  We apply reinforcement learning (RL) to robotics tasks. One of the drawbacks
of traditional RL algorithms has been their poor sample efficiency. One
approach to improve the sample efficiency is model-based RL. In our model-based
RL algorithm, we learn a model of the environment, essentially its transition
dynamics and reward function, use it to generate imaginary trajectories and
backpropagate through them to update the policy, exploiting the
differentiability of the model. Intuitively, learning more accurate models
should lead to better model-based RL performance. Recently, there has been
growing interest in developing better deep neural network based dynamics models
for physical systems, by utilizing the structure of the underlying physics. We
focus on robotic systems undergoing rigid body motion without contacts. We
compare two versions of our model-based RL algorithm, one which uses a standard
deep neural network based dynamics model and the other which uses a much more
accurate, physics-informed neural network based dynamics model. We show that,
in model-based RL, model accuracy mainly matters in environments that are
sensitive to initial conditions, where numerical errors accumulate fast. In
these environments, the physics-informed version of our algorithm achieves
significantly better average-return and sample efficiency. In environments that
are not sensitive to initial conditions, both versions of our algorithm achieve
similar average-return, while the physics-informed version achieves better
sample efficiency. We also show that, in challenging environments,
physics-informed model-based RL achieves better average-return than
state-of-the-art model-free RL algorithms such as Soft Actor-Critic, as it
computes the policy-gradient analytically, while the latter estimates it
through sampling.


  Structural, vibrational, and lattice dielectric properties of
CdCu{3}Ti{4}O{12} are studied using density-functional theory within the local
spin-density approximation, and the results are compared with those computed
previously for CaCu{3}Ti{4}O{12}. Replacing Ca with Cd is found to leave many
calculated quantities largely unaltered, although significant differences do
emerge in zone-center optical phonon frequencies and mode effective charges.
The computed phonon frequencies of CdCu{3}Ti{4}O{12} are found to be in
excellent agreement with experiment, and the computed lattice contribution to
the intrinsic static dielectric constant (~60) also agrees exceptionally well
with a recent optical absorption experiment. These results provide further
support for a picture in which the lattice dielectric response is essentially
conventional, suggesting an extrinsic origin for the anomalous low-frequency
dielectric response recently observed in both materials.


  The proton-proton fusion process p p->d e^+ nu_e is calculated at threshold
to fifth order in pionless effective field theory. There are two unknown
two-body currents contributing at the second and fourth orders. Combined with
the previous results for neutrino-deuteron and antineutrino-deuteron
scattering, computed to third order in the same approach, we conclude that a
10% measurement of reactor antineutrino-deuteron scattering measurement could
constrain the p p->d e^+ nu_e rate to ~7% while a ~3% measurement of nu_e d->
e^- p p could constrain the pp rate to ~2%.


  We perform a systematic analysis of generic string flux compactifications,
making use of Exceptional Generalized Geometry (EGG) as an organizing
principle. In particular, we establish the precise map between fluxes, gaugings
of maximal 4d supergravity and EGG, identifying the complete set of gaugings
that admit an uplift to 10d heterotic or type IIB supegravity backgrounds. Our
results reveal a rich structure, involving new deformations of 10d supergravity
backgrounds, such as the RR counterparts of the $\beta$-deformation. These new
deformations are expected to provide the natural extension of the
$\beta$-deformation to full-fledged F-theory backgrounds. Our analysis also
provides some clues on the 10d origin of some of the particularly less
understood gaugings of 4d supergravity. Finally, we derive the explicit
expression for the effective superpotential in arbitrary N = 1 heterotic or
type IIB orientifold compactifications, for all the allowed fluxes.


  We study M-separability as well as some other combinatorial versions of
separability. In particular, we show that the set-theoretic hypothesis b=d
implies that the class of selectively separable spaces is not closed under
finite products, even for the spaces of continuous functions with the topology
of pointwise convergence. We also show that there exists no maximal M-separable
countable space in the model of Frankiewicz, Shelah, and Zbierski in which all
closed P-subspaces of w^* admit an uncountable family of nonempty open mutually
disjoint subsets. This answers several questions of Bella, Bonanzinga, Matveev,
and Tkachuk.


  We study electric field quench in N=2 strongly coupled gauge theory, using
the AdS/CFT correspondence. To do so, we consider the aforementioned system
which is subjected to a time-dependent electric field indicating an out of
equilibrium system. Defining the equilibration time t_{eq}, at which the system
relaxes to its final equilibrium state after injecting the energy, we find that
the rescaled equilibriation time k^{-1}t_{eq} decreases as the transition time
k increases. Therefore, we expect that for sufficiently large transition time,
k ->infinity, the relaxation of the system to its final equilibrium can be an
adiabatic process. On the other hand, we observe a universal behavior for the
fast quenches, k << 1, meaning that the rescaled equilibration time does not
depend on the final value of the time-dependent electric field. Our
calculations generalized to systems in various dimensions also confirm
universalization process which seems to be a typical feature of all strongly
coupled gauge theories that admit a gravitational dual.


  Let G be a quasi-split reductive group over a non-archimedean local field. We
establish a local Langlands correspondence for all irreducible smooth complex
G-representations in the principal series. The parametrization map is
injective, and its image is an explicitly described set of enhanced
L-parameters. Our correspondence is determined by the choice of a Whittaker
datum for G, and it is canonical given that choice.
  We show that our parametrization satisfies many expected properties, among
others with respect to the enhanced L-parameters of generic representations,
temperedness, cuspidal supports and central characters. Along the way we
characterize genericity in terms of representations of an affine Hecke algebra.


  X-ray amorphous manganese oxides were prepared by reduction of sodium
permanganate by lithium iodide in aqueous medium (MnOx-I) and by decomposition
of manganese carbonate at moderate temperature (MnOx-C). TEM showed that these
materials are not amorphous, but nanostructured, with a prominent spinel
substructure in MnOx-C. These materials intercalate lithium with capacities up
to 200 mAh/g at first cycle (potential window 1.8-4.3 V) and 175 mAh/g at 100th
cycle. Best performances for MnOx-C are obtained with cobalt doping. Potential
electrochemical spectroscopy shows that the initial discharge induces a 2-phase
transformation in MnOx-C phases, but not in MnOx-I ones. EXAFS and XANES
confirm the participation of manganese in the redox process, with variations in
local structure much smaller than in known long-range crystallized manganese
oxides. X-ray absorption spectroscopy also shows that cobalt in MnOx-C is
divalent and does not participate in the electrochemical reaction.


  We derive a convex optimization problem for the task of segmenting sequential
data, which explicitly treats presence of outliers. We describe two algorithms
for solving this problem, one exact and one a top-down novel approach, and we
derive a consistency results for the case of two segments and no outliers.
Robustness to outliers is evaluated on two real-world tasks related to speech
segmentation. Our algorithms outperform baseline segmentation algorithms.


  Vacuum induced coherence in a strongly coupled cavity consisting of a
three-level system is studied theoretically. The effects of the strong coupling
to electromagnetic field vacuum are examined by solution of an open-system
quantum master equation. The numerical results show that the system exhibits
population trapping, and the numerical results are interpreted with analytical
expressions derived from a new basis in the weak excitation regime. We further
show that the generated effects can be probed with weak external fields.
Moreover, it is shown that the induced coherence can be controlled by the
applied field parameters like field detuning. Finally, we study the trapping
dynamics in the strong field excitation regime, and also demonstrate that a
recently proposed asymmetric pumping regime (limited to the weak coupling
regime) can remove the radiative decay of coherent Rabi oscillations, with both
weak and strong excitation fields.


  We derive an equation determining the small-x evolution of the F_2 structure
function of a large nucleus which includes all multiple pomeron exchanges in
the leading logarithmic approximation using Mueller's dipole model. We show
that in the double leading logarithmic limit this evolution equation reduces to
the GLR equation.


  Course estimation is a key component for the development of autonomous
navigation systems for robots. While state-of-the-art methods widely use
visual-based algorithms, it is worth noting that they all fail to deal with the
complexity of the real world by being computationally greedy and sometimes too
slow. They often require obstacles to be highly textured to improve the overall
performance, particularly when the obstacle is located within the focus of
expansion (FOE) where the optic flow (OF) is almost null. This study proposes
the FAst ITerative Half-plane (FAITH) method to determine the course of a micro
air vehicle (MAV). This is achieved by means of an event-based camera, along
with a fast RANSAC-based algorithm that uses event-based OF to determine the
FOE. The performance is validated by means of a benchmark on a simulated
environment and then tested on a dataset collected for indoor obstacle
avoidance. Our results show that the computational efficiency of our solution
outperforms state-of-the-art methods while keeping a high level of accuracy.
This has been further demonstrated onboard an MAV equipped with an event-based
camera, showing that our event-based FOE estimation can be achieved online
onboard tiny drones, thus opening the path towards fully neuromorphic solutions
for autonomous obstacle avoidance and navigation onboard MAVs.


  Employing a twisted superspace with eight supercharges, we describe an
off-shell formulation of N=4 D=3 twisted super Yang-Mills in the continuum
spacetime which underlies the recent proposal of N=4 D=3 twisted super
Yang-Mills on a lattice (arXiv:0707.3533[hep-lat]). By a dimensional reduction
from the N=2 D=4, we explore the two possible topological twists of N=4 D=3 and
then show that the lattice formulation given in arXiv:0707.3533[hep-lat] is
essentially categorized as the B-type. We also show that, amongst the two
inequivalent twists of N=4 D=3, only the B-type SYM can be realized on the
lattice consistently with the Leibniz rule and the gauge covariance on the
lattice.


  Large-scale structures, observed today, are generally believed to have grown
from random, small-amplitude inhomogeneities, present in the early Universe. We
investigate how gravitational instability drives the distribution of these
fluctuations away from the initial state, assumed to be Gaussian. Using second
order perturbation theory, we calculate the skewness factor, $S_3 \equiv
\langle \delta^3 \rangle \, /\, \langle \delta^2 \rangle^2$. Here the brackets,
$\langle \ldots \rangle$, denote an ensemble average, and $\delta$ is the
density contrast field, smoothed with a low pass spatial filter. We show that
$S_3$ decreases with the slope of the fluctuation power spectrum; it depends
only weakly on $\Omega$, the cosmological density parameter. We compare
perturbative calculations with N-body experiments and find excellent agreement
over a wide dynamic range. If galaxies trace the mass, measurements of $S_3$
can be used to distinguish models with Gaussian initial conditions from their
non-Gaussian alternatives.


