  In these lecture notes, we survey the landscape of Calabi-Yau threefolds, and
the use of machine learning to explore it. We begin with the compact portion of
the landscape, focusing in particular on complete intersection Calabi-Yau
varieties (CICYs) and elliptic fibrations. Non-compact Calabi-Yau manifolds are
manifest in Type II superstring theories, they arise as representation
varieties of quivers, used to describe gauge theories in the bulk familiar four
dimensions. Finally, given the huge amount of Calabi-Yau data, whether and how
machine learning can be applied to algebraic geometry and string landscape is
also discussed. These notes are directed to the beginning graduate student
interested in mathematics and in physics, and are based on lectures given by
the 2nd author at the 2019 PIMS Summer School on Algebraic Geometry in
High-Energy Physics at the University of Saskatchewan.


  The energy spectrum for high energy $\gamma$-rays ($E_\gamma \geq 10$ MeV)
from the process $pp \to \gamma \gamma X$ emitted at $90^0$ in the laboratory
frame has been measured at an energy below the pion production threshold,
namely, at 216 MeV. The resulting photon energy spectrum extracted from $\gamma
- \gamma$ coincidence events consists of a narrow peak at a photon energy of
about 24 MeV and a relatively broad peak in the energy range of (50 - 70) MeV.
The statistical significance for both the narrow and broad peaks exceeds
5$\sigma$. This behavior of the photon energy spectrum is interpreted as a
signature of the exotic dibaryon resonance $d^\star_1$ which is assumed to be
formed in the radiative process $pp \to \gamma d^\star_1$ followed by its
electromagnetic decay via the $d^\star_1 \to pp \gamma$ mode. The experimental
spectrum is compared with those obtained by means of Monte Carlo simulations.


  Inpainting has been continuously studied in the field of computer vision. As
artificial intelligence technology developed, deep learning technology was
introduced in inpainting research, helping to improve performance. Currently,
the input target of an inpainting algorithm using deep learning has been
studied from a single image to a video. However, deep learning-based inpainting
technology for panoramic images has not been actively studied. We propose a
360-degree panoramic image inpainting method using generative adversarial
networks (GANs). The proposed network inputs a 360-degree equirectangular
format panoramic image converts it into a cube map format, which has relatively
little distortion and uses it as a training network. Since the cube map format
is used, the correlation of the six sides of the cube map should be considered.
Therefore, all faces of the cube map are used as input for the whole
discriminative network, and each face of the cube map is used as input for the
slice discriminative network to determine the authenticity of the generated
image. The proposed network performed qualitatively better than existing
single-image inpainting algorithms and baseline algorithms.


  We develop a microscopic theory of the weak localization of two-dimensional
massless Dirac fermions which is valid in the whole range of classically weak
magnetic fields. The theory is applied to calculate magnetoresistance caused by
the weak localization in graphene and conducting surfaces of bulk topological
insulators.


  The relationship between various methods to calculate the physical degrees of
freedom for gauge invariant systems of a general form is established. The set
of hidden parameters caused for the superfluous degrees of freedom is revealed.


  This work is the continuation of the earlier efforts to apply the mean field
approximation to the world sheet formulation of planar phi^3 theory. The
previous attempts were either simple but without solid foundation or well
founded but excessively complicated. In this article, we present an approach
both simple, and also systematic and well founded. We are able to carry through
the leading order mean field calculation analytically, and with a suitable
tuning of the coupling constant, we find string formation.


  Loop integration results have been obtained using numerical integration and
extrapolation. An extrapolation to the limit is performed with respect to a
parameter in the integrand which tends to zero. Results are given for a
non-scalar four-point diagram. Extensions to accommodate loop integration by
existing integration packages are also discussed. These include: using
previously generated partitions of the domain and roundoff error guards.


  The Galileo Project's acoustic monitoring, omni-directional system (AMOS)
aids in the detection and characterization of aerial phenomena. It uses a
multi-band microphone suite spanning infrasonic to ultrasonic frequencies,
providing an independent signal modality for validation and characterization of
detected objects. The system utilizes infrasonic, audible, and ultrasonic
systems to cover a wide range of sounds produced by both natural and man-made
aerial phenomena. Sound signals from aerial objects can be captured given
certain conditions, such as when the sound level is above ambient noise and
isn't excessively distorted by its transmission path. Findings suggest that
audible sources can be detected up to 1 km away, infrasonic sources can be
detected over much longer distances, and ultrasonic at shorter ones. Initial
data collected from aircraft recordings with spectral analysis will help
develop algorithms and software for quick identification of known aircraft.
Future work will involve multi-sensor arrays for sound localization, larger
data sets analysis, and incorporation of machine learning and AI for detection
and identification of more types of phenomena in all frequency bands.


  For the action of the outer automorphism group of the rank two free group on
the corresponding variety of PSL(2,C) characters, two domains of discontinuity
have been known to exist that are strictly larger than the set of Schottky
characters. One is introduced by Bowditch in 1998 (followed by Tan, Wong and
Zhang in 2008) and the other by Minsky in 2013. We prove that these two domains
are equal. We then show that they are contained in the set of characters having
what we call the bounded intersection property.


  We present a novel method to solve image analogy problems : it allows to
learn the relation between paired images present in training data, and then
generalize and generate images that correspond to the relation, but were never
seen in the training set. Therefore, we call the method Conditional Analogy
Generative Adversarial Network (CAGAN), as it is based on adversarial training
and employs deep convolutional neural networks. An especially interesting
application of that technique is automatic swapping of clothing on fashion
model photos. Our work has the following contributions. First, the definition
of the end-to-end trainable CAGAN architecture, which implicitly learns
segmentation masks without expensive supervised labeling data. Second,
experimental results show plausible segmentation masks and often convincing
swapped images, given the target article. Finally, we discuss the next steps
for that technique: neural network architecture improvements and more advanced
applications.


  We develop a new stock market index that captures the chaos existing in the
market by measuring the mutual changes of asset prices. This new index relies
on a tensor-based embedding of the stock market information, which in turn
frees it from the restrictive value- or capitalization-weighting assumptions
that commonly underlie other various popular indexes. We show that our index is
a robust estimator of the market volatility which enables us to characterize
the market by performing the task of segmentation with a high degree of
reliability. In addition, we analyze the dynamics and kinematics of the
realized market volatility as compared to the implied volatility by introducing
a time-dependent dynamical system model. Our computational results which
pertain to the time period from January 1990 to December 2019 imply that there
exist a bidirectional causal relation between the processes underlying the
realized and implied volatility of the stock market within the given time
period, where it is shown that the later has a stronger causal effect on the
former as compared to the opposite. This result connotes that the implied
volatility of the market plays a key role in characterization of the market's
realized volatility.


  Cosmic ray antiprotons can originate from dark matter annihilating into
quarks that subsequently decay into antiprotons. Evaporation of primordial
black holes also can produce a significant antiproton flux. Since the spectrum
of secondary antiprotons from cosmic ray interactions peaks at ~ 2 GeV and goes
down sharply at lower energy, there is a window at energies < 1 GeV in which to
look for excess antiprotons as a signature of these exotic antiproton sources.
However, in the vicinity of the Earth low energy particles are strongly
modulated by the solar wind, which makes any analysis ambiguous. The adverse
effects of the solar wind can be avoided by placing a low energy antiproton
spectrometer aboard an interstellar probe. The theoretical predictions are
reviewed and the preliminary design of a light-weight, low-power instrument to
make the measurements and a summary of the anticipated results are given in
this paper.


  With the generalization of mobile communication systems, solicitations of all
kinds in the form of messages and emails are received by users with increasing
proportion of malicious ones. They are customized to pass anti-spam filters and
ask the person to click or to open the joined dangerous attachment. Current
filters are very inefficient against spear phishing emails. It is proposed to
improve the existing filters by taking advantage of user own analysis and
feedback to detect all kinds of phishing emails. The method rests upon an
interface displaying different warnings to receivers. Analysis shows that users
trust their first impression and are often lead to ignore warnings flagged by
proposed new interactive system.


  This work addressed the problem of learning a network with communication
between vertices. The communication between vertices is presented in the form
of perturbation on the measure. We studied the scenario where samples are drawn
from a uniform ergodic Random Graph Process (RGPs for short), which provides a
natural mathematical context for the problem of interest. For the binary
classification problem, the result we obtained gives uniform learn-ability as
the worst-case theoretical limits. We introduced the structural Rademacher
complexity, which naturally fused into the VC theory to upperbound the first
moment. With the martingale method and Marton's coupling, we establish the tail
bound for uniform convergence and give consistency guarantee for empirical risk
minimizer. The technique used in this work to obtain high probability bounds is
of independent interest to other mixing processes with and without network
structure.


  The systematic uncertainties of the measurements of the Inclusive Jet Cross
Section at the Tevatron and their effect on the chi^2 comparisons between data
and theoretical predictions are discussed.


  Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data
sets is computationally infeasible. The recently proposed stochastic gradient
Langevin dynamics (SGLD) method circumvents this problem in three ways: it
generates proposed moves using only a subset of the data, it skips the
Metropolis-Hastings accept-reject step, and it uses sequences of decreasing
step sizes. In \cite{TehThierryVollmerSGLD2014}, we provided the mathematical
foundations for the decreasing step size SGLD, including consistency and a
central limit theorem. However, in practice the SGLD is run for a relatively
small number of iterations, and its step size is not decreased to zero. The
present article investigates the behaviour of the SGLD with fixed step size. In
particular we characterise the asymptotic bias explicitly, along with its
dependence on the step size and the variance of the stochastic gradient. On
that basis a modified SGLD which removes the asymptotic bias due to the
variance of the stochastic gradients up to first order in the step size is
derived. Moreover, we are able to obtain bounds on the finite-time bias,
variance and mean squared error (MSE). The theory is illustrated with a
Gaussian toy model for which the bias and the MSE for the estimation of moments
can be obtained explicitly. For this toy model we study the gain of the SGLD
over the standard Euler method in the limit of large data sets.


