr_{1-x}O_{3-x/2} (x = 0-0.75), using infrared and Raman spectroscopy.
The results show that doping leads to significant local distortions of the
average cubic structure of these materials. By increasing the In concentration
from x = 0 to x = 0.75 new bands appear and grow in intensity in both the IR
and Raman spectra, showing that the local distortions become successively more
and more pronounced. The structural distortions are largely uncorrelated to the
presence of oxygen vacancies, but instead are mainly driven by the size
difference between the In^{3+} and Zr^{4+} ions, which leads to displacements
of the cations and to tilting of the (In/Zr)O_{6} octahedra. Based on our
results, we suggest that there is a threshold between x = 0.10 and x = 0.25
where the local structural distortions propagate throughout the whole
perovskite structure. Comparison of our spectroscopic data with the proton
conductivity reported for the same materials indicates that the presence of
extended structural distortions are favorable for fast proton transport.


  Radio halos are synchrotron diffuse sources at the centre of a fraction of
galaxy clusters. The study of large samples of clusters with adequate radio and
X-ray data is necessary to investigate the origin of radio halos and their
connection with the cluster dynamics and formation history. The aim of this
paper is to compile a well-selected sample of galaxy clusters with deep radio
observations to perform an unbiased statistical study of the properties of
radio halos. We selected 75 clusters with M > = 6e14 Msun at z=0.08-0.33 from
the Planck Sunyaev-Zel'dovich catalogue. Clusters without suitable radio data
were observed with the Giant Metrewave Radio Telescope (GMRT) and/or the Jansky
Very Large Array (JVLA) to complete the information about the possible presence
of diffuse emission. We used archival Chandra X-ray data to derive information
on the clusters' dynamical states. This observational campaign led to the
detection of several cluster-scale diffuse radio sources and candidates that
deserve future follow-up observations. Here we summarise their properties and
add information resulting from our new observations. For the clusters where we
did not detect any hint of diffuse emission, we derived new upper limits to
their diffuse flux. We have built the largest mass-selected (> 80 per cent
complete in mass) sample of galaxy clusters with deep radio observations
available to date. The statistical analysis of the sample, which includes the
connection between radio halos and cluster mergers, the radio power-mass
correlation, and the occurrence of radio halos as a function of the cluster
mass, will be presented in paper II.


  We characterize the completely determined Borel subsets of HYP as exactly the
omega_1^{ck} subsets of HYP. As a result, HYP believes there is a Borel
well-ordering of the reals, that the Borel Dual Ramsey Theorem fails, and that
every Borel d-regular bipartite graph has a Borel perfect matching, among other
examples. Therefore, the Borel Dual Ramsey Theorem and several theorems of
descriptive combinatorics are not theories of hyperarithmetic analysis. In the
case of the Borel Dual Ramsey Theorem, this answers a question of Astor,
Dzhafarov, Montalban, Solomon & the third author.


  The "Disk of satellites" (DoS) around Milky Way is a highly debated topic
with conflicting interpretations of observations and their theoretical models.
We perform a comprehensive analysis of all dwarfs detected in the Milky Way and
find that the DoS structure depends strongly on the plane identification method
and the sample size. In particular, we demonstrate that a smaller sample size
produces a higher anisotropy of the spatial distribution and a stronger
clustering of the angular momentum of the satellites. Moreover, we calculate
the evolution of the 11 classical satellites with proper motion measurements
and find that the thin DoS they currently reside in is transient. Furthermore,
we analyze two cosmological simulations using the same initial conditions of a
Milky Way-sized galaxy, an N-body run with dark matter only and a hydrodynamic
one with both baryonic and dark matter, and find that the hydrodynamic
simulation produces more anisotropic distribution of satellites than the N-body
one. Our results suggest that an anisotropic distribution of satellites in
galaxies can originate from baryonic processes in the hierarchical structure
formation model, but the claimed highly-flattened, coherently-rotating DoS of
the Milky Way may be a small-number selection effect. These findings may help
resolve the contradictory claims of DoS in other galaxies and the discrepancy
among numerical simulations.


  In this work, we propose a novel deep network for traffic sign classification
that achieves outstanding performance on GTSRB surpassing all previous methods.
Our deep network consists of spatial transformer layers and a modified version
of inception module specifically designed for capturing local and global
features together. This features adoption allows our network to classify
precisely intraclass samples even under deformations. Use of spatial
transformer layer makes this network more robust to deformations such as
translation, rotation, scaling of input images. Unlike existing approaches that
are developed with hand-crafted features, multiple deep networks with huge
parameters and data augmentations, our method addresses the concern of
exploding parameters and augmentations. We have achieved the state-of-the-art
performance of 99.81\% on GTSRB dataset.


