  Stellar radial velocity (RV) measurements have proven to be a very successful
method for detecting extrasolar planets. Analysing RV data to determine the
parameters of the extrasolar planets is a significant statistical challenge
owing to the presence of multiple planets and various degeneracies between
orbital parameters. Determining the number of planets favoured by the observed
data is an even more difficult task. Bayesian model selection provides a
mathematically rigorous solution to this problem by calculating marginal
posterior probabilities of models with different number of planets, but the use
of this method in extrasolar planetary searches has been hampered by the
computational cost of the evaluating Bayesian evidence. Nonetheless, Bayesian
model selection has the potential to improve the interpretation of existing
observational data and possibly detect yet undiscovered planets. We present a
new and efficient Bayesian method for determining the number of extrasolar
planets, as well as for inferring their orbital parameters, without having to
calculate directly the Bayesian evidence for models containing a large number
of planets. Instead, we work iteratively and at each iteration obtain a
conservative lower limit on the odds ratio for the inclusion of an additional
planet into the model. We apply this method to simulated data-sets containing
one and two planets and successfully recover the correct number of planets and
reliable constraints on the orbital parameters. We also apply our method to RV
measurements of HD 37124, 47 Ursae Majoris and HD 10180. For HD 37124, we
confirm that the current data strongly favour a three-planet system. We find
strong evidence for the presence of a fourth planet in 47 Ursae Majoris, but
its orbital period is suspiciously close to one year, casting doubt on its
validity. For HD 10180 we find strong evidence for a six-planet system.


  The stability of the recently discovered compacton solutions is studied by
means of both linear stability analysis as well as Lyapunov stability criteria.
  From the results obtained it follows that, unlike solitons, all the allowed
compacton solutions are stable, as the stability condition is satisfied for
arbitrary values of the nonlinearity parameter. The results are shown to be
true even for the higher order nonlinear dispersion equations for compactons.
Some new conservation laws for the higher order nonlinear dispersion equations
are also presented.


This paper analyses urban accessibility using a fully dynamic approach.


  The Experimental Advanced Superconducting Tokamak (EAST), a nation-level
large-scale scientific project of China, plays a key role for the research of
peaceful utilizations of fusion energy. The safety and interlock system (SIS)
is in charge of the supervision and control of all the EAST components involved
in the protection of human and tokamak from potential accidents. With the
development of physical experiment, the SIS had come close to reaching its
limits for expandability. Therefore, a prototype for upgrading EAST SIS has
been designed, and a fast architecture based on COTS FPGA is absorbed into the
new SIS. This paper presents EAST machine and human protection mechanism and
the architecture of the upgrading safety and interlock system.


  Very energetic charm and bottom hadrons may be produced in the upper
atmosphere when a primary cosmic ray or the leading hadron in an extensive air
shower collide with a nucleon. At $E\approx 10^8$ GeV their decay length
becomes of the order of 10 km, implying that they tend to interact in the air
instead of decaying. Since the inelasticity in these collisions is much smaller
than the one in proton and pion collisions, there could be rare events where a
heavy-hadron component transports a significant amount of energy deep into the
atmosphere. We have developed a module for the detailed simulation of these
processes and have included it in a new version of the air shower simulator
AIRES. We study the frequency, the energy distribution and the depth of charm
and bottom production, as well as the depth and the energy distribution of
these quarks when they decay. As an illustration, we consider the production
and decay of tau leptons (from $D_s$ decays) and the lepton flux at PeV
energies from a 30 EeV proton primary. The proper inclusion of charm and bottom
hadrons in AIRES opens the possibility to search for air-shower observables
that are sensitive to heavy quark effects.


  The search for earth abundant, efficient and stable electrocatalysts that can
enable the chemical reduction of CO2 to value-added chemicals and fuels at an
industrially relevant scale, is a high priority for the development of a global
network of renewable energy conversion and storage systems that can
meaningfully impact greenhouse gas induced climate change. Here we introduce a
straightforward, low cost, scalable and technologically relevant method to
manufacture an all-carbon, electroactive, nitrogen-doped nanoporous
carbon-carbon nanotube composite membrane, dubbed "HNCM-CNT". The membrane is
demonstrated to function as a binder-free, high-performance electrode for the
electrocatalytic reduction of CO2 to formate. The Faradaic efficiency for the
production of formate is 81%. Furthermore, the robust structural and
electrochemical properties of the membrane endow it with excellent long-term
stability.


  Natural evolution gives the impression of leading to an open-ended process of
increasing diversity and complexity. If our goal is to produce such
open-endedness artificially, this suggests an approach driven by evolutionary
metaphor. On the other hand, techniques from machine learning and artificial
intelligence are often considered too narrow to provide the sort of exploratory
dynamics associated with evolution. In this paper, we hope to bridge that gap
by reviewing common barriers to open-endedness in the evolution-inspired
approach and how they are dealt with in the evolutionary case - collapse of
diversity, saturation of complexity, and failure to form new kinds of
individuality. We then show how these problems map onto similar issues in the
machine learning approach, and discuss how the same insights and solutions
which alleviated those barriers in evolutionary approaches can be ported over.
At the same time, the form these issues take in the machine learning
formulation suggests new ways to analyze and resolve barriers to
open-endedness. Ultimately, we hope to inspire researchers to be able to
interchangeably use evolutionary and gradient-descent-based machine learning
methods to approach the design and creation of open-ended systems.


  The Metropolis implementation of the Monte Carlo algorithm has been developed
to study the equilibrium thermodynamics of many-body systems. Choosing small
trial moves, the trajectories obtained applying this algorithm agree with those
obtained by Langevin's dynamics. Applying this procedure to a simplified
protein model, it is possible to show that setting a threshold of 1 degree on
the movement of the dihedrals of the protein backbone in a single Monte Carlo
step, the mean quantities associated with the off-equilibrium dynamics (e.g.,
energy, RMSD, etc.) are well reproduced, while the good description of higher
moments requires smaller moves. An important result is that the time duration
of a Monte Carlo step depends linearly on the temperature, something which
should be accounted for when doing simulations at different temperatures.


  We combined the ISOCAM Parallel Mode Survey at 6.7 micron (LW2 filter) with
the Two Micron All Sky Survey in order to obtain a powerful tool to search for
AGN independent of dust extinction. Using moderate colour criteria H-K>0.5 and
K-LW2>2.7 we have selected a sample of 77 AGN candidates in an effective area
of about 10 square degrees. By means of optical spectroscopy we find 24 (30%)
type-1 QSOs at redshifts 0.1<z<2.3; nine of them have z>0.8. About one third of
the ISO-2MASS QSOs show so red optical colours, that they are missed in optical
and UV AGN surveys like SDSS, 2DF, or HES. With a surface density of about 2
deg^(-2) down to R<18 mag the ISO-2MASS QSOs outnumber the 1.35 deg^(-2) of the
SDSS quasar survey by 50%; we find a combined optical-IR QSO surface density of
2.7 deg^(-2). Since only two of the ISO-2MASS QSOs have also J-K>2, the
inclusion of the ISO mid-infrared photometry significantly extends the
capabilities of the pure 2MASS red AGN survey. We suggest that the newly found
red AGN resemble young members of the quasar population, and that quasars spend
much of their lifetime in a dust enshrouded phase.


  We investigate the correlation between the point disclination energies and
the surface curvature modulation of nematic liquid crystal membranes with a
Gaussian bump geometry. Due to the correlation, disclinations feel an
attractive force that confines them to an annulus region, resulting in a halo
distribution around the top of the bump. The halo formation is a direct
consequence of the nonzero Gaussian curvature of the bump that affects
preferable configurations of liquid crystal molecules around the disclination
core.


  The integrable loop model with mixed boundary conditions based on the
1-boundary extended Temperley--Lieb algebra with loop weight 1 is considered.
The corresponding qKZ equation is introduced and its minimal degree solution
described. As a result, the sum of the properly normalized components of the
ground state in size L is computed and shown to be equal to the number of
Horizontally and Vertically Symmetric Alternating Sign Matrices of size 2L+3. A
refined counting is also considered.


  We have explored the ideas that parametric resonance affects nearly geodesic
motion around a black hole or a neutron star, and that it may be relevant to
the high frequency (twin) quasi-periodic oscillations occurring in some
low-mass X-ray binaries. We have assumed the particles or fluid elements of an
accretion disc to be subject to an isotropic perturbation of a hypothetical but
rather general form. We find that the parametric resonance is indeed excited
close to the radius where epicyclic frequencies of radial and meridional
oscillations are in a 2:3 ratio. The location and frequencies of the highest
amplitude excitation vary with the strength of the perturbation. These results
agree with actual frequency ratios of twin kHz QPOs that have been reported in
some black hole candidates, and they may be consistent also with correlation of
the twin peaks in Sco X-1.


  In this paper I present a counter-example to the conjecture: The Fiedler
vector for the graph Laplacian of a tree has its most extreme values at the
verticies which are the farthest apart. This counter-example looks roughly like
a flower and so I have named it the "Fiedler rose".


  The frequency-dependent impedance is a fundamental property of electrical
components. We show that it can be determined from the equilibrium dynamical
fluctuations of the electrode charge in constant-potential molecular
simulations, extending in particular a fluctuation-dissipation for the
capacitance recovered in the low-frequency limit and provide an illustration on
water/gold nanocapacitors. This work opens the way to the interpretation of
electrochemical impedance measurements in terms of microscopic mechanisms,
directly from the dynamics of the electrolyte, or indirectly via equivalent
circuit models as in experiments.


  This article presents proposals for the design of reduced-order controllers
for high-dimensional dynamical systems. The objective is to develop efficient
control strategies that ensure stability and robustness with reduced
computational complexity. By leveraging the concept of partial pole placement,
which involves placing a subset of the closed-loop system's poles, this study
aims to strike a balance between reduced-order modeling and control
effectiveness. The proposed approach not only addresses the challenges posed by
high-dimensional systems but also provides a systematic framework for
controller design. When an infinite-dimensional operator is Riesz spectral, our
theoretical analysis highlights the potential of partial pole placement in
advancing control design. Model uncertainties, introduced by an error on the
spectral decomposition, can also be allowed. This is illustrated in particular
in the case of systems modeled by coupled ordinary-partial differential
equations (ODE-PDE).


