  To understand the impact of star formation models on galaxy evolution, we
perform cosmological zoom-in radiation-hydrodynamic simulations of a dwarf dark
matter halo with virial mass 1e9 Msun at z=6. Two different star formation
models are compared, a model based on a local gravo-thermo-turbulent condition
and a model based on a sink particle algorithm. Using idealized tests of
collapsing isothermal spheres and giant molecular clouds with different
turbulent structures, we determine the optimal accretion radius to be twice the
cell size and the resolution required to achieve reasonable convergence in star
formation efficiency to 0.3 pc for the sink algorithm. As a first study in this
series, we use cosmological zoom-in simulations with different spatial
resolutions and find that star formation is more bursty in the runs with the
sink algorithm, generating stronger outflows than in the runs with the
gravo-thermo-turbulent model. The main reason for the increased burstiness is
that the gas accretion rates on the sinks are high enough to form stars on very
short timescales, leading to more clustered star formation. As a result, the
star-forming clumps are disrupted more quickly in the sink run due to more
coherent radiation and supernova feedback. The difference in burstiness between
the two star formation models becomes even more pronounced when the supernova
explosion energy is artificially increased. Our results suggest that improving
the modelling of star formation on small, sub-molecular cloud scales can
significantly impact the global properties of simulated galaxies.


  We give a general proof showing that once irreversible processes are
involved, a class of projective measurements is impossible. Applying this no-go
result to the Schroedinger's cat paradox implies that if something is claimed
to be a real Schroedinger's cat, there will be no measurable difference between
it and a trivial classical mixture of ordinary cats in any physically
implementable process, otherwise raising the dead will become reality. Other
similar macroscopic quantum superpositions cannot be observed either due to the
lack of non-commuting measurement bases. Our proof does not involve any quantum
interpretation theory and hypothesis.


  We propose a space-based gravitational wave detector consisting of two
spatially separated, drag-free satellites sharing ultra-stable optical laser
light over a single baseline. Each satellite contains an optical lattice atomic
clock, which serves as a sensitive, narrowband detector of the local frequency
of the shared laser light. A synchronized two-clock comparison between the
satellites will be sensitive to the effective Doppler shifts induced by
incident gravitational waves (GWs) at a level competitive with other proposed
space-based GW detectors, while providing complementary features. The detected
signal is a differential frequency shift of the shared laser light due to the
relative velocity of the satellites, and the detection window can be tuned
through the control sequence applied to the atoms' internal states. This scheme
enables the detection of GWs from continuous, spectrally narrow sources, such
as compact binary inspirals, with frequencies ranging from ~3 mHz - 10 Hz
without loss of sensitivity, thereby bridging the detection gap between
space-based and terrestrial optical interferometric GW detectors. Our proposed
GW detector employs just two satellites, is compatible with integration with an
optical interferometric detector, and requires only realistic improvements to
existing ground-based clock and laser technologies.


  This paper is centred on solving differential equations by symmetry groups
for first order ODEs and is in response to Starrett (2007). It also explores
the possibility of averting the assumptions by Olver (2000) that, in practice
finding the solutions of the linearized symmetry condition is usually a much
more difficult problem than solving the original ODE but, by inspired guesswork
or geometric intuition, it is possible to ascertain a particular solution of
the linearized symmetry condition. In addition, there is the verification of
the connection between prolongation and the linearized symmetry condition as
used in solving first order ODEs.


  In order to withstand the ever-increasing invasion of privacy by CCTV cameras
and technologies, on par CCTV-aware solutions must exist that provide privacy,
safety, and cybersecurity features. We argue that a first important step
towards such CCTV-aware solutions must be a mapping system (e.g., Google Maps,
OpenStreetMap) that provides both privacy and safety routing and navigation
options. However, this in turn requires that the mapping system contains
updated information on CCTV cameras' exact geo-location, coverage area, and
possibly other meta-data (e.g., resolution, facial recognition features,
operator). Such information is however missing from current mapping systems,
and there are several ways to fix this. One solution is to perform CCTV camera
detection on geo-location tagged images, e.g., street view imagery on various
platforms, user images publicly posted in image sharing platforms such as
Flickr. Unfortunately, to the best of our knowledge, there are no computer
vision models for CCTV camera object detection as well as no mapping system
that supports privacy and safety routing options.
  To close these gaps, with this paper we introduce CCTVCV -- the first and
only computer vision MS COCO-compatible models that are able to accurately
detect CCTV and video surveillance cameras in images and video frames. To this
end, our best detectors were built using 8387 images that were manually
reviewed and annotated to contain 10419 CCTV camera instances, and achieve an
accuracy of up to 98.7%. Moreover, we build and evaluate multiple models,
present a comprehensive comparison of their performance, and outline core
challenges associated with such research.


  We determine an asymptotic formula for the number of integral points of
bounded height on a certain toric variety, which is incompatible with part of a
preprint by Chambert-Loir and Tschinkel. We provide an alternative
interpretation of the asymptotic formula we get. To do so, we construct an
analogue of Peyre's constant $\alpha$ and describe its relation to a new
obstruction to the Zariski density of integral points in certain regions of
varieties.


  The flux of neutrinos originating from cosmic ray interactions with matter in
the Sun has been calculated based on Monte Carlo models for high energy
particle interactions. The resulting flux at the Earth (within the Sun's solid
angle) is higher than the corresponding one from cosmic ray interactions with
the Earth atmosphere. The smallness of the absolute rate, however, precludes it
as a practical `standard candle' for neutrino telescopes and limits neutrino
oscillation searches. On the other hand, it facilitates dark matter searches
based on neutrinos from neutralino annihilation in the Sun.


  This work is a spin-off of an on-going programme which aims at revisiting the
original studies of Lie and Cartan on pseudogroups and geometric structures
from a modern perspective. Within the framework of Lie groupoids equipped with
a special multiplicative form - called Pfaffian groupoids - we focus on
principal bibundles and Morita equivalences. In particular, we discuss in
details the notion of Pfaffian Morita equivalence, its relation to the gauge
construction in the Pfaffian setting, and its interactions with principal
actions. We briefly present some examples and applications to transitive
pseudogroups of symmetries, which we explored in great detail in
arXiv:2211.16639.


  The existence of interacting higher-spin theories is tightly constrained by
many no-go theorems. In this paper, we construct a chiral, higher-spin
generalization of Yang-Mills theory in flat space which avoids these no-go
theorems and has non-trivial tree-level scattering amplitudes with some
higher-spin external legs. The fields and action are complex, so the theory is
non-unitary and parity-violating, yet we find surprisingly compact formulae for
all-multiplicity tree-level scattering amplitudes in the maximal helicity
violating (MHV) sector, where the two negative helicity particles have
identical but arbitrary spin. This is possible because the theory admits a
perturbative expansion around its self-dual sector. Using twistor theory, we
prove the classical integrability of this self-dual sector and show that it can
be described on spacetime by an infinite tower of interacting massless scalar
fields. We also give a twistor construction of the full theory, and use it to
derive the formula for the MHV amplitude.


  We study the best-arm identification problem in multi-armed bandits with
stochastic, potentially private rewards, when the goal is to identify the arm
with the highest quantile at a fixed, prescribed level. First, we propose a
(non-private) successive elimination algorithm for strictly optimal best-arm
identification, we show that our algorithm is $\delta$-PAC and we characterize
its sample complexity. Further, we provide a lower bound on the expected number
of pulls, showing that the proposed algorithm is essentially optimal up to
logarithmic factors. Both upper and lower complexity bounds depend on a special
definition of the associated suboptimality gap, designed in particular for the
quantile bandit problem, as we show when the gap approaches zero, best-arm
identification is impossible. Second, motivated by applications where the
rewards are private, we provide a differentially private successive elimination
algorithm whose sample complexity is finite even for distributions with
infinite support-size, and we characterize its sample complexity. Our
algorithms do not require prior knowledge of either the suboptimality gap or
other statistical information related to the bandit problem at hand.


  Phonon transport across a vacuum gap separating intrinsic silicon crystals is
predicted via the atomistic Green's function method combined with
first-principles calculations of all interatomic force constants. The overlap
of electron wave functions in the vacuum gap generates weak covalent
interaction between the silicon surfaces, thus creating a pathway for phonons.
Phonon transport, dominated by acoustic modes, exceeds near-field radiation for
vacuum gaps smaller than ~ 1 nm. The first-principles-based approach proposed
in this work is critical to accurately quantify the contribution of phonon
transport to heat transfer in the extreme near field.


  Circumplanetary disks regulate the late accretion to the giant planet and
serve as the birthplace for satellites. Understanding their characteristics via
simulations also helps to prepare for their observations. Here we study disks
around 1, 3, 5, 10 $\mathrm{M_{Jup}}$ planets with three dimensional, global
radiative hydrodynamic simulations with sub-planet peak resolution, and various
planetary temperatures. We found that as the 1 $\mathrm{M_{Jup}}$ planet
radiates away its formation heat, the circumplanetary envelope transitions to a
disk between $T_p = 6000$ K and 4000 K. In the case of 3-10 $\mathrm{M_{Jup}}$
planets a disk always forms. The temperature profile of the circumplanetary
disks is very steep, the inner 1/6th is over the silicate condensation
temperature and the entire disk is above water freezing point, making satellite
formation impossible in this early stage ($<$1 Myr). Satellites might form much
later and first in the outer parts of the disk migrating inwards later on. Our
disk masses are $1, 7, 20, 40 \times 10^{-3}\mathrm{M_{Jup}}$ for the 1, 3, 5,
10 $\mathrm{M_{Jup}}$ gas giants respectively, and we provide an empirical
formula to estimate the subdisk masses based on the planet- and circumstellar
disk mass. Our finding is that the cooler the planet, the lower the temperature
of the subdisk, the higher the vertical influx velocities, and the planetary
gap is both deeper and wider. We also show that the gaps in 2D and 3D are
different. The subdisk eccentricity increases with planetary mass and violently
interacts with the circumstellar disk, making satellite-formation less likely,
if $\mathrm{M_p} \gtrsim 5 \mathrm{M_{Jup}}$.


