  The distribution of dark matter in dwarf galaxies can have important
implications on our understanding of galaxy formation as well as the particle
physics properties of dark matter. However, accurately characterizing the dark
matter content of dwarf galaxies is challenging due to limited data and complex
dynamics that are difficult to accurately model. In this paper, we apply
spherical Jeans modeling to simulated stellar kinematic data of spherical,
isotropic dwarf galaxies with the goal of identifying the future observational
directions that can improve the accuracy of the inferred dark matter
distributions in the Milky Way dwarf galaxies. We explore how the dark matter
inference is affected by the location and number of observed stars as well as
the line-of-sight velocity measurement errors. We use mock observation to
demonstrate the difficulty in constraining the inner core/cusp of the dark
matter distribution with datasets of fewer than 10,000 stars. We also
demonstrate the need for additional measurements to make robust estimates of
the expected dark matter annihilation signal strength. For the purpose of
deriving robust indirect detection constraints, we identify Ursa Major II, Ursa
Minor, and Draco as the systems that would most benefit from additional stars
being observed.


  The microlensing surveys MACHO, EROS, MOA and OGLE (hereafter called MEMO)
have searched for microlensing toward the Large Magellanic Cloud for a
cumulated duration of 27 years. We study the potential of joining these
databases to search for very massive objects, that produce microlensing events
with a duration of several years. We identified the overlaps between the
different catalogs and compiled their time coverage to identify common regions
where a joint microlensing detection algorithm can operate. We extrapolated a
conservative global microlensing detection efficiency based on simple
hypotheses, and estimated detection rates for multi-year duration events.
Compared with the individual survey searches, we show that a combined search
for long timescale microlensing should detect about ten more events caused by
$100.M_{\odot}$ black holes if these objects have a major contribution to the
Milky Way halo. Assuming that a common analysis is feasible, meaning that the
difficulties that arise from using different passbands can be overcome, we show
that the sensitivity of such an analysis might enable us to quantify the
Galactic black hole component.


  Predicting forced displacement is an important undertaking of many
humanitarian aid agencies, which must anticipate flows in advance in order to
provide vulnerable refugees and Internally Displaced Persons (IDPs) with
shelter, food, and medical care. While there is a growing interest in using
machine learning to better anticipate future arrivals, there is little
standardized knowledge on how to predict refugee and IDP flows in practice.
Researchers and humanitarian officers are confronted with the need to make
decisions about how to structure their datasets and how to fit their problem to
predictive analytics approaches, and they must choose from a variety of
modeling options. Most of the time, these decisions are made without an
understanding of the full range of options that could be considered, and using
methodologies that have primarily been applied in different contexts - and with
different goals - as opportunistic references. In this work, we attempt to
facilitate a more comprehensive understanding of this emerging field of
research by providing a systematic model-agnostic framework, adapted to the use
of big data sources, for structuring the prediction problem. As we do so, we
highlight existing work on predicting refugee and IDP flows. We also draw on
our own experience building models to predict forced displacement in Somalia,
in order to illustrate the choices facing modelers and point to open research
questions that may be used to guide future work.


  Being inspired by Kaplan's proposal for simulating chiral fermions on a
lattice, we examine the continuum analog of his domain-wall construction for
two-dimensional chiral Schwinger models. Adopting slightly unusual dimensional
regularization, we explicitly evaluate the one-loop effective action in the
limit that the domain-wall mass goes to infinity. For anomaly-free cases, the
effective action turns out to be gauge invariant in two-dimensional sense.


  In this work, we consider the problem of pedestrian detection in natural
scenes. Intuitively, instances of pedestrians with different spatial scales may
exhibit dramatically different features. Thus, large variance in instance
scales, which results in undesirable large intra-category variance in features,
may severely hurt the performance of modern object instance detection methods.
We argue that this issue can be substantially alleviated by the
divide-and-conquer philosophy. Taking pedestrian detection as an example, we
illustrate how we can leverage this philosophy to develop a Scale-Aware Fast
R-CNN (SAF R-CNN) framework. The model introduces multiple built-in
sub-networks which detect pedestrians with scales from disjoint ranges. Outputs
from all the sub-networks are then adaptively combined to generate the final
detection results that are shown to be robust to large variance in instance
scales, via a gate function defined over the sizes of object proposals.
Extensive evaluations on several challenging pedestrian detection datasets well
demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our
method achieves state-of-the-art performance on Caltech, INRIA, and ETH, and
obtains competitive results on KITTI.


  The Bisognano-Wichmann property on the geometric behavior of the modular
group of the von Neumann algebras of local observables associated to wedge
regions in Quantum Field Theory is shown to provide an intrinsic sufficient
criterion for the existence of a covariant action of the (universal covering
of) the Poincar\'e group. In particular this gives, together with our previous
results, an intrinsic characterization of positive-energy conformal
pre-cosheaves of von Neumann algebras. To this end we adapt to our use Moore
theory of central extensions of locally compact groups by polish groups,
selecting and making an analysis of a wider class of extensions with natural
measurable properties and showing henceforth that the universal covering of the
Poincar\'e group has only trivial central extensions (vanishing of the first
and second order cohomology) within our class.


  The field of nanoscale magnetic resonance imaging (NanoMRI) was started 30
years ago. It was motivated by the desire to image single molecules and
molecular assemblies, such as proteins and virus particles, with near-atomic
spatial resolution and on a length scale of 100 nm. Over the years, the NanoMRI
field has also expanded to include the goal of useful high-resolution nuclear
magnetic resonance (NMR) spectroscopy of molecules under ambient conditions,
including samples up to the micron-scale. The realization of these goals
requires the development of spin detection techniques that are many orders of
magnitude more sensitive than conventional NMR and MRI, capable of detecting
and controlling nanoscale ensembles of spins. Over the years, a number of
different technical approaches to NanoMRI have emerged, each possessing a
distinct set of capabilities for basic and applied areas of science. The goal
of this roadmap article is to report the current state of the art in NanoMRI
technologies, outline the areas where they are poised to have impact, identify
the challenges that lie ahead, and propose methods to meet these challenges.
This roadmap also shows how developments in NanoMRI techniques can lead to
breakthroughs in emerging quantum science and technology applications.


  The physical mechanisms driving star formation (SF) in galaxies are still not
fully understood. Tidal dwarf galaxies (TDGs), made of gas ejected during
galaxy interactions, seem to be devoid of dark matter and have a near-solar
metallicity. The latter makes it possible to study molecular gas and its link
to SF using standard tracers (CO, dust) in a peculiar environment. We present a
detailed study of a nearby TDG in the Virgo Cluster, VCC 2062, using new
high-resolution CO(1--0) data from the Plateau de Bure, deep optical imaging
from the Next Generation Virgo Cluster Survey (NGVS), and complementary
multiwavelength data. Until now, there was some doubt whether VCC 2062 was a
true TDG, but the new deep optical images from the NGVS reveal a stellar bridge
between VCC 2062 and its parent galaxy, NGC 4694, which is clear proof of its
tidal origin. Several high-resolution tracers (\halpha, UV, 8~\mi, and 24~\mi)
of the star formation rate (SFR) are compared to the molecular gas distribution
as traced by the CO(1-0). Coupled with the SFR tracers, the NGVS data are used
with the CIGALE code to model the stellar populations throughout VCC 2062,
yielding a declining SFR in the recent past, consistent with the low \halpha/UV
ratio, and a high burst strength. HI emission covers VCC 2062, whereas the CO
is concentrated near the HI maxima. The CO peaks correspond to two very
distinct regions: one with moderate SF to the NE and one with only slightly
weaker CO emission but with nearly no SF. Even where SF is clearly present, the
SFR is below the value expected from the surface density of the molecular and
the total gas as compared to spiral galaxies and other TDGs. After discussing
different possible explanations, we conclude that the low surface brightness is
a crucial parameter to understand the low SFR.


  Recently two approximate Newton methods were proposed for the optimisation of
Markov Decision Processes. While these methods were shown to have desirable
properties, such as a guarantee that the preconditioner is
negative-semidefinite when the policy is $\log$-concave with respect to the
policy parameters, and were demonstrated to have strong empirical performance
in challenging domains, such as the game of Tetris, no convergence analysis was
provided. The purpose of this paper is to provide such an analysis. We start by
providing a detailed analysis of the Hessian of a Markov Decision Process,
which is formed of a negative-semidefinite component, a positive-semidefinite
component and a remainder term. The first part of our analysis details how the
negative-semidefinite and positive-semidefinite components relate to each
other, and how these two terms contribute to the Hessian. The next part of our
analysis shows that under certain conditions, relating to the richness of the
policy class, the remainder term in the Hessian vanishes in the vicinity of a
local optimum. Finally, we bound the behaviour of this remainder term in terms
of the mixing time of the Markov chain induced by the policy parameters, where
this part of the analysis is applicable over the entire parameter space. Given
this analysis of the Hessian we then provide our local convergence analysis of
the approximate Newton framework.


  Recently, Rastall gravity is undergoing a significant surge in popularity. We
obtain a power-law total mass-density profile for the inner region (within
several effective radius) of early-type galaxies (ETGs) from the space-time
structures which are described by the static spherically-symmetric solutions of
Rastall gravity under the assumption of perfect fluid matter. We find that in
the inner region of ETGs, the Rastall dimensionless parameter
$\beta=\kappa\lambda$ determines the mass distribution. We then use 118
galaxy-galaxy strong gravitational lensing systems to constrain the Rastall
dimensionless parameter $\beta$. We find that the mean value of $\beta$ for
total 118 ETGs is $\beta=0.163\pm0.001$(68\% CL) with a minor intrinsic scatter
of $\delta=0.020\pm 0.001$. Our work observationally illustrates the physical
meaning of the Rastall dimensionless parameter in galaxy scale. From the
Newtonian approximation of Rastall gravity, we also find that an absolute
isothermal mass distribution for ETGs is not allowed in the framework of
Rastall gravity.


  Visual information is processed in hierarchically organized parallel pathways
in the primate brain. In lower cortical areas, color information and shape
information are processed in a parallel manner, while in higher cortical areas,
various types of visual information, such as color, face, animate/inanimate,
are processed in a parallel manner. In the present study, the possibility of
spontaneous segregation of visual information in parallel streams was examined
by constructing a convolutional neural network with parallel architecture in
all of the convolutional layers. The results revealed that color information
was segregated from shape information in most model instances. Deletion of the
color-related stream decreased recognition accuracy in the inanimate category,
whereas deletion of the shape-related stream decreased recognition accuracy in
the animate category. The results suggest that properties of filters and
functions of a stream are spontaneously segregated in parallel streams of
neural networks.