  It is argued that Hawking radiation has indeed been measured and shown to
posses a thermal spectrum, as predicted. This contention is based on three
separate legs. The first is that the essential physics of the Hawking process
for black holes can be modelled in other physical systems. The second is the
white hole horizons are the time inverse of black hole horizons, and thus the
physics of both is the same. The third is that the quantum emission, which is
the Hawking process, is completely determined by measurements of the classical
parameters of a linear physical system. The experiment conducted in 2010
fulfills all of these requirements, and is thus a true measurement of Hawking
radiation.


  The Kepler mission has revolutionized our understanding of exoplanets, but
some of the planet candidates identified by Kepler may actually be
astrophysical false positives or planets whose transit depths are diluted by
the presence of another star. Adaptive optics images made with ARIES at the MMT
of 87 Kepler Objects of Interest place limits on the presence of fainter stars
in or near the Kepler aperture. We detected visual companions within 1" for
five stars, between 1" and 2" for seven stars, and between 2" and 4" for 15
stars. For those systems, we estimate the brightness of companion stars in the
Kepler bandpass and provide approximate corrections to the radii of associated
planet candidates due to the extra light in the aperture. For all stars
observed, we report detection limits on the presence of nearby stars. ARIES is
typically sensitive to stars approximately 5.3 Ks magnitudes fainter than the
target star within 1" and approximately 5.7 Ks magnitudes fainter within 2",
but can detect stars as faint as delta Ks = 7.5 under ideal conditions.


  Molecular Dynamics (MD) simulations have been carried out to investigate the
deformation behaviour of <110>/{111} body centered cubic (BCC) Fe nanowires
under tensile and compressive loading. An embedded atom method (EAM) potential
was used to describe the interatomic interactions. The simulations were carried
out at 10 K with a constant strain rate of $1\times10^{8}$ $s^{-1}$. The
results indicate a significant differences in deformation mechanisms under
tensile and compressive loading. Under tensile loading, the deformation occurs
by the slip of full dislocations, While under compressive loading twinning was
observed as the dominant mode of deformation. The tension-compression asymmetry
in deformation mechanisms of BCC Fe nanowires is attributed to
twinning-antitwinning asymmetry of 1/6<111> partial dislocation on {112}
planes. We further explain the mechanism of dislocation pile up in tensile
loading and twin growth in compressive loading.


  Recently, the Belle Collaboration has updated the analysis of the cross
sections for the processes $e^+ e^- \to \Upsilon(nS)\, \pi^+ \pi^-$ ($n = 1,\,
2,\, 3$) in the $e^+ e^-$ center-of-mass energy range from 10.52 to 11.02 GeV.
A new structure, called here $Y_b (10750)$, with the mass $M (Y_b) = (10752.7
\pm 5.9^{+0.7}_{-1.1})$ MeV and the Breit-Wigner width $\Gamma (Y_b) =
(35.5^{+17.6 +3.9}_{-11.3 -3.3})$ MeV was observed \cite{Abdesselam:2019gth}.
We interpret $Y_b (10750)$ as a compact $J^{PC} = 1^{--}$ state with a dominant
tetraquark component. The mass eigenstate $Y_b (10750)$ is treated as a linear
combination of the diquark-antidiquark and $b \bar b$ components due to the
mixing via gluonic exchanges shown recently to arise in the limit of large
number of quark colors. The mixing angle between $Y_b$ and $\Upsilon(5S)$ can
be estimated from the electronic width, recently determined to be $\Gamma_{ee}
(Y_b) = (13.7 \pm 1.8)$ eV. The mixing provides a plausible mechanism for $Y_b
(10750)$ production in high energy collisions from its $b \bar b$ component and
we work out the Drell-Yan and prompt production cross sections for $p p \to Y_b
(10750) \to \Upsilon (nS)\, \pi^+ \pi^-$ at the LHC. The resonant part of the
dipion invariant mass spectrum in $Y_b (10750) \to \Upsilon (1S)\, \pi^+ \pi^-$
and the corresponding angular distribution of $\pi^+$-meson in the dipion rest
frame are presented as an example.


  Based on a natural connection between ResNet and transport equation or its
characteristic equation, we propose a continuous flow model for both ResNet and
plain net. Through this continuous model, a ResNet can be explicitly
constructed as a refinement of a plain net. The flow model provides an
alternative perspective to understand phenomena in deep neural networks, such
as why it is necessary and sufficient to use 2-layer blocks in ResNets, why
deeper is better, and why ResNets are even deeper, and so on. It also opens a
gate to bring in more tools from the huge area of differential equations.


  We define the Grassmannians of an infinite-dimensional vector space $V$ as
the orbits of the action of the general linear group ${\rm GL}(V)$ on the set
of all subspaces. Let ${\mathcal G}$ be one of these Grassmannians. An
apartment in ${\mathcal G}$ is the set of all elements of ${\mathcal G}$
spanned by subsets of a certain basis of $V$. We show that every bijective
transformation $f$ of ${\mathcal G}$ such that $f$ and $f^{-1}$ send apartments
to apartments is induced by a semilinear automorphism of $V$. In the case when
${\mathcal G}$ consists of subspaces whose dimension and codimension both are
infinite, a such kind result will be proved also for the connected components
of the associated Grassmann graph.


  We study properties of a random walk in a generalized Sinai model, in which a
quenched random potential is a trajectory of a fractional Brownian motion with
arbitrary Hurst parameter H, 0< H <1, so that the random force field displays
strong spatial correlations. In this case, the disorder-average mean-square
displacement grows in proportion to log^{2/H}(n), n being time. We prove that
moments of arbitrary order k of the steady-state current J_L through a finite
segment of length L of such a chain decay as L^{-(1-H)}, independently of k,
which suggests that despite a logarithmic confinement the average current is
much higher than its Fickian counterpart in homogeneous systems. Our results
reveal a paradoxical behavior such that, for fixed n and L, the mean square
displacement decreases when one varies H from 0 to 1, while the average current
increases. This counter-intuitive behavior is explained via an analysis of
representative realizations of disorder.


  Consider an arbitrary local quantum field theory with a gap or an arbitrary
gapless free theory. We consider states in such a theory, that describe two
entangled particles localized in disjoint regions of space. We show that in
such a state, to leading order, R\'{e}nyi entropies of spatial regions,
containing only one of the particles are same as their quantum mechanical
counterparts, after subtraction of vacuum contribution. Subleading corrections
depend on overlap of wave functions. These results suggest that Von Neumann
entropy of a spatial region, after subtraction of vacuum contribution, can
serve as a measure of entanglement of indistinguishable particles in pure
states.


  The nucleus of the Milky Way is known to harbour regions of intense star
formation activity as well as a super-massive black hole. Recent Fermi space
telescope observations have revealed regions of \gamma-ray emission reaching
far above and below the Galactic Centre, the so-called Fermi bubbles. It is
uncertain whether these were generated by nuclear star formation or by
quasar-like outbursts of the central black hole and no information on the
structures' magnetic field has been reported. Here we report on the detection
of two giant, linearly-polarized radio Lobes, containing three ridge-like
sub-structures, emanating from the Galactic Centre. The Lobes each extend ~60
deg, bear a close correspondence to the Fermi bubbles, are located in the
Galactic bulge, and are permeated by strong magnetic fields of up to 15 \mu G.
Our data signal that the radio Lobes originate in a bi-conical, star-formation
(rather than black hole) driven outflow from the Galaxy's central 200 pc that
transports a massive magnetic energy of ~10^55 erg into the Galactic halo. The
ridges wind around this outflow and, we suggest, constitute a `phonographic'
record of nuclear star formation activity over at least 10 Myr.


  We consider parabolic PDEs with randomly switching boundary conditions. In
order to analyze these random PDEs, we consider more general stochastic hybrid
systems and prove convergence to, and properties of, a stationary distribution.
Applying these general results to the heat equation with randomly switching
boundary conditions, we find explicit formulae for various statistics of the
solution and obtain almost sure results about its regularity and structure.
These results are of particular interest for biological applications as well as
for their significant departure from behavior seen in PDEs forced by disparate
Gaussian noise. Our general results also have applications to other types of
stochastic hybrid systems, such as ODEs with randomly switching right-hand
sides.


  Motivated by the existence of remarkably ordered cluster arrays of bacteria
colonies growing in Petri dishes and related problems, we study the spontaneous
emergence of clustering and patterns in a simple nonequilibrium system: the
individual-based interacting Brownian bug model. We map this discrete model
into a continuous Langevin equation which is the starting point for our
extensive numerical analyses. For the two-dimensional case we report on the
spontaneous generation of localized clusters of activity as well as a
melting/freezing transition from a disordered or isotropic phase to an ordered
one characterized by hexagonal patterns. We study in detail the analogies and
differences with the well-established Kosterlitz-Thouless-Halperin-Nelson-Young
theory of equilibrium melting, as well as with another competing theory. For
that, we study translational and orientational correlations and perform a
careful defect analysis. We find a non standard one-stage, defect-mediated,
transition whose nature is only partially elucidated.


  New methods for generating synthetic handwriting images for biometric
applications have recently been developed. The temporal evolution of
handwriting from childhood to adulthood is usually left unexplored in these
works. This paper proposes a novel methodology for including temporal evolution
in a handwriting synthesizer by means of simplifying the text trajectory plan
and handwriting dynamics. This is achieved through a tailored version of the
kinematic theory of rapid human movements and the neuromotor inspired
handwriting synthesizer. The realism of the proposed method has been evaluated
by comparing the temporal evolution of real and synthetic samples both
quantitatively and subjectively. The quantitative test is based on a visual
perception algorithm that compares the letter variability and the number of
strokes in the real and synthetic handwriting produced at different ages. In
the subjective test, 30 people are asked to evaluate the perceived realism of
the evolution of the synthetic handwriting.


We discuss the nature of evolution of the magnetic field in Magnetars.