  We consider the four-dimensional nonholonomic distribution defined by the
4-potential of the electromagnetic field on the manifold. This distribution has
a metric tensor with the Lorentzian signature $(+,-,-,-)$, therefore, the
causal structure appears as in the general relativity theory. By means of the
Pontryagin's maximum principle we proved that the equations of the horizontal
geodesics for this distribution are the same as the equations of motion of a
charged particle in the general relativity theory. This is a Kaluza -- Klein
problem of classical and quantum physics solved by methods of sub-Lorentzian
geometry. We study the geodesics sphere which appears in a constant magnetic
field and its singular points. Sufficiently long geodesics are not optimal
solutions of the variational problem and define the nonholonomic wavefront.
This wavefront is limited by a convex elliptic cone. We also study variational
principle approach to the problem. The Euler -- Lagrange equations are the same
as those obtained by the Pontryagin's maximum principle if the restriction of
the metric tensor on the distribution is the same.


  An oriented link is positive if it has a link diagram whose crossings are all
positive. An oriented link is almost positive if it is not positive and has a
link diagram with exactly one negative crossing. It is known that the Rasmussen
invariant, $4$-genus and $3$-genus of a positive knot are equal. In this paper,
we prove that the Rasmussen invariant, $4$-genus and $3$-genus of an almost
positive knot are equal. Moreover, we determine the Rasmussen invariant of an
almost positive knot in terms of its almost positive knot diagram. As
corollaries, we prove that any almost positive knot is not homogeneous, and
there is no almost positive knot of $4$-genus one.


  The tetragonal FeSe phase is an intensively investigated iron based
superconductor. In this study we examined the influence of Ag addition on the
superconducting properties of selenium deficient polycrystalline FeSe0.94. The
samples were obtained by solid state reaction and melting methods. XRD analysis
shows the presence of tetragonal phase and EDX analysis establishes
inhomogeneous Ag distribution in the grains. The superconducting properties
were investigated by fundamental and third harmonic AC magnetic susceptibility.
The intergranular critical current determined from AC magnetic susceptibility
in the Ag doped sample is several times higher than that in the undoped one,
obtained by melting at approximately the same temperatures. Intra-granular
current is field independent up to almost 1000 Oe. Using the temperature
dependence of third harmonic AC magnetic susceptibility at different DC
magnetic fields, the irreversibility lines were obtained for all samples. It is
found that Ag addition increased the irreversibility field in comparison with
undoped melted and powder sintered samples. All results show that the Ag
addition in selenium deficient (FeSe0.94) samples leads to improvement of
inter- and intra- granular properties: screening ability, pinning, activation
energy and critical current and improves the irreversibility line.


  Few-shot learning is a promising approach to molecular property prediction as
supervised data is often very limited. However, many important molecular
properties depend on complex molecular characteristics -- such as the various
3D geometries a molecule may adopt or the types of chemical interactions it can
form -- that are not explicitly encoded in the feature space and must be
approximated from low amounts of data. Learning these characteristics can be
difficult, especially for few-shot learning algorithms that are designed for
fast adaptation to new tasks. In this work, we develop molecular embeddings
that encode complex molecular characteristics to improve the performance of
few-shot molecular property prediction. Our approach leverages large amounts of
synthetic data, namely the results of molecular docking calculations, and a
multi-task learning paradigm to structure the embedding space. On multiple
molecular property prediction benchmarks, training from the embedding space
substantially improves Multi-Task, MAML, and Prototypical Network few-shot
learning performance. Our code is available at
https://github.com/cfifty/IGNITE.


  A new method is presented for mesoscopic simulations of particle dispersions
in liquid crystal solvents. It allows efficient first-principle simulations of
the dispersions involving many particles with many-body interactions mediated
by the solvents. Demonstrations have been performed for the aggregation of
colloid dispersions in two-dimensional nematic and smectic-C* solvents
neglecting hydrodynamic effects, which will be taken into account in the near
future.


  The particularly abundant presence of blue straggler stars (BSS) in Galactic
open clusters offers favorable conditions for detailed studies on the
statistical properties and the origin of the blue straggler population. With
the help of Gaia DR3, the number of identified open clusters continuously
increases, and the determination of star cluster members is more reliable. We
performed a more thorough search for BSS in newly found open clusters based on
Gaia data. We implemented a uniform membership determination for over one
thousand newly identified open clusters with larger sky coverage based on the
astrometric and photometric data from Gaia DR3. The membership probabilities of
stars were assigned by the pyUPMASK algorithm. Then we estimated the physical
parameters of these clusters by isochrone fitting on their CMDs and picked out
BSS in the specific region of these CMDs. We identified 138 BSS that had not
been reported before in 50 open clusters. Compared with recent catalogs that
present more than 1500 BSS in 339 open clusters, our new catalog increased the
number of BSS in Galactic open clusters by about 10%, and the number of open
clusters with BSS by nearly 17%. In the future, more accurate abundance
measurements are anticipated to better probe the origin of BSS in open
clusters.


  We investigate the electrical conductivity of a quark-gluon plasma (QGP)
medium using non-perturbative resummation via Gribov gluon propagator. To
calculate the electrical conductivity, we utilize the relativistic Boltzmann's
kinetic equation, within the relaxation-time approximation (RTA). The
relaxation times are obtained by computing the microscopic two-body scattering
amplitude. We adopt the quasiparticle approach, which allows us to comprehend
the transport properties in both the weak and strong coupling regimes. Above
the transition temperature, we estimate the electrical conductivity of the
quark-gluon plasma using the Gribov prescription and compare our findings with
lattice results and various phenomenological models. We find our results in
close agreement with the lattice data.


  With the aim of measuring the sparsity of a real signal, Donoho and Logan
introduced the concept of maximum Nyquist density, and used it to extend
Bombieri's principle of the large sieve to bandlimited functions. This led to
several recovery algorithms based on the minimization of the $L_{1}$-norm. In
this paper we introduce the concept of {\ planar maximum} Nyquist density,
which measures the sparsity of the time-frequency distribution of a function.
We obtain a planar large sieve principle which applies to time-frequency
representations with a gaussian window, or equivalently, to Fock spaces,
$\mathcal{F}_{1}\left( \mathbb{C}\right) $, allowing for perfect recovery of
the short-Fourier transform (STFT) of functions in the modulation space $M_{1}$
(also known as Feichtinger's algebra $S_{0}$) corrupted by sparse noise and for
approximation of missing STFT data in $M_{1}$, by $L_{1}$-minimization.


  Mechanical topological insulators are well understood for linear and weakly
nonlinear systems, however traditional analysis methods break down for strongly
nonlinear systems since linear methods can not be applied in that case. We
study one such system in the form of a one-dimensional mechanical analog of the
Su-Schrieffer-Heeger interface model with strong nonlinearity of the cubic
form. The frequency-energy dependence of the nonlinear bulk modes and
topologically insulated mode is explored using Numerical continuation of the
system's nonlinear normal modes (NNMs), and the linear stability of the NNMs
are investigated using Floquet Multipliers (FMs) and Krein signature analysis.
We find that the nonlinear topological lattice supports a family of
topologically insulated NNMs that are parameterized by the total energy of the
system and are stable within a range of frequencies. Next, it is shown that
empirical calculations of the geometric Zak Phase can define an energy
threshold to predict the excitability of the nonlinear topological mode, and
that this threshold coincides with the energy that the topological NNM
intersects the linear bulk-spectrum. These predictions are validated with
numerical simulations of the nonlinear topological system. These results are
also tested for parametric perturbations which preserve and break chirality in
the system. Thus, we provide a new method for analyzing and predicting the
existence of topologically insulated modes in a strongly nonlinear lattice
based on the physical observable of band topology.


  A large variety of real-world Reinforcement Learning (RL) tasks is
characterized by a complex and heterogeneous structure that makes end-to-end
(or flat) approaches hardly applicable or even infeasible. Hierarchical
Reinforcement Learning (HRL) provides general solutions to address these
problems thanks to a convenient multi-level decomposition of the tasks, making
their solution accessible. Although often used in practice, few works provide
theoretical guarantees to justify this outcome effectively. Thus, it is not yet
clear when to prefer such approaches compared to standard flat ones. In this
work, we provide an option-dependent upper bound to the regret suffered by
regret minimization algorithms in finite-horizon problems. We illustrate that
the performance improvement derives from the planning horizon reduction induced
by the temporal abstraction enforced by the hierarchical structure. Then,
focusing on a sub-setting of HRL approaches, the options framework, we
highlight how the average duration of the available options affects the
planning horizon and, consequently, the regret itself. Finally, we relax the
assumption of having pre-trained options to show how in particular situations,
learning hierarchically from scratch could be preferable to using a standard
approach.


  We consider the class of disjoint bilinear programs $ \max \, \{
\mathbf{x}^T\mathbf{y} \mid \mathbf{x} \in \mathcal{X}, \;\mathbf{y} \in
\mathcal{Y}\}$ where $\mathcal{X}$ and $\mathcal{Y}$ are packing polytopes. We
present an $O(\frac{\log \log m_1}{\log m_1} \frac{\log \log m_2}{\log
m_2})$-approximation algorithm for this problem where $m_1$ and $m_2$ are the
number of packing constraints in $\mathcal{X}$ and $\mathcal{Y}$ respectively.
In particular, we show that there exists a near-optimal solution
$(\tilde{\mathbf{x}}, \tilde{\mathbf{y}})$ such that $\tilde{\mathbf{x}}$ and
$\tilde{\mathbf{y}}$ are ``near-integral". We give an LP relaxation of the
problem from which we obtain the near-optimal near-integral solution via
randomized rounding. We show that our relaxation is tightly related to the
widely used reformulation linearization technique (RLT). As an application of
our techniques, we present a tight approximation for the two-stage adjustable
robust optimization problem with covering constraints and right-hand side
uncertainty where the separation problem is a bilinear optimization problem. In
particular, based on the ideas above, we give an LP restriction of the
two-stage problem that is an $O(\frac{\log n}{\log \log n} \frac{\log L}{\log
\log L})$-approximation where $L$ is the number of constraints in the
uncertainty set. This significantly improves over state-of-the-art
approximation bounds known for this problem. Furthermore, we show that our LP
restriction gives a feasible affine policy for the two-stage robust problem
with the same (or better) objective value. As a consequence, affine policies
give an $O(\frac{\log n}{\log \log n} \frac{\log L}{\log \log
L})$-approximation of the two-stage problem, significantly generalizing the
previously known bounds on their performance.