  In this work, we explore a genre of puzzles ("image riddles") which involves
a set of images and a question. Answering these puzzles require both
capabilities involving visual detection (including object, activity
recognition) and, knowledge-based or commonsense reasoning. We compile a
dataset of over 3k riddles where each riddle consists of 4 images and a
groundtruth answer. The annotations are validated using crowd-sourced
evaluation. We also define an automatic evaluation metric to track future
progress. Our task bears similarity with the commonly known IQ tasks such as
analogy solving, sequence filling that are often used to test intelligence.
  We develop a Probabilistic Reasoning-based approach that utilizes
probabilistic commonsense knowledge to answer these riddles with a reasonable
accuracy. We demonstrate the results of our approach using both automatic and
human evaluations. Our approach achieves some promising results for these
riddles and provides a strong baseline for future attempts. We make the entire
dataset and related materials publicly available to the community in
ImageRiddle Website (http://bit.ly/22f9Ala).


  We review some aspects of minimal cycles in string compactifications and
their role in constructing new critical theories in six and lower dimensions as
well as in accounting for black hole entropy. (Based on a talk presented at the
Salam Memorial Meeting, the Abdus Salam International Center for Theoretical
Physics, Fall 1997)


  We study Liouville-type theorem for polyharmonic H\'enon-Lane-Emden system
$(-\Delta)^mu=|x|^av^p,\; (-\Delta)^mv=|x|^bu^q$ when $m,p,q\geq 1, pq\ne 1$,
and $a,b\geq 0$. It is a natural conjecture that the nonexistence of positive
solutions should be true if and only if $(N+a)/(p+1)+$ $(N+b)/(q+1)>N-2m$. It
is shown by Fazly [6] that the conjecture holds for radial solutions in all
dimensions and for classical solutions in dimension $N\leq 2m+1$. We here give
some partial results in dimension $N\geq 2m+2$.


  Motivated by recent numerous works on the interplay among various measures of
quantum correlations, we aim to investigate the relationship between the
violation of Clauser-Horne-Shimony-Holt (CHSH) Bell inequality and geometric
measure of quantum discord for two-qubit systems. Exact lower and upper bounds
of Bell violation versus geometric discord are obtained for a specific and
significant class of states, Bell diagonal states, and the respective states
which suffice those bounds are also characterized. The dynamical behavior of
these two quantifiers is carefully analyzed in the presence of decoherence,
including Markovian, non-Markovian, and non-back-action quantum environments.
The results suggest that Bell violation is closely related to geometric
discord, like its relationship with other entanglement monotones.


  Lead optimization is a pivotal task in the drug design phase within the drug
discovery lifecycle. The primary objective is to refine the lead compound to
meet specific molecular properties for progression to the subsequent phase of
development. In this work, we present an innovative approach, Latent
Evolutionary Optimization for Molecule Generation (LEOMol), a generative
modeling framework for the efficient generation of optimized molecules. LEOMol
leverages Evolutionary Algorithms, such as Genetic Algorithm and Differential
Evolution, to search the latent space of a Variational AutoEncoder (VAE). This
search facilitates the identification of the target molecule distribution
within the latent space. Our approach consistently demonstrates superior
performance compared to previous state-of-the-art models across a range of
constrained molecule generation tasks, outperforming existing models in all
four sub-tasks related to property targeting. Additionally, we suggest the
importance of including toxicity in the evaluation of generative models.
Furthermore, an ablation study underscores the improvements that our approach
provides over gradient-based latent space optimization methods. This
underscores the effectiveness and superiority of LEOMol in addressing the
inherent challenges in constrained molecule generation while emphasizing its
potential to propel advancements in drug discovery.


  NEMESISPY is a Python package developed to perform parametric atmospheric
modelling and radiative transfer calculation for the retrievals of exoplanetary
spectra. It is a recent development of the well-established Fortran NEMESIS
library (P. G. J. Irwin et al., 2008), which has been applied to the
atmospheric retrievals of both solar system planets and exoplanets employing
numerous different observing geometries. NEMESISPY can be easily interfaced
with Bayesian inference algorithms to retrieve atmospheric properties from
spectroscopic observations. Recently, NEMESISPY has been applied to the
retrievals of Hubble and Spitzer data of a hot Jupiter (Yang et al., 2023), as
well as to JWST/Mid-Infrared Instrument (JWST/MIRI) data of a hot Jupiter (Yang
et al., 2024).


