  The standard model for the operation of the s-process in asymptotic giant
branch (AGB) stars predicts that low-metallicity ([Fe/H] <= -1) AGB stars
should exhibit large overabundances of Pb and Bi as compared to other
s-elements. Eight CH stars (with [Fe/H] ranging from -1.5 to -2.5) are studied
in the present paper using high-resolution spectra (R = lambda/Delta lambda =
135 000), and five of them appear to be enriched in lead (with [Pb/Ce] ~ 0.7).
Abundances of heavy elements in these `lead stars' (defined as stars enriched
in s-elements and with [Pb/hs] >= 1, hs being any of Ba, La or Ce) are
consistent with the predictions for the s-process operating in low-metallicity
AGB stars as a consequence of the `partial mixing' of protons below the
convective hydrogen envelope. Two among the studied stars (HD 189711 and V Ari)
add to a growing number of low-metallicity stars which do not conform to these
predictions, however. Variations on the canonical proton-mixing scenario for
the operation of the s-process in low-metallicity stars, that could account for
these discrepant stars, are briefly discussed.


  The emphasis in this review about non-baryonic dark matter will be on
experimental approaches to this fast evolving field of astroparticle physics,
especially the direct detection method. The current status of experimental
techniques will be reviewed and recent highlights as well as future plans will
be introduced.


  Cosmic shear is a powerful probe of cosmological parameters, but its
potential can be fully utilised only if galaxy shapes are measured with great
accuracy. Two major effects have been identified which are likely to account
for most of the bias for maximum likelihood methods in recent shear measurement
challenges. Model bias occurs when the true galaxy shape is not well
represented by the fitted model. Noise bias occurs due to the non-linear
relationship between image pixels and galaxy shape. In this paper we
investigate the potential interplay between these two effects when an imperfect
model is used in the presence of high noise. We present analytical expressions
for this bias, which depends on the residual difference between the model and
real data. They can lead to biases not accounted for in previous calibration
schemes. By measuring the model bias, noise bias and their interaction, we
provide a complete statistical framework for measuring galaxy shapes with model
fitting methods from GRavitational lEnsing Accuracy Testing (GREAT) like
images. We demonstrate the noise and model interaction bias using a simple toy
model, which indicates that this effect can potentially be significant. Using
real galaxy images from the Cosmological Evolution Survey (COSMOS) we quantify
the strength of the model bias, noise bias and their interaction. We find that
the interaction term is often a similar size to the model bias term, and is
smaller than the requirements of the current and shortly upcoming galaxy
surveys.


  The determination of $V_{cb}$ relies on the Heavy-Quark Expansion and the
extraction of the non-perturbative matrix elements from inclusive $b\to c$
decays. The proliferation of these matrix elements complicates their extraction
at $1/m_b^4$ and higher, thereby limiting the $V_{cb}$ extraction.
Reparametrization invariance links different operators in the Heavy-Quark
expansion thus reducing the number of independent operators at $1/m_b^4$ to
eight for the total rate. We show that this reduction also holds for spectral
moments as long as they are defined by reparametrization invariant
weight-functions. This is valid in particular for the leptonic invariant mass
spectrum ($q^2$), i.e. the differential rate and its moments. Currently,
$V_{cb}$ is determined by fitting the energy and hadronic mass moments, which
do not manifest this parameter reduction and depend on the full set of 13
matrix elements up to $1/m_b^4$. In light of this, we propose an experimental
analysis of the $q^2$ moments to open the possibility of a model-independent
$V_{cb}$ extraction from semileptonic decays including the $1/m_b^4$ terms in a
fully data-driven way.


  In this work is elaborated the wording of Fermat`s principle for
electromagnetic waves spreading in materials with negative index of refractions
(MNR). The recent experiments with such material has confirmed their main
characteristics, predicted earlier. It is shown that for MNR not applicable
wording of Fermat`s principle through minimum (or extremum) of time of
spreading the wave between two points. The correct wording for this case is a
wording through extremum of total optical length for way between two points
moreover optical length at passing of wave through MNR is taken negative. In
paper are considered some properties of original flat lenses, executed from
MNR.


  The Pattern self-Assembly Tile set Synthesis (PATS) problem is to determine a
set of coloured tiles that self-assemble to implement a given rectangular
colour pattern. We give an exhaustive branch-and-bound algorithm to find tile
sets of minimum cardinality for the PATS problem. Our algorithm makes use of a
search tree in the lattice of partitions of the ambient rectangular grid, and
an efficient bounding function to prune this search tree. Empirical data on the
performance of the algorithm shows that it compares favourably to previously
presented heuristic solutions to the problem.


  We have fabricated electric double-layer field-effect devices to
electrostatically dope our active materials, either $x$=0.015
Ga$_{1-x}$Mn$_x$As or $x$=3.2$\times10^{-4}$ Ga$_{1-x}$Be$_x$As. The devices
are tailored for interrogation of electric field induced changes to the
frequency dependent conductivity in the accumulation or depletions layers of
the active material via infrared (IR) spectroscopy. The spectra of the
(Ga,Be)As-based device reveal electric field induced changes to the IR
conductivity consistent with an enhancement or reduction of the Drude response
in the accumulation and depletion polarities, respectively. The spectroscopic
features of this device are all indicative of metallic conduction within the
GaAs host valence band (VB). For the (Ga,Mn)As-based device, the spectra show
enhancement of the far-IR itinerant carrier response and broad mid-IR resonance
upon hole accumulation, with a decrease of these features in the depletion
polarity. These later spectral features demonstrate that conduction in
ferromagnetic (FM) Ga$_{1-x}$Mn$_x$As is distinct from genuine metallic
behavior due to extended states in the host VB. Furthermore, these data support
the notion that a Mn-induced impurity band plays a vital role in the electron
dynamics of FM Ga$_{1-x}$Mn$_x$As. We add, a sum-rule analysis of the spectra
of our devices suggests that the Mn or Be doping does not lead to a substantial
renormalization of the GaAs host VB.


  We find new decoupling limits of N=4 super Yang-Mills (SYM) on R x S^3 with
gauge group SU(N). These decoupling limits lead to decoupled theories that are
much simpler than the full N=4 SYM but still contain many of its interesting
features. The decoupling limits correspond to being in a near-critical region,
near a point with zero temperature and critical chemical potentials. The new
decoupling limits are found by generalizing the limits of hep-th/0605234 to
include not only the chemical potentials for the SU(4) R-symmetry of N=4 SYM
but also the chemical potentials corresponding to the SO(4) symmetry. In the
decoupled theories it is possible to take a strong coupling limit in a
controllable manner since the full effective Hamiltonian is known. For planar
N=4 SYM on R x S^3 all the decoupled theories correspond to fully integrable
spin chains. We study the thermodynamics of the decoupled theories and find the
Hagedorn temperature for small and large values of the effective coupling. We
find an alternative formulation of the decoupling limits in the microcanonical
ensemble. This leads to a characterization of certain regimes of weakly coupled
N=4 SYM in which there are string-like states. Finally, we find a similar
decoupling limit for pure Yang-Mills theory, which for the planar limit leads
to a fully integrable decoupled theory.


  We propose a scalable computerized approach for large-scale inference of
Liver Imaging Reporting and Data System (LI-RADS) final assessment categories
in narrative ultrasound (US) reports. Although our model was trained on reports
created using a LI-RADS template, it was also able to infer LI-RADS scoring for
unstructured reports that were created before the LI-RADS guidelines were
established. No human-labelled data was required in any step of this study; for
training, LI-RADS scores were automatically extracted from those reports that
contained structured LI-RADS scores, and it translated the derived knowledge to
reasoning on unstructured radiology reports. By providing automated LI-RADS
categorization, our approach may enable standardizing screening recommendations
and treatment planning of patients at risk for hepatocellular carcinoma, and it
may facilitate AI-based healthcare research with US images by offering large
scale text mining and data gathering opportunities from standard hospital
clinical data repositories.


  In this note, the Sherrington Kirkpatrick model of interacting spins is under
consideration. In the high temperature region, we give an asymptotic expansion
for the expected value of some genereral polynomial of the overlap of the
system when the size $N$ grows to infinity. Some of the coefficients obtained
are shown to be vanishing, while the procedure to get the nontrivial ones has
to be performed by a computer program, due to the great amount of computation
involved.