  We define geometric RG flow equations that specify the scale dependence of
the renormalized effective action Gamma[g] and the geometric entanglement
entropy S[x] of a QFT, considered as functionals of the background metric g and
the shape x of the entanglement surface. We show that for QFTs with AdS duals,
the respective flow equations are described by Ricci flow and mean curvature
flow. For holographic theories, the diffusion rate of the RG flow is much
larger, by a factor R_{AdS}^2/\ell_s^2, than the RG resolution length scale. To
derive our results. we employ the Hamilton-Jacobi equations that dictate the
dependence of the total bulk action and the minimal surface area on the
geometric QFT boundary data.


  Demosaicing and denoising of RAW images are crucial steps in the processing
pipeline of modern digital cameras. As only a third of the color information
required to produce a digital image is captured by the camera sensor, the
process of demosaicing is inherently ill-posed. The presence of noise further
exacerbates this problem. Performing these two steps sequentially may distort
the content of the captured RAW images and accumulate errors from one step to
another. Recent deep neural-network-based approaches have shown the
effectiveness of joint demosaicing and denoising to mitigate such challenges.
However, these methods typically require a large number of training samples and
do not generalize well to different types and intensities of noise. In this
paper, we propose a novel joint demosaicing and denoising method, dubbed
JDD-DoubleDIP, which operates directly on a single RAW image without requiring
any training data. We validate the effectiveness of our method on two popular
datasets -- Kodak and McMaster -- with various noises and noise intensities.
The experimental results show that our method consistently outperforms other
compared methods in terms of PSNR, SSIM, and qualitative visual perception.


  By using solid state reaction method, we have synthesized the Pd-doped
superconductor SrFe2-xPdxAs2. The systematic evolution of the lattice constants
indicated that the Fe ions were successfully replaced by the Pd. By increasing
the doping content of Pd, the antiferromagnetic state of the parent phase is
suppressed and superconductivity is induced at a doping level of about x=0.05.
Superconductivity with a maximum transition temperature Tc of about 8.7 K was
achieved at the doping level of x = 0.15. The general phase diagram of Tc
versus x was obtained and found to be similar to the case of Ni and Co doping
to the Fe sites. Our results suggest that superconductivity can be easily
induced in the FeAs family by adding electrons into the system, regardless with
the transition metals of 3d or higher d-orbital electrons.


  We show that, by going beyond the low-energy approximation for which the
dispersion relations of graphene are linear, the corresponding emergent field
theory is a specific generalization a Dirac field theory. The generalized Dirac
Hamiltonians one obtains are those compatible with specific generalizations of
the uncertainty principle. We also briefly comment on the compatibility of the
latter with noncommuting positions, $[x_i,x_j] \neq 0$, and on their possible
physical realization.


  In this work, we revisit the shear-free conjecture of general relativity and
show the violation of the well-known shear-free condition for perfect-fluid
spacetimes. It had been shown in previous investigations that, in the general
relativistic framework, the matter congruences of shear-free perfect fluid
spacetimes should be either expansion-free or rotation-free. Our current
investigation, however, indicates that a universe dominated by a Chaplygin gas
can allow a simultaneous expansion and rotation of the fluid provided that
certain non-trivial conditions, which we derive and describe below, are met. We
also show that, in the appropriate limiting cases, our results reduce to the
expected results of dust spacetimes which can only expand or rotate, but not
both, at the same time.


  Effect of 1-D photonic crystals on optical transmission of VO2 is studied by
depositing thin films of VO2 nanoparticles on SiO2/TiO2 distributed Bragg
reflectors (DBR) in the near infrared (IR) spectrum as per earlier theoretical
predictions of J. Phys. D: Appl. Phys. 51 375102 (2018). Monoclinic VO2
nanoparticles with tuned crystallinity were synthesized by a facile solution
processing method. Moderately crystalline (MC) and highly crystalline (HC) VO2
nanostructures were obtained by varying its synthesis temperature and post
growth annealing conditions. Both MC VO2 and HC VO2 films exhibit expected
reduction in optical transmission in the IR region due to its structural phase
transition from monoclinic (insulator) to rutile (metallic) around critical
temperature of 68 {\deg}C. By combining VO2 films on a 40% transmitting DBR
structure, the average optical transmission further went down to ~ 20%. Number
of stacks of DBR plays a key role in such effective reduction of optical
transmission in IR. When the number of stacks of DBR is further increased from
4 to 7, optical transmission of metallic VO2 films on DBR nearly vanishes in
NearIR spectrum in such vanadium dioxide/1D photonic crystal based composite
photonic structures. Such temperature controlled, enhanced, broad band optical
response can be a promising design for VO2 nanoparticle based hybrid photonic
absorbers for various smart window applications.


  Human operators often diagnose industrial machinery via anomalous sounds.
Automated acoustic anomaly detection can lead to reliable maintenance of
machinery. However, deep learning-driven anomaly detection methods often
require an extensive amount of computational resources which prohibits their
deployment in factories. Here we explore a machine-driven design exploration
strategy to create OutlierNets, a family of highly compact deep convolutional
autoencoder network architectures featuring as few as 686 parameters, model
sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection
accuracy matching or exceeding published architectures with as many as 4
million parameters. Furthermore, CPU-accelerated latency experiments show that
the OutlierNet architectures can achieve as much as 21x lower latency than
published networks.


  We suggest that the dark matter model based on Bose Einstein condensate or
scalar field can resolve the apparently contradictory behaviors of dark matter
in the Abell 520 and the Bullet cluster. During a collision of two galaxies in
the cluster, if initial kinetic energy of the galaxies is large enough, two
dark matter halos pass each other in a soliton-like way as observed in the
Bullet cluster. If not, the halos merge due to the tiny repulsive interaction
among dark matter particles as observed in the Abell 520. This idea can also
explain the origin of the dark galaxy and the galaxy without dark matter.


  All current Bulge-Disk models for the inner Galaxy fall short of reproducing
self-consistently the observed micro-lensing optical depth by a factor of two
($> 2\sigma$). We show that the least mass-consuming way to increase the
optical depth is to add density roughly half-way the observer and the highest
micro-lensing-source density. We present evidence for the existence of such a
density structure in the Galaxy: an inner ring, a standard feature of barred
galaxies. Judging from data on similar rings in external galaxies, an inner
ring can contribute more than 50% of a pure Bulge-Disk model to the
micro-lensing optical depth. We may thus eliminate the need for a small viewing
angle of the Bar. The influence of an inner ring on the event-duration
distribution, for realistic viewing angles, would be to increase the fraction
of long-duration events toward Baade's window. The longest events are expected
toward the negative-longitude tangent point at $\ell\sim$ -22\degr . A properly
sampled event-duration distribution toward this tangent point would provide
essential information about viewing angle and elongation of the over-all
density distribution in the inner Galaxy.


  This paper presents chemical abundance measurements for 37 damped Lya systems
at z>2.5 observed with the Echellette Spectrograph and Imager on the KeckII
telescope. We measure the HI column densities of these damped systems through
Voigt profile fits to their Lya profiles and we implement the apparent optical
depth method to determine ionic column densities. Figures and tables of all
relevant data are presented. A full analysis of the chemical enrichment history
described by these observations will be presented in a future paper. This
dataset is also valuable for efficiently planning future echelle observations
and for rough abundance pattern analyses. We aim to make this entire data set
public within three years of this publication.


  We propose a MANOVA test for semicontinuous data that is applicable also when
the dimensionality exceeds the sample size. The test statistic is obtained as a
likelihood ratio, where numerator and denominator are computed at the maxima of
penalized likelihood functions under each hypothesis. Closed form solutions for
the regularized estimators allow us to avoid computational overheads. We derive
the null distribution using a permutation scheme. The power and level of the
resulting test are evaluated in a simulation study. We illustrate the new
methodology with two original data analyses, one regarding microRNA expression
in human blastocyst cultures, and another regarding alien plant species
invasion in the island of Socotra (Yemen).


  We present new results on the cosmological evolution of the near-infrared
galaxy luminosity function, derived from the analysis of a new sample of
\~22,000 K(AB) < 22.5 galaxies selected over an area of 0.6 square degrees from
the Early Data Release of the UKIDSS Ultra Deep Survey (UDS). Our study has
exploited the multi-wavelength coverage of the UDS field provided by the new
UKIDSS WFCAM K and J-band imaging, the Subaru/XMM-Newton Deep Survey and the
Spitzer-SWIRE Survey. The unique combination of large area and depth provided
by this new survey minimises the complicating effect of cosmic variance and has
allowed us, for the first time, to trace the evolution of the brightest sources
out to z~2 with good statistical accuracy.
  In agreement with previous studies we find that the characteristic luminosity
of the near-infrared luminosity function brightens by 1 magnitude between z=0
and z~2, while the total density decreases by a factor ~2. Using the rest-frame
(U-B) colour to split the sample into red and blue galaxies, we confirm the
classic luminosity-dependent colour bimodality at z<1. However, the strength of
the colour bimodality is found to be a decreasing function of redshift, and
seems to disappear by z>1.5. Due to the large size of our sample we are able to
investigate the differing cosmological evolution of the red and blue galaxy
populations. It is found that the space density of the brightest red galaxies
(M_K < -23) stays approximately constant with redshift, and that these sources
dominate the bright-end of the luminosity function at redshifts z<1. In
contrast, the brightening of the characteristic luminosity and mild decrease in
space density displayed by the blue galaxy population leads them to dominate
the bright-end of the luminosity function at redshifts z>1.


  Prediction of lattice thermal conductivity is important to many applications
and technologies, especially for high-throughput materials screening. However,
the state-of-the-art method based on three-phonon scattering process is bound
with high computational cost while semi-empirical models such as the Slack
equation are less accurate. In this work, we examined the theoretical
background of the commonly-used computational models for high-throughput
thermal conductivity prediction and proposed an efficient and accurate method
based on an approximation for three-phonon scattering strength. This
quasi-harmonic approximation has comparable computational cost with many
widely-used thermal conductivity models but had the best performance in regard
to quantitative accuracy. As compared to many models that can only predict
lattice thermal conductivity values, this model also allows to include Normal
processes and obtain the phonon relaxation time.


