  The outskirts of galaxy clusters, near the virial radius, remain relatively
unexplored territory and yet are vital to our understanding of cluster growth,
structure, and mass. In this presentation, we show the first results from a
program to constrain the state of the outer intracluster medium (ICM) in a
large sample of galaxy clusters, exploiting the strengths of three
complementary X-ray observatories: Suzaku (low, stable background), XMM-Newton
(high sensitivity), and Chandra (good spatial resolution). By carefully
combining observations from the cluster core to beyond r_200, we are able to
identify and reduce systematic uncertainties that would impede our spatial and
spectral analysis using a single telescope. Our sample comprises nine clusters
at z ~ 0.1-0.2 fully covered in azimuth to beyond r_200, and our analysis
indicates that the ICM is not in hydrostatic equilibrium in the cluster
outskirts, where we see clear azimuthal variations in temperature and surface
brightness. In one of the clusters, we are able to measure the diffuse X-ray
emission well beyond r_200, and we find that the entropy profile and the gas
fraction are consistent with expectations from theory and numerical
simulations. These results stand in contrast to recent studies which point to
gas clumping in the outskirts; the extent to which differences of cluster
environment or instrumental effects factor in this difference remains unclear.
From a broader perspective, this project will produce a sizeable fiducial data
set for detailed comparison with high-resolution numerical simulations.


  We develop the effective theory for perturbations around black holes with
scalar hair, in two directions. First, we show that the scalar-Gauss--Bonnet
theory, often used as an example exhibiting scalar black hole hair, can be
deformed by galileon operators leading to order unity changes to its
predictions. The effective theory for perturbations thus provides an efficient
framework for describing and constraining broad classes of scalar-tensor
theories, of which the addition of galileon operators is an example. Second, we
extend the effective theory to perturbations around an axisymmetric, slowly
rotating black hole, at linear order in the black hole spin. We also discuss
the inclusion of parity-breaking operators in the effective theory.


  Neutrinos emitted during stellar core collapse up to their trapping phase
carry information about the stage from which the Supernova explosion process
initiates. The dominant $\nu_e$ emission mechanism is by electron capture on
free protons and f-p shell nuclei and the spectrum of these neutrinos is a
function of the ambient physical conditions within the core as well as the
nuclear equation of state. The number of collapse phase $\nu_e$ which can be
detected by Super-Kamioka and Sudbury Neutrino Observatory from a Supernova
within 1 kpc, and their generic energy spectra are given.


  We present the first dynamical simulation that recreates the major properties
of the archetypal nearby spiral galaxy M101. Our model describes a grazing but
relatively close (14 kpc) passage of the companion galaxy NGC 5474 through
M101's outer disk approximately 200 Myr ago. The passage is retrograde for both
disks, which yields a relatively strong gravitational response while
suppressing the formation of long tidal tails. The simulation reproduces M101's
overall lopsidedness, as well as the extended NE Plume and sharp western edge
of the galaxy's disk. The post-starburst populations observed in M101's NE
Plume are likely a result of star formation triggered at the point of contact
where the galaxies collided. Over time, this material will mix azimuthally,
leaving behind diffuse, kinematically coherent stellar streams in M101's outer
disk. At late times after the encounter, the density profile of M101's disk
shows a broken "upbending" profile similar to those seen in spiral galaxies in
denser environments, further demonstrating the connection between interactions
and long-term structural changes in galaxy disks.


  During the process of star formation, the dense gas undergoes significant
chemical evolution leading to the emergence of a rich variety of molecules
associated with hot cores and hot corinos. However, the physical and chemical
conditions involved in this evolution are poorly constrained. We provide here a
full inventory of the emission from complex organic molecules (COMs) to
investigate the physical structure and chemical composition of six high-mass
protostellar envelopes. We aim to investigate the conditions for the emergence
of COMs in hot cores. We performed an unbiased spectral survey towards six
infrared-quiet massive clumps between 159 GHz and 374 GHz with the APEX 12 m
telescope. We detect up to 11 COMs, of which at least five COMs are detected
towards all sources. Towards all the objects, most of the COM emission is found
to be cold, with respect to the typical temperatures at which COMs are found,
with a temperature of 30 K and extended with a size of ~0.3 pc. Although for
our sample of young massive clumps the bulk of the gas has a cold temperature,
we also detect emission from COMs originating from the immediate vicinity of
the protostar revealing a compact and hot component of the envelope. Only three
out of the six sources exhibit a hot gas component. We find a gradual emergence
of the warm component in terms of size and temperature, together with an
increasing molecular complexity, allowing us to establish an evolutionary
sequence for our sample based on COMs. Our findings confirm that our sample of
infrared-quiet massive clumps are in an early evolutionary stage during which
the bulk of the gas is cold. The presence of COMs is found to be characteristic
of these early evolutionary stages. We suggest that the emergence of hot cores
is preceded by a phase in which mostly O-bearing COMs appear first with similar
abundances to hot corinos albeit with larger source sizes.


  Building on the recent determination of the simplicial cohomology groups of
the convolution algebra ${\ell}^1({\mathbb Z}_+^k)$ [Gourdeau, Lykova, White,
2005] we investigate what can be said for cohomology of this algebra with more
general symmetric coefficients. Our approach leads us to a discussion of
Harrison homology and cohomology in the context of Banach algebras, and a
development of some of its basic features. As an application of our techniques
we reprove some known results on second-degree cohomology.


  Micromegas detectors, thanks to the good spatial and temporal discrimination
capabilities, are good candidates for rare event search experiments. Recent
X-ray background levels achieved by these detectors in the CAST experiment have
motivated further studies in the nature of the background levels measured. In
particular, different shielding configurations have been tested at the Canfranc
Underground Laboratory, using a microbulk type detector which was previously
running at the CAST experiment. The first results underground show that this
technology, which is made of low radiative materials, is able to reach
background levels up to $2 \times 10^{-7}$keV$^{-1}$s$^{-1}$cm$^{-2}$ with a
proper shielding. Moreover, the experimental background measurements are
complemented with Geant4 simulations which allow to understand the origin of
the background, and to optimize future shielding set-ups.


  Instance- and Label-dependent label Noise (ILN) widely exists in real-world
datasets but has been rarely studied. In this paper, we focus on Bounded
Instance- and Label-dependent label Noise (BILN), a particular case of ILN
where the label noise rates -- the probabilities that the true labels of
examples flip into the corrupted ones -- have upper bound less than $1$.
Specifically, we introduce the concept of distilled examples, i.e. examples
whose labels are identical with the labels assigned for them by the Bayes
optimal classifier, and prove that under certain conditions classifiers learnt
on distilled examples will converge to the Bayes optimal classifier. Inspired
by the idea of learning with distilled examples, we then propose a learning
algorithm with theoretical guarantees for its robustness to BILN. At last,
empirical evaluations on both synthetic and real-world datasets show
effectiveness of our algorithm in learning with BILN.


  Within the framework of density functional theory, we present a study of
approximations to the enhancement factor of the non-interacting kinetic energy
functional $T_s[\rho]$. For this purpose, we employ the model of Liu and Parr
[S. Liu and R.G. Parr, Phys. Rev. A {\bf 55}, 1792 (1997)] based on a series
expansion of $T_s[\rho]$ involving powers of the density. Applications to 34
atoms, at the Hartree-Fock level showed that the enhancement factors present
peaks that are in excellent agreement with those of the exact ones and give an
accurate description of the shell structure of these atoms. The application of
Z-dependent expansions to represent some of the terms of these approximation
for neutral atoms and for positive and negative ions, which allows $T_s[\rho]$
to be cast in a very simple form, is also explored. Indications are given as to
how these functionals may be applied to molecules and clusters


  In this work we focus on the properties of accretion onto galaxies. Through
numerical simulations we investigate the geometrical properties of accretion.
To span the scale range required in these simulations we have developed a new
numerical technique: the multi-zoom method. We run a series of Tree-SPH
simulations in smaller and smaller boxes at higher and higher mass resolution,
using data recorded at the previous level to account for the matter inflow and
the tidal field from outside matter. The code is parallelized using OpenMP. We
present a validation test to evaluate the robustness of the method: the pancake
collapse. We apply this new multizoom method to study the accretion properties.
Zooming in onto galaxies from a cosmological simulation, we select a sample of
10 well resolved galaxies (5000 baryonic particles or more). We sum up their
basic properties and plot a Tully-Fisher relation. We find that smooth
accretion of intergalactic cold gas dominates mergers for the mass growth of
galaxies at z < 2. Next we study the baryonic accretion rate which shows
different behaviours depending on the galaxy mass. The bias is also computed at
different radii and epochs. Then we present galactocentric angular maps for the
accretion integrated between z=2 and z=0, which reveal that accretion is highly
anisotropic. Average accretion rates plotted against galactocentric latitude
show a variety of behaviours. In general, accretion in the galactic plane is
favored, even more for baryonic matter than for dark matter.


  GaAs nanowires (NWs) grown by molecular-beam epitaxy may contain segments of
both the zincblende (ZB) and wurtzite (WZ) phases. Depending on the growth
conditions, we find that optical emission of such NWs occurs either
predominantly above or below the band gap energy of ZB GaAs [E(g,ZB)]. This
result is consistent with the assumption that the band gap energy of wurtzite
GaAs [E(g,WZ)] is larger than E(g,ZB) and that GaAs NWs with alternating ZB and
WZ segments along the wire axis establish a type II band alignment, where
electrons captured within the ZB segments recombine with holes of the
neighboring WZ segments. Thus, the corresponding transition energy depends on
the degree of confinement of the electrons, and transition energies exceeding
E(g,ZB) are possible for very thin ZB segments. At low temperatures, the
incorporation of carbon acceptors plays a major role in determining the
spectral profile as these can effectively bind holes in the ZB segments. From
cathodoluminescence measurements of single GaAs NWs performed at room
temperature, we deduce a lower bound of 55 meV for the difference
E(g,WZ)-E(g,ZB).


