  In the past decade, model-free reinforcement learning (RL) has provided
solutions to challenging domains such as robotics. Model-based RL shows the
prospect of being more sample-efficient than model-free methods in terms of
agent-environment interactions, because the model enables to extrapolate to
unseen situations. In the more recent past, model-based methods have shown
superior results compared to model-free methods in some challenging domains
with non-linear state transitions. At the same time, it has become apparent
that RL is not market-ready yet and that many real-world applications are going
to require model-based approaches, because model-free methods are too
sample-inefficient and show poor performance in early stages of training. The
latter is particularly important in industry, e.g. in production systems that
directly impact a company's revenue. This demonstrates the necessity for a
toolbox to push the boundaries for model-based RL. While there is a plethora of
toolboxes for model-free RL, model-based RL has received little attention in
terms of toolbox development. Bellman aims to fill this gap and introduces the
first thoroughly designed and tested model-based RL toolbox using
state-of-the-art software engineering practices. Our modular approach enables
to combine a wide range of environment models with generic model-based agent
classes that recover state-of-the-art algorithms. We also provide an experiment
harness to compare both model-free and model-based agents in a systematic
fashion w.r.t. user-defined evaluation metrics (e.g. cumulative reward). This
paves the way for new research directions, e.g. investigating uncertainty-aware
environment models that are not necessarily neural-network-based, or developing
algorithms to solve industrially-motivated benchmarks that share
characteristics with real-world problems.


  We investigate the prospects of combining a standard momentum space approach
for ultracold three-body scattering with efficient coordinate space schemes to
solve the underlying two-body problem. In many of those schemes the two-body
problem is numerically restricted up to a finite interparticle distance
$r_\mathrm{b}$. We analyze effects of this two-body restriction on the two- and
three-body level using pairwise square-well potentials that allow for analytic
two-body solutions and more realistic Lennard-Jones van der Waals potentials to
model atomic interactions. We find that the two-body $t$-operator converges
exponentially in $r_\mathrm{b}$ for the square-well interaction. Setting
$r_\mathrm{b}$ to 2000 times the range of the interaction, the three-body
recombination rate can be determined accurately up to a few percent when the
magnitude of the scattering length is small compared to $r_\mathrm{b}$, while
the position of the lowest Efimov features is accurate up to the percent level.
In addition we find that with the introduction of a momentum cut-off, it is
possible to determine the three-body parameter in good approximation even for
deep van der Waals potentials.


  A 1957 conjecture by Zdzislaw Melzak, that the unit volume polyhedron with
least edge length was a triangular right prism, with edge length
$2^{2/3}3^{11/6}$. We present a variety of necessary local criteria for any
minimizer. In the case that we are restricted to convex polyhedrons we
demonstrate that all vertices must be of degree three, the number of triangular
faces is at most 14, and we describe the behavior of quadrilateral faces should
they become arbitrarily small.


  At high energy scale the only quantum effect of any asymptotic free and
asymptotically conformal invariant GUT is the trace anomaly of the
energy-momentum tensor. Anomaly generates the new degree of freedom, that is
propagating conformal factor. At lower energies conformal factor starts to
interact with scalar field because of the violation of conformal invariance. We
estimate the effect of such an interaction and find the running of the
nonminimal coupling from conformal value $\frac{1}{6}$ to $0$. Then we discuss
the possibility of the first order phase transition induced by curvature in a
region close to the stable fixed point and calculate the induced values of
Newtonian and cosmological constants.


  Using multi-epoch observations of the Stripe 82 region done by Sloan Digital
Sky Survey, we measure precise statistical proper motions of the stars in the
Sagittarius stellar stream. The multi-band photometry and SDSS radial
velocities allow us to efficiently select Sgr members and thus enhance the
proper motion precision to ~0.1 mas/yr. We measure separately the proper motion
of a photometrically selected sample of the main sequence turn-off stars, as
well as of a spectroscopically selected Sgr giants. The data allow us to
determine the proper motion separately for the two Sgr streams in the South
found in Koposov et al.(2012). Together with the precise velocities from SDSS,
our proper motion provide exquisite constraints of the 3-D motions of the stars
in the Sgr streams.


  Timing channels enable data leakage that threatens the security of computer
systems, from cloud platforms to smartphones and browsers executing untrusted
third-party code. Preventing unauthorised information flow is a core duty of
the operating system, however, present OSes are unable to prevent timing
channels. We argue that OSes must provide time protection in addition to the
established memory protection. We examine the requirements of time protection,
present a design and its implementation in the seL4 microkernel, and evaluate
its efficacy as well as performance overhead on Arm and x86 processors.


  Macroscopic polarization plays a major role in determining the optical and
electrical properties of nitride nanostructures via polarization-induced
built-in electrostatic fields. While currently fashionable, this field of
endeavour is still by far in its early infancy. Here we contribute some
clarifications on the conceptual issues involved in determining built-in fields
in III-V nitride nanostructures, sorting out in particular the roles of
spontaneous and piezoelectric polarization.


  Generalizing a construction of P. Vanhaecke, we introduce a large class of
degenerate (i.e., associated to a degenerate Poisson bracket) completely
integrable systems on (a dense subset of) the space $\R^{2d+n+1}$, called the
generalized master systems. It turns out that certain generalized master
systems (with different Poisson brackets and different Hamiltonians) determine
the same Hamiltonian vector fields (and are therefore different descriptions of
the same Hamiltonian system), and that the Poisson brackets of these systems
are compatible. Consequently, our class of generalized master systems actually
consists of a (smaller) class of completely integrable systems, and our
construction yields a multi-Hamiltonian structure for these systems. As an
application, we construct a multi-Hamiltonian structure for the so-called
master systems introduced by D. Mumford.


  Deep learning has become the most widely used approach for cardiac image
segmentation in recent years. In this paper, we provide a review of over 100
cardiac image segmentation papers using deep learning, which covers common
imaging modalities including magnetic resonance imaging (MRI), computed
tomography (CT), and ultrasound (US) and major anatomical structures of
interest (ventricles, atria and vessels). In addition, a summary of publicly
available cardiac image datasets and code repositories are included to provide
a base for encouraging reproducible research. Finally, we discuss the
challenges and limitations with current deep learning-based approaches
(scarcity of labels, model generalizability across different domains,
interpretability) and suggest potential directions for future research.


  In this paper we derive results concerning the connected components and the
diameter of random graphs with an arbitrary i.i.d. degree sequence. We study
these properties primarily, but not exclusively, when the tail of the degree
distribution is regularly varying with exponent $1-\tau$. There are three
distinct cases: (i) $\tau>3$, where the degrees have finite variance, (ii)
$\tau\in (2,3)$, where the degrees have infinite variance, but finite mean, and
(iii) $\tau\in (1,2)$, where the degrees have infinite mean. These random
graphs can serve as models for complex networks where degree power laws are
observed.
  Our results are twofold. First, we give a criterion when there exists a
unique largest connected component of size proportional to the size of the
graph, and study sizes of the other connected components. Secondly, we
establish a phase transition for the diameter when $\tau \in (2,3)$. Indeed, we
show that for $\tau>2$ and when nodes with degree 2 are present with positive
probability, the diameter of the random graph is, with high probability,
bounded below by a constant times the logarithm of the size of the graph. On
the other hand, assuming that all degrees are at least 3 or more, we show that,
for $\tau \in (2,3)$, the diameter of the graph is with high probability
bounded from above by a constant times the $\log \log$ of the size of the
graph.


  We present simulations of an efficient high numerical aperture solid
immersion metalens concept for fluorescence microscopy. The technique exploits
the preferential emission of interfacial dipoles into a high refractive index
substrate combined with a metalens and a conventional tube lens for imaging
them. We have thus simulated dipole emission and an all-dielectric metasurface
on opposite sides of a high refractive index substrate. Our calculations
predict dipole collection efficiencies of up to 87 percent. The simulated beam
propagation through the imaging system shows excellent performance along the
optical axis, with aberrations accumulating with increasing field of view.
These aberrations can be controlled by using a metasurface with an optimized
non-hyperbolic phase profile. The high collection efficiency of dipole emission
suggests this compact solid immersion lens would be effective for fluorescence
imaging including single fluorescent centres for quantum optical application.


  For the Local Group Scd galaxy M 33 this paper presents a multi-scale study
of the relationship between the monochromatic star formation rate (SFR)
estimator based on 12 $\mu$m emission and the total SFR estimator based on a
combination of far-ultraviolet and 24 $\mu$m emission. We show the 12 $\mu$m
emission to be a linear estimator of total SFR on spatial scales from 782 pc
down to 49 pc, over almost four magnitudes in SFR. These results therefore
extend to sub-kpc length scales the analogous results from other studies for
global length scales. We use high-resolution HI and $^{12}\mathrm{CO}(J=2-1)$
image sets from the literature to compare the star formation to the neutral
gas. For the full range of length scales we find well-defined power-law
relationships between 12 $\mu$m-derived SFR surface densities and neutral gas
surface densities. For the H$_\mathrm{2}$ gas component almost all correlations
are consistent with being linear. No evidence is found for a breakdown in the
star formation law at small length scales in M 33 reported by other authors. We
show that the average star formation efficiency in M 33 is roughly $10^{-9}$
yr$^{-1}$ and that it remains constant down to giant molecular cloud length
scales. Toomre and shear-based models of the star formation threshold are shown
to inaccurately account for the star formation activity in the inner disc of M
33. Finally, we clearly show that the HI saturation limit of $\approx 9$
M$_{\odot}$ pc$^{-2}$ reported in the literature for other galaxies is not an
intrinsic property of M 33 - it is systematically introduced as an artefact of
spatially smoothing the data.


  The gauged Nambu-Jona-Lasinio model in the quenched-ladder approximation has
non-trivial dynamics near a critical scaling region (critical curve) separating
a chiral symmetric and a dynamically chiral symmetry broken phase. Scalar and
pseudoscalar composites corresponding to the four-fermion interaction become
relevant degrees of freedom at short distances, which is reflected in the
appearance of a large anomalous dimension of the four-fermion operators. A
method is introduced for solving the Schwinger-Dyson equation for the Yukawa
vertex in specific kinematic regimes. This allows one to derive an analytic
expression for the scalar propagator, which is valid along the entire critical
curve. The mass and width of the scalar composites in the critical scaling
region are reexamined and the conformal phase transition at the critical gauge
coupling is discussed.