  Experimental evidence is provided on close relation between polarization and
space charge in corona poled PVDF films. It is shown that the depolarizing
field is compensated by charges trapped in macroscopic transition zones, by
which polarized parts of the volume are separated from non-polarized ones. The
compensating charges are either injected in the bulk, or created inside due to
the thermal emission and then trapped when the ferroelectric polarization in
crystallites is formed.


  We focus on the human-humanoid interaction task optionally with an object. We
propose a new task named online full-body motion reaction synthesis, which
generates humanoid reactions based on the human actor's motions. The previous
work only focuses on human interaction without objects and generates body
reactions without hand. Besides, they also do not consider the task as an
online setting, which means the inability to observe information beyond the
current moment in practical situations. To support this task, we construct two
datasets named HHI and CoChair and propose a unified method. Specifically, we
propose to construct a social affordance representation. We first select a
social affordance carrier and use SE(3)-Equivariant Neural Networks to learn
the local frame for the carrier, then we canonicalize the social affordance.
Besides, we propose a social affordance forecasting scheme to enable the
reactor to predict based on the imagined future. Experiments demonstrate that
our approach can effectively generate high-quality reactions on HHI and
CoChair. Furthermore, we also validate our method on existing human interaction
datasets Interhuman and Chi3D.


  We study a class of models for brittle fracture: elastic theory models which
allow for cracks but not for plastic flow. We show that these models exhibit,
at all finite temperatures, a transition to fracture under applied load similar
to that at a first order liquid-gas transition. We study this transition at low
temperature for small tension. We discuss the appropriate thermodynamic limit
of these theories: a large class of boundary conditions is identified for which
the energy release for a crack becomes independent of the macroscopic shape of
the material. Using the complex variable method in a two-dimensional elastic
theory, we prove that the energy release in an isotropically stretched material
due to the creation of an arbitrary curvy cut is the same to cubic order as the
energy release for the straight cut with the same end points. We find the
normal modes and the energy spectrum for crack shape fluctuations and for crack
surface phonons, under a uniform isotropic tension. For small uniform isotropic
tension in two dimensions we calculate the essential singularity associated
with fracturing the material in a saddle point approximation including
quadratic fluctuations. This singularity determines the lifetime of the
material (half-life for fracture), and also determines the asymptotic
divergence of the high-order corrections to the zero temperature elastic
coefficients. We calculate the asymptotic ratio of the high-order elastic
coefficients of the inverse bulk modulus and argue that the result is unchanged
by nonlinearities --- the ratio of the high-order nonlinear terms are
determined solely by the linear theory.


  We define and study the Functional Aggregate Query (FAQ) problem, which
captures common computational tasks across a very wide range of domains
including relational databases, logic, matrix and tensor computation,
probabilistic graphical models, constraint satisfaction, and signal processing.
Simply put, an FAQ is a declarative way of defining a new function from a
database of input functions.
  We present "InsideOut", a dynamic programming algorithm, to evaluate an FAQ.
The algorithm rewrites the input query into a set of easier-to-compute FAQ
sub-queries. Each sub-query is then evaluated using a worst-case optimal
relational join algorithm. The topic of designing algorithms to optimally
evaluate the classic multiway join problem has seen exciting developments in
the past few years. Our framework tightly connects these new ideas in database
theory with a vast number of application areas in a coherent manner, showing
potentially that a good database engine can be a general-purpose constraint
solver, relational data store, graphical model inference engine, and
matrix/tensor computation processor all at once.
  The InsideOut algorithm is very simple, as shall be described in this paper.
Yet, in spite of solving an extremely general problem, its runtime either is as
good as or improves upon the best known algorithm for the applications that FAQ
specializes to. These corollaries include computational tasks in graphical
model inference, matrix/tensor operations, relational joins, and logic. Better
yet, InsideOut can be used within any database engine, because it is basically
a principled way of rewriting queries. Indeed, it is already part of the
LogicBlox database engine, helping efficiently answer traditional database
queries, graphical model inference queries, and train a large class of machine
learning models inside the database itself.


  The symmetric strict implication calculus $\mathsf{S^2IC}$, introduced in
[5], is a modal calculus for compact Hausdorff spaces. This is established
through de Vries duality, linking compact Hausdorff spaces with de Vries
algebras-complete Boolean algebras equipped with a special relation. Modal
compact Hausdorff spaces are compact Hausdorff spaces enriched with a
continuous relation. These spaces correspond, via modalized de Vries duality of
[3], to upper continuous modal de Vries algebras.
  In this paper we introduce the modal symmetric strict implication calculus
$\mathsf{MS^2IC}$, which extends $\mathsf{S^2IC}$. We prove that
$\mathsf{MS^2IC}$ is strongly sound and complete with respect to upper
continuous modal de Vries algebras, thereby providing a logical calculus for
modal compact Hausdorff spaces. We also develop a relational semantics for
$\mathsf{MS^2IC}$ that we employ to show admissibility of various $\Pi_2$-rules
in this system.


  In practice, the use of rounding is ubiquitous. Although researchers have
looked at the implications of rounding continuous random variables, rounding
may be applied to functions of discrete random variables as well. For example,
to infer on suicide excess deaths after a national emergency, authorities may
provide a rounded average of deaths before and after the emergency started.
Suicide rates tend to be relatively low around the world and such rounding may
seriously affect inference on the change of suicide rate. In this paper, we
study the scenario when a rounded to nearest integer average is used as a proxy
for a non-negative discrete random variable. Specifically, our interest is in
drawing inference on a parameter from the pmf of Y , when we get U = n[Y /n] as
a proxy for Y . The probability generating function of U , E(U ), and Var(U )
capture the effect of the coarsening of the support of Y . Also, moments and
estimators of distribution parameters are explored for some special cases. We
show that under certain conditions, there is little impact from rounding.
However, we also find scenarios where rounding can significantly affect
statistical inference as demonstrated in three examples. The simple methods we
propose are able to partially counter rounding error effects. While for some
probability distributions it may be difficult to derive maximum likelihood
estimators as a function of U , we provide a framework to obtain an estimator
numerically.


  We have recorded channeling patterns produced by 1 to 2 MeV protons aligned
with <111> axes in 55 nm thick silicon crystals which exhibit characteristic
angular structure for deflection angles up to and beyond the axial critical
angle, psi(a). Such large angular deflections are produced by ions incident on
atomic strings with small impact parameters, resulting in trajectories which
pass through several radial rings of atomic strings before exiting the thin
crystal. Each ring may focus, steer or scatter the channeled ions in the
transverse direction and the resulting characteristic angular structure beyond
0.6psi(a) at different depths can be related to peaks and troughs in the
nuclear encounter probability. Such radial focusing underlies other axial
channeling phenomena in thin crystals including planar channeling of small
impact parameter trajectories, peaks around the azimuthal distribution at small
tilts and large shoulders in the nuclear encounter probability at tilts beyond
psi(a).


  Saliency prediction is a well studied problem in computer vision. Early
saliency models were based on low-level hand-crafted feature derived from
insights gained in neuroscience and psychophysics. In the wake of deep learning
breakthrough, a new cohort of models were proposed based on neural network
architectures, allowing significantly higher gaze prediction than previous
shallow models, on all metrics.
  However, most models treat the saliency prediction as a \textit{regression}
problem, and accurate regression of high-dimensional data is known to be a hard
problem. Furthermore, it is unclear that intermediate levels of saliency (ie,
neither very high, nor very low) are meaningful: Something is either salient,
or it is not.
  Drawing from those two observations, we reformulate the saliency prediction
problem as a salient region \textit{segmentation} problem. We demonstrate that
the reformulation allows for faster convergence than the classical regression
problem, while performance is comparable to state-of-the-art.
  We also visualise the general features learned by the model, which are showed
to be consistent with insights from psychophysics.


  We consider a pp-wave symmetric model in the framework of the
Einstein-Maxwell-aether-axion theory. Exact solutions to the equations of axion
electrodynamics are obtained for the model, in which pseudoscalar, electric and
magnetic fields were constant before the arrival of a gravitational pp-wave. We
show that dynamo-optical interactions, i.e. couplings of the electromagnetic
field to a dynamic unit vector field, attributed to the velocity of a cosmic
substratum (aether, vacuum, dark fluid...), provide the response of axionically
active electrodynamic system to display anomalous behavior.


  The constraints of N=2 supersymmetry, in combination with several other quite
general assumptions, have recently been used to show that N=2 supersymmetric
Yang-Mills theory has a low energy quantum parameter space symmetry
characterised by the discrete group $\gu$. We show that if one also assumes the
commutativity of renormalization group flow with the action of this group on
the complexified coupling constant $\ta$, then this is sufficient to determine
the non-perturbative $\beta$-function, given knowledge of its weak coupling
behaviour. The result coincides with the outcome of direct calculations from
the Seiberg-Witten solution.


  Conventional low-power static random access memories (SRAMs) reduce read
energy by decreasing the bit-line voltage swings uniformly across the bit-line
columns. This is because the read energy is proportional to the bit-line
swings. On the other hand, bit-line swings are limited by the need to avoid
decision errors especially in the most significant bits. We propose an
information-theoretic approach to determine optimal non-uniform bit-line swings
by formulating convex optimization problems. For a given constraint on mean
squared error of retrieved words, we consider criteria to minimize energy (for
low-power SRAMs), maximize speed (for high-speed SRAMs), and minimize
energy-delay product. These optimization problems can be interpreted as
classical water-filling, ground-flattening and water-filling, and sand-pouring
and water-filling, respectively. By leveraging these interpretations, we also
propose greedy algorithms to obtain optimized discrete swings. Numerical
results show that energy-optimal swing assignment reduces energy consumption by
half at a peak signal-to-noise ratio of 30dB for an 8-bit accessed word. The
energy savings increase to four times for a 16-bit accessed word.