  Rizzo showed that the family of elliptic curves $\mathcal{W}(t)
:y^2=x^3+tx^2-(t+3)x+1$, a well-known example of Washington, has root number
$W(\mathcal{W}(t))=-1$ for all $t\in\mathbb{Z}$. In this paper we generalize
this example and identify the families of small degree on which this phenomenon
happens. Motivated by results from David, Bettin and Delaunay
(arXiv:1612.03095) and Desjardins (arXiv:1810.12787), we study in detail the
two families $\mathcal{F}_s(t):y^2=x^3+3tx^2+3sx+st$ and
$\mathcal{L}_{w,s,v}(t): wy^2=x^3+3(t^2+v)x^2+3sx+s(t^2+v)$ and describe
necessary and sufficient conditions for which subfamilies of $\mathcal{F}_s(t)$
have constant root number on integer fibres. We further prove similar but
partial results on $\mathcal{L}_{w,s,v}(t)$. Our results give examples of
subfamilies for which there is rank elevation at integer fibres.


  Gravitational wave astronomy has been firmly established with the detection
of gravitational waves from the merger of ten stellar mass binary black holes
and a neutron star binary. This paper reports on the all-sky search for
gravitational waves from intermediate mass black hole binaries in the first and
second observing runs of the Advanced LIGO and Virgo network. The search uses
three independent algorithms: two based on matched filtering of the data with
waveform templates of gravitational wave signals from compact binaries, and a
third, model-independent algorithm that employs no signal model for the
incoming signal. No intermediate mass black hole binary event was detected in
this search. Consequently, we place upper limits on the merger rate density for
a family of intermediate mass black hole binaries. In particular, we choose
sources with total masses $M=m_1+m_2\in[120,800]$M$_\odot$ and mass ratios $q =
m_2/m_1 \in[0.1,1.0]$. For the first time, this calculation is done using
numerical relativity waveforms (which include higher modes) as models of the
real emitted signal. We place a most stringent upper limit of
$0.20$~Gpc$^{-3}$yr$^{-1}$ (in co-moving units at the 90% confidence level) for
equal-mass binaries with individual masses $m_{1,2}=100$M$_\odot$ and
dimensionless spins $\chi_{1,2}= 0.8$ aligned with the orbital angular momentum
of the binary. This improves by a factor of $\sim 5$ that reported after
Advanced LIGO's first observing run.


  In a pair of recent papers (one to appear and one forthcoming), the author
develops a general version of small cancellation theory applicable in higher
dimensions, and then applies this theory to the Burnside groups of sufficiently
large exponent. The present article gives a brief introduction to the methods
and techniques involved in the proofs. Many of the ideas originate with the
recent work of A. Yu. Olshanskii and S. V. Ivanov. Some familiarity with their
work on the Burnside problem is assumed.


  In this paper, we address the problem of global-scale image geolocation,
proposing a mixed classification-retrieval scheme. Unlike other methods that
strictly tackle the problem as a classification or retrieval task, we combine
the two practices in a unified solution leveraging the advantages of each
approach with two different modules. The first leverages the EfficientNet
architecture to assign images to a specific geographic cell in a robust way.
The second introduces a new residual architecture that is trained with
contrastive learning to map input images to an embedding space that minimizes
the pairwise geodesic distance of same-location images. For the final location
estimation, the two modules are combined with a search-within-cell scheme,
where the locations of most similar images from the predicted geographic cell
are aggregated based on a spatial clustering scheme. Our approach demonstrates
very competitive performance on four public datasets, achieving new
state-of-the-art performance in fine granularity scales, i.e., 15.0% at 1km
range on Im2GPS3k.


  Effects of the mismatch between the hydrophobic length, d, of transmembrane
alpha helices of integral proteins and the hydrophobic thickness, D_h, of the
membranes they span are studied theoretically utilizing a microscopic model of
lipids. In particular, we examine the dependence of the period of a lamellar
phase on the hydrophobic length and volume fraction of a rigid, integral,
peptide. We find that the period decreases when a short peptide, such that
d<D_h, is inserted. More surprising, we find that the period increases when a
long peptide, such that d>D_h, is inserted. The effect is due to the
replacement of extensible lipid tails by rigid peptide. As the peptide length
is increased, the lamellar period continues to increase, but at a slower rate,
and can eventually decrease. The amount of peptide which fails to incorporate
and span the membrane increases with the magnitude of the hydrophobic mismatch
|d-D_h|. We explicate these behaviors which are all in accord with experiment.
Predictions are made for the dependence of the tilt of a single trans-membrane
alpha helix on hydrophobic mismatch and helix density.


  Superconductor$-$ferromagnetic-insulator heterostructures are paradigmatic
systems for studying the mutual influence of superconductivity and magnetism
via proximity effects. In particular, spin-split superconductivity is realized
in such structures. Recent experiments and theories demonstrate a rich variety
of transport phenomena occurring in devices based on such heterostructures that
suggest direct applications in thermoelectricity, low-dissipative spintronics,
radiation detection, and sensing. In this work we investigate the influence of
the electron-magnon interaction at the superconductor$-$ferromagnetic-insulator
interface on the spin-split superconductivity. It is predicted that due to the
magnon-mediated electron spin-flip processes the spin-split quasiparticle
branches are partially mixed and reconstructed, and the BCS-like spin-split
shape of the superconducting density of states, which is typical for
superconductors in the effective exchange field, is strongly modified. An
odd-frequency superconducting order parameter admixture to the leading singlet
order parameter is also found. These findings expand the physical picture of
spin-split superconductivity beyond the mean-field description of the
ferromagnet exchange field.


  Recent years have witnessed significant advancements in offline reinforcement
learning (RL), resulting in the development of numerous algorithms with varying
degrees of complexity. While these algorithms have led to noteworthy
improvements, many incorporate seemingly minor design choices that impact their
effectiveness beyond core algorithmic advances. However, the effect of these
design choices on established baselines remains understudied. In this work, we
aim to bridge this gap by conducting a retrospective analysis of recent works
in offline RL and propose ReBRAC, a minimalistic algorithm that integrates such
design elements built on top of the TD3+BC method. We evaluate ReBRAC on 51
datasets with both proprioceptive and visual state spaces using D4RL and V-D4RL
benchmarks, demonstrating its state-of-the-art performance among ensemble-free
methods in both offline and offline-to-online settings. To further illustrate
the efficacy of these design choices, we perform a large-scale ablation study
and hyperparameter sensitivity analysis on the scale of thousands of
experiments.


  Nanson's and Baldwin's voting rules select a winner by successively
eliminating candidates with low Borda scores. We show that these rules have a
number of desirable computational properties. In particular, with unweighted
votes, it is NP-hard to manipulate either rule with one manipulator, whilst
with weighted votes, it is NP-hard to manipulate either rule with a small
number of candidates and a coalition of manipulators. As only a couple of other
voting rules are known to be NP-hard to manipulate with a single manipulator,
Nanson's and Baldwin's rules appear to be particularly resistant to
manipulation from a theoretical perspective. We also propose a number of
approximation methods for manipulating these two rules. Experiments demonstrate
that both rules are often difficult to manipulate in practice. These results
suggest that elimination style voting rules deserve further study.


  Simulation of the intermediate levels of disorder found in multi-component
multi-sublattice systems in various functional materials is a challenging
issue, even for state-of-the-art methodologies based on first-principles
calculation. Here, we introduce our open-source package ab Initio Configuration
Sampling Toolkit (abICS), which combines high-throughput first-principles
calculations, machine learning, and parallel extended ensemble sampling in an
active learning setting to enable such simulations. The theoretical background
is reviewed in some detail followed by brief notes on usage of the software. In
addition, our recent applications of abICS to multi-component ionic systems and
their interfaces for energy applications are reviewed as demonstration of the
power of this approach.


  The aim of this work is to give new observational constraints on solar
surface flows by determining the horizontal scale dependence of the velocity
and intensity fields, as represented by their power spectra, and to offer some
theoretical guidelines to interpret these spectra. We use long time series of
images taken by SOT/Hinode and reconstruct both horizontal (by granule
tracking) and vertical (by Doppler effect) velocity fields in a field of view
75x75Mm^2. At small sub-granulation scales, the kinetic energy spectral density
associated with vertical motions exhibits a k^{-10/3}-like spectrum, while the
intensity fluctuation spectrum follows a k^{-3} or k^{-17/3}-like spectrum at
the two continuum levels investigated (525 and 450 nm respectively). We discuss
the physical origin of these scalings and argue that they provide a direct
observational signature of buoyancy-driven turbulent dynamics in a strongly
thermally diffusive regime. In the mesogranulation range and up to a scale of
25Mm, we find that the vertical velocity field amplitude decreases like
L^{-3/2} with the horizontal scale L. This behaviour corresponds to a k^2
spectral power law. Still in the mesoscale range, [2.5, 10]Mm, we find that
intensity fluctuations in the blue continuum follow a power law in k^2. We show
that granule tracking cannot sample scales below 2.5Mm. We locate the
supergranulation energy peak around 30Mm and show that the emergence of a pore
erases this spectral peak. Thanks to a scale height estimate, we find that
supergranular flows are shallow. (abridged abstract).


  Unmanned Aerial Vehicles (UAVs) are remote-controlled vehicles capable of
flight and are present in a variety of environments from military operations to
domestic enjoyment. These vehicles are great assets, but just as their pilot
can control them remotely, cyberattacks can be executed in a similar manner.
Cyber attacks on UAVs can bring a plethora of issues to physical and virtual
systems. Such malfunctions are capable of giving an attacker the ability to
steal data, incapacitate the UAV, or hijack the UAV. To mitigate such attacks,
it is necessary to identify and patch vulnerabilities that may be maliciously
exploited. In this paper, a new UAV vulnerability is explored with related UAV
security practices identified for possible exploitation using large streams of
data sent at specific ports. The more in-depth model involves strings of data
involving FTP-specific keywords sent to the UAV's FTP port in the form of a
fuzzing test and launching thousands of packets at other ports on the UAV as
well. During these tests, virtual and physical systems are monitored
extensively to identify specific patterns and vulnerabilities. This model is
applied to a Parrot Bebop 2, which accurately portrays a UAV that had their
network compromised by an attacker and portrays many lower-end UAV models for
domestic use. During testings, the Parrot Bebop 2 is monitored for degradation
in GPS performance, video speed, the UAV's reactivity to the pilot, motor
function, and the accuracy of the UAV's sensor data. All these points of
monitoring give a comprehensive view of the UAV's reaction to each individual
test. In this paper, countermeasures to combat the exploitation of this
vulnerability will be discussed as well as possible attacks that can branch
from the fuzzing tests.