  We prove the rationality of all the minimal series principal W-algebras
discovered by Frenkel, Kac and Wakimoto in 1992, thereby giving a new family of
rational and C_2-cofinite vertex operator algebras. A key ingredient in our
proof is the study of Zhu's algebra of simple W-algebras via the quantized
Drinfeld-Sokolov reduction. We show that the functor of taking Zhu's algebra
commutes with the reduction functor. Using this general fact we determine the
maximal spectrums of the associated graded of Zhu's algebra of all the
admissible affine vertex algebras as well.


  Physical systems that display competitive non-linear dynamics have played a
key role in the development of mathematical models of Nature. Important
examples include predator-prey models in ecology, biology, consumer-resource
models in economics, and reaction-diffusion equations in chemical reactions.
However, as real world systems are embedded in complex environments, where it
is difficult or even impossible to control external parameters, quantitative
comparison between measurements and simple models remains challenging. This
motivates the search for competitive dynamics in isolated physical systems,
with precise control. An ideal candidate is laser excitation in dilute atomic
ensembles. For example, atoms in highly-excited Rydberg states display rich
many-body dynamics including ergodicity breaking, synchronisation and time
crystals. Here, we demonstrate predator-prey dynamics by laser excitation and
ionisation of Rydberg atoms in a room temperature vapour cell. Ionisation of
excited atoms produce electric fields that suppress further excitation. This
starves the ionisation process of resource, giving rise to predator-prey
dynamics. By comparing our results to the Lotka-Volterra model, we demonstrate
that as well applications in non-linear dynamics, our experiment has
applications in metrology, and remote sensing of localised plasmas.


  The Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope
observatory is a pair conversion telescope sensitive to gamma-rays over more
than four energy decades, between 20 MeV and more than 300 GeV. Acting in
synergy with the Gamma-ray Burst Monitor (GBM) - the other instrument onboard
the mission - the LAT features unprecedented sensitivity for the study of
gamma-ray bursts (GRBs) in terms of spectral coverage, effective area, and
instrumental dead time. We will review the main results from Fermi-LAT
observation of GRB, presenting the main properties of GRBs at GeV energies.


  In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential
to identify the hazard boundary of ML Components (MLCs) in the MLAS under
analysis. Given that such boundary captures the conditions in terms of MLC
behavior and system context that can lead to hazards, it can then be used to,
for example, build a safety monitor that can take any predefined fallback
mechanisms at runtime when reaching the hazard boundary. However, determining
such hazard boundary for an ML component is challenging. This is due to the
problem space combining system contexts (i.e., scenarios) and MLC behaviors
(i.e., inputs and outputs) being far too large for exhaustive exploration and
even to handle using conventional metaheuristics, such as genetic algorithms.
Additionally, the high computational cost of simulations required to determine
any MLAS safety violations makes the problem even more challenging.
Furthermore, it is unrealistic to consider a region in the problem space
deterministically safe or unsafe due to the uncontrollable parameters in
simulations and the non-linear behaviors of ML models (e.g., deep neural
networks) in the MLAS under analysis. To address the challenges, we propose
MLCSHE (ML Component Safety Hazard Envelope), a novel method based on a
Cooperative Co-Evolutionary Algorithm (CCEA), which aims to tackle a
high-dimensional problem by decomposing it into two lower-dimensional search
subproblems. Moreover, we take a probabilistic view of safe and unsafe regions
and define a novel fitness function to measure the distance from the
probabilistic hazard boundary and thus drive the search effectively. We
evaluate the effectiveness and efficiency of MLCSHE on a complex Autonomous
Vehicle (AV) case study. Our evaluation results show that MLCSHE is
significantly more effective and efficient compared to a standard genetic
algorithm and random search.


  The asymptotic giant branch star R Sculptoris is surrounded by a detached
shell of dust and gas. The shell originates from a thermal pulse during which
the star undergoes a brief period of increased mass loss. It has hitherto been
impossible to constrain observationally the timescales and mass-loss properties
during and after a thermal pulse - parameters that determine the lifetime on
the asymptotic giant branch and the amount of elements returned by the star.
Here we report observations of CO emission from the circumstellar envelope and
shell around R Sculptoris with an angular resolution of 1.3 arcsec. What was
hitherto thought to be only a thin, spherical shell with a clumpy structure, is
revealed to contain a spiral structure. Spiral structures associated with
circumstellar envelopes have been seen previously, from which it was concluded
that the systems must be binaries. Using the data, combined with hydrodynamic
simulations, we conclude that R Sculptoris is a binary system that underwent a
thermal pulse approximately 1800 years ago, lasting approximately 200 years.
About 0.003 Msun of mass was ejected at a velocity of 14.3 km s-1 and at a rate
approximately 30 times higher than the prepulse mass-loss rate. This shows that
approximately 3 times more mass is returned to the interstellar medium during
and immediately after a pulse than previously thought.


  We study geodesically convex (g-convex) problems that can be written as a
difference of Euclidean convex functions. This structure arises in several
optimization problems in statistics and machine learning, e.g., for matrix
scaling, M-estimators for covariances, and Brascamp-Lieb inequalities. Our work
offers efficient algorithms that on the one hand exploit g-convexity to ensure
global optimality along with guarantees on iteration complexity. On the other
hand, the split structure permits us to develop Euclidean
Majorization-Minorization algorithms that help us bypass the need to compute
expensive Riemannian operations such as exponential maps and parallel
transport. We illustrate our results by specializing them to a few concrete
optimization problems that have been previously studied in the machine learning
literature. Ultimately, we hope our work helps motivate the broader search for
mixed Euclidean-Riemannian optimization algorithms


  By means of extensive computer simulations, the authors consider the
entangled coevolution of actions and social structure in a new version of a
spatial Prisoner's Dilemma model that naturally gives way to a process of
social differentiation. Diverse social roles emerge from the dynamics of the
system: leaders are individuals getting a large payoff who are imitated by a
considerable fraction of the population, conformists are unsatisfied
cooperative agents that keep cooperating, and exploiters are defectors with a
payoff larger than the average one obtained by cooperators. The dynamics
generate a social network that can have the topology of a small world network.
The network has a strong hierarchical structure in which the leaders play an
essential role in sustaining a highly cooperative stable regime. But
disruptions affecting leaders produce social crises described as dynamical
cascades that propagate through the network.


  We study the online submodular maximization problem with free disposal under
a matroid constraint. Elements from some ground set arrive one by one in
rounds, and the algorithm maintains a feasible set that is independent in the
underlying matroid. In each round when a new element arrives, the algorithm may
accept the new element into its feasible set and possibly remove elements from
it, provided that the resulting set is still independent. The goal is to
maximize the value of the final feasible set under some monotone submodular
function, to which the algorithm has oracle access.
  For $k$-uniform matroids, we give a deterministic algorithm with competitive
ratio at least $0.2959$, and the ratio approaches $\frac{1}{\alpha_\infty}
\approx 0.3178$ as $k$ approaches infinity, improving the previous best ratio
of $0.25$ by Chakrabarti and Kale (IPCO 2014), Buchbinder et al. (SODA 2015)
and Chekuri et al. (ICALP 2015). We also show that our algorithm is optimal
among a class of deterministic monotone algorithms that accept a new arriving
element only if the objective is strictly increased.
  Further, we prove that no deterministic monotone algorithm can be strictly
better than $0.25$-competitive even for partition matroids, the most modest
generalization of $k$-uniform matroids, matching the competitive ratio by
Chakrabarti and Kale (IPCO 2014) and Chekuri et al. (ICALP 2015).
Interestingly, we show that randomized algorithms are strictly more powerful by
giving a (non-monotone) randomized algorithm for partition matroids with ratio
$\frac{1}{\alpha_\infty} \approx 0.3178$.


  In this article, we review the progress made on the statistical mechanics of
liquids and fluids embedded in curved space. Our main focus will be on
two-dimensional manifolds of constant nonzero curvature and on the influence of
the latter on the phase behavior, thermodynamics and structure of simple
liquids. Reference will also be made to existing work on three-dimensional
curved space and two-dimensional manifolds with varying curvature.


  The extraordinary capabilities of large language models (LLMs) such as
ChatGPT and GPT-4 are in part unleashed by aligning them with reward models
that are trained on human preferences, which are often represented as rankings
of responses to prompts. In this paper, we document the phenomenon of
\textit{reward collapse}, an empirical observation where the prevailing
ranking-based approach results in an \textit{identical} reward distribution
\textit{regardless} of the prompts during the terminal phase of training. This
outcome is undesirable as open-ended prompts like ``write a short story about
your best friend'' should yield a continuous range of rewards for their
completions, while specific prompts like ``what is the capital of New Zealand''
should generate either high or low rewards. Our theoretical investigation
reveals that reward collapse is primarily due to the insufficiency of the
ranking-based objective function to incorporate prompt-related information
during optimization. This insight allows us to derive closed-form expressions
for the reward distribution associated with a set of utility functions in an
asymptotic regime. To overcome reward collapse, we introduce a prompt-aware
optimization scheme that provably admits a prompt-dependent reward distribution
within the interpolating regime. Our experimental results suggest that our
proposed prompt-aware utility functions significantly alleviate reward collapse
during the training of reward models.


  Polarized emission from a quasar is produced by wavelength-independent
electron scattering surrounding its accretion disc, and thus avoid the
contamination from its host galaxy and reveal the intrinsic emission spectrum
of the accretion disc. Ultra-violet (UV) emission from a quasar is normally
free from the contamination from its host galaxy. Polarization fraction of the
quasar's disc emission can therefore be determined by comparing total UV
emission with polarized visible to near-infrared (NIR) emission; and the
resulting continuum spectrum from UV to infrared can reveal the theoretically
expected Balmer edge absorption feature. We fit the polarized spectra in
visible and NIR bands together with the total UV spectra of two type-1 quasars
(3C 95, 4C 09.72), to an extended geometrically thin and optically thick
accretion disc model. In addition to the standard model, we include the Balmer
edge absorption due to co-rotational neutral gas on a narrow annulus of the
accretion disc. We find that the extended thin accretion disc model provides
adequate description on the continuum spectra of the two quasars from UV to NIR
wavelengths. A Monte-Carlo-Markov-Chain fitting to the continuum spectra is
able to well constrain the true polarization fraction of the disk emission,
which allows the Balmer edge feature to be completely revealed from polarized
visible to UV continua. The Balmer edge feature is prominent in both quasars'
spectra, and is significantly broadened due to the orbital motion of gas in the
accretion disc. The broadening of the Balmer edge feature is therefore related
to the quasar's inclination. This work proves the concept of determining
quasar's inclination from the Balmer edge feature in their continuum spectra.