  We develop a first principles, microscopic theory of impurity atom scattering
from inhomogeneous quantum liquids such as adsorbed films, slabs, or clusters
of He-4. The theory is built upon a quantitative, microscopic description of
the ground state of both the host liquid as well as the impurity atom. Dynamic
effects are treated by allowing all ground-state correlation functions to be
time-dependent.
  Our description includes both the elastic and inelastic coupling of impurity
motion to the excitations of the host liquid. As a specific example, we study
the scattering of He-3 atoms from adsorbed He-4 films. We examine the
dependence of ``quantum reflection'' on the substrate, and the consequences of
impurity bound states, resonances, and background excitations for scattering
properties.
  A thorough analysis of the theoretical approach and the physical
circumstances point towards the essential role played by inelastic processes
which determine almost exclusively the reflection probabilities. The coupling
to impurity resonances within the film leads to a visible dependence of the
reflection coefficient on the direction of the impinging particle.


  Using imaging and radio multi-wavelength observations, we studied the origin
of two homologous accelerated electron beams and a quasi-periodic
fast-propagating (QFP) wave train associated with a solar jet on 2012 July 14.
The jet occurred in a small-scale fan-spine magnetic system embedding in a
large-scale pseudostreamer, which associated with a GOES C1.4 flare, a jet-like
coronal mass ejection (CME), a type II radio burst, and a type III radio burst.
During the initial stage, a QFP wave train and a fast moving on-disk radio
source were detected in succession ahead of the jet along the outer spine of
the fan-spine system. When the jet reached a height of about 1.3 solar radii,
it underwent a bifurcation into two branches. Based on our analysis results,
all the observed phenomena in association with the jet can be explained by
using a fan-spine magnetic system. We propose that both the type III radio
burst and the on-disk fast moving radio source were caused by the same physical
process, i.e., the energetic electrons accelerated by the magnetic reconnection
at the null point, and they were along the open field lines of the
pseudostreamer and the closed outer spine of the fan-spine structure,
respectively. Due to the bifurcation of the jet body, the lower branch along
the closed outer spine of the fan-spine structure fell back to the solar
surface, while the upper branch along the open field lines of the
pseudostreamer caused the jet-like CME in the outer corona.


  Consider a branching random walk on $\mathbb Z$ in discrete time. Denote by
$L_n(k)$ the number of particles at site $k\in\mathbb Z$ at time $n\in\mathbb
N_0$. By the profile of the branching random walk (at time $n$) we mean the
function $k\mapsto L_n(k)$. We establish the following asymptotic expansion of
$L_n(k)$, as $n\to\infty$: $$ e^{-\varphi(0)n} L_n(k) = \frac{e^{-\frac 12
x_n^2(k)}}{\sqrt {2\pi \varphi''(0) n}}
  \sum_{j=0}^r \frac{F_j(x_n(k))}{n^{j/2}} + o\left(n^{-\frac{r+1}{2}}\right)
\quad a.s., $$ where $r\in\mathbb N_0$ is arbitrary, $\varphi(\beta)=\log
\sum_{k\in\mathbb Z} e^{\beta k} \mathbb E L_1(k)$ is the cumulant generating
function of the intensity of the branching random walk and $$ x_n(k) =
\frac{k-\varphi'(0) n}{\sqrt{\varphi''(0)n}}. $$ The expansion is valid
uniformly in $k\in\mathbb Z$ with probability $1$ and the $F_j$'s are
polynomials whose random coefficients can be expressed through the derivatives
of $\varphi$ and the derivatives of the limit of the Biggins martingale at $0$.
Using exponential tilting, we also establish more general expansions covering
the whole range of the branching random walk except its extreme values. As an
application of this expansion for $r=0,1,2$ we recover in a unified way a
number of known results and establish several new limit theorems. In
particular, we study the a.s. behavior of the individual occupation numbers
$L_n(k_n)$, where $k_n\in\mathbb Z$ depends on $n$ in some regular way. We also
prove a.s. limit theorems for the mode $\arg \max_{k\in\mathbb Z} L_n(k)$ and
the height $\max_{k\in\mathbb Z} L_n(k)$ of the profile. The asymptotic
behavior of these quantities depends on whether the drift parameter
$\varphi'(0)$ is integer, non-integer rational, or irrational.


