  The purpose of this paper is twofold. Firstly, we conduct an in-depth
analysis of mathematical modeling concerning thermal-mechanical curved beams,
by taking into consideration three primary forces widely accepted in the
literature: axial load, shear force, and bending moment. Additionally, we
examine their appropriate thermal couplings, shedding light on the intricate
interplay between stress-strain relationships and temperature variations. This
analysis is situated within the well-recognized context of the Bresse governing
model for arched beams. Secondly, drawing upon distinguished constitutive laws
for heat flux of conduction, we compile a comprehensive list of thermoelastic
curved beam systems in various scenarios. We introduce new categories of
problems that exhibit specific features from the thermal point of view.


  As a result of the quantum, wave-like nature of the physical world, a
harmonic oscillator can never be completely at rest. Even in the quantum ground
state, its position will always have fluctuations, called the zero-point
motion. Although the zero-point fluctuations are unavoidable, they can be
manipulated. In this work, using microwave frequency radiation pressure, we
both prepare a micron-scale mechanical system in a state near the quantum
ground state and then manipulate its thermal fluctuations to produce a
stationary, quadrature-squeezed state. We deduce that the variance of one
motional quadrature is 0.80 times the zero-point level, or 1 dB of
sub-zero-point squeezing. This work is relevant to the quantum engineering of
states of matter at large length scales, the study of decoherence of large
quantum systems, and for the realization of ultra-sensitive sensing of force
and motion.


  Geometric phases play a central role in a variety of quantum phenomena,
especially in condensed matter physics. Recently, it was shown that this
fundamental concept exhibits a connection to quantum phase transitions where
the system undergoes a qualitative change in the ground state when a control
parameter in its Hamiltonian is varied. Here we report the first experimental
study using the geometric phase as a topological test of quantum transitions of
the ground state in a Heisenberg XY spin model. Using NMR interferometry, we
measure the geometric phase for different adiabatic circuits that do not pass
through points of degeneracy.


  We investigate stability of Fredholm properties on interpolation scales of
quasi-Banach spaces. This analysis is motivated by problems arising in PDE's
and several applications are presented.


  Current status and results of the experiment on recording neutrino bursts are
presented. The observation time (since 1980) is 24.7 years. The upper bound of
collapse frequency in our Galaxy is 0.093 $y^{-1}$ (90% CL).


  We want to introduce another smoothing approach by treating each geometric
element as a player in a game: a quest for the best element quality. In other
words, each player has the goal of becoming as regular as possible. The set of
strategies for each element is given by all translations of its vertices.
Ideally, he would like to quantify this regularity using a quality measure
which corresponds to the utility function in game theory. Each player is aware
of the other players' utility functions as well as their set of strategies,
which is analogous to his own utility function and strategies. In the simplest
case, the utility functions only depend on the regularity. In more complicated
cases this utility function depends on the element size, the curvature, or even
the solution to a differential equation. This article is a sketch of a possible
game-theoretical approach to mesh smoothing and still on-going research.


  In the course of a long-term project investigating classical novae with large
outburst amplitudes, we have performed optical spectroscopy of several old-nova
candidates. We here present the spectra of the candidates V630 Sgr, XX Tau, CQ
Vel, V842 Cen, and V529 Ori, that hitherto lacked such classification. While
the first four show typical spectra for cataclysmic variables, and can thus be
identified as such, V529 Ori is probably misclassified.
  Of special interest are the two systems XX Tau and V842 Cen, which show all
signs for being low mass transfer systems. As such they can be used to judge
the evolution scenarios for novae. In particular, given the rather young age of
their outburst, it appears more likely that these systems are not on their way
into hibernation, (i.e., cutting off mass transfer for a longer period of
time), but are simply settling down towards their original configuration of
comparatively low, but steady, mass transfer, such as for dwarf novae.


  We present a deep color magnitude diagram for the galactic globular cluster
NGC 2808 (C0911-646), reaching 3 magnitudes below the main sequence turnoff. A
set of local photometric standards are provided, and the mean cluster reddening
and metallicity are constrained using several photometric techniques. The
unusual horizontal branch morphology discovered by Sosin et al. (1997, ApJ,
480, L35) is confirmed. No significant radial population gradients are
identified to within the limits of the data. We investigate the redwards color
spread on the main sequemce found by Ferraro et al. (1997, A&A, 327, 598) in a
field centered 6.3 arcmin from the cluster center, attributed to binaries. We
conclude that the spread is in the most part due to a non-Gaussian distribution
of photometric errors about the main sequence ridgeline.


  We introduce "fertility Wilf equivalence," "strong fertility Wilf
equivalence," and "postorder Wilf equivalence," three variants of Wilf
equivalence for permutation classes that formalize some phenomena that have
appeared in the study of West's stack-sorting map. We introduce "sliding
operators" and show that they induce useful bijections among sets of valid hook
configurations. Combining these maps with natural decompositions of valid hook
configurations, we give infinitely many examples of fertility, strong
fertility, and postorder Wilf equivalences. As a consequence, we obtain
infinitely many joint equidistribution results concerning many permutation
statistics. In one very special case, we reprove and extensively generalize a
result of Bouvel and Guibert. Another case reproves and generalizes a result of
the current author. A separate very special case proves and generalizes a
conjecture of the current author concerning stack-sorting preimages and the
Boolean-Catalan numbers. We end with two open questions.


  As societies around the world are ageing, the number of Alzheimer's disease
(AD) patients is rapidly increasing. To date, no low-cost, non-invasive
biomarkers have been established to advance the objectivization of AD diagnosis
and progression assessment. Here, we utilize Bayesian neural networks to
develop a multivariate predictor for AD severity using a wide range of
quantitative EEG (QEEG) markers. The Bayesian treatment of neural networks both
automatically controls model complexity and provides a predictive distribution
over the target function, giving uncertainty bounds for our regression task. It
is therefore well suited to clinical neuroscience, where data sets are
typically sparse and practitioners require a precise assessment of the
predictive uncertainty. We use data of one of the largest prospective AD EEG
trials ever conducted to demonstrate the potential of Bayesian deep learning in
this domain, while comparing two distinct Bayesian neural network approaches,
i.e., Monte Carlo dropout and Hamiltonian Monte Carlo.


  A simple "trick" is proposed, which allows to perform exactly the
site-averaging procedure required when developing integral equation theories of
interaction site models of macromolecular fluids. It shows that no
approximation is involved when the number of Ornstein-Zernike equations
coupling the site-site correlation functions is reduced to one. Its potential
practical interest for future theoretical developments is illustrated with a
rederivation of the so-called molecular closures.


  For about three years, it was known that precision spectrophotometry with
FORS2 suffered from systematic errors that made quantitative observations of
planetary transits impossible. We identified the Longitudinal Atmospheric
Dispersion Compensator (LADC) as the most likely culprit, and therefore engaged
in a project to exchange the LADC prisms with the uncoated ones from FORS1.
This led to a significant improvement in the depth of FORS2 zero points, a
reduction in the systematic noise, and should make FORS2 again competitive for
transmission spectroscopy of exoplanets.


  Several existing and successful full reference image quality assessment (IQA)
models use linear color transformation and downsampling before measuring
similarity or quality of images. This paper indicates to the right order of
these two procedures and that the existing models have not chosen the more
efficient approach. In addition, efficiency of these metrics is not compared in
a fair basis in the literature.


  This paper considers "geometric" ideal triangulations of cusped hyperbolic
3-manifolds, i.e. decompositions into positive volume ideal hyperbolic
tetrahedra. We exhibit infinitely many geometric ideal triangulations of the
figure eight knot complement. As far as we know, this is the first construction
of infinitely many geometric triangulations of a cusped hyperbolic 3-manifold.
In contrast, our approach does not extend to the figure eight sister manifold,
and it is unknown if there are infinitely many geometric triangulations for
this manifold.


  Physical layer security (PLS) is critically important for emerging wireless
communication networks to maintain the confidentiality of the information of
legitimate users. In this paper, we investigate enhancing PLS in an unmanned
aerial vehicle (UAV) based communication network where a UAV acting as an
aerial base station (BS) provides coverage in a densely packed user area (such
as a stadium or a concert area). In particular, non-orthogonal multiple access
(NOMA) together with highly-directional multi-antenna transmission techniques
in mmWave frequency bands are utilized for improving spectral efficiency. In
order to achieve PLS against potential eavesdropper attacks, we introduce a
protected zone around the user region. However, limited resource availability
refrain protected zone being extended to cover the entire eavesdropper region.
Hence, we propose an approach to optimize the protected zone shape (for fixed
area) at each UAV-BS hovering altitude. The associated secrecy performance is
evaluated considering the secrecy outage and sum secrecy rates. Numerical
results reveal the importance of protected zone shape optimization at each
altitude to maximize NOMA secrecy rates.


  Beta regression models are a suitable choice for continuous response
variables on the unity interval. Random effects add further flexibility to the
models and accommodate data structures such as hierarchical, repeated measures
and longitudinal, which typically induce extra variability and/or dependence.
Closed expressions cannot be obtained for parameter estimation and numerical
methods are required and possibly combined with sampling algorithms. We focus
on likelihood inference and related algorithms for the analysis of beta mixed
models motivated by two real problems with grouped data structures. The first
is a study on a life quality index of industry workers with data collected
according to an hierarchical sampling scheme. The second is a study with a
nested and longitudinal data structure assessing the impact of hydroelectric
power plants upon measures of water quality indexes up, downstream and at the
reservoirs of the dammed rivers. Relevant scientific hypothesis are
investigated by comparing alternative models. The analysis uses different
algorithms including data-cloning, an alternative to numerical approximations
which also assess identifiability. Confidence intervals based on profiled
likelihoods are compared to those obtained by asymptotic quadratic
approximations, showing relevant differences for parameters related to the
random effects.


  We introduce an efficient and exact algorithm, together with a faster but
approximate version, which implements with a sub-quadratic complexity the
hold-out derived from T-estimation. We study empirically the performance of
this hold-out in the context of density estimation considering well-known
competitors (hold-out derived from least-squares or Kullback-Leibler
divergence, model selection procedures, etc.) and classical problems including
histogram or bandwidth selection. Our algorithms are integrated in a companion
R-package called {\it Density.T.HoldOut} available on the CRAN:
{\url{http://cran.r-project.org/web/packages/Density.T.HoldOut/index.html}}.