  The discovery of the Higgs boson at the LHC has opened the door to clarify
the mechanism of electroweak symmetry breaking and the origin of masses of
particles. The Higgs sector in the SM is the simplest but has no theoretical
principle, so that there is a possibility of non-minimal Higgs sectors. While
the standard model is not contradict with the current data at the LHC within
the error, most of extended Higgs sectors can also reproduce the data. An
extended Higgs sector often appears in a new physics model beyond the standard
model, so that we can determine new physics from the Higgs sector. In this
talk, we discuss various aspects of extended Higgs sectors, in particular its
phenomenological properties and testability at future experiments at the
International Linear Collider.


  Face reenactment refers to the process of transferring the pose and facial
expressions from a reference (driving) video onto a static facial (source)
image while maintaining the original identity of the source image. Previous
research in this domain has made significant progress by training controllable
deep generative models to generate faces based on specific identity, pose and
expression conditions. However, the mechanisms used in these methods to control
pose and expression often inadvertently introduce identity information from the
driving video, while also causing a loss of expression-related details. This
paper proposes a new method based on Stable Diffusion, called AniFaceDiff,
incorporating a new conditioning module for high-fidelity face reenactment.
First, we propose an enhanced 2D facial snapshot conditioning approach by
facial shape alignment to prevent the inclusion of identity information from
the driving video. Then, we introduce an expression adapter conditioning
mechanism to address the potential loss of expression-related information. Our
approach effectively preserves pose and expression fidelity from the driving
video while retaining the identity and fine details of the source image.
Through experiments on the VoxCeleb dataset, we demonstrate that our method
achieves state-of-the-art results in face reenactment, showcasing superior
image quality, identity preservation, and expression accuracy, especially for
cross-identity scenarios. Considering the ethical concerns surrounding
potential misuse, we analyze the implications of our method, evaluate current
state-of-the-art deepfake detectors, and identify their shortcomings to guide
future research.


  The de Sitter spacetime is transitive under a combination of translations and
proper conformal transformations. Its usual family of geodesics, however, does
not take into account this property. As a consequence, there are points in de
Sitter spacetime which cannot be joined to each other by any one of these
geodesics. By taking into account the appropriate transitivity properties in
the variational principle, a new family of maximizing trajectories is obtained,
whose members are able to connect any two points of the de Sitter spacetime.
These geodesics introduce a new notion of motion, given by a combination of
translations and proper conformal transformations, which may possibly become
important at very-high energies, where conformal symmetry plays a significant
role.


  Static and dynamic properties of two-dimensional bidisperse dissipative
particles are numerically studied near the jamming transition. We investigate
the dependency of the critical scaling on the ratio of the different diameters
and find a new scaling of the maximum overlap (not consistent with the scaling
of the mean overlap). The ratio of kinetic and potential energies drastically
slows down near the jamming transition, i.e. the relaxation time diverges at
the jamming point.


  Echelle spectrophotometry of the planetary nebula NGC 5307 is presented. The
data consists of VLT UVES observations in the 3100 to 10360 A range. Electron
temperatures and densities have been determined using different line intensity
ratios. We determine the H, He, C, and O abundances based on recombination
lines, these abundances are almost independent of the temperature structure of
the nebula. We also determine the N, O, Ne, S, Cl, and Ar abundances based on
collisionally excited lines, the ratios of these abundances relative to that of
H depend strongly on the temperature structure of the nebula. From the
OII/[OIII] ratio we find a t^2 = 0.056+-0.005. The chemical composition of NGC
5307 is compared with those of the Sun and the Orion nebula. From the study of
the relative intensities of the OII recombination lines of multiplet 1 in this
and other nebulae it isfound that for electron densities smaller than about
5000 cm-3 collisional redistribution is not complete, this effect has to be
taken into account to derive the O abundances for those cases in which not all
the lines of the multiplet are observed. From the 4649 OII versus N_e$(ClIII)
diagram we find a critical electron density of 1325 cm-3 for collisional
redistribution of the OII lines of multiplet 1. Also based on this diagram we
argue that the OII and the [OIII] lines originate in the same regions. We also
find that the radial velocities and the FWHM of the OII and [OIII] lines in NGC
5307 are similar supporting the previous result. These two results imply that
for NGC 5307 and probably for many other gaseous nebulae chemical
inhomogeneities are not responsible for the large temperature fluctuations
observed.


  We obtain the quantum phase diagram of the Hubbard chain with alternating
on-site energy at half filling. The model is relevant for the ferroelectric
perovskites and organic mixed-stack donor-acceptor crystals. For any values of
the parameters, the band insulator is separated from the Mott insulator by a
dimer phase. The boundaries are determined accurately by crossing of excited
levels with particular discrete symmetries. We show that these crossings
coincide with jumps of charge and spin Berry phases with a clear geometrical
meaning.


  We give a brief exposition of the approach based on the methods of spectral
geometry and the spectral action principle to construction and analysis of
models on a foliated space-time.


  In this paper we design an iterative domain decomposition method for free
boundary problems with nonlinear flux jump condition. Our approach is related
to damped Newton's methods. The proposed scheme requires, in each iteration,
the approximation of the flux on (both sides of) the free interface. We present
a Finite Element implementation of our method. The numerical implementation
uses harmonically deformed triangulations to inexpensively generate finite
element meshes in subdomains. We apply our method to a simplified model for jet
flows in pipes and to a simple magnetohydrodynamics model. Finally, we present
numerical examples studying the convergence of our scheme.


  We prove that if a geodesic metric measure space satisfies a comparison
condition for isoperimetric profile and if the observable variance is maximal,
then the space is foliated by minimal geodesics, where the observable variance
is defined to be the supremum of the variance of 1-Lipschitz functions on the
space. Our result can be considered as a variant of Cheeger-Gromoll's splitting
theorem and also of Cheng's maximal diameter theorem. As an application, we
obtain a new isometric splitting theorem for a complete weighted Riemannian
manifold with a positive Bakry-\'Emery Ricci curvature.


We prove that the graph complex is a strong homotopy Lie (super) bialgebra.


  We give a generalization of the Hodge operator to spaces $(V,h)$ endowed with
a hermitian or symmetric bilinear form $h$ over arbitrary fields, including the
characteristic two case. Suitable exterior powers of $V$ become free modules
over an algebra $K$ defined using such an operator. This leads to several
exceptional homomorphisms from unitary groups (with respect to $h$) into groups
of semi-similitudes with respect to a suitable form over some subfield of $K$.
The algebra $K$ depends on $h$; it is a composition algebra unless $h$ is
symmetric and the characteristic is two.


  In this paper,we studied some properties of the Fibonacci map and find a
property of this map related to the notation of principal nest. And weproved
this property with some additional conditions equals the original definition of
Fibonacci map. Therefore give us a new method to describe Fibonacci map.


  Software process models play a pivotal role in fostering collaboration and
communication within software teams, enabling them to tackle intricate
development tasks effectively. This paper introduces LCG, a code generation
framework inspired by established software engineering practices. LCG leverages
multiple Large Language Model (LLM) agents to emulate various software process
models, namely LCGWaterfall, LCGTDD, and LCGScrum. Each model assigns LLM
agents specific roles such as requirement engineer, architect, developer,
tester, and scrum master, mirroring typical development activities and
communication patterns. Through collaborative efforts utilizing
chain-of-thought and prompt composition techniques, the agents continuously
refine themselves to enhance code quality. Utilizing GPT3.5 as the underlying
LLM and baseline (GPT), we evaluate LCG across four code generation benchmarks:
HumanEval, HumanEval-ET, MBPP, and MBPP-ET. Results indicate LCGScrum
outperforms other models, achieving Pass@1 scores of 75.2, 65.5, 82.5, and 56.7
in HumanEval, HumanEval-ET, MBPP, and MBPP-ET, respectively - an average 15%
improvement over GPT. Analysis reveals distinct impacts of development
activities on generated code, with design and code reviews contributing to
enhanced exception handling, while design, testing, and code reviews mitigate
code smells. Furthermore, temperature values exhibit negligible influence on
Pass@1 across all models. However, variations in Pass@1 are notable for
different GPT3.5 model versions, ranging from 5 to over 60 in HumanEval,
highlighting the stability of LCG across model versions. This stability
underscores the importance of adopting software process models to bolster the
quality and consistency of LLM-generated code.


  Image and video quality in Long Range Observation Systems (LOROS) suffer from
atmospheric turbulence that causes small neighbourhoods in image frames to
chaotically move in different directions and substantially hampers visual
analysis of such image and video sequences. The paper presents a real-time
algorithm for perfecting turbulence degraded videos by means of stabilization
and resolution enhancement. The latter is achieved by exploiting the turbulent
motion. The algorithm involves generation of a reference frame and estimation,
for each incoming video frame, of a local image displacement map with respect
to the reference frame; segmentation of the displacement map into two classes:
stationary and moving objects and resolution enhancement of stationary objects,
while preserving real motion. Experiments with synthetic and real-life
sequences have shown that the enhanced videos, generated in real time, exhibit
substantially better resolution and complete stabilization for stationary
objects while retaining real motion.


  This article is a first step towards the understanding of the dynamics of the
horocycle flow on foliated manifolds by hyperbolic surfaces. This is motivated
by a question formulated by M. Martinez and A. Verjovsky on the minimality of
this flow assuming that the natural affine foliation is minimal too. We have
tried to offer a simple presentation, which allows us to update and shed light
on the classical theorem proved by G. A. Hedlund in 1936 on the minimality of
the horocycle flow on compact hyperbolic surfaces. Firstly, we extend this
result to the product of PSL(2,R) and a Lie group G, which places us within the
homogeneous framework investigated by M. Ratner. Since our purpose is to deal
with non-homogeneous situations, we do not use Ratner's famous Orbit-Closure
Theorem, but we give an elementary proof. We show that this special situation
arises for homogeneous Riemannian and Lie foliations, reintroducing the
foliation point of view. Examples and counter-examples take an important place
in our work, in particular, the very instructive case of the hyperbolic torus
bundles over the circle. Our aim in writing this text is to offer to the reader
an accessible introduction to a subject that was intensively studied in the
algebraic setting, although there still are unsolved geometric problems.