  We construct a model of a relativistic fireball or light--like shell of
matter by considering a spherically symmetric moving gravitating mass
experiencing an impulsive deceleration to rest. We take this event to be
followed by the mass undergoing a deformation leading to the emission of
gravitational radiation while it returns to a spherically symmetric state. We
find that the fireball is accompanied by an impulsive gravitational wave.


  The process $e^+ e^- \to D_{s}^{*+} D_{s0}^{*-}(2317)+c.c.$ is observed for
the first time with the data sample of 567~pb$^{-1}$ collected with the BESIII
detector operating at the BEPCII collider at a center-of-mass energy $\sqrt{s}
= 4.6$~GeV. The statistical significance of the $D_{s0}^{*-}(2317)$ signal is
$5.8\sigma$ and the mass is measured to be ($2318.3\pm 1.2\pm
1.2$)~MeV/$c^{2}$. The absolute branching fraction
$\mathcal{B}(D_{s0}^{*-}(2317)\to \pi^0 D_{s}^{-})$ is measured as
$1.00^{+0.00}_{-0.14}\pm 0.14$ for the first time. The uncertainties are
statistical and systematic, respectively.


  We consider the entanglement of orthogonal generalized Bernoulli states in
two separate single-mode high-$Q$ cavities. The expectation values and the
correlations of the electric field in the cavities are obtained. We then
define, in each cavity, a dichotomic operator expressible in terms of the field
states which can be, in principle, experimentally measured by a probe atom that
``reads'' the field. Using the quantum correlations of couples of these
operators, we construct a Bell's inequality which is shown to be violated for a
wide range of the degree of entanglement and which can be tested in a simple
way. Thus the cavity fields directly show quantum non-local properties. A
scheme is also sketched to generate entangled orthogonal generalized Bernoulli
states in the two separate cavities.


  The ability to plan actions on multiple levels of abstraction enables
intelligent agents to solve complex tasks effectively. However, learning the
models for both low and high-level planning from demonstrations has proven
challenging, especially with higher-dimensional inputs. To address this issue,
we propose to use reinforcement learning to identify subgoals in expert
trajectories by associating the magnitude of the rewards with the
predictability of low-level actions given the state and the chosen subgoal. We
build a vector-quantized generative model for the identified subgoals to
perform subgoal-level planning. In experiments, the algorithm excels at solving
complex, long-horizon decision-making problems outperforming state-of-the-art.
Because of its ability to plan, our algorithm can find better trajectories than
the ones in the training set


  The probability of plunging orbits is enhanced in groups of galaxies and
indeed, observations show that ring galaxies, which are believed to form when a
galaxy passes through the center of a larger rotating disk, are often found in
small groups. Numerical simulations combined with a knowledge of the
large-scale HI distribution provide strong constraints on the dynamical history
of these systems and on the identity of the intruder. Here we present a
numerical model of the Cartwheel which supports the suggestion that the most
distant companion is the intruder. We also present high-resolution HI
observations of the more irregular system Arp 119 that reveal a possible
connection to the most distant companion.


  The re-analysis of the data reported by Mateo et al. (1998) allowed us to
obtain a partially different set of frequencies for the 20 short period
variable stars discovered in the Carina dSph galaxy. They are subdivided into 6
overtone-pulsators, 13 fundamental-pulsators; 1 case remains uncertain. The
analysis of the light curves was conducted star by star and the first Fourier
parameters were obtained for 17 variables. No clear separation between
pulsation modes is discernible in the F21-P plot, but the very narrow range of
the observed periods hampered us to consider that statement as a definitive
one. A more extensive observational study is recommended, since a preliminary
comparison with galactic variable stars displays small differences in the F21
values. Owing to the high standard deviation of the fit, the possibility that
some stars are double-mode pulsators is also suggested.