  A great deal of emphasis on timing in the RXTE era has been on pushing toward
higher and higher frequency phenomena, particularly kHz QPOs. However, the
large areas of the RXTE pointed instruments provide another capability which is
key for the understanding of accreting X-ray pulsars -- the ability to
accumulate high quality spectra in a limited observing time. For the accreting
X-ray pulsars, with their relatively modest spin frequencies, this translates
into an ability to study broad band spectra as a function of pulse phase. This
is a critical tool, as pulsar spectra are strong functions of the geometry of
the "accretion mound" and the observers' viewing angle to the ~10^12 G magnetic
field. In particular, the appearance of "cyclotron lines" is sensitively
dependent on the viewing geometry, which must change with the rotation of the
star. These spectral features, seen in only a handful of objects, are quite
important, as they give us our only direct measure of neutron star magnetic
fields. Furthermore, they carry a great deal of information as to the geometry
and physical conditions in the accretion mound. In this paper, we review the
status of cyclotron line studies with the RXTE. We present an overview of
phase-averaged results and give examples of observations which illustrate the
power of phase-resolved spectroscopy.


  Division algebras are used to explain the existence and symmetries of various
sets of auxiliary fields for super Yang-Mills in dimensions $d=3,4,6,10$.
(Contribution to G\"ursey Memorial Conference I: Strings and Symmetries)


  In this paper, adaptive pilot beam sequence design for channel estimation in
large millimeter-wave (mmWave) MIMO systems is considered. By exploiting the
sparsity of mmWave MIMO channels with the virtual channel representation and
imposing a Markovian random walk assumption on the physical movement of the
line-of-sight (LOS) and reflection clusters, it is shown that the sparse
channel estimation problem in large mmWave MIMO systems reduces to a sequential
detection problem that finds the locations and values of the non-zero-valued
bins in a two-dimensional rectangular grid, and the optimal adaptive pilot
design problem can be cast into the framework of a partially observable Markov
decision process (POMDP). Under the POMDP framework, an optimal adaptive pilot
beam sequence design method is obtained to maximize the accumulated
transmission data rate for a given period of time. Numerical results are
provided to validate our pilot signal design method and they show that the
proposed method yields good performance.


  Model compression techniques reduce the computational load and memory
consumption of deep neural networks. After the compression operation, e.g.
parameter pruning, the model is normally fine-tuned on the original training
dataset to recover from the performance drop caused by compression. However,
the training data is not always available due to privacy issues or other
factors. In this work, we present a data-free fine-tuning approach for pruning
the backbone of deep neural networks. In particular, the pruned network
backbone is trained with synthetically generated images, and our proposed
intermediate supervision to mimic the unpruned backbone's output feature map.
Afterwards, the pruned backbone can be combined with the original network head
to make predictions. We generate synthetic images by back-propagating gradients
to noise images while relying on L1-pruning for the backbone pruning. In our
experiments, we show that our approach is task-independent due to pruning only
the backbone. By evaluating our approach on 2D human pose estimation, object
detection, and image classification, we demonstrate promising performance
compared to the unpruned model. Our code is available at
https://github.com/holzbock/dfbf.


  This paper presents a mercantile framework for the decentralised sharing of
navigation expertise amongst a fleet of robots which perform regular missions
into a common but variable environment. We build on our earlier work and allow
individual agents to intermittently initiate trades based on a real-time
assessment of the nature of their missions or demand for localisation
capability, and to choose trading partners with discrimination based on an
internally evolving set of beliefs in the expected value of trading with each
other member of the team. To this end, we suggest some obligatory properties
that a formalisation of the distributed versioning of experience maps should
exhibit, to ensure the eventual convergence in the state of each agent's map
under a sequence of pairwise exchanges, as well as the uninterrupted integrity
of the representation under versioning operations. To mitigate limitations in
hardware and network resources, the "data market" is catalogued by distinct
sections of the world, which the agents treat as "products" for appraisal and
purchase. To this end, we demonstrate and evaluate our system using the
publicly available Oxford RobotCar Dataset, the hand-labelled data market
catalogue (approaching 446km of fully indexed sections-of-interest) for which
we plan to release alongside the existing raw stereo imagery. We show that, by
refining market policies over time, agents achieve improved localisation in a
directed and accelerated manner.


  Understanding the origin of the elements has been a decades long pursuit,
with many open questions still remaining. Old stars found in the Milky Way and
its dwarf satellite galaxies can provide answers because they preserve clean
elemental patterns of the nucleosynthesis processes that operated some 13
billion years ago. This enables the reconstruction of the chemical evolution of
the elements. Here we focus on the astrophysical signatures of heavy
neutron-capture elements made in the s-, i- and r-process found in old stars. A
highlight is the recently discovered r-process galaxy Reticulum II that was
apparently enriched by a neutron star merger. These results show that old stars
in dwarf galaxies provide a novel means to constrain the astrophysical site of
the r-process, ushering in much needed progress on this major outstanding
question. This nuclear astrophysics work complements the many nuclear physics
efforts into heavy-element formation, and aligns with recent results on the
gravitational wave signature of a neutron star merger.


  The Galactic HII region NGC 2579 has stayed undeservedly unexplored due to
identification problems which persisted until recently. Both NGC 2579 and its
companion ESO 370-9 have been misclassified as planetary or reflection nebula,
confused with each other and with other objects. Due to its high surface
brightness, high excitation, angular size of few arcminutes and relatively low
interstellar extinction, NGC 2579 is an ideal object for investigations in the
optical range. Located in the outer Galaxy, NGC 2579 is an excellent object for
studying the Galactic chemical abundance gradients. In this paper we present
the first comprehensive observational study on the nebular and stellar
properties of NGC 2579 and ESO 370-9, including the determination of electron
temperature, density structure, chemical composition, kinematics, distance, and
the identification and spectral classification of the ionizing stars, and
discuss the nature of ESO 370-9. Long slit spectrophotometric data in the
optical range were used to derive the nebular electron temperature, density and
chemical abundances and for the spectral classification of the ionizing star
candidates. Halpha and UBV CCD photometry was carried out to derive stellar
distances from spectroscopic parallax and to measure the ionizing photon flux.


  Non-proportional response of the scintillation yield of NaI:Tl was measured
using highly monochromatic synchrotron irradiation ranging from 9 to 100 keV.
Special attention is paid to the X-ray escape peaks. They provide us additional
information about non-proportional response in the range 0.9 to 12 keV. A rapid
variation of the non-proportional response curve is observed near the Iodine
K-electron binding energy. A dense sampling of data is performed around this
energy and that data are used to apply a method, which we call K-dip
spectroscopy. This method allows us to derive the electron response curve of
NaI:Tl down to energies as low as 30 eV. A comparison of our data with data of
others employing different methods is made. Advantages, limitations and
peculiarities of presented techniques and methods are discussed.


  As efforts to mitigate the effects of climate change grow, reliable and
thorough reporting of greenhouse gas emissions are essential for measuring
progress towards international and domestic emissions reductions targets. New
Zealand's national emissions inventories are currently reported between 15 to
27 months out-of-date. We present a machine learning approach to nowcast
(dynamically estimate) national greenhouse gas emissions in New Zealand in
advance of the national emissions inventory's release, with just a two month
latency due to current data availability. Key findings include an estimated
0.2% decrease in national gross emissions since 2020 (as at July 2022). Our
study highlights the predictive power of a dynamic view of emissions intensive
activities. This methodology is a proof of concept that a machine learning
approach can make sub-annual estimates of national greenhouse gas emissions by
sector with a relatively low error that could be of value for policy makers.


  We numerically investigate crystalline order on negative Gaussian curvature
capillary bridges. In agreement with the experimental results in [W. Irvine et
al., Nature, "Pleats in crystals on curved surfaces", 2010, (468), 947]} we
observe for decreasing integrated Gaussian curvature a sequence of transitions,
from no defects to isolated dislocations, pleats, scars and isolated sevenfold
disclinations. We especially focus on the dependency of the detached
topological charge on the integrated Gaussian curvature, for which we observe,
again in agreement with the experimental results, no net disclination for an
integrated curvature down to -10, and a linear behaviour from there on until
the disclinations match the integrated curvature of -12. The results are
obtained using a phase field crystal approach on catenoid-like surfaces and are
highly sensitive to the initialization.


  All superconformal quivers are shown to satisfy the relation c = a and are
thus good candidates for being the field theory living on D3 branes probing CY
singularities. We systematically study 3 block and 4 block chiral quivers which
admit a superconformal fixed point of the RG equation. Most of these theories
are known to arise as living on D3 branes at a singular CY manifold, namely
complex cones over del Pezzo surfaces. In the process we find a procedure of
getting a new superconformal quiver from a known one. This procedure is termed
"shrinking" and, in the 3 block case, leads to the discovery of two new models.
Thus, the number of superconformal 3 block quivers is 16 rather than the
previously known 14. We prove that this list exausts all the possibilities. We
suggest that all rank 2 chiral quivers are either del Pezzo quivers or can be
obtained by shrinking a del Pezzo quiver and verify this statement for all 4
block quivers, where a lot of "shrunk'' del Pezzo models exist.


  In the growing domain of scientific machine learning, in-context operator
learning has shown notable potential in building foundation models, as in this
framework the model is trained to learn operators and solve differential
equations using prompted data, during the inference stage without weight
updates. However, the current model's overdependence on function data overlooks
the invaluable human insight into the operator. To address this, we present a
transformation of in-context operator learning into a multi-modal paradigm. In
particular, we take inspiration from the recent success of large language
models, and propose using "captions" to integrate human knowledge about the
operator, expressed through natural language descriptions and equations. Also,
we introduce a novel approach to train a language-model-like architecture, or
directly fine-tune existing language models, for in-context operator learning.
We beat the baseline on single-modal learning tasks, and also demonstrated the
effectiveness of multi-modal learning in enhancing performance and reducing
function data requirements. The proposed method not only significantly enhanced
the development of the in-context operator learning paradigm, but also created
a new path for the application of language models.


  To date, the presence of dark matter (DM) can be judged only by its
gravitational interaction on the visible matter. It is therefore important to
find the consequences of this interaction, which can then help to determine
both the DM properties and parameters and the dynamics and evolution of visible
matter. The gravitational influence of dark matter on the stability of
interstellar medium (ISM), the progenitor of stars and star clusters, was
considered. An isothermal self-gravity gas was taken as a suitable model
describing ISM, particles interacting only gravitationally were considered as
DM. The results obtained by analytical methods show that even a small amount of
fast DM particles significantly increases the stable radius of the gas cloud
and the corresponding mass while a higher relative density of DM destabilizes
the gas. It was shown that with typical parameters of ISM and DM, its presence
increases the maximum stable mass of isothermal cloud by a factor of four and
the radius by five.