  In this paper, first we briefly describe the differences in the working
principles of uplink and downlink NOMA transmissions. Then, for both uplink and
downlink NOMA, we formulate a sum-throughput maximization problem in a cell
such that the user clustering (i.e., grouping users into a single cluster or
multiple clusters) and power allocations in NOMA cluster(s) can be optimized
under transmission power constraints, minimum rate requirements of the users,
and SIC constraints. Due to the combinatorial nature of the formulated mixed
integer non-linear programming (MINLP) problem, we solve the problem in two
steps, i.e., by first grouping users into clusters and then optimizing their
respective power allocations. In particular, we propose a low-complexity
sub-optimal user grouping scheme. The proposed scheme exploits the channel gain
differences among users in a NOMA cluster and group them into a single cluster
or multiple clusters in order to enhance the sum-throughput of the system. For
a given set of NOMA clusters, we then derive the optimal power allocation
policy that maximizes the sum throughput per NOMA cluster and in turn maximizes
the overall system throughput. Using KKT optimality conditions, closed-form
solutions for optimal power allocations are derived for any cluster size,
considering both uplink and downlink NOMA systems. Numerical results compare
the performance of NOMA over orthogonal multiple access (OMA) and illustrate
the significance of NOMA in various network scenarios.


  A spectroscopically detected Lyman alpha emitting halo at redshift 3.216 in
the GOODS-N field is found to reside at the convergence of several Lyman alpha
filaments. HST images show that some of the filaments are inhabited by
galaxies. Several of the galaxies in the field have pronounced head-tail
structures, which are partly aligned with each other. The blue colors of most
tails suggest the presence of young stars, with the emission from at least one
of the galaxies apparently dominated by high equivalent width Lyman alpha.
Faint, more diffuse, and similarly elongated, apparently stellar features, can
be seen over an area with a linear extent of at least 90 kpc. The region within
several arcseconds of the brightest galaxy exhibits spatially extended emission
by HeII, NV and various lower ionization metal lines. The gas-dynamical
features present are strongly reminiscent of ram-pressure stripped galaxies,
including evidence for recent star formation in the stripped contrails. Spatial
gradients in the appearance of several galaxies may represent a stream of
galaxies passing from a colder to a hotter intergalactic medium. The stripping
of gas from the in-falling galaxies, in conjunction with the occurrence of star
formation and stellar feedback in the galactic contrails suggests a mechanism
for the metal enrichment of the high redshift intergalactic medium that does
not depend on long-range galactic winds, at the same time opening a path for
the escape of ionizing radiation from galaxies.


  The nonlocal Cahn-Hilliard (NCH) equation with nonlocal diffusion operator is
more suitable for the simulation of microstructure phase transition than the
local Cahn-Hilliard (LCH) equation. In this paper, based on the exponential
semi-implicit scalar auxiliary variable (ESI-SAV) method, the highly effcient
and accurate schemes in time with unconditional energy stability for solving
the NCH equation are proposed. On the one hand, we have demostrated the
unconditional energy stability for the NCH equation with its high-order
semi-discrete schemes carefully and rigorously. On the other hand, in order to
reduce the calculation and storage cost in numerical simulation, we use the
fast solver based on FFT and FCG for spatial discretization. Some numerical
simulations involving the Gaussian kernel are presented and show the stability,
accuracy, efficiency and unconditional energy stability of the proposed
schemes.


  Swift observations suggest that the central compact objects of some gamma-ray
bursts (GRBs) could be newly born millisecond magnetars. Therefore, by
considering the spin evolution of the magnetars against r-mode instability, we
investigate the role of the magnetars in GRB X-ray afterglow emission. Besides
modifying the conventional energy injection model, we pay particular attention
to the internal X-ray afterglow emission, whose luminosity is assumed to track
the magnetic dipole luminosity of the magentars with a certain fraction.
Following a comparison between the model and some selected observational
samples, we suggest that some so-called "canonical" X-ray afterglows including
the shallow decay, normal decay, and steeper-than-normal decay phases could be
internally produced by the magnetars (possibly through some internal
dissipations of the magnetar winds), while the (energized) external shocks are
associated with another type of X-ray afterglows. If this is true, from those
internal X-ray afterglows, we can further determine the magnetic field
strengths and the initial spin periods of the corresponding magnetars.


  Existing guidelines for categorical color selection are heuristic, often
grounded in intuition rather than empirical studies of readers' abilities.
While design conventions recommend palettes maximize hue differences, more
recent exploratory findings indicate other factors, such as lightness, may play
a role in effective categorical palette design. We conducted a crowdsourced
experiment on mean value judgments in multi-class scatterplots using five color
palette families--single-hue sequential, multi-hue sequential,
perceptually-uniform multi-hue sequential, diverging, and multi-hue
categorical--that differ in how they manipulate hue and lightness. Participants
estimated relative mean positions in scatterplots containing 2 to 10 categories
using 20 colormaps. Our results confirm heuristic guidance that hue-based
categorical palettes are most effective. However, they also provide additional
evidence that scalable categorical encoding relies on more than hue variance.


  Recent results from the NA48/2 and NA62 kaon decay-in-flight experiments at
CERN are presented. A precision measurement of the helicity-suppressed ratio RK
of the K+- to e+- nu and K+- to mu+- nu decay rates has been performed using
the full dedicated data set collected by the NA62 experiment (RK phase); the
result is in agreement with the Standard Model expectation. New measurements of
the K+- to pi+- gamma gamma decay at the NA48/2 and NA62 experiments provide
further tests of the Chiral Perturbation Theory. A planned measurement of the
branching ratio of the ultra-rare K+ to pi+ nu anti-nu decay at 10% precision
is expected to represent a powerful test of the Standard Model.


  Consider the case where consecutive blocks of N letters of a semi-infinite
individual sequence X over a finite-alphabet are being compressed into binary
sequences by some one-to-one mapping. No a-priori information about X is
available at the encoder, which must therefore adopt a universal
data-compression algorithm. It is known that if the universal LZ77 data
compression algorithm is successively applied to N-blocks then the best
error-free compression for the particular individual sequence X is achieved, as
$N$ tends to infinity. The best possible compression that may be achieved by
any universal data compression algorithm for finite N-blocks is discussed. It
is demonstrated that context tree coding essentially achieves it. Next,
consider a device called classifier (or discriminator) that observes an
individual training sequence X. The classifier's task is to examine individual
test sequences of length N and decide whether the test N-sequence has the same
features as those that are captured by the training sequence X, or is
sufficiently different, according to some appropriatecriterion. Here again, it
is demonstrated that a particular universal context classifier with a
storage-space complexity that is linear in N, is essentially optimal. This may
contribute a theoretical "individual sequence" justification for the
Probabilistic Suffix Tree (PST) approach in learning theory and in
computational biology.


  We revise primordial black holes (PBHs) production in the axion-curvaton
model, in light of recent developments in the computation of their abundance
accounting for non-gaussianities (NGs) in the curvature perturbation up to all
orders. We find that NGs intrinsically generated in such scenarios have a
relevant impact on the phenomenology associated to PBHs and, in particular, on
the relation between the abundance and the signal of second-order gravitational
waves. We show that this model could explain both the totality of dark matter
in the asteroid mass range and the tentative signal reported by the NANOGrav
and IPTA collaborations in the nano-Hz frequency range. En route, we provide a
new, explicit computation of the power spectrum of curvature perturbations
going beyond the sudden-decay approximation.


  When experimental subjects can interact with each other, the outcome of one
individual may be affected by the treatment status of others. In many social
science experiments, such spillover effects may occur through multiple
networks, for example, through both online and offline face-to-face networks in
a Twitter experiment. Thus, to understand how people use different networks, it
is essential to estimate the spillover effect in each specific network
separately. However, the unbiased estimation of these network-specific
spillover effects requires an often-violated assumption that researchers
observe all relevant networks. We show that, unlike conventional omitted
variable bias, bias due to unobserved networks remains even when treatment
assignment is randomized and when unobserved networks and a network of interest
are independently generated. We then develop parametric and nonparametric
sensitivity analysis methods, with which researchers can assess the potential
influence of unobserved networks on causal findings. We illustrate the proposed
methods with a simulation study based on a real-world Twitter network and an
empirical application based on a network field experiment in China.


  In their recent Letter [Phys. Rev. Lett. 83, 4233 (1999)], Salazar and Toral
(ST) study numerically a finite Ising chain with non-integrable interactions
decaying like 1/r^(d+sigma) where -d <= sigma <= 0 (like ST, we discuss general
dimensionality d). In particular, they explore a presumed connection between
non-integrable interactions and Tsallis's non-extensive statistics. We point
out that (i) non-integrable interactions provide no more motivation for Tsallis
statistics than do integrable interactions, i.e., Gibbs statistics remain
meaningful for the non-integrable case, and in fact provide a {\em complete and
exact treatment}; and (ii) there are undesirable features of the method ST use
to regulate the non-integrable interactions.


  Contrary to conventional wisdom, the construction of clusters on a lattice
can easily be vectorized, namely over each ``generation'' in a breadth first
search. This applies directly to, e.g., the {\it single cluster} variant of the
Swendsen-Wang algorithm. On a Cray Y-MP, total CPU time was reduced by a factor
3.5 -- 7 in actual applications.


  In this paper a relativistic quantum theory introduced by de Groot and
\mbox{Ruijgrok} in 1975, is used as a quark model in momentum space. The
complete spectrum, with the exception of the selfconjugate light unflavoured
mesons, is calculated. The potential used consists of an one-gluon-exchange
(OGE) part and a confining part. For the confining part a relativistic
generalization of the linear plus constant potential was used, which is
well-defined in momentum space without introducing any singularities. For the
OGE part several potentials were investigated. Retardations were included at
all places. Using a fitting procedure involving 52 well-established mesons,
best results were obtained for a potential consisting of a purely vector
Richardson potential and a purely scalar confining potential. In this way a
vector confining is entirely induced by the Richardson potential. Reasonable
results were also obtained for a modified Richardson potential. Most meson
masses, with the exception of the $\pi$, the $K$ and the $K_0^*$, were found to
be reasonably well described by the model.