  Spatial quantum coherence between two separated driven-dissipative polariton
condensates created non-resonantly and with a different occupation is studied.
We identify the regions where the condensates remain coherent with the phase
difference continuously changing with the pumping imbalance and the regions
where each condensate acquires its own chemical potential with phase
differences exhibiting time-dependent oscillations. We show that in the mutual
coherence limit the coupling consists of two competing contributions: a
symmetric Heisenberg exchange and the Dzyloshinskii-Moriya asymmetric
interactions that enable a continuous tuning of the phase relation across the
dyad and derive analytic expressions for these types of interactions. The
introduction of non-equal pumping increases the complexity of the type of the
problems that can be solved by polariton condensates arranged in a graph
configuration. If equally pumped polaritons condensates arrange their phases to
solve the constrained quadratic minimisation problem with a real symmetric
matrix, the non-equally pumped condensates solve that problem for a general
Hermitian matrix.


  We reconsider QCD factorization for the leading power contribution to the
$\gamma^{\ast} \gamma \to \pi^0$ form factor $F_{\gamma^{\ast} \gamma \to
\pi^0} (Q^2)$ at one loop using the evanescent operator approach, and
demonstrate the equivalence of the resulting factorization formulae derived
with distinct prescriptions of $\gamma_5$ in dimensional regularization.
Applying the light-cone QCD sum rules (LCSRs) with photon distribution
amplitudes (DAs) we further compute the subleading power contribution to the
pion-photon form factor induced by the "hadronic" component of the real photon
at the next-to-leading-order in ${\cal O}(\alpha_s)$, with both naive
dimensional regularization and 't Hooft-Veltman schemes of $\gamma_5$.
Confronting our theoretical predictions of $F_{\gamma^{\ast} \gamma \to \pi^0}
(Q^2)$ with the experimental measurements from the BaBar and the Belle
Collaborations implies that a reasonable agreement can be achieved without
introducing an "exotic" end-point behaviour for the twist-2 pion DA.


  One of the main applications of free probability is to show that for
appropriately chosen independent copies of $d$ random matrix models, any
noncommutative polynomial in these $d$ variables has a spectral distribution
that converges asymptotically and can be described with the help of free
probability. This paper aims to show that this can be extended to
noncommutative rational functions, answering an open question by Roland
Speicher. This paper also provides a noncommutative probability approach to
approximating the free field. At the algebraic level, its construction relies
on the approximation by generic matrices. On the other hand, it admits many
embeddings in the algebra of operators affiliated with a $II_1$ factor. A
consequence of our result is that, as soon as the generators admit a random
matrix model, the approximation of any self-adjoint noncommutative rational
function by generic matrices can be upgraded at the level of convergence in
distribution.


  (abridged) We review methods for measuring the sizes, line widths, and
luminosities of giant molecular clouds (GMCs) in molecular-line data cubes with
low resolution and sensitivity. We find that moment methods are robust and
sensitive -- making full use of both position and intensity information -- and
we recommend a standard method to measure the position angle, major and minor
axis sizes, line width, and luminosity using moment methods. Without
corrections for the effects of beam convolution and sensitivity to GMC
properties, the resulting properties may be severely biased. This is
particularly true for extragalactic observations, where resolution and
sensitivity effects often bias measured values by 40% or more. We correct for
finite spatial and spectral resolutions with a simple deconvolution and we
correct for sensitivity biases by extrapolating properties of a GMC to those we
would expect to measure with perfect sensitivity. The resulting method recovers
the properties of a GMC to within 10% over a large range of resolutions and
sensitivities, provided the clouds are marginally resolved with a peak
signal-to-noise ratio greater than 10. We note that interferometers
systematically underestimate cloud properties, particularly the flux from a
cloud. The degree of bias depends on the sensitivity of the observations and
the (u,v) coverage of the observations. In the Appendix to the paper we present
a conservative, new decomposition algorithm for identifying GMCs in
molecular-line observations. This algorithm treats the data in physical rather
than observational units, does not produce spurious clouds in the presence of
noise, and is sensitive to a range of morphologies. As a result, the output of
this decomposition should be directly comparable among disparate data sets.


  Objectives: Present a novel deep learning-based skull stripping algorithm for
magnetic resonance imaging (MRI) that works directly in the information rich
k-space.
  Materials and Methods: Using two datasets from different institutions with a
total of 36,900 MRI slices, we trained a deep learning-based model to work
directly with the complex raw k-space data. Skull stripping performed by HD-BET
(Brain Extraction Tool) in the image domain were used as the ground truth.
  Results: Both datasets were very similar to the ground truth (DICE scores of
92\%-98\% and Hausdorff distances of under 5.5 mm). Results on slices above the
eye-region reach DICE scores of up to 99\%, while the accuracy drops in regions
around the eyes and below, with partially blurred output. The output of k-strip
often smoothed edges at the demarcation to the skull. Binary masks are created
with an appropriate threshold.
  Conclusion: With this proof-of-concept study, we were able to show the
feasibility of working in the k-space frequency domain, preserving phase
information, with consistent results. Future research should be dedicated to
discovering additional ways the k-space can be used for innovative image
analysis and further workflows.


  The motivation and the current status of top-down models as sources of
ultrahigh energy cosmic rays (UHECR) are reviewed. Stimulated by the AGASA
excess, they were proposed as the main source of UHECRs beyond the GZK cutoff.
Meanwhile searches for their signatures have limited their contribution to the
UHECR flux to be subdominant, while the theoretical motivation for these
searches remained strong: Topological defects are a generic consequence of
Grand Unified Theories and superheavy particles are a creditable dark matter
candidate. While Fermi/GLAST results should help to improve soon bounds on
topological defects from the diffuse gamma-ray background, the most promising
detection method are UHE neutrino searches. Superheavy dark matter can be
restricted or detected by its characteristic galactic anisotropy combined with
searches for UHE photons.


  In this work, we estimate the depth in which domestic waste are located in
space from a mobile robot in outdoor scenarios. As we are doing this calculus
on a broad range of space (0.3 - 6.0 m), we use RGB-D camera and LiDAR fusion.
With this aim and range, we compare several methods such as average, nearest,
median and center point, applied to those which are inside a reduced or
non-reduced Bounding Box (BB). These BB are obtained from segmentation and
detection methods which are representative of these techniques like Yolact,
SOLO, You Only Look Once (YOLO)v5, YOLOv6 and YOLOv7. Results shown that,
applying a detection method with the average technique and a reduction of BB of
40%, returns the same output as segmenting the object and applying the average
method. Indeed, the detection method is faster and lighter in comparison with
the segmentation one. The committed median error in the conducted experiments
was 0.0298 ${\pm}$ 0.0544 m.


  Given a compact oriented 3-manifold M in S^3 with boundary, an (M,2n)-tangle
T is a 1-manifold with 2n boundary components properly embedded in M. We say
that T embeds in a link L in S^3 if T can be completed to L by a 1-manifold
with 2n boundary components exterior to M. The link L is called a closure of T.
We define the Kauffman bracket ideal of T to be the ideal I_T generated by the
reduced Kauffman bracket polynomials of all closures of T. If this ideal is
non-trivial, then T does not embed in the unknot. We give an algorithm for
computing a finite list of generators for the Kauffman bracket ideal of any
(S^1 x D^2, 2)-tangle, also called a genus-1 tangle, and give an example of a
genus-1 tangle with non-trivial Kauffman bracket ideal. Furthermore, we show
that if a single-component genus-1 tangle S can be obtained as the partial
closure of a (B^3, 4)-tangle T, then I_T = I_S.


  An analysis is performed of instanton configurations in pure Euclidean
Yang-Mills theory containing small Lorentz-violating perturbations that
maintain gauge invariance. Conventional topological arguments are used to show
that the general classification of instanton solutions involving the
topological charge is the same as in the standard case. Explicit solutions are
constructed for general gauge invariant corrections to the action that are
quadratic in the curvature. The value of the action is found to be unperturbed
to lowest order in the Lorentz-violating parameters.


  We show that a topological investigation of the phase space of a
Semiconductor Ring Laser can be used to devise switching schemes which are
alternative to optical pulse injection of counter-propagating light. To provide
physical insight in these switching mechanisms, a full bifurcation analysis and
an investigation of the topology is performed on a two-dimensional asymptotic
model. Numerical simulations confirm the topological predictions.


  We examine a quantum dot with $N_{\rm dot}$ levels which is strongly coupled
to leads for varying number of channels $N$ in the leads. It is shown both
analytically and numerically that for strong couplings between the dot and the
leads, at least $N_{\rm dot}-N$ bound states (akin to subradiant states in
optics) remain on the dot. These bound states exhibit discrete charging and,
for a significant range of charging energies, strong Coulomb blockade behavior
as function of the chemical potential. The physics changes for large charging
energy where the same (superradiant) state is repeatedly charged.


  Let $G \curvearrowright A$ be an action of a discrete group on a unital
$C^*$-algebra by $*$-automorphisms. In this note, we give two sufficient
dynamical conditions for the $C^*$-inclusion $A \subseteq A \rtimes_r G$ to be
norming in the sense of Pop, Sinclair, and Smith. As a consequence of our
results, when $A$ is separable or simple, the inclusion $A \subseteq A
\rtimes_r G$ is norming provided it has a unique pseudo-expectation in the
sense of Pitts.


  We present a calculation of the NLO QCD corrections for Higgs-boson pair
production in gluon fusion including effects of anomalous couplings within
Standard Model Effective Field Theory (SMEFT). We study effects of different
truncation options of the EFT expansion in $1/\Lambda$ and of double operator
insertions, both at total cross-section level as well as for the distribution
of the invariant mass of the Higgs-boson pair, at $\sqrt{s}=13$ TeV. The NLO
corrections are implemented in the generator $\texttt{ggHH_SMEFT}$ in the
$\texttt{Powheg-Box-V2}$ framework.