  We investigate the impact of AGN feedback on the gravitational lensing
properties of a sample of galaxy clusters with masses in the range 10^14 -
10^15 Msol, using state-of-the-art simulations. Adopting a ray-tracing
algorithm, we compute the cross-section of giant arcs from clusters simulated
with dark-matter only physics (DM), dark matter plus gas with cooling and star
formation (CSF), and dark matter plus gas with cooling, star formation and AGN
feedback (CSFBH). Once AGN feedback is included, baryonic physics boosts the
strong lensing cross-section by much less than previously estimated using
clusters simulated with only cooling and star formation. For a cluster with a
virial mass of 7.4 X 10^14 Msol, inclusion of baryonic physics without feedback
can boost the cross-section by as much as a factor of 3, in agreement with
previous studies, whereas once AGN feedback is included this maximal figure
falls to a factor of 2 at most. Typically, clusters simulated with DM and CSFBH
physics have similar cross-sections for the production of giant arcs. We also
investigate how baryonic physics affects the weak lensing properties of the
simulated clusters by fitting NFW profiles to synthetic weak lensing data sets
using a Markov Chain Monte Carlo approach, and by performing non-parametric
mass reconstructions. Without the inclusion of AGN feedback, measured
concentration parameters can be much larger than those obtained with AGN
feedback, which are similar to the dark-matter only case.


  We analyse the 30 October, 2004, X1.2/SF solar event that occurred in AR
10691 (N13 W18) at around 11:44 UT. Observations at 212 and 405 GHz of the
Solar Submillimeter Telescope (SST), with high time resolution (5 ms), show an
intense impulsive burst followed by a long-lasting thermal phase. EUV images
from the Extreme Ultraviolet Imaging Telescope (SOHO/EIT) are used to identify
the possible emitting sources. Data from the Radio Solar Telescope Network
(RSTN) complement our spectral observations below 15 GHz. During the impulsive
phase the turnover frequency is above 15.4 GHz. The long-lasting phase is
analysed in terms of thermal emission and compared with GOES observations. From
the ratio between the two GOES soft X-ray bands, we derive the temperature and
emission measure, which is used to estimate the free-free submillimeter flux
density. Good temporal agreement is found between the estimated and observed
profiles, however the former is larger than the latter.


  The heliocentric redshifts ($z_\mathrm{hel}$) reported for 150 Type Ia
supernovae in the Pantheon compilation are significantly discrepant from their
corresponding values in the JLA compilation. Both catalogues include
corrections to the redshifts and magnitudes of the supernovae to account for
the motion of the heliocentric frame relative to the `CMB rest frame', as well
as corrections for the directionally coherent bulk motion of local galaxies
with respect to this frame. The latter is done employing modelling of peculiar
velocities which assume the $\Lambda$CDM cosmological model but nevertheless
provide evidence for residual bulk flows which are discordant with this model
(implying that the observed Universe is in fact anisotropic). Until recently
such peculiar velocity corrections in the Pantheon catalogue were made at
redshifts exceeding 0.2 although there is no data on which to base such
corrections. We study the impact of these vexed issues on the 4.4 $\sigma$
discrepancy between the Hubble constant of $H_0 = 67.4 \pm 0.5$ km/s/Mpc
inferred from observations of CMB anisotropies by Planck assuming $\Lambda$CDM,
and the measurement of $H_0 = 73.5 \pm 1.4$ km/s/Mpc by the SH0ES project which
extended the local distance ladder using Type Ia supernovae. Using the same
methodology as the latter study we find that for supernovae whose redshifts are
discrepant between Pantheon and JLA with $\Delta z_\mathrm{hel} > 0.0025$, the
Pantheon redshifts favour $H_0 \simeq 72$ km/s/Mpc, while the JLA redshifts
favour $H_0 \simeq 68$ km/s/Mpc. Thus the discrepancies between SNe Ia datasets
are sufficient to undermine the claimed `Hubble tension'. We further note the
systematic variation of $H_0$ by $\sim$ 6-9 km/s/Mpc across the sky seen in
multiple datasets, implying that it cannot be measured locally to better than
$\sim$ 10% in a model-independent manner.


  In this paper we determine the one loop radiative correction to the higgs
potential due to quantum fluctuations about the background metric of
Randall-Sundrum model. We then examine the effects of the one loop effective
potential on the stability of the classical vacuum paying particular attention
to the tadpole terms which could dominate over the classical potential for
small field configurations. We find that although the one loop potential due to
scale fluctuations could develop a tadpole term in certain regions of the
parameter space, it is positive for either sign of H. The quadratic and quartic
terms in the radiative correction are too small to cause any instability to the
classical vacuum for field configurations that do not violate the limits of
ordinary perturbation theory.


  Considering corrections produced by modified dispersion relations on the
equation of state parameter of radiation, we study the induced black hole
metric inspired by Kiselev's ansatz, thus defining a deformed
Reissner-Nordstr\"{o}m metric. In particular, we consider thermodynamic
properties of such a black hole from the combined viewpoints of the modified
equation of state parameter and the phenomenological approach to the quantum
gravity problem called rainbow gravity.


  Straight-chain (normal-propyl cyanide, n - C3H7CN) and branched-chain
(iso-propyl cyanide, i - C3H7CN) alkyl cyanides are recently identified in the
massive star-forming regions (Sgr B2(N) and Orion). These branched-chain
molecules indicate that the key amino acids (side-chain structures) may also be
present in a similar region. The process by which this branching could
propagate towards the higher-order (butyl cyanide, C4H9CN) is an active field
of research. Since the grain catalysis process could have formed a major
portion of these species, considering a realistic set of binding energies are
indeed essential. We employ quantum chemical calculations to estimate the
binding energy of these species considering water as a substrate because water
is the principal constituent of this interstellar ice. We find significantly
lower binding energy values for these species than were previously used. It is
noticed that the use of realistic binding energy values can significantly
change the abundance of these species. The branching is more favorable for the
higher-order alkyl cyanides with the new binding energies. With the inclusion
of our new binding energy values and one essential destruction reaction (i -
C3H7CN + H -> CH3C(CH3)CN + H2, having an activation barrier of 947 K),
abundances of t - C4H9CN dramatically increased.


  We study electron tunneling through Co nanoparticles in the presence of
repeated microwave pulses at 4.2K. While individual pulses are too weak to
affect the magnetic switching field, repeated microwave pulses start to reduce
the magnetic switching field at 10{\mu}s spacing. We use I-V curve as a
thermometer to show that the microwave pulses do not heat the sample, showing
that magnetization in Co nanoparticles is directly excited by microwave pulses,
and the relaxation time of the excitation energy is in the range of
microsecond.


  SNIA and CMB datasets have shown both of evolving Newton's "constant" and a
signature of the coupling of scalar field to matter. These observations
motivate the consideration of the scalar-matter coupling in Jordan frame in the
framework of scalar-tensor gravity. So far, majority of the works on the
coupling of scalar matter has performed in Einstein frame in the framework of
minimally coupled scalar fields. In this work, we generalize the original
scalar-tensor theories of gravity introducing a direct coupling of scalar to
matter in the Jordan frame. The combined consideration of both evolving
Newton's constant and scalar-matter coupling using the recent observation
datasets, shows features different from the previous works. The analysis shows
a vivid signature of the scalar-matter coupling. The variation rate of the
Newton's constant is obtained rather greater than that determined in the
previous works.


  A molecular understanding of how protein function is related to protein
structure will require an ability to understand large conformational changes
between multiple states. Unfortunately these states are often separated by high
free energy barriers and within a complex energy landscape. This makes it very
difficult to reliably connect, for example by all-atom molecular dynamics
calculations, the states, their energies and the pathways between them. A major
issue needed to improve sampling on the intermediate states is an order
parameter -- a reduced descriptor for the major subset of degrees of freedom --
that can be used to aid sampling for the large conformational change. We
present a novel way to combine information from molecular dynamics using
non-linear time series and dimensionality reduction, in order to quantitatively
determine an order parameter connecting two large-scale conformationally
distinct protein states. This new method suggests an implementation for
molecular dynamics calculations that may dramatically enhance sampling of
intermediate states.


  Fractional pebbling is a generalization of black-white pebbling introduced
recently. In this reasearch paper we solve an open problem by proving a tight
lower bound on the pebble weight required to fractionally pebble a balanced
d-ary tree of height h. This bound has close ties with branching programs and
the separation of P from NL.


  Since diffusion processes arise in so many different fields, efficient
tech-nics for the simulation of sample paths, like discretization schemes,
represent crucial tools in applied probability. Such methods permit to obtain
approximations of the first-passage times as a by-product. For efficiency
reasons, it is particularly challenging to simulate directly this hitting time
by avoiding to construct the whole paths. In the Brownian case, the
distribution of the first-passage time is explicitly known and can be easily
used for simulation purposes. The authors introduce a new rejection sampling
algorithm which permits to perform an exact simulation of the first-passage
time for general one-dimensional diffusion processes. The efficiency of the
method, which is essentially based on Girsanov's transformation , is described
through theoretical results and numerical examples.


  Because spin-orbit coupling (SOC) is invisible in the band structure when
inversion symmetry exists, whether spins are trivially degenerate or strongly
coupled to momentum due to SOC is presumed to make little difference in
transport measurements, such as magnetoresistance and quantum oscillations. In
this work, however, we show that hidden Rashba SOC in a centrosymmetric
two-dimensional material can lead to the quantum Hall effect with only
even-integer plateaus, unlike a spinless electron gas. Here, two Rashba layers
that are degenerate but with opposite SOC due to inversion symmetry, hybridize
with each other and create two doubly-degenerate bands with hidden spin
texture. Correspondingly, two branches of Landau levels interact, resulting in
significant suppression of spin splitting due to the balancing of intralayer
SOC and interlayer hybridization. Furthermore, we show that breaking inversion
symmetry restores the ordinary quantum Hall fluid by introducing spin-split
Fermi surfaces. Our theory can apply to centrosymmetric materials with strong
SOC, as demonstrated in a recent experiment on the two-dimensional
semiconductor Bi$_2$O$_2$Se.