  We present homogeneous and accurate iron abundances for almost four dozen
(47) of Galactic Cepheids using high-spectral resolution (R$\sim$40,000) high
signal-to-noise ratio (S/N $\ge$ 100) optical spectra collected with UVES at
VLT. A significant fraction of the sample (32) is located in the inner disk (RG
$le$ 6.9 kpc) and for half of them we provide new iron abundances. Current
findings indicate a steady increase in iron abundance when approaching the
innermost regions of the thin disk. The metallicity is super-solar and ranges
from 0.2 dex for RG $\sim$ 6.5 kpc to 0.4 dex for RG $\sim$ 5.5 kpc. Moreover,
we do not find evidence of correlation between iron abundance and distance from
the Galactic plane. We collected similar data available in the literature and
ended up with a sample of 420 Cepheids. Current data suggest that the mean
metallicity and the metallicity dispersion in the four quadrants of the
Galactic disk attain similar values. The first-second quadrants show a more
extended metal-poor tail, while the third-fourth quadrants show a more extended
metal-rich tail, but the bulk of the sample is at solar iron abundance.
Finally, we found a significant difference between the iron abundance of
Cepheids located close to the edge of the inner disk ([Fe/H]$\sim$0.4) and
young stars located either along the Galactic bar or in the nuclear bulge
([Fe/H]$\sim$0). Thus suggesting that the above regions have had different
chemical enrichment histories. The same outcome applies to the metallicity
gradient of the Galactic bulge, since mounting empirical evidence indicates
that the mean metallicity increases when moving from the outer to the inner
bulge regions.


  We investigate the possible detection of the Hypercharge Axion (HCA) in
colliders. The HCA is a hypothetical pseudoscalar that couples to weak
hypercharge topological density and could potentially explain the dominance of
matter over antimatter in the observable universe. If the HCA exists, it can be
produced in colliders via vector boson fusion or in association with a photon
or with a Z boson, and detected by looking for its decay into photons or Z
bosons. We find that for certain values of the HCA mass and coupling scales,
both of the order of a TeV, existing data from the Large Hadron Collider (LHC)
can already put interesting constraints and in future colliders, such as the
High Luminosity LHC, the accessible detection range is increased significantly.


  Real-world vision tasks frequently suffer from the appearance of unexpected
adverse weather conditions, including rain, haze, snow, and raindrops. In the
last decade, convolutional neural networks and vision transformers have yielded
outstanding results in single-weather video removal. However, due to the
absence of appropriate adaptation, most of them fail to generalize to other
weather conditions. Although ViWS-Net is proposed to remove adverse weather
conditions in videos with a single set of pre-trained weights, it is seriously
blinded by seen weather at train-time and degenerates when coming to unseen
weather during test-time. In this work, we introduce test-time adaptation into
adverse weather removal in videos, and propose the first framework that
integrates test-time adaptation into the iterative diffusion reverse process.
Specifically, we devise a diffusion-based network with a novel temporal noise
model to efficiently explore frame-correlated information in degraded video
clips at training stage. During inference stage, we introduce a proxy task
named Diffusion Tubelet Self-Calibration to learn the primer distribution of
test video stream and optimize the model by approximating the temporal noise
model for online adaptation. Experimental results, on benchmark datasets,
demonstrate that our Test-Time Adaptation method with Diffusion-based
network(Diff-TTA) outperforms state-of-the-art methods in terms of restoring
videos degraded by seen weather conditions. Its generalizable capability is
also validated with unseen weather conditions in both synthesized and
real-world videos.


  A new and simple statistical approach is performed to calculate the parton
distribution functions (PDFs) of the nucleon in terms of light-front kinematic
variables. We do not put in any extra arbitrary parameter or corrected term by
hand, which guarantees the stringency of our approach. Analytic expressions of
the $x$-dependent PDFs are obtained in the whole $x$ region [0,1], and some
features, especially the low-$x$ rise, are more agreeable with experimental
data than those in some previous instant-form statistical models in the
infinite-momentum frame (IMF). Discussions on heavy-flavored PDFs are also
presented.


  A quantum-mechanical Gaussian wave-packet approach to the theoretical
description of nuclear motions in a condensed-phase environment is developed.
General expressions for the time-dependent reduced density matrix are given for
a harmonic potential surface, and the exact quantum dynamics is found for a
microscopic system-plus-bath model. Particular attention is devoted to the
influence of initial correlations between system and bath for the outcome of a
pump-probe experiment. We show that the standard factorized preparation,
compared to a more realistic correlated preparation, leads to significantly
different stimulated emission spectra at high temperatures. Recent experiments
for the reaction center are analyzed using this formalism.


  A statistical study of cis-regulatory modules (CRMs) is presented based on
the estimation of similar-word set distribution. It is observed that CRMs tend
to have a fat-tail distribution. A new statistical fat-tail test with two
kurtosis-based fatness coefficients is proposed to distinguish CRMs from
non-CRMs. As compared with the existing fluffy-tail test, the first fatness
coefficient is designed to reduce computational time, making the novel fat-tail
test very suitable for long sequences and large database analysis in the
post-genome time and the second one to improve separation accuracy between CRMs
and non-CRMs. These two fatness coefficients may be served as valuable
filtering indexes to predict CRMs experimentally.


  We consider the nonlinear rheology of dense colloidal suspensions under a
time-dependent simple shear flow. Starting from the Smoluchowski equation for
interacting Brownian particles advected by shearing (ignoring fluctuations in
fluid velocity) we develop a formalism which enables the calculation of
time-dependent, far-from-equilibrium averages. Taking shear-stress as an
example we derive exactly a generalized Green-Kubo relation, and an equation of
motion for the transient density correlator, involving a three-time memory
function. Mode coupling approximations give a closed constitutive equation
yielding the time-dependent stress for arbitrary shear rate history. We solve
this equation numerically for the special case of a hard sphere glass subject
to step-strain.


  Wireless communication between an unmanned aerial vehicle (UAV) and the
ground base station (BS) is susceptible to adversarial jamming. In such
situations, it is important for the UAV to indicate a new channel to the BS.
This paper describes a method of creating spatial codes that map the chosen
channel to the motion and location of the UAVs in space, wherein the latter
physically traverses the space from a given so called "constellation point'' to
another. These points create patterns in the sky, analogous to modulation
constellations in classical wireless communications, and are detected at the BS
through a millimeter-wave (mmWave) radar sensor. A constellation point
represents a distinct n-bit field mapped to a specific channel, allowing
simultaneous frequency switching at both ends without any RF transmissions. The
main contributions of this paper are: (i) We conduct experimental studies to
demonstrate how such constellations may be formed using COTS UAVs and mmWave
sensors, given realistic sensing errors and hovering vibrations, (ii) We
develop a theoretical framework that maps a desired constellation design to
error and band switching time, considering again practical UAV movement
limitations, and (iii) We experimentally demonstrate jamming resilient
communications and validate system goodput for links formed by UAV-mounted
software defined radios.


  A method has been recently proposed to establish the geometry of the
alpha-cluster arrangement in $^{12}$C making use of polarized gamma-rays. The
ratio of intensities of scattered radiation at 90 degrees along and
perpendicular to the initial direction of the electric field vector, called
depolarization ratio, is a key quantity that allows to underpin the nature of
totally symmetric modes of vibrations. This allows to connect with the
underlying point-group structure and therefore to the geometric shape of the
nuclear molecule. This method is reviewed for $^{12}$C and extended to other
configurations, such as three unequal clusters and four identical clusters
(e.g. $^{16}$O).


  In this manuscript we present exponential inequalities for spatial lattice
processes which take values in a separable Hilbert space and satisfy certain
dependence conditions. We consider two types of dependence: spatial data under
$\alpha$-mixing conditions and spatial data which satisfies a weak dependence
condition introduced by Dedecker and Prieur [2005]. We demonstrate their
usefulness in the functional kernel regression model of Ferraty and Vieu [2004]
where we study uniform consistency properties of the estimated regression
operator on increasing subsets of the underlying function space.


  Using the BeppoSAX observatory, we have observed a nearby LINER/Seyfert 2
galaxy, NGC 3079, which is known as an outflow galaxy and a bright H_2O-maser
source. Using the PDS detector, we have revealed that the NGC 3079 nucleus
suffers from a Compton-thick absorption, with a hydrogen column density \sim
10^{25} cm^{-2}. After corrected the absorption, the 2--10 keV luminosity
becomes 10^{42-43} erg s^{-1} at a distance of 16 Mpc. It is 2-3 orders of
magnitude higher than that observed in the MECS band (below 10 kev). We also
detected a strong Fe-K line at 6.4^{+0.3}_{-0.2} keV with an equivalent width
of 2.4^{+2.9}_{-1.5} keV, which is consistent with the heavy absorption.


  A system of one-dimensional electrons interacting via a short-range potential
described by Hubbard model is considered in the regime of strong coupling using
the Bethe ansatz approach. We study its momentum distribution function at zero
temperature and find one additional singularity, at the $3k_{\mathrm{F}}$
point. We identify that the second singularity is of the same Luttinger liquid
type as the low-energy one at $k_{\mathrm{F}}$. By calculating the spectral
function simultaneously, we show that the second Luttinger liquid at
$3k_{\mathrm{F}}$ is formed by charge modes only, unlike the known one around
$k_{\mathrm{F}}$ consisting of both spin and charge modes. This result reveals
the ability of the spin-charge separation effect to split the Fermi point of
free electrons into two, demonstrating its robustness beyond the low-energy
limit of Luttinger liquid where it was originally found.


  Given a scalar parameter $q$, the $q$-deformed Heisenberg algebra
$\mathcal{H}(q)$ is the unital associative algebra with two generators $A,B$
that satisfy the $q$-deformed commutation relation $AB-qBA= I$, where $I$ is
the multiplicative identity. For $\mathcal{H}(q)$ of torsion-type, that is if
$q$ is a root of unity, characterization is obtained for all the Lie
polynomials in $A,B$ and basis and graded structure and commutation relations
for associated Lie algebras are studied.


  We revisit a classical result by Jacobi on the local minimality, as critical
points of the corresponding energy functional, of fixed-energy solutions of the
Kepler equation joining two distinct points with the same distance from the
origin. Our proof relies on the Morse index theorem, together with a
characterization of the conjugate points as points of geodesic bifurcation.