  In early dark energy (EDE) resolution of Hubble tension, the spectral index
$n_s$ of primordial scalar perturbation follows a scaling relation ${\delta
n_s}\simeq 0.4\frac{\delta H_0}{H_0}$, where $H_0$ is the Hubble constant.
However, this $n_s-H_0$ relation was obtained based on the datasets including
Planck cosmic microwave background (CMB) data. In this paper, we investigate
this scaling relation with Planck-independent CMB data, i.e. ACT and SPT-3G
combined with WMAP(+BAO+Pantheon), respectively. Our results show that the
WMAP+SPT-3G dataset also follows this scaling relation, while the WMAP+ACT
dataset seems to favor smaller $n_s$, which is related to the fact that the
critical redshift $z_c$, at which EDE is excited, favored by the WMAP+ACT
dataset is lower and closer to the recombination time.


  This paper examines the role that regularization plays in the definition of
the potential used in effective field theory (EFT) treatments of the
nucleon-nucleon interaction. I consider $NN$ scattering in $S$-wave channels at
momenta well below the pion mass. In these channels (quasi-)bound states are
present at energies well below the scale $m_\pi^2/M$ expected from naturalness
arguments. I ask whether, in the presence of such a shallow bound state, there
is a regularization scheme which leads to an EFT potential that is both useful
and systematic. In general, if a low-lying bound state is present then cutoff
regularization leads to an EFT potential which is useful but not systematic,
and dimensional regularization with minimal subtraction leads to one which is
systematic but not useful. The recently-proposed technique of dimensional
regularization with power-law divergence subtraction allows the definition of
an EFT potential which is both useful and systematic.


  In this paper, based on the basic principles of thermodynamics, we explore
the hydrodynamic regime of interacting Lifshitz field theories in the presence
of broken rotational invariance. We compute the entropy current and discover
new dissipative effects those are consistent with the principle of local
entropy production in the fluid. In our analysis, we consider both the parity
even as well as the parity odd sector upto first order in the derivative
expansion. Finally, we argue that the present construction of the paper could
be systematically identified as that of the hydrodynamic description associated
with \textit{spin waves} (away from the domain of quantum criticality) under
certain limiting conditions.


  For many decades scholars converse how to correctly include the Old
Babylonian Empire into the absolute timeline of history. A cuneiform text from
the series Enuma Anu Enlil (EAE #20) reports on the destruction of Babylon
after a lunar and solar eclipse. Eclipses provide a great tool for examining
historic events, and this account will be our basis for the investigation of
eclipse pairs to be fitted into the various chronologies proposed. We consider
three interpretations of that text: literal understanding, the inverted
sequence of the two eclipse types, and a relocation of the setting to Akkad.
All variants show imperfections. The least complications emerge when the
account draws upon the third option, i.e. if it is allotted to 2161 BCE, as the
year would mark the end of the Gutian rule in Akkad. But in any case the
account seems to support rather the Long Chronology than any other. When
dealing with eclipses such far back in time, we also allow for the shift of the
visibility zone in regard to the irregular deceleration of Earth's rotation.


  In this letter, we consider transceivers with spatially-constrained antenna
apertures of rectangular symmetry, and aim to improve of spatial degrees of
freedom (DoF) and channel capacity leveraging evanescent waves for information
transmission in near-field scenarios based on the Fourier plane-wave series
expansion. The treatment is limited to an isotropic scattering environment but
can be extended to the non-isotropic case through the linear-system theoretic
interpretation of plane-wave propagation. Numerical results show that
evanescent waves have the significant potential to provide additional DoF and
capacity in the near-field region.


  In a review on the contribution of J.B.S. Haldane to the development of the
Bayes factor hypothesis test (arXiv:1511.08180), Etz and Wagenmakers focus on
Haldane's proposition of a mixture prior in a genetic example (Haldane 1932, A
note on inverse probability. Mathematical Proceedings of the Cambridge
Philosophical Society, 28, 55-61.). As Haldane never followed up on these
ideas, it is difficult to gauge his motivation and intentions. I argue that
contrary to Haldane's stated intention of replacing flat priors with more
reasonable assumptions, he actually chose in this example an unreasonable flat
prior. Considering the information available to Haldane, I derive a superior
prior and compare to Haldane's flat prior. Haldane's main intent with his
article seems to have been to explore the different parameter regions of the
binomial and the conjugate beta. Furthermore, I agree with Etz and Wagenmakers
that Haldane serendipitously adopted a mixture prior comprising a point mass
and smooth distribution in his genetic example.


  An autonomous computer system (such as a robot) typically needs to identify,
locate, and track persons appearing in its sight. However, most solutions have
their limitations regarding efficiency, practicability, or environmental
constraints. In this paper, we propose an effective and practical system which
combines video and inertial sensors for person identification (PID). Persons
who do different activities are easy to identify. To show the robustness and
potential of our system, we propose a walking person identification (WPID)
method to identify persons walking at the same time. By comparing features
derived from both video and inertial sensor data, we can associate sensors in
smartphones with human objects in videos. Results show that the correctly
identified rate of our WPID method can up to 76% in 2 seconds.


  Krasnosselsky's art gallery theorem gives a combinatorial characterization of
star-shaped sets in Euclidean spaces, similar to Helly's characterization of
finite families of convex sets with non-empty intersection. We study colorful
and quantitative variations of Krasnosselsky's result. In particular, we are
interested in conditions on a set $K$ that guarantee there exists a measurably
large set $K'$ such that every point in $K'$ can see every point in $K$. We
prove results guaranteeing the existence of $K'$ with large volume or large
diameter.


  We present the effective lagrangian for low energy and momentum spin waves in
canted phases at next to leading order in the derivative expansion. The
symmetry breaking pattern SU(2) --> 1 of the internal spin group and that of
the crystallographic space group imply that there is one ferromagnetic and one
antiferromagnetic spin wave. The interaction of the spin waves with the charge
carriers is also discussed for canted, ferromagnetic and antiferromagnetic
phases. All this together allows us to write the doping dependence of the
dispersion relation parameters for doped manganites. We point out that the spin
waves posses distinctive characteristics which may allow us to experimentally
differentiate canted phases from phase separation regions in doped manganites.


  Single spin asymmetry in inclusive $\pi^0$-production was measured in the
polarized target fragmentation region using 50 GeV proton beam. The asymmetry
is in agreement with asymmetry measurements in the polarized beam fragmentation
region carried out at higher energies. The measurement completed 30-years
history of polarized measurements at the PROZA setup.


  We investigate the properties of objects in the Subaru Deep Field (SDF),
using public catalogs constructed from images in several optical passbands.
Using a small subset of objects most likely to be stars, we construct a stellar
locus in three-dimensional color space. We then compare the position of all
objects relative to this locus to create larger samples of stars in the SDF
with rough spectral types. The number counts of stars defined in this way are
consistent with those of current models of the Galaxy.


  Deep neural networks have become ubiquitous for applications related to
visual recognition and language understanding tasks. However, it is often
prohibitive to use typical neural networks on devices like mobile phones or
smart watches since the model sizes are huge and cannot fit in the limited
memory available on such devices. While these devices could make use of machine
learning models running on high-performance data centers with CPUs or GPUs,
this is not feasible for many applications because data can be privacy
sensitive and inference needs to be performed directly "on" device.
  We introduce a new architecture for training compact neural networks using a
joint optimization framework. At its core lies a novel objective that jointly
trains using two different types of networks--a full trainer neural network
(using existing architectures like Feed-forward NNs or LSTM RNNs) combined with
a simpler "projection" network that leverages random projections to transform
inputs or intermediate representations into bits. The simpler network encodes
lightweight and efficient-to-compute operations in bit space with a low memory
footprint. The two networks are trained jointly using backpropagation, where
the projection network learns from the full network similar to apprenticeship
learning. Once trained, the smaller network can be used directly for inference
at low memory and computation cost. We demonstrate the effectiveness of the new
approach at significantly shrinking the memory requirements of different types
of neural networks while preserving good accuracy on visual recognition and
text classification tasks. We also study the question "how many neural bits are
required to solve a given task?" using the new framework and show empirical
results contrasting model predictive capacity (in bits) versus accuracy on
several datasets.


  The mechanical and magnetic properties of the newly discovered MAB-phase
class of materials based upon AlFe2B2 were investigated. The samples were
synthesised from stoichiometric amounts of all constituent elements. X-ray
diffraction shows that the main phase is orthorhombic with an elongated b-axis,
similar to AlFe2B2. The low hardness and visual inspection of the samples after
deformation indicate that these compounds are deformed via a delamination
process. When substituting iron in AlFe2B2 with manganese, the magnetism in the
system goes from being ferro- to antiferromagnetic via a disordered
ferrimagnetic phase exhibited by AlFeMnB2. Density functional theory
calculations indicate a weakening of the magnetic interactions among the
transitions metal ions as iron is substituted by manganese in AlFe2B2. The
Mn-Mn exchange interactions in AlMn2 B2 are found to be very small.


  We report on the optimized production of a Bose-Einstein condensate of cesium
atoms using an optical trapping approach. Based on an improved trap loading and
evaporation scheme we obtain more than $10^5$ atoms in the condensed phase. To
test the tunability of the interaction in the condensate we study the expansion
of the condensate as a function of scattering length. We further excite strong
oscillations of the trapped condensate by rapidly varying the interaction
strength.


  We have developed a novel method to evaluate the potential profile of a
molecular motor at each chemical state from only the probe's trajectory and
applied it to a rotary molecular motor F$_1$-ATPase. By using this method, we
could also obtain the information regarding the mechanochemical coupling and
energetics. We demonstrate that the position-dependent transition of the
chemical states is the key feature for the highly efficient free-energy
transduction by F$_1$-ATPase.


