  In a previous paper we introduced and developed a recursive construction of
joint eigenfunctions $J_N(a_+,a_-,b;x,y)$ for the Hamiltonians of the
hyperbolic relativistic Calogero-Moser system with arbitrary particle number
$N$. In this paper we focus on the cases $N=2$ and $N=3$, and establish a
number of conjectured features of the corresponding joint eigenfunctions. More
specifically, choosing $a_+,a_-$ positive, we prove that $J_2(b;x,y)$ and
$J_3(b;x,y)$ extend to globally meromorphic functions that satisfy various
invariance properties as well as a duality relation. We also obtain detailed
information on the asymptotic behavior of similarity transformed functions
E$_2(b;x,y)$ and E$_3(b;x,y)$. In particular, we determine the dominant
asymptotics for $y_1-y_2\to\infty$ and $y_1-y_2,y_2-y_3\to\infty$, resp., from
which the conjectured factorized scattering can be read off.


  In the present paper, we are interested in classifying of Collatz sequences
on based to the different behavior of these sequences when their lengths tend
to infinity. A Collatz infinite sequence can be defined as an infinite ordered
set of positive integers such that the term of rank n is results of applying
Collatz map n times to the first term. Such term can be expressed on the form
Tn(P)=A(P,n)P+B(P,n). When n tends to infinity, each function among the two
partial coefficients denoted by A(P,n) and B(P,n) behaves in different ways.
This allows us to determine all categories of Collatz infinite sequences.
First, we carry out a classification of Collatz infinite sequences on based of
the different possible limits of the two coefficients. In second time, we
determine the different proportions of every class of the infinite sequences.
Note that results obtained do not represent a proof of a Collatz conjecture but
they have a strong relationship with this conjecture and it allows us to better
understand the behavior of Collatz sequences when n tend to infinity.


  We present a theory of the electron structure and the Zeeman effect for the
helical edge states emerging in two-dimensional topological insulators based on
HgTe/HgCdTe quantum wells with strong natural interface inversion asymmetry.
The interface inversion asymmetry, reflecting the real atomistic structure of
the quantum well, drastically modifies both bulk and edge states. For the
in-plane magnetic field, this asymmetry leads to a strong anisotropy of the
edge-state effective $g$-factor which becomes dependent on the edge
orientation. The interface inversion asymmetry also couples the counter
propagating edge states in the out-of-plane magnetic field leading to the
opening of the gap in the edge-state spectrum by arbitrary small fields.


  Negative charge transfer $AB$O$_3$ oxides may undergo electronic
metal--insulator transitions (MIT) concomitant with a dilation and contraction
of nearly rigid octahedra. On both sides of the MIT are in-phase or
out-of-phase (or both) rotations of adjacent octahedra that buckle the
$B$--O--$B$ bond angle away from 180$^\circ$. Using density functional theory
with the PBEsol$+U$ approach, we describe a novel octahedral engineering avenue
to control the $B$ 3d and O $2p$ orbital polarization through enhancement of
the $B$O$_6$ rotation "sense" rather than solely through conventional changes
to the $B$--O bond lengths, \emph{i.e.} crystal field distortions. Using
CaFeO$_3$ as a prototypical material, we show the flavor of the octahedral
rotation pattern when combined with strain--rotation coupling and thin film
engineering strategies offers a promising avenue to fine tune orbital
polarizations near electronic phase boundaries.


  This study investigates the forecasting accuracy of human experts versus
Large Language Models (LLMs) in the retail sector, particularly during standard
and promotional sales periods. Utilizing a controlled experimental setup with
123 human forecasters and five LLMs, including ChatGPT4, ChatGPT3.5, Bard,
Bing, and Llama2, we evaluated forecasting precision through Mean Absolute
Percentage Error. Our analysis centered on the effect of the following factors
on forecasters performance: the supporting statistical model (baseline and
advanced), whether the product was on promotion, and the nature of external
impact. The findings indicate that LLMs do not consistently outperform humans
in forecasting accuracy and that advanced statistical forecasting models do not
uniformly enhance the performance of either human forecasters or LLMs. Both
human and LLM forecasters exhibited increased forecasting errors, particularly
during promotional periods and under the influence of positive external
impacts. Our findings call for careful consideration when integrating LLMs into
practical forecasting processes.


  (Abridged) In this paper we have used the RIEMANN code for computational
astrophysics to study the interaction of a realistic distribution of dust
grains with gas in a vertically stratified protostellar accretion disc. The
disc was modeled to have the density and temperature of a minimum mass solar
nebula, and was driven to a fully-developed turbulence via the
magnetorotational instability (MRI). We find that the inclusion of standard
dust to gas ratios does not have any significant effect on the MRI even when
the dust sediments to the midplane of the accretion disc. The density
distribution of the dust reaches a Gaussian profile, and the scale heights for
the dust that we derive are shown to be proportional to the reciprocal of the
square root of the dust radius. The largest dust shows a strong tendency to
settle to the midplane of the accretion disc, and tends to organize itself into
elongated clumps of high density. The dynamics of these clumps is shown to be
consistent with a streaming instability. The streaming instability is seen to
be very vigorous and persistent once it forms. Each stream of high density dust
displays a reduced RMS velocity dispersion, and the densest clumpings of large
dust are shown to form where the streams intersect. We have also shown that the
mean free path and collision time for the dust that participates in the
streaming instability is reduced by almost two orders of magnitude relative to
the average mean free paths and collision times. We show that some of the large
dust in our 10 au simulations should have a propensity for grain coalescence.


  We present the results of a line identification analysis using data from the
IRAM Plateau de Bure Inferferometer, focusing on six massive star-forming hot
cores: G31.41+0.31, G29.96-0.02, G19.61-0.23, G10.62-0.38, G24.78+0.08A1 and
G24.78+0.08A2. We identify several transitions of vibrationally excited methyl
formate (HCOOCH$_3$) for the first time in these objects as well as transitions
of other complex molecules, including ethyl cyanide (C$_2$H$_5$CN), and
isocyanic acid (HNCO). We also postulate a detection of one transition of
glycolaldehyde (CH$_2$(OH)CHO) in two new hot cores. We find G29.96-0.02,
G19.61-0.23, G24.78+0.08A1 and 24.78+0.08A2 to be chemically very similar.
G31.41+0.31, however, is chemically different: it manifests a larger chemical
inventory and has significantly larger column densities. We suggest that it may
represent a different evolutionary stage to the other hot cores in the sample,
or it may surround a star with a higher mass. We derive column densities for
methyl formate in G31.41+0.31, using the rotation diagram method, of
$\times$10$^{17}$ cm$^{-2}$ and a T$_{rot}$ of $\sim$170 K. For G29.96-0.02,
G24.78+0.08A1 and G24.78+0.08A2, glycolaldehyde, methyl formate and methyl
cyanide all seem to trace the same material and peak at roughly the same
position towards the dust emission peak. For G31.41+0.31, however,
glycolaldehyde shows a different distribution to methyl formate and methyl
cyanide and seems to trace the densest, most compact inner part of hot cores.


  We propose experimentally observable signatures of of topological Majorana
quasiparticles in the few-body limit of the interacting cold-atom model of
[Iemini et al. Phys. Rev. Lett. 118 200404 (2017)]. In this limit, the total
on-site density and single-body correlations change smoothly with the model
parameters, while the calculated mutual information of opposite ends of the
lattice indicates a sharp transition of the system to a topological ground
state. Furthermore, local density and parity measurements provide an
experimentally viable path for observing the ground state Majorana
quasiparticles in ultracold atoms. Our results lay out a promising future for
utilizing few-body systems as a testing ground for Majorana physics.


  We discuss the possible usage of the compressive sampling for the early
detection of rogue waves. One of the promising techniques for the early
detection of the oceanic rogue waves is to measure the triangular Fourier
spectra which begin to appear at the early stages of their development. For the
early detection of the rogue waves it is possible to treat such a spectrum as a
sparse signal since we would mainly be interested in the high amplitude
triangular region located at the central wavenumber. Therefore compressive
sampling can be a very efficient tool for the rogue wave early warning systems.
Compressed measurements can be acquired by remote sensing techniques such as
coherent SAR which measure the ocean surface envelope or by insitu techniques
such as spectra measuring tools mounted on a ship hull or bottom mounted
pressure gauges. By employing a numerical approach we show that triangular
Fourier spectra can be sensed by compressive measurements at the early stages
of the development of rogue waves such as those in the form of Peregrine and
Akhmediev-Peregrine solitons. Our results may lead to development of the early
warning hardware systems which use the compressive sampling thus the memory
requirements for those systems can be greatly reduced.


  In this paper, we explore the Kibble-Zurek scaling of the conserved charge,
using the stachastic diffusion dynamics. After determining the characteristic
scales $\tau_{kz}$ and $l_{kz}$ and properly rescaling the traditional
correlation function and cumulant, we construct universal functions for both
the two-point correlation function $C(y_1-y_2;\tau)$ and second-order cumulant
$K(\Delta y,\tau)$ of the conserved charge in the critical regime, which are
insensitive to the initial temperature and a parameter in the mapping between
3D Ising model and the hot QCD system near the critical point.


  We study the behavior of the three-point correlation function xi_3 of dark
matter and mock galaxies, concentrating on the effects of redshift-space
distortions and the determination of galaxy bias parameters in current redshift
galaxy surveys. On large scales, redshift space distortions tend to wash out
slightly the configuration dependence of the reduced 3-point function Q3 ~
xi_3/xi_2^2. On smaller scales (< 10 Mpc/h), Q3 develops a characteristic
U-shape anisotropy between elongated and open triangles due to the effects of
velocity dispersion. We show that this shape is quite universal, very weakly
dependent on scale, initial spectral index or cosmological parameters and
should be detectable in current galaxy surveys even if affected by shot-noise
or galaxy bias. We present a detailed method for obtaining constraints on
galaxy bias parameters from measurements of Q3 in current galaxy redshift
surveys, based on the eigenmode analysis similar to the one developed for the
bispectrum. We show that our method recovers the bias parameters introduced
into mock galaxies by a HOD prescription and is also able to handle potential
systematics in the case when a smaller number than ideal of mock catalogs is
used to estimate the covariance matrix. We find that current redshift surveys
(e.g. SDSS or 2dFGRS) are just about large enough to get interesting new
constraints on bias.