  We prove the existence of a deformation quantization for integrable Poisson
structures on R^3 and give a generalization for a special class of three
dimensional manifolds.


  In this paper we are developing a theory of rational (pseudo) difference
Hamiltonian operators, focusing in particular on its algebraic aspects. We show
that a pseudo--difference Hamiltonian operator can be represented as a ratio
$AB^{-1}$ of two difference operators with coefficients from a difference field
$\mathcal{F}$ where $A$ is preHamiltonian. A difference operator $A$ is called
preHamiltonian if its image is a Lie subalgebra with respect to the Lie bracket
of evolutionary vector fields on $\mathcal{F}$. We show that a skew-symmetric
difference operator is Hamiltonian if and only if it is preHamiltonian and
satisfies simply verifiable conditions on its coefficients. We show that if $H$
is a rational Hamiltonian operator, then to find a second Hamiltonian operator
$K$ compatible with $H$ is the same as to find a preHamiltonian pair $A$ and
$B$ such that $AB^{-1}H$ is skew-symmetric. We apply our theory to non-trivial
multi-Hamiltonian structures of Narita-Itoh-Bogoyavlensky and Adler-Postnikov
equations.


  Accurate vehicular localization is important for various cooperative vehicle
safety (CVS) applications such as collision avoidance, turning assistant, etc.
In this paper, we propose a cooperative vehicular distance measurement
technique based on the sharing of GPS pseudorange measurements and a weighted
least squares method. The classic double difference pseudorange solution, which
was originally designed for high-end survey level GPS systems, is adapted to
low-end navigation level GPS receivers for its wide availability in ground
vehicles. The Carrier to Noise Ratio (CNR) of raw pseudorange measurements are
taken into account for noise mitigation. We present a Dedicated Short Range
Communications (DSRC) based mechanism to implement the exchange of pseudorange
information among neighboring vehicles. As demonstrated in field tests, our
proposed technique increases the accuracy of the distance measurement
significantly compared with the distance obtained from the GPS fixes.


  We consider a cloud-based control architecture in which the local plants
outsource the control synthesis task to the cloud. In particular, we consider a
cloud-based reinforcement learning (RL), where updating the value function is
outsourced to the cloud. To achieve confidentiality, we implement computations
over Fully Homomorphic Encryption (FHE). We use a CKKS encryption scheme and a
modified SARSA(0) reinforcement learning to incorporate the encryption-induced
delays. We then give a convergence result for the delayed updated rule of
SARSA(0) with a blocking mechanism. We finally present a numerical
demonstration via implementing on a classical pole-balancing problem.


  Graphics Processing Units (GPUs) are now powerful and flexible systems
adapted and used for other purposes than graphics calculations (General Purpose
computation on GPU -- GPGPU). We present here a prototype to be integrated into
simulation codes that estimate temperature, velocity and pressure to design
next generations of solar receptors. Such codes will delegate to our
contribution on GPUs the computation of heat transfers due to radiations. We
use Monte-Carlo line-by-line ray-tracing through finite volumes. This means
data-parallel arithmetic transformations on large data structures. Our
prototype is inspired on the source code of GPUBench. Our performances on two
recent graphics cards (Nvidia 7800GTX and ATI RX1800XL) show some speed-up
higher than 400 compared to CPU implementations leaving most of CPU computing
resources available. As there were some questions pending about the accuracy of
the operators implemented in GPUs, we start this report with a survey and some
contributed tests on the various floating point units available on GPUs.


  In this paper, we propose a deep learning-based method, deep Euler method
(DEM) to solve ordinary differential equations. DEM significantly improves the
accuracy of the Euler method by approximating the local truncation error with
deep neural networks which could obtain a high precision solution with a large
step size. The deep neural network in DEM is mesh-free during training and
shows good generalization in unmeasured regions. DEM could be easily combined
with other schemes of numerical methods, such as Runge-Kutta method to obtain
better solutions. Furthermore, the error bound and stability of DEM is
discussed.


  Positive energy unitary irreducible representations of $SU(2,2)$ can be
constructed with the aid of bosonic oscillators in (anti)fundamental
representation of $SU(2)_L\times SU(2)_R$ that are closely related to Penrose
twistors. Starting with the correspondence between the doubleton
representations, homogeneous functions on projective twistor space and on-shell
$SL(2,\mathbb C)$ generalized Weyl curvature spinors and their low-spin
counterparts, we study in the similar way the correspondence between the
massless representations, homogeneous functions on ambitwistor space and, via
the Penrose transform, with the gauge fields on Minkowski boundary of $AdS_5$.
The possibilities of reconstructing massless fields on $AdS_5$ and some
applications are also discussed.


  We report on some implications of the theory of turbulence developed by V.
Yakhot [V. Yakhot, Phys. Rev. E {\bf 57}(2) (1998)]. In particular we focus on
the expression for the scaling exponents $\zeta_{n}$. We show that Yakhot's
result contains three well known scaling models as special cases, namely K41,
K62 and the theory by V. L'vov and I. Procaccia [V. L'vov & I. Procaccia, Phys.
Rev. E {\bf 62}(6) (2000)]. The model furthermore yields a theoretical
justification for the method of extended self--similarity (ESS).


  The global and electromagnetic gyrokinetic particle-in-cell code ORB5 is
employed to investigate the self-consistent interactions between Alfv\'en modes
(AM) and ion temperature gradient (ITG) turbulence in a magnetically confined
plasma. Here, an axisymmetric magnetic equilibrium with reversed shear and
large aspect ratio is considered. An AM with toroidal mode number n=5 is driven
unstable by introducing a population of suprathermal ions. Once the AM
saturates in the presence of the fully developed turbulence, the ion heat flux
is dominated by the AM and its main harmonics. ITG-induced transport is found
to also be enhanced in the presence of the unstable AM.


  Reinforcement Learning (RL) has gained significant momentum in the
development of network protocols. However, RL-based protocols are still in
their infancy, and substantial research is required to build deployable
solutions. Developing a protocol based on RL is a complex and challenging
process that involves several model design decisions and requires significant
training and evaluation in real and simulated network topologies. Network
simulators offer an efficient training environment for RL-based protocols,
because they are deterministic and can run in parallel. In this paper, we
introduce \textit{RayNet}, a scalable and adaptable simulation platform for the
development of RL-based network protocols. RayNet integrates OMNeT++, a fully
programmable network simulator, with Ray/RLlib, a scalable training platform
for distributed RL. RayNet facilitates the methodical development of RL-based
network protocols so that researchers can focus on the problem at hand and not
on implementation details of the learning aspect of their research. We
developed a simple RL-based congestion control approach as a proof of concept
showcasing that RayNet can be a valuable platform for RL-based research in
computer networks, enabling scalable training and evaluation. We compared
RayNet with \textit{ns3-gym}, a platform with similar objectives to RayNet, and
showed that RayNet performs better in terms of how fast agents can collect
experience in RL environments.


  Supernovae feedback driven expansion has proven to be a viable mechanism to
explain the average properties of Ultra Diffuse Galaxies (UDGs) such as the
sizes, colors, mass and internal kinematics. Here, we explore the origin of
stellar metallicity gradients in feedback driven simulated UDGs from the NIHAO
project and compare them with the observed distribution of metallicity
gradients of both Local Group dwarfs as well as of the recently observed UDG
DF44. Simulated UDGs display a large variety of metallicity profiles, showing
flat to negative gradients, similarly to what is observed in LG dwarfs, while
DF44 data suggest a flat to positive gradient. The variety of metallicity
gradients in simulations is set by the interplay between the radius at which
star formation occurs and the subsequent supernovae feedback driven stellar
redistribution: rotation supported systems tend to have flat metallicity
profiles while dispersion supported galaxies show negative and steep profiles.
  Our results suggest that UDGs are not peculiar in what regards their
metallicity gradients, when compared to regular dwarfs. Desirably, a larger
observational sample of UDGs' gradients shall be available in the future, in
order to test our predictions.


  We consider the task of distinguishing between two different alternative
models that can roughly equally explain observed time series data, mainly
focusing on the period ambiguity case (aliasing). We propose a test for
checking whether the rival models are observationally equivalent for now or
they are already distinguishable. It is the Vuong closeness test, which is
based on the Kullback-Leibler Information Criterion. It is asymptotically
normal and can work (in certain sense) even in the misspecified case, when the
both proposed alternatives are actually wrong. This test is also very simple
for practical use. We apply it to several known extrasolar planetary systems
and find that our method often helps to resolve various model ambiguities
emerging in astronomical practice, but preventing us from hasty conclusions in
other cases.


  Recent advances in black hole astrophysics, particularly the first visual
evidence of a supermassive black hole at the center of the galaxy M87 by the
Event Horizon Telescope (EHT), and the detection of an orbiting "hot spot"
nearby the event horizon of Sgr A* in the Galactic center by the Gravity
Collaboration, require the development of novel numerical methods to understand
the underlying plasma microphysics. Non-thermal emission related to such hot
spots is conjectured to originate from plasmoids that form due to magnetic
reconnection in thin current layers in the innermost accretion zone.
Resistivity plays a crucial role in current sheet formation, magnetic
reconnection, and plasmoid growth in black hole accretion disks and jets. We
included resistivity in the three-dimensional general-relativistic
magnetohydrodynamics (GRMHD) code BHAC and present the implementation of an
Implicit-Explicit scheme to treat the stiff resistive source terms of the GRMHD
equations. The algorithm is tested in combination with adaptive mesh refinement
to resolve the resistive scales and a constrained transport method to keep the
magnetic field solenoidal. Several novel methods for primitive variable
recovery, a key part in relativistic magnetohydrodynamics codes, are presented
and compared for accuracy, robustness, and efficiency. We propose a new
inversion strategy that allows for resistive-GRMHD simulations of low
gas-to-magnetic pressure ratio and highly magnetized regimes as applicable for
black hole accretion disks, jets, and neutron star magnetospheres. We apply the
new scheme to study the effect of resistivity on accreting black holes,
accounting for dissipative effects as reconnection.


  Studies of 6He beta decay along with tritium can play an important role in
testing ab-initio nuclear wave-function calculations and may allow for fixing
low-energy constants in effective field theories. Here, we present an improved
determination of the 6He half-life to a relative precision of 3x10^(-4). Our
value of 806.89 \pm 0.11(stat)^{+0.23}_{-0.19}(syst) ms resolves a major
discrepancy between previous measurements. Calculating the statistical rate
function we determined the ft-value to be 803.04 ^{+0.26}_{-0.23} s. The
extracted Gamow-Teller matrix element agrees within a few percent with
ab-initio calculations.