  We consider the thermodynamics of a black hole coupled to thermal radiation
in a spatially finite (spherical) region. Thermodynamic state functions are
derived in the canonical ensemble, defined by elements of radius $r_o$ and
boundary temperature $T(r_o)$. Using recent solutions of the semi-classical
back reaction problem, we compute the $O(\hbar)$ corrections to the mass of the
black hole, thermal energy, the entropy and free energy due to the presence of
hot conformal scalars, massless spinors and U(1) gauge quantum fields in the
vicinity of the hole. The free energy is particularly important for assessing
under what conditions the nucleation of black holes from hot flat space is
likely to occur.


  We prove that the Hilbert geometry of a convex domain in the plane is Gromov
hyperbolic, if, and only if, the bottom of its spectrum is not zero


  We prove that the image of period map is algebraic, as conjectured by
Griffiths.


  We define the monomial invariants of a projective variety $Z$; they are
invariants coming from the generic initial ideal of $Z$. Using this notion, we
generalize a result of Cook: If $Z$ is an integral variety of codimension two,
satisfying the additional hypothesis $s_Z=s_\Gamma,$ then its monomial
invariants are connected.


  The time and band limiting operator is introduced to optimize the
reconstruction of a signal from only a partial part of its spectrum. In the
discrete case, this operator commutes with the so-called algebraic Heun
operator which appears in the context of the quantum integrable systems. The
construction of both operators and the proof of their commutativity is
recalled. A direct connection between their spectra is obtained. Then, the
Bethe ansatz, a well-known method to diagonalize integrable quantum
Hamiltonians, is used to diagonalize the Heun operator and to obtain insights
on the spectrum of the time and band limiting operator.


  Forthcoming surveys will extend the understanding of cosmological large scale
structures up to unprecedented redshift. According to this perspective, we
present a fully relativistic framework to evaluate the impact of stochastic
inhomogeneities on the determination of the Hubble constant. To this aim, we
work within linear perturbation theory and relate the fluctuations of the
luminosity distance-redshift relation, in the Cosmic Concordance model, to the
intrinsic uncertainty associated to the measurement of $H_0$ from high-redshift
surveys ($0.15\le z\le3.85$). We first present the detailed derivation of the
luminosity distance-redshift relation 2-point correlation function and then
provide analytical results for all the involved relativistic effects, such as
peculiar velocity, lensing, time delay and (integrated) Sachs-Wolfe, and their
angular spectra. Hence, we apply our analytical results to the study of
high-redshift Hubble diagram, according to what has been recently claimed in
literature. Following the specific of Euclid Deep Survey and LSST, we conclude
that the cosmic variance associated with the measurement of the Hubble constant
is at most of 0.1 %. Our work extends the analysis already done in literature
for closer sources, where only peculiar velocity has been taken into account.
We then conclude that deep surveys will provide an estimation of the $H_0$
which will be more precise than the one obtained from local sources, at least
in regard of the intrinsic uncertainty related to a stochastic distribution of
inhomogeneities.


  Abelian family symmetries provide a predictive framework for neutrino mass
models. In seesaw models based on an abelian family symmetry, the structures of
the Dirac and the Majorana matrices are derived from the symmetry, and the
neutrino masses and mixing angles are determined by the lepton charges under
the family symmetry. Such models can lead to mass degeneracies and large mixing
angles as well as mass hierarchies, the squared mass difference between
quasi-degenerate neutrinos being determined by the symmetry. We present two
models illustrating this approach.


  We have analyzed with pedagogical purposes the relation of the total
densities of states (TDOS/PDOS/OPDOS) and molecular orbital diagrams (MOD) for
single and isolated molecules of Water and Nitrogen. We use a calculation level
HF/6-311G, spreading the energy eigenvalues around the TDOS spikes and using
the Mulliken visual analysis in Multiwfn to showcase our findings. We discuss
the possibility to use these electrons orbit population diagrams, in
conjunction with the more intuitive MOD diagrams to teach the Quantum Mechanics
of simple molecular isolated structures.


  We present the conditions under which probabilistic error-free discrimination
of mixed states is possible, and provide upper and lower bounds on the maximum
probability of success for the case of two mixed states. We solve certain
special cases exactly, and demonstrate how the problems of state filtering and
state comparison can be recast as problems of mixed state unambiguous
discrimination.


  Histopathology images are gigapixel-sized and include features and
information at different resolutions. Collecting annotations in histopathology
requires highly specialized pathologists, making it expensive and
time-consuming. Self-training can alleviate annotation constraints by learning
from both labeled and unlabeled data, reducing the amount of annotations
required from pathologists. We study the design of teacher-student
self-training systems for Non-alcoholic Steatohepatitis (NASH) using clinical
histopathology datasets with limited annotations. We evaluate the models on
in-distribution and out-of-distribution test data under clinical data shifts.
We demonstrate that through self-training, the best student model statistically
outperforms the teacher with a $3\%$ absolute difference on the macro F1 score.
The best student model also approaches the performance of a fully supervised
model trained with twice as many annotations.


  We study selective and game-theoretic versions of properties like the ccc,
weak Lindel\"ofness and separability, giving various characterizations of them
and exploring connections between these properties and some classical cardinal
invariants of the continuum.


  In this work, we visualized Korea's traditional constellations within an
interactive 3D star map we created of the Milky Way. Unlike virtual
planetariums based on celestial star coordinates from Earth's viewpoint, our
visualization enables people to experience and interact with Korean
constellation forms and its constituent stars in 3D space, and appreciate their
historical, cultural significance from a contemporary perspective. Our
interactive constellation map is based on the most detailed and accurate
information on the stars in our Galaxy to date, and combines our expanding
scientific understanding of the stars with contextual information reflecting
Korea's unique astronomical culture and heritage.


  We introduce NeRD, a new demosaicking method for generating full-color images
from Bayer patterns. Our approach leverages advancements in neural fields to
perform demosaicking by representing an image as a coordinate-based neural
network with sine activation functions. The inputs to the network are spatial
coordinates and a low-resolution Bayer pattern, while the outputs are the
corresponding RGB values. An encoder network, which is a blend of ResNet and
U-net, enhances the implicit neural representation of the image to improve its
quality and ensure spatial consistency through prior learning. Our experimental
results demonstrate that NeRD outperforms traditional and state-of-the-art
CNN-based methods and significantly closes the gap to transformer-based
methods.


  The two-time scale nature of SAC, which is an actor-critic algorithm, is
characterised by the fact that the critic estimate has not converged for the
actor at any given time, but since the critic learns faster than the actor, it
ensures eventual consistency between the two. Various strategies have been
introduced in literature to learn better gradient estimates to help achieve
better convergence. Since gradient estimates depend upon the critic, we posit
that improving the critic can provide a better gradient estimate for the actor
at each time. Utilizing this, we propose Soft Actor Retrospective Critic
(SARC), where we augment the SAC critic loss with another loss term -
retrospective loss - leading to faster critic convergence and consequently,
better policy gradient estimates for the actor. An existing implementation of
SAC can be easily adapted to SARC with minimal modifications. Through extensive
experimentation and analysis, we show that SARC provides consistent improvement
over SAC on benchmark environments. We plan to open-source the code and all
experiment data at: https://github.com/sukritiverma1996/SARC.


  Neuroscientific data analysis has classically involved methods for
statistical signal and image processing, drawing on linear algebra and
stochastic process theory. However, digitized neuroanatomical data sets
containing labelled neurons, either individually or in groups labelled by
tracer injections, do not fully fit into this classical framework. The
tree-like shapes of neurons cannot mathematically be adequately described as
points in a vector space. There is therefore a need for new approaches. Methods
from computational topology and geometry are naturally suited to the analysis
of neuronal shapes. Here we introduce methods from Discrete Morse Theory to
extract tree-skeletons of individual neurons from volumetric brain image data,
or to summarize collections of neurons labelled by localized anterograde tracer
injections. Since individual neurons are topologically trees, it is sensible to
summarize the collection of neurons labelled by a localized anterograde tracer
injection using a consensus tree-shape. The algorithmic procedure includes an
initial pre-processing step to extract a density field from the raw volumetric
image data, followed by initial skeleton extraction from the density field
using a discrete version of a 1-(un)stable manifold of the density field.
Heuristically, if the density field is regarded as a mountainous landscape,
then the 1-(un)stable manifold follows the "mountain ridges" connecting the
maxima of the density field. We then simplify this skeleton-graph into a tree
using a shortest-path approach and methods derived from persistent homology.
The advantage of this approach is that it uses global information about the
density field and is therefore robust to local fluctuations and non-uniformly
distributed input signals. To be able to handle large data sets, we use a
divide-and-conquer approach. The resulting software DiMorSC is available on
Github.