  The current distribution across the thickness of a current-carrying
rectangular film in the Meissner state was established long ago by the London
brothers. The distribution across the width is more complicated but was later
shown to be highly non-uniform, diverging at the edges. Accordingly, the
standard view for type II superconductors is that vortices enter at the edges
and, with increasing current, are driven inwards until they self-annihilate at
the centre, causing dissipation. This condition is presumed to define the
critical current. However we have shown that, under self-field (no external
field), the transport critical current is a London surface current where the
surface current density equals the critical field divided by {\lambda}, across
the entire width. The critical current distribution must therefore be uniform.
Here we report studies of the current and field distribution across commercial
YBa2Cu3O7 conductors and confirm the accepted non-uniform distribution at low
current but demonstrate a radical crossover to a uniform distribution at
critical current. This crossover ends discontinuously at a singularity and
calculations quantitatively confirm these results in detail. The onset of
self-field dissipation is, unexpectedly, thermodynamic in character and the
implied vortex-free critical state seems to require new physics.


  Metasurfaces, a two-dimensional (2D) form of metamaterials constituted by
planar meta-atoms, exhibit exotic abilities to freely tailor electromagnetic
(EM) waves. Over the past decade, tunable metasurfaces have come to the
frontier in the field of nanophotonics, with tremendous effort focused on
developing and integrating various active materials into metasurfaces. As a
result, tunable/reconfigurable metasurfaces with multi-functionalities
triggered by various external stimuli have been successfully demonstrated,
openings a new avenue to dynamically manipulate and control EM waves for
photonic applications in demand. In this review, we first brief the progress of
tunable metasurfaces development in the last decade and highlight
representative works from the perspectives of active materials development,
design methodologies and application-driven exploration. Then, we elaborate on
the active tuning mechanisms and relevant active materials. Next, we discuss
recent achievements in theory as well as machine learning (ML) assisted design
methodologies to sustain the development of this field. After that, we
summarize and describe typical application areas of the tunable metasurfaces.
We conclude this review by analyzing existing challenges and presenting our
perspectives on future directions and opportunities in this vibrant and
fast-developing field.


  We analyzed spectra, trajectories, orbits, light curves, and decelerations of
eight Draconid meteors observed from Northern Italy on October 8, 2011. Meteor
morphologies of two of the meteors are also presented, one of them obtained
with a high resolution camera. Meteor radiants agree with theoretical
predictions, with a hint that some meteors may belong to the pre-1900 meteoroid
trails. The spectra confirm that Draconids have normal chondritic composition
of main elements (Mg, Fe, Na). There are, nevertheless, differences in the
temporal evolution of Na line emission. The differences are correlated with the
shapes of the light curves and the deceleration rates. Our data confirm that
Draconids are porous conglomerates of grains, nevertheless, significant
differences in the atmospheric fragmentation of cm-sized Draconids were found.
Various textures with various resistance to fragmentation exist among Draconid
meteoroids and even within single meteoroids.


  We study D-branes in the extended geometry appearing in exceptional field
theory (or exceptional generalised geometry). Starting from the exceptional
sigma model (an $E_{d(d)}$ covariant worldsheet action with extra target space
coordinates), we define open string boundary conditions. We write down Neumann
and Dirichlet projectors compatible with the preservation of half-maximal
supersymmetry by the brane (building on previous work on the definition of
generalised orientifold quotients in exceptional field theory). This leads to a
definition of D-branes, plus their S-duals, as particular subspaces of the
exceptional geometry, and provides an opportunity to study D-branes in U-fold
backgrounds.


  A variant of compression optical coherence elastography for mapping relative
tissue stiffness is reported. Unlike conventionally discussed
displacement-based (DB) elastorgaphy, in which the decrease in the
cross-correlation is a negative factor causing errors in mapping displacement
and strain fields, we propose to intentionally use the difference in the
correlation stability (CS) for deformed tissue regions with different
stiffness. We compare the parameter ranges (in terms of noise-to-signal ratio
and strain) in which the conventional DB- and CS-approaches are operable. It is
shown that the CS approach has such advantages as significantly wider
operability region in terms of strain and is more tolerant to noises. This is
favorable for freehand implementation of this approach. Examples of simulated
and real CS-based elastographic OCT images are given.


  We perform streaming instability simulations at Hill density and beyond, to
demonstrate that Planetesimal formation is not completed when pebble
accumulations exceed the local Hill density. We find that Hill density is not a
sufficient criterion for further gravitational collapse of a pebble cloud into
a planetesimal, but that additionally the accumulated mass has to be large
enough to overcome turbulent diffusion. A Toomre analysis of the system
indicates that linear self-gravity modes play no role on the scale of our
numerical simulation. We nevertheless find that self-gravity, by vertically
contracting the pebble layer, increases the strength of turbulence, which is
either an indication of Kelvin Helmholtz Instability or a boost of the
streaming-instability. We furthermore determine the Bonnor-Ebert central
density to which a pebble cloud of given mass has to be compressed before it
would be able to continue contraction against internal diffusion. As the
equivalent "solid body" size of the pebble cloud scales with the central
density to the power of -1/6, it is much easier to have a pebble cloud of 100
km equivalent size to collapse than one of 10 km for the same level of
turbulent diffusion. This can explain the lack of small bodies in the solar
system and predicts to have small objects formed by gravitational collapse at
large pebble to gas ratios, in the outskirts of the solar nebula and at late
times of generally reduced gas mass.


  Accretion of interstellar material by a magnetized, slowly rotating isolated
neutron star is discussed. We show that the average persistent X-ray luminosity
of these objects is unlikely to exceed 4x10^26 erg/s. They can also appear as
X-ray bursters with the burst duration of ~30 minutes and repetition time of
\~10^5 yr. This indicates that the number of the accreting isolated neutron
stars which could be observed with recent X-ray missions is a few orders of
magnitude smaller than that previously estimated. Our findings argue against
models in which the magnetic field of neutron stars is assumed to decay
exponentially on a time scale shorter than 500 Myr.


  The practical distinctions between asteroids and comets, viewed as products
of accretion on either side of the snow line, are less clear-cut than
previously understood. In this chapter, we discuss the numerous solar system
populations which have physical and dynamical properties that conflict with any
simple diagnosis of their nature and origin. Studies of these so-called
"continuum" or "transition objects", which include many of the most intriguing
bodies in the solar system, have implications for a broad range of scientific
topics from the demise of comets and the activation of asteroids to the
production of interplanetary debris and the origin of the terrestrial planet
volatiles. We present an overview of the current state of knowledge concerning
the asteroid-comet continuum and discuss the numerous physical processes behind
the activity shown by small bodies in the solar system.


  We consider an elliptic boundary problem over a bounded region $\Omega$ in
$\mathbb{R}^n$ and acting on the generalized Sobolev space
$W^{0,\chi}_p(\Omega)$ for $1 < p < \infty$. We note that similar problems for
$\Omega$ either a bounded region in $\mathbb{R}^n$ or a closed manifold acting
on $W^{0,\chi}_2(\Omega)$, called H\"{o}rmander space, have been the subject of
investigation by various authors. Then in this paper we will, under the
assumption of parameter-ellipticity, establish results pertaining to the
existence and uniqueness of solutions of the boundary problem. Furthermore,
under the further assumption that the boundary conditions are null, we will
establish results pertaining to the spectral properties of the Banach space
operator induced by the boundary problem, and in particular, to the angular and
asymptotic distribution of its eigenvalues.


  The paper considers the problem of establishing data support for the
simplifying assumption (SA) in a bivariate conditional copula model. It is
known that SA greatly simplifies the inference for a conditional copula model,
but standard tools and methods for testing SA tend to not provide reliable
results. After splitting the observed data into training and test sets, the
method proposed will use a flexible training data Bayesian fit to define tests
based on randomization and standard asymptotic theory. Theoretical
justification for the method is provided and its performance is studied using
simulated data. The paper also discusses implementations in alternative models
of interest, e.g. Gaussian, Logistic and Quantile regressions.


  We present a study of protoplanetary disks in spatially resolved low-mass
binary stars in the Orion Nebula Cluster (ONC) to assess the impact of binarity
on the properties of circumstellar disks. This is currently the largest such
study in a clustered high-stellar-density star-forming environment. We
particularly aim to determine the presence of magnetospheric accretion and dust
disks for each binary component, and measure the overall disk frequency. We
carried out spatially resolved adaptive-optics-assisted observations to acquire
near-IR photometry and spectroscopy of 26 binaries in the ONC, and determine
stellar parameters such as effective temperatures, spectral types,
luminosities, and masses, as well as accretion properties and near-infrared
excesses for the individual binary components. A fraction of 40(+10/-9)% of the
binary components in the sample can be inferred to be T Tauri stars possessing
an accretion disk, marginally fewer than the disk fraction of single stars. We
find that disks in wide binaries of >200AU separation are consistent with
random pairing, while the evolution of circumprimary and circumsecondary disks
is observed to be synchronized in close binaries (separations <200AU).
Circumbinary disks appear to be unsuitable to explain this difference.
Furthermore, we identify several mixed pairs of accreting and non-accreting
components, suggesting that these systems are common and that there is no
preference for either the more or less massive component to evolve faster. The
derived accretion luminosities and mass accretion rates of the ONC binary
components are of similar magnitude as those for both ONC single stars and
binaries in the Taurus star-forming region. The paper concludes with a
discussion of the (presumably weak) connection between the presence of inner
accretion disks in young binary systems and the existence of planets in stellar
multiples.(abridged)


  We demonstrate X-ray phase contrast microscopy performed at the European
X-ray Free-Electron Laser sampled at 1.128 MHz rate. We have applied this
method to image stochastic processes induced by an optical laser incident on
water-filled capillaries with micrometer scale spatial resolution. The
generated high speed water jet, cavitation formation and annihilation in water
and glass, as well as glass explosions are observed. The comparison between
XFEL and previous synchrotron MHz microscopy shows the superior contrast and
spatial resolution at the XFEL over the synchrotron. This work opens up new
possibilities for the characterization of dynamic stochastic systems on
nanosecond to microsecond time scales at megahertz rate with object velocities
up to few kilometers per second using X-ray Free-Electron Laser sources.