  Existing datasets for natural language inference (NLI) have propelled
research on language understanding. We propose a new method for automatically
deriving NLI datasets from the growing abundance of large-scale question
answering datasets. Our approach hinges on learning a sentence transformation
model which converts question-answer pairs into their declarative forms.
Despite being primarily trained on a single QA dataset, we show that it can be
successfully applied to a variety of other QA resources. Using this system, we
automatically derive a new freely available dataset of over 500k NLI examples
(QA-NLI), and show that it exhibits a wide range of inference phenomena rarely
seen in previous NLI datasets.


  Spectroscopic data in wavelengths 900--3000A have been obtained in a low flux
state of the nucleus of the Seyfert 1 galaxy NGC 3516. The line profiles show P
Cygni characteristics, particularly in O VI 1032A, and are compared with data
from an earlier higher state. The profiles are suggestive of, and consistent
with, an accelerating wind driven by a disk continuum source, in which the
ionisation radii have changed. This scenario may apply to the formation of
other broad emission lines in AGN.


  Back cover text: Megaprojects and Risk provides the first detailed
examination of the phenomenon of megaprojects. It is a fascinating account of
how the promoters of multibillion-dollar megaprojects systematically and
self-servingly misinform parliaments, the public and the media in order to get
projects approved and built. It shows, in unusual depth, how the formula for
approval is an unhealthy cocktail of underestimated costs, overestimated
revenues, undervalued environmental impacts and overvalued economic development
effects. This results in projects that are extremely risky, but where the risk
is concealed from MPs, taxpayers and investors. The authors not only explore
the problems but also suggest practical solutions drawing on theory and hard,
scientific evidence from the several hundred projects in twenty nations that
illustrate the book. Accessibly written, it will be essential reading in its
field for students, scholars, planners, economists, auditors, politicians,
journalists and interested citizens.


  Interpreting regulatory documents or building codes into computer-processable
formats is essential for the intelligent design and construction of buildings
and infrastructures. Although automated rule interpretation (ARI) methods have
been investigated for years, most of them highly depend on the early and manual
filtering of interpretable clauses from a building code. While few of them
considered machine interpretability, which represents the potential to be
transformed into a computer-processable format, from both clause- and
document-level. Therefore, this research aims to propose a novel approach to
automatically evaluate and enhance the machine interpretability of single
clause and building codes. First, a few categories are introduced to classify
each clause in a building code considering the requirements for rule
interpretation, and a dataset is developed for model training. Then, an
efficient text classification model is developed based on a pretrained
domain-specific language model and transfer learning techniques. Finally, a
quantitative evaluation method is proposed to assess the overall
interpretability of building codes. Experiments show that the proposed text
classification algorithm outperforms the existing CNN- or RNN-based methods,
improving the F1-score from 72.16% to 93.60%. It is also illustrated that the
proposed classification method can enhance downstream ARI methods with an
improvement of 4%. Furthermore, analyzing the results of more than 150 building
codes in China showed that their average interpretability is 34.40%, which
implies that it is still hard to fully transform the entire regulatory document
into computer-processable formats. It is also argued that the interpretability
of building codes should be further improved both from the human side and the
machine side.


  We study the necessary and sufficient complexity of ReLU neural networks---in
terms of depth and number of weights---which is required for approximating
classifier functions in $L^2$. As a model class, we consider the set
$\mathcal{E}^\beta (\mathbb R^d)$ of possibly discontinuous piecewise $C^\beta$
functions $f : [-1/2, 1/2]^d \to \mathbb R$, where the different smooth regions
of $f$ are separated by $C^\beta$ hypersurfaces. For dimension $d \geq 2$,
regularity $\beta > 0$, and accuracy $\varepsilon > 0$, we construct artificial
neural networks with ReLU activation function that approximate functions from
$\mathcal{E}^\beta(\mathbb R^d)$ up to $L^2$ error of $\varepsilon$. The
constructed networks have a fixed number of layers, depending only on $d$ and
$\beta$, and they have $O(\varepsilon^{-2(d-1)/\beta})$ many nonzero weights,
which we prove to be optimal. In addition to the optimality in terms of the
number of weights, we show that in order to achieve the optimal approximation
rate, one needs ReLU networks of a certain depth. Precisely, for piecewise
$C^\beta(\mathbb R^d)$ functions, this minimal depth is given---up to a
multiplicative constant---by $\beta/d$. Up to a log factor, our constructed
networks match this bound. This partly explains the benefits of depth for ReLU
networks by showing that deep networks are necessary to achieve efficient
approximation of (piecewise) smooth functions. Finally, we analyze
approximation in high-dimensional spaces where the function $f$ to be
approximated can be factorized into a smooth dimension reducing feature map
$\tau$ and classifier function $g$---defined on a low-dimensional feature
space---as $f = g \circ \tau$. We show that in this case the approximation rate
depends only on the dimension of the feature space and not the input dimension.


  In this article, we show that for any deformation of analytic foliations,
there exists a maximal analytic singular foliation on the space of parameters
along the leaves of which the deformation is integrable.


  We explore various instability models for the onset of star formation in
irregular galaxies. Critical gas densities are calculated for gravitational
instabilities with a thin, pure-gas disk, and with a thick disk composed of gas
and a star-like fluid. We also calculated the stability properties of three
dimensional systems including dark matter, considered the thermal state of the
gas, and used a modified threshold column density written in terms of the local
rate of shear instead of the epicyclic frequency. The model predictions were
compared to the azimuthally-averaged present day star formation activity traced
by the Halpha surface brightness, and to the 1 Gyr-integrated star formation
activity represented by the stellar surface brightness.
  The ratio of the observed gas density to the critical gas density is lower by
a factor of 2 in most of the Im galaxies than it is in spiral galaxies. Star
formation ends before this ratio drops significantly in the outer regions, and
it remains high in the inner regions where the ratio is often low. These
results suggest that the critical gas density does not trace star formation
with the same detail in irregular galaxies as it appears to trace it in giant
spiral galaxies. The only azimuthally-averaged quantity that correlates with
the current star formation activity in irregulars is the stellar surface
density. Theoretical implications are discussed.


  Magnetic spin resonance is a key non-invasive sensing and imaging technique
across the life-, material- and fundamental sciences with further medical and
commercial applications. Recent advances using paramagnetic color centers
enable magnetic resonance down on the nanoscale, and sensitivity to single
molecules is in sight. Ensemble sensing and wide field imaging improve
sensitivity and acquisition speed, but may suffer from inhomogeneous spin
control fields, produced by e.g. microstructures in integrated devices, and
limited spatial resolution. Here we demonstrate multiplexed nuclear magnetic
resonance imaging using diamond nitrogen-vacancy centers in such adverse
conditions. We image thin films of calcium fluoride down to $1.2\,\mathrm{nm}$
in thickness with a spatial resolution of $250\,\mathrm{nm}$. This corresponds
to a net moment of about 140 nuclear spins within the sensing radius of a given
NV in the ensemble.


  We define a category $\mathsf{List}$ whose objects are sets and morphisms are
mappings which assign to an element in the domain an ordered sequence (list) of
elements in the codomain. We introduce and study a category of simplicial
objects $\mathsf{slist}$ whose objects are functors $\Delta^{op} \to
\mathsf{List}$, which we call simplicial lists, and morphisms are natural
transformations which have functions as components. We demonstrate that
$\mathsf{sList}$ supports the combinatorics of (non-symmetric) operads by
constructing a fully-faithful nerve functor $N^l : \mathsf{Operad} \to
\mathsf{sList}$ from the category of operads. This leads to a reasonable model
for the theory of non-symmetric $\infty$-operads.
  We also demonstrate that $\mathsf{sList}$ has the structure of a presheaf
category. In particular, we study a subcategory $\mathsf{sList}_{\text{op}}$ of
operadic simplicial lists, in which the nerve functor takes values. The latter
category is also a presheaf category over a base whose objects may be
interpreted as levelled trees. We construct a coherent nerve functor which
outputs an $\infty$-operad for each operad enriched in Kan complexes. We also
define homology groups of simplicial lists and study first properties.


  In line with the increasing attention paid to deal with uncertainty in
ordinal data models, we propose to combine Fuzzy models with \cub models within
questionnaire analysis. In particular, the focus will be on \cub models'
uncertainty parameter and its interpretation as a preliminary measure of
heterogeneity, by introducing membership, non-membership and uncertainty
functions in the more general framework of Intuitionistic Fuzzy Sets. Our
proposal is discussed on the basis of the Evaluation of Orientation Services
survey collected at University of Naples Federico II.


  Multi-epoch photometry from AllWISE provides the opportunity to investigate
variability at 3.4 and 4.6 microns for most known brown dwarfs. WISE observed
the same patch of sky repeatedly and within a day's time, roughly 12
observations were obtained on a given patch of sky; then, another 12 were
obtained roughly six months later when that patch of sky was again in view. For
most of the sky, AllWISE contains two separate epochs of about a dozen
observations each, although ~30% of the sky has three such epochs available in
AllWISE. With the AllWISE multi-epoch photometry of ~1500 known M, L, T, and Y
dwarfs, I computed the Stetson J Index and quantified variability as a function
of spectral type. I found that the average single-exposure photometric
uncertainty in AllWISE (~0.2 magnitudes) is too large to robustly identify flux
variability smaller than ~20%. However, multi-epoch photometry from AllWISE
remains a useful resource in cases where flux variability is known to be
present with large amplitudes, or for bright nearby objects with lower
photometric uncertainties.


  Structured data in the form of tabular datasets contain features that are
distinct and discrete, with varying individual and relative importances to the
target. Combinations of one or more features may be more predictive and
meaningful than simple individual feature contributions. R's mixed effect
linear models library allows users to provide such interactive feature
combinations in the model design. However, given many features and possible
interactions to select from, model selection becomes an exponentially difficult
task. We aim to automate the model selection process for predictions on tabular
datasets incorporating feature interactions while keeping computational costs
small. The framework includes two distinct approaches for feature selection: a
Priority-based Random Grid Search and a Greedy Search method. The
Priority-based approach efficiently explores feature combinations using prior
probabilities to guide the search. The Greedy method builds the solution
iteratively by adding or removing features based on their impact. Experiments
on synthetic demonstrate the ability to effectively capture predictive feature
combinations.