  We investigate the Ponzano-Regge and Turaev-Viro topological field theories
using spin networks and their $q$-deformed analogues. I propose a new
description of the state space for the Turaev-Viro theory in terms of skein
space, to which $q$-spin networks belong, and give a similar description of the
Ponzano-Regge state space using spin networks.
  I give a definition of the inner product on the skein space and show that
this corresponds to the topological inner product, defined as the manifold
invariant for the union of two 3-manifolds.
  Finally, we look at the relation with the loop representation of quantum
general relativity, due to Rovelli and Smolin, and suggest that the above inner
product may define an inner product on the loop state space.


  The non-transitivity without extra constraints in the Euler equation in any
dimension is almost evident and can be derived, e.g., from Morse theory.


  Three possibilities to speed up the Hybrid Monte Carlo algorithm are
investigated. Changing the step-size adaptively brings no practical gain. On
the other hand, substantial improvements result from using an approximate
Hamiltonian or a preconditioned action.


  Computer-aided drug discovery is an essential component of modern drug
development. Therein, deep learning has become an important tool for rapid
screening of billions of molecules in silico for potential hits containing
desired chemical features. Despite its importance, substantial challenges
persist in training these models, such as severe class imbalance, high decision
thresholds, and lack of ground truth labels in some datasets. In this work we
argue in favor of directly optimizing the receiver operating characteristic
(ROC) in such cases, due to its robustness to class imbalance, its ability to
compromise over different decision thresholds, certain freedom to influence the
relative weights in this compromise, fidelity to typical benchmarking measures,
and equivalence to positive/unlabeled learning. We also propose new training
schemes (coherent mini-batch arrangement, and usage of out-of-batch samples)
for cost functions based on the ROC, as well as a cost function based on the
logAUC metric that facilitates early enrichment (i.e. improves performance at
high decision thresholds, as often desired when synthesizing predicted hit
compounds). We demonstrate that these approaches outperform standard deep
learning approaches on a series of PubChem high-throughput screening datasets
that represent realistic and diverse drug discovery campaigns on major drug
target families.


  We prove positive mass theorems on ALF manifolds, i.e. complete noncompact
manifolds that are asymptotic to a circle fibration over a Euclidean base, with
fibers of asymptotically constant length.


  Belnap-Dunn logic (BD), sometimes also known as First Degree Entailment, is a
four-valued propositional logic that complements the classical truth values of
True and False with two non-classical truth values Neither and Both. The latter
two are to account for the possibility of the available information being
incomplete or providing contradictory evidence. In this paper, we present a
probabilistic extension of BD that permits agents to have probabilistic beliefs
about the truth and falsity of a proposition. We provide a sound and complete
axiomatization for the framework defined and also identify policies for
conditionalization and aggregation. Concretely, we introduce four-valued
equivalents of Bayes' and Jeffrey updating and also suggest mechanisms for
aggregating information from different sources.


  In the context of requirements engineering, relation extraction is the task
of documenting the traceability between requirements artefacts. When dealing
with textual requirements (i.e., requirements expressed using natural
language), relation extraction becomes a cognitively challenging task,
especially in terms of ambiguity and required effort from domain-experts.
Hence, in highly-adaptive, large-scale environments, effective and efficient
automated relation extraction using natural language processing techniques
becomes essential. In this chapter, we present a comprehensive overview of
natural language-based relation extraction from text-based requirements. We
initially describe the fundamentals of requirements relations based on the most
relevant literature in the field, including the most common requirements
relations types. The core of the chapter is composed by two main sections: (i)
natural language techniques for the identification and categorization of
requirements relations (i.e., syntactic vs. semantic techniques), and (ii)
information extraction methods for the task of relation extraction (i.e.,
retrieval-based vs. machine learning-based methods). We complement this
analysis with the state-of-the-art challenges and the envisioned future
research directions. Overall, this chapter aims at providing a clear
perspective on the theoretical and practical fundamentals in the field of
natural language-based relation extraction.


  We study standing wave solutions to nonlinear Schr{\"o}dinger equations, on a
manifold with a rotational symmetry, which transform in a natural fashion under
the group of rotations. We call these vortex solutions. They are higher
dimensional versions of vortex standing waves that have been studied on the
Euclidean plane. We focus on two types of vortex solutions, which we call
spherical vortices and axial vortices.


  In this letter we discuss the supersymmetry issue of the self dual
supermembranes in (8+1) and (4+1)-dimensions. We find that all genuine
solutions of the (8+1)-dimensional supermembrane, based on the exceptional
group G_2, preserve one of the sixteen supersymmetries while all solutions in
(4+1)-dimensions preserve eight of them.


  Rattling-induced superconductivity in the {\beta}-pyrochlore oxide KOs2O6 is
investigated under high pressure up to 5 GPa. Resistivity measurements in a
high-quality single crystal reveal a gradual decrease in the superconducting
transition temperature Tc from 9.7 K at 1.0 GPa to 6.5 K at 3.5 GPa, followed
by a sudden drop to 3.3 K at 3.6 GPa. Powder X-ray diffraction experiments show
a structural transition from cubic to monoclinic or triclinic at a similar
pressure. The sudden drop in Tc is ascribed to this structural tran-sition, by
which an enhancement in Tc due to a strong electron-rattler interaction present
in the low-pressure cubic phase is abrogated as the rattling of the K ion is
completely suppressed or weakened in the high-pressure phase of reduced
symmetry. In addition, we find two anomalies in the temperature dependence of
resistivity in the low-pressure phase, which may be due to subtle changes in
rattling vibration.


  Genetic circuits are designed to implement certain logic in living cells,
keeping burden on the host cell minimal. However, manipulating the genome often
will have a significant impact for various reasons (usage of the cell machinery
to express new genes, toxicity of genes, interactions with native genes, etc.).
In this work we utilize Koopman operator theory to construct data-driven models
of transcriptomic-level dynamics from noisy and temporally sparse RNAseq
measurements. We show how Koopman models can be used to quantify impact on
genetic circuits. We consider an experimental example, using high-throughput
RNAseq measurements collected from wild-type E. coli, single gate components
transformed in E. coli, and a NAND circuit composed from individual gates in E.
coli, to explore how Koopman subspace functions encode increasing circuit
interference on E. coli chassis dynamics. The algorithm provides a novel method
for quantifying the impact of synthetic biological circuits on host-chassis
dynamics.


  We propose a search strategy for MeV-scale feebly interacting massive
particle (FIMP) dark matter (DM) at the $e^+e^-$ collider. We argue, detection
of a mono-$\gamma$ signal plus missing energy can indicate to an MeV-scale
reheating temperature of the Universe, after addressing observed DM abundance
and other relevant constraints.


  We conducted observations of 12CO(J=5-4) and dust thermal continuum emission
toward twenty star-forming galaxies on the main sequence at z~1.4 using ALMA to
investigate the properties of the interstellar medium. The sample galaxies are
chosen to trace the distributions of star-forming galaxies in diagrams of
stellar mass-star formation rate and stellar mass-metallicity. We detected CO
emission lines from eleven galaxies. The molecular gas mass is derived by
adopting a metallicity-dependent CO-to-H2 conversion factor and assuming a
CO(5-4)/CO(1-0) luminosity ratio of 0.23. Molecular gas masses and its
fractions (molecular gas mass/(molecular gas mass + stellar mass)) for the
detected galaxies are in the ranges of (3.9-12) x 10^{10} Msun and 0.25-0.94,
respectively; these values are significantly larger than those in local spiral
galaxies. The molecular gas mass fraction decreases with increasing stellar
mass; the relation holds for four times lower stellar mass than that covered in
previous studies, and that the molecular gas mass fraction decreases with
increasing metallicity. Stacking analyses also show the same trends. The dust
thermal emissions were clearly detected from two galaxies and marginally
detected from five galaxies. Dust masses of the detected galaxies are (3.9-38)
x 10^{7} Msun. We derived gas-to-dust ratios and found they are 3-4 times
larger than those in local galaxies. The depletion times of molecular gas for
the detected galaxies are (1.4-36) x 10^{8} yr while the results of the
stacking analysis show ~3 x 10^{8} yr. The depletion time tends to decrease
with increasing stellar mass and metallicity though the trend is not so
significant, which contrasts with the trends in local galaxies.


  A version of the situation calculus in which situations are represented as
first-order terms is presented. Fluents can be computed from the term
structure, and actions on the situations correspond to rewrite rules on the
terms. Actions that only depend on or influence a subset of the fluents can be
described as rewrite rules that operate on subterms of the terms in some cases.
If actions are bidirectional then efficient completion methods can be used to
solve planning problems. This representation for situations and actions is most
similar to the fluent calculus of Thielscher \cite{Thielscher98}, except that
this representation is more flexible and more use is made of the subterm
structure. Some examples are given, and a few general methods for constructing
such sets of rewrite rules are presented. This paper was submitted to FSCD 2020
on December 23, 2019.


  We introduce in this document a direct method allowing to solve numerically
inverse type problems for linear hyperbolic equations. We first consider the
reconstruction of the full solution of the wave equation posed in $\Omega\times
(0,T)$ - $\Omega$ a bounded subset of $\mathbb{R}^N$ - from a partial
distributed observation. We employ a least-squares technique and minimize the
$L^2$-norm of the distance from the observation to any solution. Taking the
hyperbolic equation as the main constraint of the problem, the optimality
conditions are reduced to a mixed formulation involving both the state to
reconstruct and a Lagrange multiplier. Under usual geometric optic conditions,
we show the well-posedness of this mixed formulation (in particular the inf-sup
condition) and then introduce a numerical approximation based on space-time
finite elements discretization. We prove the strong convergence of the
approximation and then discussed several examples for $N=1$ and $N=2$. The
problem of the reconstruction of both the state and the source term is also
addressed.


  The Majorana Demonstrator is an ultra-low background physics experiment
searching for the neutrinoless double beta decay of $^{76}$Ge. The Majorana
Parts Tracking Database is used to record the history of components used in the
construction of the Demonstrator. The tracking implementation takes a novel
approach based on the schema-free database technology CouchDB. Transportation,
storage, and processes undergone by parts such as machining or cleaning are
linked to part records. Tracking parts provides a great logistics benefit and
an important quality assurance reference during construction. In addition, the
location history of parts provides an estimate of their exposure to cosmic
radiation. A web application for data entry and a radiation exposure calculator
have been developed as tools for achieving the extreme radio-purity required
for this rare decay search.