  By employing a mixed SnO$_2$+Sn source, we demonstrate suboxide molecular
beam epitaxy growth of phase-pure single crystalline metastable SnO(001) thin
films at a growth rate of ~1.0nm/min without the need for additional oxygen.
These films grow epitaxially across a wide substrate temperature range from 150
to 450{\deg}C. Hence, we present an alternative pathway to overcome the
limitations of high Sn or SnO$_2$ cell temperatures and narrow growth windows
encountered in previous MBE growth of metastable SnO. In-situ laser
reflectometry and line-of-sight quadrupole mass spectrometry were used to
investigate the rate of SnO desorption as a function of substrate temperature.
While SnO ad-molecules desorption at Ts = 450{\deg}C was growth-rate
limiting,the SnO films did not desorb at this temperature after growth in
vacuum. The SnO (001) thin films are transparent and unintentionally p-type
doped, with hole concentrations and mobilities in the range of 0.9 to
6.0x10$^{18}$cm$^{-3}$ and 2.0 to 5.5 cm$^2$/V.s, respectively. These p-type
SnO films obtained at low temperatures are promising for back-end-of-line
(BEOL) compatible applications and for integration with n-type oxides in p-n
heterojunction and field-effect transistors


  Empirical growth rates of the ancient human population in Australia have been
calculated using the data for the number of rock-shelter sites. They confirm
the earlier conclusion that there was no intensification of growth between
10,000 and 1000 years BP.


  "Human-centered machine learning" (HCML) is a term that describes machine
learning that applies to human-focused problems. Although this idea is
noteworthy and generates scholarly excitement, scholars and practitioners have
struggled to clearly define and implement HCML in computer science. This
article proposes practices for human-centered machine learning, an area where
studying and designing for social, cultural, and ethical implications are just
as important as technical advances in ML. These practices bridge between
interdisciplinary perspectives of HCI, AI, and sociotechnical fields, as well
as ongoing discourse on this new area. The five practices include ensuring HCML
is the appropriate solution space for a problem; conceptualizing problem
statements as position statements; moving beyond interaction models to define
the human; legitimizing domain contributions; and anticipating sociotechnical
failure. I conclude by suggesting how these practices might be implemented in
research and practice.


  In this paper, autonomous differential equations type delta of order one on
integral domains are defined. For this we will use the autonomous ring defined
on the Hurwitz expansion ring of exponential generating functions with
coefficients in an integral domain. We will also use delta operators, which
behave like derivatives when acting on polynomials, along with Umbral calculus.
As a particular example of a delta operator we have the forward difference
operator that defines the difference equations. Then the delta-type equations
generalize to the ordinary equations and to the difference equations.


  It was recently shown by Feldbrugge et al. that the no-boundary proposal,
defined via a Lorentzian path integral and in minisuperspace, leads to unstable
fluctuations, in disagreement with early universe observations. In these
calculations many off-shell geometries summed over in the path integral in fact
contain singularities, and the question arose whether the instability might
ultimately be caused by these off-shell singularities. We address this question
here by considering a sum over purely regular geometries, by extending a
calculation pioneered by Halliwell and Louko. We confirm that the fluctuations
are unstable, even in this restricted context which, arguably, is closer in
spirit to the original proposal of Hartle and Hawking. Elucidating the reasons
for the instability of the no-boundary proposal will hopefully show how to
overcome these difficulties, or pave the way to new theories of initial
conditions for the universe.


  We present a novel approach to probabilistic electricity price forecasting
which utilizes distributional neural networks. The model structure is based on
a deep neural network that contains a so-called probability layer. The
network's output is a parametric distribution with 2 (normal) or 4 (Johnson's
SU) parameters. In a forecasting study involving day-ahead electricity prices
in the German market, our approach significantly outperforms state-of-the-art
benchmarks, including LASSO-estimated regressions and deep neural networks
combined with Quantile Regression Averaging. The obtained results not only
emphasize the importance of higher moments when modeling volatile electricity
prices, but also -- given that probabilistic forecasting is the essence of risk
management -- provide important implications for managing portfolios in the
power sector.


  The local $h^*$-polynomial is a natural invariant of a lattice polytope
appearing in Ehrhart theory and Hodge theory. In this work, we study the
question posed in [GKZ94] concerning the classification of lattice simplices
with vanishing local $h^*$-polynomial. Such simplices are called thin. We
relate this question to linear codes and hyperplane arrangements over finite
rings. This allows us to obtain a complete classification of the
$4$-dimensional thin simplices, extending the previously known results in
dimensions up to $3$.


  Let (Xt, t >= 0) be a diffusion process with jumps, sum of a Brownian motion
with drift and a compound Poisson process. We consider T_x the first hitting
time of a fixed level x > 0 by (Xt, t >= 0). We prove that the law of T_x has a
density (defective when E(X1) < 0) with respect to the Lebesgue measure.


  Polymer based electro-optic modulators have shown great potentials in high
frequency analog optical links. Existing commercial LiNibO3 Mach-Zehnder
modulators have intrinsic drawbacks in linearity to provide high fidelity
communication. In this paper, we present the design, fabrication and
characterization of a traveling wave directional coupler modulator based on
electro-optic polymer, which is able to provide high linearity, high speed, and
low optical insertion loss. A silver ground electrode is used to reduce
waveguide sidewall roughness due to the scattering of UV light in
photolithography process in addition to suppressing the RF loss. A 1-to-2
multi-mode interference 3dB-splitter, a photobleached refractive index taper
and a quasi-vertical taper are used to reduce the optical insertion loss of the
device. The symmetric waveguide structure of the MMI-fed directional coupler is
intrinsically bias-free, and the modulation is obtained at the 3-dB point
regardless of the ambient temperature. By achieving low RF loss, characteristic
impedance matching with 50{\Omega} load, and excellent velocity matching
between the RF wave and the optical wave, a travelling wave electrode is
designed to function up to 62.5GHz. Domain-inversion poling with push-pull
configuration is applied using alternating pulses on a 2-section
directional-coupler to achieve a spurious free dynamic range of 110dB/Hz2/3.
The 3-dB electrical bandwidth of device is measured to be 10GHz.


  The face super-resolution (FSR) task is to reconstruct high-resolution face
images from low-resolution inputs. Recent works have achieved success on this
task by utilizing facial priors such as facial landmarks. Most existing methods
pay more attention to global shape and structure information, but less to local
texture information, which makes them cannot recover local details well. In
this paper, we propose a novel recurrent convolutional network based framework
for face super-resolution, which progressively introduces both global shape and
local texture information. We take full advantage of the intermediate outputs
of the recurrent network, and landmarks information and facial action units
(AUs) information are extracted in the output of the first and second steps
respectively, rather than low-resolution input. Moreover, we introduced AU
classification results as a novel quantitative metric for facial details
restoration. Extensive experiments show that our proposed method significantly
outperforms state-of-the-art FSR methods in terms of image quality and facial
details restoration.


  Virtual effects induced by new physics in rare Z decays are reviewed. Since
the expected sensitivity of the giga-Z linear collider is of the order of
$10^{-8}$, we emphasize the importance of any new physics effect that gives a
prediction above this limit. It is also pointed out that an improvement on the
known experimental constraints on rare Z decays will provide us with a critical
test of the validity of the standard model at the loop level.


  We prove Alexander- and Markov-type theorems for virtual spatial trivalent
graphs and virtual trivalent braids. We provide two versions for the
Markov-type theorem: one uses an algebraic approach similar to the case of
classical braids and the other one is based on L-moves.


  A reduction procedure for stochastic differential equations based on
stochastic symmetries including Girsanov random transformations is proposed. In
this setting, a new notion of reconstruction is given, involving the
expectation values of functionals of solution to the SDE and a reconstruction
theorem for general stochastic symmetries is proved. Moreover, the notable case
of reduction under the closed subclass of quasi Doob transformations is
presented. The theoretical results are applied to stochastic models relevant in
the applications.


  We define a new notion of "b-stability" for a polarised algebraic variety,
adapted to the existence problem for Kahler-Einstein metrics on Fano manifolds.


  We extend a result of Griggs and Yeh about the maximum possible value of the
L(2,1)-labeling number of a graph in terms of its maximum degree to oriented
graphs. We consider the problem both in the usual definition of the oriented
L(2,1)-labeling number and in some variants we introduce.


  The modular multilevel converter (MMC) is a topology that consists of a high
number of capacitors, and degradation of capacitors can lead to converter
malfunction, limiting the overall system lifetime. Condition monitoring methods
can be applied to assess the health status of capacitors and realize predictive
maintenance to improve reliability. Current research works for condition
monitoring of capacitors in an MMC mainly monitor either capacitance or
equivalent series resistance (ESR), while these two health indicators can shift
at different speeds and lead to different end-of-life times. Hence, monitoring
only one of these parameters may lead to unreliable health status evaluation.
This paper proposes a data-driven method to estimate capacitance and ESR at the
same time, in which particle swarm optimization (PSO) is leveraged to update
the obtained estimations. Then, the results of the estimations are used to
predict the sub-module voltage, which is based on a capacitor voltage equation.
Furthermore, minimizing the mean square error between the predicted and actual
measured voltage makes the estimations closer to the actual values. The
effectiveness and feasibility of the proposed method are validated through
simulations and experiments.


  Graph Neural Networks are perfectly suited to capture latent interactions
between various entities in the spatio-temporal domain (e.g. videos). However,
when an explicit structure is not available, it is not obvious what atomic
elements should be represented as nodes. Current works generally use
pre-trained object detectors or fixed, predefined regions to extract graph
nodes. Improving upon this, our proposed model learns nodes that dynamically
attach to well-delimited salient regions, which are relevant for a higher-level
task, without using any object-level supervision. Constructing these localized,
adaptive nodes gives our model inductive bias towards object-centric
representations and we show that it discovers regions that are well correlated
with objects in the video. In extensive ablation studies and experiments on two
challenging datasets, we show superior performance to previous graph neural
networks models for video classification.


  In the Aharonov-Bohm (AB) effect, interference fringes are observed for a
charged particle in the absence of the local overlap with the external
electromagnetic field. This notion of the apparent nonlocality of the
interaction or the significant role of the potential has recently been
challenged and are under debate. The quantum electrodynamic approach provides a
microscopic picture of the characteristics of the interaction between a charge
and an external field. We explicitly show the gauge invariance of the local
phase shift in the magnetic AB effect, which is in contrast to the results
obtained using the usual semiclassical vector potential. Our study can resolve
the issue of the locality in the magnetic AB effect. However, the problem is
not solved in the same way in the electric counterpart wherein virtual scalar
photons play an essential role.


