  A mosaic of four UIT far-UV (FUV; 1620A) images, which covers most of the SMC
bar, is presented, with derived stellar and HII region photometry. The UV
morphology of the SMC's Bar shows that recent star formation there has left
striking features including: a) four concentrations of UV-bright stars spread
from northeast to southwest at nearly equal (~30 arcmin=0.5 kpc) spacings; b)
one concentration comprising a well-defined 8-arcmin diameter ring surrounded
by a larger H-alpha ring, suggestive of sequential star formation.
  FUV PSF photometry is obtained for 11,306 stars, and FUV photometry is
obtained for 42 H-alpha-selected HII regions, both for the stars and for the
total emission contained in the apertures defined by Kennicutt & Hodge. The
flux- weighted average ratio of total to stellar FUV flux is 2.15; the stellar
FUV luminosity function indicates that most of the excess total flux is due to
scattered FUV radiation, rather than faint stars. Both stellar and total
emission are well correlated with H-alpha fluxes, and yield FUV/H-alpha ratios
that are consistent with models of single-burst clusters with SMC metallicity,
ages from 1-5 Myr, and moderate (E(B-V)=0.0-0.1 mag) internal SMC extinction.


  The recent series of innovations in deep learning (DL) have shown enormous
potential to impact individuals and society, both positively and negatively.
The DL models utilizing massive computing power and enormous datasets have
significantly outperformed prior historical benchmarks on increasingly
difficult, well-defined research tasks across technology domains such as
computer vision, natural language processing, signal processing, and
human-computer interactions. However, the Black-Box nature of DL models and
their over-reliance on massive amounts of data condensed into labels and dense
representations poses challenges for interpretability and explainability of the
system. Furthermore, DLs have not yet been proven in their ability to
effectively utilize relevant domain knowledge and experience critical to human
understanding. This aspect is missing in early data-focused approaches and
necessitated knowledge-infused learning and other strategies to incorporate
computational knowledge. This article demonstrates how knowledge, provided as a
knowledge graph, is incorporated into DL methods using knowledge-infused
learning, which is one of the strategies. We then discuss how this makes a
fundamental difference in the interpretability and explainability of current
approaches, and illustrate it with examples from natural language processing
for healthcare and education applications.


  Fe-K$_{\beta}$ X-ray emission spectroscopy measurements reveal an asymmetric
doping dependence of the magnetic moments $\mu_\text{bare}$ in electron- and
hole-doped BaFe$_{2}$As$_{2}$. At low temperature, $\mu_\text{bare}$ is nearly
constant in hole-doped samples, whereas it decreases upon electron doping.
Increasing temperature substantially enhances $\mu_\text{bare}$ in the
hole-doped region, which is naturally explained by the theoretically predicted
crossover into a spin-frozen state. Our measurements demonstrate the importance
of Hund's coupling and electronic correlations, especially for hole-doped
BaFe$_{2}$As$_{2}$, and the inadequacy of a fully localized or fully itinerant
description of the 122 family of Fe pnictides.


  Generalised spin structures, or r-spin structures, on a 2-dimensional
orbifold \Sigma are r-fold fibrewise connected coverings (also called r-th
roots) of its unit tangent bundle ST\Sigma. We investigate such structures on
hyperbolic orbifolds. The conditions on r for such structures to exist are
given. The action of the diffeomorphism group of \Sigma on the set of r-spin
structures is described, and we determine the number of orbits under this
action and their size. These results are then applied to describe the moduli
space of taut contact circles on left-quotients of the 3-dimensional geometry
\tilde{SL}_2.


  Let $\mathcal{R}$ be the Grothendieck ring of complex smooth finite-length
representations of the sequence of p-adic groups $\{GL_n(F)\}_{n=0}^\infty$,
with multiplication defined through parabolic induction. We study the problem
of the decomposition of products of irreducible representations in
$\mathcal{R}$.
  We obtain a necessary condition on irreducible factors of a given product by
introducing a width invariant. Width $1$ representations form the previously
studied class of ladder representations.
  We later focus on the case of a product of two ladder representations, for
which we establish that all irreducible factors appear with multiplicity one.
  Finally, we propose a general rule for the composition series of a product of
two ladder representations and prove its validity for cases in which the
irreducible factors correspond to smooth Schubert varieties.


  We define the orbit category for transitive topological groupoids and their
equivariant CW-complexes. By using these constructions we define equivariant
Bredon homology and cohomology for actions of transitive topological groupoids.
We show how these theories can be obtained by looking at the action of a single
isotropy group on a fiber of the anchor map, extending equivariant results for
compact group actions. We also show how this extension from a single isotropy
group to the entire groupoid action can be applied to the structure of
principal bundles and classifying spaces.


  We prove that the asymptotic entropy of large simple graphs, as a function of
fixed edge and triangle densities, is nondifferentiable along a certain curve.


  In this paper we prove uniform a priori estimates for transmission problems
with constant coefficients on two subdomains, with a special emphasis for the
case when the ratio between these coefficients is large. In the most part of
the work, the interface between the two subdomains is supposed to be Lipschitz.
We first study a scalar transmission problem which is handled through a
converging asymptotic series. Then we derive uniform a priori estimates for
Maxwell transmission problem set on a domain made up of a dielectric and a
highly conducting material. The technique is based on an appropriate
decomposition of the electric field, whose gradient part is estimated thanks to
the first part. As an application, we develop an argument for the convergence
of an asymptotic expansion as the conductivity tends to infinity.


  We construct the non-stationary Ruijsenaars functions (affine analogue of the
Macdonald functions) in the special case $\kappa=t^{-1/N}$, using the
intertwining operators of the Ding-Iohara-Miki algebra (DIM algebra) associated
with $N$-fold Fock tensor spaces. By the $S$-duality of the intertwiners,
another expression is obtained for the non-stationary Ruijsenaars functions
with $\kappa=t^{-1/N}$, which can be regarded as a natural elliptic lift of the
asymptotic Macdonald functions to the multivariate elliptic hypergeometric
series. We also investigate some properties of the vertex operator of the DIM
algebra appearing in the present algebraic framework; an integral operator
which commutes with the elliptic Ruijsenaars operator, and the degeneration of
the vertex operators to the Virasoro primary fields in the conformal limit $q
\rightarrow 1$.


  We present a minimal Landau theory of plastic-to-crystal phase transitions in
which the key components are a multipole-moment order parameter that describes
the orientational ordering of the constituent molecules, coupling between such
order parameter and elastic strains, and thermal expansion. We illustrate the
theory with the simplest non-trivial model in which the orientational ordering
is described by a quadrupole moment, and use such model to calculate
barocaloric effects in plastic crystals that are driven by hydrostatic
pressure. The model captures characteristic features of plastic-to-crystal
phase transitions, namely, large changes in volume and entropy at the
transition, as well as the linear dependence of the transition temperature with
pressure. We identify temperature regions in the barocaloric response
associated with the individual plastic and crystal phases, and those involving
the phase transition. Our model is in overall agreement with previous
experiments in powdered samples of fullerite C$_{60}$, and predicts peak
isothermal entropy changes of $\sim90 \,{\rm J K^{-1} kg^{-1}}$ and peak
adiabatic temperature changes of $\sim35 \,{\rm K}$ under $0.60\,$GPa at
$265\,$K in fullerite single crystals.


  A physical subspace and physical Hilbert space associated with asymptotic
fields of nonrelativistic quantum electrodynamics are constructed through the
Gupta-Bleuler procedure. Asymptotic completeness is shown and a physical
Hamiltonian is defined on the physical Hilbert space.


  As quantum technology is advancing, the efficient design of quantum circuits
has become an important area of research. This paper provides an introduction
to the MCT quantum circuit design problem for reversible Boolean functions
without assuming a prior background in quantum computing. While this is a
well-studied problem, optimization models that minimize the true objective have
only been explored recently. This paper introduces a new optimization model and
symmetry-breaking constraints that improve solving time by up to two orders of
magnitude compared to earlier work when a Constraint Programming solver is
used. Experiments with up to seven qubits and using up to 15 quantum gates
result in several new best-known circuits, obtained by any method, for
well-known benchmarks. Finally, an extensive comparison with other approaches
shows that optimization models may require more time but can provide superior
circuits with optimality guarantees.


  We describe a set of coordinates on the PU(2,1)-representation variety of the
fundamental group of an oriented punctured surface $S$ with negative Euler
characteristic. The main technical tool we use is a set of geometric invariants
of a triple of flags in the complex hyperpolic plane. We establish a bijection
between a set of decorations of an ideal triangulation of $S$ and a subset of
the PU(2,1)-representation variety of $\pi_1(S)$.


  We prove positivity of the Markov operators that correspond to the
hit-and-run algorithm, random scan Gibbs sampler, slice sampler and an
Metropolis algorithm with positive proposal. In all of these cases the
positivity is independent of the state space and the stationary distribution.
In particular, the results show that it is not necessary to consider the lazy
versions of these Markov chains. The proof relies on a well known lemma which
relates the positivity of the product M T M^*, for some operators M and T, to
the positivity of T. It remains to find that kind of representation of the
Markov operator with a positive operator T.


  The frictional drag force between separated but coupled two-dimensional
electron gases of different temperatures is studied using the non-equilibrium
Green function method based on the separation of center-of-mass and relative
dynamics of electrons. As the mechanisms of producing the frictional force we
include the direct Coulomb interaction, the interaction mediated via virtual
and real TA and LA phonons, optic phonons, plasmons, and TA and LA
phonon-electron collective modes. We found that, when the distance between the
two electron gases is large, and at intermediate temperature where plasmons and
collective modes play the most important role in the frictional drag, the
possibility of having a temperature difference between two subsystems modifies
greatly the transresistivity.


  This work studies the problem of distributed optimization in heterogeneous
linear multi-agent systems. Instead of relying on a perfect communication
network as in many existing distributed optimization approaches, we considered
two important issues related to communication networks. First, assumed that
communication delays exist when each agent receives information from its
neighbors. Further, since communications networks are generally unreliable, we
assumed each agent interacted with the other agents through random digraphs.
Finally, to prove convergence to optimal solutions, delay-dependent sufficient
conditions are derived in the form of linear matrix inequality. An analysis of
numerical simulation results is presented to demonstrate the effectiveness of
the introduced approach.