  Let $(i,j)\in \mathbb{N}\times \mathbb{N}_{\geq2}$ and $S_{i,j}$ be an
infinite subset of positive integers including all prime numbers in some
arithmetic progression. In this paper, we prove the linear independence over
$\mathbb{Q}$ of the numbers \[ 1, \quad \sum_{n\in
S_{i,j}}^{}\frac{a_{i,j}(n)}{b^{in^j}},\quad (i,j)\in \mathbb{N}\times
\mathbb{N}_{\geq2}, \] where $b\geq2$ is an integer and $a_{i,j}(n)$ are
bounded nonzero integer-valued functions on $S_{i,j}$. Moreover, we also
establish a necessary and sufficient condition on the subset $\mathcal{A}$ of
$\mathbb{N}\times \mathbb{N}_{\geq2}$ for the numbers \[ 1, \quad \sum_{n\in
T_{i,j}}^{}\frac{a_{i,j}(n)}{b^{in^j}},\quad (i,j)\in \mathcal{A} \] to be
linearly independent over $\mathbb{Q}$ for any given infinite subsets $T_{i,j}$
of positive integers. Our theorems generalize a result of V. Kumar.


  We study the complexity of automatic structures via well-established concepts
from both logic and model theory, including ordinal heights (of well-founded
relations), Scott ranks of structures, and Cantor-Bendixson ranks (of trees).
We prove the following results: 1) The ordinal height of any automatic well-
founded partial order is bounded by \omega^\omega ; 2) The ordinal heights of
automatic well-founded relations are unbounded below the first non-computable
ordinal; 3) For any computable ordinal there is an automatic structure of Scott
rank at least that ordinal. Moreover, there are automatic structures of Scott
rank the first non-computable ordinal and its successor; 4) For any computable
ordinal, there is an automatic successor tree of Cantor-Bendixson rank that
ordinal.


  A class of braneworld models can lead to phantom-like acceleration of the
late universe, but without the need for any phantom matter. In the simplest
models, the universe contains only cold dark matter and a cosmological
constant. We generalize these models by introducing a quintessence field. The
new feature in our models is that quintessence leads to a crossing of the
phantom divide, $w=-1$. This is a purely gravitational effect, and there is no
phantom instability. Furthermore, the Hubble parameter is always decreasing,
and there is no big rip singularity in the future.


  In recent years, curial incidents and accidents have been reported due to
un-intended control caused by misjudgment of statistical machine learning
(SML), which include deep learning. The international functional safety
standards for Electric/Electronic/Programmable (E/E/P) systems have been widely
spread to improve safety. However, most of them do not recom-mended to use SML
in safety critical systems so far. In practical the new concepts and methods
are urgently required to enable SML to be safely used in safety critical
systems. In this paper, we organize five kinds of technical safety concepts
(TSCs) for SML components toward accordance with functional safety standards.
We discuss not only quantitative evaluation criteria, but also development
process based on XAI (eXplainable Artificial Intelligence) and Automotive SPICE
to improve explainability and reliability in development phase. Fi-nally, we
briefly compare the TSCs in cost and difficulty, and expect to en-courage
further discussion in many communities and domain.


  We show that the inter-band absorption of radiation in a 2D Dirac material
leads to a direct electric current flowing at sample edges. The photocurrent
originates from the momentum alignment of electrons and holes and is controlled
by the radiation polarization. We develop a microscopic theory of such an edge
photogalvanic effect and calculate the photocurrent for gapped and gapless 2D
Dirac materials, also in the presence of a static magnetic field which
introduces additional imbalance between the electron and hole currents.
Further, we show that the photocurrent can be considerably multiplied in a
ratchet-like structure with an array of narrow strips.


  In this work we analyze a solution that mimics the Bardeen solution with a
cosmological constant surrounded by quintessence. We show that this solution
can be obtained by Einstein equations coupled with nonlinear electrodynamics.
We also show that the solution is not always regular and what the conditions
for regularity are. We analyze the thermodynamics associated with this type of
solution by establishing the form of the Smarr formula and the first law of
thermodynamics. We obtain some thermodynamic quantities such as pressure,
temperature, heat capacity and isothermal compressibility. Once we have these
thermodynamic quantities, we check if this solution has phase transitions and
how it behaves at the points where the transitions occur. For some values of
the parameters, we find that the solution exhibits a first-order phase
transition, like a van der Waals fluid.


  In this paper we review the extragalactic propagation of ultrahigh energy
cosmic-rays (UHECR). We present the different energy loss processes of protons
and nuclei, and their expected influence on energy evolution of the UHECR
spectrum and composition. We discuss the possible implications of the recent
composition analyses provided by the Pierre Auger Observatory. The influence of
extragalactic magnetic fields and possible departures from the rectilinear case
are also mentioned as well as the production of secondary cosmogenic neutrinos
and photons and the constraints their observation would imply for the UHECRs
origin. Finally, we conclude by briefly discussing the relevance of a multi
messenger approach for solving the mystery of UHECRs.


  In offline reinforcement learning (RL) agents are trained using a logged
dataset. It appears to be the most natural route to attack real-life
applications because in domains such as healthcare and robotics interactions
with the environment are either expensive or unethical. Training agents usually
requires reward functions, but unfortunately, rewards are seldom available in
practice and their engineering is challenging and laborious. To overcome this,
we investigate reward learning under the constraint of minimizing human reward
annotations. We consider two types of supervision: timestep annotations and
demonstrations. We propose semi-supervised learning algorithms that learn from
limited annotations and incorporate unlabelled data. In our experiments with a
simulated robotic arm, we greatly improve upon behavioural cloning and closely
approach the performance achieved with ground truth rewards. We further
investigate the relationship between the quality of the reward model and the
final policies. We notice, for example, that the reward models do not need to
be perfect to result in useful policies.


  The quantum-Zeno and anti-Zeno effects (QZE/AZE) are known for a long time,
in a quantum system with coupled levels, the measurement of a particular level
population can lead to either acceleration (i.e. AZE) or retardation (i.e. QZE)
of its population transfer to other levels. Here we consider how one can
control the population flow from a coupled quantum state by measurement at a
particular time, and what system parameters are responsible for that. We
propose a framework for analysis of quantum Zeno dynamics based on
time-dependent density matrix perturbation theory. This framework allows us to
clearly separate state populations from their coherences and to predict
appearance of either QZE or AZE. We illustrate our analysis on two model
systems: 1) two coupled levels and 2) a level coupled to a continuum. In both
cases dynamics of quantum coherences play a crucial role, and perturbative
considerations allow us to predict the effect of projective measurements. In
addition, we have extended our consideration to a closely related coherent
control scenario, a unitary transformation flipping a sign of a state in a
coherent superposition describing the system wavefunction.


  Photons preferentially Compton scatter perpendicular to the plane of
polarisation. This property can be exploited to design instruments to measure
the linear polarisation of hard X-rays ($\sim$10 - 100 keV). Photons may
undergo two interactions in the sensitive volume of the instrument, i.e. a
scattering followed by an absorption. Depending on the materials used to detect
these two interactions, the Compton polarimeter can be classified as
single-phase (same material for scattering and absorption detectors) or
dual-phase (different materials). Different designs have been studied and
adopted, and current instruments are predominantly with sensors based on
scintillation- or solid-state detectors. X-ray polarimetry requires much higher
statistics than e.g. spectrometry or timing, thus systematic effects must be
accurately measured and accounted for. In this chapter we introduce the basic
formalism of the Compton effect; we describe the design schemes developed so
far for scattering polarimeters, including both the single-phase and dual-phase
approaches; we overview the calibration methods to reduce the systematic
effects; and we describe sources of background which affect the measurements.


  $k$d-trees are widely used in parallel databases to support efficient
neighborhood/similarity queries. Supporting parallel updates to $k$d-trees is
therefore an important operation. In this paper, we present BDL-tree, a
parallel, batch-dynamic implementation of a $k$d-tree that allows for efficient
parallel $k$-NN queries over dynamically changing point sets. BDL-trees consist
of a log-structured set of $k$d-trees which can be used to efficiently insert
or delete batches of points in parallel with polylogarithmic depth.
Specifically, given a BDL-tree with $n$ points, each batch of $B$ updates takes
$O(B\log^2{(n+B)})$ amortized work and $O(\log(n+B)\log\log{(n+B)})$ depth
(parallel time). We provide an optimized multicore implementation of BDL-trees.
Our optimizations include parallel cache-oblivious $k$d-tree construction and
parallel bloom filter construction.
  Our experiments on a 36-core machine with two-way hyper-threading using a
variety of synthetic and real-world datasets show that our implementation of
BDL-tree achieves a self-relative speedup of up to $34.8\times$ ($28.4\times$
on average) for batch insertions, up to $35.5\times$ ($27.2\times$ on average)
for batch deletions, and up to $46.1\times$ ($40.0\times$ on average) for
$k$-nearest neighbor queries. In addition, it achieves throughputs of up to
14.5 million updates/second for batch-parallel updates and 6.7 million
queries/second for $k$-NN queries. We compare to two baseline $k$d-tree
implementations and demonstrate that BDL-trees achieve a good tradeoff between
the two baseline options for implementing batch updates.


  Existing methods for Table Structure Recognition (TSR) from camera-captured
or scanned documents perform poorly on complex tables consisting of nested rows
/ columns, multi-line texts and missing cell data. This is because current
data-driven methods work by simply training deep models on large volumes of
data and fail to generalize when an unseen table structure is encountered. In
this paper, we propose to train a deep network to capture the spatial
associations between different word pairs present in the table image for
unravelling the table structure. We present an end-to-end pipeline, named
TSR-DSAW: TSR via Deep Spatial Association of Words, which outputs a digital
representation of a table image in a structured format such as HTML. Given a
table image as input, the proposed method begins with the detection of all the
words present in the image using a text-detection network like CRAFT which is
followed by the generation of word-pairs using dynamic programming. These
word-pairs are highlighted in individual images and subsequently, fed into a
DenseNet-121 classifier trained to capture spatial associations such as
same-row, same-column, same-cell or none. Finally, we perform post-processing
on the classifier output to generate the table structure in HTML format. We
evaluate our TSR-DSAW pipeline on two public table-image datasets -- PubTabNet
and ICDAR 2013, and demonstrate improvement over previous methods such as
TableNet and DeepDeSRT.


  Semi-supervised anomaly detection~(SSAD) is a task where normal data and a
limited number of anomalous data are available for training. In practical
situations, SSAD methods suffer adapting to domain shifts, since anomalous data
are unlikely to be available for the target domain in the training phase. To
solve this problem, we propose a domain adaptation method for SSAD where no
anomalous data are available for the target domain. First, we introduce a
domain-adversarial network to a variational auto-encoder-based SSAD model to
obtain domain-invariant latent variables. Since the decoder cannot reconstruct
the original data solely from domain-invariant latent variables, we conditioned
the decoder on the domain label. To compensate for the missing anomalous data
of the target domain, we introduce an importance sampling-based weighted loss
function that approximates the ideal loss function. Experimental results
indicate that the proposed method helps adapt SSAD models to the target domain
when no anomalous data are available for the target domain.