  Double deeply virtual Compton scattering (DDVCS) is a very precise tool for
the nucleon tomography. Its measurement requires high luminosity electron beams
and precise dedicated detectors, since its amplitude is quite small in the
interesting kinematical domain where collinear QCD factorization allows the
extraction of quark and gluon generalized parton distributions (GPDs). We
analyze the prospects for its study in the JLab energy domain as well as in
higher energy electron-ion colliders. Our results are very encouraging for
various observables both with an unpolarized and polarized lepton beam. Using
various realistic models for GPDs, we demonstrate that DDVCS measurements are
indeed very sensitive to their behaviour. Implementing our lowest order
cross-section formulae in the EpIC Monte Carlo generator, we estimate the
expected number of interesting events.


  Shadow tomography via classical shadows is a state-of-the-art approach for
estimating properties of a quantum state. We present a simplified,
combinatorial analysis of a recently proposed instantiation of this approach
based on the ensemble of unitaries that are both fermionic Gaussian and
Clifford. Using this analysis, we derive a corrected expression for the
variance of the estimator. We then show how this leads to efficient estimation
protocols for the fidelity with a pure fermionic Gaussian state (provably) and
for an $X$-like operator of the form ($|\mathbf 0\rangle\langle\psi|$ + h.c.)
(via numerical evidence). We also construct much smaller ensembles of
measurement bases that yield the exact same quantum channel, which may help
with compilation. We use these tools to show that an $n$-electron, $m$-mode
Slater determinant can be learned to within $\epsilon$ fidelity given $O(n^2
m^7 \log(m / \delta) / \epsilon^2)$ samples of the Slater determinant.


  We model the broadband enhancement of single-photon emission from color
centres in silicon carbide nanocrystals coupled to a planar hyperbolic
metamaterial, HMM resonator. The design is based on positioning the single
photon emitters within the HMM resonator, made of a dielectric index-matched
with silicon-carbide material. The broadband response results from the
successive resonance peaks of the lossy Fabry Perot structure modes arising
within the high-index HMM cavity. To capture this broadband enhancement in the
single photon emitters spontaneous emission, we placed a simple gold based
cylindrical antenna on top of the HMM resonator. We analyzed the performance of
this HMM coupled antenna structure in terms of the Purcell enhancement, quantum
efficiency, collection efficiency and overall collected photon rate. For
perpendicular dipole orientation relative to the interface, the HMM coupled
antenna resonator leads to a significantly large spontaneous emission
enhancement with Purcell factor of the order of 250 along with a very high
average total collected photon rate, CPR of about 30 over a broad emission
spectrum, 700 nm to 1000 nm. The peak CPR increases to about 80 at 900 nm,
corresponding to the emission of silicon-carbide quantum emitters. This is a
state of the art improvement considering the previous computational designs
have reported a maximum average CPR of 25 across the nitrogen-vacancy centre
emission spectrum, 600 nm to 800 nm with the highest value being about 40 at
650 nm.


  We study the gamma-ray emissions from an outer-magnetospheric potential gap
around a rotating neutron star. Migratory electrons and positrons are
accelerated by the electric field in the gap to radiate copious gamma-rays via
curvature process. Some of these gamma-rays materialize as pairs by colliding
with the X-rays in the gap, leading to a pair production cascade. Imposing the
closure condition that a single pair produces one pair in the gap on average,
we explicitly solve the strength of the acceleration field and demonstrate how
the peak energy and the luminosity of the curvature-radiated, GeV photons
depend on the strength of the surface blackbody and the power-law emissions.
Some predictions on the GeV emission from twelve rotation-powered pulsars are
presented. We further demonstrate that the expected pulsed TeV fluxes are
consistent with their observational upper limits. An implication of high-energy
pulse phase width versus pulsar age, spin, and magnetic moment is discussed.


  In this note we show that the Langlands lemma from the theory of Eisenstein
series can be used to invert the recursion relation for the Poincar\'e series
of the open substack of semi-stable $G$-bundles which was established by
Atiyah/Bott and Harder/Narasimhan.


  A k-ranking of a graph G is a labeling of the vertices of G with values from
{1,...,k} such that any path joining two vertices with the same label contains
a vertex having a higher label. The tree-depth of G is the smallest value of k
for which a k-ranking of G exists. The graph G is k-critical if it has
tree-depth k and any proper minor of G has smaller tree-depth, and it is
1-unique if for every vertex v in G, there exists an optimal ranking of G in
which v is the unique vertex with label 1.
  We present several classes of graphs that are both k-critical and 1-unique,
providing examples that satisfy conjectures on critical graphs discussed in
[M.D. Barrus and J. Sinkovic, Uniqueness and minimal obstructions for
tree-depth, submitted].


  We give an upper bound on the smallest eigenvalue of the adjacency matrix of
graphs with no p-cliques.


  Self-supervised learning (SSL) for rich speech representations has achieved
empirical success in low-resource Automatic Speech Recognition (ASR) and other
speech processing tasks, which can mitigate the necessity of a large amount of
transcribed speech and thus has driven a growing demand for on-device ASR and
other speech processing. However, advanced speech SSL models have become
increasingly large, which contradicts the limited on-device resources. This gap
could be more severe in multilingual/multitask scenarios requiring
simultaneously recognizing multiple languages or executing multiple speech
processing tasks. Additionally, strongly overparameterized speech SSL models
tend to suffer from overfitting when being finetuned on low-resource speech
corpus. This work aims to enhance the practical usage of speech SSL models
towards a win-win in both enhanced efficiency and alleviated overfitting via
our proposed S$^3$-Router framework, which for the first time discovers that
simply discarding no more than 10\% of model weights via only finetuning model
connections of speech SSL models can achieve better accuracy over standard
weight finetuning on downstream speech processing tasks. More importantly,
S$^3$-Router can serve as an all-in-one technique to enable (1) a new
finetuning scheme, (2) an efficient multilingual/multitask solution, (3) a
state-of-the-art ASR pruning technique, and (4) a new tool to quantitatively
analyze the learned speech representation. We believe S$^3$-Router has provided
a new perspective for practical deployment of speech SSL models. Our codes are
available at: https://github.com/GATECH-EIC/S3-Router.


  In this paper we study bilipschitz equivalences of germs of holomorphic
foliations in $(\mathbb{C}^2,0)$. We prove that the algebraic multiplicity of a
singularity is invariant by such equivalences. Moreover, for a large class of
singularities, we show that the projective holonomy representation is also a
bilipschitz invariant.


  We analyse the asymptotic behaviour of the eigenvalues and eigenvectors of a
Steklov problem in a dumbbell domain consisting of two Lipschitz sets connected
by a thin tube with vanishing width. All the eigenvalues are collapsing to
zero, the speed being driven by some power of the width which multiplies the
eigenvalues of a one dimensional problem. In two dimensions of the space, the
behaviour is fundamentally different from the third or higher dimensions and
the limit problems are of different nature. This phenomenon is due to the fact
that only in dimension two the boundary of the tube has not vanishing surface
measure.


  The decay $\eta'\to\eta\pi^{0}\pi^{0}$ is studied in the framework of isobar
model. It is shown, that good agreement with the experiment is achieved if
$a_0$- and $\sigma$-meson contributions are taken into account. The
contribution of $a_0$-meson is dominant, but $\sigma$-meson is necessary to
reproduce the form of experimental Dalitz plot. Instead of usual Breit-Wigner
form of $\sigma$-meson propagator we use parametrization of $\pi\pi$-amplitude,
that satisfies analiticity, crossing, unitarity and chirality constraints. This
amplitude has a pole in complex plane, that corresponds to $\sigma$-meson and
describe experimental data on $\pi\pi$-scattering in $K_{e4}$ decay.


  We study the $d\Gamma / dq^2$ spectra and the branching fractions of the $D$
meson exclusive semileptonic decays with the lepton mass effects into
consideration. We investigate their sensitivity to form factor models, and find
that the decays to a pseudoscalar meson and a lepton pair are sensitive to the
property of the form factor $F_1(q^2)$, and those to a vector meson and a
lepton pair to the form factor $A_1(q^2)$. We also analyze the experimental
results of the branching fractions $\cal{B} (D^0\to K^- (or \pi^-) \pi^+)$ and
$\cal{B} (D^0\to K^- (or \pi^-) e^+ \nu)$, and show that it is implied that
$F_1(q^2)$ is of dipole type, instead of simple pole type which is commonly
assumed.


  Analysis of the present data on the semileptonic process $B \to \pi \ell \nu$
indicates that they have not yet reached the precision to provide adequate
information on the $B \to \pi$ form factor $F_+(q^2)$, which for $q^2 =
m_\pi^2$ is known to be related to the factorized color-favored (``$T$'', or
``tree'') contribution to $B^0 \to \pi^+ \pi^-$. It is shown here that with
around 500 $B \to \pi \ell \nu$ events in which rate and spectrum are measured
one can improve the accuracy of $T$ by a significant amount. A recent CLEO
determination of the $D^* D \pi$ coupling constant is compared with an earlier
prediction, and its role in the description of the $B \to \pi$ form factors is
noted. When combined with an estimate of the penguin amplitude (``$P$'')
obtained using flavor SU(3) symmetry from $B \to K \pi$ decays, information on
$T$ allows one to gauge the effects of the penguin amplitude on extraction of
the weak phase $\alpha = \phi_2$ from the time-dependent CP-violating rate
asymmetry in $B^0 \to \pi^+ \pi^-$. The constraint on $\alpha$ implied by a
recent experimental result on this asymmetry is described.