  Reconfigurable intelligent surfaces (RISs) operate similarly to
electromagnetic (EM) mirrors and remarkably go beyond Snell law to generate an
applicable EM environment allowing for flexible adaptation and fostering
sustainability in terms of economic deployment and energy efficiency. However,
the conventional RIS is controlled through high-latency field programmable gate
array or micro-controller circuits usually implementing artificial neural
networks (ANNs) for tuning the RIS phase array that have also very high energy
requirements. Most importantly, conventional RIS are unable to function under
realistic scenarios i.e, high-mobility/low-end user equipment (UE). In this
paper, we benefit from the advanced computing power of neuromorphic processors
and design a new type of RIS named \emph{NeuroRIS}, to supporting high mobility
UEs through real time adaptation to the ever-changing wireless channel
conditions. To this end, the neuromorphic processing unit tunes all the RIS
meta-elements in the orders of $\rm{ns}$ for particular switching circuits
e.g., varactors while exhibiting significantly low energy requirements since it
is based on event-driven processing through spiking neural networks for
accurate and efficient phase-shift vector design. Numerical results show that
the NeuroRIS achieves very close rate performance to a conventional RIS-based
on ANNs, while requiring significantly reduced energy consumption with the
latter.


  We study SU(3)-structures induced on orientable hypersurfaces of
seven-dimensional manifolds with G_2-structure. Taking Gray-Hervella types for
both structures into account, we relate the type of SU(3)-structure and the
type of G_2-structure with the shape tensor of the hypersurface. Additionaly,
we show how to compute the intrinsic SU(3)-torsion and the intrinsic
G_2-torsion by means of exterior algebra.


  After two years of operation, the High-Altitude Water Cherenkov (HAWC)
Observatory has analyzed the TeV cosmic-ray sky over an energy range between
$2.0$ and $72.8$ TeV. The HAWC detector is a ground-based air-shower array
located at high altitude in the state of Puebla, Mexico. Using 300 light-tight
water tanks, it collects the Cherenkov light from the particles of extensive
air showers from primary gamma rays and cosmic rays. This detection method
allows for uninterrupted observation of the entire overhead sky (2~sr
instantaneous, 8.5~sr integrated) in the energy range from a few TeV to
hundreds of TeV. Like other detectors in the northern and southern hemisphere,
HAWC observes an energy-dependent anisotropy in the arrival direction
distribution of cosmic rays. The observed cosmic-ray anisotropy is dominated by
a dipole moment with phase $\alpha\approx40^{\circ}$ and amplitude that slowly
rises in relative intensity from $8\times10^{-4}$ at 2 TeV to $14\times10^{-4}$
around 30.3 TeV, above which the dipole decreases in strength. A significant
large-scale ($>60^{\circ}$ in angular extent) signal is also observed in the
quadrupole and octupole moments, and significant small-scale features are also
present, with locations and shapes consistent with previous observations.
Compared to previous measurements in this energy range, the HAWC cosmic-ray sky
maps improve on the energy resolution and fit precision of the anisotropy.
These data can be used in an effort to better constrain local cosmic-ray
accelerators and the intervening magnetic fields.


  This paper investigates the global structures of periodic orbits that appear
in Rayleigh-B\'enard convection, which is modeled by a two-dimensional
perturbed Hamiltonian model, by focusing upon resonance, symmetry and
bifurcation of the periodic orbits. First, we show the global structures of
periodic orbits in the extended phase space by numerically detecting the
associated periodic points on the Poincar\'e section. Then, we illustrate how
resonant periodic orbits appear and specifically clarify that there exist some
symmetric properties of such resonant periodic orbits which are projected on
the phase space; namely, the period $m$ and the winding number $n$ become odd
when an $m$-periodic orbit is symmetric with respect to the horizontal and
vertical center lines of a cell. Furthermore, the global structures of
bifurcations of periodic orbits are depicted when the amplitude $\varepsilon$
of the perturbation is varied, since in experiments the amplitude of the
oscillation of the convection gradually increases when the Rayleigh number is
raised.


  The spin-orbit splittings in the spectra of nuclei with mass numbers 5, 15
and 17 are studied within the framework of shell-model configuration mixing
calculations including 2$\hbar \omega$ excitations. The contributions of the
two-body spin-orbit and tensor components of the nucleon-nucleon interaction
are studied in various model spaces. It is found that the effects of the
two-body spin-orbit interaction are dominant and quite sensitive to the size of
the model-space considered. The effects of the tensor interaction are weaker.
The correlations effects which are included in the larger (0+2) $\hbar \omega$
shell-model space reduce the spin-orbit splitting in the case of A=5 by 20%,
and enhance it for A=15 by about the same 20%. However, it is found that the
correlations have a very small effect on the $d_{3/2} - d_{5/2}$ splitting in
A=17.


  The axion is a long-postulated boson that can simultaneously solve two
fundamental problems of modern physics: the charge-parity symmetry problem in
the strong interaction and the enigma of dark matter. In this work we estimate,
by means of Monte Carlo simulations, the sensitivity of the
Dark-photons$\&$Axion-Like particles Interferometer (DALI), a new-generation
Fabry-P\'erot haloscope proposed to probe axion dark matter in the 25-250
$\mu$eV band.


  A family of regularization functionals is said to admit a linear representer
theorem if every member of the family admits minimizers that lie in a fixed
finite dimensional subspace. A recent characterization states that a general
class of regularization functionals with differentiable regularizer admits a
linear representer theorem if and only if the regularization term is a
non-decreasing function of the norm. In this report, we improve over such
result by replacing the differentiability assumption with lower semi-continuity
and deriving a proof that is independent of the dimensionality of the space.


  We give, for each non self-dual Wadge class C contained in the class of the
Gdelta sets, a characterization of Borel sets which are not potentially in C,
among Borel sets with countable vertical sections; to do this, we use results
of partial uniformization.


  Non-contrast CT (NCCT) imaging may reduce image contrast and anatomical
visibility, potentially increasing diagnostic uncertainty. In contrast,
contrast-enhanced CT (CECT) facilitates the observation of regions of interest
(ROI). Leading generative models, especially the conditional diffusion model,
demonstrate remarkable capabilities in medical image modality transformation.
Typical conditional diffusion models commonly generate images with guidance of
segmentation labels for medical modal transformation. Limited access to
authentic guidance and its low cardinality can pose challenges to the practical
clinical application of conditional diffusion models. To achieve an equilibrium
of generative quality and clinical practices, we propose a novel Syncretic
generative model based on the latent diffusion model for medical image
translation (S$^2$LDM), which can realize high-fidelity reconstruction without
demand of additional condition during inference. S$^2$LDM enhances the
similarity in distinct modal images via syncretic encoding and diffusing,
promoting amalgamated information in the latent space and generating medical
images with more details in contrast-enhanced regions. However, syncretic
latent spaces in the frequency domain tend to favor lower frequencies, commonly
locate in identical anatomic structures. Thus, S$^2$LDM applies adaptive
similarity loss and dynamic similarity to guide the generation and supplements
the shortfall in high-frequency details throughout the training process.
Quantitative experiments confirm the effectiveness of our approach in medical
image translation. Our code will release lately.


  We study the following question: What is the largest deterministic amount of
time $T_*$ that a suitably normalized martingale $X$ can be kept inside a
convex body $K$ in $\mathbb{R}^d$? We show, in a viscosity framework, that
$T_*$ equals the time it takes for the relative boundary of $K$ to reach $X(0)$
as it undergoes a geometric flow that we call (positive) minimum curvature
flow. This result has close links to the literature on stochastic and game
representations of geometric flows. Moreover, the minimum curvature flow can be
viewed as an arrival time version of the Ambrosio--Soner codimension-$(d-1)$
mean curvature flow of the $1$-skeleton of $K$. Our results are obtained by a
mix of probabilistic and analytic methods.


  Coregulation of the expression of groups of genes has been extensively
demonstrated empirically in bacterial and eukaryotic systems. Such coregulation
can arise through the use of shared regulatory motifs, which allow the
coordinated expression of modules (and module groups) of functionally related
genes across the genome. Coregulation can also arise through the physical
association of multi-gene complexes through chromosomal looping, which are then
transcribed together. We present a general formalism for modeling coregulation
rules in the framework of Random Boolean Networks (RBN), and develop specific
models for transcription factor networks with modular structure (including
module groups, and multi-input modules (MIM) with autoregulation) and
multi-gene complexes (including hierarchical differentiation between multi-gene
complex members). We develop a mean-field approach to analyse the stability of
large networks incorporating coregulation, and show that autoregulated MIM and
hierarchical gene-complex models can achieve greater stability than networks
without coregulation whose rules have matching activation frequency. We provide
further analysis of the stability of small networks of both kinds through
simulations. We also characterize several general properties of the transients
and attractors in the hierarchical coregulation model, and show using
simulations that the steady-state distribution factorizes hierarchically as a
Bayesian network in a Markov Jump Process analogue of the RBN model.


  This article presents recent progress in the theory of quantum measurement
engines and discusses the implications of them for quantum interpretations and
philosophical implications of the theory. Several new measurement engine
designs are introduced and analyzed: We discuss a feedback based
atom-and-piston engine that sharply associates all work with successful events
and all quantum heat with the failed events, as well as an unconditional but
coherent qubit engine that can attain perfect efficiency. Any quantum
measurement of an observable that does not commute with the Hamiltonian will
necessarily change the energy of the system. We discuss different ways to
extract that energy, the efficiency and work production of that process.


  We investigate static and dynamic topologies of a 2-mode real world
university-industry cooperation network. Due to its large size and complex
structure, we choose to use statistical proxies for this goal. Among the
findings, we shall call attention to the rank-size distribution of the firm
node degrees with power law signature log-linear behavior. Which invokes hints
of a complex network architecture, as a counterpoint to the random case. We
compare furthermore the rank-size distributions of both modes with other
real-world 2-mode large-scale networks and draw parallels for their causes.
Moreover, we investigate structural change in the network by computing
Robins-Alexander clustering coefficients in a rolling window fashion in order
to capture topological change in the network temporal evolution. Findings
suggest that network stability is achieved after a short transition phase of
high clustering and cross-correlation.