  Monotone systems of polynomial equations (MSPEs) are systems of fixed-point
equations $X_1 = f_1(X_1, ..., X_n),$ $..., X_n = f_n(X_1, ..., X_n)$ where
each $f_i$ is a polynomial with positive real coefficients. The question of
computing the least non-negative solution of a given MSPE $\vec X = \vec f(\vec
X)$ arises naturally in the analysis of stochastic models such as stochastic
context-free grammars, probabilistic pushdown automata, and back-button
processes. Etessami and Yannakakis have recently adapted Newton's iterative
method to MSPEs. In a previous paper we have proved the existence of a
threshold $k_{\vec f}$ for strongly connected MSPEs, such that after $k_{\vec
f}$ iterations of Newton's method each new iteration computes at least 1 new
bit of the solution. However, the proof was purely existential. In this paper
we give an upper bound for $k_{\vec f}$ as a function of the minimal component
of the least fixed-point $\mu\vec f$ of $\vec f(\vec X)$. Using this result we
show that $k_{\vec f}$ is at most single exponential resp. linear for strongly
connected MSPEs derived from probabilistic pushdown automata resp. from
back-button processes. Further, we prove the existence of a threshold for
arbitrary MSPEs after which each new iteration computes at least $1/w2^h$ new
bits of the solution, where $w$ and $h$ are the width and height of the DAG of
strongly connected components.


  A reaction network is a chemical system involving multiple reactions and
chemical species. Stochastic models of such networks treat the system as a
continuous time Markov chain on the number of molecules of each species with
reactions as possible transitions of the chain. In many cases of biological
interest some of the chemical species in the network are present in much
greater abundance than others and reaction rate constants can vary over several
orders of magnitude. We consider approaches to approximation of such models
that take the multiscale nature of the system into account. Our primary example
is a model of a cell's viral infection for which we apply a combination of
averaging and law of large number arguments to show that the ``slow'' component
of the model can be approximated by a deterministic equation and to
characterize the asymptotic distribution of the ``fast'' components. The main
goal is to illustrate techniques that can be used to reduce the dimensionality
of much more complex models.


  We prove that a Banach lattice $X$ which does not contain the
$l^n_{\infty}$-uniformly has an equivalent norm which is uniformly Kadec-Klee
for a natural topology $\tau$ on $X$. In case the Banach lattice is purely
atomic, the topology $\tau$ is the coordinatewise convergence topology.


  State-machine replication, a fundamental approach to fault tolerance,
requires replicas to execute commands deterministically, which usually results
in sequential execution of commands. Sequential execution limits performance
and underuses servers, which are increasingly parallel (i.e., multicore). To
narrow the gap between state-machine replication requirements and the
characteristics of modern servers, researchers have recently come up with
alternative execution models. This paper surveys existing approaches to
parallel state-machine replication and proposes a novel optimistic protocol
that inherits the scalable features of previous techniques. Using a replicated
B+-tree service, we demonstrate in the paper that our protocol outperforms the
most efficient techniques by a factor of 2.4 times.


  Reverberation mapping estimates the size and kinematics of broad line regions
(BLR) in Quasars and type I AGNs. It yields size-luminosity relation, to make
QSOs standard cosmological candles, and mass-luminosity relation to study the
evolution of black holes and galaxies. The accuracy of these relations is
limited by the unknown geometry of the BLR clouds distribution and velocities.
We analyze the independent BLR structure constraints given by super-resolving
differential interferometry. We developed a three-dimensional BLR model to
compute all differential interferometry and reverberation mapping signals. We
extrapolate realistic noises from our successful observations of the QSO 3C273
with AMBER on the VLTI. These signals and noises quantify the differential
interferometry capacity to discriminate and measure BLR parameters including
angular size, thickness, spatial distribution of clouds, local-to-global and
radial-to-rotation velocity ratios, and finally central black hole mass and BLR
distance. A Markov Chain Monte Carlo model-fit, of data simulated for various
VLTI instruments, gives mass accuracies between 0.06 and 0.13 dex, to be
compared to 0.44 dex for reverberation mapping mass-luminosity fits. We
evaluate the number of QSOs accessible to measures with current (AMBER),
upcoming (GRAVITY) and possible (OASIS with new generation fringe trackers)
VLTI instruments. With available technology, the VLTI could resolve more than
60 BLRs, with a luminosity range larger than four decades, sufficient for a
good calibration of RM mass-luminosity laws, from an analysis of the variation
of BLR parameters with luminosity.