  We study the response of complex networks subject to attacks on vertices and
edges. Several existing complex network models as well as real-world networks
of scientific collaborations and Internet traffic are numerically investigated,
and the network performance is quantitatively measured by the average inverse
geodesic length and the size of the largest connected subgraph. For each case
of attacks on vertices and edges, four different attacking strategies are used:
removals by the descending order of the degree and the betweenness centrality,
calculated for either the initial network or the current network during the
removal procedure. It is found that the removals by the recalculated degrees
and betweenness centralities are often more harmful than the attack strategies
based on the initial network, suggesting that the network structure changes as
important vertices or edges are removed. Furthermore, the correlation between
the betweenness centrality and the degree in complex networks is studied.


  We present a method to generate qubits of the vibrational motion of an ion.
The method is developed in the non-rotating wave approximation regime,
therefore we consider regimes where the dynamics has not been studied. Because
the solutions are valid for a more extended range of parameters we call them
generalized qubits.


  We present Umwelt, an authoring environment for interactive multimodal data
representations. In contrast to prior approaches, which center the visual
modality, Umwelt treats visualization, sonification, and textual description as
coequal representations: they are all derived from a shared abstract data
model, such that no modality is prioritized over the others. To simplify
specification, Umwelt evaluates a set of heuristics to generate default
multimodal representations that express a dataset's functional relationships.
To support smoothly moving between representations, Umwelt maintains a shared
query predicated that is reified across all modalities -- for instance,
navigating the textual description also highlights the visualization and
filters the sonification. In a study with 5 blind / low-vision expert users, we
found that Umwelt's multimodal representations afforded complementary overview
and detailed perspectives on a dataset, allowing participants to fluidly shift
between task- and representation-oriented ways of thinking.


  In this study I develop a novel action for lattice gauge theory for finite
systems, which accommodates non-periodic boundary conditions, implements the
proper integral form of Gauss' law and exhibits an inherently symmetric energy
momentum tensor, all while realizing automatic ${\cal O}(a)$ improvement.
Taking the modern summation-by-parts formulation for finite differences as
starting point and combining it with insight from the finite volume strategies
of computational electrodynamics I show how the concept of a conserving
discretization can be realized for non-Abelian lattice gauge theory. Major
steps in the derivation are illustrated using Abelian gauge theory as example.


  A major goal of cosmology is to obtain sensitive, high resolution maps of the
Cosmic Microwave Background (CMB) anisotropy. Such maps, as would be produced
by the recently proposed Microwave Anisotropy Probe (MAP), will contain a
wealth of primary information about conditions in the early universe. To
mitigate systematic effects when observing the microwave background, it is
desirable for the raw data to be collected in differential form: as a set of
temperature differences between points in the sky. However, the production of
large (mega-pixel) maps from a set of temperature differences is a potentially
severe computational challenge. We present a new technique for producing maps
from differential radiometer data that has a computational cost that grows in
the slowest possible way with increasing angular resolution and number of map
pixels. The required central processor (CPU) time is proportional to the number
of differential data points and the required random access memory (RAM) is
proportional to the number of map pixels. We test our technique, and
demonstrate its feasibility, by simulating one year of a space-borne anisotropy
mission.


  This article presents the use of advanced tools applied to the design of
devices that can solve specific acoustic problems, improving the already
existing devices based on classic technologies. Specifically, we have used two
different configurations of a material called Sonic Crystals, which is formed
by arrays of acoustic scatterers, to obtain acoustic screens with high
diffusion properties by means of an optimization process. This design procedure
has been carried out using a multiobjective evolutionary algorithm along to an
acoustic simulation model developed with the numerical method called Finite
Difference Time Domain (FDTD). The results obtained are discussed in terms of
both the acoustic performance and the robustness of the devices achieved.


  The surroundings of globular clusters in the outer halo are one of the best
places to look for the remnants of ancient dwarf satellites. The classical
Searle & Zinn (1978) scenario of the formation of the Milky Way, in which the
halo globular clusters formed in larger dwarf galaxies, can nowadays be
considered the local manifestation of the hierarchical galaxy formation. It is
now well known that some Galactic globular clusters are actually associated
with the tidal stellar stream resulting from the accretion of dwarf satellites,
thereby confirming that this process plays an important role in the building of
the Milky Way globular cluster system. Furthermore, several recent studies have
suggested that the two most luminous Galactic globular clusters (M54,
associated with Sagittarius and Omega Centauri) might be the nuclei of dwarf
galaxies. Extending previous suggestions, van den Bergh (2000) has argued that
the ``young'' globulars located in the outer halo might be the nuclei of
extinct dwarf galaxies and suggests that a search for evidence of this may
prove worthwhile. If these clusters formed within larger stellar systems, we
would expect to find them surrounded by a stellar population that should be
revealed by deep photometry over a wide field. In this paper, we present the
first (most interesting) preliminary results from a long term project designed
to search for tidal debris around remote halo globular clusters, debris that
may be the vestiges of the larger systems in which these clusters formed.


  Let F be a germ of a singular foliation of the complex plane. Assuming that F
is a generalized curve D. Marin and J.-F. Mattei proved the incompressibility
of the foliation in a neighborhood from which a finite set of analytic curves
is removed. We show in the present work that this hypothesis cannot be eluded
by building examples of foliations, reduced after one blow-up, for which the
property does not hold. Even if we manage to prove that the individual
saddle-node foliation is incompressible, their leaves not retracting
tangentially on the boundary of the domain of definition forbids a
generalization of Marin--Mattei's construction. We finally characterize those
foliations for which the construction of Marin--Mattei's monodromy can be
carried out.


  The currently available values and confidence limits for Omega_m0, H_0 and
globular cluster ages still indicate that the dark energy that dominates the
Universe could also be a form of quintessence or phantom energy. In fact,
current cosmological values favor phantom energy. To increase the likelihood of
a cosmological constant as dark energy instead of phantom energy, the
possibilities seem to lie in reducing globular cluster ages, the Hubble
constant, or both, and possibly advancing the epoch of globular cluster
formation. For a set of possible dark energy equations of state that includes
the cosmological constant, quintessence or phantom energy, age-redshift
analytical expressions for null curvature universes that include ordinary
matter are derived together with the corresponding ages for these universes.


  Let $R$ be a commutative Noetherian ring, $\fa$ an ideal of $R$, $M$ an
arbitrary $R$-module and $N$ a finite $R$-module. We prove that \cite[Theorem
2.1]{Mel} and \cite[Proposition 3.3 (i)$\Leftrightarrow$(ii)]{B1} are true for
any Serre subcategory of $R$-modules. We also prove a characterization theorem
for $\lc_{\fa}^{i}(M)$ and $\lc_{\fa}^{i}(N,M)$ to be $\fa$-cofinite for all
$i$, whenever one of the following cases holds: (a) $\ara (\fa)\leq 1$, (b)
$\dim R/\fa \leq 1$ or (c) $\dim R\leq 2$. In the end we study Artinianness and
Artinian $\fa$-cofiniteness of local cohomology modules.


  The MUST (Mass Unspecific Supervised Tagging) method has proven to be
successful in implementing generic jet taggers capable of discriminating
various signals over a wide range of jet masses. We implement the MUST concept
by using eXtreme Gradient Boosting ({\tt XGBoost}) classifiers instead of
neural networks (NNs) as previously done. We build both fully-generic and
specific multi-pronged taggers, to identify 2, 3, and/or 4-pronged signals from
SM QCD background. We show that {\tt XGBoost}-based taggers are not only easier
to optimize and much faster than those based in NNs, but also show quite
similar performance, even when testing with signals not used in training,
providing an alternative machine-learning implementation that can potentially
be used to test the robustness of the results.


  We study a variant of classical clustering formulations in the context of
algorithmic fairness, known as diversity-aware clustering. In this variant we
are given a collection of facility subsets, and a solution must contain at
least a specified number of facilities from each subset while simultaneously
minimizing the clustering objective ($k$-median or $k$-means). We investigate
the fixed-parameter tractability of these problems and show several negative
hardness and inapproximability results, even when we afford exponential running
time with respect to some parameters.
  Motivated by these results we identify natural parameters of the problem, and
present fixed-parameter approximation algorithms with approximation ratios
$\big(1 + \frac{2}{e} +\epsilon \big)$ and $\big(1 + \frac{8}{e}+ \epsilon
\big)$ for diversity-aware $k$-median and diversity-aware $k$-means
respectively, and argue that these ratios are essentially tight assuming the
gap-exponential time hypothesis. We also present a simple and more practical
bicriteria approximation algorithm with better running time bounds. We finally
propose efficient and practical heuristics. We evaluate the scalability and
effectiveness of our methods in a wide variety of rigorously conducted
experiments, on both real and synthetic data.


  We define a notion of connection in a fibre bundle that is compatible with a
singular foliation of the base. Fibre bundles equipped with such connections
are in plentiful supply, arising naturally for any Lie groupoid-equivariant
bundle, and simultaneously generalising regularly foliated bundles in the sense
of Kamber-Tondeur and singular foliations. We define hierarchies of
diffeological holonomy groupoids associated to such bundles, which arise from
the parallel transport of jet/germinal conservation laws. We show that the
groupoids associated in this manner to trivial singularly foliated bundles are
quotients of Androulidakis-Skandalis holonomy groupoids, which coincide with
Androulidakis-Skandalis holonomy groupoids in the regular case. Finally we
prove functoriality of all our constructions under appropriate morphisms.


  We consider nonparametric regression in the context of functional data, that
is, when a random sample of functions is observed on a fine grid. We obtain a
functional asymptotic normality result allowing to build simultaneous
confidence bands (SCB) for various estimation and inference tasks. Two
applications to a SCB procedure for the regression function and to a
goodness-of-fit test for curvilinear regression models are proposed. The first
one has improved accuracy upon the other available methods while the second can
detect local departures from a parametric shape, as opposed to the usual
goodness-of-fit tests which only track global departures. A numerical study of
the SCB procedures and an illustration with a speech data set are provided.