  The physical ingredients to describe the epoch of cosmological recombination
are amazingly simple and well-understood. This fact allows us to take into
account a very large variety of processes, still finding potentially measurable
consequences. In this contribution we highlight some of the detailed physics
that were recently studied in connection with cosmological hydrogen
recombination. The impact of these considerations is two-fold: (i) the
associated release of photons during this epoch leads to interesting and unique
deviations of the Cosmic Microwave Background (CMB) energy spectrum from a
perfect blackbody, which, in particular at decimeter wavelength, may become
observable in the near future. Observing these distortions, in principle would
provide an additional way to determine some of the key parameters of the
Universe (e.g. the specific entropy, the CMB monopole temperature and the
pre-stellar abundance of helium), not suffering from limitations set by cosmic
variance. Also it permits us to confront our detailed understanding of the
recombination process with direct observational evidence. In this contribution
we illustrate how the theoretical spectral template for the cosmological
recombination spectrum may be utilized for this purpose. (ii) with the advent
of high precision CMB data, e.g. as will be available using the Planck Surveyor
or Cmbpol, a very accurate theoretical understanding of the ionization history
of the Universe becomes necessary for the interpretation of the CMB temperature
and polarization anisotropies. (abridged)


  Unsupervised pre-training was a critical technique for training deep neural
networks years ago. With sufficient labeled data and modern training
techniques, it is possible to train very deep neural networks from scratch in a
purely supervised manner nowadays. However, unlabeled data is easier to obtain
and usually of very large scale. How to make use of them better to help
supervised learning is still a well-valued topic. In this paper, we investigate
convolutional denoising auto-encoders to show that unsupervised pre-training
can still improve the performance of high-level image related tasks such as
image classification and semantic segmentation. The architecture we use is a
convolutional auto-encoder network with symmetric shortcut connections. We
empirically show that symmetric shortcut connections are very important for
learning abstract representations via image reconstruction. When no extra
unlabeled data are available, unsupervised pre-training with our network can
regularize the supervised training and therefore lead to better generalization
performance. With the help of unsupervised pre-training, our method achieves
very competitive results in image classification using very simple
all-convolution networks. When labeled data are limited but extra unlabeled
data are available, our method achieves good results in several semi-supervised
learning tasks.


  Let $\mathbb{F}$ be a fixed finite field, and let $A \subset \mathbb{F}^n$.
It is a well-known fact that there is a subspace $V \leq \mathbb{F}^n$,
$\mbox{codim} V \ll_{\delta} 1$, and an $x$, such that $A$ is $\delta$-uniform
when restricted to $x + V$ (that is, all non-trivial Fourier coefficients of
$A$ restricted to $x + V$ have magnitude at most $\delta$). We show that if
$\mathbb{F} = \mathbb{F}_2$ then it is possible to take $x = 0$; that is, $A$
is $\delta$-uniform on a subspace $V \leq \mathbb{F}^n$. We give an example to
show that this is not necessarily possible when $\mathbb{F} = \mathbb{F}_3$.
  ADDED July 2016: shortly after this paper appeared on the arxiv, F. Manners
showed us a rather short argument he had found in 2013, giving a better bound
for our main theorem. We do not, therefore, intend to publish this note. The
example over $\mathbb{F}_3$ may still be of interest to some readers and so we
will not withdraw the paper from the arxiv.


  The effect of pressure on the unique electronic state of the
antiferromagnetic (AF) compound EuCu2Ge2 has been measured in a wide
temperature range from 10 mK to 300 K by electrical resistivity measurements up
to 10 GPa. The Neel temperature of TN = 15 K at ambient pressure increases
monotonically with increasing pressure and becomes a maximum of TN = 27 K at
6.2 GPa but suddenly drops to zero at Pc = 6.5 GPa, suggesting the quantum
critical point (QCP) of the valence transition of Eu from a nearly divalent
state to that with trivalent weight. The rhomag0 and A values obtained from the
low-temperature electrical resistivity based on the Fermi liquid relation of
rhomag = rhomag0 + AT^2 exhibit huge and sharp peaks around Pc. The exponent n
obtained from the power law dependence rhomag = rhomag0 + BT^n is clearly less
than 1.5 at P = Pc = 6. 5 GPa, which is expected at the AF-QCP. These results
indicate that Pc coincides with Pv, corresponding to the quantum criticality of
the valence transition pressure Pv. The electronic specific heat coefficient,
estimated from the generalized Kadowaki-Woods relation, is about 510 mJ/mol K^2
around Pc, suggesting the formation of a heavy-fermion state.


  One-dimensional systems obtained as low-energy limits of hybrid
superconductor-topological insulator devices provide means of production,
transport, and destruction of Majorana bound states (MBSs) by variations of the
magnetic flux. When two or more pairs of MBSs are present in the intermediate
state, there is a possibility of a Landau-Zener transition, wherein even a slow
variation of the flux leads to production of a quasiparticle pair. We study
numerically a version of this process, with four MBSs produced and subsequently
destroyed, and find that, quite universally, the probability of quasiparticle
production in it is 50%. This implies that the effect may be a limiting factor
in applications requiring a high degree of quantum coherence.


  We present the statistical results of a systematic, unbiased search for
subpulse modulation of 187 pulsars performed with the Westerbork Synthesis
Radio Telescope (WSRT) in the Netherlands at an observing wavelength of 21 cm
(Weltevrede et al. 2006). We have increased the list of pulsars that show the
drifting subpulse phenomenon by 42, indicating that more than 55% of the
pulsars show this phenomenon. The large number of new drifters we have found
allows us, for the first time, to do meaningful statistics on the drifting
phenomenon. We find that the drifting phenomenon is correlated with the pulsar
age such that drifting is more likely to occur in older pulsars. Pulsars that
drift more coherently seem to be older and have a lower modulation index.
Contrary claims from older studies, both P3 (the repetition period of the
drifting subpulse pattern) and the drift direction are found to be uncorrelated
with other pulsar parameters.


  Non Born-Oppenheimer quantum dynamics of H$_{2}^{+}$ excited by shaped
one-cycle laser pulses linearly polarized along the molecular axis have been
studied by the numerical solution of the time-dependent Schr\"odinger equation
within a %three-body three-dimensional model, including the internuclear
separation, $R$, and the electron coordinates $z$ and $\rho$. Laser carrier
frequencies corresponding to the wavelengths $\lambda_{l}=25$~nm through
$\lambda_{l}=400$~nm were used and the amplitudes of the pulses were chosen
such that the energy of H$_{2}^{+}$ was close to its dissociation threshold at
the end of any laser pulse applied. It is shown that there exists a
characteristic oscillation frequency $\omega_{\rm osc} \simeq 0.2265$~au
(corresponding to the period of $\tau_{\rm osc} \simeq 0.671$~fs and the
wavelength of $\lambda_{\rm osc} \simeq 200$~nm) that manifests itself as a
"carrier" frequency of temporally shaped oscillations of the time-dependent
expectation values $\langle z \rangle$ and $\langle \partial V/\partial z
\rangle$ that emerge at the ends of the laser pulses and exist on a timescale
of at least 50~fs. Time-dependent expectation values $\langle \rho \rangle$ and
$\langle \partial V/\partial \rho \rangle$ of the optically-passive degree of
freedom, $\rho$, demonstrate post-laser-field oscillations at two basic
frequencies $\omega^{\rho}_{1} \approx \omega_{\rm osc}$ and $\omega^{\rho}_{2}
\approx 2\omega_{\rm osc}$. Power spectra associated with the electronic motion
show higher- and lower-order harmonics with respect to the driving field.


  As a distinctive feature unique to non-Hermitian systems, non-Hermitian skin
effect displays fruitful exotic phenomena in one or higher dimensions,
especially when conventional topological phases are involved. Among them,
hybrid skin-topological effect is theoretically proposed recently, which
exhibits anomalous localization of topological boundary states at
lower-dimensional boundaries accompanied by extended bulk states. Here we
experimentally realize the hybrid skin-topological effect in a non-Hermitian
phononic crystal. The phononic crystal, before tuning to be non-Hermitian, is
an ideal acoustic realization of the Kane-Mele model, which hosts gapless
helical edge states at the boundaries. By introducing a staggered distribution
of loss, the spin-dependent edge modes pile up to opposite corners, leading to
a direct observation of the spin-dependent hybrid skin-topological effect. Our
work highlights the interplay between topology and non-Hermiticity and opens
new routes to non-Hermitian wave manipulations.


  Heavy ion collisions provide a unique probe of the quark-gluon plasma
properties. The role of partonic degrees of freedom in p-Pb collisions at
$\sqrt{s_{NN}}$ = 5 TeV by studying the production cross section is studied.
Experimental observable like the number of K$_S$ mesons is sensitive to the
kinematic properties of the reaction. Our results demonstrate the importance of
angular distribution for the partonic degrees of freedom in hadronization
process.


  We prove for a large class of knots that the meridional rank coincides with
the bridge number. This class contains all knots whose exterior is a graph
manifold. This gives a partial answer to a question of S. Cappell and J.
Shaneson, see problem 1.11 on Kirby's list.


  We study the one photon scattering problem for a super cavity (SC) coupling
with two two-level atoms. With atomic decay, we find a sudden drop in
reflection at $\Delta=0$ for the two atoms in the node-antinode configuration
but not in the antinode-node one. The underlying mechanism is due to the
scatterer has a configuration dependent localized dissipated eigen-mode at
$\Delta=0$. In the node-antinode configuration, the eigen-mode localized near
the input side, which can transport the photon into the SC. By exciting the
atom at the node, the photon can leak into the reservoir due to atomic decay,
which causes the sudden drop at $\Delta=0$ in reflection. In the antinode-node
case, however, the eigen-mode is localized near the output side, no photon can
be transported into the SC and leads to completely reflection at $\Delta=0$. A
similar phenomenon has been observed in a recent experiment of X-ray quantum
optics [Nature \textbf{482}, 199 (2012)] but with a much more complicated
explanation due to electromagnetically induced transparency.