  Estimating accurate and reliable fruit and vegetable counts from images in
real-world settings, such as orchards, is a challenging problem that has
received significant recent attention. Estimating fruit counts before harvest
provides useful information for logistics planning. While considerable progress
has been made toward fruit detection, estimating the actual counts remains
challenging. In practice, fruits are often clustered together. Therefore,
methods that only detect fruits fail to offer general solutions to estimate
accurate fruit counts. Furthermore, in horticultural studies, rather than a
single yield estimate, finer information such as the distribution of the number
of apples per cluster is desirable. In this work, we formulate fruit counting
from images as a multi-class classification problem and solve it by training a
Convolutional Neural Network. We first evaluate the per-image accuracy of our
method and compare it with a state-of-the-art method based on Gaussian Mixture
Models over four test datasets. Even though the parameters of the Gaussian
Mixture Model-based method are specifically tuned for each dataset, our network
outperforms it in three out of four datasets with a maximum of 94\% accuracy.
Next, we use the method to estimate the yield for two datasets for which we
have ground truth. Our method achieved 96-97\% accuracies. For additional
details please see our video here:
https://www.youtube.com/watch?v=Le0mb5P-SYc}{https://www.youtube.com/watch?v=Le0mb5P-SYc.


  Instead of controlling "symmetric" risks measured by central moments of
investment return or terminal wealth, more and more portfolio models have
shifted their focus to manage "asymmetric" downside risks that the investment
return is below certain threshold. Among the existing downside risk measures,
the lower-partial moments (LPM) and conditional value-at-risk (CVaR) are
probably most promising. In this paper we investigate the dynamic mean-LPM and
mean-CVaR portfolio optimization problems in continuous-time, while the current
literature has only witnessed their static versions. Our contributions are
two-fold, in both building up tractable formulations and deriving corresponding
analytical solutions. By imposing a limit funding level on the terminal wealth,
we conquer the ill-posedness exhibited in the class of mean-downside risk
portfolio models. The limit funding level not only enables us to solve both
dynamic mean-LPM and mean-CVaR portfolio optimization problems, but also offers
a flexibility to tame the aggressiveness of the portfolio policies generated
from such mean - downside risk models. More specifically, for a general market
setting, we prove the existence and uniqueness of the Lagrangian multiplies,
which is a key step in applying the martingale approach, and establish a
theoretical foundation for developing efficient numerical solution approaches.
Moreover, for situations where the opportunity set of the market setting is
deterministic, we derive analytical portfolio policies for both dynamic
mean-LPM and mean-CVaR formulations.


  We introduce a direct generalization of the Weinstein conjecture to closed,
Lichnerowicz exact, locally conformally symplectic manifolds, (for short $\lcs$
manifolds). This conjectures existence of certain 2-curves in the manifold,
which we call Reeb 2-curves. The conjecture readily holds for all closed exact
lcs surfaces. In higher dimensions, we give partial verifications of this
conjecture, based on certain extended ($\mathbb{Q} ^{} \sqcup \{\pm \infty\}$
valued) Gromov-Witten, elliptic curve counts in $\lcs$ manifolds. As a basic
application we get some novel results in classical Reeb dynamics. The most
basic such result gives sufficient conditions for a strict contactomorphism to
fix the image of some closed Reeb orbit on a closed contact manifold. Along the
way we give a Gromov-Witten theoretic construction of the classical dynamical
Fuller index (for Reeb vector field), which among other things explains its
rationality.


  This paper explores the application of machine learning to enhance our
understanding of water accessibility issues in underserved communities called
Colonias located along the northern part of the United States - Mexico border.
We analyzed more than 2000 such communities using data from the Rural Community
Assistance Partnership (RCAP) and applied hierarchical clustering and the
adaptive affinity propagation algorithm to automatically group Colonias into
clusters with different water access conditions. The Gower distance was
introduced to make the algorithm capable of processing complex datasets
containing both categorical and numerical attributes. To better understand and
explain the clustering results derived from the machine learning process, we
further applied a decision tree analysis algorithm to associate the input data
with the derived clusters, to identify and rank the importance of factors that
characterize different water access conditions in each cluster. Our results
complement experts' priority rankings of water infrastructure needs, providing
a more in-depth view of the water insecurity challenges that the Colonias
suffer from. As an automated and reproducible workflow combining a series of
tools, the proposed machine learning pipeline represents an operationalized
solution for conducting data-driven analysis to understand water access
inequality. This pipeline can be adapted to analyze different datasets and
decision scenarios.


  We show that a slowly decaying current pulse can lead to nearly deterministic
precessional switching in the presence of noise. We consider a biaxial
macrospin, with an easy axis in the plane and a hard axis out-of-the plane,
typical of thin film nanomagnets patterned into asymmetric shapes. Out-of-plane
precessional magnetization orbits are excited with a current pulse with a
component of spin polarization normal to the film plane. By numerically
integrating the stochastic Landau-Lifshitz-Gilbert-Slonczewski equation we show
that thermal noise leads to strong dephasing of the magnetization orbits.
However, an adiabatically decreasing pulse amplitude overwhelmingly leads to
magnetization reversal, with a final state that {\em only} depends on the pulse
polarity, not on the pulse amplitude. We develop an analytic model to explain
this phenomena and to determine the pulse decay time necessary for adiabatic
magnetization relaxation and thus precessional magnetization switching.


  Prior proposes the term "egocentric" for logical systems that study
properties of agents rather than properties of possible worlds. In such a
setting, the paper introduces two different modalities capturing de re and de
dicto knowledge and proves that these two modalities are not definable through
each other.


  We investigate the Taylor expansion of the baryon number susceptibility, and
hence, pressure, in a series in the baryon chemical potential (mu_B) through a
lattice simulation with light dynamical staggered quarks at a finer lattice
cutoff a=1/6T. We determine the QCD cross over coupling at mu_B=0. We find the
radius of convergence of the series at various temeperatures, and bound the
location of the QCD critical point to be T^E/T_c = 0.94 and mu_B^E/T < 1.8. We
also investigate the extrapolation of various susceptibilities and linkages to
finite chemical potential.


  Stoichiometric SnTe is theoretically a small gap semiconductor that undergoes
a ferroelectric distortion on cooling. In reality however, crystals are always
non-stoichiometric and metallic; the ferroelectric transition is therefore more
accurately described as a polar structural transition. Here we study the Fermi
surface using quantum oscillations as a function of pressure. We find the
oscillation spectrum changes at high pressure, due to the suppression of the
polar transition and less than 10 kbar is sufficient to stabilize the
undistorted cubic lattice. This is accompanied by a large decrease in the Hall
and electrical resistivity. Combined with our density functional theory (DFT)
calculations and angle resolved photoemission spectroscopy (ARPES) measurements
this suggests the Fermi surface $L$-pockets have lower mobility than the
tubular Fermi surfaces that connect them. Also captured in our DFT calculations
is a small widening of the band gap and shift in density of states for the
polar phase. Additionally we find the unusual phenomenon of a linear
magnetoresistance that exists irrespective of the distortion that we attribute
to regions of the Fermi surface with high curvature.


  We prove the convergence of a damped Newton's method for the nonlinear system
resulting from a discretization of the second boundary value problem for the
Monge-Ampere equation. The boundary condition is enforced through the use of
the notion of asymptotic cone. The differential operator is discretized based
on a discrete analogue of the subdifferential.


  Compressed sensing, allows to acquire compressible signals with a small
number of measurements. In applications, a hardware implementation often
requires a calibration as the sensing process is not perfectly known. Blind
calibration, that is performing at the same time calibration and compressed
sensing is thus particularly appealing. A potential approach was suggested by
Sch\"ulke and collaborators in Sch\"ulke et al. 2013 and 2015, using
approximate message passing (AMP) for blind calibration (cal-AMP). Here, the
algorithm is extended from the already proposed offline case to the online
case, where the calibration is refined step by step as new measured samples are
received. Furthermore, we show that the performance of both the offline and the
online algorithms can be theoretically studied via the State Evolution (SE)
formalism. Through numerical simulations, the efficiency of cal-AMP and the
consistency of the theoretical predictions are confirmed.


  Statisticians and quantitative neuroscientists have actively promoted the use
of independence relationships for investigating brain networks, genomic
networks, and other measurement technologies. Estimation of these graphs
depends on two steps. First is a feature extraction by summarizing measurements
within a parcellation, regional or set definition to create nodes. Secondly,
these summaries are then used to create a graph representing relationships of
interest. In this manuscript we study the impact of dimension reduction on
graphs that describe different notions of relations among a set of random
variables. We are particularly interested in undirected graphs that capture the
random variables' independence and conditional independence relations. A
dimension reduction procedure can be any mapping from high dimensional spaces
to low dimensional spaces. We exploit a general framework for modeling the raw
data and advocate that in estimating the undirected graphs, any acceptable
dimension reduction procedure should be a graph-homotopic mapping, i.e., the
graphical structure of the data after dimension reduction should inherit the
main characteristics of the graphical structure of the raw data. We show that,
in terms of inferring undirected graphs that characterize the conditional
independence relations among random variables, many dimension reduction
procedures, such as the mean, median, or principal components, cannot be
theoretically guaranteed to be a graph-homotopic mapping. The implications of
this work are broad. In the most charitable setting for researchers, where the
correct node definition is known, graphical relationships can be contaminated
merely via the dimension reduction. The manuscript ends with a concrete
example, characterizing a subset of graphical structures such that the
dimension reduction procedure using the principal components can be a
graph-homotopic mapping.


  Connected and Automated Vehicles use sensors and wireless communication to
improve road safety and efficiency. However, attackers may target
Vehicle-to-Everything communication. Indeed, an attacker may send authenticated
but wrong data to send false location information, alert incorrect events or
report a bogus object endangering the safety of other CAVs. Currently,
Standardization Development Organizations are working on developing security
standards against such attacks. Unfortunately, current standardization efforts
do not include misbehavior specifications for advanced V2X services such as
Collective Perception yet. This work assesses the security of Collective
Perception Messages and proposes inputs for consideration in existing
standards.


  Since November 2004, we attempt to detect GRB optical afterglows in near
real-time on images taken at the Canada France Hawaii Telescope within the Very
Wide Survey, component of the CFHT Legacy survey. To do so, a Real Time
Analysis System automatically and quickly analyzes MegaCAM images and extracts
from them a list of photometrically and astrometrically variable objects which
is then validated by a member of the collaboration. Each month, we repeatedly
observe 15 to 30 square degrees down to magnitude i' = 22.5. A few objects are
classified as candidates and analyzed more deeply, and statistics are done
showing the treatment's performance.