  The leptogenesis with supersymmetric Higgs triplets is studied in the light
of experimental verification in the TeV region. The lepton number asymmetry
appears just after the inflation via multiscalar coherent evolution of Higgs
triplets and antislepton on a flat manifold. If the Higgs triplet mass terms
dominate over the negative thermal-log term for the Hubble parameter H
comparable to the Higgs triplet mass M_\Delta, the asymmetry is fixed readily
to some significant value by the redshift and rotation of these scalar fields,
providing the sufficient lepton-to-entropy ratio n_L / s \sim 10^-10. This can
be the case even with M_\Delta \sim 1 TeV for the reheating temperature T_R
\sim 10^6 GeV and the mass parameter M / \lambda \sim 10^22 GeV of the
nonrenormalizable superpotential terms relevant for leptogenesis.


  During planet formation gravitational interaction between a planetary embryo
and the protoplanetary gas disc causes orbital migration of the planetary
embryo, which plays an important role in shaping the final planetary system.
While migration sometimes occurs in the supersonic regime, wherein the relative
velocity between the planetary embryo and the gas is higher than the sound
speed, migration prescriptions proposed thus far describing the planet-disc
interaction force and the timescales of orbital change in the supersonic regime
are inconsistent with one another. Here we discuss the details of existing
prescriptions in the literature and derive a new simple and intuitive
formulation for planet-disc interactions based on dynamical friction that can
be applied in both supersonic and subsonic cases. While the existing
prescriptions assume particular disc models, ours include the explicit
dependence on the disc parameters; hence it can be applied to discs with any
radial surface density and temperature dependence (except for the local
variations with radial scales less than the disc scale height). Our
prescription will reduce the uncertainty originating from different literature
formulations of planet migration and will be an important tool to study planet
accretion processes, especially when studying the formation of close-in
low-mass planets that are commonly found in exoplanetary systems.


  We investigate the properties of satellite galaxies formed in N-body/SPH
simulations of galaxy formation in the $\Lambda$CDM cosmology. The simulations
include the main physical effects thought to be important in galaxy formation
and, in several cases, produce realistic spiral discs. In total, a sample of 9
galaxies of luminosity comparable to the Milky Way was obtained. At magnitudes
brighter than the resolution limit, $M_V=-12$, the luminosity function of the
satellite galaxies in the simulations is in excellent agreement with data for
the Local Group. The radial number density profile of the model satellites, as
well as their gas fractions also match observations very well. In agreement
with previous N-body studies, we find that the satellites tend to be
distributed in highly flattened configurations whose major axis is aligned with
the major axis of the (generally triaxial) dark halo. In 2 out of 3 systems
with sufficiently large satellite populations, the satellite system is nearly
perpendicular to the plane of the galactic disc, a configuration analogous to
that observed in the Milk Way. The discs themselves are perpendicular to the
minor axis of their host halos in the inner parts, and the correlation between
the orientation of the galaxy and the shape of the halo persists even out to
the virial radius. However, in one case the disc's minor axis ends up, at the
virial radius, perpendicular to the minor axis of the halo. The angular momenta
of the galaxies and their host halo tend to be well aligned.


  Recently Andrews and Bryan [3] discovered a comparison function which allows
them to shorten the classical proof of the well-known fact that the curve
shortening flow shrinks embedded closed curves in the plane to a round point.
Using this comparison function they estimate the length of any chord from below
in terms of the arc length between its endpoints and elapsed time. They apply
this estimate to short segments and deduce directly that the maximum curvature
decays exponentially to the curvature of a circle with the same length. We
consider the expansion of convex curves under inverse (mean) curvature flow and
show that the above comparison function also works in this case to obtain a new
proof of the fact that the flow exists for all times and becomes round in
shape, i.e. converges smoothly to the unit circle after an appropriate
rescaling.


  We investigate the Busy Beaver Game introduced by Rado (1962) generalized to
non-binary alphabets. Harland (2016) conjectured that activity (number of
steps) and productivity (number of non-blank symbols) of candidate machines
grow as the alphabet size increases. We prove this conjecture for any alphabet
size under the condition that the number of states is sufficiently large. For
the measure activity we show that increasing the alphabet size from two to
three allows an increase. By a classical construction it is even possible to
obtain a two-state machine increasing activity and productivity of any machine
if we allow an alphabet size depending on the number of states of the original
machine. We also show that an increase of the alphabet by a factor of three
admits an increase of activity.


  The simplest scheme for predicting real galaxy properties after performing a
dark matter simulation is to rank order the real systems by stellar mass and
the simulated systems by halo mass and then simply assume monotonicity - that
the more massive halos host the more massive galaxies. This has had some
success, but we study here if a better motivated and more accurate matching
scheme is easily constructed by looking carefully at how well one could predict
the simulated IllustrisTNG galaxy sample from its dark matter computations. We
find that using the dark matter rotation curve peak velocity, $v_{max}$, for
normal galaxies reduces the error of the prediction by 30% (18% for central
galaxies and 60% for satellite systems) - following expectations from the
physics of monolithic collapse. For massive systems with halo mass $>$
10$^{12.5}$ M$_{\odot}$ hierarchical merger driven formation is the better
model and dark matter halo mass remains the best single metric. Using a new
single variable that combines these effects, $\phi$ $=$
$v_{max}$/$v_{max,12.7}$ + M$_{peak}$/(10$^{12.7}$ M$_{\odot}$) allows further
improvement and reduces the error, as compared to ranking by dark matter mass
at $z=0$ by another 6% from $v_{max}$ ranking. Two parameter fits -- including
environmental effects produce only minimal further impact.


  In the standard basis exact expressions for the components of SIC vectors
(belonging to a symmetric informationally complete POVM) are typically very
complicated. We show that a simple transformation to a basis adapted to the
symmetries of a fiducial SIC vector can result in a massive reduction in
complexity. We rely on a conjectural number theoretic connection between SICs
in dimension $d_j$ and SICs in dimension $d_{j+1} = d_j(d_j-2)$. We focus on
the sequence 5, 15, 195, ... . We rewrite Zauner's exact solution for the SIC
in dimension 5 to make its simplicity manifest, and use our adapted basis to
convert numerical solutions in dimensions 15 and 195 to exact solutions.
Comparing to the known exact solutions in dimension 15 we find that the
simplification achieved is dramatic. The proof that the exact vectors are
indeed SIC fiducial vectors, also in dimension 195, is a long calculation
guided by the standard ray class hypothesis about the algebraic number fields
generated by the SICs. We conjecture that our result generalizes to every
dimension in the particular sequence we consider.


  Neutron stars (NSs) are excellent natural laboratories to constrain gravity
on strong field regime and nuclear matter in extreme conditions. Motivated by
the recent discovery of a compact object with $2.59^{+0.08}_{-0.09} M_\odot$ in
the binary merger GW190814, if this object was a NS, it serves as a strong
constraint on the NS equation of state (EoS), ruling out several soft EoSs
favored by GW170817 event. In this work, we revisit the question of the maximum
mass of NSs considering a chameleon screening (thin-shell effect) on the NS
mass-radius relation, where the microscopic physics inside the NS is given by
realistic soft EoSs. We find that from appropriate and reasonable combination
of modified gravity, rotation effects and realistic soft EoSs, that it is
possible to achieve high masses and explain GW190814 secondary component, and
in return also NSs like PSR J0740+6620 (the most NS massive confirmed to date).
It is shown that gravity can play an important role in estimating maximum mass
of NSs, and even with soft EoSs, it is possible to generate very high masses.
Therefore, in this competition on the hydrostatic equilibrium between gravity
and EoS, some soft EoSs, in principle, cannot be completely be ruled out
without first taking into account gravitational effects.


  In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge
is to infer hidden dynamic cellular processes from measured static cell
snapshots. To tackle this challenge, many computational methods have been
developed from distinct perspectives. Besides the common perspectives of
inferring trajectories (or pseudotime) and RNA velocity, another important
perspective is to estimate the differentiation potential of cells, which is
commonly referred to as "cell potency." In this review, we provide a
comprehensive summary of 11 computational methods that estimate cell potency
from scRNA-seq data under different assumptions, some of which are even
conceptually contradictory. We divide these methods into three categories:
mean-based, entropy-based, and correlation-based methods, depending on how a
method summarizes gene expression levels of a cell or cell type into a potency
measure. Our review focuses on the key similarities and differences of the
methods within each category and between the categories, providing a high-level
intuition of each method. Moreover, we use a unified set of mathematical
notations to detail the 11 methods' methodologies and summarize their usage
complexities, including the number of ad-hoc parameters, the number of required
inputs, and the existence of discrepancies between the method description in
publications and the method implementation in software packages. Realizing the
conceptual contradictions of existing methods and the difficulty of fair
benchmarking without single-cell-level ground truths, we conclude that accurate
estimation of cell potency from scRNA-seq data remains an open challenge.


  We compare dynamical heterogeneities in equilibrated supercooled liquids and
in the nonequilibrium glassy state within the framework of the random first
order transition theory. Fluctuating mobility generation and transport in the
glass are treated by numerically solving stochastic continuum equations for
mobility and fictive temperature fields that arise from an extended mode
coupling theory containing activated events. Fluctuating spatiotemporal
structures in aging and rejuvenating glasses lead to dynamical heterogeneity in
glasses with characteristics distinct from those found in the equilibrium
supercooled liquid. The non-Gaussian distribution of activation free energies,
the stretching exponent $\beta$, and the growth of characteristic lengths are
studied along with the four-point correlation function. Asymmetric
thermodynamic responses upon heating and cooling are predicted to be the
results of the heterogeneity and the out-of-equilibrium behavior of glasses
below $T_g$. Numerical results agree with experimental calorimetry. We
numerically confirm the prediction of Lubchenko and Wolynes in the glass that
the dynamical heterogeneity can lead to noticeably bimodal distributions of
local fictive temperatures which explains in a unified way recent experimental
observations that have been interpreted as coming from two distinct
equilibration mechanisms in glasses.