  (abbreviated) Measuring the surface abundances of AGB stars is an important
tool for studying the effects of nucleosynthesis and mixing in the interior of
low- to intermediate mass stars during their final evolutionary phases. The
atmospheres of AGB stars can be strongly affected by stellar pulsation and the
development of a stellar wind, though, and the abundance determination of these
objects should therefore be based on dynamic model atmospheres. We investigate
the effects of stellar pulsation and mass loss on the appearance of selected
spectral features (line profiles, line intensities) and on the derived
elemental abundances by performing a systematic comparison of hydrostatic and
dynamic model atmospheres. High-resolution synthetic spectra in the near
infrared range were calculated based on two dynamic model atmospheres (at
various phases during the pulsation cycle) as well as a grid of hydrostatic
COMARCS models. Equivalent widths of a selection of atomic and molecular lines
were derived in both cases and compared with each other. In the case of the
dynamic models, the equivalent widths of all investigated features vary over
the pulsation cycle. A consistent reproduction of the derived variations with a
set of hydrostatic models is not possible, but several individual phases and
spectral features can be reproduced well with the help of specific hydrostatic
atmospheric models. In addition, we show that the variations in equivalent
width that we found on the basis of the adopted dynamic model atmospheres agree
qualitatively with observational results for the Mira R Cas over its light
cycle. The findings of our modelling form a starting point to deal with the
problem of abundance determination in strongly dynamic AGB stars (i.e.,
long-period variables).


  We describe the impact of the full one-loop Electro-Weak terms of
O($\alpha_s\alpha^3_{EM}$) entering the electron-positron into three-jet
cross-section. We include both factorisable and non-factorisable virtual
corrections, photon bremsstrahlung but not the real emission of $W^\pm$ and $Z$
bosons. We show preliminary results and we discuss the impact of the
Electro-Weak corrections on three-jet observables.


  The existing zero-shot detection approaches project visual features to the
semantic domain for seen objects, hoping to map unseen objects to their
corresponding semantics during inference. However, since the unseen objects are
never visualized during training, the detection model is skewed towards seen
content, thereby labeling unseen as background or a seen class. In this work,
we propose to synthesize visual features for unseen classes, so that the model
learns both seen and unseen objects in the visual domain. Consequently, the
major challenge becomes, how to accurately synthesize unseen objects merely
using their class semantics? Towards this ambitious goal, we propose a novel
generative model that uses class-semantics to not only generate the features
but also to discriminatively separate them. Further, using a unified model, we
ensure the synthesized features have high diversity that represents the
intra-class differences and variable localization precision in the detected
bounding boxes. We test our approach on three object detection benchmarks,
PASCAL VOC, MSCOCO, and ILSVRC detection, under both conventional and
generalized settings, showing impressive gains over the state-of-the-art
methods. Our codes are available at
https://github.com/nasir6/zero_shot_detection.


  Consumer protection agencies are charged with safeguarding the public from
hazardous products, but the thousands of products under their jurisdiction make
it challenging to identify and respond to consumer complaints quickly. From the
consumer's perspective, online reviews can provide evidence of product defects,
but manually sifting through hundreds of reviews is not always feasible. In
this paper, we propose a system to mine Amazon.com reviews to identify products
that may pose safety or health hazards. Since labeled data for this task are
scarce, our approach combines positive unlabeled learning with domain
adaptation to train a classifier from consumer complaints submitted to the U.S.
Consumer Product Safety Commission. On a validation set of manually annotated
Amazon product reviews, we find that our approach results in an absolute F1
score improvement of 8% over the best competing baseline. Furthermore, we apply
the classifier to Amazon reviews of known recalled products; the classifier
identifies reviews reporting safety hazards prior to the recall date for 45% of
the products. This suggests that the system may be able to provide an early
warning system to alert consumers to hazardous products before an official
recall is announced.


  Medical image segmentation is a vital healthcare endeavor requiring precise
and efficient models for appropriate diagnosis and treatment. Vision
transformer (ViT)-based segmentation models have shown great performance in
accomplishing this task. However, to build a powerful backbone, the
self-attention block of ViT requires large-scale pre-training data. The present
method of modifying pre-trained models entails updating all or some of the
backbone parameters. This paper proposes a novel fine-tuning strategy for
adapting a pretrained transformer-based segmentation model on data from a new
medical center. This method introduces a small number of learnable parameters,
termed prompts, into the input space (less than 1\% of model parameters) while
keeping the rest of the model parameters frozen. Extensive studies employing
data from new unseen medical centers show that the prompt-based fine-tuning of
medical segmentation models provides excellent performance regarding the
new-center data with a negligible drop regarding the old centers. Additionally,
our strategy delivers great accuracy with minimum re-training on new-center
data, significantly decreasing the computational and time costs of fine-tuning
pre-trained models.


  Efficient approaches to quantum control and feedback are essential for
quantum technologies, from sensing to quantum computation. Open-loop control
tasks have been successfully solved using optimization techniques, including
methods like gradient-ascent pulse engineering (GRAPE), relying on a
differentiable model of the quantum dynamics. For feedback tasks, such methods
are not directly applicable, since the aim is to discover strategies
conditioned on measurement outcomes. In this work, we introduce feedback-GRAPE,
which borrows some concepts from model-free reinforcement learning to
incorporate the response to strong stochastic (discrete or continuous)
measurements, while still performing direct gradient ascent through the quantum
dynamics. We illustrate its power considering various scenarios based on cavity
QED setups. Our method yields interpretable feedback strategies for state
preparation and stabilization in the presence of noise. Our approach could be
employed for discovering strategies in a wide range of feedback tasks, from
calibration of multi-qubit devices to linear-optics quantum computation
strategies, quantum-enhanced sensing with adaptive measurements, and quantum
error correction.


  The performance of supervised semantic segmentation methods highly relies on
the availability of large-scale training data. To alleviate this dependence,
few-shot semantic segmentation (FSS) is introduced to leverage the model
trained on base classes with sufficient data into the segmentation of novel
classes with few data. FSS methods face the challenge of model generalization
on novel classes due to the distribution shift between base and novel classes.
To overcome this issue, we propose a class-shared memory (CSM) module
consisting of a set of learnable memory vectors. These memory vectors learn
elemental object patterns from base classes during training whilst re-encoding
query features during both training and inference, thereby improving the
distribution alignment between base and novel classes. Furthermore, to cope
with the performance degradation resulting from the intra-class variance across
images, we introduce an uncertainty-based feature augmentation (UFA) module to
produce diverse query features during training for improving the model's
robustness. We integrate CSM and UFA into representative FSS works, with
experimental results on the widely-used PASCAL-5$^i$ and COCO-20$^i$ datasets
demonstrating the superior performance of ours over state of the art.


  The recent detections of the gravitational wave (GW) event GW170817 and its
electromagnetic counterpart GRB 170817A produced by a binary neutron star (NS)
merger is a new milestone of multimessenger astronomy. The time interval
between these two signals has attracted widespread attention from physicists.
In the braneworld scenario, GWs could propagate through the bulk while
electromagnetic waves (EMWs) are bounded on the brane, i.e., our Universe.
Therefore, the trajectories of GWs and EMWs may follow different pathes. If GWs
and EMWs are originated simultaneously from the same source on the brane, they
are expected to arrive at the observer successively. Consequently, the time
delay between GW170817 and GRB 170817A may carry the information of the extra
dimension. In this paper, we try to investigate the phenomenon in the context
of a five-dimensional dS ($\text{dS}_5$) spacetime. We first study two special
Universe models, i.e., de Sitter and Einstein-de Sitter models, and calculate
the gravitation horizon radius in each case. For the real Universe, we then
consider the $\Lambda$CDM model. Our results show that for the de Sitter model
of the Universe, the $\text{dS}_5$ radius could not contribute to the time
delay. With the data of the observation, we constrain the $\text{dS}_5$ radius
to $\ell\gtrsim7.5\times10^{2}\,\text{Tpc}$ for the Einstein-de Sitter model
and $\ell\gtrsim2.4\times10^{3}\,\text{Tpc}$ for the $\Lambda$CDM model. After
considering the uncertainty in the source redshift and the time-lags given by
different astrophysical processes of the binary NS merger, we find that our
constraints are not sensitive to the redshift in the range of (0.005, 0.01) and
the time-lag in the range of (-100s, 1.734s).


  The usual notion of primeness for virtual links does not correspond to the
notion of primeness typically found in the literature on links in thickened
surfaces. We compare these and related notions (split, stabilized,
irreducible). Our main result, generalizing a classical result of Menasco, is
that an alternating virtual diagram $V$ represents a prime virtual link unless
$V$ is "obviously" nonprime. (Adams et al proved a related result.) We also
introduce "lassos" for virtual link diagrams and describe how to use lassos to
determine by inspection whether or not an alternating virtual diagram is weakly
prime, pairwise prime, or nonsplit. This is a companion paper to $\textit{The
virtual flyping theorem}$.


  Causal discovery amounts to unearthing causal relationships amongst features
in data. It is a crucial companion to causal inference, necessary to build
scientific knowledge without resorting to expensive or impossible randomised
control trials. In this paper, we explore how reasoning with symbolic
representations can support causal discovery. Specifically, we deploy
assumption-based argumentation (ABA), a well-established and powerful knowledge
representation formalism, in combination with causality theories, to learn
graphs which reflect causal dependencies in the data. We prove that our method
exhibits desirable properties, notably that, under natural conditions, it can
retrieve ground-truth causal graphs. We also conduct experiments with an
implementation of our method in answer set programming (ASP) on four datasets
from standard benchmarks in causal discovery, showing that our method compares
well against established baselines.


  Previously, the controllability problem of a linear time-invariant dynamical
system was mapped to the maximum matching (MM) problem on the bipartite
representation of the underlying directed graph, and the sizes of MMs on random
bipartite graphs were calculated analytically with the cavity method at zero
temperature limit. Here we present an alternative theory to estimate MM sizes
based on the core percolation theory and the perfect matching of cores. Our
theory is much more simplified and easily interpreted, and can estimate MM
sizes on random graphs with or without symmetry between out- and in-degree
distributions. Our result helps to illuminate the fundamental connection
between the controllability problem and the underlying structure of complex
systems.