  Quantum models based on few-mode Master equations have been a central tool in
the study of resonator quantum electrodynamics, extending the seminal
single-mode Jaynes-Cummings model to include loss and multiple modes. Despite
their broad application range, previous approaches within this framework have
either relied on a Markov approximation or a fitting procedure. By combining
ideas from pseudomode and quasinormal mode theory, we develop a certification
criterion for multi-mode effects in lossy resonators. It is based on a witness
observable, and neither requires a fitting procedure nor a Markov
approximation. Using the resulting criterion, we demonstrate that such
multi-mode effects are important for understanding previous experiments in
X-ray cavity QED with M\"ossbauer nuclei and that they allow one to tune the
nuclear ensemble properties.


  We use deep HST/ACS observations to calculate the star formation history
(SFH) of the Cetus dwarf spheroidal (dSph) galaxy. Our photometry reaches below
the oldest main sequence turn-offs, which allows us to estimate the age and
duration of the main episode of star formation in Cetus. This is well
approximated by a single episode that peaked roughly 12+/-0.5 Gyr ago and
lasted no longer than about 1.9+/-0.5 Gyr (FWHM). Our solution also suggests
that essentially no stars formed in Cetus during the past 8 Gyrs. This makes
Cetus' SFH comparable to that of the oldest Milky Way dSphs. Given the current
isolation of Cetus in the outer fringes of the Local Group, this implies that
Cetus is a clear outlier in the morphology-Galactocentric distance relation
that holds for the majority of Milky Way dwarf satellites. Our results also
show that Cetus continued forming stars through z ~ 1, long after the Universe
was reionized, and that there is no clear signature of the epoch of
reionization in Cetus' SFH. We discuss briefly the implications of these
results for dwarf galaxy evolution models. Finally, we present a comprehensive
account of the data reduction and analysis strategy adopted for all galaxies
targeted by the LCID (Local Cosmology from Isolated Dwarfs) project. We employ
two different photometry codes (DAOPHOT/ALLFRAME and DOLPHOT), three different
SFH reconstruction codes (IAC-pop/MinnIAC, MATCH, COLE), and two stellar
evolution libraries (BaSTI and Padova/Girardi), allowing for a detailed
assessment of the modeling and observational uncertainties.


  In the first part of this paper, we give a new analytical proof of a theorem
of C. Sabbah on integrable deformations of meromorphic connections on $\mathbb
P^1$ with coalescing irregular singularities of Poincar\'e rank 1, and
generalizing a previous result of B. Malgrange. In the second part of this
paper, as an application, we prove that any semisimple formal Frobenius
manifold (over $\mathbb C$), with unit and Euler field, is the completion of an
analytic pointed germ of a Dubrovin-Frobenius manifold. In other words, any
formal power series, which provides a quasi-homogenous solution of WDVV
equations and defines a semisimple Frobenius algebra at the origin, is actually
convergent under no further tameness assumptions.


  We explore the existence of $D \Xi$ and $D^* \Xi$ molecular states within the
one boson exchange model. We regularize the potential derived in this model
with a form factor and a cut-off of the order of $1\,{\rm GeV}$. To determine
the cut-off, we use the condition that the $X(3872)$ is reproduced as a pole in
the $J^{PC} = 1^{++}$ $D^*\bar{D}$ amplitude. From this we find that the $J^P=
{\frac{1}{2}}^{-}$ $D^*\,\Xi$ system is on the verge of binding and has an
unnaturally large scattering length. For the $J^P= {\frac{1}{2}}^{-}$ $D\,\Xi$
and the $J^P= {\frac{3}{2}}^{-}$ $D^*\,\Xi$ systems the attraction is not
enough to form a bound state. From heavy quark symmetry and the quark model we
can extend the previous model to the $P \Xi_{QQ}$ and $P^* \Xi_{QQ}$ systems,
with $P = D, \bar{B}$, $P^* = D^*, \bar{B}^*$ and $\Xi_{QQ} = \Xi_{cc},
\Xi_{bb}$. In this case we predict a series of triply heavy pentaquark-like
molecules.


  Dynamical correlations of the spin and the energy density are investigated in
the critical region of the random transverse-field Ising chain by numerically
exact calculations in large finite systems (L<=128). The spin-spin
autocorrelation function is found to decay proportional to (log t)^{-2x_m} and
(log t)^{-2x_m^s} in the bulk and on the surface, respectively, with x_m and
x_m^s the bulk and surface magnetization exponents, respectively. On the other
hand the critical energy-energy autocorrelation functions have a power law
decay, which are characterized by novel critical exponents eta_e~2.2 in the
bulk and eta_e^s~2.5 at the surface, respectively. The numerical results are
compared with the predictions of a scaling theory.


  During five ROSAT HRI observations in July 1995, a bright X-ray source
(designated 1RXH J132519.8-430312) was detected 2.5' south-west of the nucleus
of NGC 5128 (Centaurus A) at the outer regions of the elliptical galaxy. At
that time it was the brightest point source in the ROSAT HRI field-of-view. All
other ROSAT observations made before and after these observations show no trace
of the source suggesting that this is a highly variable object. Chandra
observations of Cen A made in September 1999 show a source at the ROSAT
position which may be the quiescent counterpart of the transient. It is very
probable that the transient is located in NGC 5128. Given the existence of a
transient source that close to the direction of NGC 5128, all observations (at
least in soft X-rays) of Cen A, with instruments of low spatial resolution,
must take the presence and variability of this object into account.


  Graph-based semantic representations are valuable in natural language
processing, where it is often simple and effective to represent linguistic
concepts as nodes, and relations as edges between them. Several attempts has
been made to find a generative device that is sufficiently powerful to
represent languages of semantic graphs, while at the same allowing efficient
parsing. We add to this line of work by introducing graph extension grammar,
which consists of an algebra over graphs together with a regular tree grammar
that generates expressions over the operations of the algebra. Due to the
design of the operations, these grammars can generate graphs with
non-structural reentrancies; a type of node-sharing that is excessively common
in formalisms such as abstract meaning representation, but for which existing
devices offer little support. We provide a parsing algorithm for graph
extension grammars, which is proved to be correct and run in polynomial time.


  While all the information required for the folding of a protein is contained
in its amino acid sequence, one has not yet learnt how to extract this
information so as to predict the detailed, biological active, three-dimensional
structure of a protein whose sequence is known. This situation is not
particularly satisfactory, in keeping with the fact that while linear
sequencing of the amino acids specifying a protein is relatively simple to
carry out, the determination of the folded-native-conformation can only be done
by an elaborate X-ray diffraction analysis performed on crystals of the protein
or, if the protein is very small, by nuclear magnetic resonance techniques.
Using insight obtained from lattice model simulations of the folding of small
proteins (fewer than 100 residues), in particular of the fact that this
phenomenon is essentially controlled by conserved contacts among strongly
interacting amino acids, which also stabilize local elementary structures
formed early in the folding process and leading to the (post-critical) folding
core when they assemble together, we have worked out a successful strategy for
reading the three-dimensional structure of a notional protein from its amino
acid sequence.


  We study strong (pathwise) approximation of Cox-Ingersoll-Ross processes. We
propose a Milstein-type scheme that is suitably truncated close to zero, where
the diffusion coefficient fails to be locally Lipschitz continuous. For this
scheme we prove polynomial convergence rates for the full parameter range
including the accessible boundary regime. The error criterion is given by the
maximal $L_p$-distance of the solution and its approximation on a compact
interval. In the particular case of a squared Bessel process of dimension
$\delta>0$ the polynomial convergence rate is given by $\min(1,\delta)/(2p)$.


  We show that, in second-order phase transformations induced by an
inhomogeneous quench, the density of topological defects is drastically
suppressed as the velocity with which the quench propagates becomes smaller
than the speed at which the front of the broken symmetry phase spreads. The
velocity of the broken symmetry phase front is approximately given by the ratio
of the healing length to relaxation time at freeze-out, that is at the instant
when the critical slowing down results in a transition from the adiabatic to
the impulse behavior in the order parameter. Experimental implications are
briefly discussed.


  In this paper we provide an abstract aproach to the study of classes of
multiple summing multilinear operators between Banach spaces. The main purpose
is unify the study of several known classes and results, for example multiple
$(p, q_1,\dots, q_n)$-summing operators, multiple mixing $(s, q, p)$-summing
operators and multiple strong $(s, q, p)$-mixing summing operators. We also
define new class of multiple summing multilinear operator that are particular
cases of our construction and, therefore, satisfy the results proved in the
paper.


  We construct several families of functors on the homotopy category of
singular Soergel bimodules that mimic cabling and insertion of column-colored
projectors. We use these functors to identify the intrinsically-colored
homology of Webster--Williamson and the projector-colored homology of
Elias--Hogancamp for an arbitrary link, up to multiplication by a polynomial in
the quantum degree $q$. Combined with the results of arXiv:2303.16271, this
establishes parity results for the intrinsic column-colored homology of
positive torus knots, partially resolving a conjecture of
Hogancamp--Rose--Wedrich in arXiv:2107.09590.


  A variable temperature neutron and synchrotron diffraction study have been
performed on the giant magnetoresistant oxypnictides LnMnAsO (Ln = La, Nd). The
low temperature magnetic structures have been studied and results show a spin
reorientation of the Mn2+ spins below TN (Nd) for NdMnAsO. The Mn2+ spins
rotate from alignment along c to alignment into the basal plane and the Mn2+
and Nd3+ moments refine to 3.54(4) \mu B and 1.93(4) \mu B respectively at 2 K.
In contrast there is no change in magnetic structure with temperature for
LaMnAsO. There is no evidence of a structural transition down to 2 K, however
discontinuities in the cell volume, Ln-O and Mn-As bond lengths are detected at
\sim 150 K for both materials. This temperature coincides with the electronic
transition previously reported and suggests a coupling between electronic and
lattice degrees of freedom.


  We discuss the possibility of same-sign dileptons as a signature for Majorana
neutrinos. The production mechanism is given by a single heavy neutrino
production and decay proton-proton -> l^{\pm} N X -> l^{\pm} l^{\pm} X'. Cross
section and distributions are presented for the LHC energies.