  We study the kinetics of protein folding via statistical energy landscape
theory. We concentrate on the local-connectivity case, where the
configurational changes can only occur among neighboring states, with the
folding progress described in terms of an order parameter given by the fraction
of native conformations. The non-Markovian diffusion dynamics is analyzed in
detail and an expression for the mean first-passage time (MFPT) from non-native
unfolded states to native folded state is obtained. It was found that the MFPT
has a V-shaped dependence on the temperature. We also find that the MFPT is
shortened as one increases the gap between the energy of the native and average
non-native folded states relative to the fluctuations of the energy landscape.
The second- and higher-order moments are studied to infer the first-passage
time (FPT) distribution. At high temperature, the distribution becomes close to
a Poisson distribution, while at low temperatures the distribution becomes a
L\'evy-like distribution with power-law tails, indicating a non-self-averaging
intermittent behavior of folding dynamics. We note the likely relevance of this
result to single-molecule dynamics experiments, where a power law (L\'evy)
distribution of the relaxation time of the underlined protein energy landscape
is observed.


  Many particle dark matter models predict that the dark matter undergoes
cascade annihilations, i.e. the annihilation products are 4-body final states.
In the context of model-independent cascade annihilation models, we study the
compatibility of the dark matter interpretation of the Fermi- LAT Galactic
center gamma-ray emission with null detections from dwarf spheroidal galaxies.
For canonical values of the Milky Way density profile and the local dark matter
density, we find that the dark matter interpretation to the Galactic center
emission is strongly constrained. However, uncertainties in the dark matter
distribution weaken the constraints and leave open dark matter interpretations
over a wide range of mass scales.


  A general scheme to find tachyon boundary states is developed within the
framework of the theory of KP hierarchy. The method is applied to calculate
correlation function of intersecting D-branes and rederived the results of our
previous works as special examples. A matrix generalization of this scheme
provides a method to study dynamics of coincident multi D-branes.


  It is consistent that there is a compact space of character less than the
splitting number in which there are no converging sequences. Such a space is an
Efimov space.


  We study the available data in polarized e-p deep inelastic scattering to
test two different solutions to the so called spin crisis: one of them based on
the axial gluon anomaly and consistent with the Bjorken sum rule and another
one, where the defects in the spin sum rules and in the Gottfried sum rule are
related. In this case a defect is also expected for the Bjorken sum rule.
Experimental data, especially the very recent SLAC E154, favour the first
solution and demand a gluon polarization $\Delta G = 2.25 \pm 1.39$.


  With the proliferation of social media platforms and e-commerce sites,
several cross-domain collaborative filtering strategies have been recently
introduced to transfer the knowledge of user preferences across domains. The
main challenge of cross-domain recommendation is to weigh and learn users'
different behaviors in multiple domains. In this paper, we propose a
Cross-Domain collaborative filtering model following a Translation-based
strategy, namely CDT. In our model, we learn the embedding space with
translation vectors and capture high-order feature interactions in users'
multiple preferences across domains. In doing so, we efficiently compute the
transitivity between feature latent embeddings, that is if feature pairs have
high interaction weights in the latent space, then feature embeddings with no
observed interactions across the domains will be closely related as well. We
formulate our objective function as a ranking problem in factorization machines
and learn the model's parameters via gradient descent. In addition, to better
capture the non-linearity in user preferences across domains we extend the
proposed CDT model by using a deep learning strategy, namely DeepCDT. Our
experiments on six publicly available cross-domain tasks demonstrate the
effectiveness of the proposed models, outperforming other state-of-the-art
cross-domain strategies.


  For a simple symmetric random walk in dimension $d\geq 3$, a uniform strong
law of large numbers is proved for the number of sites with given local time up
to time $n$.


  We present the results of a spectroscopic search for close binaries among
horizontal branch (HB) stars in NGC6752. We used the ESO VLT-FORS2 instrument
to obtain medium resolution (R=4100) spectra of 51 hot HB stars with 8000 <
Teff < 32000 K during four consecutive nights. Eighteen of our targets are
extreme horizontal branch (EHB) stars with Teff > 22000 K. Radial velocity
variations were measured with cross-correlation techniques and we carefully
evaluated the statistical and systematic errors associated with them. No close
binary system has been detected among our 51 targets. The data corrected for
instrumental effects indicate that the radial velocity variations are always
below 15 km/s (3sigma level). From a statistical analysis of our results, we
conclude that (at 95 confidence level) the fraction of binaries with a ~0.5
solar masses companion among EHB stars in NGC6752 is smaller than 20%. This
empirical evidence sharply contrasts with what has been found for hot subdwarfs
in the field, and opens new questions about the formation of EHB stars in
globular clusters (and possibly in the field as well.


  High-dimensional transport equations frequently occur in science and
engineering. Computing their numerical solution, however, is challenging due to
its high dimensionality.
  In this work we develop an algorithm to efficiently solve the transport
equation in moderately complex geometrical domains using a Galerkin method
stabilized by streamline diffusion. The ansatz spaces are a tensor product of a
sparse grid in space and discontinuous piecewise polynomials in time. Here, the
sparse grid is constructed upon nested multilevel finite element spaces to
provide geometric flexibility. This results in an implicit time-stepping scheme
which we prove to be stable and convergent. If the solution has additional
mixed regularity, the convergence of a $2d$-dimensional problem equals that of
a $d$-dimensional one up to logarithmic factors.
  For the implementation, we rely on the representation of sparse grids as a
sum of anisotropic full grid spaces. This enables us to store the functions and
to carry out the computations on a sequence regular full grids exploiting the
tensor product structure of the ansatz spaces. In this way existing finite
element libraries and GPU acceleration can be used. The combination technique
is used as a preconditioner for an iterative scheme to solve the transport
equation on the sequence of time strips.
  Numerical tests show that the method works well for problems in up to six
dimensions. Finally, the method is also used as a building block to solve
nonlinear Vlasov-Poisson equations.


  Diffusion magnetic resonance imaging (dMRI) data allow to reconstruct the 3D
pathways of axons within the white matter of the brain as a tractography. The
analysis of tractographies has drawn attention from the machine learning and
pattern recognition communities providing novel challenges such as finding an
appropriate representation space for the data. Many of the current learning
algorithms require the input to be from a vectorial space. This requirement
contrasts with the intrinsic nature of the tractography because its basic
elements, called streamlines or tracks, have different lengths and different
number of points and for this reason they cannot be directly represented in a
common vectorial space. In this work we propose the adoption of the
dissimilarity representation which is an Euclidean embedding technique defined
by selecting a set of streamlines called prototypes and then mapping any new
streamline to the vector of distances from prototypes. We investigate the
degree of approximation of this projection under different prototype selection
policies and prototype set sizes in order to characterise its use on
tractography data. Additionally we propose the use of a scalable approximation
of the most effective prototype selection policy that provides fast and
accurate dissimilarity approximations of complete tractographies.


  We develop a geometric criterion that unambiguously characterizes and also
provides a systematic and effective computational procedure for finding all the
residual symmetries of any gravitational Ansatz . We apply the criterion to
several examples starting with the Collinson Ansatz for circular stationary
axisymmetric spacetimes. We reproduce the residual symmetries already known for
this Ansatz including their conformal symmetry, for which we identify the
corresponding infinite generators spanning the two related copies of the Witt
algebra. We also consider the noncircular generalization of this Ansatz and
show how the noncircular contributions on the one hand break the conformal
invariance and on the other hand enhance the standard translations symmetries
of the circular Killing vectors to supertranslations depending on the direction
along which the circularity is lost. As another application of the method, the
well-known relation defining conjugate gravitational potentials introduced by
Chandrasekhar, that makes possible the derivation of the Kerr black hole from a
trivial solution of the Ernst equations, is deduced as a special point of the
general residual symmetry of the Papapetrou Ansatz. We emphasize how the
election of Weyl coordinates, which determines the Papapetrou Ansatz, breaks
also the conformal freedom of the stationary axisymmetric spacetimes.
Additionally, we study AdS waves for any dimension generalizing the residual
symmetries already known for lower dimensions and exhibiting a very complex
infinite-dimensional Lie algebra containing three families: two of them span
the semidirect sum of the Witt algebra and scalar supertranslations and the
third generates vector supertranslations. We manage to comprehend that the
infinite connected group is the precise diffeomorphisms subgroup allowing to
locally deform the AdS background into AdS waves.