  We investigate a multi-locus evolutionary model which is based on the DNA
shuffling protocol widely applied in \textit{in vitro} directed evolution. This
model incorporates selection, recombination and point mutations. The simplicity
of the model allows us to obtain a full analytical treatment of both its
dynamical and equilibrium properties, for the case of an infinite population.
We also briefly discuss finite population size corrections.


  This paper introduces a method for constructing an upper bound for
exploration policy using either the weighted variance of return sequences or
the weighted temporal difference (TD) error. We demonstrate that the variance
of the return sequence for a specific state-action pair is an important
information source that can be leveraged to guide exploration in reinforcement
learning. The intuition is that fluctuation in the return sequence indicates
greater uncertainty in the near future returns. This divergence occurs because
of the cyclic nature of value-based reinforcement learning; the evolving value
function begets policy improvements which in turn modify the value function.
Although both variance and TD errors capture different aspects of this
uncertainty, our analysis shows that both can be valuable to guide exploration.
We propose a two-stream network architecture to estimate weighted variance/TD
errors within DQN agents for our exploration method and show that it
outperforms the baseline on a wide range of Atari games.


  Cholesteric Liquid Crystals (CLCs), subject to externally applied magnetic
fields and confined between two parallel planar surfaces with strong
homeotropic anchoring conditions, are found to undergo transitions to different
types of helicoidal configurations with disclinations. Analytical and numerical
studies are performed in order to characterise their properties. In particular,
we produce a phase diagram for the transitions from the nematic state to the
helicoidal phases in terms of the molecular chirality and the strength of the
applied magnetic field.


  It is increasingly realized that taking stochastic effects into account is
important in order to study biological cells. However, the corresponding
mathematical formulation, the chemical master equation (CME), suffers from the
curse of dimensionality and thus solving it directly is not feasible for most
realistic problems. In this paper we propose a dynamical low-rank algorithm for
the CME that reduces the dimensionality of the problem by dividing the reaction
network into partitions. Only reactions that cross partitions are subject to an
approximation error (everything else is computed exactly). This approach,
compared to the commonly used stochastic simulation algorithm (SSA, a Monte
Carlo method), has the advantage that it is completely noise-free. This is
particularly important if one is interested in resolving the tails of the
probability distribution. We show that in some cases (e.g. for the lambda
phage) the proposed method can drastically reduce memory consumption and run
time and provide better accuracy than SSA.


  Modern geodesy is subject to a dramatic change from the Newtonian paradigm to
Einstein's theory of general relativity. This is motivated by the ongoing
advance in development of quantum sensors for applications in geodesy including
quantum gravimeters and gradientometers, atomic clocks and fiber optics for
making ultra-precise measurements of the geoid and multipolar structure of the
Earth's gravitational field. At the same time, VLBI, SLR, and GNSS have
achieved an unprecedented level of accuracy in measuring coordinates of the
reference points of the ITRF and the world height system. The main geodetic
reference standard is a normal gravity field represented in the Newtonian
gravity by the field of a Maclaurin ellipsoid. The present paper extends the
concept of the normal gravity field to the realm of general relativity. We
focus our attention on the calculation of the first post-Newtonian
approximation of the normal field that is sufficient for applications. We show
that in general relativity the level surface of the uniformly rotating fluid is
no longer described by the Maclaurin ellipsoid but is an axisymmetric spheroid
of the forth order. We parametrize the mass density distribution and derive the
post-Newtonian normal gravity field of the rotating spheroid which is given in
a closed form by a finite number of the ellipsoidal harmonics. We employ
transformation from the ellipsoidal to spherical coordinates to deduce the
post-Newtonian multipolar expansion of the metric tensor given in terms of
scalar and vector gravitational potentials of the rotating spheroid. We compare
these expansions with that of the normal gravity field generated by the Kerr
metric and demonstrate that the Kerr metric has a fairly limited application in
relativistic geodesy. Finally, we derive the post-Newtonian generalization of
the Somigliana formula for the gravity field on the reference ellipsoid.


  The emergence of the Internet of Things has seen the introduction of numerous
connected devices used for the monitoring and control of even Critical
Infrastructures. Distributed stream processing has become key to analyzing data
generated by these connected devices and improving our ability to make
decisions. However, optimizing these systems towards specific Quality of
Service targets is a difficult and time-consuming task, due to the large-scale
distributed systems involved, the existence of so many configuration
parameters, and the inability to easily determine the impact of tuning these
parameters.
  In this paper we present an approach for the effective testing of system
configurations for critical IoT analytics pipelines. We demonstrate our
approach with a prototype that we called Timon which is integrated with
Kubernetes. This tool allows pipelines to be easily replicated in parallel and
evaluated to determine the optimal configuration for specific applications. We
demonstrate the usefulness of our approach by investigating different
configurations of an exemplary geographically-based traffic monitoring
application implemented in Apache Flink.


  In the attempts to apply Finsler geometry to construct an extension of
general relativity, the question about a suitable generalization of the
Einstein equations is still under debate. Since Finsler geometry is based on a
scalar function on the tangent bundle, the field equation which determines this
function should also be a scalar equation. In the literature two such equations
have been suggested: the one by Rutz and the one by one of the authors. Here we
employ the method of canonical variational completion to show that Rutz
equation can not be obtained from a variation of an action and that its
variational completion yields the latter field equations. Moreover, to improve
the mathematical rigor in the derivation of the Finsler gravity field equation,
we formulate the Finsler gravity action on the positive projective tangent
bundle. This has the advantage of allowing us to apply the classical
variational principle, by choosing the domains of integration to be compact and
independent of the dynamical variable. In particular in the pseudo-Riemannian
case, the vacuum field equation becomes equivalent to the vanishing of the
Ricci tensor.


  The dynamical equations for particles in the Parrinello-Rahman Molecular
Dynamics were compared with the Newton's Second Law. The discrepancy is due to
using the in-complete particles' kinetic energy in the Lagrangian.


  In a Hamiltonian formalism we study chiral symmetry for lattice Fermions
formulated in terms of Shockley surface states bound to a wall in an extra
spatial dimension. For hadronic physics this provides a natural scheme for
taking quark masses to zero without requiring a precise tuning of parameters.
We illustrate the chiral anomaly as a flow of states in this extra dimension.
We discuss two alternatives for extending the picture to a chiral coupling of
gauge fields to such Fermions; one with a small explicit breaking of gauge
symmetry and one one with heavy mirror Fermions.


  To investigate the structure of individual differences in performance on
behavioral tasks, Haaf and Rouder (2017) developed a class of hierarchical
Bayesian mixed models with varying levels of constraint on the individual
effects. The models are then compared via Bayes factors, telling us which model
best predicts the observed data. One common criticism of their method is that
the observed data are assumed to be drawn from a normal distribution. However,
for most cognitive tasks, the primary measure of performance is a response
time, the distribution of which is well known to not be normal. In this paper,
I investigate the assumption of normality for two datasets in numerical
cognition. Specifically, I show that using a shifted lognormal model for the
response times does not change the overall pattern of inference. Further, since
the model-estimated effects are now on a logarithmic scale, the interpretation
of the modeling becomes more difficult, particularly because the estimated
effect is now multiplicative rather than additive. As a result, I recommend
that even though response times are not normally distributed in general, the
simplification afforded by the Haaf and Rouder (2017) approach provides a
pragmatic approach to modeling individual differences in behavioral tasks.


  In this paper, we develop a method which we call OnlineGCP for computing the
Generalized Canonical Polyadic (GCP) tensor decomposition of streaming data.
GCP differs from traditional canonical polyadic (CP) tensor decompositions as
it allows for arbitrary objective functions which the CP model attempts to
minimize. This approach can provide better fits and more interpretable models
when the observed tensor data is strongly non-Gaussian. In the streaming case,
tensor data is gradually observed over time and the algorithm must
incrementally update a GCP factorization with limited access to prior data. In
this work, we extend the GCP formalism to the streaming context by deriving a
GCP optimization problem to be solved as new tensor data is observed, formulate
a tunable history term to balance reconstruction of recently observed data with
data observed in the past, develop a scalable solution strategy based on
segregated solves using stochastic gradient descent methods, describe a
software implementation that provides performance and portability to
contemporary CPU and GPU architectures and integrates with Matlab for enhanced
useability, and demonstrate the utility and performance of the approach and
software on several synthetic and real tensor data sets.


  In this paper we characterise the equilibrium measure for a nonlocal and
anisotropic weighted energy describing the interaction of positive dislocations
in the plane. We prove that the minimum value of the energy is attained by a
measure supported on the vertical axis and distributed according to the
semi-circle law, a well-known measure which also arises as the minimiser of
purely logarithmic interactions in one dimension. In this way we give a
positive answer to the conjecture that positive dislocations tend to form
vertical walls. This result is one of the few examples where the minimiser of a
nonlocal energy is explicitly computed and the only one in the case of
anisotropic kernels.


  The goal of protein representation learning is to extract knowledge from
protein databases that can be applied to various protein-related downstream
tasks. Although protein sequence, structure, and function are the three key
modalities for a comprehensive understanding of proteins, existing methods for
protein representation learning have utilized only one or two of these
modalities due to the difficulty of capturing the asymmetric interrelationships
between them. To account for this asymmetry, we introduce our novel asymmetric
multi-modal masked autoencoder (AMMA). AMMA adopts (1) a unified multi-modal
encoder to integrate all three modalities into a unified representation space
and (2) asymmetric decoders to ensure that sequence latent features reflect
structural and functional information. The experiments demonstrate that the
proposed AMMA is highly effective in learning protein representations that
exhibit well-aligned inter-modal relationships, which in turn makes it
effective for various downstream protein-related tasks.