  We show that gas in a large fraction of low mass dark matter halos may form
Toomre stable disks, if angular momentum is conserved when the gas contracts.
Such halos would be stable to star formation and therefore remain dark. This
may potentially explain the discrepancy between the predicted and observed
number of dwarf satellites in the Local Group, as well as the deviation between
the predicted and the observed faint end slope of the luminosity function. The
above mechanism does not require a strong variation of the baryon fraction with
the virial mass of the dark halo. We show that model fits to rotation curves
are also consistent with this hypothesis: none of the observed galaxies lie in
the region of parameter space forbidden by the Toomre stability criterion.


  The metallicity of active galactic nuclei (AGNs), which can be measured by
emission line ratios in their broad and narrow line regions (BLRs and NLRs),
provides invaluable information about the physical connection between the
different components of AGNs. From the archival databases of the International
Ultraviolet Explorer, the Hubble Space Telescope and the Sloan Digital Sky
Survey, we have assembled the largest sample available of AGNs which have
adequate spectra in both the optical and ultraviolet bands to measure the
narrow line ratio [N II]/H{\alpha} and also, in the same objects, the
broad-line N V/C IV ratio. These permit the measurement of the metallicities in
the NLRs and BLRs in the same objects. We find that neither the BLR nor the NLR
metallicity correlate with black hole masses or Eddington ratios, but there is
a strong correlation between NLR and BLR metallicities. This metallicity
correlation implies that outflows from BLRs carry metal-rich gas to NLRs at
characteristic radial distances of ~ 1.0 kiloparsec. This chemical connection
provides evidence for a kinetic feedback of the outflows to their hosts. Metals
transported into the NLR enhance the cooling of the ISM in this region, leading
to local star formation after the AGNs turn to narrow line LINERs. This
post-AGN star formation is predicted to be observable as an excess continuum
emission from the host galaxies in the near infrared and ultraviolet, which
needs to be further explored.


  We use a physically motivated semi-analytic model, based on the mass function
of dark matter halos, to predict the number of radio-loud quasars as a function
of redshift and luminosity. Simple models in which the central BH mass scales
with the velocity dispersion of its host halo as M(bh) sigma(halo)^5 have been
previously found to be consistent with a number of observations, including the
optical and X-ray quasar luminosity functions. We find that similar models,
when augmented with an empirical prescription for radio emission, overpredict
the number of faint (10 micro-Jy) radio sources by 1-2 orders of magnitude.
This translates into a more stringent constraint on the low-mass end of the
quasar black hole mass function than is available from the Hubble and Chandra
Deep Fields. We interpret this discrepancy as evidence that black holes with
masses below 10^7 Msun are either rare or are not as radio-loud as their more
massive counterparts. Models that exclude BHs with masses below 10^7 Msun are
in agreement with the deepest existing radio observations, but still produce a
significant tail of high-redshift objects. In the 1-10GHz bands, at the
sensitivity of 10 micro-Jy, we find surface densities of 100, 10, and 0.3
deg^-2 for sources located at z>6, 10, and 15, respectively. The discovery of
these sources with instruments such as the Allen Telescope Array (ATA),
Extended Very Large Array (EVLA), and the Square Kilometer Array (SKA) would
open a new window for the study of supermassive BHs at high redshift. We also
find surface densities of 0.1 deg^-2 at z > 6 for mJy sources that can be used
to study 21 cm absorption from the epoch of reionization. We suggest that,
although not yet optically identified, the FIRST survey may have already
detected 10^3-10^4 such sources.


  We observe that a recently proposed supersymmetric model with Q6 flavor
symmetry admits a new CP violating ground state. A new sum rule for the quark
mixing parameters emerges, which is found to be consistent with data. Simple
extensions of the model to the neutrino sector suggest an inverted hierarchical
mass spectrum with nearly maximal CP violation (|delta_{MNS}| simeq pi/2).
Besides reducing the number of parameters in the fermion sector, these models
also provide solutions to the SUSY flavor problem and the SUSY CP problem. We
construct a renormalizable scalar potential that leads to the spontaneous
breaking of CP symmetry and the family symmetry.


  Recently, Digitized-Counterdiabatic (CD) Quantum Approximate Optimization
Algorithm (QAOA) has been proposed to make QAOA converge to the solution of an
optimization problem in fewer steps, inspired by Trotterized counterdiabatic
driving in continuous-time quantum annealing. In this paper, we critically
revisit this approach by focusing on the paradigmatic weighted and unweighted
one-dimensional MaxCut problem. We study two variants of QAOA with first and
second-order CD corrections. Our results show that, indeed, higher order CD
corrections allow for a quicker convergence to the exact solution of the
problem at hand by increasing the complexity of the variational cost function.
Remarkably, however, the total number of free parameters needed to achieve this
result is independent of the particular QAOA variant analyzed.


  Axion-like particles (ALPs) are ubiquitous in models of new physics
explaining some of the most pressing puzzles of the Standard Model. However,
until relatively recently, little attention has been paid to its interplay with
flavour. In this work, we study in detail the phenomenology of ALPs that
exclusively interact with up-type quarks at the tree-level, which arise in some
well-motivated ultra-violet completions such as QCD-like dark sectors or
Froggatt-Nielsen type models of flavour. Our study is performed in the
low-energy effective theory to highlight the key features of these scenarios in
a model independent way. We derive all the existing constraints on these models
and demonstrate how upcoming experiments at fixed-target facilities and the LHC
can probe a vast region of the parameter space, which is currently not excluded
by cosmological and astrophysical bounds. We also emphasize how a future
measurement of the currently unavailable meson decay $D \to \pi +
\rm{invisible}$ could complement these upcoming searches and help to probe a
large unexplored region of their parameter space.


  Out-of-domain (OOD) detection for low-resource text classification is a
realistic but understudied task. The goal is to detect the OOD cases with
limited in-domain (ID) training data, since we observe that training data is
often insufficient in machine learning applications. In this work, we propose
an OOD-resistant Prototypical Network to tackle this zero-shot OOD detection
and few-shot ID classification task. Evaluation on real-world datasets show
that the proposed solution outperforms state-of-the-art methods in zero-shot
OOD detection task, while maintaining a competitive performance on ID
classification task.


  We have studied charge transport in a one-dimensional chain of small
Josephson junctions using a single-electron transistor. We observe a crossover
from time-correlated tunneling of single electrons to that of Cooper pairs as a
function of both magnetic field and current. At relatively high magnetic field,
single-electron transport dominates and the tunneling frequency is given by
f=I/e, where I is the current through the chain and e is the electron's charge.
As the magnetic field is lowered, the frequency gradually shifts to f=I/2e for
I>200 fA, indicating Cooper-pair transport. For the parameters of the measured
sample, we expect the Cooper-pair transport to be incoherent.


  We prove an extended convexity for quantum Fisher information of a mixed
state with a given convex decomposition. This convexity introduces a bound
which has two parts: i. classical part associated to the Fisher information of
the probability distribution of the states contributing to the decomposition,
and ii. quantum part given by the average quantum Fisher information of the
states in this decomposition. Next we use a non-Hermitian extension of
symmetric logarithmic derivative in order to obtain another upper bound on
quantum Fisher information, which enables to derive a closed form for a fairly
general class of system dynamics given by a dynamical semigroup. We combine our
two upper bounds together in a general (open system) metrology framework where
the dynamics is described by a quantum channel, and derive the ultimate
precision limit for quantum metrology. We illustrate our results and their
applications through two examples, where we also demonstrate that how the
extended convexity allows to track transition between quantum and classical
behaviors for an estimation precision.


  Distributed Systems involve two or more computer systems which may be
situated at geographically distinct locations and are connected by a
communication network. Due to failures in the communication link, faults arise
which may make the entire system dysfunctional. To enable seamless operation of
the distributed system, these faults need to be detected and located
accurately. This paper examines various techniques of handling faults in
distributed systems and proposes and innovative technique which uses percent
accuracy for detecting faulty nodes in the system. Every node in the system
acts as an initiator and votes for certifying faulty nodes in the system. This
certification is done on the basis of percent accuracy value of each faulty
node which should exceed a predefined threshold value to qualify node as
faulty. As the threshold increases, the number of faulty nodes detected in the
system reduces. This is a decentralized approach with no dependency on a single
node to act as a leader for diagnosis. This technique is also applicable to
ad-hoc networks, which are static in nature.


  We report initialization, complete electrical control, and single-shot
readout of an exchange-only spin qubit. Full control via the exchange
interaction is fast, yielding a demonstrated 75 qubit rotations in under 2 ns.
Measurement and state tomography are performed using a maximum-likelihood
estimator method, allowing decoherence, leakage out of the qubit state space,
and measurement fidelity to be quantified. The methods developed here are
generally applicable to systems with state leakage, noisy measurements, and
non-orthogonal control axes.


  We give four constructions of non-$\partial\bar\partial$ (hence non-K\"ahler)
manifolds: (1) A simply connected page-$1$-$\partial\bar\partial$-manifold (2)
A simply connected $dd^c+3$-manifold (3) For any $r\geq 2$, a simply connected
compact manifold with nonzero differential on the $r$-th page of the
Fr\"olicher spectral sequence. (4) For any $r\geq 2$, a pluriclosed nilmanifold
with nonzero differential on the $r$-th page of the Fr\"olicher spectral
sequence. The latter disproves a conjecture by Popovici. A main ingredient in
the first three constructions is a simple resolution construction of certain
quotient singularities with control on the cohomology.


  We present a new approach to derive the observed properties of synchrotron
outbursts in relativistic jets. The idea is to use the very well sampled
submillimetre-to-radio long-term light curves of 3C 273 to extract the spectral
and temporal evolution of a typical outburst. The method consists in a
decomposition of these light curves into a series of twelve self-similar
flares. With a model-independent parameterization, we find that the obtained
outburst's evolution is in good qualitative agreement with the expectations of
shock models in relativistic jets. We then derive, by a second approach, the
relevant parameters of three-stage shock models.
  We observe for the first time that the optically thin spectral index is
steeper during the initial rising phase of the evolution than during the final
declining phase as expected by the shock model of Marscher & Gear (1985). We
obtain that this index flattens from alpha=-1.1 to alpha=-0.5, in good
agreement with what is expected from a power law electron energy distribution
with an index of -2.0. The observed flattening gives support to the idea that
radiative (synchrotron and/or Compton) losses are the dominant cooling process
of the electrons during the initial phase of the shock evolution.
  Two other results give us confidence in our decomposition: 1) the outbursts
that we identify do well correspond to the VLBI components observed in the jet
and 2) there is strong evidence that high-frequency peaking outbursts evolve
faster than low-frequency peaking outbursts. We propose that this last
correlation is related to the distance from the core of the jet at which the
shock forms.