  For the purpose of propagating information and ideas through a social
network, a seeding strategy aims to find a small set of seed users that are
able to maximize the spread of the influence, which is termed as influence
maximization problem. Despite a large number of works have studied this
problem, the existing seeding strategies are limited to the static social
networks. In fact, due to the high speed data transmission and the large
population of participants, the diffusion processes in real-world social
networks have many aspects of uncertainness. Unfortunately, as shown in the
experiments, in such cases the state-of-art seeding strategies are pessimistic
as they fails to trace the dynamic changes in a social network. In this paper,
we study the strategies selecting seed users in an adaptive manner. We first
formally model the Dynamic Independent Cascade model and introduce the concept
of adaptive seeding strategy. Then based on the proposed model, we show that a
simple greedy adaptive seeding strategy finds an effective solution with a
provable performance guarantee. Besides the greedy algorithm an efficient
heuristic algorithm is provided in order to meet practical requirements.
Extensive experiments have been performed on both the real-world networks and
synthetic power-law networks. The results herein demonstrate the superiority of
the adaptive seeding strategies to other standard methods.


  Recent experimental observations of laser-induced adsorption at the interface
between an alkali vapor and a dielectric surface have demonstrated the
possibility of growing metallic films of nanometric thickness on dielectric
surfaces, with arbitrary shapes determined by the intensity profile of the
light. The mechanisms directly responsible for the accumulation of atoms at the
irradiated surface have been shown to involve photo-ionization of atoms very
close to the surface. However, the existence of a vapor-pressure threshold for
initiating the film growth still raises questions on the processes occurring at
the surface. In this letter, we report on the observation that the
vapor-pressure threshold corresponds to a minimum adatom coverage necessary for
the surface to effectively neutralize the incoming ions and make possible the
growth of a multilayer film. We discuss the hypothesis that the coverage
threshold is a surface conductivity threshold.


  We prove that for any singular measure $\mu$ on $\mathbb{R}^n$ it is possible
to cover $\mu$-almost every point with $n$ families of Lipschitz slabs of
arbitrarily small total width. More precisely, up to a rotation, for every
$\delta>0$ there are $n$ countable families of $1$-Lipschitz functions
$\{f_i^1\}_{i\in\mathbb{N}},\ldots, \{f_i^n\}_{i\in\mathbb{N}},$
$f_i^j:\{x_j=0\}\subset\mathbb{R}^n\to\mathbb{R}$, and $n$ sequences of
positive real numbers $\{\varepsilon_i^1\}_{i\in\mathbb{N}},\ldots,
\{\varepsilon_i^n\}_{i\in\mathbb{N}}$ such that, denoting $\hat x_j$ the
orthogonal projection of the point $x$ onto $\{x_j=0\}$ and
$$I_i^j:=\{x=(x_1,\ldots,x_n)\in \mathbb{R}^n:f_i^j(\hat x_j)-\varepsilon_i^j<
x_j< f_i^j(\hat x_j)+\varepsilon_i^j\},$$ it holds
$\sum_{i,j}\varepsilon_i^j\leq \delta$ and
$\mu(\mathbb{R}^n\setminus\bigcup_{i,j}I_i^j)=0.$
  We apply this result to show that, if $\mu$ is not absolutely continuous, it
is possible to approximate the identity with a sequence $g_h$ of smooth
equi-Lipschitz maps satisfying
$$\limsup_{h\to\infty}\int_{\mathbb{R}^n}{\rm{det}}(\nabla g_h)
d\mu<\mu(\mathbb{R}^n).$$ From this, we deduce a simple proof of the fact that
every top-dimensional Ambrosio-Kirchheim metric current in $\mathbb{R}^n$ is a
Federer-Fleming flat chain.


  Huffman-coded sphere shaping (HCSS) is an algorithm for finite-length
probabilistic constellation shaping, which provides nearly optimal energy
efficiency at low implementation complexity. In this paper, we experimentally
study the nonlinear performance of HCSS employing dual-polarization 64-ary
quadrature amplitude modulation (DP-64QAM) in an extended reach single-span
link comprising 200 km of standard single mode fiber (SSMF). We investigate the
effects of shaping sequence length, dimensionality of symbol mapping, and
shaping rate. We determine that the na\"ive approach of Maxwell-Boltzmann
distribution matching - which is optimal in the additive white Gaussian noise
channel - provides a maximum achievable information rate gain of 0.18
bits/4D-symbol in the infinite length regime. Conversely, HCSS can achieve a
gain of 0.37 bits/4Dsymbol using amplitude sequence lengths of 32, which may be
implemented without multiplications, using integer comparison and addition
operations only. Coded system performance, with a net data rate of
approximately 425 Gb/s for both shaped and uniform inputs, is also analyzed.


  We use the method of evaluating the decay rate in terms of the imaginary part
of a probe brane action to study the holographic Schwinger effect. In the
confining D3-branes case, we find that the Schwinger effect occurs at energy
scales higher than the Kaluza-Klein mass, indicating the absence of such effect
when the dual gauge field theory can be regarded as an 2+1 dimensional theory.
This property is independent of the configuration of the probe brane. In the
case of D3-branes with a B field dual to a noncommutative super Yang-Mills
theory, we study how the decay rate is affected by the noncommutative effect.


  We introduce a flexible approach for the enumeration of the down-sets of a
finite poset and test it with the calculation of the Dedekind numbers $b(5) =
7581$ and $b(6) = 7828354$. For the calculation of $b(5)$, we develop two
methods of which the first one (without pre-calculations) requires simple
evaluation of 80 posets and the second one (with pre-calculations) of 34
posets. The calculation of $b(6)$ (with pre-calculations) is done by evaluating
245 posets.


  Type Ia supernovae (SNe Ia) have been intensively investigated due to its
great homogeneity and high luminosity, which make it possible to use them as
standardizable candles for the determination of cosmological parameters. In
2011, the physics Nobel prize was awarded for the discovery of the accelerating
expansion of the Universe through observations of distant supernovae. This is a
pedagogical article, aimed at those starting their study of that subject, in
which we dwell on some topics related to the analysis of SNe Ia and their use
in luminosity distance estimators. Here we investigate their spectral
properties and light curve standardization, paying careful attention to the
fundamental quantities directly related to the SNe Ia observables. Finally, we
describe our own step-by-step implementation of a classical light curve fitter,
the stretch, applying it to real data from the Carnegie Supernova Project.


  We provide sufficient conditions for polynomial rate of convergence in the
weak law of large numbers for supercritical general indecomposable multi-type
branching processes. The main result is derived by investigating the embedded
single-type process composed of all individuals having the same type as the
ancestor. As an important intermediate step, we determine the (exact)
polynomial rate of convergence of Nerman's martingale in continuous time to its
limit. The techniques used also allow us to give streamlined proofs of the weak
and strong laws of large numbers and ratio convergence for the processes in
focus.


  In this paper we consider the Fermi's paradox (FP) and propose a possible
resolution in the context of super-advanced alien civilizations. In this sense
it can be regarded as to be the weak Fermi paradox (WFP). By assuming that
superadvanced extraterrestrials (ET) exist and are capable of constructing a
huge ring-like megastructure around a pulsar or a main sequence star (MSS) to
consume its emitted energy, we consider the stability problem of the ring,
studying the out of plane dynamics. It has been shown that for normal pulsars
the oscillation timescale, thus the timescale of the spectral variability is of
the order of several days, whereas for MSS it can vary from minutes up to
hundreds of years. We have shown that by means of the high resolving power and
sensitivity, the European Southern Observatory's Very Large Telescope can
detect either cold ($T=300$ K) or hot ($T=4000$ K) megastructures throughout
the whole galaxy even for very small oscillation amplitudes of rings. As it is
shown the observed variability can have characteristics similar to those of
some other variables, which can be considered as one of the resolutions of the
WFP.


  We consider self-interacting scalar fields with a conformal coupling in the
dS background and study the quantum corrections from bubble loop diagrams.
Incorporating the perturbative in-in formalism, we calculate the quantum
corrections in the vacuum zero point energy and pressure of self-interacting
fields with the potential $V \propto \Phi^n $ for even values of $n$. We
calculate the equation of state corresponding to these quantum corrections and
examine the scaling of the divergent terms in the vacuum zero point energy and
pressure associated to the dimensional regularization scheme. In particular, we
show that for quartic self-interacting scalar field the conformal invariance is
respected at two-loop order at the conformal point.