  We present the results of hydrodynamical simulations of the disk photosphere
irradiated by strong X-rays produced in the inner most part of the disk. As
expected, the irradiation heats the photosphere and drives a thermal wind. To
apply our results to the well-studied X-ray transient source GRO J1655-40, we
adopted the observed mass of its black hole, and the observed properties of its
X-ray radiation. To compare the results with the observations, we also computed
transmitted X-ray spectra based on the wind solution. Our main finding is: the
density of the fast moving part of the wind is more than one order of magnitude
lower than that inferred from the observations. Consequently, the model fails
to predict spectra with line absorption as strong and as blueshifted as those
observed. However, despite the thermal wind being weak and Compton thin, the
ratio between the mass-loss rate and the mass accretion rate is about seven.
This high ratio is insensitive to the accretion luminosity, in the limit of
lower luminosities. Most of the mass is lost from the disk between 0.07 and 0.2
of the Compton radius. We discovered that beyond this range the wind solution
is self-similar. In particular, soon after it leaves the disk, the wind flows
at a constant angle with respect to the disk. Overall, the thermal winds
generated in our comprehensive simulations do not match the wind spectra
observed in GRO J1655-40. This supports the conclusion of Miller et al. and
Kallman et al. that the wind in GRO J1655-40, and possibly other X-ray
transients, may be driven by magnetic processes. This in turn implies that the
disk wind carries even more material than our simulations predict and as such
has a very significant impact on the accretion disk structure and dynamics.


  Source code summarization is the task of creating short, natural language
descriptions of source code. Code summarization is the backbone of much
software documentation such as JavaDocs, in which very brief comments such as
"adds the customer object" help programmers quickly understand a snippet of
code. In recent years, automatic code summarization has become a high value
target of research, with approaches based on neural networks making rapid
progress. However, as we will show in this paper, the production of good
summaries relies on the production of the action word in those summaries: the
meaning of the example above would be completely changed if "removes" were
substituted for "adds." In this paper, we advocate for a special emphasis on
action word prediction as an important stepping stone problem towards better
code summarization -- current techniques try to predict the action word along
with the whole summary, and yet action word prediction on its own is quite
difficult. We show the value of the problem for code summaries, explore the
performance of current baselines, and provide recommendations for future
research.


  There is no doubt that Search Engines are playing a great role in Internet
usage. But all the top search engines Google, Yahoo and Bing are having a
critical flaw called "Openness of a Search Engine". An Internet user should be
allowed to get the search results only when requested through Search engine's
web page but the user must not be allowed to get the search results when
requested through any web page that does not belong to the Search Engine. Only
results of a search engine should be available to the Internet user but not the
Search Engine. This paper explains the critical flaw called "Openness of Search
Engine" with a case study on top 3 search engines 'Google', 'Yahoo' and 'Bing'.
This paper conducts an attack based test using J2EE framework and proves that
'Google' passed the test and it strongly protects its Critical Search System,
where 'Yahoo' and 'Bing' are failed to protect their Search Engines. But
previously 'Google' also had other high severity issues with the Openness of
search engine; this paper reveals those issues also. Finally this paper appeals
strongly to the all top Search Engines to fix their critical flaws of "Openness
of Search Engine".


  Rotating turbulent flows form a challenging test case for large-eddy
simulation (LES). We, therefore, propose and validate a new subgrid-scale (SGS)
model for such flows. The proposed SGS model consists of a dissipative eddy
viscosity term as well as a nondissipative term that is nonlinear in the
rate-of-strain and rate-of-rotation tensors. The two corresponding model
coefficients are a function of the vortex stretching magnitude. Therefore, the
model is consistent with many physical and mathematical properties of the
Navier-Stokes equations and turbulent stresses, and is easy to implement. We
determine the two model constants using a nondynamic procedure that takes into
account the interaction between the model terms. Using detailed direct
numerical simulations (DNSs) and LESs of rotating decaying turbulence and
spanwise-rotating plane-channel flow, we reveal that the two model terms
respectively account for dissipation and backscatter of energy, and that the
nonlinear term improves predictions of the Reynolds stress anisotropy near
solid walls. We also show that the new SGS model provides good predictions of
rotating decaying turbulence and leads to outstanding predictions of
spanwise-rotating plane-channel flow over a large range of rotation rates for
both fine and coarse grid resolutions. Moreover, the new nonlinear model
performs as well as the dynamic Smagorinsky and scaled anisotropic
minimum-dissipation models in LESs of rotating decaying turbulence and
outperforms these models in LESs of spanwise-rotating plane-channel flow,
without requiring (dynamic) adaptation or near-wall damping of the model
constants.


  The eta Chamaeleontis star cluster is one of the most important nearby
groupings of pre-main sequence (PMS) stars. With accurately known distance,
compact structure and highly-coeval intermediate-aged population, the eta Cha
cluster is an important laboratory for enhancing our understanding of all PMS
star evolutionary issues. In this paper, recent results concerning the stellar
population are discussed including the discovery of a rare example of a ~10
Myr-old Classical T Tauri (CTT) star. This and other objects point to an
X-ray-faint late-type population residing amongst the ROSAT-detected
membership.


  The ability to dynamically adapt neural networks to newly-available data
without performance deterioration would revolutionize deep learning
applications. Streaming learning (i.e., learning from one data example at a
time) has the potential to enable such real-time adaptation, but current
approaches i) freeze a majority of network parameters during streaming and ii)
are dependent upon offline, base initialization procedures over large subsets
of data, which damages performance and limits applicability. To mitigate these
shortcomings, we propose Cold Start Streaming Learning (CSSL), a simple,
end-to-end approach for streaming learning with deep networks that uses a
combination of replay and data augmentation to avoid catastrophic forgetting.
  Because CSSL updates all model parameters during streaming, the algorithm is
capable of beginning streaming from a random initialization, making base
initialization optional. Going further, the algorithm's simplicity allows
theoretical convergence guarantees to be derived using analysis of the Neural
Tangent Random Feature (NTRF). In experiments, we find that CSSL outperforms
existing baselines for streaming learning in experiments on CIFAR100, ImageNet,
and Core50 datasets. Additionally, we propose a novel multi-task streaming
learning setting and show that CSSL performs favorably in this domain. Put
simply, CSSL performs well and demonstrates that the complicated, multi-step
training pipelines adopted by most streaming methodologies can be replaced with
a simple, end-to-end learning approach without sacrificing performance.


  In the beginning, the synchrotron radiation (SR) was studied by classical
methods using the Li\'{e}nard-Wiechert potentials of electric currents.
Subsequently, quantum corrections to the obtained classical formulas were
studied, considering the emission of photons arising from electronic
transitions between spectral levels, described in terms of the Dirac equation.
In this paper, we consider an intermediate approach, in which electric currents
generating the radiation are considered classically, whereas the quantum nature
of the radiation is taken into account exactly. Such an approximate approach
may be helpful in some cases, it allows one to study the one-photon and
multi-photon radiation without complicating calculations using corresponding
solutions of the Dirac equation. We construct exact quantum states of the
electromagnetic field interacting with classical currents and study their
properties. By their help, we calculate a probability of photon emission by
classical currents and obtain relatively simple formulas for the one-photon and
multi-photon radiation. Using the specific circular electric current, we
calculate the corresponding SR. We discuss a relation of obtained results with
known before, for example, with the Schott formula, with the Schwinger
calculations, with one-photon radiation of scalar particles due to transitions
between Landau levels, and with some previous results of calculating the
two-photon SR.


  We present a framework for learning to plan hierarchically in domains with
unknown dynamics. We enhance planning performance by exploiting problem
structure in several ways: (i) We simplify the search over plans by leveraging
knowledge of skill objectives, (ii) Shorter plans are generated by enforcing
aggressively hierarchical planning, (iii) We learn transition dynamics with
sparse local models for better generalisation. Our framework decomposes
transition dynamics into skill effects and success conditions, which allows
fast planning by reasoning on effects, while learning conditions from
interactions with the world. We propose a simple method for learning new
abstract skills, using successful trajectories stemming from completing the
goals of a curriculum. Learned skills are then refined to leverage other
abstract skills and enhance subsequent planning. We show that both conditions
and abstract skills can be learned simultaneously while planning, even in
stochastic domains. Our method is validated in experiments of increasing
complexity, with up to 2^100 states, showing superior planning to classic
non-hierarchical planners or reinforcement learning methods. Applicability to
real-world problems is demonstrated in a simulation-to-real transfer experiment
on a robotic manipulator.


  In this paper, we present a quality of service (QoS)-aware priority-based
spectrum management scheme to guarantee the minimum required bit rate of
vertical sector players (VSPs) in the 5G and beyond generation, including the
6th generation (6G). VSPs are considered as spectrum leasers to optimize the
overall spectrum efficiency of the network from the perspective of the mobile
network operator (MNO) as the spectrum licensee and auctioneer. We exploit a
modified Vickrey-Clarke-Groves (VCG) auction mechanism to allocate the spectrum
to them where the QoS and the truthfulness of bidders are considered as two
important parameters for prioritization of VSPs. The simulation is done with
the help of deep deterministic policy gradient (DDPG) as a deep reinforcement
learning (DRL)-based algorithm. Simulation results demonstrate that deploying
the DDPG algorithm results in significant advantages. In particular, the
efficiency of the proposed spectrum management scheme is about %85 compared to
the %35 efficiency in traditional auction methods.


  There is no unique way to quantify the degree of delocalization of quantum
states in unbounded continuous spaces. In this work, we explore a recently
introduced localization measure that quantifies the portion of the classical
phase space occupied by a quantum state. The measure is based on the
$\alpha$-moments of the Husimi function and is known as the R\'enyi occupation
of order $\alpha$. With this quantity and random pure states, we find a general
expression to identify states that are maximally delocalized in phase space.
Using this expression and the Dicke model, which is an interacting spin-boson
model with an unbounded four-dimensional phase space, we show that the R\'enyi
occupations with $\alpha>1$ are highly effective at revealing quantum scars.
Furthermore, by analyzing the high moments ($\alpha>1$) of the Husimi function,
we are able to identify qualitatively and quantitatively the unstable periodic
orbits that scar some of the eigenstates of the model.


  In our current interpretation of the hierarchical structure of the universe
it is well established that galaxies collide and merge with each other during
their lifetime. If massive black holes (MBHs) reside in galactic centres, we
expect them to form binaries in galactic nuclei surrounded by a circumbinary
disc. If cooling is efficient enough, the gas in the disc will clump and
trigger stellar formation in situ. In this first paper we address the evolution
of the binary under the influence of the newly formed stars, which form
individually and also clustered. We use SPH techniques to evolve the gas in the
circumbinary disc and to study the phase of star formation. When the amount of
gas in the disc is negligible, we further evolve the system with a
high-accurate direct-summation $N-$body code to follow the evolution of the
stars, the innermost binary and tidal disruption events (TDEs). For this, we
modify the direct N-body code to (i) include treatment of TDEs and to (ii)
include "gas cloud particles" that mimic the gas, so that the stellar clusters
do not disolve when we follow their infall on to the MBHs. We find that the
amount of stars disrupted by either infalling stellar clusters or individual
stars is as large as 10^{-4}/yr per binary, higher than expected for typical
galaxies.