  The primary X-ray diagnostic lines in He-like ions are mainly excited by
electron impact from the ground level to the n = 2 levels, but at high
temperatures n > 2 levels are also excited. In order to describe the atomic
processes more completely collision strengths are computed for OVII including
for the first time all of the following: (i) relativistic fine structure, (ii)
levels up to the n = 4, and (iii) radiation damping of autoionizing resonances.
The calculations are carried out using the Breit-Pauli R-matrix (BPRM) method
with a 31-level eigenfunction expansion. Resonance structures in collision
strengths are delineated in detail up to the n = 4 thresholds. For highly
charged He-like ions radiation damping of autoionizing resonances is known to
be significant. We investigate this effect in detail and find that while
resonances are discernibly damped radiatively as the series limit n --> infty
is approached from below, the overall effect on effective cross sections and
rate coefficients is found to be very small. Collision strengths for the
principal lines important in X-ray plasma diagnostics, w,x,y and z,
corresponding to the 4 transitions to the ground level 1s^2 (^1S_0) <-- 1s2p
(^1P^o_1), 1s2p (^3P^o_2), 1s2p (^3P^o_1), 1s2s (^3S_1), are explicitly shown.
It is found that the effective collision strength of the forbidden z-line is up
to a factor of 4 higher at T < 10^6 K than previous values. This is likely to
be of considerable importance in the diagnostics of photoionized astrophysical
plasmas. Significant differences are also found with previous works for several
other transitions. This work is carried out as part of the Iron Project-RmaX
Network.


  In the problem of learning disentangled representations, one of the promising
methods is to factorize aggregated posterior by penalizing the total
correlation of sampled latent variables. However, this well-motivated strategy
has a blind spot: there is a disparity between the sampled latent
representation and its corresponding mean representation. In this paper, we
provide a theoretical explanation that low total correlation of sampled
representation cannot guarantee low total correlation of the mean
representation. Indeed, we prove that for the multivariate normal
distributions, the mean representation with arbitrarily high total correlation
can have a corresponding sampled representation with bounded total correlation.
We also propose a method to eliminate this disparity. Experiments show that our
model can learn a mean representation with much lower total correlation, hence
a factorized mean representation. Moreover, we offer a detailed explanation of
the limitations of factorizing aggregated posterior: factor disintegration. Our
work indicates a potential direction for future research of disentangled
learning.


  Within the variational approach of Bose liquids we analyze the g.s. energy of
charge neutral alpha matter at $T$=0. As a prerequisite for such calculation we
take from the literature or propose new $\alp-\alp$ potentials that are
particularly suitable for this task, i.e. posses a repulsive core and/or
reproduce the low energy scattering data and the resonance properties of the
$\alp-\alp$ system. The alpha matter EOS is then obtained with the HNC method
using Pandharipande-Bethe correlation derived variationally in the lowest order
expansion of the energy functional or a simple gaussian function with a healing
range determined by the normalization of the radial distribution function in
the lowest order. We show that saturation is achieved only via repulsive and
shallow potentials that are not consistent with the scattering and resonance
constraints.


  The Kondo effect between localized $f$-electrons and conductive carriers
leads to exotic physical phenomena. Among them, heavy-fermion (HF) systems, in
which massive effective carriers appear due to the Kondo effect, have
fascinated many researchers. Dimensionality is also an important characteristic
of the HF system, especially because it is strongly related to quantum
criticality [S. Sachdev, Science 288, 475 (2000)]. However, perfect
two-dimensional (2D) HF materials have not been reported yet. Here, we report
the surface electronic structure of the monoatomic-layer Kondo lattice YbCu$_2$
on a Cu(111) surface observed by synchrotron-based angle-resolved photoelectron
spectroscopy. The 2D conducting band and the Yb 4$f$ state, located very close
to the Fermi level, are observed. These bands are hybridized at
low-temperature, forming the 2D HF state, with an evaluated coherent
temperature of about 30 K. The effective mass of the 2D state is enhanced by a
factor of 100 by the development of the HF state. Furthermore, clear evidence
of the hybridization gap formation in the temperature dependence of the
Kondo-resonance peak has been observed below the coherent temperature. Our
study provides a new candidate as an ideal 2D HF material for understanding the
Kondo effect at low dimensions.


  Content delivery networks deliver much of the web and video content in the
world by deploying a large distributed network of servers. We model and analyze
a simple paradigm for client-side server selection that is commonly used in
practice where each user independently measures the performance of a set of
candidate servers and selects the one that performs the best. For web (resp.,
video) delivery, we propose and analyze a simple algorithm where each user
randomly chooses two or more candidate servers and selects the server that
provided the best hit rate (resp., bit rate). We prove that the algorithm
converges quickly to an optimal state where all users receive the best hit rate
(resp., bit rate), with high probability. We also show that if each user chose
just one random server instead of two, some users receive a hit rate (resp.,
bit rate) that tends to zero. We simulate our algorithm and evaluate its
performance with varying choices of parameters, system load, and content
popularity.


  Recurrent neural networks (RNNs), including long short-term memory (LSTM)
RNNs, have produced state-of-the-art results on a variety of speech recognition
tasks. However, these models are often too large in size for deployment on
mobile devices with memory and latency constraints. In this work, we study
mechanisms for learning compact RNNs and LSTMs via low-rank factorizations and
parameter sharing schemes. Our goal is to investigate redundancies in recurrent
architectures where compression can be admitted without losing performance. A
hybrid strategy of using structured matrices in the bottom layers and shared
low-rank factors on the top layers is found to be particularly effective,
reducing the parameters of a standard LSTM by 75%, at a small cost of 0.3%
increase in WER, on a 2,000-hr English Voice Search task.


  Two-loop electroweak corrections to the muon anomalous magnetic moment are
automatically calculated by using GRACE-FORM system, as a trial to extend our
system for two-loop calculation. We adopt the non-linear gauge (NLG) to check
the reliability of our calculation. In total 1780 two-loop diagrams consisting
of 14 different topological types and 70 one-loop diagrams composed of counter
terms are calculated. We check UV- and IR-divergences cancellation and the
independence of the results from NLG parameters. As for the numerical
calculation, we adopt trapezoidal rule with Double Exponential method (DE).
Linear extrapolation method (LE) is introduced to regularize UV- and IR-
divergence and to get finite values.


  Object detection remains an active area of research in the field of computer
vision, and considerable advances and successes has been achieved in this area
through the design of deep convolutional neural networks for tackling object
detection. Despite these successes, one of the biggest challenges to widespread
deployment of such object detection networks on edge and mobile scenarios is
the high computational and memory requirements. As such, there has been growing
research interest in the design of efficient deep neural network architectures
catered for edge and mobile usage. In this study, we introduce YOLO Nano, a
highly compact deep convolutional neural network for the task of object
detection. A human-machine collaborative design strategy is leveraged to create
YOLO Nano, where principled network design prototyping, based on design
principles from the YOLO family of single-shot object detection network
architectures, is coupled with machine-driven design exploration to create a
compact network with highly customized module-level macroarchitecture and
microarchitecture designs tailored for the task of embedded object detection.
The proposed YOLO Nano possesses a model size of ~4.0MB (>15.1x and >8.3x
smaller than Tiny YOLOv2 and Tiny YOLOv3, respectively) and requires 4.57B
operations for inference (>34% and ~17% lower than Tiny YOLOv2 and Tiny YOLOv3,
respectively) while still achieving an mAP of ~69.1% on the VOC 2007 dataset
(~12% and ~10.7% higher than Tiny YOLOv2 and Tiny YOLOv3, respectively).
Experiments on inference speed and power efficiency on a Jetson AGX Xavier
embedded module at different power budgets further demonstrate the efficacy of
YOLO Nano for embedded scenarios.


  Classical dynamics of spinning zero-size objects in an external gravitational
field is derived from the conservation law of the stress-energy and spin
tensors. The resulting world line equations differ from those in the existing
literature. In particular, the spin of the Dirac particle does not couple to
the background curvature. As a check of consistency, the wave packet solution
of the free Dirac equation is considered. The resulting equations are shown to
include a constraint that relates the wave packet spin to its orbital angular
momentum. In the zero-size limit, both contributions to the total angular
momentum disappear simultaneously.


  In this paper we consider error sums of the form \[\sum_{m=0}^{\infty}
\varepsilon_m\Big( \,b_m\alpha - \frac{a_m}{c_m}\,\Big) \,,\] where $\alpha$ is
a real number, $a_m$, $b_m$, $c_m$ are integers, and $\varepsilon_m=1$ or
$\varepsilon_m ={(-1)}^m$. In particular, we investigate such sums for \[\alpha
\in \big\{ \pi, e,e^{1/2},e^{1/3},\dots, \log (1+t), \zeta(2), \zeta(3) \big\}
\] and exhibit some connections between rational coefficients occurring in
error sums for Ap\'ery's continued fraction for $\zeta(2)$ and well-known
integer sequences. The concept of the paper generalizes the theory of ordinary
error sums, which are given by $b_m=q_m$ and $a_m/c_m=p_m$ with the convergents
$p_m/q_m$ from the continued fraction expansion of $\alpha$.


  Digital democracy and new forms for direct digital participation in policy
making gain unprecedented momentum. This is particularly the case for
preferential voting methods and decision-support systems designed to promote
fairer, more inclusive and legitimate collective decision-making processes in
citizens assemblies, participatory budgeting and elections. However, a
systematic human experimentation with different voting methods is cumbersome
and costly. This paper introduces VoteLab, an open-source and
thoroughly-documented platform for modular and adaptive design of voting
experiments. It supports to visually and interactively build reusable campaigns
with a choice of different voting methods, while voters can easily respond to
subscribed voting questions on a smartphone. A proof-of-concept with four
voting methods and questions on COVID-19 in an online lab experiment have been
used to study the consistency of voting outcomes. It demonstrates the
capability of VoteLab to support rigorous experimentation of complex voting
scenarios.