  Vision-and-language navigation (VLN) agents are trained to navigate in
real-world environments by following natural language instructions. A major
challenge in VLN is the limited availability of training data, which hinders
the models' ability to generalize effectively. Previous approaches have
attempted to address this issue by introducing additional supervision during
training, often requiring costly human-annotated data that restricts
scalability. In this paper, we introduce a masked path modeling (MPM)
objective, which pretrains an agent using self-collected data for downstream
navigation tasks. Our proposed method involves allowing the agent to actively
explore navigation environments without a specific goal and collect the paths
it traverses. Subsequently, we train the agent on this collected data to
reconstruct the original path given a randomly masked subpath. This way, the
agent can actively accumulate a diverse and substantial amount of data while
learning conditional action generation. To evaluate the effectiveness of our
technique, we conduct experiments on various VLN datasets and demonstrate the
versatility of MPM across different levels of instruction complexity. Our
results exhibit significant improvements in success rates, with enhancements of
1.32\%, 1.05\%, and 1.19\% on the val-unseen split of the Room-to-Room,
Room-for-Room, and Room-across-Room datasets, respectively. Furthermore, we
conduct an analysis that highlights the potential for additional improvements
when the agent is allowed to explore unseen environments prior to testing.


  Many-body localization (MBL) in a one-dimensional Fermi Hubbard model with
random on-site interactions is studied. While for this model all
single-particle states are trivially delocalized, it is shown that for
sufficiently strong disordered interactions the model is many-body localized.
It is therefore argued that MBL does not necessary rely on localization of the
single-particle spectrum. This model provides a convenient platform to study
pure MBL phenomenology, since Anderson localization in this model does not
exist. By examining various forms of the interaction term a dramatic effect of
symmetries on charge transport is demonstrated. A possible realization in a
cold atom experiments is proposed.


  Precision studies of the Higgs boson at future $e^+e^-$ colliders can help to
shed light on fundamental questions related to electroweak symmetry breaking,
baryogenesis, the hierarchy problem, and dark matter. The main production
process, $e^+e^- \to HZ$, will need to be controlled with sub-percent
precision, which requires the inclusion of next-to-next-to-leading order (NNLO)
electroweak corrections. The most challenging class of diagrams are planar and
non-planar double-box topologies with multiple massive propagators in the
loops. This article proposes a technique for computing these diagrams
numerically, by transforming one of the sub-loops through the use of Feynman
parameters and a dispersion relation, while standard one-loop formulae can be
used for the other sub-loop. This approach can be extended to deal with tensor
integrals. The resulting numerical integrals can be evaluated in minutes on a
single CPU core, to achieve about 0.1% relative precision.


  A new hybrid experiment has been started by AS{\gamma} experiment at Tibet,
China, since August 2011, which consists of a low threshold burst-detector-grid
(YAC-II, Yangbajing Air shower Core array), the Tibet air-shower array
(Tibet-III) and a large underground water Cherenkov muon detector (MD). In this
paper, the capability of the measurement of the chemical components (proton,
helium and iron) with use of the (Tibet-III+YAC-II) is investigated by means of
an extensive Monte Carlo simulation in which the secondary particles are
propagated through the (Tibet-III+YAC-II) array and an artificial neural
network (ANN) method is applied for the primary mass separation. Our simulation
shows that the new installation is powerful to study the chemical compositions,
in particular, to obtain the primary energy spectrum of the major component at
the knee.


  Text-to-Image models such as Stable Diffusion have shown impressive image
generation synthesis, thanks to the utilization of large-scale datasets.
However, these datasets may contain sexually explicit, copyrighted, or
undesirable content, which allows the model to directly generate them. Given
that retraining these large models on individual concept deletion requests is
infeasible, fine-tuning algorithms have been developed to tackle concept
erasing in diffusion models. While these algorithms yield good concept erasure,
they all present one of the following issues: 1) the corrupted feature space
yields synthesis of disintegrated objects, 2) the initially synthesized content
undergoes a divergence in both spatial structure and semantics in the generated
images, and 3) sub-optimal training updates heighten the model's susceptibility
to utility harm. These issues severely degrade the original utility of
generative models. In this work, we present a new approach that solves all of
these challenges. We take inspiration from the concept of classifier guidance
and propose a surgical update on the classifier guidance term while
constraining the drift of the unconditional score term. Furthermore, our
algorithm empowers the user to select an alternative to the erasing concept,
allowing for more controllability. Our experimental results show that our
algorithm not only erases the target concept effectively but also preserves the
model's generation capability.


  In this paper, a dense Internet of Things (IoT) monitoring system is studied
in which a large number of devices contend for transmitting timely status
packets to their corresponding receivers over wireless noisy channels, using a
carrier sense multiple access (CSMA) scheme. When each device completes one
transmission, due to possible transmission failure, two cases with and without
transmission feedback to each device must be considered. Particularly, for the
case with no feedback, the device uses policy (I): It will go to an idle state
and release the channel regardless of the outcome of the transmission. For the
case with perfect feedback, if the transmission succeeds, the device will go to
an idle state, otherwise, it uses either policy (W), i.e., it will go to a
waiting state and re-contend for channel access; or it uses policy (S), i.e.,
it will stay at a service state and occupy this channel to attempt another
transmission. For those three policies, the closed-form expressions of the
average age of information (AoI) of each device are characterized under schemes
with and without preemption in service. It is shown that, for each policy, the
scheme with preemption in service always achieves a smaller average AoI,
compared with the scheme without preemption. Then, a mean-field approximation
approach with guaranteed accuracy is developed to analyze the asymptotic
performance for the considered system with an infinite number of devices and
the effects of the system parameters on the average AoI are characterized.
Simulation results show that the proposed mean-field approximation is accurate
even for a small number of devices. The results also show that policy (S)
achieves the smallest average AoI compared with policies (I) and (W), and the
average AoI does not always decrease with the arrival rate for all three
policies.


  We present a review on the self-resonant dark matter scenarios where multiple
components of dark matter give rise to a resonant condition in the $u$-channel
diagrams for their comparable masses. In this case, there is no need of lighter
mediators for enhancing the self-scattering and annihilation cross sections for
dark matter. We discuss the velocity-dependent self-scattering for the
small-scale problems, the relic density of self-resonant dark matter, and the
observable signatures in indirect and detection experiments.


  Resonances in an electric circuit occur when capacitive and inductive
components are present together. Such resonances appear in admittance
measurements depending on the circuit's parameters and the driving AC
frequency. In this study, we analyze the impedance characteristics of
nontrivial topolectrical circuits such as one- and two-dimensional
Su-Schrieffer-Heeger circuits and reveal that size-dependent anomalous
impedance resonances inevitably arise in finite $LC$ circuits. Through the
\textit{method of images}, we study how resonance modes in a multi-dimensional
circuit array can be nontrivially modified by the reflection and interference
of current from the structure and boundaries of the lattice. We derive analytic
expressions for the impedance across two corner nodes of various lattice
networks with homogeneous and heterogeneous circuit elements. We also derive
the irregular dependency of the impedance resonance on the lattice size, and
provide integral and dimensionally-reduced expressions for the impedance in
three dimensions and above.


  Data from the Michelson Doppler Imager (MDI) and Helioseismic and Magnetic
Imager (HMI) are analyzed from 1996 to 2023 to investigate tilt angles
($\gamma$) of bipolar magnetic regions and Joy's Law for Cycles 23, 24, and a
portion of 25. The HMI radial magnetic field ($B_{r}$) and MDI magnetogram
($B_{los}$) data are used to calculate ($\gamma$) using the flux-weighted
centroids of the positive and negative polarities. Each AR is only sampled
once. The analysis includes only Beta ($\beta$)-class active regions since
computing $\gamma$ of complex active regions is less meaningful. During the
emergence of the ARs, we find that the average tilt angle ($\bar{\gamma}$)
increases from 3.30$^{\circ}\pm$0.75 when 20\% of the flux has emerged to
6.79$^{\circ}\pm$0.66 when the ARs are at their maximum flux. Cycle 24 had a
larger average tilt $\bar{\gamma}_{24}$=6.67$\pm$0.66 than Cycle 23,
$\bar{\gamma}_{23}$=5.11$\pm$0.61. There are persistent differences in
$\bar{\gamma}$ in the hemispheres with the southern hemisphere having higher
${\bar{\gamma}}$ in Cycles 23 and 24 but the errors are such that these
differences are not statistically significant.