  We consider the problem of joint three-dimensional localization and
synchronization for a single-input single-output (SISO) system in the presence
of a reconfigurable intelligent surface (RIS), equipped with a uniform planar
array. First, we derive the Cram\'er-Rao bounds (CRBs) on the estimation error
of the channel parameters, namely, the angle-of-departure (AOD), composed of
azimuth and elevation, from RIS to the user equipment (UE) and times-of-arrival
(TOAs) for the path from the base station (BS) to UE and BS-RIS-UE reflection.
In order to avoid high-dimensional search over the parameter space, we devise a
low-complexity estimation algorithm that performs two 1D searches over the TOAs
and one 2D search over the AODs. Simulation results demonstrate that the
considered RIS-aided wireless system can provide submeter-level positioning and
synchronization accuracy, materializing the positioning capability of Beyond 5G
networks even with single-antenna BS and UE. Furthermore, the proposed
estimator is shown to attain the CRB at a wide interval of distances between UE
and RIS. Finally, we also investigate the scaling of the position error bound
with the number of RIS elements.


  Topological insulators show important properties, such as topological phase
transitions and topological edge states. Although these properties and
phenomena can be simulated by well-designed circuits, it is undoubtedly
difficult to design such topological circuits due to the complex physical
principles and calculations involved. Therefore, achieving a framework that can
automatically to complete bidirectional design of topology circuits is very
significant. Here, we propose an effective bidirectional collaborative design
framework with strong task adaptability, which can automatically generate
specific results according to our requirements. In the framework, a composable
generation strategy is employed, which involves building a shared multimodal
space by bridging alignment in the diffusion process. For simplicity, a series
of two-dimensional (2D) Su-Schrieffer-Heeger (SSH) circuits are constructed
with different structural parameters. The framework at first is applied to find
the relationship between the structural information and topological features.
Then, the correctness of the results through experimental measurements can be
verified by the automatically generated circuit diagram following the
manufacture of Printed Circuit Board (PCB). The framework is demonstrated by
achieving good results in the reverse design of circuit structures and forward
prediction of topological edge states, reaching an accuracy of 94%. Overall,
our research demonstrates the enormous potential of the proposed bidirectional
deep learning framework in complex tasks and provides insights for
collaborative design tasks.


  We present the delay time distribution (DTD) estimates of Type Ia supernovae
(SNe~Ia) using spatially resolved SN~Ia host galaxy spectra from MUSE and
MaNGA. By employing a grouping algorithm based on k-means and earth mover's
distances (EMD), we separated the host galaxy star formation histories (SFHs)
into spatially distinct regions and used maximum likelihood method to constrain
the DTD of SNe Ia progenitors. When a power-law model of the form
$DTD(t)\propto t^{s} (t>\tau)$ is used, we found an SN rate decay slope
$s=-1.41^{+0.32}_{-0.33}$ and a delay time $\tau=120^{+142}_{-83} Myr$ .
Moreover, we tested other DTD models such as a broken power law model and a
two-component power law model, and found no statistically significant support
to these alternative models.


  Tools, models and statistical methods for signal processing and medical image
analysis and training deep learning models to create research prototypes for
eventual clinical applications are of special interest to the biomedical
imaging community. But material and optical properties of biological tissues
are complex and not easily captured by imaging devices. Added complexity can be
introduced by datasets with underrepresentation of medical images from races
and ethnicities for deep learning, and limited knowledge about the regulatory
framework needed for commercialization and safety of emerging Artificial
Intelligence (AI) and Machine Learning (ML) technologies for medical image
analysis. This extended version of the workshop paper presented at the special
session of the 2022 IEEE 19th International Symposium on Biomedical Imaging,
describes strategy and opportunities by University of California professors
engaged in machine learning (section I) and clinical research (section II), the
Office of Science and Engineering Laboratories (OSEL) section III, and
officials at the US FDA in Center for Devices & Radiological Health (CDRH)
section IV. Performance evaluations of AI/ML models of skin (RGB), tissue
biopsy (digital pathology), and lungs and kidneys (Magnetic Resonance, X-ray,
Computed Tomography) medical images for regulatory evaluations and real-world
deployment are discussed.


  We give a new sufficient condition on a spectral triple to ensure that the
quantum group of orientation and volume preserving isometries defined in
\cite{qorient} has a $C^*$-action on the underlying $C^*$ algebra.


  Recent convolutional neural networks (CNNs) have led to impressive
performance but often suffer from poor calibration. They tend to be
overconfident, with the model confidence not always reflecting the underlying
true ambiguity and hardness. In this paper, we propose angular visual hardness
(AVH), a score given by the normalized angular distance between the sample
feature embedding and the target classifier to measure sample hardness. We
validate this score with an in-depth and extensive scientific study, and
observe that CNN models with the highest accuracy also have the best AVH
scores. This agrees with an earlier finding that state-of-art models improve on
the classification of harder examples. We observe that the training dynamics of
AVH is vastly different compared to the training loss. Specifically, AVH
quickly reaches a plateau for all samples even though the training loss keeps
improving. This suggests the need for designing better loss functions that can
target harder examples more effectively. We also find that AVH has a
statistically significant correlation with human visual hardness. Finally, we
demonstrate the benefit of AVH to a variety of applications such as
self-training for domain adaptation and domain generalization.


  Typical fMRI studies have focused on either the mean trend in the
blood-oxygen-level-dependent (BOLD) time course or functional connectivity
(FC). However, other statistics of the neuroimaging data may contain important
information. Despite studies showing links between the variance in the BOLD
time series (BV) and age and cognitive performance, a formal framework for
testing these effects has not yet been developed. We introduce the Variance
Design General Linear Model (VDGLM), a novel framework that facilitates the
detection of variance effects. We designed the framework for general use in any
fMRI study by modeling both mean and variance in BOLD activation as a function
of experimental design. The flexibility of this approach allows the VDGLM to i)
simultaneously make inferences about a mean or variance effect while
controlling for the other and ii) test for variance effects that could be
associated with multiple conditions and/or noise regressors. We demonstrate the
use of the VDGLM in a working memory application and show that engagement in a
working memory task is associated with whole-brain decreases in BOLD variance.


  Metastable dynamics of a hyperbolic variation of the Allen-Cahn equation with
homogeneous Neumann boundary conditions are considered. Using the "dynamical
approach" proposed by Carr-Pego [10] and Fusco-Hale [19] to study
slow-evolution of solutions in the classic parabolic case, we prove existence
and persistence of metastable patterns for an exponentially long time. In
particular, we show the existence of an "approximately invariant"
$N$-dimensional manifold $\mathcal{M}_0$ for the hyperbolic Allen-Cahn
equation: if the initial datum is in a tubular neighborhood of $\mathcal{M}_0$,
the solution remains in such neighborhood for an exponentially long time.
Moreover, the solution has $N$ transition layers and the transition points move
with exponentially small velocity. In addition, we determine the explicit form
of a system of ordinary differential equations describing the motion of the
transition layers and we analyze the differences with the corresponding motion
valid for the parabolic case.


  In this paper, we study the semilinear integro-differential equations
\begin{equation*} \mathcal{L}_{K}u(x)\equiv
C_n\text{P.V.}\int_{\R^n}\left(u(x)-u(y)\right)K(x-y)dy=f(x,u), \end{equation*}
and the full nonlinear integro-differential equations \begin{equation*}
F_{G,K}u(x)\equiv C_n\text{P.V.}\int_{\R^n}G(u(x)-u(y))K(x-y)dy=f(x,u),
\end{equation*} where $K(\cdot)$ is a symmetric jumping kernel and
$K(\cdot)\geq C|\cdot|^{-n-\alpha}$, $G(\cdot)$ is some nonlinear function
without non-degenerate condition. We adopt the direct method of moving planes
to study the symmetry and monotonicity of solutions for the
integro-differential equations, and investigate the limit of some non-local
operators $\mathcal{L}_{K}$ as $\alpha\to2.$ Our results extended some results
obtained in \cite{CL} and \cite{CLLG}.


  The behaviour of the geomagnetic field characteristics (mean values of the
paleointensity, amplitude of its variations and reversal frequency) during the
last 170 million years was analyzed. It was found that the mean values of the
paleointensity were in direct relation to amplitude of its variations and in
reverse relation to reversal frequency. The most considerable changes of the
Earth magnetic field characteristics occurred in Early Cretaceous, Middle
Paleogene and Neogene. The analysis of conformity of the reconstructed
characteristics behaviour of a geomagnetic field to an alfa-omega- dynamo model
was made.


  Let $A$ be an integral $k$-algebra of finite type over an algebraically
closed field $k$ of characteristic $p>0$. Given a collection ${\cal{D}}$ of
$k$-derivations on $A$, that we interpret as algebraic vector fields on
$X=Spec(A)$, we study the group spanned by the hypersurfaces $V(f)$ of $X$
invariant for ${\cal{D}}$ modulo the rational first integrals of ${\cal{D}}$.
We prove that this group is always a finite $\mathbb{Z}/p$-vector space, and we
give an estimate for its dimension. This is to be related to the results of
Jouanolou and others on the number of hypersurfaces invariant for a foliation
of codimension 1. As an application, given a $k$-algebra $B$ between $A^p$ and
$A$, we show that the kernel of the pull-back morphism $Pic(B)\rightarrow
Pic(A)$ is a finite $\mathbb{Z}/p$-vector space. In particular, if $A$ is a
UFD, then the Picard group of $B$ is finite.


  We demonstrate the trapping and manipulation of single neutral atoms in
reconfigurable arrays of optical tweezers. Our approach offers unparalleled
speed by using a Texas Instruments Digital Micro-mirror Device (DMD) as a
holographic amplitude modulator with a frame rate of 20,000 per second. We show
the trapping of static arrays of up to 20 atoms, as well as transport of
individually selected atoms over a distance of 25{\mu}m with laser cooling and
4{\mu}m without. We discuss the limitations of the technique and the scope for
technical improvements.