  Radio relics are Mpc-scale diffuse synchrotron sources found in galaxy
cluster outskirts. They are believed to be associated with large-scale shocks
propagating through the intra-cluster medium, although the connection between
radio relics and the cluster merger shocks is not yet proven conclusively. We
present a first tentative detection of a pressure jump in the well-known relic
of the Coma cluster through Sunyaev-Zel'dovich (SZ) effect imaging.The SZE data
are extracted from the first public all-sky data release of Planck and we use
high-frequency radio data at 2.3 GHz to constrain the shock-front geometry. The
SZE data provide evidence for a pressure discontinuity, consistent with the
relic position, without requiring any additional prior on the shock-front
location. The derived Mach number M = 2.9 (+0.8/-0.6) is consistent with X-ray
and radio results. A high-pressure "filament" without any pressure
discontinuity is disfavoured by X-ray measurements and a "sub-cluster" model
based on the infalling group NGC 4839 can be ruled out considering the
published mass estimates for this group. These results signify a first attempt
towards directly measuring the pressure discontinuity for a radio relic and the
first SZ-detected shock feature observed near the virial radius of a galaxy
cluster.


  We prove some $C^\infty$ and Gevrey well-posedness results for hyperbolic
equations whose coefficients lose regularity at one point.


  We present a novel construction for a Higgs-VEV sensitive operator, which can
be used as a trigger operator in cosmic selection models for the electroweak
hierarchy problem. Our operator does not contain any degrees of freedom charged
under the SM gauge symmetries, leading to reduced tuning in the resulting
models. Our construction is based on the extension of a two Higgs doublet model
(2HDM) with a softly broken approximate global $D_8$ symmetry (the symmetry
group of a square). A cosmic crunching model based on our extended Higgs sector
has only a percent level tuning corresponding to the usual little hierarchy
problem. In large regions of parameter space the 2HDM is naturally pushed
towards the alignment limit. A complete model requires the introduction of
fermionic top partners to ensure the approximate $D_8$ symmetry in the fermion
sector. We also show that the same extended Higgs sector can be used for a
novel implementation of the seesaw mechanism of neutrino masses.


  Autonomous vehicles are a promising solution to traffic congestion, air
pollution, accidents, and wasted time and resources. However, remote driver
intervention may be necessary for extreme situations to ensure safe roadside
parking or complete remote takeover. In such cases, high-quality real-time
video streaming is crucial for practical remote driving. In a preliminary
study, we already presented a region of interest (ROI) HEVC data compression
where the image was segmented into two categories of ROI and background,
allocating more bandwidth to the ROI, yielding an improvement in the visibility
of the classes that essential for driving while transmitting the background
with lesser quality. However, migrating bandwidth to the large ROI portion of
the image doesn't substantially improve the quality of traffic signs and
lights. This work categorized the ROIs into either background, weak ROI, or
strong ROI. The simulation-based approach uses a photo-realistic driving
scenario database created with the Cognata self-driving car simulation
platform. We use semantic segmentation to categorize the compression quality of
a Coding Tree Unit (CTU) according to each pixel class. A background CTU can
contain only sky, trees, vegetation, or building classes. Essentials for remote
driving include significant classes such as roads, road marks, cars, and
pedestrians. And most importantly, traffic signs and traffic lights. We apply
thresholds to decide if the number of pixels in a CTU of a particular category
is enough to declare it as belonging to the strong or weak ROI. Then, we
allocate the bandwidth according to the CTU categories. Our results show that
the perceptual quality of traffic signs, especially textual signs and traffic
lights, improves significantly by up to 5.5 dB compared to the only background
and foreground partition, while the weak ROI classes at least retain their
original quality.


  This paper considers the problems of modeling and predicting a long-term and
``blurry'' relapse that occurs after a medical act, such as a surgery. The
relapse is observed only indirectly, in a ``blurry'' fashion, through
longitudinal prescriptions of drugs over a long period of time after the
medical act. We introduce a new model, called ZiMM (Zero-inflated Mixture of
Multinomial distributions) in order to capture long-term and blurry relapses.
On top of it, we build an end-to-end deep-learning architecture called ZiMM
Encoder-Decoder (ZiMM ED) that can learn from the complex, irregular, highly
heterogeneous and sparse patterns of health events that are observed through a
claims-only database. ZiMM ED is applied on a ``non-clinical'' claims database,
that contains only timestamped reimbursement codes for drug purchases, medical
procedures and hospital diagnoses, the only available clinical feature being
the age of the patient. This setting is more challenging than a setting where
bedside clinical signals are available. Our motivation for using such a
non-clinical claims database is its exhaustivity population-wise, compared to
clinical electronic health records coming from a single or a small set of
hospitals. Indeed, we consider a dataset containing the claims of almost
\emph{all French citizens} who had surgery for prostatic problems, with a
history between 1.5 and 5 years. We consider a long-term (18 months) relapse
(urination problems still occur despite surgery), which is blurry since it is
observed only through the reimbursement of a specific set of drugs for
urination problems. Our experiments show that ZiMM ED improves several
baselines, including non-deep learning and deep-learning approaches, and that
it allows working on such a dataset with minimal preprocessing work.


  The properties of the quark and hadron Universe are explored. Kinetic theory
considerations are presented proving that hadron abundances after phase
transformation from quarks to hadrons remain intact till abundances of hadrons
become irrelevant. The hadronization process and the evolution of hadron yields
is described in detail.


  We study the spectrum and the nature of the excitations of an
antiferromagnetic (AFM) Heisenberg chain with staggered long range
interactions, both numerically using the time-dependent density matrix
renormalization group (tDMRG) method and by means of a multi-spinon
approximation that qualitatively explains its main features. The unfrustrated
long-range nature of the exchange effectively increases the dimensionality of
the system and the chain is able to undergo true symmetry breaking and develop
long range order, transitioning from a gapless spin liquid to a gapless ordered
AFM phase. We calculated the momentum resolved spin dynamical structure factor
and found that for weakly decaying interactions the emergence of N\'eel order
can be associated to the formation of bound states of spinons that become
coherent magnons. The quasiparticle band leaks out from the two-spinon
continuum that is pushed up to higher energies. Our physical picture is also
supported by an analysis of the behavior of the excitations in real-time.


  We analyze a class of stochastically stable quenched measures. We prove that
stochastic stability is fully characterized by an infinite family of zero
average polynomials in the covariance matrix entries.


  The evolutionary paths taken by massive stars with $M \gtrsim 60 \,
\mathrm{M}_\odot$ remain substantially uncertain. They begin their lives as
main sequence (MS) O-stars. Depending on their masses, rotation rates, and
metallicities, they can then encounter a wide range of evolutionary states with
an equally broad set of possible surface compositions and spectral
classifications. We present a new grid of calculations for the evolution of
such stars that covers a broad range in mass, M/M$_\odot = 60$ to $150$,
rotation rate, $v \, / \, v_{\rm crit} = 0$ to $0.6$, metallicity,
$[\mathrm{Fe}/\mathrm{H}] = -4$ to $0$, and $\alpha$-element enhancement,
$[\alpha/\mathrm{Fe}] = 0$ to $0.4$. We show that rotating stars undergo
rotationally-induced dredge-up of nucleosynthetic products, mostly He and N, to
their surfaces while still on the MS. Non-rotating metal-rich stars also reveal
the products of nucleosynthesis on their surfaces because even modest amounts
of mass loss expose their "fossil" convective cores: regions that are no longer
convective, but which were part of the convective core at an early stage in the
star's evolution. Thus surface enhancement of He and N is expected for rotating
stars at all metallicities, and for non-rotating stars if they are relatively
metal-rich. We calculate a stellar atmosphere for a representative model from
our grid, properly accounting for He- and N-enhancement, and show that the
resulting spectrum provides a good match to observed WNL stars, strongly
suggesting that the physical mechanisms we have identified are the ultimate
cause of the WNL phase.


  This paper presents a new color image enhancement technique based on modified
MultiScale Retinex(MSR) algorithm and visual quality of the enhanced images are
evaluated using a new metric, namely, wavelet energy. The color image
enhancement is achieved by down sampling the value component of HSV color space
converted image into three scales (normal, medium and fine) following the
contrast stretching operation. These down sampled value components are enhanced
using the MSR algorithm. The value component is reconstructed by averaging each
pixels of the lower scale image with that of the upper scale image subsequent
to up sampling the lower scale image. This process replaces dark pixel by the
average pixels of both the lower scale and upper scale, while retaining the
bright pixels. The quality of the reconstructed images in the proposed method
is found to be good and far better then the other researchers method. The
performance of the proposed scheme is evaluated using new wavelet domain based
assessment criterion, referred as wavelet energy. This scheme computes the
energy of both original and enhanced image in wavelet domain. The number of
edge details as well as wavelet energy is less in a poor quality image compared
with naturally enhanced image. Experimental results presented confirms that the
proposed wavelet energy based color image quality assessment technique
efficiently characterizes both the local and global details of enhanced image.


  This paper presents a family of autonomous Unmanned Aerial Vehicles (UAVs)
platforms designed for a diverse range of indoor and outdoor applications. The
proposed UAV design is highly modular in terms of used actuators, sensor
configurations, and even UAV frames. This allows to achieve, with minimal
effort, a proper experimental setup for single, as well as, multi robot
scenarios. Presented platforms are intended to facilitate the transition from
simulations, and simplified laboratory experiments, into the deployment of
aerial robots into uncertain and hard-to-model real-world conditions. We
present mechanical designs, electric configurations, and dynamic models of the
UAVs, followed by numerous recommendations and technical details required for
building such a fully autonomous UAV system for experimental verification of
scientific achievements. To show strength and high variability of the proposed
system, we present results of tens of completely different real-robot
experiments in various environments using distinct actuator and sensory
configurations.


  We train a single, goal-conditioned policy that can solve many robotic
manipulation tasks, including tasks with previously unseen goals and objects.
We rely on asymmetric self-play for goal discovery, where two agents, Alice and
Bob, play a game. Alice is asked to propose challenging goals and Bob aims to
solve them. We show that this method can discover highly diverse and complex
goals without any human priors. Bob can be trained with only sparse rewards,
because the interaction between Alice and Bob results in a natural curriculum
and Bob can learn from Alice's trajectory when relabeled as a goal-conditioned
demonstration. Finally, our method scales, resulting in a single policy that
can generalize to many unseen tasks such as setting a table, stacking blocks,
and solving simple puzzles. Videos of a learned policy is available at
https://robotics-self-play.github.io.