  We investigate the performance of gate-defined devices fabricated on high
mobility InAs metamorphic heterostructures. We find that heterostructures
capped with In$_{0.75}$Ga$_{0.25}$As often show signs of parallel conduction
due to proximity of their surface Fermi level to the conduction band minimum.
Here, we introduce a technique that can be used to estimate the density of this
surface charge that involves cool-downs from room temperature under gate bias.
We have been able to remove the parallel conduction under high positive bias,
but achieving full depletion has proven difficult. We find that by using
In$_{0.75}$Al$_{0.25}$As as the barrier without an In$_{0.75}$Ga$_{0.25}$As
capping, a drastic reduction in parallel conduction can be achieved. Our
studies show that this does not change the transport properties of the quantum
well significantly. We achieved full depletion in InAlAs capped
heterostructures with non-hysteretic gating response suitable for fabrication
of gate-defined mesoscopic devices.


  We deal with non negative functions satisfying \[ \left\{ \begin{array}{ll}
(-\Delta)^s u_s=0 & \mathrm{in}\quad C, u_s=0 & \mathrm{in}\quad
\mathbb{R}^n\setminus C, \end{array}\right. \] where $s\in(0,1)$ and $C$ is a
given cone on $\mathbb R^n$ with vertex at zero. We consider the case when $s$
approaches $1$, wondering whether solutions of the problem do converge to
harmonic functions in the same cone or not. Surprisingly, the answer will
depend on the opening of the cone through an auxiliary eigenvalue problem on
the upper half sphere. These conic functions are involved in the study of the
nodal regions in the case of optimal partitions and other free boundary
problems and play a crucial role in the extension of the
Alt-Caffarelli-Friedman monotonicity formula to the case of fractional
diffusions.


  Recent advances in reprogrammable hardware (e.g., FPGAs) and memory
technology (e.g., DDR4, HBM) promise to solve performance problems inherent to
graph processing like irregular memory access patterns on traditional hardware
(e.g., CPU). While several of these graph accelerators were proposed in recent
years, it remains difficult to assess their performance and compare them on
common graph workloads and accelerator platforms, due to few open source
implementations and excessive implementation effort.
  In this work, we build on a simulation environment for graph processing
accelerators, to make several existing accelerator approaches comparable. This
allows us to study relevant performance dimensions such as partitioning schemes
and memory technology, among others. The evaluation yields insights into the
strengths and weaknesses of current graph processing accelerators along these
dimensions, and features a novel in-depth comparison.


  The weights of a deep neural network model are optimized in conjunction with
the governing flow equations to provide a model for sub-grid-scale stresses in
a temporally developing plane turbulent jet at Reynolds number $Re_0=6\,000$.
The objective function for training is first based on the instantaneous
filtered velocity fields from a corresponding direct numerical simulation, and
the training is by a stochastic gradient descent method, which uses the adjoint
Navier--Stokes equations to provide the end-to-end sensitivities of the model
weights to the velocity fields. In-sample and out-of-sample testing on multiple
dual-jet configurations show that its required mesh density in each coordinate
direction for prediction of mean flow, Reynolds stresses, and spectra is half
that needed by the dynamic Smagorinsky model for comparable accuracy. The same
neural-network model trained directly to match filtered sub-grid-scale stresses
-- without the constraint of being embedded within the flow equations during
the training -- fails to provide a qualitatively correct prediction. The
coupled formulation is generalized to train based only on mean-flow and
Reynolds stresses, which are more readily available in experiments. The
mean-flow training provides a robust model, which is important, though a
somewhat less accurate prediction for the same coarse meshes, as might be
anticipated due to the reduced information available for training in this case.
The anticipated advantage of the formulation is that the inclusion of resolved
physics in the training increases its capacity to extrapolate. This is assessed
for the case of passive scalar transport, for which it outperforms established
models due to improved mixing predictions.


  An old problem of P. Levy is to characterize those Banach spaces which embed
isometrically in $L_p.$ We show a new criterion in terms of the second
derivative of the norm. As an application, we show that if $M$ is a twice
differentiable Orlicz function with $M'(0)=M''(0)=0$ then the $n$-dimensional
Orlicz space $\ell_M^n,\ n\ge 3,$ does not embed isometrically in $L_p$ with
$0<p\le 2.$ These results generalize and clear up the recent solution to the
1938 Schoenberg's problem on positive definite functions.


  The new coronavirus 2019 (COVID-2019) has rapidly become a pandemic and has
had a devastating effect on both everyday life, public health and the global
economy. It is critical to detect positive cases as early as possible to
prevent the further spread of this epidemic and to treat affected patients
quickly. The need for auxiliary diagnostic tools has increased as accurate
automated tool kits are not available. This paper presents a work in progress
that proposes the analysis of images of lung ultrasound scans using a
convolutional neural network. The trained model will be used on a Raspberry Pi
to predict on new images.


  We demonstrate ultrashort pulse generation from a fluoride fiber laser
co-doped with holmium and praseodymium. To date the majority of work focused on
short pulse generation from this class of fiber laser has employed loss
modulators in the cavity, both real and artificial. In this work we
alternatively employ a frequency shifting element: an acousto-optic modulator
(AOM) in the cavity. This results in mode-locked output of sub-5 ps pulses with
10 nJ of energy at a center wavelength of 2.86 {\mu}m, and a pulse repetition
frequency of 30.1 MHz, equating to a peak power of 1.9 kW. Additional
experimental investigation of the relationship between frequency shift and
cavity round trip offer insight into the complex underlying dynamics. As a
complementary mode-locking technique to conventional loss modulation, this
method of pulse formation may greatly expand the design flexibility of pulsed
mid-infrared fiber lasers.


  The main objective of this paper and the accompanying one \cite{ETZ2} is to
provide a notion of viscosity solutions for fully nonlinear parabolic
path-dependent PDEs. Our definition extends our previous work \cite{EKTZ},
focused on the semilinear case, and is crucially based on the nonlinear optimal
stopping problem analyzed in \cite{ETZ0}. We prove that our notion of viscosity
solutions is consistent with the corresponding notion of classical solutions,
and satisfies a stability property and a partial comparison result. The latter
is a key step for the wellposedness results established in \cite{ETZ2}. We also
show that the value processes of path-dependent stochastic control problems are
viscosity solutions of the corresponding path dependent dynamic programming
equation.


  We propose a Wilsonian action compatible with special geometry and higher
dimension N=2 corrections, and show that the holomorphic contribution F to the
low energy effective action is independent of the infrared cutoff. We further
show that for asymptotically free SU(2) super Yang-Mills theories, the infrared
cutoff can be tuned to cancel leading corrections to F. We also classify all
local higher-dimensional contributions to the N=2 superspace effective action
that produce corrections to the Kahler potential when reduced to N=1
superspace.


  In mixed-valence or heavy-fermion systems, the hybridization between local
$f$ orbitals and conduction band states can cause the suppression of long-range
magnetic order, which competes with strong spin fluctuations. Ce- and Yb-based
systems have been found to exhibit fascinating physical properties
(heavy-fermion superconductivity, non-Fermi-liquid states, etc.) when tuned to
the vicinity of magnetic quantum critical points by use of various external
control parameters (temperature, magnetic field, chemical composition).
Recently, similar effects (mixed-valence, Kondo fluctuations, heavy Fermi
liquid) have been reported to exist in some Eu-based compounds. Unlike Ce (Yb),
Eu has a multiple electron (hole) occupancy of its $4f$ shell, and the magnetic
Eu$^{2+}$ state ($4f^7$) has no orbital component in the usual $LS$ coupling
scheme, which can lead to a quite different and interesting physics. In the
EuCu$_{2}$(Si$_{x}$Ge$_{1-x}$)$_{2}$ series, where the valence can be tuned by
varying the Si/Ge ratio, it has been reported that a significant valence
fluctuation can exist even in the magnetic order regime. This paper presents a
detailed study of the latter material using different microscopic probes
(XANES, M\"ossbauer spectroscopy, elastic and inelastic neutron scattering), in
which the composition dependence of the magnetic order and dynamics across the
series is traced back to the change in the Eu valence state. In particular, the
results support the persistence of valence fluctuations into the
antiferromagnetic state over a sizable composition range below the critical Si
concentration $x_c \approx 0.65$. The sequence of magnetic ground states in the
series is shown to reflect the evolution of the magnetic spectral response.


  Let Lf(x)=-\Delta f(x) + V(x)f(x), V\geq 0, V\in L^1_{loc}(R^d), be a
non-negative self-adjoint Schr\"odinger operator on R^d. We say that an
L^1-function f belongs to the Hardy space H^1_L if the maximal function M_L
f(x)=\sup_{t>0} |e^{-tL} f(x)| belongs to L^1(R^d). We prove that under certain
assumptions on V the space H^1_L is also characterized by the Riesz transforms
R_j=\frac{\partial}{\partial x_j} L^{-1/2}, j=1,...,d, associated with L. As an
example of such a potential V one can take any V\geq 0, V\in L^1_{loc}, in one
dimension.


  The center of the Milky Way is a prime site to search for signals of dark
matter (DM) annihilation due to its proximity and expected high concentration
of DM. The amplification of the dispersion velocity of DM particles in the
Galactic center (GC), caused by baryonic contraction and feedback, makes this
particular region of the sky an even more promising target for exploring
velocity-dependent DM models. Here we demonstrate that current GC observations
with the H.E.S.S. telescope, presently the most sensitive TeV-scale gamma-ray
telescope in operation in this region of the sky, set the strongest constraints
on velocity-dependent annihilating DM particles with masses above 200 GeV. For
p-wave annihilations, they improve the current constraints by a factor of
$\sim$4 for a DM mass of 1 TeV. For the spatial distribution of DM, we use the
results of the latest FIRE-2 zoom cosmological simulation of Milky Way-size
halos. In addition, we utilize the newest version of the GALPROP cosmic-ray
propagation framework to simulate the Galactic diffuse gamma-ray emission in
the GC. We have found that p-wave (d-wave) DM particles with a mass of
approximately 1.7 TeV and annihilating into the $W^+$$W^-$ channel exhibit a
velocity-weighted annihilation cross-section upper limit of 4.6$\times$
10$^{-22}$ cm$^3$s$^{-1}$ (9.2$\times$10$^{-17}$ cm$^3$s$^{- 1}$) at a 95\%
confidence level. This is about 460 (2$\times$ 10$^{6}$) times greater than the
thermal relic cross-section for p-wave (d-wave) DM models.