  We study the local thermal entanglement in a spin-1/2 Ising-$XXZ$ diamond
structure with two impurities. In this spin chain, we have the two impurities
with an isolated $XXZ$ dimer between them. We focus on the study of the thermal
entanglement in this dimer. The main goal of this paper is to provide a good
understanding of the effect of impurities in the entanglement of the model.
This model is exactly solved by a rigorous treatment based on the
transfer-matrix method. Our results show that the entanglement can be tuned by
varying the impurities parameters in this system. In addition, it is shown that
the thermal entanglement for such a model exhibits a clear performance
improvement when we control and manipulate the impurities compared to the
original model without impurities. Finally, the impurities can be manipulated
to locally control the thermal entanglement, unlike the original model where it
is done globally.


  We describe a system for deep reinforcement learning of robotic manipulation
skills applied to a large-scale real-world task: sorting recyclables and trash
in office buildings. Real-world deployment of deep RL policies requires not
only effective training algorithms, but the ability to bootstrap real-world
training and enable broad generalization. To this end, our system combines
scalable deep RL from real-world data with bootstrapping from training in
simulation, and incorporates auxiliary inputs from existing computer vision
systems as a way to boost generalization to novel objects, while retaining the
benefits of end-to-end training. We analyze the tradeoffs of different design
decisions in our system, and present a large-scale empirical validation that
includes training on real-world data gathered over the course of 24 months of
experimentation, across a fleet of 23 robots in three office buildings, with a
total training set of 9527 hours of robotic experience. Our final validation
also consists of 4800 evaluation trials across 240 waste station
configurations, in order to evaluate in detail the impact of the design
decisions in our system, the scaling effects of including more real-world data,
and the performance of the method on novel objects. The projects website and
videos can be found at
\href{http://rl-at-scale.github.io}{rl-at-scale.github.io}.


  We present in this paper a routine which construct the ideal generated by a
list of elements in a matrix Lie algebra at any particular characteristic. We
have used this algorithm to analyze the problem of the simplicity of some Lie
algebras.


  This paper examines the relationship between a profile curve of a surface in
3 dimensions and the isotopy class of the surface.


  Energy conversion is most efficient for micro or nano machines with tight
coupling between input and output power. To reach meaningful amounts of power,
ensembles of $N$ such machines must be considered. We use a model system to
demonstrate that interactions between $N$ tightly coupled nanomachines can
enhance the power output per machine. Furthermore, while interactions break
tight coupling and thus lower efficiency in finite ensembles, the macroscopic
limit ($N \rightarrow \infty$) restores it and enhances both the efficiency and
the output power per nanomachine.


  The recently introduced concept of $\mathcal{D}$-variation unifies previous
concepts of variation of multivariate functions. In this paper, we give an
affirmative answer to the open question from Pausinger \& Svane (J. Complexity,
2014) whether every function of bounded Hardy--Krause variation is Borel
measurable and has bounded $\mathcal{D}$-variation. Moreover, we show that the
space of functions of bounded $\mathcal{D}$-variation can be turned into a
commutative Banach algebra.


  Deep reinforcement learning provides a promising approach for vision-based
control of real-world robots. However, the generalization of such models
depends critically on the quantity and variety of data available for training.
This data can be difficult to obtain for some types of robotic systems, such as
fragile, small-scale quadrotors. Simulated rendering and physics can provide
for much larger datasets, but such data is inherently of lower quality: many of
the phenomena that make the real-world autonomous flight problem challenging,
such as complex physics and air currents, are modeled poorly or not at all, and
the systematic differences between simulation and the real world are typically
impossible to eliminate. In this work, we investigate how data from both
simulation and the real world can be combined in a hybrid deep reinforcement
learning algorithm. Our method uses real-world data to learn about the dynamics
of the system, and simulated data to learn a generalizable perception system
that can enable the robot to avoid collisions using only a monocular camera. We
demonstrate our approach on a real-world nano aerial vehicle collision
avoidance task, showing that with only an hour of real-world data, the
quadrotor can avoid collisions in new environments with various lighting
conditions and geometry. Code, instructions for building the aerial vehicles,
and videos of the experiments can be found at github.com/gkahn13/GtS


  We prove sub-convex bounds on the fourth moment of Hecke-Laplace eigenforms
on $S^3$. As a corollary, we get a bound on the sup-norm on an individual
eigenform, which constitutes an improvement over what is achievable through
employing the Iwaniec-Sarnak amplifier.


  We present the results of multi wavelength, co-spatial and near co-temporal
observations of jets above a sunspot light bridge. The data were obtained with
the Solar Optical Telescope (SOT) on board Hinode, the Interface Region
Spectrograph (IRIS) and the Atmospheric Imaging Assembly (AIA) on board the
Solar Dynamic Observatory (SDO). Most of the jets in the Ca II H images show
decreasing brightness with height while in the IRIS slit jaw images at 1330
\AA~ jets show a bright leading edge. These jets show rising and falling motion
as evident from the parabolic profile obtained from the time-distance diagram.
The rising and falling speeds of the jets are similar. These jets show a
coordinated behaviour between neighbouring jets moving jointly up and down.
Some of the jets show a plasma ejection from the leading edge which is also
hotter at the transition region (TR) and coronal temperatures . A Similar
behaviour is seen in the AIA wave bands that suggests that jets above the LB
reach up to the lower corona and the leading edges are heated up to coronal
temperatures. Such jets are important means of transfer mass and energy to the
transition region and corona above sunspots.


  Occlusion is probably the biggest challenge for human pose estimation in the
wild. Typical solutions often rely on intrusive sensors such as IMUs to detect
occluded joints. To make the task truly unconstrained, we present AdaFuse, an
adaptive multiview fusion method, which can enhance the features in occluded
views by leveraging those in visible views. The core of AdaFuse is to determine
the point-point correspondence between two views which we solve effectively by
exploring the sparsity of the heatmap representation. We also learn an adaptive
fusion weight for each camera view to reflect its feature quality in order to
reduce the chance that good features are undesirably corrupted by ``bad''
views. The fusion model is trained end-to-end with the pose estimation network,
and can be directly applied to new camera configurations without additional
adaptation. We extensively evaluate the approach on three public datasets
including Human3.6M, Total Capture and CMU Panoptic. It outperforms the
state-of-the-arts on all of them. We also create a large scale synthetic
dataset Occlusion-Person, which allows us to perform numerical evaluation on
the occluded joints, as it provides occlusion labels for every joint in the
images. The dataset and code are released at
https://github.com/zhezh/adafuse-3d-human-pose.


  A new concept is proposed to solve the solar neutrino problem, that is based
on a hypothesis about the existence of semi-weak interaction of electron
neutrinos with nucleons mediated by massless pseudoscalar bosons. Owing to
about 10 collisions of a solar neutrino with nucleons of the Sun, the fluxes of
left- and right-handed solar neutrinos at the Earth surface are approximately
equal, and their spectrum is changed in comparison with the one at the
production moment. The postulated model with one free parameter provides a good
agreement between the calculated and experimental characteristics of the
processes with solar neutrinos: ${}^{37}{\rm Cl} \rightarrow {}^{37}{\rm Ar}$,
${}^{71}{\rm Ga} \rightarrow {}^{71}{\rm Ge}$, $\nu_{e} e^{-}\rightarrow
\nu_{e} e^{-}$, and $\nu_{e}D \rightarrow e^{-}pp$.


  In this note, we provide a different proof of Hill's criterion of freeness
for abelian groups. Our proof hinges on the construction of suitable families
of subgroups of the links in Hill's theorem and, ultimately, on the
construction of such a family of pure subgroups of the group itself.


  In simulations of some economic gas-like models, the asymptotic regime shows
an exponential wealth distribution, independently of the initial wealth
distribution given to the system. The appearance of this statistical
equilibrium for this type of gas-like models is explained in a rigorous
analytical way.


  We argue that the intractable part of the measurement problem -- the 'big'
measurement problem -- is a pseudo-problem that depends for its legitimacy on
the acceptance of two dogmas. The first dogma is John Bell's assertion that
measurement should never be introduced as a primitive process in a fundamental
mechanical theory like classical or quantum mechanics, but should always be
open to a complete analysis, in principle, of how the individual outcomes come
about dynamically. The second dogma is the view that the quantum state has an
ontological significance analogous to the significance of the classical state
as the 'truthmaker' for propositions about the occurrence and non-occurrence of
events, i.e., that the quantum state is a representation of physical reality.
We show how both dogmas can be rejected in a realist information-theoretic
interpretation of quantum mechanics as an alternative to the Everett
interpretation. The Everettian, too, regards the 'big' measurement problem as a
pseudo-problem, because the Everettian rejects the assumption that measurements
have definite outcomes, in the sense that one particular outcome, as opposed to
other possible outcomes, actually occurs in a quantum measurement process. By
contrast with the Everettians, we accept that measurements have definite
outcomes. By contrast with the Bohmians and the GRW 'collapse' theorists who
add structure to the theory and propose dynamical solutions to the 'big'
measurement problem, we take the problem to arise from the failure to see the
significance of Hilbert space as a new kinematic framework for the physics of
an indeterministic universe, in the sense that Hilbert space imposes kinematic
(i.e., pre-dynamic) objective probabilistic constraints on correlations between
events.


  Coupling hemodynamics with vessel wall growth and remodeling (G&R) is crucial
for understanding pathology at distal vasculature to study progression of
incurable vascular diseases, such as pulmonary arterial hypertension. The
present study is the first modeling attempt that focuses on defining
homeostatic baseline values in distal pulmonary vascular bed via, a so-called,
homeostatic optimization. To define the vascular homeostasis and total
hemodynamics in the vascular tree, we consider two time-scales: a cardiac cycle
and a longer period of vascular adaptations. An iterative homeostatic
optimization is performed at the slow-time scale and incorporates: an extended
Murray's law, wall metabolic cost function, stress equilibrium, and
hemodynamics. The pulmonary arterial network of small vessels is represented by
a fractal bifurcating tree. The pulsatile blood flow is described by a
Womersley's deformable wall analytical solution. A vessel wall mechanical
response is described by the constrained mixture theory for an orthotropic
membrane and then linearized around mean pressure. Wall material parameters are
characterized by using available porcine pulmonary artery experiments and human
data from literature. Illustrative examples for symmetric and asymmetric
fractal trees are presented to provide homeostatic values in normal subjects.
We also outline the key ideas for the derivation of a temporal multiscale
formalism to justify the proposed one-way coupled system of governing equations
and identify the inherent assumptions. The developed framework demonstrates a
potential for advanced parametric studies and future G&R and hemodynamics
modeling in pulmonary arterial hypertension.