  We develop a form factor bootstrap program to determine the matrix elements
of local, boundary condition changing operators. We propose axioms for these
form factors and determine their solutions in the free boson and Lee-Yang
models. The sudden change in the boundary condition, caused by an operator
insertion, can be interpreted as a local quench and the form factors provide
the overlap of any state before the quench with any outgoing state after the
quench.


  A large international effort is under way to assess the presence of a shadow
in the radio emission from the compact source at the centre of our Galaxy,
Sagittarius A$^*$ (Sgr A$^*$). If detected, this shadow would provide the first
direct evidence of the existence of black holes and that Sgr A$^*$ is a
supermassive black hole. In addition, the shape of the shadow could be used to
learn about extreme gravity near the event horizon and to determine which
theory of gravity better describes the observations. The mathematical
description of the shadow has so far used a number of simplifying assumptions
that are unlikely to be met by the real observational data. We here provide a
general formalism to describe the shadow as an arbitrary polar curve expressed
in terms of a Legendre expansion. Our formalism does not presume any knowledge
of the properties of the shadow, e.g. the location of its centre, and offers a
number of routes to characterize the distortions of the curve with respect to
reference circles. These distortions can be implemented in a coordinate
independent manner by different teams analysing the same data. We show that the
new formalism provides an accurate and robust description of noisy
observational data, with smaller error variances when compared to previous
approaches for the measurement of the distortion.


  The large single spin asymmetries observed at high momentum fractions x_F and
transverse momenta k_T in p^\uparrow p -> \pi(x_F,k_T)+X as well as in pp ->
\Lambda^\uparrow(x_F,k_T)+X suggest that soft helicity flip processes are
coherent with hard scattering. Such coherence can be maintained if x_F -> 1 as
k_T -> \infty, while k_T^2(1-x_F) \sim \Lambda_QCD^2 stays fixed. The entire
hadron wave function, rather than a single quark, then contributes to the
scattering process. Analogous coherence effects have been seen experimentally
in the Drell-Yan process at high x_F. We find that the p^\uparrow p ->
\pi(x_F,k_T)+X production amplitudes have large dynamic phases and that
helicity flip contributions are unsuppressed in this limit, giving rise to
potentially large single spin asymmetries.


  The relational data model offers unrivaled rigor and precision in defining
data structure and querying complex data. Yet the use of relational databases
in scientific data pipelines is limited due to their perceived unwieldiness. We
propose a simplified and conceptually refined relational data model named
DataJoint. The model includes a language for schema definition, a language for
data queries, and diagramming notation for visualizing entities and
relationships among them. The model adheres to the principle of entity
normalization, which requires that all data -- both stored and derived -- must
be represented by well-formed entity sets. DataJoint's data query language is
an algebra on entity sets with five operators that provide matching
capabilities to those of other relational query languages with greater clarity
due to entity normalization. Practical implementations of DataJoint have been
adopted in neuroscience labs for fluent interaction with scientific data
pipelines.


  We have computed the even-$N$ moments $N \leq 20$ of the gluon-to-quark
splitting function $P_{\rm qg}$ at the fourth order of perturbative QCD via the
renormalization of off-shell operator matrix elements. Our results, derived
analytically for a general gauge group, agree with all results obtained for
this function so far, in particular with the lowest five moments obtained via
physical cross sections. Using our new moments and all available endpoint
constraints, we construct approximations for the four-loop $P_{\rm qg}(x)$ that
should be sufficient for a wide range of collider-physics applications. The
N$^3$LO corrections resulting from these and the corresponding quark-quark
splitting functions lead to a marked improvement of the perturbative accuracy
for the scale derivative of the singlet quark distribution, with effects of 1%
or less at $x \gtrsim 10^{\,-4}$ at a standard reference scale with $\alpha_s =
0.2$.


  A new method for generating fitting grids for least-squares tensor
hypercontraction (LS-THC) is presented. This method draws inspiration from the
related interpolative separable density fitting (ISDF) technique, but uses only
a pivoted Cholesky decomposition of the metric matrix, S, already computed as a
matter of course in LS-THC. The size and quality of the resulting grid is
controlled by a user-defined cutoff parameter and the size of the starting
grid. Additionally, the Cholesky-based method provides an alternative and
possible more numerically stable method for performing the least-squares fit.
The quality of the grids produced is evaluated for LS-DF-THC-MP2 calculations
on retinal and benzene, the former with a large starting grid and small cc-pVDZ
basis set, and the latter with a wide range of grids and basis sets. The error
and grid size is found to be well-controlled by either the cutoff parameter
(with a large starting grid) or the starting grid size (with a tight cutoff)
and highly predictable. The Cholesky-based method is also able to generate
unique grids tailored to different charge distributions, for example the (ab|,
(ai|, and (ij| distributions that arise in the molecular orbital integrals.
While only the (ai| grid directly affects the MP2 energy, the relative sizes of
the other grids are examined.


  We have imaged magnetization textures of permalloy films patterned into
Penrose P2 tilings (P2T) using scanning electron microscopy with polarization
analysis (SEMPA). P2T film segments have near-uniform, bipolar magnetization,
similar to artificial spin ices, but with asymmetric vertex coordination that
induces a more complex spin ice behavior mediated by exchange interactions in
vertex domain walls. Numerical simulations including long-range dipole
interactions agree with SEMPA images of as-grown P2T, and predict a
ferromagnetic ground state for a 2D P2T lattice of classical Ising spins.


  The main focus of this paper is on bisimulation-invariant MSO, and more
particularly on giving a novel model-theoretic approach to it. In model theory,
a model companion of a theory is a first-order description of the class of
models in which all potentially solvable systems of equations and non-equations
have solutions. We show that bisimulation-invariant MSO on trees gives the
model companion for a new temporal logic, "fair CTL", an enrichment of CTL with
local fairness constraints. To achieve this, we give a completeness proof for
the logic fair CTL which combines tableaux and Stone duality, and a fair CTL
encoding of the automata for the modal {\mu}-calculus. Moreover, we also show
that MSO on binary trees is the model companion of binary deterministic fair
CTL.


  In this paper, we investigate proof-theoretic aspects of the logics of
evidence and truth LETJ and LETF. These logics extend, respectively, Nelson's
logic N and the logic of first-degree entailment FDE, also known as Belnap-Dunn
four-valued logic, with a classicality operator that recovers classical logic
for formulas in its scope. We will present natural deduction and sequent
systems for LETJ and LETF, together with proofs of normalization and
cut-elimination theorems, respectively. As a corollary, we obtain decidability
for both logics.


  We consider vortex-type solutions in $d=5$ dimensions of the Einstein gravity
coupled to a nonabelian SU(2) field posessing a nonzero electric part. After
the dimensional reduction, this corresponds to a $d=4$
Einstein-Yang-Mills-Higgs-U(1)-dilaton model. A general axially symmetric
ansatz is presented, and the properties of the spherically symmetric solutions
are analysed.


  In this Letter, we show that the explosive percolation is a novel continuous
phase transition. The order-parameter-distribution histogram at the percolation
threshold is studied in Erd\H{o}s-R\'{e}nyi networks, scale-free networks, and
square lattice. In finite system, two well-defined Gaussian-like peaks coexist,
and the valley between the two peaks is suppressed with the system size
increasing. This finite-size effect always appears in typical first-order phase
transition. However, both of the two peaks shift to zero point in a power law
manner, which indicates the explosive percolation is continuous in the
thermodynamic limit. The nature of explosive percolation in all the three
structures belongs to this novel continuous phase transition. Various scaling
exponents concerning the order-parameter-distribution are obtained.


  We discuss kernels on complact classical groups $G=\U(n)$, $\OO(2n)$,
$\Sp(n)$ defined by the formula $K(z,u)=|\det(1-zu^*)|^s$. We obtain the
explicit Plancherel formula for these kernels and the interval of
positive-definiteness. We also obtain explicit models for Sahi's 'unipotent'
representations.


  Two-dimensional stealthy hyperuniform distributions of rigid scatterers
embedded in a waveguide are experimentally characterized the wave transport
properties for scalar waves in airborne audible acoustics. The non resonant
nature of the scatterers allows us to directly links the these properties to
the geometric distribution of points through the structure factor. The
transport properties are analyzed as a function of the stealthiness $\chi$ of
their hyperuniform point pattern and compared to those of a disordered material
in the diffusive regime, which are characterized by the Ohm's law through the
mean free path. Different scattering regimes are theoretically and numerically
identified showing transparent regions, isotropic band gaps, and anisotropic
scattering depending on $\chi$. The robustness of these scattering regimes to
losses which are unavoidable in audible acoustics is experimentally unvealed.


  The interaction between the electric dipole moment of a trapped molecular ion
and the configuration of the confined Coulomb crystal couples the orientation
of the molecule to its motion. We consider the practical feasibility of
harnessing this interaction to initialize, process, and read out quantum
information encoded in molecular ion qubits without optically illuminating the
molecules. We present two schemes wherein a molecular ion can be entangled with
a co-trapped atomic ion qubit, providing, among other things, a means for
molecular state preparation and measurement. We also show that virtual phonon
exchange can significantly boost range of the intermolecular dipole-dipole
interaction, allowing strong coupling between widely-separated molecular ion
qubits.


  Green bonds have been shown to be effective tool for sustainability however
market growth is impeded by high issuance and transaction costs. The lack of
appropriate standardisation and frameworks raise fear of greenwashing.
  In this paper, we propose a platform for green bond issuance on the Algorand
blockchain. It offers "Green Bonds as a Service", increasing accessibility
through automation. The solution has minimal associated costs and supports
fractional asset ownership, both of which will help adoption especially in
developing countries. A financial regulator must preapprove an investor and can
freeze assets in the case of financial irregularities. Green bonds can be
bought directly from an issuer or in the secondary market. We also introduce a
novel mechanism whereby an issuer can upload proof of impact reports. A green
verifier uses these to submit a green rating; poor green ratings result in
reputational damage and economic penalties.