  The holy grail in deep neural network research is porting the memory- and
computation-intensive network models on embedded platforms with a minimal
compromise in model accuracy. To this end, we propose a novel approach, termed
as Variational Student, where we reap the benefits of compressibility of the
knowledge distillation (KD) framework, and sparsity inducing abilities of
variational inference (VI) techniques. Essentially, we build a sparse student
network, whose sparsity is induced by the variational parameters found via
optimizing a loss function based on VI, leveraging the knowledge learnt by an
accurate but complex pre-trained teacher network. Further, for sparsity
enhancement, we also employ a Block Sparse Regularizer on a concatenated tensor
of teacher and student network weights. We demonstrate that the marriage of KD
and the VI techniques inherits compression properties from the KD framework,
and enhances levels of sparsity from the VI approach, with minimal compromise
in the model accuracy. We benchmark our results on LeNet MLP and VGGNet (CNN)
and illustrate a memory footprint reduction of 64x and 213x on these MLP and
CNN variants, respectively, without a need to retrain the teacher network.
Furthermore, in the low data regime, we observed that our method outperforms
state-of-the-art Bayesian techniques in terms of accuracy.


  In this paper, under Acquistpace-Terreni conditions, we make extensive use of
interpolation spaces and exponential dichotomy techniques to obtain the
existence of weighted pseudo almost periodic solutions to some classes of
nonautonomous partial evolution equations. Applications include the existence
of weighted pseudo almost periodic solutions to a nonautonomous heat equation
with gradient coefficients.


  The emergence of artificial intelligence has profoundly impacted
computational chemistry, particularly through machine-learned potentials
(MLPs), which offer a balance of accuracy and efficiency in calculating atomic
energies and forces to be used in molecular dynamics simulations. These MLPs
have significantly advanced molecular dynamics simulations across various
applications, including large-scale simulations of materials, interfaces, and
chemical reactions. Despite these advances, the construction of training
datasets - a critical component for the accuracy of MLPs - has not received
proportional attention. This is particularly critical for chemical reactivity
which depends on rare barrier-crossing events. Here we address this gap by
introducing ArcaNN, a comprehensive framework designed for generating training
datasets for reactive MLPs. ArcaNN employs a concurrent learning approach
combined with advanced sampling techniques to ensure accurate representation of
high-energy geometries. The framework integrates automated processes for
iterative training, exploration, new configuration selection, and energy and
force labeling, while ensuring reproducibility and documentation. We
demonstrate ArcaNN's capabilities through a paradigm nucleophilic substitution
reaction in solution, showcasing its effectiveness, the uniformly low error of
the resulting MLP everywhere along the chemical reaction coordinate, and its
potential for broad applications in reactive molecular dynamics. We also
provide guidelines on how to assess the quality of a NNP for a reactive system.


  News Discourse Profiling seeks to scrutinize the event-related role of each
sentence in a news article and has been proven useful across various downstream
applications. Specifically, within the context of a given news discourse, each
sentence is assigned to a pre-defined category contingent upon its depiction of
the news event structure. However, existing approaches suffer from an
inadequacy of available human-annotated data, due to the laborious and
time-intensive nature of generating discourse-level annotations. In this paper,
we present a novel approach, denoted as Intra-document Contrastive Learning
with Distillation (ICLD), for addressing the news discourse profiling task,
capitalizing on its unique structural characteristics. Notably, we are the
first to apply a semi-supervised methodology within this task paradigm, and
evaluation demonstrates the effectiveness of the presented approach.


  We present a residual-based a posteriori error estimator for the hybrid
high-order (HHO) method for the Stokes model problem. Both the proposed HHO
method and error estimator are valid in two and three dimensions and support
arbitrary approximation orders on fairly general meshes. The upper bound and
lower bound of the error estimator are proved, in which proof, the key
ingredient is a novel stabilizer employed in the discrete scheme. By using the
given estimator, adaptive algorithm of HHO method is designed to solve model
problem. Finally, the expected theoretical results are numerically demonstrated
on a variety of meshes for model problem.


  Motivated by a model for lipid bilayer cell membranes, we study the
minimization of the Willmore functional in the class of oriented closed
surfaces with prescribed total mean curvature, prescribed area, and prescribed
genus. Adapting methods previously developed by Keller-Mondino-Rivi\`ere,
Bauer-Kuwert, and Ndiaye-Sch\"atzle, we prove existence of smooth minimizers
for a large class of constraints. Moreover, we analyze the asymptotic behaviour
of the energy profile close to the unit sphere and consider the total mean
curvature of axisymmetric surfaces.


  We study precursors of failure in hierarchical random fuse network models
which can be considered as idealizations of hierarchical (bio)materials where
fibrous assemblies are held together by multi-level (hierarchical) cross-links.
When such structures are loaded towards failure, the patterns of precursory
avalanche activity exhibit generic scale invariance: Irrespective of load,
precursor activity is characterized by power-law avalanche size distributions
without apparent cut-off, with power-law exponents that decrease continuously
with increasing load. This failure behavior and the ensuing super-rough crack
morphology differ significantly from the findings in non-hierarchical
structures.