  Twisted rapid passage is a type of non-adiabatic rapid passage that gives
rise to controllable quantum interference effects that were first observed
experimentally in 2003. We show that twisted rapid passage sweeps can be used
to implement a universal set of quantum gates that operate with high-fidelity.
For each gate in the universal set, sweep parameter values are provided which
simulations indicate will yield a quantum gate with error probability P_{e} <
10**(-4). Note that all gates in this universal set are driven by a **single**
type of control field (twisted rapid passage), and the error probability for
each gate falls below the rough-and-ready estimate for the accuracy threshold
P_{a} ~ 10**(-4). The simulations suggest that the universal gate set produced
by twisted rapid passage shows promise for use in a fault-tolerant scheme for
quantum computing.


  We show that if a flavour-less vector meson remains bound after
deconfinement, and if its limiting velocity in the quark-gluon plasma is
subluminal, then this meson produces a distinct peak in the spectrum of thermal
photons emitted by the plasma. We also demonstrate that this effect is a
universal property of all strongly coupled, large-Nc plasmas with a gravity
dual. For the J/psi the corresponding peak lies between 3 and 5 GeV and could
be observed at LHC.


  We investigate a number of simple toy models to explore interesting
relationships between dynamics and typicality. We start with an infinite model
that has been proposed as an illustration of how non-ergodic dynamics can
produce interesting features that are suggestive for cosmological applications.
We consider various attempts to define the infinite model more rigorously as a
limit of a finite system. None of our attempts at such rigor were able to
preserve the attractive properties. We hope our work will challenge others to
find more successful toy models. The difficulty of finding such models suggests
that connections between dynamics and typicality we hope for in cosmological
theories such as eternal inflation may not be so easy to achieve.


  We study the relation of Type Ia Supernovae (SNe Ia) with host galaxies from
a large low redshift sample. By examining the Hubble residuals of the entire
sample from the best-fit cosmology, we show that SNe Ia in passive hosts are
brighter than those in star-forming hosts after light curve correction at
2.1{\sigma}confidence level. We find that SNe Ia in high luminosity hosts are
brighter after light-curve correction at>3{\sigma}confidence level. We also
find that SNe Ia in large size galaxies are brighter after light-curve
correction at?2{\sigma} confidence level. We demonstrate that the residual
linearly depends on host luminosity at a confidence of 4{\sigma}or host size at
a confidence of 3.3{\sigma}.


  Among the various approaches for producing point distributions with blue
noise spectrum, we argue for an optimization framework using Gaussian kernels.
We show that with a wise selection of optimization parameters, this approach
attains unprecedented quality, provably surpassing the current state of the art
attained by the optimal transport (BNOT) approach. Further, we show that our
algorithm scales smoothly and feasibly to high dimensions while maintaining the
same quality, realizing unprecedented high-quality high-dimensional blue noise
sets. Finally, we show an extension to adaptive sampling.


  Jupiter has nearly 8000~known co-orbital asteroids orbiting in the L4 and L5
Lagrange points called Jupiter Trojan asteroids. Aside from the greater number
density of the L4 cloud the two clouds are in many ways considered to be
identical. Using sparse photometric data taken by the Asteroid
Terrestrial-impact Last Alert System (ATLAS) for 863 L4 Trojans and 380 L5
Trojans we derive the shape distribution for each of the clouds and find that,
on average, the L4 asteroids are more elongated than the L5 asteroids. This
shape difference is most likely due to the greater collision rate in the L4
cloud that results from its larger population. We additionally present the
phase functions and $c-o$ colours of 266~objects.


  The evolution on the AGB and beyond is reviewed with respect to the origin of
Wolf-Rayet central stars. We focus on thermal pulses due to their particular
importance for the evolution of hydrogen deficient stars. It is shown that
overshoot applied to all convection regions is a key ingredient to model these
objects leading to intershell abundances already close to the surface
abundances of Wolf-Rayet central stars. In contrast to standard evolutionary
calculations, overshoot models do show dredge up for very low envelope masses
and efficient dredge up was found even during the post-AGB stage. Three thermal
pulse scenarios for Wolf-Rayet central stars can now be distinguished: an AGB
Final Thermal Pulse (AFTP) occurring at the very end of the AGB evolution, a
Late Thermal Pulse (LTP) occurring during the post-AGB evolution when hydrogen
burning is still on, and a Very Late Thermal Pulse (VLTP) occurring on the
cooling branch when hydrogen burning has already ceased. All scenarios lead to
hydrogen-deficient post-AGB stars with carbon and oxygen abundances as observed
for Wolf-Rayet stars. Hydrogen is either diluted by dredge up (AFTP, LTP) or
completely burnt (VLTP).


  We present temperature-dependent infrared spectroscopic studies of CaMnSb2, a
proposed threedimensional topological material. The low plasma edge in the
reflectivity spectrum and small Drude component in the optical conductivity
indicate a very low carrier density. The low-frequency optical conductivity is
well described by the superposition of a narrow and a broad Drude terms.
Several linear components have been observed in the low-temperature optical
conductivity, but none of them extrapolates to the origin, at odds with the
optical response expected for three-dimensional Dirac fermions. A series of
absorption peaks have been resolved in the high-frequency optical conductivity.
The energy of these peaks agrees well with the interband transitions expected
for the band structures from first-principles calculations. Intriguingly, the
lowest band gap increases with decreasing temperature, mimic the temperature
evolution of inverted bands. Furthermore, our theoretical calculations
demonstrate the existence of weak coupling between two Sb-chains layers results
in the topological trivial surface states in CaMnSb2.


  We consider the degenerate Einsteins Brownian motion model when the time
interval of the moving particles before the collisions, is reciprocal to the
number of particles per unit volume u(x,t), at the point of observation x at
time t. The parameter 0 < tau < C, which controls the characteristics of the
fluid, almost increases unboundedly, as u approaches 0. This degeneration leads
to the localization of the particle distribution in the media. In the paper, we
present a structural condition of the time interval and the frequency of these
free jumps, as functions of u which guarantees the finite speed of propagation
of u.


  Optical technology may provide important architectures for future computing,
such as analog optical computing and image processing. Compared with
traditional electric operation, optical operation has shown some unique
advantages including faster operating speeds and lower power consumption. Here,
we propose an optical full differentiator based on the spin-orbit interaction
of light at a simple optical interface. The broadband optical operation is
independent on the wavelength due to the nature of purely geometric. As an
important application of the fully differential operation, the broadband image
processing of edge detection is demonstrated. By adjusting the polarization of
the incident beam, the one-dimension edge imaging at any desirable direction
can be obtained. The broadband image processing of edge detection provides
possible applications in autonomous driving, target recognition, microscopic
imaging, and augmented reality.


  Susceptibility of the transverse field Ising model on the square lattice is
calculated numerically in the paramagnetic phase in a wide range of
temperatures and transverse fields. An expression with one constant $\pi$, that
determines both the critical exponent $\gamma$ and the critical transverse
field, compellingly represents the data asymptotically near the quantum
critical point, except for a narrow classical region close to the phase
transition line, and shows two crossovers as temperature varies.


  We show that classical thermodynamics has a formulation in terms of
Hamilton-Jacobi theory, analogous to mechanics. Even though the thermodynamic
variables come in conjugate pairs such as pressure/volume or
temperature/entropy, the phase space is odd-dimensional. For a system with n
thermodynamic degrees of freedom it is (2n+1)-dimensional. The equations of
state of a substance pick out an n-dimensional submanifold. A family of
substances whose equations of state depend on n parameters define a
hypersurface of co-dimension one. This can be described by the vanishing of a
function which plays the role of a Hamiltonian. The ordinary differential
equations (characteristic equations) defined by this function describe a
dynamical system on the hypersurface. Its orbits can be used to reconstruct the
equations of state. The `time' variable associated to this dynamics is related
to, but is not identical to, entropy. After developing this formalism on
well-grounded systems such as the van der Waals gases and the Curie-Weiss
magnets, we derive a Hamilton-Jacobi equation for black hole thermodynamics in
General Relativity. The cosmological constant appears as a constant of
integration in this picture.


  FPU models, in dimension one, are perturbations either of the linear model or
of the Toda model; perturbations of the linear model include the usual
$\beta$-model, perturbations of Toda include the usual $\alpha+\beta$ model. In
this paper we explore and compare two families, or hierarchies, of FPU models,
closer and closer to either the linear or the Toda model, by computing
numerically, for each model, the maximal Lyapunov exponent $\chi$. We study the
asymptotics of $\chi$ for large $N$ (the number of particles) and small
$\epsilon$ (the specific energy $E/N$), and find, for all models, asymptotic
power laws $\chi\simeq C\epsilon^a$, $C$ and $a$ depending on the model. The
asymptotics turns out to be, in general, rather slow, and producing accurate
results requires a great computational effort. We also revisit and extend the
analytic computation of $\chi$ introduced by Casetti, Livi and Pettini,
originally formulated for the $\beta$-model. With great evidence the theory
extends successfully to all models of the linear hierarchy, but not to models
close to Toda.


  The ratios of the codon usage in the quartets and sextets for the vertebrate
series exhibit a correlated behaviour which fits naturally in the framework of
the crystal basis model of the genetic code. Moreover the observed universal
behaviour of these suitably normalized ratios can be easily explained.


  We give a self-contained survey of some approaches aimed at a global
description of the geometry underlying double field theory. After reviewing the
geometry of Courant algebroids and their incarnations in the AKSZ construction,
we develop the theory of metric algebroids including their graded geometry. We
use metric algebroids to give a global description of doubled geometry,
incorporating the section constraint, as well as an AKSZ-type construction of
topological doubled sigma-models. When these notions are combined with
ingredients of para-Hermitian geometry, we demonstrate how they reproduce
kinematical features of double field theory from a global perspective,
including solutions of the section constraint for Riemannian foliated doubled
manifolds, as well as a natural notion of generalized T-duality for polarized
doubled manifolds. We describe the $L_\infty$-algebras of symmetries of a
doubled geometry, and briefly discuss other proposals for global doubled
geometry in the literature.


  In this paper, the definition of generalized spacelike Mannheim curve in
Minkowski space-time $E_1^4$ is given. The necessary and sufficient conditions
for the generalized spacelike Mannheim curve are obtained. Also, some
characterizations of Mannheim curve are given.