  Intelligent Cyber-physical systems can be modelled as multi-agent systems
with planning capability to impart adaptivity for changing contexts. In such
multi-agent systems, the protocol for plan execution must result in the proper
completion and ordering of actions in spite of their distributed execution.
However, in untrusted scenarios, there is a possibility of agents not
respecting the protocol either due to faults or due to malicious reasons
thereby resulting in plan failure. In order to prevent such situations, we
propose to implement the execution of agents through smart contracts. This
points to a generic architecture seamlessly integrating intelligent
planning-based CPS and smart-contracts.


  Motivated by COVID-19 vaccine allocation, where vulnerable subpopulations are
simultaneously more impacted in terms of health and more disadvantaged in terms
of access to the vaccine, we formalize and study the problem of resource
allocation when there are inherent access differences that correlate with
advantage and disadvantage. We identify reducing resource disparity as a key
goal in this context and show its role as a proxy to more nuanced downstream
impacts. We develop a concrete access model that helps quantify how a given
allocation translates to resource flow for the advantaged vs. the
disadvantaged, based on the access gap between them. We then provide a
methodology for access-aware allocation. Intuitively, the resulting allocation
leverages more vaccines in locations with higher vulnerable populations to
mitigate the access gap and reduce overall disparity. Surprisingly, knowledge
of the access gap is often not needed to perform access-aware allocation. To
support this formalism, we provide empirical evidence for our access model and
show that access-aware allocation can significantly reduce resource disparity
and thus improve downstream outcomes. We demonstrate this at various scales,
including at county, state, national, and global levels.


  For random compositions of independent and identically distributed measurable
maps on a Polish space, we study the existence and finitude of absolutely
continuous ergodic stationary probability measures (which are, in particular,
physical measures) whose basins of attraction cover the whole space almost
everywhere. We characterize and hierarchize such random maps in terms of their
associated Markov operators, as well as show the difference between classes in
the hierarchy by plenty of examples, including additive noise, multiplicative
noise, and iterated function systems. We also provide sufficient practical
conditions for a random map to belong to these classes. For instance, we
establish that any continuous random map on a compact Riemannian manifold with
absolutely continuous transition probability has finitely many physical
measures whose basins of attraction cover Lebesgue almost all the manifold.


  This document describes the details and the motivation behind a new dataset
we collected for the semi-supervised recognition challenge~\cite{semi-aves} at
the FGVC7 workshop at CVPR 2020. The dataset contains 1000 species of birds
sampled from the iNat-2018 dataset for a total of nearly 150k images. From this
collection, we sample a subset of classes and their labels, while adding the
images from the remaining classes to the unlabeled set of images. The presence
of out-of-domain data (novel classes), high class-imbalance, and fine-grained
similarity between classes poses significant challenges for existing
semi-supervised recognition techniques in the literature. The dataset is
available here: \url{https://github.com/cvl-umass/semi-inat-2020}


  By applying Noether method to N=1 local supersymmetry in eleven dimensions,
we obtained two candidates of R^4 corrections to the supergravity. The bosonic
parts of these two completely match with the results obtained by type IIA
string perturbative calculations. We also obtained 13 parameters which relate
only fermionic terms.


  The scattering parameter extraction method of metamaterial homogenization is
reviewed to show that the only ambiguity is the one related to the choice of
the branch of the complex logarithmic function (or the complex inverse cosine
function), whereas it has no ambiguity for the sign of the wave number and
intrinsic impedance. While the method indeed yields two signs of the intrinsic
impedance, and thus the wave number, the signs are dependent, and moreover,
both sign combinations lead to the same permittivity and permeability, and are
thus permissible. This observation is in distinct contrast to a number of
statements in the literature where the correct sign of the intrinsic impedance
and wave number, resulting from the scattering parameter method, is chosen by
imposing additional physical requirements such as passivity. The scattering
parameter method is reviewed through an investigation of a uniform plane wave
normally incident on a planar slab in free-space, and the severity of the
branch ambiguity is illustrated through simulations of a known metamaterial
realization. Several approaches for proper branch selection are reviewed and
their suitability to metamaterial samples is discussed.


  Text-based representations of chemicals and proteins can be thought of as
unstructured languages codified by humans to describe domain-specific
knowledge. Advances in natural language processing (NLP) methodologies in the
processing of spoken languages accelerated the application of NLP to elucidate
hidden knowledge in textual representations of these biochemical entities and
then use it to construct models to predict molecular properties or to design
novel molecules. This review outlines the impact made by these advances on drug
discovery and aims to further the dialogue between medicinal chemists and
computer scientists.


  The horofunction boundary is a means of compactifying metric spaces that was
introduced by Gromov in the 1970s. We describe explicitly the horofunction
boundary of the Hilbert geometry, and sketch how it may be used to study the
isometry group of this space.


  In this recreational mathematics note, we address a simple, yet instructive
question:
  Justin has to travel a distance of d miles along a bus route. Along this
route, there are n bus stops i, each spaced at a distance of d_i from the
starting point. At each bus stop, Justin is faced with a choice: to walk or to
wait. If he walks on, he can still catch a bus at the next bus stop--but if a
bus passes him while he walks, he is almost assured a longer wait.
  We model Justin's decision constraint and completely solve the model in a
special case. The answer is intuitive: the optimal strategy is the laziest.


  When performing manipulation-based activities such as picking objects, a
mobile robot needs to position its base at a location that supports successful
execution. To address this problem, prominent approaches typically rely on
costly grasp planners to provide grasp poses for a target object, which are
then are then analysed to identify the best robot placements for achieving each
grasp pose. In this paper, we propose instead to first find robot placements
that would not result in collision with the environment and from where picking
up the object is feasible, then evaluate them to find the best placement
candidate. Our approach takes into account the robot's reachability, as well as
RGB-D images and occupancy grid maps of the environment for identifying
suitable robot poses. The proposed algorithm is embedded in a service robotic
workflow, in which a person points to select the target object for grasping. We
evaluate our approach with a series of grasping experiments, against an
existing baseline implementation that sends the robot to a fixed navigation
goal. The experimental results show how the approach allows the robot to grasp
the target object from locations that are very challenging to the baseline
implementation.


  Tracking data in the NFL is a sequence of spatial-temporal measurements that
vary in length depending on the duration of the play. In this paper, we
demonstrate how model-based curve clustering of observed player trajectories
can be used to identify the routes run by eligible receivers on offensive
passing plays. We use a Bernstein polynomial basis function to represent
cluster centers, and the Expectation Maximization algorithm to learn the route
labels for each of the 34,698 routes run on the 6,963 passing plays in the data
set. We go on to suggest ideas for new potential receiver metrics that account
for receiver deployment. The resulting route labels can also be paired with
film to enable streamlined queries of game film.


  For positive $p$-harmonic functions on Riemannian manifolds, we derive a
gradient estimate and Harnack inequality with constants depending only on the
lower bound of the Ricci curvature, the dimension $n$, $p$ and the radius of
the ball on which the function is defined. Our approach is based on a careful
application of the Moser iteration technique and is different from Cheng-Yau's
method employed by Kostchwar and Ni, in which a gradient estimate for positive
$p$-harmonic functions is derived under the assumption that the sectional
curvature is bounded from below.


  In this methodological note we discuss some details and peculiarities of the
cosmic expansion as viewed by a realistic observer. We show that the velocity
$v_\Theta$ related to a change (measured by observer's clock) of the angular
distance, plays an important role in formation of a meaningful observed picture
of the expansion of the universe. Usage of this velocity and the angular
distance (in addition to the standard approach --- proper distance and
corresponding velocity) allows to present the cosmic expansion in a more
illustrative manner. These parameters play a key role in visualization of the
expansion.


  We report on the controllable pinning of domain walls in stripes with
perpendicular magnetic anisotropy by magnetostatic coupling to magnetic
vortices in disks located above the stripe. Pinning mechanisms and depinning
fields are reported. This novel pinning strategy, which can be realized by
current nanofabrication techniques, opens up new possibilities for the
non-destructive control of domain wall mobility in domain wall based spintronic
devices.


  Large language models (LLMs) are gaining increasing attention for their
capability to process graphs with rich text attributes, especially in a
zero-shot fashion. Recent studies demonstrate that LLMs obtain decent text
classification performance on common text-rich graph benchmarks, and the
performance can be improved by appending encoded structural information as
natural languages into prompts. We aim to understand why the incorporation of
structural information inherent in graph data can improve the prediction
performance of LLMs. First, we rule out the concern of data leakage by curating
a novel leakage-free dataset and conducting a comparative analysis alongside a
previously widely-used dataset. Second, as past work usually encodes the
ego-graph by describing the graph structure in natural language, we ask the
question: do LLMs understand the graph structure in accordance with the intent
of the prompt designers? Third, we investigate why LLMs can improve their
performance after incorporating structural information. Our exploration of
these questions reveals that (i) there is no substantial evidence that the
performance of LLMs is significantly attributed to data leakage; (ii) instead
of understanding prompts as graph structures as intended by the prompt
designers, LLMs tend to process prompts more as contextual paragraphs and (iii)
the most efficient elements of the local neighborhood included in the prompt
are phrases that are pertinent to the node label, rather than the graph
structure.


  Current multimodal large language models (MLLMs) face significant challenges
in visual document understanding (VDU) tasks due to the high resolution, dense
text, and complex layouts typical of document images. These characteristics
demand a high level of detail perception ability from MLLMs. While increasing
input resolution improves detail perception, it also leads to longer sequences
of visual tokens, increasing computational costs and straining the models'
ability to handle long contexts. To address these challenges, we introduce
DocKylin, a document-centric MLLM that performs visual content slimming at both
the pixel and token levels, thereby reducing token sequence length in VDU
scenarios. DocKylin utilizes an Adaptive Pixel Slimming (APS) preprocessing
module to perform pixel-level slimming, increasing the proportion of
informative pixels. Moreover, DocKylin incorporates a novel Dynamic Token
Slimming (DTS) module to conduct token-level slimming, filtering essential
tokens and removing others to create a compressed, adaptive visual sequence.
Experiments demonstrate DocKylin's promising performance across various VDU
benchmarks. Notably, both the proposed APS and DTS are parameter-free,
facilitating easy integration into existing MLLMs, and our experiments indicate
their potential for broader applications.