  Aerosols found in the atmosphere affect the climate and worsen air quality.
To mitigate these adverse impacts, aerosol formation and aerosol chemistry in
the atmosphere need to be better mapped out and understood. Currently, mass
spectrometry is the single most important analytical technique in atmospheric
chemistry and is used to track and identify compounds and processes. Vast
amounts of data are collected in each measurement of current time-of-flight and
orbitrap mass spectrometers using modern rapid data acquisition practices.
However, compound identification remains as a major bottleneck during data
analysis due to lacking reference libraries and analysis tools. Data-driven
compound identification approaches could alleviate the problem, yet remain rare
to non-existent in atmospheric science. In this perspective, we review the
current state of data-driven compound identification with mass spectrometry in
atmospheric science, and discuss current challenges and possible future steps
towards a digital mass spectrometry era in atmospheric science.


  Strong deep earthquakes occurred in the Sea of Okhotsk, May 24, 2013 at 05:44
UTC. The magnitude of the earthquake is M = 8.3, the depth of the hypocenter is
629 km. The earthquake was accompanied by the deep-focus aftershocks. The paper
is devoted to the study of the space-time properties of aftershocks. An
estimate is made of the deactivation coefficient of the earthquake source,
which "cools down" after the main shock. The modulation of aftershocks by
toroidal oscillations of the Earth was discovered. The questions about the
round-the-world seismic echo and the ribbed structure of the spatio-temporal
distribution of epicenters are considered. Keywords: Omori law, deactivation
coefficient, inverse problem, modulation, free oscillations, round-the-world
echo, space-time distribution


  Three-dimensional integer partitions provide a convenient representation of
codimension-one three-dimensional random rhombus tilings. Calculating the
entropy for such a model is a notoriously difficult problem. We apply
transition matrix Monte Carlo simulations to evaluate their entropy with high
precision. We consider both free- and fixed-boundary tilings. Our results
suggest that the ratio of free- and fixed-boundary entropies is
$\sigma_{free}/\sigma_{fixed}=3/2$, and can be interpreted as the ratio of the
volumes of two simple, nested, polyhedra. This finding supports a conjecture by
Linde, Moore and Nordahl concerning the ``arctic octahedron phenomenon'' in
three-dimensional random tilings.


  Integrative modeling of macromolecular assemblies allows for structural
characterization of large assemblies that are recalcitrant to direct
experimental observation. A Bayesian inference approach facilitates combining
data from complementary experiments along with physical principles, statistics
of known structures, and prior models, for structure determination. Here, we
review recent methods for integrative modeling based on statistical inference
and machine learning. These methods improve over the current state-of-the-art
by enhancing the data collection, optimizing coarse-grained model
representations, making scoring functions more accurate, sampling more
efficient, and model analysis more rigorous. We also discuss three new
frontiers in integrative modeling: incorporating recent deep learning-based
methods, integrative modeling with in situ data, and metamodeling.


  Solvation is essential for protein activities. To study internal solvation of
protein, site-directed mutagenesis is applied. Intrinsic fluorescent probe,
tryptophan, is inserted into desired position inside protein molecule for
ultrafast spectroscopic study. Here we review this unique method for protein
dynamics researches. We introduce the frontiers of protein solvation,
site-directed mutagenesis, protein stability and characteristics, and the
spectroscopic methods. Then we present time-resolved spectroscopic dynamics of
solvation dynamics inside caves of active sites. The studies are carried out on
a globular protein, staphylococcal nuclease. The solvation at internal sites of
the caves indicate clear characteristics of local environment. These solvation
behaviors correlated to the enzyme activity directly.


  We study the baby Skyrme model as a theory that interpolates between two
distinct BPS systems. For this a near-BPS approximation can be used which,
however, involves a small deviation from each of the two BPS limits. We provide
analytical explanation and numerical support for the validity of this
approximation. We then study the set of all possible supersymmetric extensions
of the baby Skyrme model with ${\cal N}=1$ and the particular ones with
extended ${\cal N}=2$ supersymmetries and relate this to the above mentioned
almost-BPS approximation.


  Pre-trained vision-language models (VLMs), exemplified by CLIP, demonstrate
remarkable adaptability across zero-shot classification tasks without
additional training. However, their performance diminishes in the presence of
domain shifts. In this study, we introduce CLIP Adaptation duRing Test-Time
(CLIPArTT), a fully test-time adaptation (TTA) approach for CLIP, which
involves automatic text prompts construction during inference for their use as
text supervision. Our method employs a unique, minimally invasive text prompt
tuning process, wherein multiple predicted classes are aggregated into a single
new text prompt, used as pseudo label to re-classify inputs in a transductive
manner. Additionally, we pioneer the standardization of TTA benchmarks (e.g.,
TENT) in the realm of VLMs. Our findings demonstrate that, without requiring
additional transformations nor new trainable modules, CLIPArTT enhances
performance dynamically across non-corrupted datasets such as CIFAR-10,
corrupted datasets like CIFAR-10-C and CIFAR-10.1, alongside synthetic datasets
such as VisDA-C. This research underscores the potential for improving VLMs'
adaptability through novel test-time strategies, offering insights for robust
performance across varied datasets and environments. The code can be found at:
https://github.com/dosowiechi/CLIPArTT.git


  Three-parametric family of non-Hermitian but ${\cal PT}-$symmetric six-by-six
matrix Hamiltonians $H^{(6)}(x,y,z)$ is considered. The ${\cal PT}-$symmetry
remains spontaneously unbroken (i.e., the spectrum of the bound-state energies
remains real so that the unitary-evolution stability of the quantum system in
question is shown guaranteed) in a non-empty domain ${\cal D}^{(physical)}$ of
parameters $x,y,z$. The construction of the exceptional-point (EP) boundary
$\partial{\cal D}^{(physical)}$ of the physical domain is preformed using an
innovative non-numerical implicit-function-construction strategy. The topology
of the resulting EP boundary of the spontaneous ${\cal PT}-$symmetry breakdown
(i.e., of the physical "horizon of stability") is shown similar to its much
more elementary $N=4$ predecessor. Again, it is shown to consist of two
components, viz., of the region of the quantum phase transitions of the first
kind (during which at least some of the energies become complex) and of the
quantum phase transitions of the second kind (during which some of the level
pairs only cross but remain real).


  We provide a complete classification of Fano threefolds X having canonical
Gorenstein singularities and the anticanonical degree (-KX)^3 equal 64.


  Studying inequalities between subgraph- or homomorphism-densities is an
important topic in graph theory. Sums of squares techniques have proven useful
in dealing with such questions. Using an approach from real algebraic geometry,
we strengthen a Positivstellensatz for simple quantum graphs by Lov\'asz and
Szegedy, and we prove several new Positivstellens\"atze for nonnegativity of
quantum multigraphs. We provide new examples and counterexamples.


  We present the first analytical superposition of a charged black hole with an
annular disk of extremal dust. In order to obtain the solutions, we first solve
the Einstein-Maxwell field equations for sources that represent disk-like
configurations of matter in confomastatic spacetimes by assuming a functional
dependence among the metric function, the electric potential and an auxiliary
function,which is taken as a solution of the Laplace equation. We then employ
the Lord Kelvin Inversion Method applied to models of finite extension in order
to obtain annular disks. The structures obtained extend to infinity, but their
total masses are finite and all the energy conditions are satisfied. Finally,
we observe that the extremal Reissner-Nordstr\"{o}m black hole can be embedded
into the center of the disks by adding a boundary term in the inversion.


  The first motivation of this paper is to study stationarity and ergodic
properties for a general class of time series models defined conditional on an
exogenous covariates process. The dynamic of these models is given by an
autoregressive latent process which forms a Markov chain in random
environments. Contrarily to existing contributions in the field of Markov
chains in random environments, the state space is not discrete and we do not
use small set type assumptions or uniform contraction conditions for the random
Markov kernels. Our assumptions are quite general and allows to deal with
models that are not fully contractive, such as threshold autoregressive
processes. Using a coupling approach, we study the existence of a limit, in
Wasserstein metric, for the backward iterations of the chain. We also derive
ergodic properties for the corresponding skew-product Markov chain. Our results
are illustrated with many examples of autoregressive processes widely used in
statistics or in econometrics, including GARCH type processes, count
autoregressions and categorical time series.


  Pretrained language models often do not perform tasks in ways that are in
line with our preferences, e.g., generating offensive text or factually
incorrect summaries. Recent work approaches the above issue by learning from a
simple form of human evaluation: comparisons between pairs of model-generated
task outputs. Comparison feedback conveys limited information about human
preferences per human evaluation. Here, we propose to learn from natural
language feedback, which conveys more information per human evaluation. We
learn from language feedback on model outputs using a three-step learning
algorithm. First, we condition the language model on the initial output and
feedback to generate many refinements. Second, we choose the refinement with
the highest similarity to the feedback. Third, we finetune a language model to
maximize the likelihood of the chosen refinement given the input. In synthetic
experiments, we first evaluate whether language models accurately incorporate
feedback to produce refinements, finding that only large language models (175B
parameters) do so. Using only 100 samples of human-written feedback, our
learning algorithm finetunes a GPT-3 model to roughly human-level summarization
ability.


  This is a remark that by using an adaptation of the technique invented by A.
Kiselev, F. Nazarov, and A. Voldberg, with a modified scaling argument, we can
prove global regularity of the critical 2-D dissipative quasi-geostrophic
equation with smooth periodic force, under the assumption that the initial data
is smooth and periodic, and the force is $\alpha$-H\"older continuous in space,
$\alpha > 0$.


  We present a generalized version of the Path Interchange Zel'dovich
Approximation (PIZA), for use with realistic galaxy redshift surveys. PIZA is a
linear particle-based Lagrangian method which uses the present day positions of
galaxies to reconstruct both the initial density field and the present day
peculiar velocity field. We generalize the method by mapping galaxy positions
from redshift-space to real-space in the Local Group frame, and take the
selection function into account by minimizing the mass weighted action. We have
applied our new method to mock galaxy catalogues, and find it offers an
improvement in the accuracy of reconstructions over linear theory. We have
applied our method to the all-sky IRAS Point Source Catalogue Redshift Survey.
Applying PIZA to PSCz, we are able to obtain the peculiar velocity field, the
dipole, bulk velocities, and the distortion parameter. From the dipole, we find
that beta=0.51+/-0.14. We compare the PSCz bulk velocity with that of Mk III
and conclude that beta=0.5+/-0.15. We compare the PSCz dipole with that of SFI
and find that beta=0.5+/-0.1.