  The Lya emission has been observed from galaxies over a redshift span z ~ 0 -
8.6. However, the evolution of high-redshift Lya emitters (LAEs), and the link
between these populations and local galaxies, remain poorly understood. Here,
we investigate the Lya properties of progenitors of a local L* galaxy by
combining cosmological hydrodynamic simulations with three-dimensional
radiative transfer calculations using the new ART^2 code. We find that the main
progenitor (the most massive one) of a Milky Way-like galaxy has a number of
Lya properties close to those of observed LAEs at z ~ 2 - 6, but most of the
fainter ones appear to fall below the detection limits of current surveys. The
Lya photon escape fraction depends sensitively on a number of physical
properties of the galaxy, such as mass, star formation rate, and metallicity,
as well as galaxy morphology and orientation. Moreover, we find that
high-redshift LAEs show blue-shifted Lya line profiles characteristic of gas
inflow, and that the Lya emission by excitation cooling increases with
redshift, and becomes dominant at z > 6. Our results suggest that some observed
LAEs at z ~ 2-6 with luminosity of L_Lya ~ 10^{42-43} ergs/s may be similar to
the main progenitor of the Milky Way at high redshift, and that they may evolve
into present-day L* galaxies.


  We discuss two two-layered logics formalising reasoning with paraconsistent
probabilities that combine the Lukasiewicz $[0,1]$-valued logic with Baaz
$\triangle$ operator and the Belnap--Dunn logic.


  At sufficiently high baryon densities, the physics of a dense quark-gluon
plasma may be investigated through the tools of perturbative QCD. This approach
has recently been successfully applied to the study of color superconductivity,
where the dominant di-quark pairing interaction arises from one gluon exchange.
Screening in the plasma leads to novel behaviour, including a remarkable
non-BCS scaling of T_C, the transition temperature to the color superconducting
phase. Radiative corrections to one gluon exchange were previously considered
and found to affect T_C. In particular, the quark self-energy in a plasma leads
to non-Fermi liquid behaviour and suppresses T_C. However, at the same time,
the quark-gluon vertex was shown not to modify the result at leading order.
This dichotomy between the effects of the radiative corrections at first
appears rather surprising, as the BRST identity connects the self-energy to the
vertex corrections. Nevertheless, as we demonstrate, there is in fact no
contradiction with the BRST identity, at least to leading log order. This
clarifies some of the previous statements on the importance of the higher order
corrections to the determination of T_C and the zero temperature gap in color
superconductivity.


  The gauge dependence problem existing in the original Gribov-Zwanziger theory
is discussed.


  We construct geometric categorical Lie algebra actions on the derived
category of coherent sheaves on Nakajima quiver varieties. These actions
categorify Nakajima's construction of Kac-Moody algebra representations on the
K-theory of quiver varieties. We define an induced affine braid group action on
these derived categories.


  The kinetic-MHD hybrid simulation approach for macroscopic instabilities in
plasmas can be extended to include the kinetic effects of both thermal ions and
energetic ions. The new coupling scheme includes synchronization of density and
parallel velocity between thermal ions and MHD, in addition to pressure
coupling, to ensure the quasineutrality condition and avoid numerical errors.
The new approach has been implemented in the kinetic-MHD code M3D-C1-K, and was
used to study the thermal ion kinetic effects and Landau damping in fishbone
modes in both DIII-D and NSTX. It is found that the thermal ion kinetic effects
can cause an increase of the frequencies of the non-resonant $n=1$ fishbone
modes driven by energetic particles for $q_\mathrm{min}>1$, and Landau damping
can provide additional stabilization effects. A nonlinear simulation for $n=1$
fishbone mode in NSTX is also performed, and the perturbation on magnetic flux
surfaces and the transport of energetic particles are calculated.