  We investigate the geometry near the horizon of a generic, four-dimensional
extremal black hole. When the cosmological constant is negative, we show that
(in almost all cases) tidal forces diverge as one crosses the horizon, and this
singularity is stronger for larger black holes. In particular, this applies to
generic nonspherical black holes, such as those satisfying inhomogeneous
boundary conditions. Nevertheless, all scalar curvature invariants remain
finite. Moreover, we show that nonextremal black holes have tidal forces that
diverge in the extremal limit. Holographically, this singularity is reflected
in anomalous scaling of the specific heat with temperature. Similar (albeit
weaker) effects are present when the cosmological constant is positive, but not
when it vanishes.


  We report the design of highly efficient optical antennas employing a
judicious synthesis of metallic and dielectric materials. In the proposed
scheme, a pair of metallic coupled nanoparticles permits large enhancements in
both excitation strength and radiative decay rates, while a high refractive
index dielectric microsphere is employed to efficiently collect light without
spoiling the emitter quantum efficiency. Our simulations indicate potential
fluorescence rate enhancements of 3 orders of magnitude over the entire optical
frequency range.


  Existing metaverse systems lack rich relation types between entities and
events. The challenge is that there is no portable framework to introduce rich
concepts, relations, events into the metaverse. This paper introduces a new
metaverse framework, MetaOnce. This framework proposes to build multi-scene
graphs. This framework not only describes rich relations in a single scene but
also combines multiple scene graphs into a complete graph for more
comprehensive analysis and inference. Prior social network systems mainly
describe friend relations. They ignore the effect of entity-relation-event
games on the metaverse system and existing rule constraints. We propose a rule
controller and impose constraints on the relations that allow the framework to
behave in a compliant manner. We build a metaverse system to test the features
of the framework, and experimental results show that our framework can build a
multi-scene metaverse with memory and rule constraints.


  We present an experimental and theoretical study of the magnetic field
dependence of the mode frequency of thermally excited spin waves in rectangular
shaped nanopillars of lateral sizes 60x100, 75x150, and 105x190 nm2, patterned
from MgO-based magnetic tunnel junctions. The spin wave frequencies were
measured using spectrally resolved electrical noise measurements. In all
spectra, several independent quantized spin wave modes have been observed and
could be identified as eigenexcitations of the free layer and of the synthetic
antiferromagnet of the junction. Using a theoretical approach based on the
diagonalization of the dynamical matrix of a system of three coupled, spatially
confined magnetic layers, we have modeled the spectra for the smallest pillar
and have extracted its material parameters. The magnetization and exchange
stiffness constant of the CoFeB free layer are thereby found to be
substantially reduced compared to the corresponding thin film values. Moreover,
we could infer that the pinning of the magnetization at the lateral boundaries
must be weak. Finally, the interlayer dipolar coupling between the free layer
and the synthetic antiferromagnet causes mode anticrossings with gap openings
up to 2 GHz. At low fields and in the larger pillars, there is clear evidence
for strong non-uniformities of the layer magnetizations. In particular, at zero
field the lowest mode is not the fundamental mode, but a mode most likely
localized near the layer edges.


  The study of exoplanet atmospheres showed large diversity compared to the
planets in our solar system. Especially Jupiter type exoplanets orbiting their
host star in close orbits, the so-called hot and ultra-hot Jupiters, have been
studied in detail due to their enhanced atmospheric signature. Due to their
tidally locked status, the temperature difference between the day- and
nightside triggers atmospheric winds which can lead to various fingerprints in
the observations. Spatially resolved absorption lines during transit such as
sodium (Na) could be a good tracer for such winds. Different works resolved the
Na$^-$ absorption lines on different exoplanets which show different line
widths. Assuming that this could be attributed to such zonal jet streams, this
work models the effect of such winds on synthetic absorption lines. For this,
transiting Jupiter type planets with rotational velocities similar to hot and
ultra-hot Jupiter are considered. The investigation shows that high wind
velocities could reproduce the broadening of Na-line profiles inferred in
different high-resolution transit observations. There is a tendency that the
broadening values decrease for planets with lower equilibrium temperature. This
could be explained by atmospheric drag induced by the ionization of alkali
lines which slow down the zonal jet streams, favoring their existence on hot
Jupiter rather than ultra-hot Jupiter.


  Many photonic quantum information processing applications would benefit from
a high brightness, fiber-coupled source of triggered single photons. Here, we
present a fiber-coupled photonic-crystal waveguide single-photon source relying
on evanescent coupling of the light field from a tapered out-coupler to an
optical fiber. A two-step approach is taken where the performance of the
tapered out-coupler is recorded first on an independent device containing an
on-chip reflector. Reflection measurements establish that the chip-to-fiber
coupling efficiency exceeds 80 %. The detailed characterization of a
high-efficiency photonic-crystal waveguide extended with a tapered out-coupling
section is then performed. The corresponding overall single-photon source
efficiency is 10.9 % $\pm$ 2.3 %, which quantifies the success probability to
prepare an exciton in the quantum dot, couple it out as a photon in the
waveguide, and subsequently transfer it to the fiber. The applied out-coupling
method is robust, stable over time, and broadband over several tens of
nanometers, which makes it a highly promising pathway to increase the
efficiency and reliability of planar chip-based single-photon sources.


  A mid-infrared atlas of part of the Galactic plane ($75^\circ < l <
148^\circ, b = \pm6^\circ$) has been constructed using HIRES processed infrared
data to provide a mid-infrared data set for the Canadian Galactic Plane Survey
(CGPS). The addition of this data set to the CGPS will enable the study of the
emission from the smallest components of interstellar dust at an angular
resolution comparable to that of the radio, millimetre, and far-infrared data
in the CGPS. The Mid-Infrared Galaxy Atlas (MIGA) is a mid-infrared (12 $\mu$m
and 25 $\mu$m) counterpart to the far-infrared IRAS Galaxy Atlas (IGA), and
consists of resolution enhanced ($\sim 0.5'$ resolution) HIRES images along
with ancillary maps. This paper describes the processing and characteristics of
the atlas, the cross-beam simulation technique used to obtain high-resolution
ratio maps, and future plans to extend both the IGA and MIGA.


  We analyze robustness of decoherence-free (DF) subspace in charge qubits when
there are a local structure and non-uniformity that violate collective
decoherence measurement condition. We solve master equations of up to four
charge qubits and a detector as two serially coupled quantum point contacts
(QPC) with an island structure. We show that robustness of DF states is
strongly affected by local structure as well as by non-uniformities of qubits.


  Depending on how much information an adversary can access to, adversarial
attacks can be classified as white-box attack and black-box attack. For
white-box attack, optimization-based attack algorithms such as projected
gradient descent (PGD) can achieve relatively high attack success rates within
moderate iterates. However, they tend to generate adversarial examples near or
upon the boundary of the perturbation set, resulting in large distortion.
Furthermore, their corresponding black-box attack algorithms also suffer from
high query complexities, thereby limiting their practical usefulness. In this
paper, we focus on the problem of developing efficient and effective
optimization-based adversarial attack algorithms. In particular, we propose a
novel adversarial attack framework for both white-box and black-box settings
based on a variant of Frank-Wolfe algorithm. We show in theory that the
proposed attack algorithms are efficient with an $O(1/\sqrt{T})$ convergence
rate. The empirical results of attacking the ImageNet and MNIST datasets also
verify the efficiency and effectiveness of the proposed algorithms. More
specifically, our proposed algorithms attain the best attack performances in
both white-box and black-box attacks among all baselines, and are more time and
query efficient than the state-of-the-art.


  The cryo-electron microscopy (Cryo-EM) becomes popular for macromolecular
structure determination. However, the 2D images which Cryo-EM detects are of
high noise and often mixed with multiple heterogeneous conformations or
contamination, imposing a challenge for denoising. Traditional image denoising
methods can not remove Cryo-EM image noise well when the signal-noise-ratio
(SNR) of images is meager. Thus it is desired to develop new effective
denoising techniques to facilitate further research such as 3D reconstruction,
2D conformation classification, and so on. In this paper, we approach the
robust image denoising problem in Cryo-EM by a joint Autoencoder and Generative
Adversarial Networks (GAN) method. Equipped with robust $\ell_1$ Autoencoder
and some designs of robust $\beta$-GANs, one can stabilize the training of GANs
and achieve the state-of-the-art performance of robust denoising with low SNR
data and against possible information contamination. The method is evaluated by
both a heterogeneous conformational dataset on the Thermus aquaticus RNA
Polymerase (RNAP) and a homogenous dataset on the Plasmodium falciparum 80S
ribosome dataset (EMPIRE-10028), in terms of Mean Square Error (MSE), Peak
Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), as
well as heterogeneous conformation clustering. These results suggest that our
proposed methodology provides an effective tool for Cryo-EM 2D image denoising.
Our code is available in "https://github.com/ghl1995/denoise-gan-in-cryo-em".


  The recent fabrication of graphene nanoribbon (GNR) field-effect transistors
poses a challenge for first-principles modeling of carbon nanoelectronics due
to many thousand atoms present in the device. The state of the art quantum
transport algorithms, based on the nonequilibrium Green function formalism
combined with the density functional theory (NEGF-DFT), were originally
developed to calculate self-consistent electron density in equilibrium and at
finite bias voltage (as a prerequisite to obtain conductance or current-voltage
characteristics, respectively) for small molecules attached to metallic
electrodes where only a few hundred atoms are typically simulated. Here we
introduce combination of two numerically efficient algorithms which make it
possible to extend the NEGF-DFT framework to device simulations involving large
number of atoms. We illustrate fusion of these two algorithms into the
NEGF-DFT-type code by computing charge transfer, charge redistribution and
conductance in zigzag-GNR/variable-width-armchair-GNR/zigzag-GNR two-terminal
device covered with a gate electrode made of graphene layer as well. The total
number of carbon and edge-passivating hydrogen atoms within the simulated
central region of this device is ~7000. Our self-consistent modeling of the
gate voltage effect suggests that rather large gate voltage might be required
to shift the band gap of the proposed AGNR interconnect and switch the
transport from insulating into the regime of a single open conducting channel.