  In this paper we discuss generalizations of discrete torsion to noninvertible
symmetries in 2d QFTs. One point of this paper is to explain that there are two
complementary generalizations. Both generalizations are counted by
$H^2(G,U(1))$ when one specializes to ordinary finite groups $G$. However, the
counting is different for more general fusion categories. Furthermore, only one
generalizes the picture of discrete torsion as differences in choices of gauge
actions on B fields. Explaining this in detail, how one of the generalizations
of discrete torsion to noninvertible cases encodes actions on B fields, is the
other point of this paper. In particular, this generalizes old results in
ordinary orbifolds that discrete torsion is a choice of group action on the B
field. We also explain how this same generalization of discrete torsion gives
rise to physically-sensible twists on gaugeable algebras and fiber functors.


  When a rod is vertically withdrawn from a granular layer, oblique force
chains can be developed by effective shearing. In this study, the force-chain
structure in a rod-withdrawn granular layer was experimentally investigated
using a photoelastic technique. The rod is vertically withdrawn from a
two-dimensional granular layer consisting of bidisperse photoelastic disks.
During the withdrawal, the development process of force chains is visualized by
the photoelastic effect. By systematic analysis of photoelastic images, force
chain structures newly developed by the rod withdrawing are identified and
analyzed. In particular, the relation between the rod-withdrawing force
$F_\mathrm{w}$, total force-chains force $F_\mathrm{t}$, and their average
orientation $\theta$ are discussed. We find that the oblique force chains are
newly developed by withdrawing. The force-chain angle $\theta$ is almost
constant (approximately $20^{\circ}$ from the horizontal), and the total force
$F_\mathrm{t}$ gradually increases by the withdrawal. In addition,
$F_\mathrm{t}\sin\theta$ shows a clear correlation with $F_\mathrm{w}$.


  We analyze the spatial distributions of young stars in Taurus-Auriga and
Upper Sco as determined from the two-point correlation function (i.e. the mean
surface density of neighbors). The corresponding power-law fits allow us to
determine the fractal dimensions of each association's spatial distribution,
measure the stellar velocity dispersions, and distinguish between the bound
binary population and chance alignments of members. We find that the fractal
dimension of Taurus is D~1.05, consistent with its filamentary structure. The
fractal dimension of Upper Sco may be even shallower (D~0.7), but this fit is
uncertain due to the limited area and possible spatially-variable
incompleteness. We also find that random stellar motions have erased all
primordial structure on scales of <0.07 degrees in Taurus and <1.7 degrees in
Upper Sco; given ages of ~1 Myr and ~5 Myr, the corresponding internal velocity
dispersions are ~0.2 km/s and ~1.0 km/s, respectively. Finally, we find that
binaries can be distinguished from chance alignments at separations of <120"
(17000 AU) in Taurus and 75" (11000 AU) in Upper Sco. The binary populations in
these associations that we previously studied, spanning separations of 3-30",
are dominated by binary systems. However, the few lowest-mass pairs (M_prim <
0.3 M_sun) might be chance alignments.


  The field of natural language processing (NLP) has witnessed a transformative
shift with the emergence of large language models (LLMs), revolutionizing
various language tasks and applications, and the integration of LLM into
specialized domains enhances their capabilities for domain-specific
applications. Notably, NLP has made significant strides in organic chemistry,
particularly in predicting synthetic tasks, paving the way for the development
of LLMs tailored to the organic chemistry field. In this work, we introduce
SynAsk, a comprehensive organic chemistry domain-specific LLM platform
developed by AIChemEco Inc. By finetuning an LLM with domain-specific data and
integrating it with a chain of thought approach, SynAsk seamlessly accesses our
knowledge base and advanced chemistry tools in a question-and-answer format.
This includes functionalities such as a basic chemistry knowledge base,
molecular information retrieval, reaction performance prediction,
retrosynthesis prediction, chemical literature acquisition, and more. This
novel methodology synergizes fine-tuning techniques with external resource
integration, resulting in an organic chemistry-specific model poised to
facilitate research and discovery in the field. Accessible via
http://synask.aichemeco.com, SynAsk represents a significant advancement in
leveraging NLP for synthetic applications.


  Tor is a popular low-latency anonymity network. However, Tor does not protect
against the exploitation of an insecure application to reveal the IP address
of, or trace, a TCP stream. In addition, because of the linkability of Tor
streams sent together over a single circuit, tracing one stream sent over a
circuit traces them all. Surprisingly, it is unknown whether this linkability
allows in practice to trace a significant number of streams originating from
secure (i.e., proxied) applications. In this paper, we show that linkability
allows us to trace 193% of additional streams, including 27% of HTTP streams
possibly originating from "secure" browsers. In particular, we traced 9% of Tor
streams carried by our instrumented exit nodes. Using BitTorrent as the
insecure application, we design two attacks tracing BitTorrent users on Tor. We
run these attacks in the wild for 23 days and reveal 10,000 IP addresses of Tor
users. Using these IP addresses, we then profile not only the BitTorrent
downloads but also the websites visited per country of origin of Tor users. We
show that BitTorrent users on Tor are over-represented in some countries as
compared to BitTorrent users outside of Tor. By analyzing the type of content
downloaded, we then explain the observed behaviors by the higher concentration
of pornographic content downloaded at the scale of a country. Finally, we
present results suggesting the existence of an underground BitTorrent ecosystem
on Tor.


  This thesis is concerned with quantitative verification, that is, the
verification of quantitative properties of quantitative systems. These systems
are found in numerous applications, and their quantitative verification is
important, but also rather challenging. In particular, given that most systems
found in applications are rather big, compositionality and incrementality of
verification methods are essential.
  In order to ensure robustness of verification, we replace the Boolean yes-no
answers of standard verification with distances. Depending on the application
context, many different types of distances are being employed in quantitative
verification. Consequently, there is a need for a general theory of system
distances which abstracts away from the concrete distances and develops
quantitative verification at a level independent of the distance. It is our
view that in a theory of quantitative verification, the quantitative aspects
should be treated just as much as input to a verification problem as the
qualitative aspects are. In this work we develop such a general theory of
quantitative verification. We assume as input a distance between traces, or
executions, and then employ the theory of games with quantitative objectives to
define distances between quantitative systems. Different versions of the
quantitative bisimulation game give rise to different types of distances,
viz.~bisimulation distance, simulation distance, trace equivalence distance,
etc., enabling us to construct a quantitative generalization of van Glabbeek's
linear-time--branching-time spectrum. We also extend our general theory of
quantitative verification to a theory of quantitative specifications. For this
we use modal transition systems, and we develop the quantitative properties of
the usual operators for behavioral specification theories.


  It is known that in a water solution with multivalent counterions (Z-ions),
two likely charged macroions can attract each other due to correlations of
Z-ions adsorbed on their surfaces. This "correlation" attraction is
short-ranged and decays exponentially with increasing distance between
macroions at characteristic distance A/2\pi, where A is the average distance
between Z-ions on the surfaces of macroions. In this work, we show that an
additional long range "polarization" attraction exists when the bare surface
charge densities of the two macroions have the same sign, but are different in
absolute values. The key idea is that with adsorbed Z-ions, two insulating
macroions can be considered as conductors with fixed but different electric
potentials. Each potential is determined by the difference between the entropic
bulk chemical potential of a Z-ion and its correlation chemical potential at
the surface of the macroion determined by its bare surface charge density. When
the two macroions are close enough, they get polarized in such a way that their
adjacent spots form a charged capacitor, which leads to attraction. In a salt
free solution this polarization attractive force is long ranged: it decays as a
power of the distance between the surfaces of two macroions, d. The
polarization force decays slower than the van der Waals attraction and
therefore is much larger than it in a large range of distances. In the presence
of large amount of monovalent salt, when A/2\pi<< d<< r_s (r_s is the
Debye-H\"{u}ckel screening radius), this force is still much stronger than the
van der Waals attraction and the correlation attraction mentioned above.


  A 227 ksec Chandra Observatory X-ray image of the hot plasma in the Hydra A
cluster has revealed an extensive cavity system. The system was created by a
continuous outflow or a series of bursts from the nucleus of the central galaxy
over the past 200-500 Myr. The cavities have displaced 10% of the plasma within
a 300 kpc radius of the central galaxy, creating a swiss-cheese-like topology
in the hot gas. The surface brightness decrements are consistent with empty
cavities oriented within 40 degrees of the plane of the sky. The outflow has
deposited upward of 10^61 erg into the cluster gas, most of which was propelled
beyond the inner ~100 kpc cooling region. The supermassive black hole has
accreted at a rate of approximately 0.1-0.25 solar masses per year over this
time frame, which is a small fraction of the Eddington rate of a ~10^9 solar
mass black hole, but is dramatically larger than the Bondi rate. Given the
previous evidence for a circumnuclear disk of cold gas in Hydra A, these
results are consistent with the AGN being powered primarily by infalling cold
gas. The cavity system is shadowed perfectly by 330 MHz radio emission. Such
low frequency synchrotron emission may be an excellent proxy for X-ray cavities
and thus the total energy liberated by the supermassive black hole.


  The projection predictive variable selection is a decision-theoretically
justified Bayesian variable selection approach achieving an outstanding
trade-off between predictive performance and sparsity. Its projection problem
is not easy to solve in general because it is based on the Kullback-Leibler
divergence from a restricted posterior predictive distribution of the so-called
reference model to the parameter-conditional predictive distribution of a
candidate model. Previous work showed how this projection problem can be solved
for response families employed in generalized linear models and how an
approximate latent-space approach can be used for many other response families.
Here, we present an exact projection method for all response families with
discrete and finite support, called the augmented-data projection. A simulation
study for an ordinal response family shows that the proposed method performs
better than or similarly to the previously proposed approximate latent-space
projection. The cost of the slightly better performance of the augmented-data
projection is a substantial increase in runtime. Thus, in such cases, we
recommend the latent projection in the early phase of a model-building workflow
and the augmented-data projection for final results. The ordinal response
family from our simulation study is supported by both projection methods, but
we also include a real-world cancer subtyping example with a nominal response
family, a case that is not supported by the latent projection.


  This PhD Thesis is devoted to the study of Hodge structures on a special type
of complex algebraic varieties, the so-called character varieties. For this
purpose, we propose to use a powerful algebro-geometric tool coming from
theoretical physics, known as Topological Quantum Field Theory (TQFT). With
this idea in mind, in the present Thesis we develop a formalism that allows us
to construct TQFTs from two simpler pieces of data: a field theory (geometric
data) and a quantisation (algebraic data). As an application, we construct a
TQFT computing Hodge structures on representation varieties and we use it for
computing explicity the Deligne-Hodge polynomials of parabolic
$\mathrm{SL}_2(\mathbb{C})$-character varieties.