  CP violating signals in semileptonic $\tau$ decays induced by an exotic
scalar exchange are studied in a completely model-independent way. These can be
observed in decays of unpolarized single $\tau$'s even if their rest frame
cannot be reconstructed. No beam polarization is required. The importance of
the two-meson channel, in particular the $K\pi$ final state is emphasized.


  Recently, Lipowski [cond-mat/0002378] investigated a stochastic lattice model
which exhibits a discontinuous transition from an active phase into infinitely
many absorbing states. Since the transition is accompanied by an apparent
power-law singularity, it was conjectured that the model may combine features
of first- and second-order phase transitions. In the present work it is shown
that this singularity emerges as an artifact of the definition of the model in
terms of products. Instead of a power law, we find a logarithmic singularity at
the transition. Moreover, we generalize the model in such a way that the
second-order phase transition becomes accessible. As expected, this transition
belongs to the universality class of directed percolation.


  Estimating frequencies of elements appearing in a data stream is a key task
in large-scale data analysis. Popular sketching approaches to this problem
(e.g., CountMin and CountSketch) come with worst-case guarantees that
probabilistically bound the error of the estimated frequencies for any possible
input. The work of Hsu et al. (2019) introduced the idea of using machine
learning to tailor sketching algorithms to the specific data distribution they
are being run on. In particular, their learning-augmented frequency estimation
algorithm uses a learned heavy-hitter oracle which predicts which elements will
appear many times in the stream. We give a novel algorithm, which in some
parameter regimes, already theoretically outperforms the learning based
algorithm of Hsu et al. without the use of any predictions. Augmenting our
algorithm with heavy-hitter predictions further reduces the error and improves
upon the state of the art. Empirically, our algorithms achieve superior
performance in all experiments compared to prior approaches.


  We investigate the collective phase dynamics in conventional long Josephson
junction (LJJ) stacks and in layered superconductors, exhibiting intrinsic LJJ
behaviors. Using a theoretical model which accounts for both the magnetic
induction effect and the breakdown of local charge neutrality (i.e., charging
effect), we show that the collective motion of Josephson vortices, including
the dispersion of Josephson plasma mode and the Swihart-type velocity, in an
intrinsic LJJ stack such as Bi$_2$Sr$_2$CaCu$_2$O$_{8+y}$ (BSCCO) is
significantly modified from those in a conventional LJJ stack. In BSCCO, the
strength of the charging effect $\alpha$ is small (i.e., $\alpha \sim
0.1-0.4$), but it leads to notable changes in collective phase dynamics,
including changes to the stability condition. Also, we show that splitting of
the supercurrent branch in the resistive state is due to collective motion of
Josephson vortices. The width of spread of these sub-branches in the linear
current-voltage regime depends on $\alpha$, suggesting another way to measure
the charging effect in BSCCO.


  The general misconception regarding velocity measurements of a test particle
as it approaches black hole is addressed by introducing generalized observer
set. For a general static spherically symmetric metric applicable to both
Einstein and alternative gravities as well as for some well known solutions in
alternative gravity theories, we find that velocity of the test particle do not
approach that of light at event horizon by considering ingoing observers and
test particles.


  The concept of causality has a controversial history. The question of whether
it is possible to represent and address causal problems with probability
theory, or if fundamentally new mathematics such as the do-calculus is required
has been hotly debated, In this paper we demonstrate that, while it is critical
to explicitly model our assumptions on the impact of intervening in a system,
provided we do so, estimating causal effects can be done entirely within the
standard Bayesian paradigm. The invariance assumptions underlying causal
graphical models can be encoded in ordinary Probabilistic graphical models,
allowing causal estimation with Bayesian statistics, equivalent to the
do-calculus.


  A central area of research in nonlinear science is the study of instabilities
that drive the emergence of extreme events. Unfortunately, experimental
techniques for measuring such phenomena often provide only partial
characterization. For example, real-time studies of instabilities in nonlinear
fibre optics frequently use only spectral data, precluding detailed predictions
about the associated temporal properties. Here, we show how Machine Learning
can overcome this limitation by predicting statistics for the maximum intensity
of temporal peaks in modulation instability based only on spectral
measurements. Specifically, we train a neural network based Machine Learning
model to correlate spectral and temporal properties of optical fibre modulation
instability using data from numerical simulations, and we then use this model
to predict the temporal probability distribution based on high-dynamic range
spectral data from experiments. These results open novel perspectives in all
systems exhibiting chaos and instability where direct time-domain observations
are difficult.


  The theory of relativity, which was proposed in the beginning of the 20th
century, applies to particles and frames of reference whose velocity is less
than the velocity of light. In this paper we shall show how this theory can be
extended to particles and frames of reference which move faster than light.


  Face occlusions, covering either the majority or discriminative parts of the
face, can break facial perception and produce a drastic loss of information.
Biometric systems such as recent deep face recognition models are not immune to
obstructions or other objects covering parts of the face. While most of the
current face recognition methods are not optimized to handle occlusions, there
have been a few attempts to improve robustness directly in the training stage.
Unlike those, we propose to study the effect of generative face completion on
the recognition. We offer a face completion encoder-decoder, based on a
convolutional operator with a gating mechanism, trained with an ample set of
face occlusions. To systematically evaluate the impact of realistic occlusions
on recognition, we propose to play the occlusion game: we render 3D objects
onto different face parts, providing precious knowledge of what the impact is
of effectively removing those occlusions. Extensive experiments on the Labeled
Faces in the Wild (LFW), and its more difficult variant LFW-BLUFR, testify that
face completion is able to partially restore face perception in machine vision
systems for improved recognition.


  We have designed a tiered security system for mobile devices where each
security tier holds user-defined security triggers and actions. It has a
friendly interface that allows users to easily define and configure the
different circumstances and actions they need according to context. The system
can be set up and activated from any browser or directly on the mobile device
itself. When the security system is operated from a Web site or server, its
configuration can be readily shared across multiple devices. When operated
directly from the mobile device, no server is needed for activation. Many
different types of security circumstances and actions can be set up and
employed from its tiers. Security circumstances can range from temporary
misplacement of a mobile device at home to malicious theft in a hostile region.
Security actions can range from ringing a simple alarm to automatically
erasing, overwriting, and re-erasing drives.


  Modern data centers are becoming increasingly equipped with RDMA-capable
NICs. These devices enable distributed systems to rely on algorithms designed
for shared memory. RDMA allows consensus to terminate within a few microsecond
in failure-free scenarios, yet, RDMA-optimized algorithms still use expensive
two-sided operations in case of failure. In this work, we present a new
leader-based consensus algorithm that relies solely on one-sided RDMA verbs.
Our algorithm is based on Paxos, it decides in a single one-sided RDMA
operation in the common case, and changes leader also in a single one-sided
RDMA operation in case of failure. We implement our algorithm in the form of an
SMR system named Velos, and we evaluated our system against the
state-of-the-art competitor Mu. Compared to Mu, our solution adds a small
overhead of approximately 0.6 microseconds in failure-free executions and
shines during failover periods during which it is 13 times faster in changing
leader.


  We consider the possibility that some supersymmetric gauge theories which are
not asymptotically free can be governed by ultraviolet-stable fixed points. If
this scenario can be realized, gaugino masses will exhibit power-law running
with scale, providing a possible solution to the supersymmetric flavor problem.
While naive perturbative calculations hint at the appearance of
ultraviolet-stable fixed points in certain theories, there are strong
constraints following from limits on the scaling dimensions of gauge-invariant
operators, positivity of central charges, and Cardy's conjectured constraint on
the flow of the Euler coefficient in the stress tensor trace anomaly. Also, we
prove that if ultraviolet-stable fixed points do exist, they cannot occur in
the perturbative regime of a renormalizable model, and that all-orders results
in the limit of large numbers of chiral superfields are necessarily
inconclusive. However, we argue that the general idea of ultraviolet-stable
fixed points in supersymmetric gauge theories is viable, and exhibit models
that can satisfy all known constraints.


  The spectral theory of the Karoubi operator due to Cuntz and Quillen is
extended to general mixed (duchain) complexes, that is, chain complexes which
are simultaneously cochain complexes. Connes' coboundary map B can be viewed as
a perturbation of the noncommutative De Rham differential d by a polynomial in
the Karoubi operator. The homological impact of such perturbations is expressed
in terms of two short exact sequences.


  Continuous-wave (CW) gravitational waves (GWs) call for
computationally-intensive methods. Low signal-to-noise ratio signals need
templated searches with long coherent integration times and thus fine
parameter-space resolution. Longer integration increases sensitivity. Low-mass
x-ray binaries (LMXBs) such as Scorpius X-1 (Sco X-1) may emit accretion-driven
CWs at strains reachable by current ground-based observatories. Binary orbital
parameters induce phase modulation. This paper describes how resampling
corrects binary and detector motion, yielding source-frame time series used for
cross-correlation. Compared to the previous, detector-frame, templated
cross-correlation method, used for Sco X-1 on data from the first Advanced LIGO
observing run (O1), resampling is about 20x faster in the costliest,
most-sensitive frequency bands. Speed-up factors depend on integration time and
search setup. The speed could be reinvested into longer integration with a
forecast sensitivity gain, 20 to 125 Hz median, of approximately 51%, or from
20 to 250 Hz, 11%, given the same per-band cost and setup. This paper's timing
model enables future setup optimization. Resampling scales well with longer
integration, and at 10x unoptimized cost could reach respectively 2.83x and
2.75x median sensitivities, limited by spin-wandering. Then an O1 search could
yield a marginalized-polarization upper limit reaching torque-balance at 100
Hz. Frequencies from 40 to 140 Hz might be probed in equal observing time with
2x improved detectors.


  In this work, we consider the problem of predicting the course of a
progressive disease, such as cancer or Alzheimer's. Progressive diseases often
start with mild symptoms that might precede a diagnosis, and each patient
follows their own trajectory. Patient trajectories exhibit wild variability,
which can be associated with many factors such as genotype, age, or sex. An
additional layer of complexity is that, in real life, the amount and type of
data available for each patient can differ significantly. For example, for one
patient we might have no prior history, whereas for another patient we might
have detailed clinical assessments obtained at multiple prior time-points. This
paper presents a probabilistic model that can handle multiple modalities
(including images and clinical assessments) and variable patient histories with
irregular timings and missing entries, to predict clinical scores at future
time-points. We use a sigmoidal function to model latent disease progression,
which gives rise to clinical observations in our generative model. We
implemented an approximate Bayesian inference strategy on the proposed model to
estimate the parameters on data from a large population of subjects.
Furthermore, the Bayesian framework enables the model to automatically
fine-tune its predictions based on historical observations that might be
available on the test subject. We applied our method to a longitudinal
Alzheimer's disease dataset with more than 3000 subjects [23] and present a
detailed empirical analysis of prediction performance under different
scenarios, with comparisons against several benchmarks. We also demonstrate how
the proposed model can be interrogated to glean insights about temporal
dynamics in Alzheimer's disease.