  Learning a joint language-visual embedding has a number of very appealing
properties and can result in variety of practical application, including
natural language image/video annotation and search. In this work, we study
three different joint language-visual neural network model architectures. We
evaluate our models on large scale LSMDC16 movie dataset for two tasks: 1)
Standard Ranking for video annotation and retrieval 2) Our proposed movie
multiple-choice test. This test facilitate automatic evaluation of
visual-language models for natural language video annotation based on human
activities. In addition to original Audio Description (AD) captions, provided
as part of LSMDC16, we collected and will make available a) manually generated
re-phrasings of those captions obtained using Amazon MTurk b) automatically
generated human activity elements in "Predicate + Object" (PO) phrases based on
"Knowlywood", an activity knowledge mining model. Our best model archives
Recall@10 of 19.2% on annotation and 18.9% on video retrieval tasks for subset
of 1000 samples. For multiple-choice test, our best model achieve accuracy
58.11% over whole LSMDC16 public test-set.


  We derive a geometric integration formula for the partition function of a
classical dynamical system and use it to show that corrections to the WKB
approximation vanish for any Hamiltonian which generates conformal motions of
some Riemannian geometry on the phase space. This generalizes previous cases
where the Hamiltonian was taken as an isometry generator. We show that this
conformal symmetry is similar to the usual formulations of the
Duistermaat-Heckman integration formula in terms of a supersymmetric Ward
identity for the dynamical system. We present an explicit example of a
localizable Hamiltonian system in this context and use it to demonstrate how
the dynamics of such systems differ from previous examples of the
Duistermaat-Heckman theorem.


  We consider the problem of detecting malware with deep learning models, where
the malware may be combined with significant amounts of benign code. Examples
of this include piggybacking and trojan horse attacks on a system, where
malicious behavior is hidden within a useful application. Such added
flexibility in augmenting the malware enables significantly more code
obfuscation. Hence we focus on the use of static features, particularly
Intents, Permissions, and API calls, which we presume cannot be ultimately
hidden from the Android system, but only augmented with yet more such features.
We first train a deep neural network classifier for malware classification
using features of benign and malware samples. Then we demonstrate a steep
increase in false negative rate (i.e., attacks succeed), simply by randomly
adding features of a benign app to malware. Finally we test the use of data
augmentation to harden the classifier against such attacks. We find that for
API calls, it is possible to reject the vast majority of attacks, where using
Intents or Permissions is less successful.


  In 2006, Varacca and V\"olzer proved that on finite graphs, omega-regular
large sets coincide with omega-regular sets of probability 1, by using the
existence of positional strategies in the related Banach-Mazur games. Motivated
by this result, we try to understand relations between sets of probability 1
and various notions of simple strategies (including those introduced in a
recent paper of Gr\"adel and Lessenich). Then, we introduce a generalisation of
the classical Banach-Mazur game and in particular, a probabilistic version
whose goal is to characterise sets of probability 1 (as classical Banach-Mazur
games characterise large sets). We obtain a determinacy result for these games,
when the winning set is a countable intersection of open sets.


  We study $C^2$ extremal quasiconformal mappings in space and establish
necessary and sufficient conditions for a `localized' form of extremality in
the spirit of the work of G. Aronsson on absolutely minimizing Lipschitz
extensions. We also prove short time existence for smooth solutions of a
gradient flow of QC diffeomorphisms associated to the extremal problem.


  In the last decades we have witnessed the success of applications of
Artificial Intelligence to playing games. In this work we address the
challenging field of games with hidden information and card games in
particular. Jass is a very popular card game in Switzerland and is closely
connected with Swiss culture. To the best of our knowledge, performances of
Artificial Intelligence agents in the game of Jass do not outperform top
players yet. Our contribution to the community is two-fold. First, we provide
an overview of the current state-of-the-art of Artificial Intelligence methods
for card games in general. Second, we discuss their application to the use-case
of the Swiss card game Jass. This paper aims to be an entry point for both
seasoned researchers and new practitioners who want to join in the Jass
challenge.


  We present an exact RG (renormalization group) analysis of $O(N)$-invariant
scalar field theory about the Gaussian fixed point. We prove a series of
statements that taken together show that the non-polynomial eigen-perturbations
found in the LPA (local potential approximation) at the linearised level, do
not lead to new interactions, \textit{i.e.} enlarge the universality class,
neither in the LPA or treated exactly. Non-perturbatively, their RG flow does
not emanate from the fixed point. For the equivalent Wilsonian effective action
they can be re-expressed in terms of the usual couplings to polynomial
interactions, which can furthermore be tuned to be as small as desired for all
finite RG time. For the infrared cutoff Legendre effective action, this can
also be done for the infrared evolution. We explain why this is nevertheless
consistent with the fact that the large field behaviour is fixed by these
perturbations.


  We propose a protocol for generating multi-particle entangled states using
coherent manipulation of atoms trapped in an optical cavity. We show how
entanglement can be adiabatically produced with two control beams and by
exploiting cavity-mediated interactions between the atoms. Our methods will
allow for optimal generation of entanglement for the measurement protocol we
propose. We discuss an experimental implementation and compare the performance
of the states produced with those of classical states and ideal
maximally-entangled Dicke states. We find that our states always feature
metrological gain and even outperform ideal Dicke states in the measurement of
magnetic field gradients. Due to the easy scalability, our entanglement
protocol is a promising tool for quantum state engineering.


  We investigate the Fokker-Planck description of the equal-time statistics of
the three-dimensional Lorenz-63 attractor with additive white noise. The
invariant measure is found by computing the zero (or null) mode of the linear
Fokker-Planck operator as a problem of sparse linear algebra. Two variants are
studied: A self-adjoint construction of the linear operator, and the
replacement of diffusion with hyperdiffusion. We also access the low-order
statistics of the system by a perturbative expansion in equal-time cumulants.
Comparison is made to statistics obtained by the standard approach of
accumulation via direct numerical simulation. Theoretical and computational
aspects of the Fokker-Planck and cumulant expansion methods are discussed.


  A new mechanism of supersymmetry breaking involving a dynamical parameter is
introduced. It is independent of particle phenomenology and gauge groups. An
explicit realization of this mechanism takes place in Type II superstring
compactifications which admit eight supercharges (also known as generalized
SU(3) structures). The resulting Kahler potentials are expressed in terms of
Hitchin functionals.


  We characterize the Markovian and affine structure of the Volterra Heston
model in terms of an infinite-dimensional adjusted forward process and specify
its state space. More precisely, we show that it satisfies a stochastic partial
differential equation and displays an exponentially-affine characteristic
functional. As an application, we deduce an existence and uniqueness result for
a Banach-space valued square-root process and provide its state space. This
leads to another representation of the Volterra Heston model together with its
Fourier-Laplace transform in terms of this possibly infinite system of affine
diffusions.


  In the \emph{shuffle model} of differential privacy, data-holding users send
randomized messages to a secure shuffler, the shuffler permutes the messages,
and the resulting collection of messages must be differentially private with
regard to user data. In the \emph{pan-private} model, an algorithm processes a
stream of data while maintaining an internal state that is differentially
private with regard to the stream data. We give evidence connecting these two
apparently different models.
  Our results focus on \emph{robustly} shuffle private protocols, whose privacy
guarantees are not greatly affected by malicious users. First, we give robustly
shuffle private protocols and upper bounds for counting distinct elements and
uniformity testing. Second, we use pan-private lower bounds to prove robustly
shuffle private lower bounds for both problems. Focusing on the dependence on
the domain size $k$, we find that robust approximate shuffle privacy and
approximate pan-privacy have additive error $\Theta(\sqrt{k})$ for counting
distinct elements. For uniformity testing, we give a robust approximate shuffle
private protocol with sample complexity $\tilde O(k^{2/3})$ and show that an
$\Omega(k^{2/3})$ dependence is necessary for any robust pure shuffle private
tester. Finally, we show that this connection is useful in both directions: we
give a pan-private adaptation of recent work on shuffle private histograms and
use it to recover further separations between pan-privacy and interactive local
privacy.


  We characterize the convexity of functions and the monotonicity of vector
fields on metric measure spaces with Riemannian Ricci curvature bounded from
below. Our result offers a new approach to deal with some rigidity theorems
such as `splitting theorem' and `volume cone implies metric cone theorem' in
non-smooth context.


  We show that the cophylogenetic distance, k-interval cospeciation, is
distinct from other metrics and accounts for global congruence between locally
incongruent trees. The growth of the neighborhood of trees which satisfy the
largest possible k-interval cospeciation with a given tree indicates that
k-interval cospeciation is useful for analyzing simulated data.


  Although the favored glide planes in hexagonal close-packed Zr are prismatic,
screw dislocations can escape their habit plane to glide in either pyramidal or
basal planes. Using abinitio calculations within the nudged elastic band
method, we show that, surprisingly, both events share the same thermally
activated process with an unusual conservative motion of the prismatic stacking
fault perpendicularly to itself. Halfway through the migration, the screw
dislocation adopts a nonplanar metastable configuration with stacking faults in
adjacent prismatic planes joined by a two-layer pyramidal twin.


  We quantify the accuracy of different non-self-consistent and self-consistent
spin-orbit coupling (SOC) treatments in Kohn-Sham and hybrid density-functional
theory by providing a band structure benchmark set for the valence and
low-lying conduction energy bands of 103 inorganic compounds, covering chemical
elements up to Po. Reference energy band structures for the PBE density
functional are obtained using the full-potential (linearized) augmented plane
wave code Wien2k, employing its self-consistent treatment of SOC including
Dirac-like p$^{1/2}$ orbitals in the basis set. We use this benchmark set to
benchmark a computationally simpler, non-self-consistent all-electron treatment
of SOC based on scalar-relativistic orbitals and numeric atom-centered orbital
basis functions. For elements up to Z$\approx$50, both treatments agree
virtually exactly. For the heaviest elements considered (Tl, Pb, Bi, Po), the
band structure changes due to SOC are captured with a relative deviation of 11%
or less. For different density functionals (PBE vs. the hybrid HSE06), we show
that the effect of spin-orbit coupling is usually similar but can be dissimilar
if the qualitative features of the predicted underlying scalar-relativistic
band structures do not agree. All band structures considered in this work are
available online via the NOMAD Repository to aid in future benchmark studies
and methods development.