  We point out a flaw in the unfair case of the quantum Prisoner's Dilemma as
introduced in the pioneering Letter "Quantum Games and Quantum Strategies" of
Eisert, Wilkens and Lewenstein. It is not true that the so-called miracle move
therein always gives quantum Alice a large reward against classical Bob and
outperforms tit-for-tat in an iterated game. Indeed, we introduce a new
classical strategy that becomes Bob's dominant strategy, should Alice play the
miracle move. Finally, we briefly survey the subsequent literature and turn to
the 3-parameter strategic space instead of the 2-parameter one of Eisert et al.


  We have identified intracluster planetary nebulae in a Virgo cluster core
field by imaging with the Subaru Suprime-Cam through two narrow band filters
centered at the redshifted wavelengths of the [OIII] $\lambda=5007$ \AA and the
H$\alpha$ $\lambda = 6563$ \AA lines; broad-band images in V and R were
acquired to check for emission in the adjacent continuum. Emission line objects
in Virgo are then selected from the two-color diagram ([OIII] -- Halpha) vs.
([OIII] -- (V+R)), which is calibrated using PNe in M84 (Jacoby et al. 1990).
Using both [OIII] and Halpha allows us to distinguish bona-fide planetary
nebulae from high redshift emission-line galaxies at the bright end of the
[OIII] luminosity function. Spectroscopic observations of a subsample of these
objects were made at the TNG and at the VLT, in a region around M84 and in an
intracluster field respectively. The observations confirm the efficiency of the
combined [OIII]+H$\alpha$ imaging to identify true PNe. We also obtained the
first spectrum of an intracluster PN which shows the [OIII] doublet with S/N >
10 and its Halpha emission. From the results based on the spectroscopic
follow-up, we derive a lower limit to the fraction of the Virgo cluster light
contributed by the intracluster stars at the surveyed position in the cluster
core: it amounts to 10%.


  We prove homological stability for sequences of "oriented configuration
spaces" as the number of points in the configuration goes to infinity. These
are spaces of configurations of n points in a connected manifold M of dimension
at least 2 which 'admits a boundary', with labels in a path-connected space X,
and with an orientation: an ordering of the points up to even permutations.
They are double covers of the corresponding unordered configuration spaces,
where the points do not have this orientation. To prove our result we adapt
methods from a paper of Randal-Williams, which proves homological stability in
the unordered case. Interestingly the oriented configuration spaces stabilise
more slowly than the unordered ones: the stability slope we obtain is
one-third, compared to one-half in the unordered case (these are the best
possible slopes in their respective cases). This result can also be interpreted
as homological stability for unordered configuration spaces with certain
twisted coefficients.


  We have measured the Stark shift of the $A^2\Pi_{1/2} - X^2\Sigma^+$
transition in YbF. We use a molecular beam triple resonance method, with two
laser transitions acting as pump and probe, assisted by an rf transition that
tags a single hyperfine transition of the X state. After subtracting the known
ground state Stark shift, we obtain a value of $70.3(1.5) $Hz/(V/cm)$^2$ for
the static electric polarizability of the state $A^2\Pi_{1/2}(J=1/2,f)$. From
this we calculate a value $\mu_e=2.46(3)$D for the electric dipole moment of
the $A^2\Pi_{1/2}(v=0)$ state.


  Vertex models are a popular approach to modeling the mechanical and dynamical
properties of dense biological tissues, describing the tissue as a network of
connected polygons representing the cells. Recently a class of two-dimensional
vertex models was shown to exhibit a disordered rigidity transition controlled
by the preferred cellular geometry, echoing experimental findings. An
attractive variant of these models uses a Voronoi tessellation to describe the
cells and endows them with a non-equilibrium model of cellular motility,
leading to rich, glassy behavior. This glassy behavior was suggested to be
inextricably linked to an underlying jamming transition. We test this
conjecture, exploring the low-effective-temperature limit of the Voronoi model
by studying cell trajectories from detailed dynamical simulations in
combination with rigidity measurements of energy-minimized disordered cell
configurations. We find that the zero-temperature limit of this model has no
unjamming transition.


  Mobile Data Offloading permits users to use cheap communication media,
whenever feasible, for delivering their personal data instead of using the
infrastructure which is more expensive. Having good procedures to assess and
compare the different possibilities a device has to send his data is crucial.
In the following paper, we propose an evaluating approach that takes into
consideration the topology changes history in order to provide an efficient way
to calculate the quality of a specific medium.
  In particular \textit{Bayesian Networks} are used as basis to provide our
solution to the Maniac Challenge 2013. Bayesian Networks is a statistical model
for the generation of inferences starting with very little information. In the
next sections we will give a definition of the Bayesian Networks focusing on
the simplest and computationally efficient of the Bayesian Networks versions,
named \textit{\naive Bayes}. Finally, we will show our method for the challenge
to evaluate the medium and the bid strategy.


  Zener ordering is a phenomenon that octahedral interstitial atoms such as
carbon occupy the same sublattice inside bcc matrix such as iron. The original
formulation relies on a mean field theory, which is still most in use today. We
employ multiple methods, such as Molecular Dynamics, Metropolis Monte Carlo,
Mean Field Theory with chemical interactions and finite temperature effects to
show that the Zener ordering for iron carbon systems is governed by local
chemical interactions and finite temperature effects and less of mean field
nature as described originally by Zener.


  Using two approaches to strongly correlated systems, the extremely correlated
Fermi liquid theory and the dynamical mean field theory, we compute the
transverse transport coefficients, namely the Hall constants $R_H$ and Hall
angles $\theta_H$, and also the longitudinal and transverse optical response of
the $U=\infty$ Hubbard model in the limit of infinite dimensions. We focus on
two successive low-temperature regimes, the Gutzwiller correlated Fermi liquid
(GCFL) and the Gutzwiller correlated strange metal (GCSM). We find that the
Hall angles $\cot \theta_H \propto T^2$ in the GCFL regime, on warming into the
strange metal regime, it passes through a downward bend and continues as $T^2$.
Equivalently, $R_H$ is weakly temperature dependent in the GCFL regime, and
becomes strongly $T$-dependent in the GCSM regime. Drude peaks are found for
both the longitudinal optical conductivity $\sigma_{xx}(\omega)$ and the
optical Hall angles $\tan \theta_H(\omega)$ below certain characteristic energy
scales. By comparing the relaxation rates extracted from fitting to the Drude
formula, we find that in the GCFL regime there is a single relaxation rate
controlling both longitudinal and transverse transport, while in the GCSM
regime two independent relaxation rates emerge. We trace the origin of this
behavior to the dynamical particle-hole asymmetry of the Dyson self-energy,
arguably a generic feature of doped Mott insulators.


  The standard alpha-disc model predicts an anti-correlation between the
density of the inner accretion disc and the black hole mass times square of the
accretion rate, as seen in higher mass ($M_{\rm BH}>10^{6} M_{\odot}$) active
galactic nuclei (AGNs). In this work, we test the predictions of the alpha-disc
model and study the properties of the inner accretion flow for the low-mass end
($M_{\rm BH}\approx 10^{5-6}M_{\odot}$) of AGNs. We utilize a new high-density
disc reflection model where the density parameter varies from $n_{\rm
e}=10^{15}$ to $10^{20}$ cm$^{-3}$ and apply it to the broadband X-ray (0.3-10
keV) spectra of the low-mass AGN sample. The sources span a wide range of
Eddington fractions and are consistent with being sub-Eddington or
near-Eddington. The X-ray spectra reveal a soft X-ray excess below $\sim 1.5$
keV which is well modeled by high-density reflection from an ionized accretion
disc of density $n_{\rm e}\sim 10^{18}$ cm$^{-3}$ on average. The results
suggest a radiation pressure-dominated disc with an average of 70% fraction of
the disc power transferred to the corona, consistent with that observed in
higher mass AGNs. We show that the disc density higher than $10^{15}$ cm$^{-3}$
can result from the radiation pressure compression when the disc surface does
not hold a strong magnetic pressure gradient. We find tentative evidence for a
drop in black hole spin at low-mass regimes.


  Testing the validity of probabilistic models containing unmeasured (hidden)
variables is shown to be a hard task. We show that the task of testing whether
models are structurally incompatible with the data at hand, requires an
exponential number of independence evaluations, each of the form: "X is
conditionally independent of Y, given Z." In contrast, a linear number of such
evaluations is required to test a standard Bayesian network (one per vertex).
On the positive side, we show that if a network with hidden variables G has a
tree skeleton, checking whether G represents a given probability model P
requires the polynomial number of such independence evaluations. Moreover, we
provide an algorithm that efficiently constructs a tree-structured Bayesian
network (with hidden variables) that represents P if such a network exists, and
further recognizes when such a network does not exist.


  Social media platforms, particularly Telegram, play a pivotal role in shaping
public perceptions and opinions on global and national issues. Unlike
traditional news media, Telegram allows for the proliferation of user-generated
content with minimal oversight, making it a significant venue for the spread of
controversial and misinformative content. During the COVID-19 pandemic,
Telegram's popularity surged in Singapore, a country with one of the highest
rates of social media use globally. We leverage Singapore-based Telegram data
to analyze information flows within groups focused on COVID-19 and climate
change. Using k-means clustering, we identified distinct user archetypes,
including Skeptic, Engaged Advocate, Observer, and Analyst, each contributing
uniquely to the discourse. We developed a model to classify users into these
clusters (Precision: Climate change: 0.99; COVID-19: 0.95). By identifying
these user archetypes and examining their contributions to information
dissemination, we sought to uncover patterns to inform effective strategies for
combating misinformation and enhancing public discourse on pressing global
issues.