  We investigate the positional order of the two-dimensional hard disk model
with short-time dynamics and equilibrium simulations. The melting density and
the critical exponents z and eta are determined. Our results rule out a phase
transition as predicted by the Kosterlitz-Thouless-Halperin-Nelson-Young theory
as well as a first-order transition.


  The C language is getting more and more popular as a design and verification
language (DVL). SystemC, ParC [1] and Cx [2] are based on C. C-models of the
design and verification environment can also be generated from new DVLs (e.g.
Chisel [3]) or classical DVLs such as VHDL or Verilog. The execution of these
models is usually license free and presumably faster than their alternative
counterparts (simulators). This paper proposes activity-dependent, ordered,
cycle-accurate (AOC) C-models to speed up simulation time. It compares the
results with alternative concepts. The paper also examines the execution of the
AOC C-model on a multithreaded processor environment.


  In real-time applications the characteristics and properties of a signal vary
inconsistently. So, to maintain the integrity of such signals there is a need
for effective adaptive filters. The conventional Least Mean Squared(LMS)
algorithm is widely used because of its computational simplicity and ease of
implementation. But, its convergence speed rapidly reduces when colored noise
is present in the signal. Affine projection(AP) algorithms are used to speed up
the convergence but have high computational costs. In this paper, the mean
square performance of LMS and AP algorithms is analyzed when subject to white
noise and colored noise.


  Using self-consistent magnetohydrodynamic (MHD) simulations, we explore the
hypothesis that nonlinear MHD waves dominate the internal dynamics of galactic
molecular clouds. We employ an isothermal equation of state and allow for
self-gravity. We adopt ``slab-symmetry,'' which permits motions $\bf v_\perp$
and fields $\bf B_\perp$ perpendicular to the mean field, but permits gradients
only parallel to the mean field. The Alfv\'en speed $v_A$ exceeds the sound
speed $c_s$ by a factor $3-30$. We simulate the free decay of a spectrum of
Alfv\'en waves, with and without self-gravity. We also perform simulations with
and without self-gravity that include small-scale stochastic forcing.
  Our major results are as follows: (1) We confirm that fluctuating transverse
fields inhibit the mean-field collapse of clouds when the energy in Alfv\'en-
like disturbances remains comparable to the cloud's gravitational binding
energy. (2) We characterize the turbulent energy spectrum and density structure
in magnetically-dominated clouds. The spectra evolve to approximately
$v_{\perp,\,k}^2\approx B_{\perp,\,k}^2/4\pi\rho\propto k^{-s}$ with $s\sim 2$,
  i.e. approximately consistent with a ``linewidth-size'' relation $\sigma_v(R)
\propto R^{1/2}$. The simulations show large density contrasts, with high
density regions confined in part by the fluctuating magnetic fields. (3) We
evaluate the input power required to offset dissipation through shocks, as a
function of $c_s/v_A$, the velocity dispersion $\sigma_v$, and the scale
$\lambda$ of the forcing. In equilibrium, the volume dissipation rate is
$5.5(c_s/v_a)^{1/2} (\lambda/L)^{-1/2}\times \rho \sigma_v^3/L$, for a cloud of
linear size $L$ and density $\rho$. (4) Somewhat speculatively, we apply our
results to a ``typical'' molecular cloud. The mechanical power input required


  As robots become more ubiquitous and capable, it becomes ever more important
to enable untrained users to easily interact with them. Recently, this has led
to study of the language grounding problem, where the goal is to extract
representations of the meanings of natural language tied to perception and
actuation in the physical world. In this paper, we present an approach for
joint learning of language and perception models for grounded attribute
induction. Our perception model includes attribute classifiers, for example to
detect object color and shape, and the language model is based on a
probabilistic categorial grammar that enables the construction of rich,
compositional meaning representations. The approach is evaluated on the task of
interpreting sentences that describe sets of objects in a physical workspace.
We demonstrate accurate task performance and effective latent-variable concept
induction in physical grounded scenes.


  A feasible route towards implementing long-distance quantum key distribution
(QKD) systems relies on probabilistic schemes for entanglement distribution and
swapping as proposed in the work of Duan, Lukin, Cirac, and Zoller (DLCZ)
[Nature 414, 413 (2001)]. Here, we calculate the conditional throughput and
fidelity of entanglement for DLCZ quantum repeaters, by accounting for the DLCZ
self-purification property, in the presence of multiple excitations in the
ensemble memories as well as loss and other sources of inefficiency in the
channel and measurement modules. We then use our results to find the generation
rate of secure key bits for QKD systems that rely on DLCZ quantum repeaters. We
compare the key generation rate per logical memory employed in the two cases of
with and without a repeater node. We find the cross-over distance beyond which
the repeater system outperforms the non-repeater one. That provides us with the
optimum inter-node distancing in quantum repeater systems. We also find the
optimal excitation probability at which the QKD rate peaks. Such an optimum
probability, in most regimes of interest, is insensitive to the total distance.


  The analysis of adaptive carrier phase estimation is investigated in
long-haul high speed n-level phase shift keying (n-PSK) optical fiber
communication systems based on the one-tap normalized least-mean-square (LMS)
algorithm. The close-form expressions for the estimated carrier phase and the
bit-error-rate floor have been derived in the n-PSK coherent optical
transmission systems. The results show that the one-tap normalized LMS
algorithm performs pretty well in the carrier phase estimation, but will be
less effective with the increment of modulation levels, in the compensation of
both intrinsic laser phase noise and equalization enhanced phase noise.


  The article gives the motivations for the measurement of the hyperfine
splitting (hfs) in the ground state of muonic hydrogen to explore the
properties of the proton at low momentum transfer. It summarizes these proposed
measurement methods and finally describes the FAMU experiment in more detail.


  Graph neural networks (GNNs) are emerging in chemical engineering for the
end-to-end learning of physicochemical properties based on molecular graphs. A
key element of GNNs is the pooling function which combines atom feature vectors
into molecular fingerprints. Most previous works use a standard pooling
function to predict a variety of properties. However, unsuitable pooling
functions can lead to unphysical GNNs that poorly generalize. We compare and
select meaningful GNN pooling methods based on physical knowledge about the
learned properties. The impact of physical pooling functions is demonstrated
with molecular properties calculated from quantum mechanical computations. We
also compare our results to the recent set2set pooling approach. We recommend
using sum pooling for the prediction of properties that depend on molecular
size and compare pooling functions for properties that are molecular
size-independent. Overall, we show that the use of physical pooling functions
significantly enhances generalization.


  One-dimensional (1D) infinite periodic systems exhibit vanishing group
velocity and diverging density of states (DOS) near band edges. However, in
practice, systems have finite sizes and inevitably this prompts the question of
whether helpful physical quantities related to infinite systems, such as the
group velocity that is deduced from the band structure, remain relevant in
finite systems. For instance, one may wonder how the DOS divergence can be
approached with finite systems. Intuitively, one may expect that the
implementation of larger and larger DOS, or equivalently smaller and smaller
group velocities, would critically increase the system length. Based on general
1D-wave-physics arguments, we demonstrate that the large slow-light DOS
enhancement of periodic systems can be observed with very short systems, whose
lengths scale with the logarithm of the inverse of the group velocities. The
understanding obtained for 1D systems leads us to propose a novel sort of
microstructure to enhance light-matter interaction, a sort of photonic speed
bump that abruptly changes the speed of light by a few orders of magnitude
without any reflection. We show that the DOS enhancements of speed bumps result
from a classical electromagnetic resonance characterized by a single resonance
mode and also that the nature and the properties of the resonance are markedly
different from those of classical defect-mode photonic-crystal cavities.


  We experimentally demonstrate a three-dimensional chiral optical metamaterial
that exhibits an asymmetric transmission for forwardly and backwardly
propagating linearly polarized light. The observation of this novel effect
requires a metamaterial composed of three-dimensional chiral metaatoms without
any rotational symmetry. Our analysis is supported by a systematic
investigation of the transmission matrices for arbitrarily complex, lossy media
that allows deriving a simple criterion for asymmetric transmission in an
arbitrary polarization base. Contrary to physical intuition, in general the
polarization eigenstates in such three-dimensional and low-symmetry
metamaterials do not obey fxed relations and the associated transmission
matrices cannot be symmetrized.


  Deep generative models, such as diffusion models, GANs, and IMLE, have shown
impressive capability in tackling inverse problems. However, the validity of
model-generated solutions w.r.t. the forward problem and the reliability of
associated uncertainty estimates remain understudied. This study evaluates
recent diffusion-based, GAN-based, and IMLE-based methods on three inverse
problems, i.e., $16\times$ super-resolution, colourization, and image
decompression. We assess the validity of these models' outputs as solutions to
the inverse problems and conduct a thorough analysis of the reliability of the
models' estimates of uncertainty over the solution. Overall, we find that the
IMLE-based CHIMLE method outperforms other methods in terms of producing valid
solutions and reliable uncertainty estimates.


  This paper proposes a novel reinforcement learning (RL) algorithm using
improved Monte Carlo tree search (IMCTS) formulation for discrete optimum
design of truss structures. IMCTS with multiple root nodes includes update
process, the best reward, accelerating technique, and terminal condition.
Update process means that once a final solution is found, it is used as the
initial solution for next search tree. The best reward is used in the
backpropagation step. Accelerating technique is introduced by decreasing the
width of search tree and reducing maximum number of iterations. The agent is
trained to minimize the total structural weight under various constraints until
the terminal condition is satisfied. Then, optimal solution is the minimum
value of all solutions found by search trees. These numerical examples show
that the agent can find optimal solution with low computational cost, stably
produces an optimal design, and is suitable for multi-objective structural
optimization and large-scale structures.


  I investigate the discrepancy between the evolution and pulsation masses for
Cepheid variables. A number of recent works have proposed that non-canonical
mass-loss can account for the mass discrepancy. This mass-loss would be such
that a 5Mo star loses approximately 20% of its mass by arriving at the Cepheid
instability strip; a 14Mo star, none. Such findings would pose a serious
challenge to our understanding of mass-loss. I revisit these results in light
of the Padova stellar evolutionary models and find evolutionary masses are
($17\pm5$)% greater than pulsation masses for Cepheids between 5<M/Mo<14. I
find that mild internal mixing in the main-sequence progenitor of the Cepheid
are able to account for this mass discrepancy.