  We consider both gauged and ungauged minimal supergravities in five
dimensions and analyse the charged rotating solutions with two equal angular
momenta $J$. When the electric charge $Q\sim J^{2/3}$ with some specific
coefficient, we find new extremal black objects emerge that are asymptotic to
either Minkowski or global AdS spacetimes and can be best described as
degenerate black rings. Their near-horizon geometry is locally AdS$_3\times
S^2$, where the periodic $U(1)$ fibre coordinate in $S^3$ untwists and
collapses to be the degenerate part of the AdS$_3$ horizon. It turns out that
there are two branches of extremal rotating black holes, starting as the
extremal RN black holes of the same mass, but opposite charges. With the
increasing of the angular momentum, they will join to become the same
degenerate black ring, where the Gibbs free energies however are not continuous
at the joining. For the same $Q(J)$ relation, we find that there is in addition
a rotating soliton whose mass is smaller than the degenerate black ring.


  We investigate the structure of the centralizer and the normalizer of a local
analytic or formal differential system at a nondegenerate stationary point,
using the theory of Poincar\'e-Dulac normal forms. Our main results are
concerned with the formal case. We obtain a description of the relation between
centralizer and normalizer, sharp dimension estimates when the centralizer of
the linearization has finite dimension, and lower estimates for the dimension
of the centralizer in general. For a distinguished class of linear vector
fields (which is sufficiently large to be of interest) we obtain a precise
characterization of the centralizer for corresponding normal forms in the
generic case. Moreover, in view of their relation to normalizers, we discuss
inverse Jacobi multipliers and obtain existence criteria and nonexistence
results for several classes of vector fields.


  Deep learning methods have shown remarkable performance in image denoising,
particularly when trained on large-scale paired datasets. However, acquiring
such paired datasets for real-world scenarios poses a significant challenge.
Although unsupervised approaches based on generative adversarial networks offer
a promising solution for denoising without paired datasets, they are difficult
in surpassing the performance limitations of conventional GAN-based
unsupervised frameworks without significantly modifying existing structures or
increasing the computational complexity of denoisers. To address this problem,
we propose a SC strategy for multiple denoisers. This strategy can achieve
significant performance improvement without increasing the inference complexity
of the GAN-based denoising framework. Its basic idea is to iteratively replace
the previous less powerful denoiser in the filter-guided noise extraction
module with the current powerful denoiser. This process generates better
synthetic clean-noisy image pairs, leading to a more powerful denoiser for the
next iteration. This baseline ensures the stability and effectiveness of the
training network. The experimental results demonstrate the superiority of our
method over state-of-the-art unsupervised methods.


  This paper addresses the floating body problem which consists in studying the
interaction of surface water waves with a floating body. We propose a new
formulation of the water waves problem that can easily be generalized in order
to take into account the presence of a floating body. The resulting equations
have a compressible-incompressible structure in which the interior pressure
exerted by the fluid on the floating body is a Lagrange multiplier that can be
determined through the resolution of a $d$-dimensional elliptic equation, where
$d$ is the horizontal dimension. In the case where the object is freely
floating, we decompose the hydrodynamic force and torque exerted by the fluid
on the solid in order to exhibit an added mass effect; in the one dimensional
case $d=1$, the computations can be carried out explicitly. We also show that
this approach in which the interior pressure appears as a Lagrange multiplier
can be implemented on reduced asymptotic models such as the nonlinear shallow
water equations and the Boussinesq equations; we also show that it can be
transposed to the discrete version of these reduced models and propose simple
numerical schemes in the one dimensional case. We finally present several
numerical computations based on these numerical schemes; in order to validate
these computations we exhibit explicit solutions in some particular
configurations such as the return to equilibrium problem in which an object is
dropped from a non-equilibrium position in a fluid which is initially at rest.


  Bound states in the continuum (BICs) refer to physical states that possess
intrinsic zero dissipation loss even though they are located in the continuous
energy spectrum. BICs have been widely explored in optical and acoustic
structures, leading to applications in sensing and lasing. Realizing BICs in
micromechanical structures is of significant importance for both fundamental
research and engineering applications. Here, we fabricated, with
CMOS-compatible processes on a silicon chip, a wheel-shaped micromechanical
resonator, in which we experimentally observed the BIC in the micromechanical
domain. Such BICs result from destructive interference between two dissipative
modes of the mechanical structure under broken azimuthal symmetry. These BICs
are found to be robust against size variations of the dissipation channels. The
demonstrated mechanical BIC can be obtained with a large and robust supporting
structure, which substantially reduces device fabrication difficulty and allows
for its operation in versatile environments for broader application areas. Our
results open a new way of phonon trapping in micromechanical structures with
dissipation channels, and produce long phonon lifetimes that are desired in
many mechanical applications such as mechanical oscillators, sensors, and
quantum information processors.


  We present a new method for the determination of the surface properties of
airless bodies from measurements of the emitted infrared flux. Our approach
uses machine learning techniques to train, validate, and test a neural network
representation of the thermophysical behavior of the atmosphereless body given
shape model, illumination and observational geometry of the remote sensors. The
networks are trained on a dataset of thermal simulations of the emitted
infrared flux for different values of surface rock abundance, roughness, and
values of the thermal inertia of the regolith and of the rock components. These
surrogate models are then employed to retrieve the surface thermal properties
by Markov Chain Monte Carlo Bayesian inversion of observed infrared fluxes. We
apply the method to the inversion of simulated infrared fluxes of asteroid
(101195) Bennu -- according to a geometry of observations similar to those
planned for NASA's OSIRIS-REx mission -- and infrared observations of asteroid
(25143) Itokawa. In both cases, the surface properties of the asteroid -- such
as surface roughness, thermal inertia of the regolith and rock component, and
relative rock abundance -- are retrieved; the contribution from the regolith
and rock components are well separated. For the case of Itokawa, we retrieve a
rock abundance of about 85% for pebbles larger than the diurnal skin depth,
which is about 2 cm. The thermal inertia of the rock is found to be lower than
the expected value for LL chondrites, indicating that the rocks on Itokawa
could be fractured. The average thermal inertia of the surface is around 750 $J
s^{-1/2} K^{-1} m^{-2}$ and the measurement of thermal inertia of the regolith
corresponds to an average regolith particle diameter of about 10 mm,
consistently with in situ measurements as well as results from previous
studies.


  This paper deals with the stability analysis of a mass-spring system subject
to friction using Lyapunov-based arguments. As the described system presents a
stick-slip phenomenon, the mass may then periodically sticks to the ground. The
objective consists of developing numerically tractable conditions ensuring the
global asymptotic stability of the unique equilibrium point. The proposed
approach merges two intermediate results: The first one relies on the
characterization of an attractor around the origin, to which converges the
closed-loop trajectories. The second result assesses the regional asymptotic
stability of the equilibrium point by estimating its basin of attraction. The
main result relies on conditions allowing to ensure that the attractor issued
from the first result is included in the basin of attraction of the origin
computed from the second result. An illustrative example draws the interest of
the approach.


  We study the effects of the shear and bulk viscosities in the hadronic phase
on the expansion of the fireball and on the particle production in relativistic
heavy ion collisions. Comparing simulation with or without viscosity in the
hadronic matter we find that elliptic flow observables strongly dependent on
dissipative effects in the late stage. On the other hand, interferometry radii
are sensitive, through the early transverse flow, on the value of the viscosity
at high temperatures. We present first calculations including the effects of
bulk viscosity in the hadronic phase and in the hadron emission. We find them
important in obtaining a small freeze-out temperature consistent with the
measured transverse momentum spectra and elliptic flow of identified particles.


  Calculations of the cosmic rate of core collapses, and the associated
neutrino flux, commonly assume that a fixed fraction of massive stars collapse
to black holes. We argue that recent results suggest that this fraction instead
increases with redshift. With relatively more stars vanishing as "unnovae" in
the distant universe, the detectability of the cosmic MeV neutrino background
is improved due to their hotter neutrino spectrum, and expectations for
supernova surveys are reduced. We conclude that neutrino detectors, after the
flux from normal SNe is isolated via either improved modeling or the next
Galactic SN, can probe the conditions and history of black hole formation.


  This master's thesis contains an introduction to $A_\infty$-algebras and
homological perturbation theory. We then discuss the formality of compact
K\"ahler manifolds and present a direct proof of a homotopy transfer principle
of $A_\infty$-algebras, also known as Kadeishvili's Theorem.


  An elementary proof is given for the fact that every locally compact
subsemigroup of a compact topological group is a closed subgroup. A sample
consequence is that every commutative cancellative pseudocompact locally
compact Hausdorff topological semigroup with open shifts is a compact
topological group.


  In the paper we obtain some new applications of well--known W. Rudin's
theorem concerning lacunary series to problems of combinatorial number theory.
We generalize a result of M.-C. Chang on L_2 (L)-norm of Fourier coefficients
of a set (here L is a dissociated set), and prove a dual version of the
theorem. Our main instrument is computing of eigenvalues of some operators.


  In addition to the three standard operations on posets which are dual of
poset or ordinal and cardinal sums of partial ordered sets one adds the natural
join of posets. This is especially natural natural join operation for graded
posets including very special and important the so called cobweb posets. The
main aims of this article are the presentation of the authors update
applications of natural join of posets in the domain of graded posets and
summary of the general properties of natural join of posets with posing some
questions arising on the way. Thus in this note apart from revealing some
general properties of natural join of posets we also deliver the authors
combinatorial interpretations of cobweb posets including discrete hyperboxes
coding of the so called cobweb posets .Various explicit formulas for the zeta
functions and zeta matrices as well as the inverse of zeta matrix for any
graded posets are supplied. Whitney numbers formulas and those for
characteristic polynomials are given too.