  We propose an SO(10) grand unified theory which has the simplest Higgs
structure discussed so far in the literature. We include only two Higgs
scalars, a 210-plet and a 16-plet. In addition to the regular fermions we
include one singlet, whose mass term breaks chiral symmetry, so that fermions
can get masses. All fermions acquire see-saw masses, since there are no Higgs
bi-doublets. Required neutrino masses with large mixing as well as leptogenesis
are possible in this model.


  We generalize many recent uniqueness results on the fractional Calder\'on
problem to cover the cases of all domains with nonempty exterior. The highlight
of our work is the characterization of uniqueness and nonuniqueness of partial
data inverse problems for the fractional conductivity equation on domains that
are bounded in one direction for conductivities supported in the whole
Euclidean space and decaying to a constant background conductivity at infinity.
We generalize the uniqueness proof for the fractional Calder\'on problem by
Ghosh, Salo and Uhlmann to a general abstract setting in order to use the full
strength of their argument. This allows us to observe that there are also
uniqueness results for many inverse problems for higher order local
perturbations of a lower order fractional Laplacian. We give concrete example
models to illustrate these curious situations and prove Poincar\'e inequalities
for the fractional Laplacians of any order on domains that are bounded in one
direction. We establish Runge approximation results in these general settings,
improve regularity assumptions also in the cases of bounded sets and prove
general exterior determination results. Counterexamples to uniqueness in the
inverse fractional conductivity problem with partial data are constructed in
another companion work.


  Given a 4d ${\mathcal N}=2$ supersymmetric theory with an ${\mathcal
N}=(2,2)$ supersymmetric surface defect, a marginal perturbation of the bulk
theory induces a complex structure deformation of the defect moduli space. We
describe a concrete way of computing this deformation using the bulk-defect
OPE.


  We present a four-dimensional Planck-scale corrected quadratic extension of
General Relativity (GR) where no a priori relation between metric and
connection is imposed (Palatini formalism). Static spherically symmetric
electrovacuum solutions are obtained in exact analytical form. The macroscopic
properties of these solutions are in excellent agreement with GR, though the
region around the central singularity is modified. In fact, the singularity is
generically replaced by a wormhole supported by the electric field, which
provides a non-trivial topology to the space-time. Moreover, for a certain
charge-to-mass ratio the geometry is completely regular everywhere. For such
regular solutions, the horizon disappears in the microscopic regime below a
critical number of charges ($N<17$), yielding a set of massive objects that
could be naturally identified as black hole remnants.


  We review results for the mass spectrum of orbitally excited baryons obtained
in the $1/N_c$ expansion. We show the dependence of various contributions to
the mass operator as a function of the excitation energy.


  In the standard model of cosmology, the Universe began its expansion with an
anomalously low entropy, which then grew dramatically to much larger values
consistent with the physical conditions at decoupling, roughly 380,000 years
after the Big Bang. There does not appear to be a viable explanation for this
`unnatural' history, other than via the generalized second law of
thermodynamics (GSL), in which the entropy of the bulk, S_bulk, is combined
with the entropy of the apparent (or gravitational) horizon, S_h. This is not
completely satisfactory either, however, since this approach seems to require
an inexplicable equilibrium between the bulk and horizon temperatures. In this
paper, we explore the thermodynamics of an alternative cosmology known as the
R_h=ct universe, which has thus far been highly successful in resolving many
other problems or inconsistencies in LCDM. We find that S_bulk is constant in
this model, eliminating the so-called initial entropy problem simply and
elegantly. The GSL may still be relevant, however, principally in selecting the
arrow of time, given that S_h ~ t^2 in this model.


  Let $V$ be the weighted projective variety defined by a weighted homogeneous
ideal $J$ and $C$ a maximal cone in the Gr\"obner fan of $J$ with $m$ rays. We
construct a flat family over $\mathbb A^m$ that assembles the Gr\"obner
degenerations of $V$ associated with all faces of $C$. This is a
multi-parameter generalization of the classical one-parameter Gr\"obner
degeneration associated to a weight. We explain how our family can be
constructed from Kaveh-Manon's recent work on the classification of toric flat
families over toric varieties: it is the pull-back of a toric family defined by
a Rees algebra with base $X_C$ (the toric variety associated to $C$) along the
universal torsor $\mathbb A^m \to X_C$. We apply this construction to the
Grassmannians ${\rm Gr}(2,\mathbb C^n)$ with their Pl\"ucker embeddings and the
Grassmannian ${\rm Gr}\big(3,\mathbb C^6\big)$ with its cluster embedding. In
each case, there exists a unique maximal Gr\"obner cone whose associated
initial ideal is the Stanley-Reisner ideal of the cluster complex. We show that
the corresponding cluster algebra with universal coefficients arises as the
algebra defining the flat family associated to this cone. Further, for ${\rm
Gr}(2,\mathbb C^n)$ we show how Escobar-Harada's mutation of Newton-Okounkov
bodies can be recovered as tropicalized cluster mutation.


  The brain has a great capacity for computation and efficient resolution of
complex problems, far surpassing modern computers. Neuromorphic engineering
seeks to mimic the basic principles of the brain to develop systems capable of
achieving such capabilities. In the neuromorphic field, navigation systems are
of great interest due to their potential applicability to robotics, although
these systems are still a challenge to be solved. This work proposes a
spike-based robotic navigation and environment pseudomapping system formed by a
bio-inspired hippocampal memory model connected to a Posterior Parietal Cortex
model. The hippocampus is in charge of maintaining a representation of an
environment state map, and the PPC is in charge of local decision-making. This
system was implemented on the SpiNNaker hardware platform using Spiking Neural
Networks. A set of real-time experiments was applied to demonstrate the correct
functioning of the system in virtual and physical environments on a robotic
platform. The system is able to navigate through the environment to reach a
goal position starting from an initial position, avoiding obstacles and mapping
the environment. To the best of the authors knowledge, this is the first
implementation of an environment pseudo-mapping system with dynamic learning
based on a bio-inspired hippocampal memory.


  Classical persistent homology is a powerful mathematical tool for shape
comparison. Unfortunately, it is not tailored to study the action of
transformation groups that are different from the group Homeo(X) of all
self-homeomorphisms of a topological space X. This fact restricts its use in
applications. In order to obtain better lower bounds for the natural
pseudo-distance d_G associated with a subgroup G of Homeo(X), we need to adapt
persistent homology and consider G-invariant persistent homology. Roughly
speaking, the main idea consists in defining persistent homology by means of a
set of chains that is invariant under the action of G. In this paper we
formalize this idea, and prove the stability of the persistent Betti number
functions in G-invariant persistent homology with respect to the natural
pseudo-distance d_G. We also show how G-invariant persistent homology could be
used in applications concerning shape comparison, when the invariance group is
a proper subgroup of the group of all self-homeomorphisms of a topological
space. In this paper we will assume that the space X is triangulable, in order
to guarantee that the persistent Betti number functions are finite without
using any tameness assumption.


  We propose a method for compressively acquiring a dynamic light field (a 5-D
volume) through a single-shot coded image (a 2-D measurement). We designed an
imaging model that synchronously applies aperture coding and pixel-wise
exposure coding within a single exposure time. This coding scheme enables us to
effectively embed the original information into a single observed image. The
observed image is then fed to a convolutional neural network (CNN) for
light-field reconstruction, which is jointly trained with the camera-side
coding patterns. We also developed a hardware prototype to capture a real 3-D
scene moving over time. We succeeded in acquiring a dynamic light field with
5x5 viewpoints over 4 temporal sub-frames (100 views in total) from a single
observed image. Repeating capture and reconstruction processes over time, we
can acquire a dynamic light field at 4x the frame rate of the camera. To our
knowledge, our method is the first to achieve a finer temporal resolution than
the camera itself in compressive light-field acquisition. Our software is
available from our project webpage


  We image with unprecedented spatial resolution and sensitivity disk features
that could be potential signs of planet-disk interaction. Two companion
candidates have been claimed in the disk around the young Herbig Ae/Be star
HD100546. Thus, this object serves as an excellent target for our investigation
of the natal environment of giant planets. We exploit the power of extreme
adaptive optics operating in conjunction with the new high-contrast imager
SPHERE to image HD100546 in scattered light. We obtain the first polarized
light observations of this source in the visible (with resolution as fine as 2
AU) and new H and K band total intensity images that we analyze with the
Pynpoint package. The disk shows a complex azimuthal morphology, where multiple
scattering of photons most likely plays an important role. High brightness
contrasts and arm-like structures are ubiquitous in the disk. A double-wing
structure (partly due to ADI processing) resembles a morphology newly observed
in inclined disks. Given the cavity size in the visible (11 AU), the CO
emission associated to the planet candidate 'c' might arise from within the
circumstellar disk. We find an extended emission in the K band at the expected
location of 'b'. The surrounding large-scale region is the brightest in
scattered light. There is no sign of any disk gap associated to 'b'.


  Logarithmic spin-1/3 superconformal field theories are investigated. the
chiral and full two-point functions of two-(or more-) dimensional Jordanian
blocks of arbitrary weights, are obtained.


  Given a holomorphic family $f:\mathcal{X} \to S$ of compact complex manifolds
and a relative ample line bundle $L\to \mathcal{X}$, the higher direct images
$R^{n-p}f_*\Omega^p_{\mathcal{X}/S}(L)$ carry a natural hermitian metric. Using
the explicit formula for the curvature tensor of these direct images, we prove
that the determinant of the twisted Hodge filtration $F^p_L=\oplus_{i\geq
p}R^{n-i}\Omega^i_{\mathcal{X}/S}(L)$ is (semi-) positive on the base $S$ if
$L$ itself is (semi-) positive on $\mathcal{X}$.