  We obtain nontrivial solutions for a class of double-phase problems using
Morse theory. In the absence of a direct sum decomposition, we use a
cohomological local splitting to get an estimate of the critical groups at
zero.


  Using a high resolution DM simulation of the Local Group (LG), conducted
within the framework of the Constrained Local UniversE Simulation (CLUES)
project, we investigate the nature of how satellites of the MW and M31 are
accreted. Satellites of these galaxies are accreted anisotropically, entering
the virial radius of their hosts from specific "spots" with respect to the
large scale structure. Furthermore, material which is tidally stripped from
these satellites is also, at z=0, distributed anisotropically and is
characterized by an ellipsoidal sub-volume embedded in the halo. The angular
pattern created by the locus of satellite infall points and the projected
stripped DM is investigated within a coordinate system determined by the
location of the LG companion and the simulated Virgo cluster across concentric
shells ranging from 0.1 to 5 r_vir. Remarkably, the principal axis of the
ellipsoidal sub-volume shows a coherent alignment extending from well within
the halo to a few r_vir. A spherical harmonics transform applied to the angular
distributions confirms the visual impression: namely, the angular distributions
of both the satellites entry points and stripped DM is dominated by the l=2
quadrupole term, whose major principal axis is aligned across the shells
considered. It follows that the structure of the outer (r>0.5 r_vir) regions of
the main halos is closely related to the cosmic web within which it is
embedded. Given the hypothesis that a large fraction of the stellar halo of the
Milky Way has been accreted from satellites, the present results can be applied
to the stellar halo of the MW and M31. We predict that the remnants of tidally
stripped satellites should be embedded in streams of material composed of dark
matter and stars. The present results can therefore shed light on the existence
of satellites embedded within larger streams of matter, such as the Segue 2
satellite.


  We explore a scenario of high scale supersymmetry where all supersymmetric
particles except gauginos stay at a high energy scale $M_{\rm SUSY}$ which is
much larger than the reheating temperature $T_\text{RH}$. The dark matter is
dominated by bino component with mass around the electroweak scale and the
observed relic abundance is mainly generated by the freeze-in process during
the early universe. Considering the various constraints, we identify two
available scenarios in which the supersymmetric sector at an energy scale below
$T_\text{RH}$ consists of: a) bino; b) bino and wino. Typically, for a bino
mass around 0.1-1 TeV and a wino mass around 2 TeV, we find that $M_{\rm SUSY}$
should be around $10^{12-14}$ GeV with $T_\text{RH}$ around $10^{4-6}$ GeV.


  We provide a theoretical framework for encoding arbitrary logical states of a
quantum bit (qubit) into a continuous-variable quantum mode through quantum
walks. Starting with a squeezed-vacuum state of the quantum mode, we show that
quantum walks of the state in phase space can generate output states that are
variants of codeword states originally put forward by Gottesman, Kitaev, and
Preskill (GKP) [Phys. Rev. A {\bf 64}, 012310 (2001)]. In particular, with a
coin-toss transformation that projects the quantum coin onto the diagonal
coin-state, we show that the resulting {\em dissipative} quantum walks can
generate qubit encoding akin to the prototypical GKP encoding. We analyze the
performance of these codewords for error corrections and find that even without
optimization our codewords outperform the GKP ones by a narrow margin. Using
the circuit representation, we provide a general architecture for the
implementation of this encoding scheme and discuss its possible realization
through circuit quantum-electrodynamics systems.


  We consider the problem of speaker diarization, the problem of segmenting an
audio recording of a meeting into temporal segments corresponding to individual
speakers. The problem is rendered particularly difficult by the fact that we
are not allowed to assume knowledge of the number of people participating in
the meeting. To address this problem, we take a Bayesian nonparametric approach
to speaker diarization that builds on the hierarchical Dirichlet process hidden
Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006)
1566--1581]. Although the basic HDP-HMM tends to over-segment the audio
data---creating redundant states and rapidly switching among them---we describe
an augmented HDP-HMM that provides effective control over the switching rate.
We also show that this augmentation makes it possible to treat emission
distributions nonparametrically. To scale the resulting architecture to
realistic diarization problems, we develop a sampling algorithm that employs a
truncated approximation of the Dirichlet process to jointly resample the full
state sequence, greatly improving mixing rates. Working with a benchmark NIST
data set, we show that our Bayesian nonparametric architecture yields
state-of-the-art speaker diarization results.


  Mathematical core of quantum mechanics is the theory of unitary
representations of symmetries of physical systems. We argue that quantum
behavior is a natural result of extraction of "observable" information about
systems containing "unobservable" elements in their descriptions. Since our aim
is physics where the choice between finite and infinite descriptions can not
have any empirical consequences, we consider the problem in the finite
background. Besides, there are many indications from observations - from the
lepton mixing data, for example - that finite groups underly phenomena in
particle physics at the deep level. The "finite" approach allows to reduce any
quantum dynamics to the simple permutation dynamics, and thus to express
quantum observables in terms of permutation invariants of symmetry groups and
their integer characteristics such as sizes of conjugate classes, sizes of
group orbits, class coefficients, dimensions of representations. Our study has
been accompanied by computations with finite groups, their representations and
invariants. We have used both our C implementation of algorithms for working
with groups and computer algebra system GAP.


  How should we learn visual representations for embodied agents that must see
and move? The status quo is tabula rasa in vivo, i.e. learning visual
representations from scratch while also learning to move, potentially augmented
with auxiliary tasks (e.g. predicting the action taken between two successive
observations). In this paper, we show that an alternative 2-stage strategy is
far more effective: (1) offline pretraining of visual representations with
self-supervised learning (SSL) using large-scale pre-rendered images of indoor
environments (Omnidata), and (2) online finetuning of visuomotor
representations on specific tasks with image augmentations under long learning
schedules. We call this method Offline Visual Representation Learning (OVRL).
We conduct large-scale experiments - on 3 different 3D datasets (Gibson, HM3D,
MP3D), 2 tasks (ImageNav, ObjectNav), and 2 policy learning algorithms (RL, IL)
- and find that the OVRL representations lead to significant across-the-board
improvements in state of art, on ImageNav from 29.2% to 54.2% (+25% absolute,
86% relative) and on ObjectNav from 18.1% to 23.2% (+5.1% absolute, 28%
relative). Importantly, both results were achieved by the same visual encoder
generalizing to datasets that were not seen during pretraining. While the
benefits of pretraining sometimes diminish (or entirely disappear) with long
finetuning schedules, we find that OVRL's performance gains continue to
increase (not decrease) as the agent is trained for 2 billion frames of
experience.


  Heterogeneous Graph Neural Networks (HGNNs) have gained significant
popularity in various heterogeneous graph learning tasks. However, most
existing HGNNs rely on spatial domain-based methods to aggregate information,
i.e., manually selected meta-paths or some heuristic modules, lacking
theoretical guarantees. Furthermore, these methods cannot learn arbitrary valid
heterogeneous graph filters within the spectral domain, which have limited
expressiveness. To tackle these issues, we present a positive spectral
heterogeneous graph convolution via positive noncommutative polynomials. Then,
using this convolution, we propose PSHGCN, a novel Positive Spectral
Heterogeneous Graph Convolutional Network. PSHGCN offers a simple yet effective
method for learning valid heterogeneous graph filters. Moreover, we demonstrate
the rationale of PSHGCN in the graph optimization framework. We conducted an
extensive experimental study to show that PSHGCN can learn diverse
heterogeneous graph filters and outperform all baselines on open benchmarks.
Notably, PSHGCN exhibits remarkable scalability, efficiently handling large
real-world graphs comprising millions of nodes and edges. Our codes are
available at https://github.com/ivam-he/PSHGCN.


  Adjoint functor theorems give necessary and sufficient conditions for a
functor to admit an adjoint. In this paper we prove general adjoint functor
theorems for functors between $\infty$-categories. One of our main results is
an $\infty$-categorical generalization of Freyd's classical General Adjoint
Functor Theorem. As an application of this result, we recover Lurie's adjoint
functor theorems for presentable $\infty$-categories. We also discuss the
comparison between adjunctions of $\infty$-categories and homotopy adjunctions,
and give a treatment of Brown representability for $\infty$-categories based on
Heller's purely categorical formulation of the classical Brown representability
theorem.


  We stabilized the frequencies of two independent Nd:YAG lasers to two
adjacent longitudinal modes of a high-finesse Fabry-P\'erot resonator and
obtained a beat frequency instability of 6.3 mHz at an integration time of 40
s. Referred to a single laser, this is $1.6\times10^{-17}$ relative to the
laser frequency, and $1.3\times10^{-6}$ relative to the full width at half
maximum of the cavity resonance. The amplitude spectrum of the beat signal had
a FWHM of 7.8 mHz. This stable frequency locking is of importance for
next-generation optical clock interrogation lasers and fundamental physics
tests.