  Over the past two decades, helioseismology has revolutionized our
understanding of the interior structure and dynamics of the Sun.
Asteroseismology will soon place this knowledge into a broader context by
providing structural data for hundreds of Sun-like stars. Solar-like
oscillations have already been detected from the ground in several stars, and
NASA's Kepler mission is poised to unleash a flood of stellar pulsation data.
Deriving reliable asteroseismic information from these observations demands a
significant improvement in our analysis methods. In this paper we report the
initial results of our efforts to develop an objective stellar model-fitting
pipeline for asteroseismic data. The cornerstone of our automated approach is
an optimization method using a parallel genetic algorithm. We describe the
details of the pipeline and we present the initial application to Sun-as-a-star
data, yielding an optimal model that accurately reproduces the known solar
properties.


  We carry out a thermal energy census of hot baryons at $z < 1$, by
cross-correlating the \emph{Planck} MILCA y-map with 0.8 million
clusters/groups selected from the Yang et.al (2021) catalog. The thermal
Sunyaev-Zel'dovich (tSZ) effect around these clusters/groups are reliably
obtained, which enables us to make our model constraints based on one-halo (1h)
and two-halo (2h) contributions, respectively. (1) The total measurement S/N of
the one-halo term is 63. We constrain the $Y$-$M$ relation over the halo mass
range of $10^{13}$-$10^{15} M_\odot/h$, and find $Y\propto M^{\alpha}$ with
$\alpha= 1.8$ at $z=0.14$ ($\alpha=2.1$ at $z=0.75$). The total thermal energy
of gas bound to clusters/groups increases from $0.1\ \rm meV/cm^3$ at $z=0.14$
to $0.22\ \rm meV/cm^3$ at $z=0.75$. (2) The two-halo term is used to constrain
the bias-weighted electron pressure $\langle b_yP_e \rangle$. We find that
$\langle b_yP_e \rangle$ (in unit of $\rm meV/cm^3$) increases from $0.24\pm
0.02$ at $z=0.14$ to $0.45\pm 0.02$ at $z=0.75$. These results lead to several
implications. (i) The hot gas fraction $f_{\rm gas}$ in clusters/groups
monotonically increase with halo mass, where $f_{\rm gas}$ of a $10^{14}
M_\odot/h$ halo is $\sim 50\%$ ($25\%$) of the cosmic mean at $z=0.14\ (0.75)$.
(ii) By comparing the 1h- and 2h-terms, we obtain tentative constraint on the
thermal energy of unbound gas. (iii) The above results lead to significant
suppression of matter and weak lensing power spectrum at small scales. These
implications are important for astrophysics and cosmology, and we will further
investigate them with improved data and gas modeling.


  The Tayler-Spruit dynamo is one of the most promising mechanisms proposed to
explain angular momentum transport during stellar evolution. Its development in
proto-neutron stars spun-up by supernova fallback has also been put forward as
a scenario to explain the formation of very magnetized neutron stars called
magnetars. Using three-dimensional direct numerical simulations, we model the
proto-neutron star interior as a stably stratified spherical Couette flow with
the outer sphere that rotates faster than the inner one. We report the
existence of two subcritical dynamo branches driven by the Tayler instability.
They differ by their equatorial symmetry (dipolar or hemispherical) and the
magnetic field scaling, which is in agreement with different theoretical
predictions (by Fuller and Spruit, respectively). The magnetic dipole of the
dipolar branch is found to reach intensities compatible with observational
constraints on magnetars.


  We consider the problem of estimating a nested structure of two expectations
taking the form $U_0 = E[\max\{U_1(Y), \pi(Y)\}]$, where $U_1(Y) = E[X\ |\ Y]$.
Terms of this form arise in financial risk estimation and option pricing. When
$U_1(Y)$ requires approximation, but exact samples of $X$ and $Y$ are
available, an antithetic multilevel Monte Carlo (MLMC) approach has been
well-studied in the literature. Under general conditions, the antithetic MLMC
estimator obtains a root mean squared error $\varepsilon$ with order
$\varepsilon^{-2}$ cost. If, additionally, $X$ and $Y$ require approximate
sampling, careful balancing of the various aspects of approximation is required
to avoid a significant computational burden. Under strong convergence criteria
on approximations to $X$ and $Y$, randomised multilevel Monte Carlo techniques
can be used to construct unbiased Monte Carlo estimates of $U_1$, which can be
paired with an antithetic MLMC estimate of $U_0$ to recover order
$\varepsilon^{-2}$ computational cost. In this work, we instead consider biased
multilevel approximations of $U_1(Y)$, which require less strict assumptions on
the approximate samples of $X$. Extensions to the method consider an
approximate and antithetic sampling of $Y$. Analysis shows the resulting
estimator has order $\varepsilon^{-2}$ asymptotic cost under the conditions
required by randomised MLMC and order $\varepsilon^{-2}|\log\varepsilon|^3$
cost under more general assumptions.


  We study the collective quark excitations in an extremely anisotropic system
of two interpenetrating streams of the quark-gluon plasma. In contrast to the
gluon modes, all quark ones appear to be stable in such a system. Even more,
the quark modes in the two-stream system are very similar to those in the
isotropic plasma.


  The field of Natural Language Processing (NLP) is growing rapidly, with new
research published daily along with an abundance of tutorials, codebases and
other online resources. In order to learn this dynamic field or stay up-to-date
on the latest research, students as well as educators and researchers must
constantly sift through multiple sources to find valuable, relevant
information. To address this situation, we introduce TutorialBank, a new,
publicly available dataset which aims to facilitate NLP education and research.
We have manually collected and categorized over 6,300 resources on NLP as well
as the related fields of Artificial Intelligence (AI), Machine Learning (ML)
and Information Retrieval (IR). Our dataset is notably the largest
manually-picked corpus of resources intended for NLP education which does not
include only academic papers. Additionally, we have created both a search
engine and a command-line tool for the resources and have annotated the corpus
to include lists of research topics, relevant resources for each topic,
prerequisite relations among topics, relevant sub-parts of individual
resources, among other annotations. We are releasing the dataset and present
several avenues for further research.


  It is well known that the entanglement of a quantum state is invariant under
local unitary transformations. It dictates, for example, that the degree of
entanglement of a photon pair in a Bell state remains maximally entangled
during propagation in free-space. Here we outline a scenario where this
paradigm does not hold. Using local Bell states engineered from classical
vector vortex beams with non-separable degrees of freedom, so-called
classically entangled states, we demonstrate that the entanglement evolves
during propagation, oscillating between maximally entangled (purely vector) and
product states (purely scalar). We outline the theory behind these novel
propagation dynamics and confirm the results experimentally. Crucially, our
approach allows delivering a tunable degree of local entanglement to a distant
receiver by simply altering a modal phase delay holographically, or, in
essence, a tractor beam for entanglement. This demonstration highlights a
hitherto unnoticed property of classical entanglement and offers at the same
time a device for on-demand delivery of vector states to targets, e.g., for
dynamic laser materials processing as well as switchable resolution within STED
systems.


  Recent studies on metamorphic petrology as well as microstructural
observations suggest the influence of mechanical effects upon chemically active
metamorphic minerals. Thus, the understanding of such a coupling is crucial to
describe the dynamics of geomaterials. In this effort, we derive a
thermodynamically-consistent framework to characterize the evolution of
chemically active minerals. We model the metamorphic mineral assemblages as a
solid-species solution where the species mass transport and chemical reaction
drive the stress generation process. The theoretical foundations of the
framework rely on modern continuum mechanics, thermodynamics far from
equilibrium, and the phase-field model. We treat the mineral solid solution as
a continuum body, and following the Larch\'e and Cahn network model, we define
displacement and strain fields. Consequently, we obtain a set of coupled
chemo-mechanical equations. We use the aforementioned framework to study single
minerals as solid solutions during metamorphism. Furthermore, we emphasise the
use of the phase-field framework as a promising tool to model complex
multi-physics processes in geoscience. Without loss of generality, we use
common physical and chemical parameters found in the geoscience literature to
portrait a comprehensive view of the underlying physics. Thereby, we carry out
2D and 3D numerical simulations using material parameters for metamorphic
minerals to showcase and verify the chemo-mechanical interactions of mineral
solid solutions that undergo spinodal decomposition, chemical reactions, and
deformation.


  In this paper, we explore the following question: Are synonym substitution
attacks really synonym substitution attacks (SSAs)? We approach this question
by examining how SSAs replace words in the original sentence and show that
there are still unresolved obstacles that make current SSAs generate invalid
adversarial samples. We reveal that four widely used word substitution methods
generate a large fraction of invalid substitution words that are ungrammatical
or do not preserve the original sentence's semantics. Next, we show that the
semantic and grammatical constraints used in SSAs for detecting invalid word
replacements are highly insufficient in detecting invalid adversarial samples.


  We discuss the interconnections between AO*, adversarial game-searching
algorithms, e.g., proof number search and minimax search. The former was
developed in the context of a general AND/OR graph model, while the latter were
mostly presented in game-trees which are sometimes modeled using AND/OR trees.
It is thus worth investigating to what extent these algorithms are related and
how they are connected. In this paper, we explicate the interconnections
between these search paradigms. We argue that generalized proof number search
might be regarded as a more informed replacement of AO* for solving arbitrary
AND/OR graphs, and the minimax principle might also extended to use dual
heuristics.


  This work investigates interference mitigation techniques in multi-user
multiple input multiple output (MU-MIMO) Intelligent Reflecting Surface
(IRS)-aided networks, focusing on the base station end. Two methods of precoder
design based on block diagonalization are proposed. The first method does not
consider the interference caused by the IRS, seeking to mitigate only the
multi-user interference. The second method mitigates both the IRS-caused
interference and the multi-user interference. A comparison between both methods
within an no-IRS MU-MIMO network with strong direct links is provided. The
results show that, although in some circumstances IRS interference can be
neglected, treating it can improve system capacity and provide higher spectral
efficiency


  We show that layer potential groupoids for conical domains constructed in an
earlier paper (Carvalho-Qiao, Central European J. Math., 2013) are Fredholm
groupoids, which enables us to deal with many analysis problems on singular
spaces in a unified treatment. As an application, we obtain Fredholm criteria
for operators on layer potential groupoids.