  Codimension-two bifurcations are fundamental and interesting phenomena in
dynamical systems. Fold-Hopf and double-Hopf bifurcations are the most
important among them. We study the unfoldings of these two codimension-two
bifurcations, and obtain sufficient conditions for their nonintegrability in
the meaning of Bogoyavlenskij. We reduce the problems of the unfoldings to
those of planar polynomial vector fields and analyze the nonintegrability of
the planar vector fields, based on Ayoul and Zung's version of the
Morales-Ramis theory. New useful criteria for nonintegrability of planar
polynomial vector fields are also given. The approaches used here are
applicable to many problems including circular symmetric systems.


  Robust and accurate detection of small moving targets in cluttered moving
backgrounds is a significant and challenging problem for robotic visual systems
to perform search and tracking tasks. Inspired by the neural circuitry of
elementary motion vision in the mammalian retina, this paper proposes a
bioinspired retinal neural network based on a new neurodynamics-based temporal
filtering and multiform 2-D spatial Gabor filtering. This model can estimate
motion direction accurately via only two perpendicular spatiotemporal filtering
signals, and respond to small targets of different sizes and velocities by
adjusting the dendrite field size of the spatial filter. Meanwhile, an
algorithm of directionally selective inhibition is proposed to suppress the
target-like features in the moving background, which can reduce the influence
of background motion effectively. Extensive synthetic and real-data experiments
show that the proposed model works stably for small targets of a wider size and
velocity range, and has better detection performance than other bioinspired
models. Additionally, it can also extract the information of motion direction
and motion energy accurately and rapidly.


  We show that the size of a 4-critical graph of girth at least five is bounded
by a linear function of its genus. This strengthens the previous bound on the
size of such graphs given by Thomassen. It also serves as the basic case for
the description of the structure of 4-critical triangle-free graphs embedded in
a fixed surface, presented in a future paper of this series.


  We study a tensor network formulation of the two dimensional lattice
$\mathcal{N}=1$ Wess-Zumino model with Wilson derivatives for both fermions and
bosons. The tensor renormalization group allows us to compute the partition
function without the sign problem, and basic ideas to obtain a tensor network
for both fermion and scalar boson systems were already given in previous works.
In addition to improving the methods, we have constructed a tensor network
representation of the model including the Yukawa-type interaction of Majorana
fermions and real scalar bosons. We present some numerical results.


  On 31 December 2018 at 10:36:34 UTC shallow earthquake of ML=4.3 took place
25 Km east of Cairo and in the vicinity of the under-construction new Capital
city of Egypt (Fig.1). The event was interesting as it was felt with intensity
IV up to 50 Km epicentral distance with relatively small magnitude Mw=3.8. the
focal mechanism of the event showed Normal faulting with a slight component of
strike-slip movement which is in good agreement with the tectonic regime of the
area and estimated source parameters are found to be in agreement with
intraplate earthquakes. These results could support that the event is triggered
by the Pre-Tertiary E-W faults as an on land continuation of the Gulf of Suez
extensional process.


  We introduce a novel geometry-oriented methodology, based on the emerging
tools of topological data analysis, into the change point detection framework.
The key rationale is that change points are likely to be associated with
changes in geometry behind the data generating process. While the applications
of topological data analysis to change point detection are potentially very
broad, in this paper we primarily focus on integrating topological concepts
with the existing nonparametric methods for change point detection. In
particular, the proposed new geometry-oriented approach aims to enhance
detection accuracy of distributional regime shift locations. Our simulation
studies suggest that integration of topological data analysis with some
existing algorithms for change point detection leads to consistently more
accurate detection results. We illustrate our new methodology in application to
the two closely related environmental time series datasets -ice phenology of
the Lake Baikal and the North Atlantic Oscillation indices, in a research query
for a possible association between their estimated regime shift locations.


  Galaxy clusters are useful laboratories to investigate the evolution of the
Universe, and accurately measuring their total masses allows us to constrain
important cosmological parameters. However, estimating mass from observations
that use different methods and spectral bands introduces various systematic
errors. This paper evaluates the use of a Convolutional Neural Network (CNN) to
reliably and accurately infer the masses of galaxy clusters from the Compton-y
parameter maps provided by the Planck satellite. The CNN is trained with mock
images generated from hydrodynamic simulations of galaxy clusters, with
Planck's observational limitations taken into account. We observe that the CNN
approach is not subject to the usual observational assumptions, and so is not
affected by the same biases. By applying the trained CNNs to the real Planck
maps, we find cluster masses compatible with Planck measurements within a 15%
bias. Finally, we show that this mass bias can be explained by the well known
hydrostatic equilibrium assumption in Planck masses, and the different
parameters in the Y500-M500 scaling laws. This work highlights that CNNs,
supported by hydrodynamic simulations, are a promising and independent tool for
estimating cluster masses with high accuracy, which can be extended to other
surveys as well as to observations in other bands.


  We propose an efficient method to compute Lyapunov exponents and Lyapunov
eigenvectors of long-range interacting many-particle systems, whose dynamics is
described by the Vlasov equation. We show that an expansion of a distribution
function using Hermite modes (in momentum variable) and Fourier modes (in
configuration variable) converges fast if an appropriate scaling parameter is
introduced and identified with the inverse of the system temperature. As a
consequence, dynamics and linear stability properties of many-particle states,
both in the close-to and in the far-from equilibrium regimes can be predicted
using a small number of expansion coefficients. As an example of a long-range
interacting system we investigate stability properties of stationary states of
the Hamiltonian mean-field model.


  We study large D effective theory for D dimensional charged (Anti) de Sitter
black holes. Then we show that de Sitter Reissner-Nordstrom black hole becomes
unstable against gravitational perturbations at larger charge than certain
critical value in higher dimension. Furthermore we find that there is a
non-trivial zero-mode static perturbation at the critical charge. The existence
of static perturbations suggests the appearance of non-spherical symmetric
solution branches of static charged de Sitter black hole. This expectation is
confirmed by constructing the non-spherical symmetric static solutions of large
D effective equations.


  We present $\Phi$- and $J/\Psi$--nuclear bound state energies and absorption
widths for some selected nuclei, using potentials in the local density
approximation computed from an effective Lagrangian approach combined with the
quark-meson coupling model. Our results suggest that these mesons should form
bound states with all the nuclei considered provided that these mesons are
produced in nearly recoilless kinematics.


  We present a loop-by-loop method for computing the differential equations of
Feynman integrals using the recently developed dual form formalism. We give
explicit prescriptions for the loop-by-loop fibration of multi-loop dual forms.
Then, we test our formalism on a simple, but non-trivial, example: the two-loop
three-mass elliptic sunrise family of integrals. We obtain an epsilon-form
differential equation within the correct function space in a sequence of
relatively simple algebraic steps. In particular, none of these steps relies on
the analysis of $q$-series. Then, we discuss interesting properties satisfied
by our dual basis as well as its simple relation to the known epsilon-form
basis of Feynman integrands. The underlying K3-geometry of the three-loop
four-mass sunrise integral is also discussed. Finally, we speculate on how to
construct a "good" loop-by-loop basis at three-loop.


  The conformational mobility of organic molecules defined as a variability of
practically accessible conformers plays a critical role in determining
electronic, chemical, and physical properties within computational methods. At
the same time, there is a challenge of identifying compact set of global and
local conformers for the comprehensive description of potential energy surface.
Here we apply nature-inspired algorithms to resolve this issue. Among all the
considered algorithms, the artificial bee colony optimizer exhibits the highest
performance in discovering conformers detected both in gas and condensed
phases. We hope that our approach enables researchers to make a next step in
organic crystal packing studies.


  We present a statistical analysis of spectra of transfer matrices of
classical lattice spin models; this continues the work on the eight-vertex
model of the preceding paper. We show that the statistical properties of these
spectra can serve as a criterion of integrability. It provides also an
operational numerical method to locate integrable varieties. In particular, we
distinguish the notions of integrability and criticality considering the two
examples of the three-dimensional Ising critical point and the two-dimensional
three-state Potts critical point. For complex spectra which appear frequently
in the context of transfer matrices, we show that the notion of independence of
eigenvalues for integrable models still holds.


  We introduce a unified Gaussian quantum operator representation for fermions
and bosons. The representation extends existing phase-space methods to Fermi
systems as well as the important case of Fermi-Bose mixtures. It enables
simulations of the dynamics and thermal equilibrium states of many-body quantum
systems from first principles. As an example, we numerically calculate
finite-temperature correlation functions for the Fermi Hubbard model, with no
evidence of the Fermi sign problem.


  Multigrid algorithms are among the fastest iterative methods known today for
solving large linear and some non-linear systems of equations. Greatly
optimized for serial operation, they still have a great potential for
parallelism not fully realized. In this work, we present a novel multigrid
algorithm designed to work entirely inside many-core architectures like the
graphics processing units (GPUs), without memory transfers between the GPU and
the central processing unit (CPU), avoiding low bandwitdth communications. The
algorithm is denoted as the high occupancy multigrid (HOMG) because it makes
use of entire grid operations with interpolations and relaxations fused into
one task, providing useful work for every thread in the grid. For a given
accuracy, its number of operations scale linearly with the total number of
nodes in the grid. Perfect scalability is observed for a large number of
processors.


  In this letter we propose a general principle for how to build up a quantum
neural network with high learning efficiency. Our stratagem is based on the
equivalence between extracting information from input state to readout qubit
and scrambling information from the readout qubit to input qubits. We
characterize the quantum information scrambling by operator size growth, and by
Haar random averaging over operator sizes, we propose an averaged operator size
to describe the information scrambling ability for a given quantum neural
network architectures, and argue this quantity is positively correlated with
the learning efficiency of this architecture. As examples, we compute the
averaged operator size for several different architectures, and we also
consider two typical learning tasks, which are a regression task of a quantum
problem and a classification task on classical images, respectively. In both
cases, we find that, for the architecture with a larger averaged operator size,
the loss function decreases faster or the prediction accuracy in the testing
dataset increases faster as the training epoch increases, which means higher
learning efficiency. Our results can be generalized to more complicated quantum
versions of machine learning algorithms.