  In order to characterize the global circulation of the subsurface ocean of
Jovian and Saturnian moons, we analyze the properties of 21 three-dimensional
simulations of Boussinesq thermal convection in a rapidly rotating spherical
shell. Flow is driven by an adverse temperature contrast imposed across the
domain, and is subjected to no-slip boundary conditions. We cover a region of
parameter space previously unexplored by global simulations, both in terms of
rapid rotation and vigor of convective forcing, closer to, yet still admittedly
far from, the conditions appropriate for the subsurface ocean of Ganymede,
Europa, Enceladus, and Titan. Our most extreme simulations exhibit a dynamic
global circulation that combines powerful east-west zonal jets, planetary
waves, and vortices. A spectral analysis of the kinetic energy distribution
performed in cylindrical geometry reveals a high degree of anisotropy of the
simulated flows. Specifically, the axisymmetric zonal energy spectra follow a
steep $-5$ slope in wavenumber space, with the energy amplitude exclusively
controlled by the rotation rate. In contrast, the non-axisymmetric residual
spectra display a gentle $-5/3$ slope, with the energy amplitude controlled by
the thermal buoyancy input power. This spectral behavior conforms with the
theory of zonostrophic turbulence and allows us to propose tentative
extrapolations of these findings to the more extreme conditions of icy
satellites. By assuming that kinetic energy dissipates via Ekman friction we
predict an upper bound for the zonal velocity ranging from a few centimeters
per second for Enceladus to about one meter per second for Ganymede, with
residual velocities smaller than the zonal velocity by an order of magnitude on
each moon. These predictions yield typical jets size approaching the ocean
depth of Titan, Ganymede and Europa and $10$ to $40\%$ of the ocean depth on
Enceladus.


  Deep learning systems extensively use convolution operations to process input
data. Though convolution is clearly defined for structured data such as 2D
images or 3D volumes, this is not true for other data types such as sparse
point clouds. Previous techniques have developed approximations to convolutions
for restricted conditions. Unfortunately, their applicability is limited and
cannot be used for general point clouds. We propose an efficient and effective
method to learn convolutions for non-uniformly sampled point clouds, as they
are obtained with modern acquisition techniques. Learning is enabled by four
key novelties: first, representing the convolution kernel itself as a
multilayer perceptron; second, phrasing convolution as a Monte Carlo
integration problem, third, using this notion to combine information from
multiple samplings at different levels; and fourth using Poisson disk sampling
as a scalable means of hierarchical point cloud learning. The key idea across
all these contributions is to guarantee adequate consideration of the
underlying non-uniform sample distribution function from a Monte Carlo
perspective. To make the proposed concepts applicable to real-world tasks, we
furthermore propose an efficient implementation which significantly reduces the
GPU memory required during the training process. By employing our method in
hierarchical network architectures we can outperform most of the
state-of-the-art networks on established point cloud segmentation,
classification and normal estimation benchmarks. Furthermore, in contrast to
most existing approaches, we also demonstrate the robustness of our method with
respect to sampling variations, even when training with uniformly sampled data
only. To support the direct application of these concepts, we provide a
ready-to-use TensorFlow implementation of these layers at
https://github.com/viscom-ulm/MCCNN


  Apart from the high accuracy of machine learning models, what interests many
researchers in real-life problems (e.g., fraud detection, credit scoring) is to
find hidden patterns in data; particularly when dealing with their challenging
imbalanced characteristics. Interpretability is also a key requirement that
needs to accompany the used machine learning model. In this concern, often,
intrinsically interpretable models are preferred to complex ones, which are in
most cases black-box models. Also, linear models are used in some high-risk
fields to handle tabular data, even if performance must be sacrificed. In this
paper, we introduce Self-Reinforcement Attention (SRA), a novel attention
mechanism that provides a relevance of features as a weight vector which is
used to learn an intelligible representation. This weight is then used to
reinforce or reduce some components of the raw input through element-wise
vector multiplication. Our results on synthetic and real-world imbalanced data
show that our proposed SRA block is effective in end-to-end combination with
baseline models.


  We generalize the Stein-Tomas [17] $L^2$-restricition theorem and the uniform
Sobolev estimates of Kenig, Ruiz and the second author [11] by allowing
critically singular potential. We also obtain Strichartz estimates for
Schr\"odinger and wave operators with such potentials. Due to the fact that
there may be nontrivial eigenfunctions we are required to make certain spectral
assumptions, such as assuming that the solutions only involve sufficiently
large frequencies.


  We use the theory of dynamical invariants to yield a simple derivation of
noncyclic analogues of the Abelian and non-Abelian geometric phases. This
derivation relies only on the principle of gauge invariance and elucidates the
existing definitions of the Abelian noncyclic geometric phase. We also discuss
the adiabatic limit of the noncyclic geometric phase and compute the adiabatic
non-Abelian noncyclic geometric phase for a spin 1 magnetic (or electric)
quadrupole interacting with a precessing magnetic (electric) field.


  InAs semiconductor quantum dots (QDs) emitting in the near infrared are
promising platforms for on-demand single-photon sources and spin-photon
interfaces. However, the realization of quantum-photonic nanodevices emitting
in the second and third telecom windows with similar performance remains an
open challenge. Here, we report an optimized heterostructure design for QDs
emitting in the O- and C-bands grown by means of molecular beam epitaxy. The
InAs QDs are grown on compositionally graded InGaAs buffers, which act as
virtual substrates, and are embedded in mostly relaxed active regions.
Reciprocal space maps of the indium profiles and optical absorption spectra are
used to optimize In0.22Ga0.78As and In0.30Ga0.70As active regions, accounting
for the chosen indium grading profile. This approach results in a tunable QD
photoluminescence (PL) emission from 1200 up to 1600 nm. Power and polarization
dependent micro-PL measurements performed at 4 K reveal exciton-biexciton
complexes from quantum dots emitting in the telecom O- and C-bands. The
presented study establishes a flexible platform that can be an essential
component for advanced photonic devices based on InAs/GaAs that serve as
building blocks for future quantum networks.


  Biharmonic or polyharmonic curves and surfaces in 3-dimensional contact
manifolds are investigated.


  We investigate the role of electron-phonon scattering and gate bias in
degrading the drive current of nanotube MOSFETs. Our central results are: (i)
Optical phonon scattering significantly decreases the drive current only when
gate voltage is higher than a well-defined threshold. It means that elastic
scattering mechanisms are most detrimental to nanotube MOSFETs. (ii) For
comparable mean free paths, a lower phonon energy leads to a larger degradation
of drive current. Thus for semiconducting nanowire FETs, the drive current will
be more sensitive than carbon nanotube FETs because of the smaller phonon
energies in semiconductors. (iii) Radial breathing mode phonons cause an
appreciable reduction in drive current.


  The Python package fluidsim is introduced in this article as an extensible
framework for Computational Fluid Mechanics (CFD) solvers. It is developed as a
part of FluidDyn project (Augier et al., 2018), an effort to promote
open-source and open-science collaboration within fluid mechanics community and
intended for both educational as well as research purposes. Solvers in fluidsim
are scalable, High-Performance Computing (HPC) codes which are powered under
the hood by the rich, scientific Python ecosystem and the Application
Programming Interfaces (API) provided by fluiddyn and fluidfft packages
(Mohanan et al., 2018). The present article describes the design aspects of
fluidsim, viz. use of Python as the main language; focus on the ease of use,
reuse and maintenance of the code without compromising performance. The
implementation details including optimization methods, modular organization of
features and object-oriented approach of using classes to implement solvers are
also briefly explained. Currently, fluidsim includes solvers for a variety of
physical problems using different numerical methods (including
finite-difference methods). However, this metapaper shall dwell only on the
implementation and performance of its pseudo-spectral solvers, in particular
the two- and three-dimensional Navier-Stokes solvers. We investigate the
performance and scalability of fluidsim in a state of the art HPC cluster.
Three similar pseudo-spectral CFD codes based on Python (Dedalus, SpectralDNS)
and Fortran (NS3D) are presented and qualitatively and quantitatively compared
to fluidsim. The source code is hosted at Bitbucket as a Mercurial repository
bitbucket.org/fluiddyn/fluidsim and the documentation generated using Sphinx
can be read online at fluidsim.readthedocs.io.


  As the interface between the Sun's photosphere and corona, the chromosphere
and transition region play a key role in the formation and acceleration of the
solar wind. Observations from the Interface Region Imaging Spectrograph reveal
the prevalence of intermittent small-scale jets with speeds of 80-250 km/s from
the narrow bright network lanes of this interface region. These jets have
lifetimes of 20-80 seconds and widths of 300 km or less. They originate from
small-scale bright regions, often preceded by footpoint brightenings and
accompanied by transverse waves with ~20 km/s amplitudes. Many jets reach
temperatures of at least ~100000 K and constitute an important element of the
transition region structures. They are likely an intermittent but persistent
source of mass and energy for the solar wind.


  A strong converse bound for constant composition codes of the form
$P_{e}^{(n)} \geq 1- A n^{-0.5(1-E_{sc}'(R,W,p))} e^{-n E_{sc}(R,W,p)}$ is
established using the Berry-Esseen theorem through the concepts of Augustin
information and Augustin mean, where $A$ is a constant determined by the
channel $W$, the composition $p$, and the rate $R$, i.e., $A$ does not depend
on the block length $n$.


  We show, using a Knapp-type homogeneity argument, that the $(L^p, L^2)$
restriction theorem implies a growth condition on the hypersurface in question.
We further use this result to show that the optimal $(L^p, L^2)$ restriction
theorem implies the sharp isotropic decay rate for the Fourier transform of the
Lebesgue measure carried by compact convex finite hypersurfaces.


  Multiple populations (MPs), characterized by variations in light elemental
abundances, have been found in stellar clusters in the Milky Way, Magellanic
Clouds, as well as several other dwarf galaxies. Based on a large amount of
observations, mass has been suggested to be a key parameter affecting the
presence and appearance of MPs in stellar clusters. To further investigate the
existence of MPs in low-mass clusters and explore the mass threshold for MP
formation, we carried out a project studying the stellar population composition
in several low-mass Galactic globular clusters. Here we present our study on
the cluster Eridanus. With blue-UV low-resolution spectra obtained with the
OSIRIS/Multi-object spectrograph on the Gran Telescopio Canarias, we computed
the spectral indices of CH and CN for the sample giant stars, and derived their
carbon and nitrogen abundances using model spectra. A significant dispersion in
the initial surface abundance of nitrogen was found in the sample, indicating
the existence of MPs in Eridanus. Inspecting the age-initial mass distribution
of in-situ clusters with MPs, we find a slight trend that initial mass
increases with increasing age, and the lowest initial mass of log Minitial
~4.98 and 5.26 are found at the young and old end, respectively, which might
provide a rough reference for the mass threshold for clusters to form MPs.
However, more observations of clusters with low initial masses are still
necessary before any firm conclusion can be drawn.


  We provide an elementary and self-contained derivation of formulae for
products and ratios of characteristic polynomials from classical groups using
classical results due to Weyl and Littlewood.