  We report the detection of a z~7 galaxy strongly lensed by the massive galaxy
cluster Abell 2218 (z=0.175) at 3.6 and 4.5 um using the Spitzer Observatory
and at 1.1 um using the Hubble Space Telescope. The new data indicate a refined
photometric redshift in the range of 6.6-6.8 depending on the presence of
Ly-alpha emission. The spectral energy distribution is consistent with having a
significant Balmer break, suggesting that the galaxy is in the poststarburst
stage with an age of at least ~50 Myr and quite possibly a few hundred Myr.
This suggests the possibility that a mature stellar population is already in
place at such a high redshift. Compared with typical Lyman break galaxies at
z~3-4, the stellar mass is an order of magnitude smaller (~10^{9} Msun), but
the specific star formation rate (star formation rate/M_{star}) is similarly
large (> 10^{-9} yr^{-1}), indicating equally vigorous star-forming activity.


  The aim of this paper is first to introduce and study Rota-Baxter cosystems
and bisystems as generalization of Rota-Baxter coalgebras and bialgebras,
respectively, with various examples. The second purpose is to provide an
alternative definition of covariant bialgebras via coderivations. Furthermore,
we consider coquasitriangular covariant bialgebras which are generalization of
coquasitriangular infinitesimal bialgebras, coassociative Yang-Baxter pairs,
coassociative Yang-Baxter equation, double coalgebras and dendriform
coalgebras. We study some of their properties and relationships with
Rota-Baxter cosystems and bisystems.


  We study differential shot noise in mesoscopic diffusive
normal-superconducting (NS) heterostructures at finite voltages where nonlinear
effects due to the superconducting proximity effect arise. A numerical
scattering-matrix approach is adopted. Through an NS contact, we observe that
the shot noise shows a reentrant dependence on voltage due to the
superconducting proximity effect but the differential Fano factor stays
approximately constant. Furthermore, we consider differential shot noise in the
structures where an insulating barrier is formed between normal and
superconducting regions and calculate the differential Fano factor as a
function of barrier height.


  This article summarizes the status of Indirect and Direct searches for Dark
Matter, with a special focus and a critical look on the former. It is the
write-up of a rapporteur talk given at the 34th International Cosmic Ray
Conference (ICRC 2015) in The Hague, The Netherlands.


  We prove that the iterates produced by, either the scalar step size variant,
or the coordinatewise variant of AdaGrad algorithm, are convergent sequences
when applied to convex objective functions with Lipschitz gradient. The key
insight is to remark that such AdaGrad sequences satisfy a variable metric
quasi-Fej\'er monotonicity property, which allows to prove convergence.


  If we consider a q-analogue of linear differential equation, Galoois group of
the q-analogue difference equation is still a linear algebraic group. Namely,
by a quantization of linear differential equation, Galois group is not
quantized. We show by Examples that if we consider non-linear equations Galois
group is quantized. The results depend on general differential Galois theory
for non-linear differential equations developed by the second author.


  Quadratic divergencies which lead to the usual fine-tuning or hierarchy
problem are discussed in top condensation models. As in the Standard Model a
cancellation of quadratic divergencies is not possible without the boson
contributions in the radiative corrections which are absent in lowest order of
an $1/N_c$-expansion. To deal with the cancellation of quadratic divergencies
we propose therefore an expansion in the flavor degrees of freedom. In leading
order we find the remarkable result that quadratic divergencies automatically
disappear.


  Motivated by the debate of possible definitions of mass and width of
resonances for $Z$-boson and hadrons, we suggest a definition of unstable
particles by ``minimally complex'' semigroup representations of the Poincar\'e
group characterized by $(j,{\mathsf s}=(m-i\Gamma/2)^{2})$ in which the Lorentz
subgroup is unitary. This definition, though decidedly distinct from those
based on various renormalization schemes of perturbation theory, is intimately
connected with the first order pole definition of the $S$-matrix theory in that
the complex square mass $(m-i\Gamma/2)^{2}$ characterizing the representation
of the Poincar\'e semigroup is exactly the position ${\mathsf s}_R$ at which
the $S$-matrix has a simple pole. Wigner's representations $(j,m)$ are the
limit case of the complex representations for $\Gamma=0$. These representations
have generalized vectors (Gamow kets) which have, in addition to the $S$-matrix
pole at ${\mathsf s}=(m-i\Gamma/2)^{2}$, all the other properties that
heuristically the unstable states need to possess: a Breit-Wigner distribution
in invariant square mass and a lifetime $\tau=\frac{1}{\Gamma}$ defined by the
exactly exponential law for the decay probability ${\cal P}(t)$ and rate
$\dot{\cal P}(t)$ given by an exact Golden Rule which becomes Dirac's Golden
Rule in the Born-approximation. In addition and unintended, they have an
asymmetric time evolution.


  Important models in insurance, for example the Carm{\'e}r--Lundberg theory
and the Sparre Andersen model, essentially rely on the Poisson process. The
process is used to model arrival times of insurance claims.
  This paper extends the classical framework for ruin probabilities by
proposing and involving the fractional Poisson process as a counting process
and addresses fields of applications in insurance.
  The interdependence of the fractional Poisson process is an important feature
of the process, which leads to initial stress of the surplus process. On the
other hand we demonstrate that the average capital required to recover a
company after ruin does not change when switching to the fractional Poisson
regime. We finally address particular risk measures, which allow simple
evaluations in an environment governed by the fractional Poisson process.


  When a dyon encircles an infinitely-long solenoid enclosing uniform electric
and magnetic fields, its wave function accumulates a duality-invariant quantum
phase, which is topological because it depends on a winding number and is
nonlocal because the enclosed fields act on the dyon in regions where these
fields vanish. Here, we derive this dyon phase and show how its duality
symmetry unifies the Aharonov-Bohm phase with its dual phase. We obtain the
energy levels, the two-slit interference shift, and the scattering amplitude
associated with the duality-invariant quantum phase. Assuming that the dyon has
spin 1/2, we show that this spin does not affect the introduced phase. We argue
that a spin 1/2 dyon has electric and magnetic moments, the former being
greater than the latter because of the Schwinger-Zwanziger quantisation
condition.


  We consider the communication scenario where a source-destination pair wishes
to keep the information secret from a relay node despite wanting to enlist its
help. For this scenario, an interesting question is whether the relay node
should be deployed at all. That is, whether cooperation with an untrusted relay
node can ever be beneficial. We first provide an achievable secrecy rate for
the general untrusted relay channel, and proceed to investigate this question
for two types of relay networks with orthogonal components. For the first
model, there is an orthogonal link from the source to the relay. For the second
model, there is an orthogonal link from the relay to the destination. For the
first model, we find the equivocation capacity region and show that answer is
negative. In contrast, for the second model, we find that the answer is
positive. Specifically, we show by means of the achievable secrecy rate based
on compress-and-forward, that, by asking the untrusted relay node to relay
information, we can achieve a higher secrecy rate than just treating the relay
as an eavesdropper. For a special class of the second model, where the relay is
not interfering itself, we derive an upper bound for the secrecy rate using an
argument whose net effect is to separate the eavesdropper from the relay. The
merit of the new upper bound is demonstrated on two channels that belong to
this special class. The Gaussian case of the second model mentioned above
benefits from this approach in that the new upper bound improves the previously
known bounds. For the Cover-Kim deterministic relay channel, the new upper
bound finds the secrecy capacity when the source-destination link is not worse
than the source-relay link, by matching with the achievable rate we present.


  We argue that the random-matrix like energy spectra found in pseudointegrable
billiards with pointlike scatterers are related to the quantum violation of
scale invariance of classical analogue system. It is shown that the behavior of
the running coupling constant explains the key characteristics of the level
statistics of pseudointegrable billiards.


  We settle a problem of Havel by showing that there exists an absolute
constant d such that if G is a planar graph in which every two distinct
triangles are at distance at least d, then G is 3-colorable. In fact, we prove
a more general theorem. Let G be a planar graph, and let H be a set of
connected subgraphs of G, each of bounded size, such that every two distinct
members of H are at least a specified distance apart and all triangles of G are
contained in \bigcup{H}. We give a sufficient condition for the existence of a
3-coloring phi of G such that for every B\in H, the restriction of phi to B is
constrained in a specified way.


  We present a fluctuation relation for heat dissipation in a nonequilibrium
system. A nonequilibrium work is known to obey the fluctuation theorem in any
time interval $t$. A heat, which differs from a work by an energy change, is
shown to satisfy a modified fluctuation relation. Modification is brought by
correlation between a heat and an energy change during nonequilibrium processes
whose effect may not be negligible even in the $t\to\infty$ limit. The
fluctuation relation is derived for overdamped Langevin equation systems, and
tested in a linear diffusion system.


  In 2018, Kalck and Yang showed that the singularity categories associated
with 3-dimensional Gorenstein quotient singularities are triangle equivalent
(up to direct summands) to small cluster categories associated with McKay
quivers with potential. We introduce graded McKay quivers with potential and
generalize Kalck-Yang's theorem to arbitrary dimensions. The singularity
categories we consider occur as stable categories of categories of maximal
Cohen-Macaulay modules. We refine our description of the singularity categories
by showing that these categories of maximal Cohen-Macaulay modules are
equivalent to Higgs categories in the sense of Wu. Moreover, we describe the
singularity categories in the non-Gorenstein case.


  This White Paper is an input to the ongoing discussion about the extension
and refinement of simplified Dark Matter (DM) models. Based on two concrete
examples, we show how existing simplified DM models (SDMM) can be extended to
provide a more accurate and comprehensive framework to interpret and
characterise collider searches. In the first example we extend the canonical
SDMM with a scalar mediator to include mixing with the Higgs boson. We show
that this approach not only provides a better description of the underlying
kinematic properties that a complete model would possess, but also offers the
option of using this more realistic class of scalar mixing models to compare
and combine consistently searches based on different experimental signatures.
The second example outlines how a new physics signal observed in a visible
channel can be connected to DM by extending a simplified model including
effective couplings. This discovery scenario uses the recently observed excess
in the high-mass diphoton searches of ATLAS and CMS for a case study to show
that such a pragmatic approach can aid the experimental search programme to
verify/falsify a potential signal and to study its underlying nature. In the
next part of the White Paper we outline other interesting options for SDMM that
could be studied in more detail in the future. Finally, we discuss important
aspects of supersymmetric models for DM and how these could help to develop of
more complete SDMM.