  An exact formula \[ B(n) = \frac{n}{2}(\lfloor \lg n \rfloor + 1) - \sum
_{k=0} ^{\lfloor \lg n \rfloor} 2^k Zigzag(\frac{n}{2^{k+1}}), \] where \[
Zigzag (x) = \min (x - \lfloor x \rfloor, \lceil x \rceil - x), \] for the
minimal number $ B(n) $ of comparisons of keys performed by $ {\tt MergeSort} $
on an $ n $-element array is derived and analyzed. The said formula is less
complex than any other known formula for the same and can be evaluated in $
O(\log ^{c}) $ time, where $ c $ is a constant. It is shown that there is no
closed-form formula for the above.
  Since the recurrence relation for the minimal number of comparisons of keys
for $ {\tt MergeSort} $ is identical with a recurrence relation for the number
of 1s in binary expansions of all integers between $ 0 $ and $ n $
(exclusively), the above results extend to the sum of binary digits problem.


  CrI3 is a prototypical van der Waals ferromagnet with a magnetic honeycomb
lattice. Previous inelastic neutron scattering studies have suggested
topological nature of its magnetic excitations with a magnon gap at the Dirac
points, which are anticipated to give rise to magnon thermal Hall effect. Here
we report thermal transport properties of CrI3 and show that the long-sought
thermal Hall signal anticipated for topological magnons is fairly small. In
contrast, we find that CrI3 exhibits an appreciable anomalous thermal Hall
signal at lower temperature which may arise from magnon-phonon hybridization or
magnon-phonon scattering. These findings are anticipated to stimulate further
neutron scattering studies on CrI3 single crystal, which can shed light not
only on the intrinsic nature of magnetic excitations but also on the
magnon-phonon interaction.


  In this research paper, we provide a concise overview of fractal calculus
applied to fractal sets. We introduce and solve a second $\alpha$-order fractal
differential equation with constant coefficients across different scenarios. We
propose a uniqueness theorem for second $\alpha$-order fractal linear
differential equations. We define the solution space as a vector space with
non-integer orders. We establish precise conditions for second $\alpha$-order
fractal linear differential equations and derive the corresponding fractal
adjoint differential equation.


  We consider the problem of learning soft assignments of $N$ items to $K$
categories given two sources of information: an item-category similarity
matrix, which encourages items to be assigned to categories they are similar to
(and to not be assigned to categories they are dissimilar to), and an item-item
similarity matrix, which encourages similar items to have similar assignments.
We propose a simple quadratic programming model that captures this intuition.
We give necessary conditions for its solution to be unique, define an
out-of-sample mapping, and derive a simple, effective training algorithm based
on the alternating direction method of multipliers. The model predicts
reasonable assignments from even a few similarity values, and can be seen as a
generalization of semisupervised learning. It is particularly useful when items
naturally belong to multiple categories, as for example when annotating
documents with keywords or pictures with tags, with partially tagged items, or
when the categories have complex interrelations (e.g. hierarchical) that are
unknown.


  Grating cloaks are a variation of dielectric carpet (or ground- plane)
cloaks. The latter were introduced by Li and Pendry. In contrast to the
numerical work involved in the quasi-conformal carpet cloak, the
refractive-index profile of a conformal grating cloak follows a closed and
exact analytical form. We have previously mentioned that finite-size conformal
grating cloaks may exhibit better cloaking than usual finite-size carpet
cloaks. In this letter, we directly visualize their performance using
photorealistic ray-tracing simulations. We employ a Newtonian approach that is
advantageous compared to conventional ray tracing based on Snell's law.
Furthermore, we quantify the achieved cloaking quality by computing the
cross-correlations of rendered images. The cross-correlations for the grating
cloak are much closer to 100% (i.e., ideal) than those for the Gaussian carpet
cloak.


  The aim of this paper is to open the problem of construction of a nonlinear
connection $\Gamma=(M^{(i)}_{(\alpha)\beta}, N^{(i)}_{(\alpha)j})$ on the jet
bundle of first order $J^1(T,M)$, which to be canonically produced by a
Kronecker product vertical metrical d-tensor
$G^{(\alpha)(\beta)}_{(i)(j)}=h^{\alpha\beta}g_{ij}$, possibly provided by
multi-time dependent quadratic Lagrangians coming from various branches of
theoretical physics: bosonic strings theory, electrodynamics or elasticity.


  This paper presents a class of linear predictors for nonlinear controlled
dynamical systems. The basic idea is to lift the nonlinear dynamics into a
higher dimensional space where its evolution is approximately linear. In an
uncontrolled setting, this procedure amounts to a numerical approximation of
the Koopman operator associated to the nonlinear dynamics. In this work, we
extend the Koopman operator to controlled dynamical systems and compute a
finite-dimensional approximation of the operator in such a way that this
approximation has the form a linear controlled dynamical system. In numerical
examples, the linear predictors obtained in this way exhibit a performance
superior to existing linear predictors such as those based on local
linearization or the so-called Carleman linearization. Importantly, the
procedure to construct these linear predictors is completely data-driven and
extremely simple -- it boils down to a nonlinear transformation of the data
(the lifting) and a linear least squares problem in the lifted space that can
be readily solved for large data sets. These linear predictors can be readily
used to design controllers for the nonlinear dynamical system using linear
controller design methodologies. We focus in particular on model predictive
control (MPC) and show that MPC controllers designed in this way enjoy
computational complexity of the underlying optimization problem comparable to
that of MPC for a linear dynamical system of the same size. Importantly, linear
inequality constraints on the state and control inputs as well as nonlinear
constraints on the state can be imposed in a linear fashion in the proposed MPC
scheme. Similarly, cost functions nonlinear in the state variable can be
handled in a linear fashion. Numerical examples (including a high-dimensional
nonlinear PDE control) demonstrate the approach with the source code available
online.


  A test dataset is generated using temperature cycled operation with a WO3
metal oxide semiconductor (MOS) gas sensor. Six concentrations of naphthalene
from 0 to 40 ppb are measured and, subsequently, used to evaluate the
performance of three variants of Partial Least Squares Regression (PLSR).
Ordinary PLSR produces highly non-linear models due to the non-linear response
of the sensor. Double-logarithmic data results in a model with much better
linearity which has a resolution of 4 ppb in the range from 0 to 20 ppb. The
more complex Locally Weighted PLSR (LW-PLSR) produces an even better model,
especially for higher concentrations, without making any assumptions for
relationships in the underlying data.


  We provide a covariant framework to study classically the stability of small
perturbations on the so-called gonihedric string model by making precise use of
variational techniques. The local action depends of the square root of the
quadratic mean extrinsic curvature of the worldsheet swept out by the string,
and is reparametrization invariant. A general expression for the worldsheet
perturbations, guided by Jacobi equations without any early gauge fixing, is
obtained. This is manifested through a set of highly coupled nonlinear
differential partial equations where the perturbations are described by scalar
fields, $\Phi^i$, living in the worldsheet. This model contains, as a special
limit, to the linear model in the mean extrinsic curvature. In such a case the
Jacobi equations specialize to a single wave-like equation for $\Phi$.


  Pre-trained speech Transformers in speech translation (ST) have facilitated
state-of-the-art (SotA) results; yet, using such encoders is computationally
expensive. To improve this, we present a novel Reducer Adaptor block, RedApt,
that could be seamlessly integrated within any Transformer-based speech
encoding architecture. Integrating the pretrained wav2vec 2 speech encoder with
RedAptbrings 41% speedup, 33% memory reduction with 24% fewer FLOPs at
inference. To our positive surprise, our ST model with RedApt outperforms the
SotA architecture by an average of 0.68 BLEU score on 8 language pairs from
Must-C.


  Recently, Dicus and Repko computed nu gamma --> nu gamma gamma for energies
below the threshold for e^{+}e^{-} pair production. They found across section
on the order of 10^{-52} omega ^{gamma} with gamma =10, where omega is the CMS
energy of one of the initial particles in MeV. Cross sections for the crossed
processes are the same to factors of order one. This note investigates the
extent to which these processes could, if their result extrapolates past 1 MeV:
affect supernova dynamics; cut off the energy distribution of very high energy
cosmic photons and neutrinos; and possibly give rise to an observable gamma
signal from scattering of neutrinos from one supernova by those of a second
supernova close in space and time. We also estimate, from Supernova 1987A,
that, in the region above a few MeV, gamma must fall below 8.4.


  We have constructed three-dimensional models for the equilibrium abundance of
molecular hydrogen within diffuse interstellar clouds of arbitrary geometry
that are illuminated by ultraviolet radiation. The position-dependent photo-
dissociation rate of H$_2$ within such clouds was computed using a 26-ray
approximation to model the attenuation of the incident ultraviolet radiation
field by dust and by H$_2$ line absorption. We have applied our modeling
technique to the isolated diffuse cloud G236+39, assuming that the cloud has a
constant density and that the thickness of the cloud along the line of sight is
at every point proportional to the 100 um continuum intensity measured by IRAS.
We find that our model can successfully account for observed variations in the
ratio of 100 umu continuum intensity to HI column density, with larger values
of that ratio occurring along lines of sight in which the molecular hydrogen
fraction is expected to be largest. Using a standard chi^2 analysis to assess
the goodness of fit of our models, we find (at the 60sigma level) that a
three-dimensional model is more successful in matching the observational data
than a one-dimensional model in which the geometrical extent of the cloud along
the line of sight is assumed to be very much smaller than its extent in the
plane-of-the-sky. If D is the distance to G236+39, and given standard
assumptions about the rate of grain-catalysed H_2 formation, we find that the
cloud has an extent along the line of sight that is 0.9+-0.1 times its mean
extent projected onto the plane of the sky; a gas density of (53+-8)(100 pc/D)
cm^-3; and is illuminated by a radiation field of (1.1+-0.2) (100 pc/D) times
the mean interstellar radiation field estimated by Draine (1978). The derived
100 um emissivity per nucleon is (1.13+-0.06)x10^-20 MJy sr^-1 cm^2.