  Using Monte Carlo simulation, we studied the electrical conductivity of
two-dimensional films. The films consisted of a poorly conductive host matrix
and highly conductive rod-like fillers (rods). The rods were of various lengths
fitting a log-normal distribution. They could be aligned along a direction.
Special attention was paid to films having completely aligned rods. The impact
of length dispersity and the extent of rod alignment on the
insulator-to-conductor phase transition was studied. The greater the length
dispersity the smaller the critical concentration. The anisotropy of the
electrical conductivity was more pronounced in the vicinity of the phase
transition. A finite size effect was found to be significant only in the
vicinity of the phase transition.


  Spatial and channel re-calibration have become powerful concepts in computer
vision. Their ability to capture long-range dependencies is especially useful
for those networks that extract local features, such as CNNs. While
re-calibration has been widely studied for image analysis, it has not yet been
used on shape representations. In this work, we introduce re-calibration
modules on deep neural networks for 3D point clouds. We propose a set of
re-calibration blocks that extend Squeeze and Excitation blocks and that can be
added to any network for 3D point cloud analysis that builds a global
descriptor by hierarchically combining features from multiple local
neighborhoods. We run two sets of experiments to validate our approach. First,
we demonstrate the benefit and versatility of our proposed modules by
incorporating them into three state-of-the-art networks for 3D point cloud
analysis: PointNet++, DGCNN, and RSCNN. We evaluate each network on two tasks:
object classification on ModelNet40, and object part segmentation on ShapeNet.
Our results show an improvement of up to 1% in accuracy for ModelNet40 compared
to the baseline method. In the second set of experiments, we investigate the
benefits of re-calibration blocks on Alzheimer's Disease (AD) diagnosis. Our
results demonstrate that our proposed methods yield a 2% increase in accuracy
for diagnosing AD and a 2.3% increase in concordance index for predicting AD
onset with time-to-event analysis. Concluding, re-calibration improves the
accuracy of point cloud architectures, while only minimally increasing the
number of parameters.


  In connection with the discussion and the measurements fulfilled in [R. de
Sangro et al., Eur. Phys. J. C 75 (2015) 137], the full identity is
demonstrated between the Feynman formula for the field of a moving charge and
the Lienard-Wiechert potentials.


  We briefly introduce the generic framework of Disordered Elastic Systems
(DES), giving a short `recipe' of a DES modeling and presenting the quantities
of interest in order to probe the static and dynamical disorder-induced
properties of such systems. We then focus on a particular low-dimensional DES,
namely the one-dimensional interface in short-ranged elasticity and
short-ranged quenched disorder. Illustrating different elements given in the
introductory sections, we discuss specifically the consequences of the
interplay between a finite temperature T>0 and a finite interface width \xi>0
on the static geometrical fluctuations at different lengthscales, and the
implications on the quasistatic dynamics.


  The destiny of complex organic molecules (COMs) in star-forming regions is
interlinked with various evolutionary phases. Therefore, identifying these
species in diversified environments of identical star-forming regions would
help to comprehend their physical and chemical heritage. We identified multiple
COMs utilizing the Large Program `Astrochemical Surveys At IRAM' (ASAI) data,
dedicated to chemical surveys in Sun-like star-forming regions with the IRAM 30
m telescope. It was an unbiased survey in the millimetre regime, covering the
prestellar core, protostar, outflow region, and protoplanetary disk phase.
Here, we have reported some transitions of seven COMs, namely, methanol
(CH3OH), acetaldehyde (CH3CHO), methyl formate (CH3OCHO), ethanol (C2H5OH),
propynal (HCCCHO), dimethyl ether (CH3OCH3), and methyl cyanide (CH3CN) in some
sources L1544, B1-b, IRAS4A, and SVS13A. We found a trend among these species
from the derived abundances using the rotational diagram method and MCMC fit.
We have found that the abundances of all of the COMs, except for HCCCHO,
increase from the L1544 (prestellar core) and peaks at IRAS16293-2422 (class 0
phase). It is noticed that the abundance of these molecules correlate with the
luminosity of the sources. The obtained trend is also visible from the previous
interferometric observations and considering the beam dilution effect.


  We propose new parametric frameworks of regression analysis with the
conditional mode of a bounded response as the focal point of interest.
Covariate effects estimation and prediction based on the maximum likelihood
method under two new classes of regression models are demonstrated. We also
develop graphical and numerical diagnostic tools to detect various sources of
model misspecification. Predictions based on different central tendency
measures inferred using various regression models are compared using synthetic
data in simulations. Finally, we conduct regression analysis for data from the
Alzheimer's Disease Neuroimaging Initiative to demonstrate practical
implementation of the proposed methods. Supplementary materials that contain
technical details, and additional simulation and data analysis results are
available online.


  We present the first ground-based detection of thermal emission from an
exoplanet in the H-band. Using HAWK-I on the VLT, we observed an occultation of
WASP-19b by its G8V-type host star. WASP-19b is a Jupiter-mass planet with an
orbital period of only 19 h, and thus, being highly irradiated, is expected to
be hot. We measure an H-band occultation depth of (0.259 +0.046 -0.044) %,
which corresponds to an H-band brightness temperature of T_H = 2580 +/- 125 K.
A cloud-free model of the planet's atmosphere, with no redistribution of energy
from day-side to night-side, under predicts the planet/star flux density ratio
by a factor of two. As the stellar parameters, and thus the level of planetary
irradiation, are well-constrained by measurement, it is likely that our model
of the planet's atmosphere is too simple.


  Choosing and developing performant database solutions helps organizations
optimize their operational practices and decision-making. Since graph data is
becoming more common, it is crucial to develop and use them in big data with
complex relationships with high and consistent performance. However, legacy
database technologies such as MySQL are tailored to store relational databases
and need to perform more complex queries to retrieve graph data. Previous
research has dealt with performance aspects such as CPU and memory usage. In
contrast, energy usage and temperature of the servers are lacking. Thus, this
paper evaluates and compares state-of-the-art graphs and relational databases
from the performance aspects to allow a more informed selection of
technologies. Graph-based big data applications benefit from informed selection
database technologies for data retrieval and analytics problems. The results
show that Neo4j performs faster in querying connected data than MySQL and
ArangoDB, and energy, CPU, and memory usage performances are reported in this
paper.


  The generalized Langevin equation (GLE) is a stochastic integro-differential
equation that has been used to describe the velocity of microparticles in
viscoelastic fluids. In this work, we consider the large-time asymptotic
properties of a Markovian approximation to the GLE in the presence of a wide
class of external potential wells. The qualitative behavior of the GLE is
largely determined by its memory kernel $K$, which summarizes the delayed
response of the fluid medium on the particles past movement. When $K$ can be
expressed as a finite sum of exponentials, it has been shown that long-term
time-averaged properties of the position and velocity do not depend on $K$ at
all. In certain applications, however, it is important to consider the GLE with
a power law memory kernel. Using the fact that infinite sums of exponentials
can have power law tails, we study the infinite-dimensional version of the
Markovian GLE in a potential well. In the case where the memory kernel $K$ is
integrable (i.e. in the asymptotically diffusive regime), we are able to extend
previous results and show that there is a unique stationary distribution for
the GLE system and that the long-term statistics of the position and velocity
do not depend on $K$. However, when $K$ is not integrable (i.e. in the
asymptotically subdiffusive regime), we are able to show the existence of an
invariant probability measure but uniqueness remains an open question. In
particular, the method of asymptotic coupling used in the integrable case to
show uniqueness does not apply when $K$ fails to be integrable.


  For the two-user MISO broadcast channel with imperfect and delayed channel
state information at the transmitter (CSIT), the work explores the tradeoff
between performance on the one hand, and CSIT timeliness and accuracy on the
other hand. The work considers a broad setting where communication takes place
in the presence of a random fading process, and in the presence of a feedback
process that, at any point in time, may provide CSIT estimates - of some
arbitrary accuracy - for any past, current or future channel realization. This
feedback quality may fluctuate in time across all ranges of CSIT accuracy and
timeliness, ranging from perfectly accurate and instantaneously available
estimates, to delayed estimates of minimal accuracy. Under standard
assumptions, the work derives the degrees-of-freedom (DoF) region, which is
tight for a large range of CSIT quality. This derived DoF region concisely
captures the effect of channel correlations, the accuracy of predicted,
current, and delayed-CSIT, and generally captures the effect of the quality of
CSIT offered at any time, about any channel.
  The work also introduces novel schemes which - in the context of imperfect
and delayed CSIT - employ encoding and decoding with a phase-Markov structure.
The results hold for a large class of block and non-block fading channel
models, and they unify and extend many prior attempts to capture the effect of
imperfect and delayed feedback. This generality also allows for consideration
of novel pertinent settings, such as the new periodically evolving feedback
setting, where a gradual accumulation of feedback bits progressively improves
CSIT as time progresses across a finite coherence period.