  In the last years it is becoming more and more evident the importance in
providing to the students, and people in general, a more realistic view on the
scientific research world. In this framework many initiatives are beeing
carried out to put students closer to scientists. The Astrophysics Masterclass,
held in our Department, is an example of such activities, consisting in a
one-day outreach event during which high school students experience the
research methods in the astrophysics field. In this paper we describe this
activity, which allows the participants to work on real research data in a real
research center.


  A widespread practice to implement a flexible array is to consider the
storage area into two parts: the used area, which is already available for
read/write operations, and the supply area, which is used in case of
enlargement of the array. The main purpose of the supply area is to avoid as
much as possible the reallocation of the whole storage area in case of
enlargement. As the supply area is not used by the application, the main idea
of the paper is to convey the information to the garbage collector, making it
possible to avoid completely the marking of the supply area. We also present a
simple method to analyze the types of objects, which are stored in an array as
well as the possible presence of NULL values within the array. This allows us
to better specialize the work of the garbage collector when marking the used
area, and also, by transitivity, to improve overall results for type analysis
of all expressions of the source code. After introducing several abstract data
types, which represent the main arrays concerned by our technique (i.e., zero
or variable indexing, circular arrays and hash maps), we measure its impact
during the bootstrap of two compilers whose libraries are equipped with these
abstract data types. We then measure, on various software products we have not
written, the frequency of certain habits of manipulation of arrays, to assess
the validity of our approach.


  Regional Climate Models (RCMs) are an essential tool for analysing regional
climate change impacts as they provide simulations with more small-scale
details and expected smaller errors than global climate models. There has been
much effort to increase the spatial resolution and simulation skill of RCMs,
yet the extent to which this improves the projection of hydrological change is
unclear. Here, we evaluate the skill of five reanalysis-driven Euro-CORDEX RCMs
in simulating precipitation and temperature, and as drivers of a hydrological
model to simulate river flow on four UK catchments covering different physical,
climatic and hydrological characteristics. We test whether high-resolution RCMs
provide added value, through analysis of two RCM resolutions, 50 km and 12.5
km, which are also bias-corrected employing the parametric quantile-mapping
(QM) method, using the normal distribution for temperature, and the Gamma (GQM)
and Double Gamma (DGQM) distributions for precipitation. In a small catchment
with complex topography, the 12.5 km RCMs outperform their 50 km version for
precipitation and temperature, but when used in combination with the
hydrological model, fail to capture the observed river flow distribution. In
the other (larger) catchments, only one high-resolution RCM consistently
outperforms its low-resolution version, implying that in general there is no
added value from using the high-resolution RCMs in those catchments. GQM
decreases most of the simulation biases, except for extreme precipitation and
high flows, which are further decreased by DGQM. Bias correction does not
improve the representation of daily temporal variability, but it does for
monthly variability, in particular when applying DGQM. Overall, an increase in
RCM resolution does not imply a better simulation of hydrology and
bias-correction represents an alternative to ease decision-making.


  The Apache Point Observatory Galactic Evolution Experiment (APOGEE), part of
the Sloan Digital Sky Survey III, explores the stellar populations of the Milky
Way using the Sloan 2.5-m telescope linked to a high resolution (R~22,500),
near-infrared (1.51-1.70 microns) spectrograph with 300 optical fibers. For
over 150,000 predominantly red giant branch stars that APOGEE targeted across
the Galactic bulge, disks and halo, the collected high S/N (>100 per
half-resolution element) spectra provide accurate (~0.1 km/s) radial
velocities, stellar atmospheric parameters, and precise (~0.1 dex) chemical
abundances for about 15 chemical species. Here we describe the basic APOGEE
data reduction software that reduces multiple 3D raw data cubes into
calibrated, well-sampled, combined 1D spectra, as implemented for the
SDSS-III/APOGEE data releases (DR10, DR11 and DR12). The processing of the
near-IR spectral data of APOGEE presents some challenges for reduction,
including automated sky subtraction and telluric correction over a 3 degree
diameter field and the combination of spectrally dithered spectra. We also
discuss areas for future improvement.


  NGC 1624-2 is the most strongly magnetized O-type star known. Previous
spectroscopic observations of this object in the ultraviolet provided evidence
that it hosts a large and dense circumstellar magnetosphere. Follow-up
observations obtained with the \textit{Hubble Space Telescope} not only confirm
that previous inference, but also suggest that NGC 1624-2's magnetosphere has a
complex structure. Furthermore, an expanded spectropolarimetric time series
shows a potential departure from a dipolar magnetic field geometry, which could
mean that the strongest field detected at the surface of an O-type star is also
topologically complex. This result raises important questions regarding the
origin and evolution of magnetic fields in massive stars.


  Another facet of the elegant link between random processes on graphs and
Laplacian-based numerical linear algebra is uncovered: based on random spanning
forests, novel Monte-Carlo estimators for graph signal smoothing are proposed.
These random forests are sampled efficiently via a variant of Wilson's
algorithm --in time linear in the number of edges. The theoretical variance of
the proposed estimators are analyzed, and their application to several problems
are considered, such as Tikhonov denoising of graph signals or semi-supervised
learning for node classification on graphs.


  We report on the observation of the rearrangement of vortices in the ns time
scale following a fast magnetic field variation due to selective local heating
of a high Tc superconducting film. Detailed simulations describing the measured
photoinduced voltage signals along the film lead to a better understanding of
vortex dynamics in fast regimes, where mean vortex velocities reach 10000 m/s


  Ion Coulomb crystals are currently establishing themselves as a highly
controllable test-bed for mesoscopic systems of statistical mechanics. The
detailed experimental interrogation of the dynamics of these crystals however
remains an experimental challenge. In this work, we show how to extend the
concepts of multi-dimensional nonlinear spectroscopy to the study of the
dynamics of ion Coulomb crystals. The scheme we present can be realized with
state-of-the-art technology and gives direct access to the dynamics, revealing
nonlinear couplings even in the presence of thermal excitations. We illustrate
the advantages of our proposal showing how two-dimensional spectroscopy can be
used to detect signatures of a structural phase transition of the ion crystal,
as well as resonant energy exchange between modes. Furthermore, we demonstrate
in these examples how different decoherence mechanisms can be identified.


  We study the disorder driven superconductor to insulator transition in
amorphous films of high carrier-concentration indium-oxide. Using thin films
with various sizes and aspect ratios we show that the `critical'
sheet-resistance $R_{{\small \square}}$ depends systematically on sample
geometry; superconductivity disappears when $R_{{\small \square}}$ exceeds
$\approx6 $k$\Omega$ in large samples. On the other hand, wide and sufficiently
short samples of the same batch exhibit superconductivity (judged by
conductivity versus temperature) up to $R_{{\small \square}}$ which is
considerably larger. These results support the inhomogeneous scenario for the
superconductor-insulator transition.


  Comprehensive semantic segmentation on renal pathological images is
challenging due to the heterogeneous scales of the objects. For example, on a
whole slide image (WSI), the cross-sectional areas of glomeruli can be 64 times
larger than that of the peritubular capillaries, making it impractical to
segment both objects on the same patch, at the same scale. To handle this
scaling issue, prior studies have typically trained multiple segmentation
networks in order to match the optimal pixel resolution of heterogeneous tissue
types. This multi-network solution is resource-intensive and fails to model the
spatial relationship between tissue types. In this paper, we propose the
Omni-Seg+ network, a scale-aware dynamic neural network that achieves
multi-object (six tissue types) and multi-scale (5X to 40X scale) pathological
image segmentation via a single neural network. The contribution of this paper
is three-fold: (1) a novel scale-aware controller is proposed to generalize the
dynamic neural network from single-scale to multi-scale; (2) semi-supervised
consistency regularization of pseudo-labels is introduced to model the
inter-scale correlation of unannotated tissue types into a single end-to-end
learning paradigm; and (3) superior scale-aware generalization is evidenced by
directly applying a model trained on human kidney images to mouse kidney
images, without retraining. By learning from ~150,000 human pathological image
patches from six tissue types at three different resolutions, our approach
achieved superior segmentation performance according to human visual assessment
and evaluation of image-omics (i.e., spatial transcriptomics). The official
implementation is available at https://github.com/ddrrnn123/Omni-Seg.


  Electromagnetic observations have been used over the past decades to
understand the nature of black holes and the material around them. Our ability
to learn about the fundamental physics relies on our understanding of two key
ingredients in the modeling of these electromagnetic observations: the gravity
theory that describes the black hole, and the astrophysics that produces the
observed radiation. In this work we study our current ability to constrain and
detect deviations from General Relativity using the accretion disk spectrum of
stellar-mass black holes in binary systems. Our analysis combines relativistic
ray-tracing and Markov-Chain Monte-Carlo sampling techniques to determine how
well such tests of General Relativity can be carried out in practice. We show
that even when a very simple astrophysical model for the accretion disk is
assumed a priori, the uncertainties and covariances between the parameters of
the model and the parameters that control the deformation from General
Relativity make any test of General Relativity very challenging with accretion
disk spectrum observations. We also discuss the implications of assuming that
General Relativity is correct a priori on the estimation of parameters of the
astrophysical model when the data is not described by Einstein's theory, which
can lead to a fundamental systematic bias.