  Reliable bipedal walking over complex terrain is a challenging problem, using
a curriculum can help learning. Curriculum learning is the idea of starting
with an achievable version of a task and increasing the difficulty as a success
criteria is met. We propose a 3-stage curriculum to train Deep Reinforcement
Learning policies for bipedal walking over various challenging terrains. In the
first stage, the agent starts on an easy terrain and the terrain difficulty is
gradually increased, while forces derived from a target policy are applied to
the robot joints and the base. In the second stage, the guiding forces are
gradually reduced to zero. Finally, in the third stage, random perturbations
with increasing magnitude are applied to the robot base, so the robustness of
the policies are improved. In simulation experiments, we show that our approach
is effective in learning walking policies, separate from each other, for five
terrain types: flat, hurdles, gaps, stairs, and steps. Moreover, we demonstrate
that in the absence of human demonstrations, a simple hand designed walking
trajectory is a sufficient prior to learn to traverse complex terrain types. In
ablation studies, we show that taking out any one of the three stages of the
curriculum degrades the learning performance.


  Binary stars are as common as single stars. Binary stars are of immense
importance to astrophysicists because that they allow us to determine the
masses of the stars independent of their distances. They are the cornerstone of
the understanding of stellar evolutionary theory and play an essential role in
cosmic distance measurement, galactic evolution, nucleosynthesis and the
formation of important objects such as cataclysmic variable stars, X-ray
binaries, Type Ia supernovae, and gravitational wave-producing double compact
objects. In this article, we review the significant theoretical and
observational progresses in addressing binary stars in the new millennium.
Increasing large survey projects have led to the discovery of enormous numbers
of binary stars, which enables us to conduct statistical studies of binary
populations, and therefore provide unprecedented insight into the stellar and
binary evolution physics. Meanwhile, the rapid development of theoretical
concepts and numerical approaches for binary evolution have made a substantial
progress on the alleviation of some long-standing binary-related problems such
as the stability of mass transfer and common envelope evolution. Nevertheless,
it remains a challenge to have a full understanding of fundamental problems of
stellar and binary astrophysics. The upcoming massive survey projects and
increasingly sophisticated computational methods will lead to future progress.


  Approximate Bayesian computing is a powerful likelihood-free method that has
grown increasingly popular since early applications in population genetics.
However, complications arise in the theoretical justification for Bayesian
inference conducted from this method with a non-sufficient summary statistic.
In this paper, we seek to re-frame approximate Bayesian computing within a
frequentist context and justify its performance by standards set on the
frequency coverage rate. In doing so, we develop a new computational technique
called approximate confidence distribution computing, yielding theoretical
support for the use of non-sufficient summary statistics in likelihood-free
methods. Furthermore, we demonstrate that approximate confidence distribution
computing extends the scope of approximate Bayesian computing to include
data-dependent priors without damaging the inferential integrity. This
data-dependent prior can be viewed as an initial `distribution estimate' of the
target parameter which is updated with the results of the approximate
confidence distribution computing method. A general strategy for constructing
an appropriate data-dependent prior is also discussed and is shown to often
increase the computing speed while maintaining statistical inferential
guarantees. We supplement the theory with simulation studies illustrating the
benefits of the proposed method, namely the potential for broader applications
and the increased computing speed compared to the standard approximate Bayesian
computing methods.


  Asymptotics of solutions to relativistic fractional elliptic equations with
Hardy type potentials is established in this paper. As a consequence, unique
continuation properties are obtained.


  Real-world systems are made of interacting entities that evolve with time.
Creating models that can forecast interactions by learning the dynamics of
entities is an important problem in numerous fields. Earlier works used dynamic
graph models to achieve this. However, real-world interactions are more complex
than pairwise, as they involve more than two entities, and many of these
higher-order interactions have directional components. Examples of these can be
seen in communication networks such as email exchanges that involve a sender,
and multiple recipients, citation networks, where authors draw upon the work of
others, and so on. In this paper, we solve the problem of higher-order directed
interaction forecasting by proposing a deep neural network-based model
\textit{Directed HyperNode Temporal Point Process} for directed hyperedge event
forecasting, as hyperedge provides a native framework for modeling
relationships among the variable number of nodes. Our proposed technique
reduces the search space by initially forecasting the nodes at which events
will be observed and then forecasting hyperedge sizes and adjacency vectors for
the nodes observing events. Based on these, it generates candidate hyperedges,
which are then used by a hyperedge predictor to identify the ground truth. To
demonstrate the efficiency of our model, we curated five datasets and conducted
an extensive empirical study. We believe that this is the first work that
solves the problem of forecasting higher-order directional interactions.


  Frequency-domain representations of multirate systems are essential for
controller design and performance evaluation of multirate systems and
sampled-data control. The aim of this paper is to develop a time-efficient
closed-loop identification approach for multirate systems in the
frequency-domain. The developed method utilizes local polynomial modeling for
lifted representations of LPTV systems, which enables direct identification of
closed-loop multirate systems in a single identification experiment. Unlike LTI
identification techniques, the developed method does not suffer from bias due
to ignored LPTV dynamics. The developed approach is demonstrated on a multirate
example, resulting in accurate and fast identification in the frequency domain.


  We analyze the effects of a nonlinear cubic perturbation on the delta-Kicked
Rotor. We consider two different models, in which the nonlinear term acts
either in the position or in the momentum representation. We numerically
investigate the modifications induced by the nonlinearity in the quantum
transport in both localized and resonant regimes and a comparison between the
results for the two models is presented. Analyzing the momentum distributions
and the increase of the mean square momentum, we find that the quantum
resonances asymptotically are very stable with respect to the nonlinear
perturbation of the rotor's phase evolution. For an intermittent time regime,
the nonlinearity even enhances the resonant quantum transport, leading to
superballistic motion.


  We propose to estimate 3D human pose from multi-view images and a few IMUs
attached at person's limbs. It operates by firstly detecting 2D poses from the
two signals, and then lifting them to the 3D space. We present a geometric
approach to reinforce the visual features of each pair of joints based on the
IMUs. This notably improves 2D pose estimation accuracy especially when one
joint is occluded. We call this approach Orientation Regularized Network (ORN).
Then we lift the multi-view 2D poses to the 3D space by an Orientation
Regularized Pictorial Structure Model (ORPSM) which jointly minimizes the
projection error between the 3D and 2D poses, along with the discrepancy
between the 3D pose and IMU orientations. The simple two-step approach reduces
the error of the state-of-the-art by a large margin on a public dataset. Our
code will be released at https://github.com/CHUNYUWANG/imu-human-pose-pytorch.


  D. Voiculescu [2] proved that a standard family of independent random unitary
k by k matrices and a constant k by k unitary matrix is asymtotically free as k
goes to infinity. This result was a key ingredient in Voiculescu's proof [3]
that his free entropy is additive when the variables are free. In this paper,
we give a very elementary proof of a more detailed version of this result [2].


  In this paper, we introduce the relevant concepts of $n$-ary multiplicative
Hom-Nambu-Lie superalgebras and construct three classes of $n$-ary
multiplicative Hom-Nambu-Lie superalgebras. As a generalization of the notion
of derivations for $n$-ary multiplicative Hom-Nambu-Lie algebras, we discuss
the derivations of $n$-ary multiplicative Hom-Nambu-Lie superalgebras. In
addition, the theory of one parameter formal deformation of $n$-ary
multiplicative Hom-Nambu-Lie superalgebras is developed by choosing a suitable
cohomology.


  Allowed weak interaction rates for sd-shell nuclei in stellar environment are
calculated using a generalized form of proton-neutron quasiparticle RPA model
with separable Gamow-Teller forces. Twelve different weak rates are calculated
for each nucleus as a function of temperature and density. This project
consists of calculation of weak rates for a total of 709 nuclei with masses
ranging from A = 18 to 100. This paper contains calculated weak rates for
sd-shell nuclei. The calculated capture and decay rates take into consideration
the latest experimental energy levels and ft value compilations. The results
are also compared with earlier works. Particle emission processes from excited
states, previously ignored, are taken into account, and are found to
significantly affect some beta decay rates.


  Sometimes we need the approximate value of the partition number in a simple
and efficient way. There are already several formulae to calculate the
partition number p(n). But they are either inconvenient for most people (not
majored in math) who do not want do write programs, or unsatisfying in
accuracy. By bringing in two parameters in the Hardy-Ramanujan's Asymptotic
formula and fitting the data of the two parameters by least square method,
iteration method and some other special designed methods, several revised
elementary estimation formulae with high accuracy for p(n) are obtained. With
these estimation formulae, the approximate value of p(n) can be calculated by a
pocket calculator without programming function. The main difficulty is that the
usual methods to fit the data of the two parameters by an elementary function
is defective here. These method could be used in finding the fitting functions
of some other complex data.


  The Set-union Knapsack Problem (SUKP) is a generalization of the popular 0-1
knapsack problem. Given a set of weighted elements and a set of items with
profits where each item is composed of a subset of elements, the SUKP involves
packing a subset of items in a capacity-constrained knapsack such that the
total profit of the selected items is maximized while their weights do not
exceed the knapsack capacity. In this work, we present an effective iterated
two-phase local search algorithm for this NP-hard combinatorial optimization
problem. The proposed algorithm iterates through two search phases: a local
optima exploration phase that alternates between a variable neighborhood
descent search and a tabu search to explore local optimal solutions, and a
local optima escaping phase to drive the search to unexplored regions. We show
the competitiveness of the algorithm compared to the state-of-the-art methods
in the literature. Specifically, the algorithm discovers 18 improved best
results (new lower bounds) for the 30 benchmark instances and matches the
best-known results for the 12 remaining instances. We also report the first
computational results with the general CPLEX solver, including 6 proven optimal
solutions. Finally, we investigate the effectiveness of the key ingredients of
the algorithm on its performance.


  We perform an explicit two-loop computation of the Higgs mass in a
five-dimensional supersymmetric theory compactified on the orbifold S^1/Z_2,
with superpotential localized on a fixed-point brane and supersymmetry broken
in the bulk of the extra dimension by Scherk-Schwarz boundary conditions. At
one-loop, the Higgs mass is finite and proportional to 1/R^2, R being the
radius of the extra dimension. The two-loop corrections contain finite
proportional to 1/R^2 and linearly divergent Lambda/R terms, where Lambda is
the cutoff of the five-dimensional theory. The divergent terms are cancelled by
diagrams containing one-loop wave-function renormalization counterterms, i.e.
by linear threshold effects of Yukawa (and gauge) couplings, at the scale 1/R.
As a result there is no counterterm for the Higgs mass, which is finite and
calculable to the considered order.