  In a cloud chamber, the quantum measurement problem amounts to explaining the
first droplet in a charged-particle track; subsequent droplets are explained by
Mott's 1929 wave-theoretic argument about collision-induced wavefunction
collimation. I formulate a mechanism for how the first droplet in a cloud
chamber track arises, making no reference to quantum measurement axioms. I look
specifically at tracks of charged particles emitted in the simplest slow
decays, because I can reason about rather than guess the form that wave packets
take. The first visible droplet occurs when a randomly occurring,
barely-subcritical vapor droplet is pushed past criticality by ionization
triggered by the faint wavefunction of the emitted charged particle. This is
possible because potential energy incurred when an ionized vapor molecule
polarizes the other molecules in a droplet can balance the excitation energy
needed for the emitted charged particle to create the ion in the first place.
This degeneracy is a singular condition for Coulombic scattering, leading to
infinite or near-infinite ionization cross sections, and from there to an
emergent Born rule in position space, but not an operator projection as in the
projection postulate. Analogous mechanisms may explain canonical quantum
measurement behavior in detectors such as ionization chambers, proportional
counters, photomultiplier tubes or bubble chambers. This work is important
because attempts to understand canonical quantum measurement behavior and its
limitations have become urgent in view of worldwide investment in quantum
computing and in searches for super-rare process (e.g., proton decay).


  Motivated by the swift global transmission of infectious diseases, we present
a comprehensive framework for network-based epidemic control. Our aim is to
curb epidemics using two different approaches. In the first approach, we
introduce an optimization strategy that optimally reduces travel rates. We
analyze the convergence of this strategy and show that it hinges on the network
structure to minimize infection spread. In the second approach, we expand the
classic SIR model by incorporating and optimizing quarantined states to
strategically contain the epidemic. We show that this problem reduces to the
problem of matrix balancing. We establish a link between optimization
constraints and the epidemic's reproduction number, highlighting the
relationship between network structure and disease dynamics. We demonstrate
that applying augmented primal-dual gradient dynamics to the optimal quarantine
problem ensures exponential convergence to the KKT point. We conclude by
validating our approaches using simulation studies that leverage public data
from counties in the state of Massachusetts.


  Nuclear spin-1/2 lattices where each spin has a small effective number of
interacting neighbors represent a particular challenge for first-principles
calculations of free induction decays (FIDs) observed by nuclear magnetic
resonance (NMR). The challenge originates from the fact that these lattices are
far from the limit where classical spin simulations perform well. Here we use
the recently developed method of hybrid quantum-classical simulations to
compute nuclear FIDs for $^{29}$Si-enriched silicon and fluorapatite. In these
solids, small effective number of interacting neighbors is either due to the
partition of the lattice into pairs of strongly coupled spins (silicon), or due
to the partition into strongly coupled chains (fluorapatite). We find a very
good overall agreement between the hybrid simulation results and the
experiments. In addition, we introduce an extension of the hybrid method, which
we call the method of "coupled quantum clusters". It is tested on
$^{29}$Si-enriched silicon and found to exhibit excellent performance.


  In a new approach to the theory of integration over Wess-Zumino
supermanifolds, we suggest that a fundamental principle is their consistency
with an ``Ethereal Conjecture'' that asserts the topology of the supermanifold
must be generated essentially from its bosonic submanifold. This naturally
leads to a theory of ``ectoplasmic'' integration based on super p-forms. One
consequence of this approach is that the derivation of ``density projection
operators'' becomes trivial in a number of supergravity theories.


  This is the talk given at ``Gribov-75 Memorial Workshop on Quarks, Hadrons,
and Strong Interactions", May 22-24, 2005, Budapest. Hungary. In this talk we
discuss the BFKL Pomeron Calculus and its interrelation with thecolour dipole
approach. The two key problems we consider are the probabilistic interpretation
of the BFKL Pomeron Calculus and the possiblescenario for the asymptotic
behaviour of the scattering amplitude at high energyin QCD.


  The generalized Hubbard model is an important theoretical model for modeling
strongly correlated materials. To be able to theoretically predict the
properties of the materials, the model requires Hubbard's $U$ and Hund's $J$
parameter that represent the on-site Coulomb and exchange interaction,
respectively. For bare Coulomb interactions, the analytic expression of $U$ and
$J$ are analytically described by Racah's $A,B,C$ parameters. However, the
values of $U$ are too large for transition-metal-based materials. To obtain
more accurate values for realistic materials, we employ a Yukawa-type screened
Coulomb interaction, characterized by screening constant $\lambda$. We
characterize $\lambda$ for transition metals. The modified $A,B,C$ parameters
give a more realistic $U$ and $J$ values.


  We study an unbiased estimator for the density of a sum of random variables
that are simulated from a computer model. A numerical study on examples with
copula dependence is conducted where the proposed estimator performs favourably
in terms of variance compared to other unbiased estimators. We provide
applications and extensions to the estimation of marginal densities in Bayesian
statistics and to the estimation of the density of sums of random variables
under Gaussian copula dependence.


  We provide a study of how induced model sparsity can help achieve
compositional generalization and better sample efficiency in grounded language
learning problems. We consider simple language-conditioned navigation problems
in a grid world environment with disentangled observations. We show that
standard neural architectures do not always yield compositional generalization.
To address this, we design an agent that contains a goal identification module
that encourages sparse correlations between words in the instruction and
attributes of objects, composing them together to find the goal. The output of
the goal identification module is the input to a value iteration network
planner. Our agent maintains a high level of performance on goals containing
novel combinations of properties even when learning from a handful of
demonstrations. We examine the internal representations of our agent and find
the correct correspondences between words in its dictionary and attributes in
the environment.


  In this paper we study the fault codiagnosis problem for discrete event
systems given by finite automata (FA) and timed systems given by timed automata
(TA). We provide a uniform characterization of codiagnosability for FA and TA
which extends the necessary and sufficient condition that characterizes
diagnosability. We also settle the complexity of the codiagnosability problems
both for FA and TA and show that codiagnosability is PSPACE-complete in both
cases. For FA this improves on the previously known bound (EXPTIME) and for TA
it is a new result. Finally we address the codiagnosis problem for TA under
bounded resources and show it is 2EXPTIME-complete.


  We report on stacked multiple quantum dots (QDs) formed inside inverted
pyramidal recesses, which allow for the precise positioning of the QDs
themselves. Specifically we fabricated double QDs with varying inter-dot
distance and ensembles with more than two nominally highly symmetric QDs. For
each, the effect of the interaction between QDs is studied by characterizing a
large number of QDs through photoluminescence spectroscopy. A clear red-shift
of the emission energy is observed together with a change in the orientation of
its polarization, suggesting an increasing interaction between the QDs. Finally
we show how stacked QDs can help influencing the charging of the excitonic
complexes.


  Starting from Nahm's equations, we explore BPS magnetic monopoles in the
Yang-Mills Higgs theory of gauge group $Sp(4)$ which is broken to $SU(2)\times
U(1)$. A family of BPS field configurations with purely Abelian magnetic charge
describe two identical massive monopoles and one massless monopole. We
construct the field configurations with axial symmetry by employing the ADHMN
construction, and find the explicit expression of the metrics for the
12-dimensional moduli space of Nahm data and its submanifolds.


  We present results of extensive quantum Monte Carlo simulations of the
three-dimensional (3D) S=1/2 Heisenberg antiferromagnet. Finite-size scaling of
the spin stiffness and the sublattice magnetization gives the critical
temperature Tc/J = 0.946 +/- 0.001. The critical behavior is consistent with
the classical 3D Heisenberg universality class, as expected. We discuss the
general nature of the transition from quantum mechanical to classical (thermal)
order parameter fluctuations at a continuous Tc > 0 phase transition.


  We calculate one-loop string amplitudes of open and closed strings with
N=1,2,4 supersymmetry in four and six dimensions, by compactification on
Calabi-Yau and K3 orbifolds. In particular, we develop a method to combine
contributions from all spin structures for an arbitrary number of legs at
minimal supersymmetry. Each amplitude is cast into a compact form by
reorganizing the kinematic building blocks and casting the worldsheet integrals
in a basis. Infrared regularization plays an important role to exhibit the
expected factorization limits. We comment on implications for the one-loop
string effective action.


  Photoproduction of $\pi\eta$-pairs from nucleons has been investigated from
threshold up to incident photon energies of $\approx$~1.4~GeV. The quasi-free
reactions $\gamma p\rightarrow p\pi^0\eta$, $\gamma n\rightarrow n\pi^0\eta$,
$\gamma p\rightarrow n\pi^+\eta$, and $\gamma n\rightarrow p\pi^-\eta$ were for
the first time measured from nucleons bound in the deuteron. The corresponding
reactions from a free-proton target were also studied to investigate
final-state interaction effects (for neutral pions the free-proton results
could be compared to previous measurements; the $\gamma p\rightarrow
n\pi^+\eta$ reaction was measured for the first time). For the $\pi^0\eta$
final state coherent production via the $\gamma d\rightarrow d\pi^0\eta$
reaction was also investigated. The experiments were performed at the tagged
photon beam of the Mainz MAMI accelerator using an almost $4\pi$ coverage
electromagnetic calorimeter composed of the Crystal Ball and TAPS detectors.
The total cross sections for the four different final states obey the relation
$\sigma(p\pi^0\eta)$ $\approx$ $\sigma(n\pi^0\eta)$ $\approx$
$2\sigma(p\pi^-\eta)$ $\approx$ $2\sigma(n\pi^+\eta)$ as expected for a
dominant contribution from a
$\Delta^{\star}\rightarrow\eta\Delta(1232)\rightarrow\pi\eta N$ reaction chain,
which is also supported by the shapes of the invariant-mass distributions of
nucleon-meson and $\pi$-$\eta$ pairs. The experimental results are compared to
the predictions from an isobar reaction model.


  Magneto transmission spectroscopy was employed to study the valley Zeeman
effect in large-area monolayer MoS$_{2}$ and MoSe$_{2}$. The extracted values
of the valley g-factors for both A- and B-exciton were found be similar with
$g_v \simeq -4.5$. The samples are expected to be strained due to the CVD
growth on sapphire at high temperature ($700^\circ$C). However, the estimated
strain, which is maximum at low temperature, is only $\simeq 0.2\%$.
Theoretical considerations suggest that the strain is too small to
significantly influence the electronic properties. This is confirmed by the
measured value of valley g-factor, and the measured temperature dependence of
the band gap, which are almost identical for CVD and mechanically exfoliated
MoS$_2$.