  Early diagnosis of Alzheimer's disease and its prodromal stage, also known as
mild cognitive impairment (MCI), is critical since some patients with
progressive MCI will develop the disease. We propose a multi-stream deep
convolutional neural network fed with patch-based imaging data to classify
stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's
disease with cognitively normal subjects to identify distinct anatomical
landmarks using a multivariate statistical test. These landmarks are then used
to extract patches that are fed into the proposed multi-stream convolutional
neural network to classify MRI images. Next, we train the architecture in a
separate scenario using samples from Alzheimer's disease images, which are
anatomically similar to the progressive MCI ones and cognitively normal images
to compensate for the lack of progressive MCI training data. Finally, we
transfer the trained model weights to the proposed architecture in order to
fine-tune the model using progressive MCI and stable MCI data. Experimental
results on the ADNI-1 dataset indicate that our method outperforms existing
methods for MCI classification, with an F1-score of 85.96%.


  Large Multimodal Models (LMM) are built across modalities and the
misalignment between two modalities can result in "hallucination", generating
textual outputs that are not grounded by the multimodal information in context.
To address the multimodal misalignment issue, we adapt the Reinforcement
Learning from Human Feedback (RLHF) from the text domain to the task of
vision-language alignment, where human annotators are asked to compare two
responses and pinpoint the more hallucinated one, and the vision-language model
is trained to maximize the simulated human rewards. We propose a new alignment
algorithm called Factually Augmented RLHF that augments the reward model with
additional factual information such as image captions and ground-truth
multi-choice options, which alleviates the reward hacking phenomenon in RLHF
and further improves the performance. We also enhance the GPT-4-generated
training data (for vision instruction tuning) with previously available
human-written image-text pairs to improve the general capabilities of our
model. To evaluate the proposed approach in real-world scenarios, we develop a
new evaluation benchmark MMHAL-BENCH with a special focus on penalizing
hallucinations. As the first LMM trained with RLHF, our approach achieves
remarkable improvement on the LLaVA-Bench dataset with the 94% performance
level of the text-only GPT-4 (while previous best methods can only achieve the
87% level), and an improvement by 60% on MMHAL-BENCH over other baselines. We
opensource our code, model, data at https://llava-rlhf.github.io.


  The spin-charge-family theory offers a possible explanation for the
assumptions of the standard model - for the charges of a family members, for
the gauge fields, for the appearance of families, for the the scalar fields -
interpreting the standard model as its low energy effective manifestation. The
spin-charge-family theory predicts at the low energy regime two decoupled
groups of four families of quarks and leptons. The predicted fourth family
waits to be observed, while the stable fifth family is the candidate to form
the dark matter. The Higss and Yukawa couplings are the low energy effective
manifestation of several scalar fields, all with the bosonic (adjoint)
representations with respect to all the charge groups, with the family groups
included. Properties of the families are analysed and relations among coherent
contributions of the loop corrections to fermion properties discussed,
including the one which enables the existence of the Majorana neutrinos. The
appearance of several scalar fields is presented, their properties discussed,
it is explained how these scalar fields can effectively be interpreted as the
{\em standard model} Higgs (with the fermion kind of charges) and the Yukawa
couplings, and a possible explanation why the Higgs has not yet been observed
offered. The relation to proposals that the Yukawas follow from the SU(3)
family (flavour) group, having the family charges in the fundamental
representations of these groups, is discussed. The spin-charge-family theory
predicts that there are no supersymmetric partners of the observed fermions and
bosons.


  The chemical reaction network (CRN) is a widely used formalism to describe
macroscopic behavior of chemical systems. Available tools for CRN modelling and
simulation require local access, installation, and often involve local file
storage, which is susceptible to loss, lacks searchable structure, and does not
support concurrency. Furthermore, simulations are often single-threaded, and
user interfaces are non-trivial to use. Therefore there are significant hurdles
to conducting efficient and collaborative chemical research. In this paper, we
introduce a new enterprise chemistry simulation framework, COEL, which
addresses these issues. COEL is the first web-based framework of its kind. A
visually pleasing and intuitive user interface, simulations that run on a large
computational grid, reliable database storage, and transactional services make
COEL ideal for collaborative research and education. COEL's most prominent
features include ODE-based simulations of chemical reaction networks and
multicompartment reaction networks, with rich options for user interactions
with those networks. COEL provides DNA-strand displacement transformations and
visualization (and is to our knowledge the first CRN framework to do so), GA
optimization of rate constants, expression validation, an application-wide
plotting engine, and SBML/Octave/Matlab export. We also present an overview of
the underlying software and technologies employed and describe the main
architectural decisions driving our development. COEL is available at
http://coel-sim.org for selected research teams only. We plan to provide a part
of COEL's functionality to the general public in the near future.


  Techniques for generating saliency maps continue to be used for
explainability of deep learning models, with efforts primarily applied to the
image classification task. Such techniques, however, can also be applied to
object detectors, not only with the classification scores, but also for the
bounding box parameters, which are regressed values for which the relevant
pixels contributing to these parameters can be identified. In this paper, we
present ODSmoothGrad, a tool for generating saliency maps for the
classification and the bounding box parameters in object detectors. Given the
noisiness of saliency maps, we also apply the SmoothGrad algorithm to visually
enhance the pixels of interest. We demonstrate these capabilities on one-stage
and two-stage object detectors, with comparisons using classifier-based
techniques.


  Instabilities of fluid flows often generate turbulence. Using extensive
direct numerical simulations, we study two-dimensional turbulence driven by a
wavenumber-localised instability superposed on stochastic forcing, in contrast
to previous studies of state-independent forcing. As the contribution of the
instability forcing, measured by a parameter $\gamma$, increases, the system
undergoes two transitions. For $\gamma$ below a first threshold, a regular
large-scale vortex condensate forms. Above this threshold, shielded vortices
(SVs) emerge within the condensate. At a second, larger value of $\gamma$, the
condensate breaks down, and a gas of weakly interacting vortices with broken
symmetry spontaneously emerges, characterised by preponderance of vortices of
one sign only and suppressed inverse energy cascade. The latter transition is
shown to depend on the damping mechanism. The number density of SVs in the
broken symmetry state slowly increases via a random nucleation process.
Bistability is observed between the condensate and mixed SV-condensate states.
Our findings provide new evidence for a strong dependence of two-dimensional
turbulence phenomenology on the forcing.


  We extend the correspondence between adS-supergravities and superconformal
field theories on the adS boundary to a correspondence between gauged
supergravities (typically with non-compact gauge groups) and quantum field
theories on domain walls.


  We study experimentally the dynamics of long waves among turbulent bending
waves in a thin elastic plate set into vibration by a monochromatic forcing at
a frequency $f_0$. This frequency is chosen large compared with the
characteristic frequencies of bending waves. As a consequence, a range of
conservative scales, without energy flux in average, exists for frequencies
$f<f_0$. Within this range, we report a flat power density spectrum for the
orthogonal velocity, corresponding to energy equipartition between modes. Thus,
the average energy per mode $\beta^{-1}$ -- analogous to a temperature -- fully
characterizes the large-scale turbulent wave field. We present an expression
for $\beta$ as a function of the forcing frequency and amplitude, and of the
plate characteristics.


  We develop a theory for stable maps to curves with divisible ramification.
For a fixed integer $r>0$, we show that the condition of every ramification
locus being divisible by $r$ is equivalent to the existence of an $r$th root of
a canonical section. We consider this condition in regards to both absolute and
relative stable maps and construct natural moduli spaces in these situations.
We construct an analogue of the Fantechi-Pandharipande branch morphism and when
the domain curves are genus zero we construct a virtual fundamental class. This
theory is anticipated to have applications to $r$-spin Hurwitz theory. In
particular it is expected to provide a proof of the $r$-spin ELSV formula
[SSZ'15, Conj. 1.4] when used with virtual localisation.


  In this article, we obtain a super-exponential rate of convergence in total
variation between the traces of the first $m$ powers of an $n\times n$ random
unitary matrices and a $2m$-dimensional Gaussian random variable. This
generalizes previous results in the scalar case to the multivariate setting,
and we also give the precise dependence on the dimensions $m$ and $n$ in the
estimate with explicit constants. We are especially interested in the regime
where $m$ grows with $n$ and our main result basically states that if $m\ll
\sqrt{n}$, then the rate of convergence in the Gaussian approximation is
$\Gamma(\frac nm+1)^{-1}$ times a correction. We also show that the Gaussian
approximation remains valid for all $m\ll n^{2/3}$ without a fast rate of
convergence.


  Network assisted full-duplex (NAFD) is a spatial-division duplex technique
for future wireless networks with cell-free massive multiple-input
multiple-output (CF massive MIMO) network, where a large number of remote
antenna units (RAUs), either using half-duplex or full-duplex, jointly support
truly flexible duplex including time-division duplex, frequency-division duplex
and full duplex on demand of uplink and downlink traffic by using network MIMO
methods. With NAFD, bi-directional data rates of the wireless network could be
increased and end-to-end delay could be reduced. In this paper, the spectral
efficiency of NAFD communications in CF massive MIMO network with imperfect
channel state information (CSI) is investigated under spatial correlated
channels. Based on large dimensional random matrix theory, the deterministic
equivalents for the uplink sum-rate with minimum-mean-square-error (MMSE)
receiver as well as the downlink sum-rate with zero-forcing (ZF) and
regularized zero-forcing (RZF) beamforming are derived. Numerical results show
that under various environmental settings, the deterministic equivalents are
accurate in both a large-scale system and system with a finite number of
antennas. It is also shown that with the downlink-to-uplink interference
cancellation, the uplink spectral efficiency of CF massive MIMO with NAFD could
be improved. The spectral efficiencies of NAFD with different duplex
configurations such as in-band full-duplex, and half-duplex are compared. With
the same total numbers of transmit and receive antennas, NAFD with half-duplex
RAUs offers a higher spectral efficiency. To alleviate the uplink-to-downlink
interference, a novel genetic algorithm based user scheduling strategy (GAS) is
proposed. Simulation results show that the achievable downlink sum-rate by
using the GAS is greatly improved compared to that by using the random user
scheduling.