  Jet quenching studies play a prominent role in our current understanding of
ultra-relativistic heavy ion collisions. In this review I first present the
available formalism to compute medium-induced gluon radiation. Then I discuss
its effect on single particle spectra, with dedicated attention to the case of
the radiating parton being a massive quark. Next I examine more differential
observables like jet shapes and multiplicities, and the consequences of flow on
radiative energy loss. I conclude with some remarks.


  We explore the physics of crystallization in the deep interiors of white
dwarf stars using the color-magnitude diagram and luminosity function
constructed from proper motion cleaned Hubble Space Telescope photometry of the
globular cluster NGC 6397. We demonstrate that the data are consistent with the
theory of crystallization of the ions in the interior of white dwarf stars and
provide the first empirical evidence that the phase transition is first order:
latent heat is released in the process of crystallization as predicted by van
Horn (1968). We outline how this data can be used to observationally constrain
the value of Gamma = E_{Coulomb}/E_{thermal} near the onset of crystallization,
the central carbon/oxygen abundance, and the importance of phase separation.


  There have lately been several suggestions for parametrized distances on a
graph that generalize the shortest path distance and the commute time or
resistance distance. The need for developing such distances has risen from the
observation that the above-mentioned common distances in many situations fail
to take into account the global structure of the graph. In this article, we
develop the theory of one family of graph node distances, known as the
randomized shortest path dissimilarity, which has its foundation in statistical
physics. We show that the randomized shortest path dissimilarity can be easily
computed in closed form for all pairs of nodes of a graph. Moreover, we come up
with a new definition of a distance measure that we call the free energy
distance. The free energy distance can be seen as an upgrade of the randomized
shortest path dissimilarity as it defines a metric, in addition to which it
satisfies the graph-geodetic property. The derivation and computation of the
free energy distance are also straightforward. We then make a comparison
between a set of generalized distances that interpolate between the shortest
path distance and the commute time, or resistance distance. This comparison
focuses on the applicability of the distances in graph node clustering and
classification. The comparison, in general, shows that the parametrized
distances perform well in the tasks. In particular, we see that the results
obtained with the free energy distance are among the best in all the
experiments.


  Recent advances in deep clustering and unsupervised representation learning
are based on the idea that different views of an input image (generated through
data augmentation techniques) must either be closer in the representation
space, or have a similar cluster assignment. Bootstrap Your Own Latent (BYOL)
is one such representation learning algorithm that has achieved
state-of-the-art results in self-supervised image classification on ImageNet
under the linear evaluation protocol. However, the utility of the learnt
features of BYOL to perform clustering is not explored. In this work, we study
the clustering ability of BYOL and observe that features learnt using BYOL may
not be optimal for clustering. We propose a novel consensus clustering based
loss function, and train BYOL with the proposed loss in an end-to-end way that
improves the clustering ability and outperforms similar clustering based
methods on some popular computer vision datasets.


  We propose a numerical approach, of the BGK kinetic type, that is able to
approximate with a given, but arbitrary, order of accuracy the solution of
linear and non-linear convection-diffusion type problems: scalar
advection-diffusion, non-linear scalar problems of this type and the
compressible Navier-Stokes equations. Our kinetic model can use \emph{finite}
advection speeds that are independent of the relaxation parameter, and the time
step does not suffer from a parabolic constraint. Having finite speeds is in
contrast with many of the previous works about this kind of approach, and we
explain why this is possible: paraphrasing more or less
\cite{golse:hal-00859451}, the convection-diffusion like PDE is not a limit of
the BGK equation, but a correction of the same PDE without the parabolic term
at the second order in the relaxation parameter that is interpreted as Knudsen
number. We then show that introducing a matrix collision instead of the
well-known BGK relaxation makes it possible to target a desired
convection-diffusion system.
  Several numerical examples, ranging from a simple pure diffusion model to the
compressible Navier-Stokes equations illustrate our approach


  Paul Erdos asked how frequently the ternary expansion of 2^n omits the digit
2. He conjectured this happens only for finitely many values of n. We
generalize this question to consider iterates of two discrete dynamical
systems. The first is over the real numbers, and considers the integer part of
lambda 2^n for a real input lambda. The second is over the 3-adic integers, and
considers the sequence lambda 2^n for a 3-adic integer input lambda.
  We show that the number of input values that have infinitely many iterates
omitting the digit 2 in their ternary expansion is small in a suitable sense.
For each nonzero input we give an asymptotic upper bound on the number of the
first k iterates that omit the digit 2, as k goes to infinity. We also study
auxiliary problems concerning the Hausdorff dimension of intersections of
multiplicative translates of 3-adic Cantor sets.


  The interface properties between gold (Au) contacts and molybdenum disulfide
(MoS2) are critical for optimizing the performance of semiconductor devices.
This study investigates the impact of metal dopants (D) on the transport
properties of MoS2 devices with top Au contacts, aiming to reduce contact
resistance and enhance device performance. Using density functional theory
(DFT) and non-equilibrium Green's function (NEGF)- based first-principles
calculations, we examine the structural, electronic, and quantum transport
properties of Au-contacted, metal-doped MoS2. Our results indicate that Cd, Re,
and Ru dopants significantly improve the structural stability and electronic
properties of MoS2. Specifically, formation energy calculations show that Cd
and Re are stable at hollow sites, while Ru prefers bond sites. Remarkably,
Au-Ru-MoS2-based device exhibits tunnel resistance (RT ) up to 4.82 ohm-um.
Furthermore, a dual-gated Au-Ru-MoS2 field effect transistor (FET) demonstrates
an impressive Ion/Ioff ratio of 10^8 at Vgs of 2 V, highlighting its potential
for nano-switching applications.


  The first generation of E-ELT instruments will include an optical-infrared
High Resolution Spectrograph, conventionally indicated as EELT-HIRES, which
will be capable of providing unique breakthroughs in the fields of exoplanets,
star and planet formation, physics and evolution of stars and galaxies,
cosmology and fundamental physics. A 2-year long phase A study for EELT-HIRES
has just started and will be performed by a consortium composed of institutes
and organisations from Brazil, Chile, Denmark, France, Germany, Italy, Poland,
Portugal, Spain, Sweden, Switzerland and United Kingdom. In this paper we
describe the science goals and the preliminary technical concept for EELT-HIRES
which will be developed during the phase A, as well as its planned development
and consortium organisation during the study.


  Consider bivariate observations $(X_1,Y_1), \ldots, (X_n,Y_n) \in
\mathbb{R}\times \mathbb{R}$ with unknown conditional distributions $Q_x$ of
$Y$, given that $X = x$. The goal is to estimate these distributions under the
sole assumption that $Q_x$ is isotonic in $x$ with respect to likelihood ratio
order. If the observations are identically distributed, a related goal is to
estimate the joint distribution $\mathcal{L}(X,Y)$ under the sole assumption
that it is totally positive of order two in a certain sense. An algorithm is
developed which estimates the unknown family of distributions $(Q_x)_x$ via
empirical likelihood. The benefit of the stronger regularization imposed by
likelihood ratio order over the usual stochastic order is evaluated in terms of
estimation and predictive performances on simulated as well as real data.


  The quantum approximate optimization algorithm (QAOA) is a hybrid variational
quantum-classical algorithm that solves combinatorial optimization problems.
While there is evidence suggesting that the fixed form of the standard QAOA
ansatz is not optimal, there is no systematic approach for finding better
ans\"atze. We address this problem by developing an iterative version of QAOA
that is problem-tailored, and which can also be adapted to specific hardware
constraints. We simulate the algorithm on a class of Max-Cut graph problems and
show that it converges much faster than the standard QAOA, while simultaneously
reducing the required number of CNOT gates and optimization parameters. We
provide evidence that this speedup is connected to the concept of shortcuts to
adiabaticity.


  The existing work in cross-view geo-localization is based on images where a
ground panorama is matched to an aerial image. In this work, we focus on ground
videos instead of images which provides additional contextual cues which are
important for this task. There are no existing datasets for this problem,
therefore we propose GAMa dataset, a large-scale dataset with ground videos and
corresponding aerial images. We also propose a novel approach to solve this
problem. At clip-level, a short video clip is matched with corresponding aerial
image and is later used to get video-level geo-localization of a long video.
Moreover, we propose a hierarchical approach to further improve the clip-level
geolocalization. It is a challenging dataset, unaligned and limited field of
view, and our proposed method achieves a Top-1 recall rate of 19.4% and 45.1%
@1.0mile. Code and dataset are available at following link:
https://github.com/svyas23/GAMa.


  Multiple Indicator Cluster Surveys (MICS), supported by UNICEF, are one of
the most important global household survey programs that provide data on health
and education of women and children. We analyze the Serbia 2014-15 MICS dataset
using topological data analysis which treats the data cloud as a topological
space and extracts information about its intrinsic geometric properties. In
particular, our analysis uses the Mapper algorithm, a dimension-reduction and
clustering method which produces a graph from the data cloud. The resulting
Mapper graph provides insight into various relationships between household
wealth - as expressed by the wealth index, an important indicator extracted
from the MICS data - and other parameters such as urban/rural setting,
ownership of items, and prioritization of possessions. Among other uses, these
findings can serve to inform policy by providing a hierarchy of essential
amenities. They can also potentially be used to refine the wealth index or
deepen our understanding of what it captures.


  Assessing pain in patients unable to speak (also called non-verbal patients)
is extremely complicated and often is done by clinical judgement. However, this
method is not reliable since patients vital signs can fluctuate significantly
due to other underlying medical conditions. No objective diagnosis test exists
to date that can assist medical practitioners in the diagnosis of pain. In this
study we propose the use of functional near-infrared spectroscopy (fNIRS) and
deep learning for the assessment of human pain. The aim of this study is to
explore the use deep learning to automatically learn features from fNIRS raw
data to reduce the level of subjectivity and domain knowledge required in the
design of hand-crafted features. Four deep learning models were evaluated,
multilayer perceptron (MLP), forward and backward long short-term memory
net-works (LSTM), and bidirectional LSTM. The results showed that the Bi-LSTM
model achieved the highest accuracy (90.6%)and faster than the other three
models. These results advance knowledge in pain assessment using neuroimaging
as a method of diagnosis and represent a step closer to developing a
physiologically based diagnosis of human pain that will benefit vulnerable
populations who cannot self-report pain.