  The analysis of Belief Propagation and other algorithms for the {\em
reconstruction problem} plays a key role in the analysis of community detection
in inference on graphs, phylogenetic reconstruction in bioinformatics, and the
cavity method in statistical physics.
  We prove a conjecture of Evans, Kenyon, Peres, and Schulman (2000) which
states that any bounded memory message passing algorithm is statistically much
weaker than Belief Propagation for the reconstruction problem. More formally,
any recursive algorithm with bounded memory for the reconstruction problem on
the trees with the binary symmetric channel has a phase transition strictly
below the Belief Propagation threshold, also known as the Kesten-Stigum bound.
The proof combines in novel fashion tools from recursive reconstruction,
information theory, and optimal transport, and also establishes an asymptotic
normality result for BP and other message-passing algorithms near the critical
threshold.


  It is shown that the low-energy single-particle excitation-spectrum of the
three-band Hubbard model at hole-dopings away from half-filling agrees
remarkably well with Quantum Monte Carlo data and spectroscopic experiments
within the framework of a conserving approximation that includes
self-consistently the interaction with charge, spin, and two-particle
fluctuations. The dispersion of the quasiparticle-peak obtained from the
spectral-weight function is flat around the $(\pi,0)$ and $(0,\pi)$ points as
has been observed in recent angle-resolved photoemission measurement. The
significant reduction of the quasiparticle-dispersion near the Fermi-energy is
due to spin-fluctuations, rather than being induced by band effects.


  We explore the scales and the extent of disagreement between $Planck$ PR3 and
Atacama Cosmology Telescope (ACT) DR4 data. $Planck$ and ACT data have
substantial overlap in the temperature anisotropy data between scales
corresponding to multipoles $\ell\simeq 600-2500$ with complementing coverage
of larger angular scales by $Planck$ and smaller angular scales by ACT. Since
the same cosmology should govern the anisotropy spectrum at all scales, we
probe this disagreement in the primordial power spectrum. We use a parametric
form of power law primordial spectrum that allows changes in the spectral tilt.
We also reconstruct the primordial spectrum with a non-parametric method from
both $Planck$ and ACT temperature data. We find the disagreement exists within
scales 0.08 - 0.16 ${\rm Mpc}^{-1}$ where ACT temperature data prefers a scale
invariant/blue spectrum. At scales larger and smaller than this window, ACT
data strongly prefers a red tilt, which is consistent with $Planck$. This
change in the spectral tilt can be identified in the ACT data at 2$\sigma$ C.L.
without using $Planck$ data, indicating that the tension is driven by different
preferences for tilts within the ACT data. The addition of $Planck$ data up to
intermediate scales ($\ell\le650$) increases this significance to 3$\sigma$.
Given the large overlap between $Planck$ and ACT within 0.08 - 0.16 ${\rm
Mpc}^{-1}$ and considering the internal consistency between different $Planck$
temperature and polarization spectra, the scope of new physics as a solution to
the tension remains limited. Our results -- a strong preference for an
intermediate transition in spectral tilt and the variation of this preference
in different data combinations -- indicate that systematic effects can be
misperceived as new physics emerging from different non-standard cosmological
processes.


  It is now routine to measure the weak gravitational lensing shear signal from
the mean ellipticity of distant galaxies. However, conversion between
ellipticity and shear assumes local linearity of the lensing potential (ie that
the spatial derivatives of the shear are small), and this condition is not
satisfied in some of the most interesting regions of the sky. We extend a
derivation of lensing equations to include higher order terms, and assess the
level of biases introduced by assuming that first-order weak lensing theory
holds in a relatively strong shear regime. We find that, even in a worst-case
scenario, a fully linear analysis is accurate to within 1% outside ~1.07 times
the Einstein radius of a lens, by deriving an analytic function that can be
used to estimate the applicability of any first-order analysis. The effect is
too small to explain the discrepancy between weak- and strong-lensing estimates
of the mass of the bullet cluster, and should not impact cluster surveys for
the forseeable future. In fact, it means that arclets can be used to measure
shears closer to a cluster core than has been generally appreciated. However,
at the level of accuracy demanded by future lensing surveys, this bias ought to
be considered in measurements of the inner slope of cluster mass distributions
and the small-scale end of the mass power spectrum. Both of these are central
in determining the relationship between baryonic and dark matter.


  Ultrafast transient reflectivity changes caused by collective and single
particle excitations in the quasi one-dimensional charge-density wave (CDW)
semiconductor K0.3MoO3 are investigated with optical pump-probe spectroscopy.
The temperature-dependence of non-equilibrium single particle excitations
across the CDW gap and their recombination dynamics are reported for the first
time. In addition, amplitude mode reflectivity oscillations are observed in
real time. A T-dependent overdamped response is also observed which is
attributed to relaxation of the phason mode.


  We have investigated shot noise and conduction of graphene field effect
nanoribbon devices at low temperature. By analyzing the exponential $I-V$
characteristics of our devices in the transport gap region, we found out that
transport follows variable range hopping laws at intermediate bias voltages $1
< V_{bias} < 12$ mV. In parallel, we observe a strong shot noise suppression
leading to very low Fano factors. The strong suppression of shot noise is
consistent with inelastic hopping, in crossover from one- to two-dimensional
regime, indicating that the localization length $l_{loc} < W$ in our
nanoribbons.


  In this work we explore a new cosmological solution for an universe filled
with one dissipative fluid, described by a barotropic EoS $p = \omega \rho$, in
the framework of the full Israel-Stewart theory. The form of the bulk viscosity
has been assumed of the form $\xi = \xi_{0}\rho^{1/2}$. The relaxation time is
taken to be a function of the EoS, the bulk viscosity and the speed of bulk
viscous perturbations, $c_{b}$. The solution presents an initial singularity,
where the curvature scalar diverges as the scale factor goes to zero. Depending
on the values for $\omega$, $\xi_{0}$, $c_{b}$ accelerated and decelerated
cosmic expansion can be obtained. In the case of accelerated expansion, the
viscosity drives the effective EoS to be of quintessence type, for the single
fluid with positive pressure. Nevertheless, we show that only the solution with
decelerated expansion satisfies the thermodynamics conditions $dS/dt > 0$
(growth of the entropy) and $d^{2}S/dt^{2} < 0$ (convexity condition). We show
that an exact stiff matter EoS is not allowed in the framework of the full
causal thermodynamic approach; and in the case of a EoS very close to the stiff
matter regime, we found that dissipative effects becomes negligible so the
entropy remains constant. Finally, we show numerically that the solution is
stable under small perturbations.


  Medical Artificial Intelligence (AI) involves the application of machine
learning algorithms to biomedical datasets in order to improve medical
practices. Products incorporating medical AI require certification before
deployment in most jurisdictions. To date, clear pathways for regulating
medical AI are still under development. Below the level of formal pathways lies
the actual practice of developing a medical AI solution. This Perspective
proposes best practice guidelines for development compatible with the
production of a regulatory package which, regardless of the formal regulatory
path, will form a core component of a certification process. The approach is
predicated on a statistical risk perspective, typical of medical device
regulators, and a deep understanding of machine learning methodologies. These
guidelines will allow all parties to communicate more clearly in the
development of a common Good Machine Learning Practice (GMLP), and thus lead to
the enhanced development of both medical AI products and regulations.


  We study the Koszul property of a standard graded $K$-algebra $R$ defined by
the binomial edge ideal of a pair of graphs $(G_1,G_2)$. We show that the
following statements are equivalent: (i) $R$ is Koszul; (ii) the defining ideal
$J_{G_1,G_2}$ of $R$ has a quadratic Gr\"obner basis; (iii) the graded maximal
ideal of $R$ has linear quotients with respect to a suitable order of its
generators


  Discrete series Whittaker functions on $Spin(2n, 2)$ are studied. The
dimensions of the space of both algebraic and continuous Whittaker models are
explicitly determined. They are described by a sum of dimensions of irreducible
representations of $Spin(2n-3, 2)$. Also obtained are the Mellin-Barnes type
integral formulas of the Whittaker functions associated with minimal $K$-type
vectors.


  The spacetime singularities in classical general relativity are inevitable,
which are also predicated by the celebrated singularity theorems. However, it
is general belief that singularities do not exist in the nature and they are
the limitations of the general relativity. In the absence of a well defined
quantum gravity, models of regular black holes have been studied. We employ
probability distribution inspired mass function $m(r)$ to replace Kerr black
hole mass $M$ to present a nonsingular rotating black hole that is identified
asymptotically ($r \gg k$, $k>0$ constant) exactly as the Kerr-Newman black
hole, and as the Kerr black hole when $k=0$. The radiating counterpart renders
a nonsingular generalization of Carmeli's spacetime as well as Vaidya's
spacetime, in the appropriate limits. The exponential correction factor
changing the geometry of the classic black hole to remove curvature singularity
can be also motivated by the quantum arguments. The regular rotating spacetime
can also be understood as a black hole of general relativity coupled to
nonlinear electrodynamics.


  Developing a robust object tracker is a challenging task due to factors such
as occlusion, motion blur, fast motion, illumination variations, rotation,
background clutter, low resolution and deformation across the frames. In the
literature, lots of good approaches based on sparse representation have already
been presented to tackle the above problems. However, most of the algorithms do
not focus on the learning of sparse representation. They only consider the
modeling of target appearance and therefore drift away from the target with the
imprecise training samples. By considering all the above factors in mind, we
have proposed a visual object tracking algorithm by integrating a
coarse-to-fine search strategy based on sparse representation and the weighted
multiple instance learning (WMIL) algorithm. Compared with the other trackers,
our approach has more information of the original signal with less complexity
due to the coarse-to-fine search method, and also has weights for important
samples. Thus, it can easily discriminate the background features from the
foreground. Furthermore, we have also selected the samples from the un-occluded
sub-regions to efficiently develop the strong classifier. As a consequence, a
stable and robust object tracker is achieved to tackle all the aforementioned
problems. Experimental results with quantitative as well as qualitative
analysis on challenging benchmark datasets show the accuracy and efficiency of
our method.