  Both unitary chiral theories and lattice QCD simulations show that the $DK$
interaction is attractive and can form a bound state, namely, $D^*_{s0}(2317)$.
Assuming the validity of the heavy antiquark-diquark symmetry (HADS), the
$\Xi_{cc}\bar{K}$ interaction is the same as the $DK$ interaction, which
implies the existence of a $\Xi_{cc}\bar{K}$ bound state with a binding energy
of $49-64$ MeV. In this work, we study whether a $\Xi_{cc}\Xi_{cc}\bar{K}$
three-body system binds. The $\Xi_{cc}\Xi_{cc}$ interaction is described by
exchanging $\pi$, $\sigma$, $\rho$, and $\omega$ mesons, with the corresponding
couplings related to those of the $NN$ interaction via the quark model. We
indeed find a $\Xi_{cc}\Xi_{cc}\bar{K}$ bound state, with quantum numbers
$J^P=0^-$, $I=\frac{1}{2}$, $S=1$ and $C=4$, and a binding energy of $80-118$
MeV. It is interesting to note that this system is very similar to the
well-known $NN\bar{K}$ system, which has been studied extensively both
theoretically and experimentally. Within the same framework, we show the
existence of a $NN\bar{K}$ state with a binding energy of $35-43$ MeV,
consistent with the results of other theoretical works and experimental data,
which serves as a consistency check on the predicted $\Xi_{cc}\Xi_{cc}\bar{K}$
bound state.


  In contrast with the classical gauge group cases, any method to prove exactly
the scaling relation which relates moduli and prepotential is not known in the
case of exceptional gauge groups. This paper provides a direct method to
establish this relation by using Picard-Fuchs equations. In particular, it is
shown that the scaling relation found by Ito in N=2 supersymmetric G_2
Yang-Mills theory actually holds exactly.


  We leverage blockchain technology for drone node authentication in internet
of drone things (IoDT). During the authentication procedure, the credentials of
drone nodes are examined to remove malicious nodes from the system. In IoDT,
drones are responsible for gathering data and transmitting it to cluster heads
(CHs) for further processing. The CH collects and organizes data. Due to
computational load, their energy levels rapidly deplete. To overcome this
problem, we present a low-energy adaptive clustering hierarchy (R2D) protocol
based on distance, degree, and residual energy. R2D is used to replace CHs with
normal nodes based on the biggest residual energy, the degree, and the shortest
distance from BS. The cost of keeping a big volume of data on the blockchain is
high. We employ the Interplanetary File System (IPFS), to address this issue.
Moreover, IPFS protects user data using the industry-standard encryption
technique AES-128. This standard compares well to other current encryption
methods. Using a consensus mechanism based on proof of work requires a high
amount of computing resources for transaction verification. The suggested
approach leverages a consensus mechanism known as proof of authority (PoA) to
address this problem . The results of the simulations indicate that the
suggested system model functions effectively and efficiently. A formal security
analysis is conducted to assess the smart contract's resistance to attacks.


  We study the ground-state correlation energy $E_{\rm c}$ of two electrons of
opposite spin confined within a $D$-dimensional ball ($D \ge 2$) of radius $R$.
In the high-density regime, we report accurate results for the exact and
restricted Hartree-Fock energy, using a Hylleraas-type expansion for the former
and a simple polynomial basis set for the latter. By investigating the exact
limiting correlation energy $E_{\rm c}^{(0)} = \lim_{R \to 0} \Ec$ for various
values of $D$, we test our recent conjecture [J. Chem. Phys. {\bf 131} (2009)
241101] that, in the large-$D$ limit, $E_{\rm c}^{(0)} \sim -\delta^2/8$ for
any spherically-symmetric confining external potential, where $\delta=1/(D-1)$.


  At 2-loop order in the Coulomb gauge, individual Feynman graphs contributing
to the effective action have energy divergences. It is proved that these cancel
in suitable combinations of graphs. This has previously been shown only for
transverse external fields. The calculation results in a generalization of the
Christ-Lee term which was inserted into the Hamiltonian.


  One of the remaining challenges within the standard model is to gain a good
understanding of QCD in the non-perturbative regime. A key step towards this
aim is baryon spectroscopy, investigating the spectrum and the properties of
baryon resonances. To gain access to resonances with small $\pi N$ partial
width, photoproduction experiments provide essential information. Partial wave
analyses need to be performed to extract the contributing resonances. Here, a
complete experiment is required to unambiguously determine the contributing
amplitudes. This involves the measurement of carefully chosen single and double
polarisation observables. In a joint endeavour by MAMI, ELSA, and Jefferson
Laboratory, a new generation of experiments with polarised beams, polarised
proton and neutron targets, and $4\pi$ particle detectors have been performed
in recent years. Many results of unprecedented quality were recently published
by all three experiments, and included by the various partial wave analysis
groups in their analyses, leading to substantial improvements, e.g. a more
precise determination of resonance parameters. An overview of recent results is
given, with an emphasis on results from the CBELSA/TAPS experiment, and their
impact on our understanding of the nucleon excitation spectrum is discussed.


  We report on ongoing efforts to tackle an important open problem in QCD
thermodynamics, namely an evaluation of the pressure to order $g^6$ in a
weak-coupling expansion, corresponding to four loops. In particular, we
identify a class of contributing Feynman sum-integrals with lower-loop factors,
describe the formalism to tensor decompose those, and manage to map them onto
scalar master sum-integrals that have already been evaluated in the literature.


  We give a new proof of uniqueness of solutions to the Gross-Pitaevskii
hierarchy, first established by Erdos, Schlein and Yau, in a different space,
based on space-time estimates.


  In the low temperature phase of the square Ising model, we describe the
inverse temperature beta as the function of a squared mass M and study the
critical behavior of beta(M) via the large M expansion. Using the
delta-expansion by which the large mass expansion is transformed into a series
exhibiting expected scaling behavior, we perform the estimation of the critical
inverse temperature beta_{c} with the help of linear differential equation to
be satisfied by ansatz of beta(M) near the critical point M=0. To improve the
estimation, the leading correction exponent nu is independently estimated from
beta^{(2)}/beta^{(1)} and is used in the estimation of beta_{c}, giving rise to
remarkable accuracy improvement.


  Transverse momentum integrated multiplicities in the central region of pp
collisions at LHC energies satisfy Koba-Nielsen-Olesen scaling. We attempt to
relate this finding to multiplicity distributions of soft gluons. KNO scaling
emerges if the effective theory describing color charge fluctuations at a scale
on the order of the saturation momentum is approximately Gaussian. From an
evolution equation for quantum corrections which includes both saturation as
well as fluctuations we find that evolution with the QCD \beta-function
satisfies KNO scaling while fixed-coupling evolution does not. Thus, non-linear
saturation effects and running-coupling evolution are both required in order to
reproduce geometric scaling of the DIS cross section and KNO scaling of virtual
dipoles in a hadron wave function.


  Granger causality has been used for the investigation of the inter-dependence
structure of the underlying systems of multi-variate time series. In
particular, the direct causal effects are commonly estimated by the conditional
Granger causality index (CGCI). In the presence of many observed variables and
relatively short time series, CGCI may fail because it is based on vector
autoregressive models (VAR) involving a large number of coefficients to be
estimated. In this work, the VAR is restricted by a scheme that modifies the
recently developed method of backward-in-time selection (BTS) of the lagged
variables and the CGCI is combined with BTS. Further, the proposed approach is
compared favorably to other restricted VAR representations, such as the
top-down strategy, the bottom-up strategy, and the least absolute shrinkage and
selection operator (LASSO), in terms of sensitivity and specificity of CGCI.
This is shown by using simulations of linear and nonlinear, low and
high-dimensional systems and different time series lengths. For nonlinear
systems, CGCI from the restricted VAR representations are compared with
analogous nonlinear causality indices. Further, CGCI in conjunction with BTS
and other restricted VAR representations is applied to multi-channel scalp
electroencephalogram (EEG) recordings of epileptic patients containing
epileptiform discharges. CGCI on the restricted VAR, and BTS in particular,
could track the changes in brain connectivity before, during and after
epileptiform discharges, which was not possible using the full VAR
representation.


  Black holes are among the most exciting phenomena predicted by General
Relativity and play a key role in fundamental physics. Many interesting
phenomena involve dynamical black hole configurations in the high curvature
regime of gravity. In these lecture notes I will summarise the main numerical
relativity techniques to explore highly dynamical phenomena, such as black hole
collisions, in generic $D$-dimensional spacetimes.


  Measuring the generalization performance of a Deep Neural Network (DNN)
without relying on a validation set is a difficult task. In this work, we
propose exploiting Latent Geometry Graphs (LGGs) to represent the latent spaces
of trained DNN architectures. Such graphs are obtained by connecting samples
that yield similar latent representations at a given layer of the considered
DNN. We then obtain a generalization score by looking at how strongly connected
are samples of distinct classes in LGGs. This score allowed us to rank 3rd on
the NeurIPS 2020 Predicting Generalization in Deep Learning (PGDL) competition.


  Neurons in the nervous system exhibit an outstanding variety of morphological
and physiological properties. However, close to threshold, this remarkable
richness may be grouped succinctly into two basic types of excitability, often
referred to as type I and type II. The dynamical traits of these two neuron
types have been extensively characterized. It would be interesting, however, to
understand the information-processing consequences of their dynamical
properties. To that end, here we determine the differences between the stimulus
features inducing firing in type I and in type II neurons. We work both with
realistic conductance-based models and minimal normal forms. We conclude that
type I neurons fire in response to scale-free depolarizing stimuli. Type II
neurons, instead, are most efficiently driven by input stimuli containing both
depolarizing and hyperpolarizing phases, with significant power in the
frequency band corresponding to the intrinsic frequencies of the cell.


  The study of galaxy cluster outskirts has emerged as one of the new frontiers
in extragalactic astrophysics and cosmology with the advent of new observations
in X-ray and microwave. However, the thermodynamic properties and chemical
enrichment of this diffuse and azimuthally asymmetric component of the
intracluster medium (ICM) are still not well understood. This work, for the
first time, systematically explores potential observational biases in these
regions. To assess X-ray measurements of galaxy cluster properties at large
radii ($>{R}_{500c}$), we use mock Chandra analyses of cosmological galaxy
cluster simulations. The pipeline is identical to that used for Chandra
observations, but the biases discussed in this paper are relevant for all X-ray
observations outside of ${R}_{500c}$. We find the following from our analysis:
(1) filament regions can contribute as much as $50\%$ at $R_{200c}$ to the
emission measure; (2) X-ray temperatures and metal abundances from model fitted
mock X-ray spectra in a multi-temperature ICM respectively vary to the level of
$10\%$ and $50\%$; (3) resulting density profiles vary to within $10\%$ out to
$R_{200c}$, and gas mass, total mass, and baryon fractions all vary to within a
few percent; (4) the bias from a metal abundance extrapolated a factor of five
higher than the true metal abundance results in total mass measurements biased
high by $20\%$ and total gas measurements biased low by $10\%$; and (5)
differences in projection and dynamical state of a cluster can lead to gas
density slope measurements that differ by a factor of $15\%$ and $30\%$,
respectively. The presented results can partially account for some of the
recent gas profile measurements in cluster outskirts by, e.g., Suzaku. Our
findings are pertinent to future X-ray cosmological constraints from cluster
outskirts.