  The use of Pade approximants for the description of QCD matrix elements is
discussed in this talk. We will see how they prove to be an extremely useful
tool, specially in the case of resonant amplitudes. It will allow the inclusion
of high-energy Euclidian data to improve the determination of low-energy
properties, such as the quadratic vector radius. This does not mean that the
rational approximations can be arbitrarily employed for the extraction of any
desired hadronic parameter. A discussion about the validity, limitations and
possible issues of the Pade analysis is carried on along the paper. Finally,
based on the de Montessus de Ballore's theorem, a theoretically safe new
procedure for the extraction of the pole mass and width of resonances is
proposed here and illustrated with the example of the rho(770).


  The exact ground state of a strongly interacting quantum many-body system can
be obtained by evolving a trial state with finite overlap with the ground state
to infinite imaginary time. In this work, we use a newly discovered fourth
order positive factorization scheme which requires knowing both the potential
and its gradients. We show that the resultaing fourth order wave function
alone, without further iterations, gives an excellent description of strongly
interacting quantum systems such as liquid 4He, comparable to the best
variational results in the literature.


  The (In,Fe)Sb layers with the Fe content up to 13 at. % have been grown on
(001) GaAs substrates using the pulsed laser deposition. The TEM investigations
show that the (In,Fe)Sb layers are epitaxial and free of the inclusions of a
second phase. The observation of the hysteretic magnetoresistance curves at
temperatures up to 300 K reveals that the Curie point is above room
temperature. The resonant character of magnetic circular dichroism confirms the
intrinsic ferromagnetism in the (In,Fe)Sb layers. We suggest that the
ferromagnetism of the (In,Fe)Sb matrix is not carrier-mediated and apparently
is determined by the mechanism of superexchange interaction between Fe atoms
(This work was presented at the XXI Symposium Nanophysics and Nanoelectronics,
Nizhny Novgorod, March, 13-16, 2017 (book of proceedings v.1, p. 195),
http://nanosymp.ru/UserFiles/Symp/2017_v1.pdf).


  Layout analysis is a task of uttermost importance in ancient handwritten
document analysis and represents a fundamental step toward the simplification
of subsequent tasks such as optical character recognition and automatic
transcription. However, many of the approaches adopted to solve this problem
rely on a fully supervised learning paradigm. While these systems achieve very
good performance on this task, the drawback is that pixel-precise text labeling
of the entire training set is a very time-consuming process, which makes this
type of information rarely available in a real-world scenario. In the present
paper, we address this problem by proposing an efficient few-shot learning
framework that achieves performances comparable to current state-of-the-art
fully supervised methods on the publicly available DIVA-HisDB dataset.


  We determine those k-tuples of conjugacy classes of matrices, from which it
is possible to choose matrices which have no common invariant subspace and have
sum zero. This is an additive version of the Deligne-Simpson problem. We deduce
the result from earlier work of ours on preprojective algebras and the moment
map for representations of quivers. Our answer depends on the root system for a
Kac-Moody Lie algebra.


  In reinforcement learning for visual navigation, it is common to develop a
model for each new task, and train that model from scratch with task-specific
interactions in 3D environments. However, this process is expensive; massive
amounts of interactions are needed for the model to generalize well. Moreover,
this process is repeated whenever there is a change in the task type or the
goal modality. We present a unified approach to visual navigation using a novel
modular transfer learning model. Our model can effectively leverage its
experience from one source task and apply it to multiple target tasks (e.g.,
ObjectNav, RoomNav, ViewNav) with various goal modalities (e.g., image, sketch,
audio, label). Furthermore, our model enables zero-shot experience learning,
whereby it can solve the target tasks without receiving any task-specific
interactive training. Our experiments on multiple photorealistic datasets and
challenging tasks show that our approach learns faster, generalizes better, and
outperforms SoTA models by a significant margin.


  Temporal graph neural networks (temporal GNNs) have been widely researched,
reaching state-of-the-art results on multiple prediction tasks. A common
approach employed by most previous works is to apply a layer that aggregates
information from the historical neighbors of a node. Taking a different
research direction, in this work, we propose tBDFS -- a novel temporal GNN
architecture. tBDFS applies a layer that efficiently aggregates information
from temporal paths to a given (target) node in the graph. For each given node,
the aggregation is applied in two stages: (1) A single representation is
learned for each temporal path ending in that node, and (2) all path
representations are aggregated into a final node representation. Overall, our
goal is not to add new information to a node, but rather observe the same exact
information in a new perspective. This allows our model to directly observe
patterns that are path-oriented rather than neighborhood-oriented. This can be
thought as a Depth-First Search (DFS) traversal over the temporal graph,
compared to the popular Breath-First Search (BFS) traversal that is applied in
previous works. We evaluate tBDFS over multiple link prediction tasks and show
its favorable performance compared to state-of-the-art baselines. To the best
of our knowledge, we are the first to apply a temporal-DFS neural network.


  Recent advances in Deep Gaussian Processes (DGPs) show the potential to have
more expressive representation than that of traditional Gaussian Processes
(GPs). However, there exists a pathology of deep Gaussian processes that their
learning capacities reduce significantly when the number of layers increases.
In this paper, we present a new analysis in DGPs by studying its corresponding
nonlinear dynamic systems to explain the issue. Existing work reports the
pathology for the squared exponential kernel function. We extend our
investigation to four types of common stationary kernel functions. The
recurrence relations between layers are analytically derived, providing a
tighter bound and the rate of convergence of the dynamic systems. We
demonstrate our finding with a number of experimental results.


  Beautiful theories of magnetic hysteresis based on random microscopic
disorder have been developed over the past ten years. Our goal was to directly
compare these theories with precise experiments. We first developed and then
applied coherent x-ray speckle metrology to a series of thin multilayer
perpendicular magnetic materials. To directly observe the effects of disorder,
we deliberately introduced increasing degrees of disorder into our films. We
used coherent x-rays to generate highly speckled magnetic scattering patterns.
The apparently random arrangement of the speckles is due to the exact
configuration of the magnetic domains in the sample. In effect, each speckle
pattern acts as a unique fingerprint for the magnetic domain configuration.
Small changes in the domain structure change the speckles, and comparison of
the different speckle patterns provides a quantitative determination of how
much the domain structure has changed. How is the magnetic domain configuration
at one point on the major hysteresis loop related to the configurations at the
same point on the loop during subsequent cycles? The microscopic return-point
memory(RPM) is partial and imperfect in the disordered samples, and completely
absent when the disorder was not present. We found the complementary-point
memory(CPM) is also partial and imperfect in the disordered samples and
completely absent when the disorder was not present. We found that the RPM is
always a little larger than the CPM. We also studied the correlations between
the domains within a single ascending or descending loop. We developed new
theoretical models that do fit our experiments.


  We present a geometrically enhanced Markov chain Monte Carlo sampler for
networks based on a discrete curvature measure defined on graphs. Specifically,
we incorporate the concept of graph Forman curvature into sampling procedures
on both the nodes and edges of a network explicitly, via the transition
probability of the Markov chain, as well as implicitly, via the target
stationary distribution, which gives a novel, curved Markov chain Monte Carlo
approach to learning networks. We show that integrating curvature into the
sampler results in faster convergence to a wide range of network statistics
demonstrated on deterministic networks drawn from real-world data.


  Periods of moduli spaces of stable sheaves on K3 surfaces were computed by
Mukai, O'Grady and the author. In this paper, we shall treat moduli spaces of
stable sheaves on abelian surfaces.


  Ordinal pattern dependence is a multivariate dependence measure based on the
co-movement of two time series. In strong connection to ordinal time series
analysis, the ordinal information is taken into account to derive robust
results on the dependence between the two processes. This article deals with
ordinal pattern dependence for long-range dependent time series including mixed
cases of short- and long-range dependence. We investigate the limit
distributions for estimators of ordinal pattern dependence. In doing so we
point out the differences that arise for the underlying time series having
different dependence structures. Depending on these assumptions, central and
non-central limit theorems are proven. The limit distributions for the latter
ones can be included in the class of multivariate Rosenblatt processes.
Finally, a simulation study is provided to illustrate our theoretical findings.


  The paper describes in detail the recovery effort of one of the official
MediaWiki grammars. Over two hundred grammar transformation steps are reported
and annotated, leading to delivery of a level 2 grammar, semi-automatically
extracted from a community created semi-formal text using at least five
different syntactic notations, several non-enforced naming conventions,
multiple misspellings, obsolete parsing technology idiosyncrasies and other
problems commonly encountered in grammars that were not engineered properly.
Having a quality grammar will allow to test and validate it further, without
alienating the community with a separately developed grammar.


  We determine the nonlinear stability of shock-fronted travelling waves
arising in a reaction-nonlinear diffusion PDE, subject to a fourth-order
spatial derivative term multiplied by a small parameter $\varepsilon$ that
models {\it nonlocal regularization}. Motivated by the authors' recent
stability analysis of shock-fronted travelling waves under viscous relaxation,
our numerical analysis is guided by the observation that there is a fast-slow
decomposition of the associated eigenvalue problem for the linearised operator.
In particular, we observe an astonishing reduction of the complex
four-dimensional eigenvalue problem into a {\it real} one-dimensional problem
defined along the slow manifolds; i.e. slow eigenvalues defined near the tails
of the shock-fronted wave for $\varepsilon = 0$ govern the point spectrum of
the linearised operator when $0 < \varepsilon \ll 1$.


  The development of four-dimensional (4D) scanning transmission electron
microscopy (STEM) using fast detectors has opened-up new avenues for addressing
some of long-standing challenges in electron imaging. One of these challenges
is how to image severely distorted crystal lattices, such as at a dislocation
core. Here we introduce a new 4D-STEM technique, called Cepstral STEM, for
imaging disordered crystals using electron diffuse scattering. Local
fluctuations of diffuse scattering are captured by scanning electron
nanodiffraction (SEND) using a coherent probe. The harmonic signals in electron
diffuse scattering are detected through Cepstral analysis and used for imaging.
By integrating Cepstral analysis with 4D-STEM, we demonstrate that information
about the distortive part of electron scattering potential can be separated and
imaged at nm spatial resolution. We apply our technique to the analysis of a
dislocation core in SiGe and lattice distortions in high entropy alloy.


  In this note we make a comment on the paper by Jarlskog (math-ph/0504049) in
which she gave a parametrisation to unitary matrices. Namely, we show that her
recursive method is essentially obtained by the canonical coordinate of the
second kind in the Lie group theory, and propose a problem on constructing
unitary gates in quantum computation by making use of the parametrisation.