  We prove that, consistently, there exists a weakly but not strongly
inaccessible cardinal $\lambda$ for which the sequence $\langle
2^\theta:\theta<\lambda\rangle$ is not eventually constant and the weak diamond
fails at $\lambda$. We also prove that consistently diamond fails but a
parametrized version of weak diamond holds at some strongly inaccessible
$\lambda$.


  Unlike the case when using a balanced training dataset, the per-class recall
(i.e., accuracy) of neural networks trained with an imbalanced dataset are
known to vary a lot from category to category. The convention in long-tailed
recognition is to manually split all categories into three subsets and report
the average accuracy within each subset. We argue that under such an evaluation
setting, some categories are inevitably sacrificed. On one hand, focusing on
the average accuracy on a balanced test set incurs little penalty even if some
worst performing categories have zero accuracy. On the other hand, classes in
the "Few" subset do not necessarily perform worse than those in the "Many" or
"Medium" subsets. We therefore advocate to focus more on improving the lowest
recall among all categories and the harmonic mean of all recall values.
Specifically, we propose a simple plug-in method that is applicable to a wide
range of methods. By simply re-training the classifier of an existing
pre-trained model with our proposed loss function and using an optional
ensemble trick that combines the predictions of the two classifiers, we achieve
a more uniform distribution of recall values across categories, which leads to
a higher harmonic mean accuracy while the (arithmetic) average accuracy is
still high. The effectiveness of our method is justified on widely used
benchmark datasets.


  The new version of the Ultra-relativistic Quantum Molecular Dynamics model
(UrQMD-2.3) is presented. The Ultra-relativistic Quantum Molecular Dynamics
model (UrQMD) is a microscopic many body approach to pp, pA and AA interactions
at relativistic energies. The major updates and changes are explained and a
comparison to the previous version (UrQMD-1.3p1) in the context of the
available data is performed. The plots and numerical data tables for hadron
(i.e. $\pi$, K, p, $\bar{p}$, $\Lambda$, $\bar{\Lambda}$, $\Xi$, $\bar{\Xi}$,
$\Omega$, $\bar{\Omega}$) multiplicities, and mean transverse mass momenta,
$<m_T > -m_0$ excitation functions, transverse mass spectra and rapidity
distributions in pp and central Au+Au/Pb+Pb reactions from $E_{\rm lab}=2A $GeV
to $\sqrt{s}_{\rm NN}=200$ GeV are provided in this paper. The source code of
UrQMD-2.3 is available at http://www.th.physik.uni-frankfurt.de/~urqmd


  Bayesian networks are powerful statistical models to study the probabilistic
relationships among set random variables with major applications in disease
modeling and prediction. Here, we propose a continuous time Bayesian network
with conditional dependencies, represented as Poisson regression, to model the
impact of exogenous variables on the conditional dependencies of the network.
We also propose an adaptive regularization method with an intuitive early
stopping feature based on density based clustering for efficient learning of
the structure and parameters of the proposed network. Using a dataset of
patients with multiple chronic conditions extracted from electronic health
records of the Department of Veterans Affairs we compare the performance of the
proposed approach with some of the existing methods in the literature for both
short-term (one-year ahead) and long-term (multi-year ahead) predictions. The
proposed approach provides a sparse intuitive representation of the complex
functional relationships between multiple chronic conditions. It also provides
the capability of analyzing multiple disease trajectories over time given any
combination of prior conditions.


  (abridged) We present a comprehensive study of a sample of 23 genuine
radio-loud NLS1 galaxies which have the radio-loudness parameters greater than
100. The radio sources of the sample are ubiquitously compact. A significant
fraction of these objects show interesting radio to X-ray properties that are
unusual to most of the previously known radio-loud NLS1 AGN, but are
reminiscent of blazars. These include flat radio spectra, large amplitude flux
and spectral variability, compact VLBI cores, very high brightness temperatures
derived from variability, enhanced optical emission in excess of the normal
ionising continuum, flat X-ray spectra, and blazar-like SEDs. We interpret them
as evidence for the postulated blazar nature of these very radio-loud NLS1 AGN,
which might possess at least moderately relativistic jets. Intrinsically, some
of the objects have relatively low radio power and would have been classified
as radio-intermediate AGN. The black hole masses are estimated to be within
10^{6-8}Msun, and the inferred Eddington ratios are around unity. The results
imply that radio-loud AGN may be powered by black holes with moderate masses
(10^{6-7}Msun) accreting at high rates. We find that a significant fraction of
the objects, despite having strong emission lines, resemble high-energy peaked
BL Lacs (HBL) in their SED. Given the peculiarities of blazar-like NLS1
galaxies, questions arise as to whether they are plain downsizing extensions of
normal radio-loud AGN, or whether they form a previously unrecognised
population.


  We study sample-to-sample fluctuations of the gap ratio in the energy spectra
in finite disordered spin chains. The chains are described by the random-field
Ising model and the Heisenberg model. We show that away from the
ergodic/nonergodic crossover, the fluctuations are correctly captured by the
Rosenzweig-Porter (RP) model. However, fluctuations in the microscopic models
significantly exceed those in the RP model in the vicinity of the crossover. We
show that upon introducing an extension to the RP model, one correctly
reproduces the fluctuations in all regimes, i.e., in the ergodic and nonergodic
regimes as well as at the crossover between them. Finally, we demonstrate how
to reduce the sample-to-sample fluctuations in both studied microscopic models.


  Many important real-world applications involve time-series data with skewed
distribution. Compared to conventional imbalance learning problems, the
classification of imbalanced time-series data is more challenging due to high
dimensionality and high inter-variable correlation. This paper proposes a
structure preserving Oversampling method to combat the High-dimensional
Imbalanced Time-series classification (OHIT). OHIT first leverages a
density-ratio based shared nearest neighbor clustering algorithm to capture the
modes of minority class in high-dimensional space. It then for each mode
applies the shrinkage technique of large-dimensional covariance matrix to
obtain accurate and reliable covariance structure. Finally, OHIT generates the
structure-preserving synthetic samples based on multivariate Gaussian
distribution by using the estimated covariance matrices. Experimental results
on several publicly available time-series datasets (including unimodal and
multimodal) demonstrate the superiority of OHIT against the state-of-the-art
oversampling algorithms in terms of F1, G-mean, and AUC.


  Learning sentence embeddings is a fundamental problem in natural language
processing. While existing research primarily focuses on enhancing the quality
of sentence embeddings, the exploration of sentence embedding dimensions is
limited. Here we present a comprehensive and empirical analysis of the
dimensionality of sentence embeddings. First, we demonstrate that the optimal
dimension of sentence embeddings is usually smaller than the default value.
Subsequently, to compress the dimension of sentence embeddings with minimum
performance degradation, we identify two components contributing to the overall
performance loss: the encoder's performance loss and the pooler's performance
loss. Therefore, we propose a two-step training method for sentence
representation learning models, wherein the encoder and the pooler are
optimized separately to mitigate the overall performance loss in low-dimension
scenarios. Experimental results on seven STS tasks and seven sentence
classification tasks demonstrate that our method significantly improves the
performance of low-dimensional sentence embeddings.


  The packing chromatic number $\chi_\rho(G)$ of a graph $G$ is the smallest
integer $k$ needed to proper color the vertices of $G$ in such a way the
distance between any two vertices having color $i$ be at least $i+1$. We obtain
$\chi_\rho(H_{q,m})$ for $m=3$, where $H_{q,m}$ is the Hamming graph of words
of length $m$ and alphabet with $q$ symbols, and tabulate bounds of them for $m
\geq 4$ up to 10000 vertices. We also give a polynomial reduction from the
problem of finding $\chi_\rho(G)$ to the Maximum Stable Set problem.


  The factorial moments (FM) of multiplicity distributions in one- and
two-dimensions are studied for Pb+Pb collisions at 158 AGeV/c. The results are
compared with FRITIOF, RQMD and VENUS predictions. In the relation between FM
and the number of partitions, $M$, obvious {\it positive} intermittence
exponents, $\phi_q$, are observed. The exponents, $\phi_q$, are compared with
the anomalous dimensions, $d_q$. Then they are expressed in terms of L\'evy
stable indices, $\mu$. Meanwhile the experimental data within the interval,
$2<\eta <6$, fulfills the requirements of the L\'evy stable region ($0 \le \mu
\le 2$), the results from the other intervals are away from this region. All
their corresponding indices have negative values.


  Optimization of the stability of synchronized states between a pair of
symmetrically coupled reaction-diffusion systems exhibiting rhythmic
spatiotemporal patterns is studied in the framework of the phase reduction
theory. The optimal linear filter that maximizes the linear stability of the
in-phase synchronized state is derived for the case where the two systems are
linearly coupled. The nonlinear optimal interaction function that theoretically
gives the largest linear stability of the in-phase synchronized state is also
derived. The theory is illustrated by using typical rhythmic patterns in
FitzHugh-Nagumo systems as examples.


  Recent years have seen a rapid rise of artificial neural networks being
employed in a number of cognitive tasks. The ever-increasing computing
requirements of these structures have contributed to a desire for novel
technologies and paradigms, including memristor-based hardware accelerators.
Solutions based on memristive crossbars and analog data processing promise to
improve the overall energy efficiency. However, memristor nonidealities can
lead to the degradation of neural network accuracy, while the attempts to
mitigate these negative effects often introduce design trade-offs, such as
those between power and reliability. In this work, we design nonideality-aware
training of memristor-based neural networks capable of dealing with the most
common device nonidealities. We demonstrate the feasibility of using
high-resistance devices that exhibit high $I$-$V$ nonlinearity -- by analyzing
experimental data and employing nonideality-aware training, we estimate that
the energy efficiency of memristive vector-matrix multipliers is improved by
three orders of magnitude ($0.715\ \mathrm{TOPs}^{-1}\mathrm{W}^{-1}$ to $381\
\mathrm{TOPs}^{-1}\mathrm{W}^{-1}$) while maintaining similar accuracy. We show
that associating the parameters of neural networks with individual memristors
allows to bias these devices towards less conductive states through
regularization of the corresponding optimization problem, while modifying the
validation procedure leads to more reliable estimates of performance. We
demonstrate the universality and robustness of our approach when dealing with a
wide range of nonidealities.