  The Asteroid Redirect Mission (ARM) under development by NASA is being
planned to collect a multi-meter boulder from a near-Earth asteroid (NEA), and
to bring it to the cis-lunar space in the mid-2020's for future study and
exploitation by a crewed mission. The MarcoPolo-M5 project is being proposed in
2016 for the M5 mission opportunity by ESA, to bring back to Earth a sample
from a very primitive D-type NEA.
  We aim to further characterize the physical properties of two optimal targets
for sample return space missions, the low-DeltaV NEAs (341843) 2008 EV5 and
(52381) 1993 HA. 2008 EV5 is the baseline target of ARM, but only one spectrum
of this object exists in the literature. 1993 HA is a very favourable target
for a space mission based on its dynamical properties: here we intend to assess
if it is a suitable target for MarcoPolo-M5.
  We obtained visible spectroscopy of 2008 EV5 with the FORS2 instrument at
ESO-VLT, at different rotational phases. We also obtained visible and
near-infrared spectroscopy of 1993 HA, using the EFOSC2 and SOfI instruments at
ESO-NTT. Visible photometry of 1993 HA was carried out within the IMPACTON
project at the Observatorio Astronomico do Sertao de Itaparica (Brazil).
  Our new observations are in agreement with the C-type classification of 2008
EV5. We obtained five visible spectra which do not show any variability within
the limits of noise, suggesting a homogeneous surface. We obtained the first
ever spectroscopic dataset for 1993 HA, finding a featureless, red-sloped
behaviour typical of D-types. We found that the synodic rotation period of 1993
HA is 4.107+-0.002 h. The derived lightcurve also suggests an elongated shape
(axis ratio a/b>=1.71). At this stage 1993 HA does indeed seem to be the most
favourable target for MarcoPolo-M5, though future observations are necessary to
study it further.


  We prove the filling area conjecture in the hyperelliptic case. In
particular, we establish the conjecture for all genus 1 fillings of the circle,
extending P. Pu's result in genus 0. We translate the problem into a question
about closed ovalless real surfaces. The conjecture then results from a
combination of two ingredients. On the one hand, we exploit integral geometric
comparison with orbifold metrics of constant positive curvature on real
surfaces of even positive genus. Here the singular points are Weierstrass
points. On the other hand, we exploit an analysis of the combinatorics on
unions of closed curves, arising as geodesics of such orbifold metrics.


  Choosing four entangled stets to form an orthogonal and complete basis for a
two-particle system, we argue that a local hidden variable model should give
the probability of each entangled state if the two-particle system is described
by a mixed state. Under this condition, a new Bell inequality is derived for
the Werner states, and a nonlocality proof can be given for nonsequential
measurements PACS number: 03.65.Bz


  Experience replay enables online reinforcement learning agents to store and
reuse the previous experiences of interacting with the environment. In the
original method, the experiences are sampled and replayed uniformly at random.
A prior work called prioritized experience replay was developed where
experiences are prioritized, so as to replay experiences seeming to be more
important more frequently. In this paper, we develop a method called
double-prioritized state-recycled (DPSR) experience replay, prioritizing the
experiences in both training stage and storing stage, as well as replacing the
experiences in the memory with state recycling to make the best of experiences
that seem to have low priorities temporarily. We used this method in Deep
Q-Networks (DQN), and achieved a state-of-the-art result, outperforming the
original method and prioritized experience replay on many Atari games.


  We revisit echelle spectra (spectral resolution R ~ 40000) of 8 Gamma-Ray
Burst afterglows to obtain the incidence (dN/dz) of weak intervening Mg II
systems at a mean redshift of <z> = 1.5. We show that dN/dz of systems having
restframe equivalent widths 0.07 A < W_r(MgII) < 1 A toward GRBs is
statistically consistent with the incidence toward QSOs. Our result is in
contrast to the results for Mg II systems having W_r > 1 A, where dN/dz toward
GRBs has been found to be larger than toward QSOs by a factor of ~ 4. We
confirm the overdensity albeit at a factor of ~ 3 only. This suggests that any
explanation for the GRB/QSO discrepancy, be it intrinsic to the absorbers or a
selection effect, should be inherent only to the galaxies that host strong
absorbers in the line-of-sight to GRBs. We argue that, of all scenarios that
have been proposed, lensing amplification is the one that could explain the
strong Mg II enhancement while allowing for no significant enhancement in the
weak absorbers.


  We introduce the notions of w-lower semicontinuous and almost w-lower
semicontinuous correspondence with respect to a given set and prove a new
fixed-point theorem. We also introduce the notion of correspondence with
e-LSCS-property. As applications we obtain some new equilibrium theorems for
abstract economies and for generalized multiobjective games.


  It is believed that soon after the Planck time, Einstein's general relativity
theory should be corrected to an effective quadratic theory. In this work we
present the 3+1 decomposition for the zero vorticity case for arbitrary
spatially homogenous spaces. We specialize for the particular Bianchi $I$
diagonal case. The 3- curvature can be understood as a generalized potential,
and the Bianchi $I$ case is a limiting case where this potential is negligible
to the dynamics. The spirit should be analogous, in some sense to the BKL
solution. In this sense, a better understanding of the Bianchi $I$ case could
shed some light into the general Bianchi case.


  Aluminum monochloride (AlCl) has been proposed as a promising candidate for
laser cooling to ultracold temperatures, and recent spectroscopy results
support this prediction. It is challenging to produce large numbers of AlCl
molecules because it is a highly reactive open-shell molecule and must be
generated in situ. Here we show that pulsed-laser ablation of stable, non-toxic
mixtures of Al with an alkali or alkaline earth chlorides, denoted XCln, can
provide a robust and reliable source of cold AlCl molecules. Both the chemical
identity of XCln and the Al:XCln molar ratio are varied, and the yield of AlCl
is monitored using absorption spectroscopy in a cryogenic gas. For KCl, the
production of Al and K atoms was also monitored. We model the AlCl production
in the limits of nonequilibrium recombination dominated by first-encounter
events. The non-equilibrium model is in agreement with the data and also
reproduces the observed trend with different XCln precursors. We find that AlCl
production is limited by the solid-state densities of Al and Cl atoms and the
recondensation of Al atoms in the ablation plume. We suggest future directions
for optimizing the production of cold AlCl molecules using laser ablation.


  Today's software systems like cyber-physical production systems or big data
systems have to process large volumes and diverse types of data which heavily
influences the quality of these so-called data-intensive systems. However,
traditional software testing approaches rather focus on functional behavior
than on data aspects. Therefore, the role of data in testing has to be
rethought and specific testing approaches for data-intensive software systems
are required. Thus, the aim of this chapter is to contribute to this area by
(1) providing basic terminology and background on data-intensive software
systems and their testing, and (2) presenting the state of the research and the
hot topics in the area. Finally, the directions of research and the new
frontiers on testing data-intensive software systems are discussed.


  One of the factors limiting the performance of handwritten text recognition
(HTR) for stenography is the small amount of annotated training data. To
alleviate the problem of data scarcity, modern HTR methods often employ data
augmentation. However, due to specifics of the stenographic script, such
settings may not be directly applicable for stenography recognition. In this
work, we study 22 classical augmentation techniques, most of which are commonly
used for HTR of other scripts, such as Latin handwriting. Through extensive
experiments, we identify a group of augmentations, including for example
contained ranges of random rotation, shifts and scaling, that are beneficial to
the use case of stenography recognition. Furthermore, a number of augmentation
approaches, leading to a decrease in recognition performance, are identified.
Our results are supported by statistical hypothesis testing. Links to the
publicly available dataset and codebase are provided.


  We present a partial analysis of a multi-wavelength study of the X-ray binary
Cir X-1, a system harboring the most relativistic outflow in our galaxy so far.
The data were taken (almost) simultaneously in radio and X ray during a survey
carried out in October 2000 and December 2002. Cir X-1 was observed at the
radio frequencies of 4.8 and 8.6 GHz with the Australia Telescope Compact Array
(ATCA). In the X-ray spectral domain we used the Rossi X-Ray Timing Explorer
(RXTE). We found strong evidence for flaring activity in radio not only at the
periastron but also at the apoastron passages. A comparison of our data against
different correlations between radio and X ray found in other neutron star
systems shows that Cir X-1 does not seem to follow the general trend. However,
the fact that Cir X-1 is an `exotic' X-ray binary makes any interpretation more
complicated.


  We show that the observed rotation curves of spiral galaxies constrain the
sound speed of the dark matter to be $c_s < 10^{-4} c$, where $c$ is the speed
of light in vacuum. Using the Modified Chaplygin Gas as a representative
example of a class of unified dark energy models incorporating an effective
dark matter component with a non-zero sound speed, we determine the most
stringent constraint to date on the value of the constant contribution to the
equation of state parameter in this class of models. Finally, we explain the
reason why previous constraints using the Cosmic Microwave Background and
Baryonic Acoustic Oscillations were not as competitive as the one presented in
this paper and discuss the limitations of the recently proposed Extended
Chaplygin Gas.


  We give a survey on some recent results concerning the Landau-Lifshitz
equation, a fundamental nonlinear PDE with a strong geometric content,
describing the dynamics of the magnetization in ferromagnetic materials. We
revisit the Cauchy problem for the anisotropic Landau-Lifshitz equation,
without dissipation, for smooth solutions, and also in the energy space in
dimension one. We also examine two approximations of the Landau-Lifshitz
equation given by of the Sine-Gordon equation and cubic Schr\"odinger
equations, arising in certain singular limits of strong easy-plane and
easy-axis anisotropy, respectively.
  Concerning localized solutions, we review the orbital and asymptotic
stability problems for a sum of solitons in dimension one, exploiting the
variational nature of the solitons in the hydrodynamical framework.
  Finally, we survey results concerning the existence, uniqueness and stability
of self-similar solutions (expanders and shrinkers) for the isotropic
Landau-Lifshitz equation with Gilbert term. Since expanders are associated with
a singular initial condition with a jump discontinuity, we also review their
well-posedness in spaces linked to the BMO space.