  We present a comprehensive analysis of the SU(3) octet and decuplet baryon
masses and $\sigma$-terms using high-precision lattice QCD data and chiral
SU(3) effective theory with finite range regularization. The effects of various
systematic uncertainties, including from the scale setting of the lattice data
and the regularization prescriptions, are quantified. We find the pion-nucleon
and strange nucleon $\sigma$-terms to be $\sigma_{\pi N} = 44(3)(3)$ MeV and
$\sigma_{N s} = 50(6)(1)$ MeV, respectively. The results provide constraints on
the energy-momentum tensor mass decompositions of the SU(3) octet and decuplet
baryons, where we find the trace anomaly and quark/gluon energies decrease for
strange baryons due to their larger strange $\sigma$-terms.


  First, the solution uniqueness and existence of a stationary anisotropic
(linear) Stokes system with constant viscosity coefficients in a compressible
framework on $n$-dimensional flat torus are analysed in a range of periodic
Sobolev (Bessel-potential) spaces. By employing the Leray-Schauder fixed point
theorem, the linear results are employed to show existence of solution to the
stationary anisotropic (non-linear) Navier-Stokes incompressible system on
torus in a periodic Sobolev space. Then the solution regularity results for
stationary anisotropic Navier-Stokes system on torus are established.


  The stationary state of a spherically symmetric plasma configuration is
investigated in the limit of immobile ions and weak collisions. Configurations
with small radii are positively charged as a significant fraction of the
electron population evaporates during the equilibration process, leaving behind
an electron distribution function with an energy cutoff. Such charged plasma
configurations are of interest for the study of Coulomb explosions and ion
acceleration from small clusters irradiated by ultraintense laser pulses and
for the investigation of ion bunches propagation in a plasma.


  Recent strides in economic complexity have shown that the future economic
development of nations can be predicted with a single "economic fitness"
variable, which captures countries' competitiveness in international trade. The
predictions by this low-dimensional approach could match or even outperform
predictions based on much more sophisticated methods, such as those by the
International Monetary Fund (IMF). However, all prior works in economic
complexity aimed to quantify countries' fitness from World Trade export data,
without considering the possibility to infer countries' potential for growth
from alternative sources of data. Here, motivated by the long-standing
relationship between technological development and economic growth, we aim to
forecast countries' growth from patent data. Specifically, we construct a
citation network between countries from the European Patent Office (EPO)
dataset. Initial results suggest that the H-index centrality in this network is
a potential candidate to gauge national economic performance. To validate this
conjecture, we construct a two-dimensional plane defined by the H-index and GDP
per capita, and use a forecasting method based on dynamical systems to test the
predicting accuracy of the H-index. We find that the predictions based on the
H-index-GDP plane outperform the predictions by IMF by approximately 35%, and
they marginally outperform those by the economic fitness extracted from trade
data. Our results could inspire further attempts to identify predictors of
national growth from different sources of data related to scientific and
technological innovation.


  Meta-learning agents excel at rapidly learning new tasks from open-ended task
distributions; yet, they forget what they learn about each task as soon as the
next begins. When tasks reoccur - as they do in natural environments -
metalearning agents must explore again instead of immediately exploiting
previously discovered solutions. We propose a formalism for generating
open-ended yet repetitious environments, then develop a meta-learning
architecture for solving these environments. This architecture melds the
standard LSTM working memory with a differentiable neural episodic memory. We
explore the capabilities of agents with this episodic LSTM in five
meta-learning environments with reoccurring tasks, ranging from bandits to
navigation and stochastic sequential decision problems.


  The intensive care units (ICUs) are responsible for generating a wealth of
useful data in the form of Electronic Health Record (EHR). This data allows for
the development of a prediction tool with perfect knowledge backing. We aimed
to build a mortality prediction model on 2012 Physionet Challenge mortality
prediction database of 4000 patients admitted in ICU. The challenges in the
dataset, such as high dimensionality, imbalanced distribution, and missing
values were tackled with analytical methods and tools via feature engineering
and new variable construction. The objective of the research is to utilize the
relations among the clinical variables and construct new variables which would
establish the effectiveness of 1-Dimensional Convolutional Neural Network (1- D
CNN) with constructed features. Its performance with the traditional machine
learning algorithms like XGBoost classifier, Support Vector Machine (SVM),
K-Neighbours Classifier (K-NN), and Random Forest Classifier (RF) is compared
for Area Under Curve (AUC). The investigation reveals the best AUC of 0.848
using 1-D CNN model.


  We consider the Schr\"odinger operator with a periodic potential on quasi-1D
models of zigzag single-wall carbon nanotubes in magnetic field. The spectrum
of this operator consists of an absolutely continuous part (intervals separated
by gaps) plus an infinite number of eigenvalues with infinite multiplicity. We
obtain identities and a priori estimates in terms of effective masses and gap
lengths.


  Based on a construction due to B. G\"{u}neysu and S. Pigola (\textit{Adv.
Math.} \textbf{281} (2015), pp.353--393), for each $p \in [1,\infty]$ and $m
\in \mathbb{Z}_{\geq 2}$, we exhibit an $m$-dimensional Riemannian open
manifold $\mathcal{M}$ on which the $L^p$-Calder\'{o}n--Zygmund estimate
\begin{equation*} \|\nabla \nabla f\|^p_{L^p} \leq C_1 \|\Delta f\|_{L^p}^p +
C_2 \| f\|_{L^p}^p \qquad \text{ for all } f \in C^\infty_c(\mathcal{M})
\end{equation*} is false for any $C_1, C_2$ depending on $m$ and $p$.
Therefore, one must impose further geometric conditions on the manifold to
ensure the validity of the Calder\'{o}n--Zygmund estimate.


  In this work we report recent results of our experimental and theoretical
studies related to plasma conversion of liquid ethanol into hydrogen-enriched
syngas in the plasma-liquid system with the electric discharge in a gas channel
with liquid wall using available diagnostics and numerical modeling.


  Despite a recent flurry of experimental and simulation studies, an accurate
estimate of the interaction strength of water molecules with hexagonal boron
nitride is lacking. Here we report quantum Monte Carlo results for the
adsorption of a water monomer on a periodic hexagonal boron nitride sheet,
which yield a water monomer interaction energy of -84 +/- 5 meV. We use the
results to evaluate the performance of several widely used density functional
theory (DFT) exchange correlation functionals, and find that they all deviate
substantially. Differences in interaction energies between different adsorption
sites are however better reproduced by DFT.


  Slow working nodes, known as stragglers, can greatly reduce the speed of
distributed computation. Coded matrix multiplication is a recently introduced
technique that enables straggler-resistant distributed multiplication of large
matrices. A key property is that the finishing time depends only on the work
completed by a set of the fastest workers, while the work done by the slowest
workers is ignored completely. This paper is motivated by the observation that
in real-world commercial cloud computing systems such as Amazon's Elastic
Compute Cloud (EC2) the distinction between fast and slow nodes is often a soft
one. Thus, if we could also exploit the work completed by stragglers we may
realize substantial performance gains. To realize such gains, in this paper we
use the idea of hierarchical coding (Ferdinand and Draper, IEEE Int. Symp. Inf.
Theory, 2018). We decompose the overall matrix multiplication task into a
hierarchy of heterogeneously sized subtasks. The duty to complete each subtask
is shared amongst all workers and each subtask is (generally) of a different
complexity. The motivation for the hierarchical decomposition is the
recognition that more workers will finish the first subtask than the second (or
third, forth, etc.). Connecting to error correction coding, earlier subtasks
can therefore be designed to be of a higher rate than later subtasks. Through
this hierarchical design our scheme exploits the work completed by stragglers,
rather than ignoring it, even if that amount is much less than that completed
by the fastest workers. We numerically show that our method realizes a 60%
improvement in the expected finishing time for a widely studied statistical
model of the speed of computation and, on Amazon EC2, the gain is 35%.


  Renormalisation group analysis with the present measurements of the top quark
mass $m_t = 172.69\pm 0.30$ GeV indicates that the Standard Model (SM) Higgs
potential becomes unstable at energy scales $\sim 10^{10}$ GeV. This may be
interpreted as hinting at new particles at high energy. The minimal extension
of the SM that can avoid this instability while leaving the SM Higgs as the
sole scalar particle of the theory is obtained by adding suitable fermions to
the SM. These fermions are good dark matter candidates and the model is known
as the minimal dark matter model. We revisit the inflationary scenario based on
the minimal dark matter model, taking into account updated parameter
constraints and recent understanding of reheating dynamics. We explore the
model with different values of the right-handed neutrino mass and find that the
cosmological prediction is insensitive to such details. We obtained a spectral
index of the cosmic microwave background $n_s=0.9672$ and a tensor-to-scalar
ratio $r=0.0031$ as a robust prediction of this scenario.


  Autonomous mobile robots deployed in outdoor environments must reason about
different types of terrain for both safety (e.g., prefer dirt over mud) and
deployer preferences (e.g., prefer dirt path over flower beds). Most existing
solutions to this preference-aware path planning problem use semantic
segmentation to classify terrain types from camera images, and then ascribe
costs to each type. Unfortunately, there are three key limitations of such
approaches -- they 1) require pre-enumeration of the discrete terrain types, 2)
are unable to handle hybrid terrain types (e.g., grassy dirt), and 3) require
expensive labelled data to train visual semantic segmentation. We introduce
Visual Representation Learning for Preference-Aware Path Planning (VRL-PAP), an
alternative approach that overcomes all three limitations: VRL-PAP leverages
unlabeled human demonstrations of navigation to autonomously generate triplets
for learning visual representations of terrain that are viewpoint invariant and
encode terrain types in a continuous representation space. The learned
representations are then used along with the same unlabeled human navigation
demonstrations to learn a mapping from the representation space to terrain
costs. At run time, VRL-PAP maps from images to representations and then
representations to costs to perform preference-aware path planning. We present
empirical results from challenging outdoor settings that demonstrate VRL-PAP 1)
is successfully able to pick paths that reflect demonstrated preferences, 2) is
comparable in execution to geometric navigation with a highly detailed manually
annotated map (without requiring such annotations), 3) is able to generalize to
novel terrain types with minimal additional unlabeled demonstrations.