  The interactions between $e_g$ orbitals in neighboring sites are investigated
in LaMnO$_3$ by taking into account virtual exchange of electrons and phonons.
The spin and orbital ordering temperatures and the spin wave dispersion
relation are calculated. We find that the orbital ordering is mainly caused by
the electronic interactions and that the Jahn-Teller coupling is much smaller
than that reported previously. We propose that the elastic constant shows a
characteristic change at the N{\'e}el temperature by the spin and orbital
coupling and the higher-order Jahn-Teller coupling.


  Learning skills by imitation is a promising concept for the intuitive
teaching of robots. A common way to learn such skills is to learn a parametric
model by maximizing the likelihood given the demonstrations. Yet, human
demonstrations are often multi-modal, i.e., the same task is solved in multiple
ways which is a major challenge for most imitation learning methods that are
based on such a maximum likelihood (ML) objective. The ML objective forces the
model to cover all data, it prevents specialization in the context space and
can cause mode-averaging in the behavior space, leading to suboptimal or
potentially catastrophic behavior. Here, we alleviate those issues by
introducing a curriculum using a weight for each data point, allowing the model
to specialize on data it can represent while incentivizing it to cover as much
data as possible by an entropy bonus. We extend our algorithm to a Mixture of
(linear) Experts (MoE) such that the single components can specialize on local
context regions, while the MoE covers all data points. We evaluate our approach
in complex simulated and real robot control tasks and show it learns from
versatile human demonstrations and significantly outperforms current SOTA
methods. A reference implementation can be found at
https://github.com/intuitive-robots/ml-cur


  The incidence and properties of present-day dwarf galaxies hosting massive
black holes (BHs) can provide important constraints on the origin of
high-redshift BH seeds. Here we present high-resolution X-ray and radio
observations of the low-metallicity, star-forming, dwarf-galaxy system Mrk 709
with the Chandra X-ray Observatory and the Karl G. Jansky Very Large Array.
These data reveal spatially coincident hard X-ray and radio point sources with
luminosities suggesting the presence of an accreting massive BH (M_BH ~
10^5-10^7 Msun). Based on imaging from the Sloan Digital Sky Survey (SDSS), we
find that Mrk 709 consists of a pair of compact dwarf galaxies that appear to
be interacting with one another. The position of the candidate massive BH is
consistent with the optical center of the southern galaxy (Mrk 709 S), while no
evidence for an active BH is seen in the northern galaxy (Mrk 709 N). We derive
stellar masses of M ~ 2.5 x 10^9 Msun and M ~ 1.1 x 10^9 Msun for Mrk 709 S and
Mrk 709 N, respectively, and present an analysis of the SDSS spectrum of the
BH-host Mrk 709 S. At a metallicity of just ~10% solar, Mrk 709 is among the
most metal-poor galaxies with evidence for an active galactic nucleus.
Moreover, this discovery adds to the growing body of evidence that massive BHs
can form in dwarf galaxies and that deep, high-resolution X-ray and radio
observations are ideally suited to reveal accreting massive BHs hidden at
optical wavelengths.


  In this paper, we propose a fast, well-performing, and consistent method for
segmenting a piecewise-stationary, linear time series with an unknown number of
breakpoints. The time series model we use is the nonparametric Locally
Stationary Wavelet model, in which a complete description of the
piecewise-stationary second-order structure is provided by wavelet periodograms
computed at multiple scales and locations. The initial stage of our method is a
new binary segmentation procedure, with a theoretically justified and rapidly
computable test criterion that detects breakpoints in wavelet periodograms
separately at each scale. This is followed by within-scale and across-scales
post-processing steps, leading to consistent estimation of the number and
locations of breakpoints in the second-order structure of the original process.
An extensive simulation study demonstrates good performance of our method.


  We present an approximate semiclassical (SC) framework for mixed quantized
dynamics in Wigner phase space in a two-part series. In the first article, we
introduced the Adiabatic Hybrid Wigner Dynamics (AHWD) method that allows for a
few important `system' degrees of freedom to be quantized using high-level
Double Herman-Kluk SC theory while describing the rest (the `bath') using
classical-limit Linearized SC theory. In this second article, we extend our
hybrid Wigner dynamics to nonadiabatic processes. The resulting Nonadiabatic
Hybrid Wigner Dynamics (NHWD) has two variants that differ in the choice of
degrees of freedom to be quantized. Specifically, we introduce NHWD(E) where
only the electronic state variables are quantized and the NHWD(V) where both
electronic state variables and a handful of strongly coupled nuclear modes are
quantized. We show that while NHWD(E) proves accurate for a wide range of
scattering models and spin-boson models, systems where a few nuclear modes are
strongly coupled to electronic states require NHWD(V) to accurately capture the
long-time dynamics. Taken together, we show that AHWD and NHWD represent a new
framework for SC simulations of high-dimensional systems with significant
quantum effects.


  The Generative Adversarial Networks (GAN) framework is a well-established
paradigm for probability matching and realistic sample generation. While recent
attention has been devoted to studying the theoretical properties of such
models, a full theoretical understanding of the main building blocks is still
missing. Focusing on generative models with Optimal Transport metrics as
discriminators, in this work we study how the interplay between the latent
distribution and the complexity of the pushforward map (generator) affects
performance, from both statistical and modelling perspectives. Motivated by our
analysis, we advocate learning the latent distribution as well as the
pushforward map within the GAN paradigm. We prove that this can lead to
significant advantages in terms of sample complexity.


  The gap between classical mechanics and quantum mechanics has an important
interpretive implication: the Universe must have an irreducible fundamental
level, which determines the properties of matter at higher levels of
organization. We show that the main parameters of any fundamental model must be
theory-independent. They cannot be predicted, because they cannot have internal
causes. However, it is possible to describe them in the language of classical
mechanics. We invoke philosophical reasons in favor of a specific model, which
treats particles as sources of real waves. Experimental considerations for
gravitational, electromagnetic, and quantum phenomena are outlined.


  An encoder wishes to minimize the bit rate necessary to guarantee that a
decoder is able to calculate a symbol-wise function of a sequence available
only at the encoder and a sequence that can be measured only at the decoder.
This classical problem, first studied by Yamamoto, is addressed here by
including two new aspects: (i) The decoder obtains noisy measurements of its
sequence, where the quality of such measurements can be controlled via a
cost-constrained "action" sequence, which is taken at the decoder or at the
encoder; (ii) Measurement at the decoder may fail in a way that is
unpredictable to the encoder, thus requiring robust encoding. The considered
scenario generalizes known settings such as the Heegard-Berger-Kaspi and the
"source coding with a vending machine" problems. The rate-distortion-cost
function is derived in relevant special cases, along with general upper and
lower bounds. Numerical examples are also worked out to obtain further insight
into the optimal system design.


  The topological states of matters arising from the nontrivial magnetic
configuration provide a better understanding of physical properties and
functionalities of solid materials. Such studies benefit from the active
control of spin orientation in any solid, which is yet known to rarely take
place in the two-dimensional (2D) limit. Here we demonstrate by the
first-principles calculations that spin-orientation dependent topological
states can appear in the geometrically frustrated monolayer antiferromagnet.
Different topological states including quantum anomalous Hall (QAH) effect and
time-reversal-symmetry (TRS) broken quantum spin Hall (QSH) effect can be
obtained by changing spin orientation in the NiTl2S4 monolayer. Remarkably, the
dilated nc-AFM NiTl2S4 monolayer gives birth to the QAH effect with hitherto
reported largest number of quantized conducting channels (Chern number C = -4)
in 2D materials. Interestingly, under tunable chemical potential, the nc-AFM
NiTl2S4 monolayer hosts a novel state supporting the coexistence of QAH and TRS
broken QSH effects with a Chern number C = 3 and spin Chern number C_s = 1.
This work manifests a promising concept and material realization toward
topological spintronics in 2D antiferromagnets by manipulating its spin degree
of freedom.