  We present a new high-resolution N-body algorithm for cosmological
simulations. The algorithm employs a traditional particle-mesh technique on a
cubic grid and successive multilevel relaxations on the finer meshes,
introduced recursively in a fully adaptive manner in the regions where the
density exceeds a predefined threshold. The mesh is generated to effectively
match an arbitrary geometry of the underlying density field -- a property
particularly important for cosmological simulations. In a simulation the mesh
structure is not created at every time step but is properly adjusted to the
evolving particle distribution. The algorithm is fast and effectively parallel.
We present a detailed description of the methodology, implementation, and tests
of the code.
  We further use the code to study the structure of dark matter halos in
high-resolution (2/h kpc) simulations of standard CDM (Omega=1, h=0.5,
sigma_8=0.63) and LCDM (Omega_Lambda=1-Omega_0=0.7, h=0.7, sigma_8=1.0) models.
We find that halo density profiles in both CDM and LCDM models are well fitted
by the analytical model presented recently by Navarro et al., which predicts a
singular [$\rho(r)\propto r^{-1}$] behavior of the halo density profiles at
small radii. We therefore conclude that halos formed in the $\Lambda$CDM model
have structure similar to CDM halos and thus cannot explain the dynamics of the
central parts of dwarf spiral galaxies, as inferred from the galaxies' rotation
curves.


  We simulate two variants of quenched twisted mass QCD (tmQCD), with
degenerate Wilson quarks of masses equal to or heavier than half the strange
quark mass. We use Ward identities in order to measure the twist angles of the
theory and thus check the quality of the tuning of mass parameters to a physics
condition which stays constant as the lattice spacing is varied. Flavour
symmetry breaking in tmQCD is studied in a framework of two fully twisted and
two standard Wilson quark flavours, tuned to be degenerate in the continuum.
Comparing pseudoscalar masses, obtained from connected quark diagrams made of
tmQCD and/or standard Wilson quark propagators, we confirm that flavour
symmetry breaking effects, which are at most 5%, decrease as we approach the
continuum limit. We also compute the pseudoscalar decay constant in the
continuum limit, with reduced systematics. As a consequence of improved tuning
of the mass parameters at $\beta = 6.1$, we reanalyse our previous $B_K$
results. Our main phenomenological findings are $r_0 f_K = 0.421(7)$ and $\hat
B_K = 0.735(71)$.


  We use ALEPH data on hadronic $\tau$ decays in order to calculate Euclidean
coordinate space correlation functions in the vector and axial-vector channels.
The linear combination $V-A$ receives no perturbative contribution and is
quantitatively reproduced by the instanton liquid model. In the case of $V+A$
the instanton calculation is in good agreement with the data once perturbative
corrections are included. These corrections clearly show the evolution of
$\alpha_s$. We also analyze the range of validity of the Operator Product
Expansion (OPE). In the $V-A$ channel we find a dimension $d=6$ contribution
which is comparable to the original SVZ estimate, but the instanton model
provides a different non-singular term of the same magnitude. In the $V+A$ case
both the OPE and the instanton model predict the same $d=4$ power correction
induced by the gluon condensate, but it is masked by much larger perturbative
contributions. We conclude that the range of validity of the OPE is limited to
$x\lsim0.3$ fm, whereas the instanton model describes the data over the entire
range.


  The fields produced by a long beam with a given transverse charge
distribution in a homogeneous vacuum chamber are studied. Signals induced by a
displaced finite-size beam on electrodes of a beam position monitor (BPM) are
calculated and compared to those produced by a pencil beam. The non-linearities
and corrections to BPM signals due to a finite transverse beam size are
calculated for an arbitrary chamber cross section. Simple analytical
expressions are given for a few particular transverse distributions of the beam
current in a circular or rectangular chamber. Of particular interest is a
general proof that in an arbitrary homogeneous chamber the beam-size
corrections vanish for any axisymmetric beam current distribution.


  These notes are designed for those who either plan to work in differential
geometry, or at least want to have a good reason not to do it. We discuss
smooth curves and surfaces -- the main gate to differential geometry. We focus
on the techniques that are absolutely essential for further study, keeping it
problem-centered, elementary, visual, and virtually rigorous.


  This paper studies the set of finite groups appearing as
$\pi_1(M)/\pi_1(M)^{(n)}$, where $M$ is a closed, orientable 3-manifold and
$\pi_1(M)^{(n)}$ denotes the $n$-th term of the derived series of $\pi_1(M)$.
Our main result is that if $M$ is a closed, orientable 3-manifold, $n\ge 2$,
and $G\cong \pi_1(M)/\pi_1(M)^{(n)}$ is finite, then the cup product pairing
$H^2(G)\otimes H^2(G)\to H^4(G)$ has cyclic image $C$, and the pairing
$H^2(G)\otimes H^2(G)\stackrel{\smile}{\longrightarrow} C$ is isomorphic to the
linking pairing $H_1(M)_{\textrm{Tors}}\otimes H_1(M)_{\textrm{Tors}}\to
\mathbb{Q}/\mathbb{Z}$.


  A group of trans-Neptunian objects (TNO) are dynamically related to the dwarf
planet 136108 Haumea. Ten of them show strong indications of water ice on their
surfaces, are assumed to have resulted from a collision, and are accepted as
the only known TNO collisional family. Nineteen other dynamically similar
objects lack water ice absorptions and are hypothesized to be dynamical
interlopers. We have made observations to determine sizes and geometric albedos
of six of the accepted Haumea family members and one dynamical interloper. Ten
other dynamical interlopers have been measured by previous works. We compare
the individual and statistical properties of the family members and
interlopers, examining the size and albedo distributions of both groups. We
also examine implications for the total mass of the family and their ejection
velocities. We use far-infrared space-based telescopes to observe the target
TNOs near their thermal peak and combine these data with optical magnitudes to
derive sizes and albedos using radiometric techniques. We determine the
power-law slope of ejection velocity as a function of effective diameter. The
detected Haumea family members have a diversity of geometric albedos $\sim$
0.3-0.8, which are higher than geometric albedos of dynamically similar objects
without water ice. The median geometric albedo for accepted family members is
$p_V=0.48_{-0.18}^{+0.28}$, compared to 0.08$_{-0.05}^{+0.07}$ for the
dynamical interlopers. In the size range $D=175-300$ km, the slope of the
cumulative size distribution is $q$=3.2$_{-0.4}^{+0.7}$ for accepted family
members, steeper than the $q$=2.0$\pm$0.6 slope for the dynamical interlopers
with D$< $500 km. The total mass of Haumea's moons and family members is 2.4%
of Haumea's mass. The ejection velocities required to emplace them on their
current orbits show a dependence on diameter, with a power-law slope of
0.21-0.50.


  The problem of team formation in a social network asks for a set of
individuals who not only have the required skills to perform a task but who can
also communicate effectively with each other. Existing work assumes that all
links in a social network are positive, that is, they indicate friendship or
collaboration between individuals. However, it is often the case that the
network is signed, that is, it contains both positive and negative links,
corresponding to friend and foe relationships. Building on the concept of
structural balance, we provide definitions of compatibility between pairs of
users in a signed network, and algorithms for computing it. We then define the
team formation problem in signed networks, where we ask for a compatible team
of individuals that can perform a task with small communication cost. We show
that the problem is NP-hard even when there are no communication cost
constraints, and we provide heuristic algorithms for solving it. We present
experimental results with real data to investigate the properties of the
different compatibility definitions, and the effectiveness of our algorithms.


  One of the main challenges in identifying structural changes in stochastic
processes is to carry out analysis for time series with dependency structure in
a computationally tractable way. Another challenge is that the number of true
change points is usually unknown, requiring a suitable model selection
criterion to arrive at informative conclusions. To address the first challenge,
we model the data generating process as a segment-wise autoregression, which is
composed of several segments (time epochs), each of which modeled by an
autoregressive model. We propose a multi-window method that is both effective
and efficient for discovering the structural changes. The proposed approach was
motivated by transforming a segment-wise autoregression into a multivariate
time series that is asymptotically segment-wise independent and identically
distributed. To address the second challenge, we derive theoretical guarantees
for (almost surely) selecting the true number of change points of segment-wise
independent multivariate time series. Specifically, under mild assumptions, we
show that a Bayesian Information Criterion (BIC)-like criterion gives a
strongly consistent selection of the optimal number of change points, while an
Akaike Information Criterion (AIC)-like criterion cannot. Finally, we
demonstrate the theory and strength of the proposed algorithms by experiments
on both synthetic and real-world data, including the Eastern US temperature
data and the El Nino data from 1854 to 2015. The experiment leads to some
interesting discoveries about temporal variability of the summer-time
temperature over the Eastern US, and about the most dominant factor of ocean
influence on climate.


  Thanks to decades of observations using HST, the structure of galaxies at
redshift $z>2$ has been widely studied in the rest-frame ultraviolet regime,
which traces recent star formation from young stellar populations. But, we
still have little information about the spatial distribution of the older, more
evolved, stellar populations, constrained by the rest-frame infrared portion of
galaxies' spectral energy distribution. We present the morphological
characterization of a sample of 21 massive galaxies
($\log(M_{\star}/M_{\odot})>9.5$) at redshift $3<z<5.5$. These galaxies are
observed as part of the GTO program MIDIS with the Mid-Infrared Instrument
(MIRI) onboard JWST. The deep MIRI 5.6~$\mu$m imaging allows us to characterize
for the first time the rest-frame near-infrared structure of galaxies beyond
cosmic noon, at higher redshifts than possible with NIRCam, tracing their older
stellar populations. We derive the galaxies' non-parametric morphology and
model the galaxies' light distribution with a S\'ersic component. We find that
at $z>3$ massive galaxies show a smooth distribution of their rest-infrared
light, strongly supporting the increasing number of regular disk galaxies
already in place at early epochs. On the contrary, the ultraviolet structure
obtained from HST observations is generally more irregular, catching the most
recent episodes of star formation. Importantly, we find a segregation of
morphologies across cosmic time, having massive galaxies at redshift $z>4$
later-type morphologies compared to $z\sim3$ galaxies. These findings suggest a
transition phase in galaxy assembly and central mass build up already taking
place at $z\sim3-4$. MIRI provides unique information about the structure of
the mature stellar population of high-redshift galaxies, unveiling that massive
galaxies beyond cosmic noon are prevalently compact disk galaxies with smooth
mass distribution.