  Traditional ways of storing and querying data do not work well in scenarios
where data is being generated continuously and quick decisions need to be
taken. For example, in hospital intensive care units, signals from multiple
devices need to be monitored and the occurrence of any anomaly should raise
alarms immediately. A typical design would take the average from a window of
say 10 seconds (time-based) or 10 successive (count-based) readings and look
for sudden deviations. Existing stream processing systems either restrict the
windows to time or count-based windows or let users define customized windows
in imperative programming languages. These are subject to the implementers'
interpretation of what is desired and hard to understand for others. We
introduce a formalism for specifying windows based on Monadic Second Order
logic. It offers several advantages over ad-hoc definitions written in
imperative languages. We demonstrate four such advantages. First, we illustrate
how practical streaming data queries can be easily written with precise
semantics. Second, we can get different but expressively equivalent formalisms
for defining windows. We use one of them (regular expressions) to design an
end-user-friendly language for defining windows. Third, we use another
expressively equivalent formalism (automata) to design a processor that
automatically generates windows according to specifications. The fourth
advantage we demonstrate is more sophisticated. Some window definitions have
the problem of too many windows overlapping with each other, overwhelming the
processing engine. This is handled in different ways by different engines, but
all the options are about what to do when this happens at runtime. We study
this as a static analysis question and prove that it is undecidable to check
whether such a scenario can ever arise for a given window definition. We
identify a decidable fragment...


  Let $F \subseteq \mathbb{R}^2$ be a Bedford-McMullen carpet defined by
multiplicatively independent exponents, and suppose that either $F$ is not a
product set, or it is a product set with marginals of dimension strictly
between $0$ and $1$. We prove that any similarity $g$ such that $g(F) \subseteq
F$ is an isometry composed of reflections about lines parallel to the axes. Our
approach utilizes the structure of tangent sets of $F$, obtained by "zooming
in" on points of $F$, projection theorems for products of self-similar sets,
and logarithmic commensurability type results for self similar sets in the
line.


  Hole detection is a crucial task for monitoring the status of wireless sensor
networks (WSN) which often consist of low-capability sensors. Holes can form in
WSNs due to the problems during placement of the sensors or power/hardware
failure. In these situations, sensing or transmitting data could be affected
and can interrupt the normal operation of the WSNs. It may also decrease the
lifetime of the network and sensing coverage of the sensors. The problem of
hole detection is especially challenging in WSNs since the exact location of
the sensors is often unknown. In this paper, we propose a novel hole detection
approach called FD-CNN which is based on Force-directed (FD) Algorithm and
Convolutional Neural Network (CNN). In contrast to existing approaches, FD-CNN
is a centralized approach and is able to detect holes from WSNs without relying
on the information related to the location of the sensors. The proposed
approach also alleviates the problem of high computational complexity in
distributed approaches. The proposed approach accepts the network topology of a
WSN as an input and generates the identity of the nodes surrounding each
detected hole in the network as the final output. In the proposed approach, an
FD algorithm is used to generate the layout of the wireless sensor networks
followed by the identification of the holes in the layouts using a trained CNN
model. In order to prepare labeled datasets for training the CNN model, an
unsupervised pre-processing method is also proposed in this paper. After the
holes are detected by the CNN model, two algorithms are proposed to identify
the regions of the holes and corresponding nodes surrounding the regions.
Extensive experiments are conducted to evaluate the proposed approach based on
different datasets. Experimental results show that FD-CNN can achieve 80%
sensitivity and 93% specificity in less than 2 minutes.


  We study the physics of quark deconfinement on domain walls in
four-dimensional supersymmetric SU(N) Yang-Mills theory, compactified on a
small circle with supersymmetric boundary conditions. We numerically examine
the properties of BPS domain walls connecting vacua k units apart. We also
determine their electric fluxes and use the results to show that Wilson loops
of any nonzero N-ality exhibit perimeter law on all k-walls. Our results
confirm and extend, to all N and k, the validity of the semiclassical picture
of deconfinement of Anber, Sulejmanpasic and one of us (EP), arXiv:1501.06773,
providing a microscopic explanation of mixed 0-form/1-form anomaly inflow.


  Recent technological advances could make interstellar travel possible, using
ultra-lightweight sails pushed by lasers or solar photon pressure, at speeds of
a few percent the speed of light. Obtaining remote observational data from such
probes is not trivial because of their minimal instrumentation (gram scale) and
large distances (pc). We derive the optimal communication scheme to maximize
the data rate between a remote probe and home-base. he framework requires
coronagraphic suppression of the stellar background at the level of $10^{-9}$
within a few tenths of an arcsecond of the bright star. Our work includes
models for the loss of photons from diffraction, technological limitations,
interstellar extinction, and atmospheric transmission. Major noise sources are
atmospheric, zodiacal, stellar and instrumental. We examine the maximum
capacity using the "Holevo bound" which gives an upper limit to the amount of
information (bits) that can be encoded through a quantum state (photons), which
is a few bits per photon for optimistic signal and noise levels. This allows
for data rates of order bits per second per Watt from a transmitter of size 1 m
at a distance of $\alpha\,$Centauri (1.3 pc) to an earth-based large receiving
telescope (E-ELT, 39 m). The optimal wavelength for this distance is 300 nm
(space-based receiver) to 400 nm (earth-based) and increases with distance, due
to extinction, to a maximum of $\approx3\,\mu$m to the center of the galaxy at
8 kpc.


  Without mutation and migration, evolutionary dynamics ultimately leads to the
extinction of all but one species. Such fixation processes are well understood
and can be characterized analytically with methods from statistical physics.
However, many biological arguments focus on stationary distributions in a
mutation-selection equilibrium. Here, we address the equilibration time
required to reach stationarity in the presence of mutation, this is known as
the mixing time in the theory of Markov processes. We show that mixing times in
evolutionary games have the opposite behaviour from fixation times when the
intensity of selection increases: In coordination games with bistabilities, the
fixation time decreases, but the mixing time increases. In coexistence games
with metastable states, the fixation time increases, but the mixing time
decreases. Our results are based on simulations and the WKB approximation of
the master equation.


  High performance calculation is increasingly used within society. Previously
reserved for an elite, based on large computing and storage infrastructures, it
is now a core module for many companies. Indeed, high performance calculation
makes it possible to design and optimize many elements for a limited cost
,compared to the production of prototypes or tests in situ. It is also widely
used in big data and artificial intelligence.It seems essential to ask about
theenvironmental impact of these digital practices. A number of actions have
already been initiated in this community: GREEN500; European CoC
eco-responsibility label for Data centres... but these actions generally look
at specific or even idealised situations and/or software.The software
qualification process in the field of high performance calculation consists in
looking at the scalability of the software. The originality of this study is to
focus on energy scalability (calculation return time depending on the power
consumed), by considering several architectures (three TOP500 machines and a
laboratory cluster).The energy cost of an example calculation could be
estimated, which shows that the most efficient machine in terms of calculation
time is not necessarily the most energy efficient, and depending on the number
of cores/processes chosen, it is not always the same architecture that is the
most energy-efficient. It was therefore possible to show that the longer the
user is prepared to wait, the less energy is used by the calculation.


  Recent advances in nanofluidics have enabled the confinement of water down to
a single molecular layer. Such monolayer electrolytes show promise in achieving
bio-inspired functionalities through molecular control of ion transport.
However, the understanding of ion dynamics in these systems is still scarce.
Here, we develop an analytical theory, backed up by molecular dynamics
simulations, predicting strongly nonlinear effects in ion transport across
quasi-two-dimensional slits. We show that under an electric field, ions
assemble into elongated clusters, whose slow dynamics result in hysteretic
conduction. This phenomenon, known as memristor effect, can be harnessed to
build an elementary neuron. As a proof-of-concept, we carry out molecular
simulations of two nanofluidic slits reproducing the Hodgkin-Huxley model, and
observe spontaneous emission of voltage spikes characteristic of neuromorphic
activity.


  We present XMM-Newton observations of three AGN taken as part of a hunt to
find very heavily obscured Compton-thick AGN. For obscuring columns greater
than 10^25 cm^-2, AGN are only visible at energies below 10 keV via
reflected/scattered radiation, characterized by a flat power-law. We therefore
selected three objects (ESO 417-G006, IRAS 05218-1212, and MCG -01-05-047) from
the Swift BAT hard X-ray survey catalog with Swift X-ray Telescope XRT 0.5-10
keV spectra with flat power-law indices as candidate Compton-thick sources for
follow-up observations with the more sensitive instruments on XMM-Newton. The
XMM spectra, however, rule out reflection-dominated models based on the
weakness of the observed Fe K-alpha lines. Instead, the spectra are well-fit by
a model of a power-law continuum obscured by a Compton-thin absorber, plus a
soft excess. This result is consistent with previous follow-up observations of
two other flat-spectrum BAT-detected AGN. Thus, out of the six AGN in the
22-month BAT catalog with apparently flat Swift XRT spectra, all five that have
had follow-up observations are not likely Compton-thick. We also present new
optical spectra of two of these objects, IRAS 05218-1212 and MCG -01-05-047.
Interestingly, though both these AGN have similar X-ray spectra, their optical
spectra are completely different, adding evidence against the simplest form of
the geometric unified model of AGN. IRAS 05218-1212 appears in the optical as a
Seyfert 1, despite the ~8.5x10^22 cm^-2 line-of-sight absorbing column
indicated by its X-ray spectrum. MCG -01-05-047's optical spectrum shows no
sign of AGN activity; it appears as a normal galaxy.


  Scientific research organizations that are developing and deploying
Artificial Intelligence (AI) systems are at the intersection of technological
progress and ethical considerations. The push for Responsible AI (RAI) in such
institutions underscores the increasing emphasis on integrating ethical
considerations within AI design and development, championing core values like
fairness, accountability, and transparency. For scientific research
organizations, prioritizing these practices is paramount not just for
mitigating biases and ensuring inclusivity, but also for fostering trust in AI
systems among both users and broader stakeholders. In this paper, we explore
the practices at a research organization concerning RAI practices, aiming to
assess the awareness and preparedness regarding the ethical risks inherent in
AI design and development. We have adopted a mixed-method research approach,
utilising a comprehensive survey combined with follow-up in-depth interviews
with selected participants from AI-related projects. Our results have revealed
certain knowledge gaps concerning ethical, responsible, and inclusive AI, with
limitations in awareness of the available AI ethics frameworks. This revealed
an overarching underestimation of the ethical risks that AI technologies can
present, especially when implemented without proper guidelines and governance.
Our findings reveal the need for a holistic and multi-tiered strategy to uplift
capabilities and better support science research teams for responsible,
ethical, and inclusive AI development and deployment.