  We study the one-way number-on-the-forehead (NOF) communication complexity of
the $k$-layer pointer jumping problem with $n$ vertices per layer. This classic
problem, which has connections to many aspects of complexity theory, has seen a
recent burst of research activity, seemingly preparing the ground for an
$\Omega(n)$ lower bound, for constant $k$. Our first result is a surprising
sublinear -- i.e., $o(n)$ -- upper bound for the problem that holds for $k \ge
3$, dashing hopes for such a lower bound. A closer look at the protocol
achieving the upper bound shows that all but one of the players involved are
collapsing, i.e., their messages depend only on the composition of the layers
ahead of them. We consider protocols for the pointer jumping problem where all
players are collapsing. Our second result shows that a strong $n - O(\log n)$
lower bound does hold in this case. Our third result is another upper bound
showing that nontrivial protocols for (a non-Boolean version of) pointer
jumping are possible even when all players are collapsing. Our lower bound
result uses a novel proof technique, different from those of earlier lower
bounds that had an information-theoretic flavor. We hope this is useful in
further study of the problem.


  We derive general conditions for 100 percent frequency conversion in any
doubly resonant nonlinear cavity, for both second- and third-harmonic
generation via chi2 and chi3 nonlinearities. We find that conversion efficiency
is optimized for a certain critical power depending on the cavity parameters,
and assuming reasonable parameters we predict 100 percent conversion using
milliwatts of power or less. These results follow from a semi-analytical
coupled-mode theory framework which is generalized from previous work to
include both chi2 and chi3 media as well as inhomogeneous (fully vectorial)
cavities, analyzed in the high-efficiency limit where down-conversion processes
lead to a maximum efficiency at the critical power, and which is verified by
direct finite-difference time-domain (FDTD) simulations of the nonlinear
Maxwell equations. Explicit formulas for the nonlinear coupling coefficients
are derived in terms of the linear cavity eigenmodes, which can be used to
design and evaluate cavities in arbitrary geometries.


  The local nuclear slope $B(s,t) = {d \over d t} (\ln {d\sigma_n (s,t)\over
dt})$ is reconstructed from the experimental angular distributions with a
procedure that uses overlapping $t$-bins, for an energy that ranges from the
ISR to the $S\bar ppS$ and the Tevatron. Predictions of several models of
($p,p$) and ($\bar p,p$) elastic scattering at high energy are tested in
$B(s,t)$ at small $|t|$. Only a model with two-components Pomeron and Odderon
gives a satisfactory agreement with the (non fitted) slope data, in particular
for the evolution of $B(s,t)$ with $s$ as a function of $t$ in $\bar pp$
scattering. This model predicts a similar behavior for $pp$ and $\bar pp$
scattering at small $|t|$. A detailed confirmation for $pp$ collisions would be
expected from RHIC.


  We consider discrete denoising of two-dimensional data with characteristics
that may be varying abruptly between regions.
  Using a quadtree decomposition technique and space-filling curves, we extend
the recently developed S-DUDE (Shifting Discrete Universal DEnoiser), which was
tailored to one-dimensional data, to the two-dimensional case. Our scheme
competes with a genie that has access, in addition to the noisy data, also to
the underlying noiseless data, and can employ $m$ different two-dimensional
sliding window denoisers along $m$ distinct regions obtained by a quadtree
decomposition with $m$ leaves, in a way that minimizes the overall loss. We
show that, regardless of what the underlying noiseless data may be, the
two-dimensional S-DUDE performs essentially as well as this genie, provided
that the number of distinct regions satisfies $m=o(n)$, where $n$ is the total
size of the data. The resulting algorithm complexity is still linear in both
$n$ and $m$, as in the one-dimensional case. Our experimental results show that
the two-dimensional S-DUDE can be effective when the characteristics of the
underlying clean image vary across different regions in the data.


  We investigate one of the most common analytic continuation techniques in
condensed matter physics, namely the Pad\'{e} approximant. Aspects concerning
its implementation in the exact muffin-tin orbitals (EMTO) method are
scrutinized with special regard towards making it stable and free of artificial
defects. The electronic structure calculations are performed for solid
hydrogen, and the performance of the analytical continuation is assessed by
monitoring the density of states constructed directly and via the Pad\'{e}
approximation. We discuss the difference between the \textbf{k}-integrated and
\textbf{k}-resolved analytical continuations, as well as describing the use of
random numbers and pole residues to analyze the approximant. It is found that
the analytic properties of the approximant can be controlled by appropriate
modifications, making it a robust and reliable tool for electronic structure
calculations. At the end, we propose a route to perform analytical continuation
for the EMTO + dynamical mean field theory (DMFT) method.


  We propose a new model based on the Ising model with the aim to study
synaptic plasticity phenomena in neural networks. It is today well established
in biology that the synapses or connections between certain types of neurons
are strengthened when the neurons are co-active, a form of the so called
synaptic plasticity. Such mechanism is believed to mediate the formation and
maintenance of memories. The proposed model describes some features from that
phenomenon. Together with the spin-flip dynamics, in our model the coupling
constants are also subject to stochastic dynamics, so that they interact with
each other. The evolution of the system is described by a continuous-time
Markov jump process.
  Keyword Markov chain, Stochastic Ising model, synaptic plasticity, neural
networks, transience


  We study the structure of CSC-1 reducible conformal metrics on a compact
Riemann surface with finite conical singularities. We prove that any compact
Riemann surface with a CSC-1 reducible conformal metric of finite conical
singularities can be divided into a finite number of pieces by cutting along
geodesics where each piece is isometric to some football. This allows us to
study the existence of CSC-1 reducible conformal metrics with finite conical
singularities on a compact Riemann surface. As an application, we give an angle
condition of the existence of CSC-1 reducible conformal metrics with finite
conical singularities on a compact Riemann surface.


  We introduce a family of models for quantum mechanical, one-dimensional
random walks, called quantum multibaker maps (QMB). These are Weyl
quantizations of the classical multibaker models previously considered by
Gaspard, Tasaki and others. Depending on the properties of the phases
parametrizing the quantization, we consider only two classes of the QMB maps:
uniform and random. Uniform QMB maps are characterized by phases which are the
same in every unit cell of the multibaker chain. Random QMB maps have phases
that vary randomly from unit cell to unit cell. The eigenstates in the former
case are extended while in the latter they are localized. In the uniform case
and for large $\hbar$, analytic solutions can be obtained for the time
dependent quantum states for periodic chains and for open chains with absorbing
boundary conditions. Steady state solutions and the properties of the
relaxation to a steady state for a uniform QMB chain in contact with
``particle'' reservoirs can also be described analytically. The analytical
results are consistent with, and confirmed by, results obtained from numerical
methods. We report here results for the deep quantum regime (large $\hbar$) of
the uniform QMB, as well as some results for the random QMB. We leave the
moderate and small $\hbar$ results as well as further consideration of the
other versions of the QMB for further publications.


  Finding an optimal individualized treatment regimen is considered one of the
most challenging precision medicine problems. Various patient characteristics
influence the response to the treatment, and hence, there is no
one-size-fits-all regimen. Moreover, the administration of an unsafe dose
during the treatment can have adverse effects on health. Therefore, a treatment
model must ensure patient \emph{safety} while \emph{efficiently} optimizing the
course of therapy. We study a prevalent medical problem where the treatment
aims to keep a physiological variable in a safe range and preferably close to a
target level, which we refer to as \emph{leveling}. Such a task may be relevant
in numerous other domains as well. We propose ESCADA, a novel and generic
multi-armed bandit (MAB) algorithm tailored for the leveling task, to make
safe, personalized, and context-aware dose recommendations. We derive high
probability upper bounds on its cumulative regret and safety guarantees.
Following ESCADA's design, we also describe its Thompson sampling-based
counterpart. We discuss why the straightforward adaptations of the classical
MAB algorithms such as GP-UCB may not be a good fit for the leveling task.
Finally, we make \emph{in silico} experiments on the bolus-insulin dose
allocation problem in type-1 diabetes mellitus disease and compare our
algorithms against the famous GP-UCB algorithm, the rule-based dose
calculators, and a clinician.


  In the past year, a new non-supersymmetric framework for electroweak symmetry
breaking (with or without Higgs) involving SU(2)_L * SU(2)_R * U(1)_{B-L} in
higher dimensional warped geometry has been suggested. In this work, we embed
this gauge structure into a GUT such as SO(10) or Pati-Salam. We showed
recently (in hep-ph/0403143) that in a warped GUT, a stable Kaluza-Klein
fermion can arise as a consequence of imposing proton stability. Here, we
specify a complete realistic model where this particle is a weakly interacting
right-handed neutrino, and present a detailed study of this new dark matter
candidate, providing relic density and detection predictions. We discuss
phenomenological aspects associated with the existence of other light (<~ TeV)
KK fermions (related to the neutrino), whose lightness is a direct consequence
of the top quark's heaviness. The AdS/CFT interpretation of this construction
is also presented. Most of our qualitative results do not depend on the nature
of the breaking of the electroweak symmetry provided that it happens near the
TeV brane.


  An accurate and simple analytical model for the computation of the reflection
amplitude and phase of Reconfigurable Intelligent Surfaces is presented. The
model is based on a transmission-line circuit representation of the RIS which
takes into account the physics behind the structure including the effect of all
relevant geometrical and electrical parameters. The proposed representation of
the RIS allows to take into account the effect of incidence angle, mutual
coupling among elements and the effect of the interaction of the periodic
surface with the RIS ground plane. It is shown that the proposed approach
allows to design a physically realisable RIS without recurring to onerous
electromagnetic simulations. The proposed model aims at filling the gap between
RIS assisted communications algorithms and physical implementation issues which
determine realistic performance of these surfaces.


  This is a brief review of the present status, of some recent developments and
of the open challenges in string/M theory.