  Using the rigid body as an example, we illustrate some features of stochastic
geometric mechanics. These features include: i) a geometric variational
motivation for the noise structure involving Lie-Poisson brackets and momentum
maps, ii) stochastic coadjoint motion with double bracket dissipation, iii) the
Lie-Poisson Fokker-Planck description and its stationary solutions, iv) random
dynamical systems, random attractors and SRB measures connected to statistical
physics.


  We analyze neutral Higgs boson decays into squark pairs in the minimal
supersymmetric extension of the Standard Model and improve previous analyses.
In particular the treatment of potentially large higher-order corrections due
to the soft SUSY breaking parameters A_b, the trilinear Higgs coupling to
sbottoms, and mu, the Higgsino mass parameter, is investigated. The remaining
theoretical uncertainties including the SUSY-QCD corrections are analyzed
quantitatively.


  We present PyGFit, a program designed to measure PSF-matched photometry from
images with disparate pixel scales and PSF sizes. While PyGFit has a number of
uses, its primary purpose is to extract robust spectral energy distributions
(SEDs) from crowded images. It does this by fitting blended sources in crowded,
low resolution images with models generated from a higher resolution image.
This approach minimizes the impact of crowding and also yields consistently
measured fluxes in different filters, minimizing systematic uncertainty in the
final SEDs. We present an example of applying PyGFit to real data and perform
simulations to test its fidelity. The uncertainty in the best-fit flux rises
sharply as a function of nearest-neighbor distance for objects with a neighbor
within 60% of the PSF size. Similarly, the uncertainty increases quickly for
objects blended with a neighbor more than four times brighter. For all other
objects the fidelity of PyGFit's results depends only on flux, and the
uncertainty is primarily limited by sky noise.


  Multifragmentation reactions are dominating processes for the decomposition
of highly excited nuclei leading to the fragment production in heavy-ion
collisions. At high energy reactions strange particles are abundantly produced.
We present a novel development of the Statistical multifragmentation model
(SMM) as its generalization for the hyper-matter which is formed after the
hyperon capture. In this way, it is possible to describe its disintegration
into normal and hyper-nuclei. Some properties of hyper-nuclei and their binding
energies can be determined from the comparison of the isotope yields. The main
focus of this method is to investigate strange and multi-strange hypernuclei
since their properties are not easy to measure in traditional hyper-nuclei
experiments.


  We present a detailed study of the composition of 20 M giants in the Galactic
center with 15 of them confirmed to be in the Nuclear Star Cluster. As a
control sample we have also observed 7 M giants in the Milky Way Disk with
similar stellar parameters. All 27 stars are observed using the NIRSPEC
spectograph on the KECK II telescope in the K-band at a resolving power of
R=23,000. We report the first silicon abundance trends versus [Fe/H] for stars
in the Galactic center. While finding a disk/bulge like trend at subsolar
metallicities, we find that [Si/Fe] is enhanced at supersolar metallicities. We
speculate on possible enrichment scenarios to explain such a trend. However,
the sample size is modest and the result needs to be confirmed by additional
measurements of silicon and other \textalpha-elements. We also derive a new
distribution of [Fe/H] and find the most metal rich stars at [Fe/H]=+0.5 dex,
confirming our earlier conclusions that the Galactic center hosts no stars with
extreme chemical composition.


  Low-temperature (below 60K) phase changes of free clusters containing 50,
137, and 229 methane molecules have been observed in isoenergetic Molecular
Dynamics computations. Bulk solid methane exhibits structural phase
transformation at 20.4 K. However, clusters of 50 molecules already melt at
about 25 K, which makes the observation of solid-solid transformations rather
challenging. Molecular Dynamics calculations of isoenergetic free methane
clusters show that they exhibit transformations from an orientationally-ordered
(~ 10K) and a disordered phase (above 20K). There is no hysteresis in the
cooling-heating regime - a signal for continuous transition.


  We apply Zwanziger formalism to Cho restricted $ SU(2) $ theory to obtain the
potential in a static quark-antiquark pair. Cho restricted theory is a
self-consistent subset of a non-Abelian $ SU(2) $ gauge theory which tries to
describe the infrared regime of Yang-Mills gauge theories. In Zwanziger
formalism, a local Lagrangian depending on two electric and magnetic gauge
fields is constructed for the theories where both electric and magnetic charges
exist. Based on this local Lagrangian the propagator and then the potential
between quarks is calculated in two limits: $ m_{C} r \ll 1 $ and $ m_{C} r \gg
1$, where $ m_{C} $ is the mass of the dual gauge boson and $ r $ is the
distance between the quark and the antiquark.


  Pull-tabbing is an evaluation approach for functional logic computations,
based on a graph transformation recently proposed, which avoids making
irrevocable non-deterministic choices that would jeopardize the completeness of
computations. In contrast to other approaches with this property, it does not
require an upfront cloning of a possibly large portion of the choice's context.
We formally define the pull-tab transformation, characterize the class of
programs for which the transformation is intended, extend the computations in
these programs to include the transformation, and prove the correctness of the
extended computations.


  We propose several prediction intervals procedures for the individual
treatment effect with either finite-sample or asymptotic coverage guarantee in
a non-parametric regression setting, where non-linear regression functions,
heteroskedasticity and non-Gaussianity are allowed. The construct the
prediction intervals we use the conformal method of Vovk et al. (2005). In
extensive simulations, we compare the coverage probability and interval length
of our prediction interval procedures. We demonstrate that complex learning
algorithms, such as neural networks, can lead to narrower prediction intervals
than simple algorithms, such as linear regression, if the sample size is large
enough.


  This work presents the differential charmonia production cross sections in
high energy p+p collisions calculated using NRQCD formalism. The NRQCD
formalism, factorizes the quarkonia production cross sections in terms of short
distance QCD cross sections and long distance matrix elements (LDMEs). The
short distance cross sections are calculated in terms of perturbative QCD and
LDMEs are obtained by fitting the experimental data. Measured transverse
momentum distributions of $\chi_{\rm c}$, $\psi$(2S) and J/$\psi$ in p +{$\bar
{\rm p}$} collisions at $\sqrt{s}=$ 1.8, 1.96 TeV and in p+p collisions at
$\sqrt{s}=$ 7, 8 and 13 TeV are used to constrain LDMEs. The feed-down
contribution to each state from the higher states are taken into account. The
formalism provides a very good description of the data in a wide energy range.
The values of LDMEs are used to predict the charmonia cross sections in p+p
collisions at 13 and 5 TeV in kinematic bins relevant for the LHC detectors.


  Generalizations of the classical affine Lelieuvre formula to surfaces in
projective three-dimensional space and to hypersurfaces in multi- dimensional
projective space are given. A discrete version of the projective Lelieuvre
formula is presented too.


  The demand for mobile multimedia streaming services has been steadily growing
in recent years. Mobile multimedia broadcasting addresses the shortage of radio
resources but introduces a network error recovery problem. Retransmitting
multimedia segments that are not correctly broadcast can cause service
disruptions and increased service latency, affecting the quality of experience
perceived by end users. With the advent of networking paradigms based on
virtualization technologies, mobile networks have been enabled with more
flexibility and agility to deploy innovative services that improve the
utilization of available network resources. This paper discusses how mobile
multimedia broadcast services can be designed to prevent service degradation by
using the computing capabilities provided by multiaccess edge computing (MEC)
platforms in the context of a 5G network architecture. An experimental platform
has been developed to evaluate the feasibility of a MEC application to provide
adaptive error recovery for multimedia broadcast services. The results of the
experiments carried out show that the proposal provides a flexible mechanism
that can be deployed at the network edge to lower the impact of transmission
errors on latency and service disruptions.


  In this paper we consider a system of non-linear stochastic heat equations on
$\mathbb{R}^d$ driven by a Gaussian noise which is white in time and has a
homogeneous spatial covariance. Under some suitable regularity and non
degeneracy conditions, the smoothness of the joint density of the solution for
this system has been studied by E. Nualart in [11]. The purpose of this paper
is further to study the lower and upper bounds of the density. The main tools
are the Malliavin calculus and the method developed by Kohatasu-Higa in [6] or
E. Nualart and Quer-Sardanyons in [12].


  The existence of bright quasars at high redshifts implies that supermassive
black holes were able to form in the early Universe. Though a number of
mechanisms to achieve this have been proposed, none yet stands out. A recent
suggestion is the formation of quasi-stars, initially stellar-mass black holes
accreting from hydrostatic giant-like envelopes of gas, formed from the
monolithic collapse of pre-galactic gas clouds. In this work, we modify the
Cambridge STARS stellar evolution package to construct detailed models of the
evolution of these objects. We find that, in all of our models, the black hole
inside the envelope is able to reach slightly more than one-tenth of the total
mass of the system before hydrostatic equilibrium breaks down. This breakdown
occurs after a few million years of evolution. We show that the mechanism which
causes the hydrostatic evolution to end is present in polytropic models. We
also show that the solutions are highly sensitive to the size of the inner
boundary radius and that no physical solutions exist if the inner boundary is
chosen to be less than about 0.3 of the Bondi radius.


  We propose a robust method for averaging numbers contaminated by a large
proportion of outliers. Our method, dubbed RODIAN, is inspired by the key idea
of MINPRAN [1]: We assume that the outliers are uniformly distributed within
the range of the data and we search for the region that is least likely to
contain outliers only. The median of the data within this region is then taken
as RODIAN. Our approach can accurately estimate the true mean of data with more
than 50% outliers and runs in time $O(n\log n)$. Unlike other robust
techniques, it is completely deterministic and does not rely on a known inlier
error bound. Our extensive evaluation shows that RODIAN is much more robust
than the median and the least-median-of-squares. This result also holds in the
case of non-uniform outlier distributions.


  Recent advances on the quest to determine the primordial or pregalactic
helium abundance, Yp, are reviewed. There are seven problems affecting the He/H
abundance determinations of H II regions that are briefly discussed: the
underlying absorption lines in the observed spectra, the ionization,
temperature, and density structures of each object, the collisional
contribution to the intensity of the He I and H I lines, the optical thickness
of the He I lines, and the extrapolation to derive Yp based on the Y and O/H
values.