  This study investigates the spin susceptibility in U-based superconductor
UTe$_2$ in the superconducting (SC) state by using Knight shift measurements
for a magnetic field $H$ along the $a$ axis, which is the magnetic easy axis of
UTe$_2$. Although a tiny anomaly ascribed to the SC diamagnetic effect was
observed just below the SC transition temperature $T_{\rm c}$, the $a$-axis
Knight shift in the SC state shows no significant decrease, following the
extrapolation from the normal-state temperature dependence. This indicates that
the spin susceptibility is nearly unchanged below $T_{\rm c}$. Considering the
previous Knight shift results for $H \parallel b$ and $H \parallel c$, the
dominant SC state is determined to be $B_{\rm 3u}$ in the spin-triplet pairing,
which is consistent with the spin anisotropy in the normal state. The present
result shows that UTe$_2$ is a spin-triplet superconductor with spin degrees of
freedom.


  Spontaneous collapse models and Bohmian mechanics are two different solutions
to the measurement problem plaguing orthodox quantum mechanics. They have, a
priori nothing in common. At a formal level, collapse models add a non-linear
noise term to the Schr\"odinger equation, and extract definite measurement
outcomes either from the wave function (e.g. mass density ontology) or the
noise itself (flash ontology). Bohmian mechanics keeps the Schr\"odinger
equation intact but uses the wave function to guide particles (or fields),
which comprise the primitive ontology. Collapse models modify the predictions
of orthodox quantum mechanics, whilst Bohmian mechanics can be argued to
reproduce them. However, it turns out that collapse models and their primitive
ontology can be exactly recast as Bohmian theories. More precisely, considering
(i) a system described by a non-Markovian collapse model, and (ii) an extended
system where a carefully tailored bath is added and described by Bohmian
mechanics, the stochastic wave-function of the collapse model is exactly the
wave-function of the original system conditioned on the Bohmian hidden
variables of the bath. Further, the noise driving the collapse model is a
linear functional of the Bohmian variables. The randomness that seems
progressively revealed in the collapse models lies entirely in the initial
conditions in the Bohmian-like theory. Our construction of the appropriate bath
is not trivial and exploits an old result from the theory of open quantum
systems. This reformulation of collapse models as Bohmian theories brings to
the fore the question of whether there exists `unromantic' realist
interpretations of quantum theory that cannot ultimately be rewritten this way,
with some guiding law. It also points to important foundational differences
between `true' (Markovian) collapse models and non-Markovian models.


  Benkart, Sottile, and Stroomer have completely characterized by Knuth and
dual Knuth equivalence a bijective proof of the conjugation symmetry of the
Littlewood-Richardson coefficients. Tableau-switching provides an algorithm to
produce such a bijective proof. Fulton has shown that the White and the
Hanlon-Sundaram maps are versions of that bijection. In this paper one exhibits
explicitly the Yamanouchi word produced by that conjugation symmetry map which
on its turn leads to a new and very natural version of the same map already
considered independently. A consequence of this latter construction is that
using notions of Relative Computational Complexity we are allowed to show that
this conjugation symmetry map is linear time reducible to the Schutzenberger
involution and reciprocally. Thus the Benkart-Sottile-Stroomer conjugation
symmetry map with the two mentioned versions, the three versions of the
commutative symmetry map, and Schutzenberger involution, are linear time
reducible to each other. This answers a question posed by Pak and Vallejo.


  We describe a Lohner-type algorithm for the computation of rigorous upper
bounds for reachable set for control systems, solutions of ordinary
differential inclusions and perturbations of ODEs.


  Facing a global epidemic of new infectious diseases such as COVID-19,
non-pharmaceutical interventions (NPIs), which reduce transmission rates
without medical actions, are being implemented around the world to mitigate
spreads. One of the problems in assessing the effects of NPIs is that different
NPIs have been implemented at different times based on the situation of each
country; therefore, few assumptions can be shared about how the introduction of
policies affects the patient population. Mathematical models can contribute to
further understanding these phenomena by obtaining analytical solutions as well
as numerical simulations. In this study, an NPI was introduced into the SIR
model for a conceptual study of infectious diseases under the condition that
the transmission rate was reduced to a fixed value only once within a finite
time duration, and its effect was analyzed numerically and theoretically. It
was analytically shown that the maximum fraction of infected individuals and
the final size could be larger if the intervention starts too early. The
analytical results also suggested that more individuals may be infected at the
peak of the second wave with a stronger intervention. This study provides
quantitative relationship between the strength of a one-shot intervention and
the reduction in the number of patients with no approximation. This suggests
the importance of the strength and time of NPIs, although detailed studies are
necessary for the implementation of NPIs in complicated real-world environments
as the model used in this study is based on various simplifications.


  Facial expression recognition is a challenging task when neural network is
applied to pattern recognition. Most of the current recognition research is
based on single source facial data, which generally has the disadvantages of
low accuracy and low robustness. In this paper, a neural network algorithm of
facial expression recognition based on multimodal data fusion is proposed. The
algorithm is based on the multimodal data, and it takes the facial image, the
histogram of oriented gradient of the image and the facial landmarks as the
input, and establishes CNN, LNN and HNN three sub neural networks to extract
data features, using multimodal data feature fusion mechanism to improve the
accuracy of facial expression recognition. Experimental results show that,
benefiting by the complementarity of multimodal data, the algorithm has a great
improvement in accuracy, robustness and detection speed compared with the
traditional facial expression recognition algorithm. Especially in the case of
partial occlusion, illumination and head posture transformation, the algorithm
also shows a high confidence.


  We obtained near-infrared spectra of 26 SDSS quasars at $0.7<z<2.5$ with
reported rest-frame ultraviolet $M_{\rm BH} \sim 10^{10}M_{\odot}$ to
critically examine the systematic effects involved with their mass estimations.
We find that AGNs heavier than $10^{10}M_{\odot}$ often display double-peaked
H$\alpha$ emission, extremely broad FeII complex emission around MgII, and
highly blueshifted and broadened CIV emission. The weight of this evidence,
combined with previous studies, cautions against the use of $M_{\rm BH}$ values
based on any emission line with a width over 8000 km/s. Also, the $M_{\rm BH}$
estimations are not positively biased along the presence of ionized narrow line
outflows, anisotropic radiation, or the use of line FWHM instead of $\sigma$
for our sample, and unbiased with variability, scatter in broad line equivalent
width, or obscuration for general type-1 quasars. Removing the systematically
uncertain $M_{\rm BH}$ values, $\sim10^{10}M_{\odot}$ BHs in $1\lesssim z
\lesssim 2$ AGNs can still be explained by anisotropic motion of the broad line
region from $\sim10^{9.5}M_{\odot}$ BHs, although current observations support
they are intrinsically most massive, and overmassive to the host's bulge mass.


  We investigate higher order effects in electromagnetic dissociation of
neutron halo nuclei using a simple and realistic zero range model for the
neutron-core interaction. In the sudden (or Glauber) approximation all orders
in the target-core interaction are taken into account. Small deviations from
the sudden approximation are readily calculated. We obtain very simple
analytical results for the next-to-leading order effects, which have a simple
physical interpretation. For intermediate energy electromagnetic dissociation,
higher order effects are generally small. We apply our model to Coulomb
dissociation of $^{19}$C at 67 A MeV. The analytical results are compared to
numerical results from the integration of the time-dependent Schroedinger
equation. Good agreement is obtained. We conclude that higher order
electromagnetic effects are well under control.


  The pandemic has affected every facet of human life. Apart from individuals
psychological and mental health issues, the concern regarding mobility, access
and communication with high risk infection is a challenging situation. People
with disability are more likely vulnerable to infections. The new changes in
our social lifestyle (social distancing, limiting touch) can profoundly impact
the day today life of people with disability. In this paper, we will briefly
discuss the situation faced by individuals with disabilities, some known
remedies, and yet to be identified and curated technological remedies; the
impact due to transition of special education toward online mode. Tips and
tricks for better utilization of work from home concept by people with
disabilities. Accessibility must be universal, accommodating all and
encouraging inclusivity. As rightly said by Helen Keller, 'The only thing worse
than being blind is having sight but no vision'; subsequently, going by the
demand of the time, we should contribute toward the universal design approach
by supporting people with disabilities and commit to the changes required in
disability care to reduce the impact of pandemic. Keywords: Disabilities,
pandemic, corona virus, inclusive


  A muon storage ring neutrino source uses an induction linac for muon capture,
a 200 MHz copper cavity system for cooling, and 200 and 400 MHz superconducting
rf systems for acceleration to a final energy of up to 50 GeV. These systems
and their requirements are discussed in this paper.


  Recent results have demonstrated that samplers constructed with flow-based
generative models are a promising new approach for configuration generation in
lattice field theory. In this paper, we present a set of methods to construct
flow models for targets with multiple separated modes (i.e. theories with
multiple vacua). We demonstrate the application of these methods to modeling
two-dimensional real scalar field theory in its symmetry-broken phase. In this
context we investigate the performance of different flow-based sampling
algorithms, including a composite sampling algorithm where flow-based proposals
are occasionally augmented by applying updates using traditional algorithms
like HMC.


  Supersymmetry exhibts new sources of CP violation. We discuss the
implications of these new contributions to CP violation both in the K and B
physics. We show that CP violation puts severe constraints on low energy SUSY,
but it represents also a promising ground to look for signals of new physics.


  Recent approaches to word sense disambiguation (WSD) utilize encodings of the
sense gloss (definition), in addition to the input context, to improve
performance. In this work we demonstrate that this approach can be adapted for
use in multiword expression (MWE) identification by training models which use
gloss and context information to filter MWE candidates produced by a rule-based
extraction pipeline. Our approach substantially improves precision,
outperforming the state-of-the-art in MWE identification on the DiMSUM dataset
by up to 1.9 F1 points and achieving competitive results on the PARSEME 1.1
English dataset. Our models also retain most of their WSD performance, showing
that a single model can be used for both tasks. Finally, building on similar
approaches using Bi-encoders for WSD, we introduce a novel Poly-encoder
architecture which improves MWE identification performance.