  We investigated an out-of-plane exchange bias system that is based on the
antiferromagnet MnN. Polycrystalline, highly textured film stacks of Ta / MnN /
CoFeB / MgO / Ta were grown on SiO$_x$ by (reactive) magnetron sputtering and
studied by x-ray diffraction and Kerr magnetometry. Nontrivial modifications of
the exchange bias and the perpendicular magnetic anisotropy were observed both
as functions of film thicknesses as well as field cooling temperatures. In
optimized film stacks, a giant perpendicular exchange bias of 3600 Oe and a
coercive field of 350 Oe were observed at room temperature. The effective
interfacial exchange energy is estimated to be $J_\mathrm{eff} = 0.24$ mJ/m$^2$
and the effective uniaxial anisotropy constant of the antiferromagnet is
$K_\mathrm{eff} = 24$ kJ/m$^3$. The maximum effective perpendicular anisotropy
field of the CoFeB layer is $H_\mathrm{ani} = 3400$ Oe. These values are larger
than any previously reported values. These results possibly open a route to
magnetically stable, exchange biased perpendicularly magnetized spin valves.


  We study the dynamics of identical Leaky Integrate-and-Fire (LIF) neurons on
a multiplex composed of two ring networks with symmetric nonlocal coupling
within each ring and one-to-one connections between rings. We investigate the
impact of different intra-ring coupling strengths in the two rings for
attractive and repulsive inter-ring coupling and show that they can lead to
subthreshold oscillations. The corresponding parameter spaces where this
phenomenon occurs are determined numerically. Moreover, we show that depending
on whether the couplings between the two rings are attractive or repulsive, the
interaction produces qualitatively different behavior in the synchronization
patterns and the mean frequency profiles.


  The study of the X-ray properties of z>4 quasars has grown substantially over
the last few years, mostly thanks to Chandra. Here we focus on (1) the average
X-ray properties of an optically selected, luminous sample of nine z>4 Palomar
Digital Sky Survey quasars recently observed by Chandra, and (2) the X-ray
spectral properties of three z>4 AGNs in the Chandra Deep Field-North (CDF-N)
using the 2 Ms exposure.


  The resonance $D_{sJ}(2632)$ observed by SELEX, has attracted great interests
and meanwhile brought up serious dispute. Its spin-parity, so far has not
finally determined and if it exists, its quark-structure might be exotic.
Following the previous literature where $D_{sJ}(2632)$ is assumed to be a
radial-excited state of $1^-$, we consider the possibilities that it might be a
$q\bar q$ ground state of $2^+$ or the first radial-excited state of $0^+$
$D_{sJ}(2317)$ and re-calculate its strong decay widths in terms of the
Bethe-Salpeter equation. Our results indicate that there still is a sharp
discrepancy between the theoretical evaluation and data.


  NMR is the technique of election to probe the local properties of materials.
Herein we present the results of density functional theory (DFT) \textit{ab
initio} calculations of the NMR parameters for fluorapatite (FAp), a calcium
orthophosphate mineral belonging to the apatite family, by using the GIPAW
method [Pickard and Mauri, 2001]. Understanding the local effects of pressure
on apatites is particularly relevant because of their important role in many
solid state and biomedical applications. Apatites are open structures, which
can undergo complex anisotropic deformations, and the response of NMR can
elucidate the microscopic changes induced by an applied pressure. The computed
NMR parameters proved to be in good agreement with the available experimental
data. The structural evaluation of the material behavior under hydrostatic
pressure (from --5 to +100 kbar) indicated a shrinkage of the diameter of the
apatitic channel, and a strong correlation between NMR shielding and pressure,
proving the sensitivity of this technique to even small changes in the chemical
environment around the nuclei. This theoretical approach allows the exploration
of all the different nuclei composing the material, thus providing a very
useful guidance in the interpretation of experimental results, particularly
valuable for the more challenging nuclei such as $^{43}$Ca and $^{17}$O.


  In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC)
that improves classification accuracy and minimizes the number of classifiers
is proposed. Using pre-trained classifiers, an ensemble in which base
classifiers votes according to assigned weights is formed. These assigned
weights directly affect classifier accuracy. In the proposed method, ensemble
weights finding problem is modeled as a cost function with the following terms:
(a) a data fidelity term aiming to decrease misclassification rate, (b) a
sparsity term aiming to decrease the number of classifiers, and (c) a
non-negativity constraint on the weights of the classifiers. As the proposed
cost function is non-convex thus hard to solve, convex relaxation techniques
and novel approximations are employed to obtain a numerically efficient
solution. Sparsity term of cost function allows trade-off between accuracy and
testing time when needed. The efficiency of SDWEC was tested on 11 datasets and
compared with the state-of-the art classifier ensemble methods. The results
show that SDWEC provides better or similar accuracy levels using fewer
classifiers and reduces testing time for ensemble.