  In medical research, it is often needed to obtain subgroups with
heterogeneous survivals, which have been predicted from a prognostic factor.
For this purpose, a binary split has often been used once or recursively;
however, binary partitioning may not provide an optimal set of well separated
subgroups. We propose a multi-way partitioning algorithm, which divides the
data into K heterogeneous subgroups based on the information from a prognostic
factor. The resulting subgroups show significant differences in survival. Such
a multi-way partition is found by maximizing the minimum of the subgroup
pairwise test statistics. An optimal number of subgroups is determined by a
permutation test. Our developed algorithm is compared with two binary recursive
partitioning algorithms. In addition, its usefulness is demonstrated with a
real data of colorectal cancer cases from the Surveillance Epidemiology and End
Results program. We have implemented our algorithm into an R package maps,
which is freely available in the Comprehensive R Archive Network (CRAN).


  A novel method for characterising the full spectrum of deuteron ions emitted
by laser driven multi-species ion sources is discussed. The procedure is based
on using differential filtering over the detector of a Thompson parabola ion
spectrometer, which enables discrimination of deuterium ions from heavier ion
species with the same charge-to-mass ratio (such as C6+, O8+, etc.). Commonly
used Fuji Image plates were used as detectors in the spectrometer, whose
absolute response to deuterium ions over a wide range of energies was
calibrated by using slotted CR-39 nuclear track detectors. A typical deuterium
ion spectrum diagnosed in a recent experimental campaign is presented.


  Implicit neural representation (INR) is a popular approach for
arbitrary-scale image super-resolution (SR), as a key component of INR,
position encoding improves its representation ability. Motivated by position
encoding, we propose orthogonal position encoding (OPE) - an extension of
position encoding - and an OPE-Upscale module to replace the INR-based
upsampling module for arbitrary-scale image super-resolution. Same as INR, our
OPE-Upscale Module takes 2D coordinates and latent code as inputs; however it
does not require training parameters. This parameter-free feature allows the
OPE-Upscale Module to directly perform linear combination operations to
reconstruct an image in a continuous manner, achieving an arbitrary-scale image
reconstruction. As a concise SR framework, our method has high computing
efficiency and consumes less memory comparing to the state-of-the-art (SOTA),
which has been confirmed by extensive experiments and evaluations. In addition,
our method has comparable results with SOTA in arbitrary scale image
super-resolution. Last but not the least, we show that OPE corresponds to a set
of orthogonal basis, justifying our design principle.


  We present an efficient quasi-Newton orbital solver optimized to reduce the
number of gradient (Fock matrix) evaluations. The solver optimizes orthogonal
orbitals by sequences of unitary rotations generated by the (preconditioned)
limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm
incorporating trust-region step restriction. Low-rank structure of the inverse
(approximate) Hessian is exploited not only in L-BFGS but also when solving the
trust-region problem. The efficiency of the proposed ``Quasi-Newton Unitary
Optimization with Trust-Region'' (QUOTR) method is compared to that of the
standard Roothaan-Hall approach accelerated by the Direct Inversion of
Iterative Subspace (DIIS), and other exact and approximate Newton solvers for
mean-field (Hartree-Fock and Kohn-Sham) problems.


  The difficulty of measuring and controlling suction in unsaturated soils is
one of the reasons why the development of the mechanics of unsaturated soils
has not been as advanced as that of saturated soils. However, significant
developments have been carried out in the last decade in this regard. In this
paper, a re-view of some developments carried out in the techniques of
controlling suction by using the axis translation, the osmotic method and the
vapour control technique is presented. The paper also deals with some recent
de-velopments in the direct measurement of suction by using high capacity
tensiometers and in the measurement of high suction by using high range
psychrometers. The recent progresses made in these techniques have been
significant and will certainly help further experimental investigation of the
hydromechanical behaviour of un-saturated soils.


  In a prime number decomposition of integers in a given set, the occurrence
frequencies of prime numbers are shown to satisfy a general forms of Zipf's
law.


  We have searched for the decay $B^+\to\omega l^+\nu$ ($l=e$ or $\mu$) in 78
fb$^{-1}$ of $\Upsilon(4S)$ data (85 million $B\bar B$ events) accumulated with
the Belle detector. The final state is fully reconstructed using the $\omega$
decay into $\pi^+\pi^-\pi^0$, combined with detector hermeticity to estimate
the neutrino momentum. A signal of $414\pm 125$ events is found in the data,
corresponding to a branching fraction of $(1.3\pm 0.4\pm 0.2\pm 0.3)\times
10^{-4}$, where the first two errors are statistical and systematic,
respectively. The third error reflects the estimated form-factor uncertainty.


  Radio loud active galactic nuclei (RLAGNs) are often morphologically complex
objects that can consist of multiple, spatially separated, components.
Astronomers often rely on visual inspection to resolve radio component
association. However, applying visual inspection to all the hundreds of
thousands of well-resolved RLAGNs that appear in the images from the Low
Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) at $144$ MHz, is a
daunting, time-consuming process, even with extensive manpower.
  Using a machine learning approach, we aim to automate the radio component
association of large ($> 15$ arcsec) radio components.
  We turned the association problem into a classification problem and trained
an adapted Fast region-based convolutional neural network to mimic the expert
annotations from the first LoTSS data release. We implemented a rotation data
augmentation to reduce overfitting and simplify the component association by
removing unresolved radio sources that are likely unrelated to the large and
bright radio components that we consider using predictions from an existing
gradient boosting classifier.
  For large ($> 15$ arcsec) and bright ($> 10$ mJy) radio components in the
LoTSS first data release, our model provides the same associations for
$85.3\%\pm0.6$ of the cases as those derived when astronomers perform the
association manually. When the association is done through public crowd-sourced
efforts, a result similar to that of our model is attained.
  Our method is able to efficiently carry out manual radio-component
association for huge radio surveys and can serve as a basis for either
automated radio morphology classification or automated optical host
identification. This opens up an avenue to study the completeness and
reliability of samples of radio sources with extended, complex morphologies.


  This paper investigates risk bounds for quantile additive trend filtering, a
method gaining increasing significance in the realms of additive trend
filtering and quantile regression. We investigate the constrained version of
quantile trend filtering within additive models, considering both fixed and
growing input dimensions. In the fixed dimension case, we discover an error
rate that mirrors the non-quantile minimax rate for additive trend filtering,
featuring the main term $n^{-2r/(2r+1)}V^{2/(2r+1)}$, when the underlying
quantile function is additive, with components whose $(r-1)$th derivatives are
of bounded variation by $V$. In scenarios with a growing input dimension $d$,
quantile additive trend filtering introduces a polynomial factor of
$d^{(2r+2)/(2r+1)}$. This aligns with the non-quantile variant, featuring a
linear factor $d$, particularly pronounced for larger $r$ values. Additionally,
we propose a practical algorithm for implementing quantile trend filtering
within additive models, using dimension-wise backfitting. We conduct
experiments with evenly spaced data points or data that samples from a uniform
distribution in the interval $[0,1]$, applying distinct component functions and
introducing noise from normal and heavy-tailed distributions. Our findings
confirm the estimator's convergence as $n$ increases and its superiority,
particularly in heavy-tailed distribution scenarios. These results deepen our
understanding of additive trend filtering models in quantile settings, offering
valuable insights for practical applications and future research.


  We demonstrate that future radius measurement of the NICER mission have the
potential to reveal the existence of a strong phase transition in dense neutron
star matter by confirming the existence of so called twin stars, compact star
configurations with the same mass but different radii. The latest radius
constraints from NICER for the pulsars J0740+6620 as well as J0030+0451 are
discussed using relativistic mean field equations of state with varying
stiffness, connected with a first order phase transition to quark matter. We
show, that twin star solutions are compatible with the new radius constraint
but are located at radii \emph{below} the present constraints from NICER
serving as a smoking gun for a strong phase transition in neutron star matter.
This scenario is realized if a strong phase transition takes place in neutron
stars of the first branch with masses above $2\,M_\odot$.


  Given a domain Y in a complex manifold X, it is a difficult problem with no
general solution to determine whether Y has a schlicht envelope of holomorphy
in X, and if it does, to describe the envelope. The purpose of this paper is to
tackle the problem with the help of a smooth 1-dimensional foliation F of X
with no compact leaves. We call a domain Y in X an interval domain with respect
to F if Y intersects every leaf of F in a nonempty connected set. We show that
if X is Stein and if F satisfies a new property called quasiholomorphicity,
then every interval domain in X has a schlicht envelope of holomorphy, which is
also an interval domain. This result is a generalization and a global version
of a well-known lemma from the mid-1980s. We illustrate the notion of
quasiholomorphicity with sufficient conditions, examples, and counterexamples,
and present some applications, in particular to a little-studied boundary
regularity property of domains called local schlichtness.


  Indigenous languages are historically under-served by Natural Language
Processing (NLP) technologies, but this is changing for some languages with the
recent scaling of large multilingual models and an increased focus by the NLP
community on endangered languages. This position paper explores ethical
considerations in building NLP technologies for Indigenous languages, based on
the premise that such projects should primarily serve Indigenous communities.
We report on interviews with 17 researchers working in or with Aboriginal
and/or Torres Strait Islander communities on language technology projects in
Australia. Drawing on insights from the interviews, we recommend practices for
NLP researchers to increase attention to the process of engagements with
Indigenous communities, rather than focusing only on decontextualised
artefacts.


  We present our Agent-Based Market Microstructure Simulation (ABMMS), an
Agent-Based Financial Market (ABFM) that captures much of the complexity
present in the US National Market System for equities (NMS). Agent-Based models
are a natural choice for understanding financial markets. Financial markets
feature a constrained action space that should simplify model creation, produce
a wealth of data that should aid model validation, and a successful ABFM could
strongly impact system design and policy development processes. Despite these
advantages, ABFMs have largely remained an academic novelty. We hypothesize
that two factors limit the usefulness of ABFMs. First, many ABFMs fail to
capture relevant microstructure mechanisms, leading to differences in the
mechanics of trading. Second, the simple agents that commonly populate ABFMs do
not display the breadth of behaviors observed in human traders or the trading
systems that they create. We investigate these issues through the development
of ABMMS, which features a fragmented market structure, communication
infrastructure with propagation delays, realistic auction mechanisms, and more.
As a baseline, we populate ABMMS with simple trading agents and investigate
properties of the generated data. We then compare the baseline with
experimental conditions that explore the impacts of market topology or
meta-reinforcement learning agents. The combination of detailed market
mechanisms and adaptive agents leads to models whose generated data more
accurately reproduce stylized facts observed in actual markets. These
improvements increase the utility of ABFMs as tools to inform design and policy
decisions.