  We present infrared H- and K-band spectra of a companion candidate 3" north
of the young star GSC 08047-00232, a probable member of the nearby young
Horologium association. From previously obtained JHK-band colors and the
magnitude difference between primary and companion candidate, the latter could
well be substellar (Neuh"auser et al. 2003) with the spectral type being
roughly M7-L9 from the JHK colors (Chauvin et al. 2003). With the H- and K-band
spectra now obtained with ISAAC at the VLT, the spectral type of the companion
candidate is found to be M6-9.5. Assuming the same age and distance as for the
primary star (~35 Myrs, 50 to 85 pc), this yields a mass of ~25 Jupiter masses
for the companion, hence indeed substellar. After TWA-5 B and HR 7329 B, this
is the third brown dwarf companion around a nearby (up to 100 pc) young (up to
100 Myrs) star. A total of three confirmed brown dwarf companions (any mass,
separation above 50 AU) around 79 stars surveyed in three young nearby
associations corresponds to a frequency of 6 \pm 4 % (with a correction for
missing companions which are almost on the same line-of-sight as the primary
star instead of being separated well), consistent with the expectation, if
binaries have the same mass function as field stars. Hence, it seems that there
is no brown dwarf desert at wide separations.


  In recent studies in hyperspectral imaging, biometrics and energy analytics,
the framework of deep dictionary learning has shown promise. Deep dictionary
learning outperforms other traditional deep learning tools when training data
is limited; therefore hyperspectral imaging is one such example that benefits
from this framework. Most of the prior studies were based on the unsupervised
formulation; and in all cases, the training algorithm was greedy and hence
sub-optimal. This is the first work that shows how to learn the deep dictionary
learning problem in a joint fashion. Moreover, we propose a new discriminative
penalty to the said framework. The third contribution of this work is showing
how to incorporate stochastic regularization techniques into the deep
dictionary learning framework. Experimental results on hyperspectral image
classification shows that the proposed technique excels over all
state-of-the-art deep and shallow (traditional) learning based methods
published in recent times.


  This paper is concerned with relationships of Lyapunov exponents with
sensitivity and stability for non-autonomous discrete systems. Some new
concepts are introduced for non-autonomous discrete systems, including Lyapunov
exponents, strong sensitivity at a point and in a set, Lyapunov stability, and
exponential asymptotical stability. It is shown that the positive Lyapunov
exponent at a point implies strong sensitivity for a class of non-autonomous
discrete systems. Furthermore, the uniformly positive Lyapunov exponents in a
totally invariant set imply strong sensitivity in this set under certain
conditions. It is also shown that the negative Lyapunov exponent at a point
implies exponential asymptotical stability for a class of non-autonomous
discrete systems. The related existing results for autonomous discrete systems
are generalized to non-autonomous discrete systems and their conditions are
weaken. One example is provided for illustration.


  This paper presents fifteen problems about mapping class groups. It is an
expanded and updated version of the author's preprint "Ten problems on the
mapping class groups". The paper will appear in the book "Problems on Mapping
Class Groups and Related Topics", ed. by B. Farb, Proc. Symp. Pure Math.
series, Amer. Math. Soc.


  Transmitting messages in the most efficient way as possible has always been
one of politicians main concerns during electoral processes. Due to the rapidly
growing number of users, online social networks have become ideal platforms for
politicians to interact with their potential voters. Exploiting the available
potential of these tools to maximize their influence over voters is one of
politicians actual challenges. To step in this direction, we have analyzed the
user activity in the online social network Twitter, during the 2011 Spanish
Presidential electoral process, and found that such activity is correlated with
the election results. We introduce a new measure to study political support in
Twitter, which we call the Relative Support. We have also characterized user
behavior by analyzing the structural and dynamical patterns of the complex
networks emergent from the mention and retweet networks. Our results suggest
that the collective attention is driven by a very small fraction of users.
Furthermore we have analyzed the interactions taking place among politicians,
observing a lack of debate. Finally we develop a network growth model to
reproduce the interactions taking place among politicians.


  We study direct limits $(G,K) = \varinjlim (G_n,K_n)$ of Gelfand pairs of the
form $G_n = N_n\rtimes K_n$ with $N_n$ nilpotent, in other words pairs
$(G_n,K_n)$ for which $G_n/K_n$ is a commutative nilmanifold. First, we extend
the criterion of \cite{W3} for a direct limit representation to be multiplicity
free. Then we study direct limits $G/K = \varinjlim G_n/K_n$ of commutative
nilmanifolds and look to see when the regular representation of $G = \varinjlim
G_n$ on an appropriate Hilbert space $\varinjlim L^2(G_n/K_n)$ is multiplicity
free. One knows that the $N_n$ are commutative or 2--step nilpotent. In many
cases where the derived algebras $[\gn_n,\gn_n]$ are of bounded dimension we
construct $G_n$--equivariant isometric maps $\zeta_n : L^2(G_n/K_n) \to
L^2(G_{n+1}/K_{n+1})$ and prove that the left regular representation of $G$ on
the Hilbert space $L^2(G/K) := \varinjlim \{L^2(G_n/K_n),\zeta_n\}$ is a
multiplicity free direct integral of irreducible unitary representations. The
direct integral and its irreducible constituents are described explicitly. One
constituent of our argument is an extension of the classical Peter--Weyl
Theorem to parabolic direct limits of compact groups.


  We theoretically studied the quasiparticle transport in a 2D electron gas
biased in the quantum Hall regime and in the presence of a lateral potential
barrier. The lateral junction hosts the specific magnetic field dependent
quasiparticle states highly localized in the transverse direction. The quantum
tunnelling across the barrier provides a complex bands structure of a
one-dimensional energy spectrum of these bound states, $\epsilon_n(p_y)$, where
$p_y$ is the electron momentum in the longitudinal direction $y$. Such a
spectrum manifests itself by a large number of peaks and drops in the
dependence of the magnetic edge states transmission coefficient $D(E)$ on the
electron energy $E$. E.g., the high value of $D$ occurs as soon as the electron
energy $E$ reaches gaps in the spectrum. These peaks and drops of $D(E)$ result
in giant oscillations of the transverse conductance $G_x$ with the magnetic
field and/or the transport voltage. Our theoretical analysis based on the
coherent macroscopic quantum superposition of the bound states and the magnetic
edge states propagating along the system boundaries, is in a good accord with
the experimental observations found in Ref. W. Kang et al., Letters to Nature,
403, 59 (2000).


  This work investigates the existence and bifurcation structure of multi-pulse
steady-state solutions to bistable lattice dynamical systems. Such solutions
are characterized by multiple compact disconnected regions where the solution
resembles one of the bistable states and resembles another trivial bistable
state outside of these compact sets. It is shown that the bifurcation curves of
these multi-pulse solutions lie along closed and bounded curves (isolas), even
when single-pulse solutions lie along unbounded curves. These results are
applied to a discrete Nagumo differential equation and we show that the
hypotheses of this work can be confirmed analytically near the anti-continuum
limit. Results are demonstrated with a number of numerical investigations.


  We discuss the use of field theoretical techniques in the lattice
determination of the free energy dependence on the $\theta$ angle in SU(N)
Yang-Mills theories.


  Very little has been explored about the narrative as a process when
constructing entertainment for interactive media. Simultaneously, the interest
in narrative vehicles increases while certain occupations, seeing the narrative
as a structure, obscure the examination of the process of selecting, arranging
and rendering story material. To correct this deficiency, a method for a
narrative bridging that encourages research and design while exploring
narration as a process, is proposed with the aim to not diminish the properties
of the interactive media. This method focuses on the initial phase where
establishing and handling the information takes place and creates a foundation
that precedes its systematization and computation. The aim is to give designers
a comfortable design tool that firmly aids the design without interfering with
creativity, and at the same time aids the construction of interplay between
narration, spatiality and interactivity. The method aided the practise of a
discipline that was established through a qualitative study conducted as part
of a university course in rapid prototyping. The results demonstrated that the
method aided time-constrained design processes, simultaneously detecting
inconsistencies that would prevent the team from making improvements. The
method gave the team a shared vocabulary and outlook, allowing them to progress
without interfering with the creative flow. This enabled the team to reason
about the process and easily advice design stakeholders. The study also
provides directions for future developments within research of narrative
processes in game design.


  We prove two explicit formulae for one-part double Hurwitz numbers with
completed 3-cycles. We define "combinatorial Hodge integrals" from these
numbers in the spirit of the celebrated ELSV formula. The obtained results
imply some explicit formulae and properties of the combinatorial Hodge
integrals.


  We report an absolute calibration of the ionization yields($\textit{Q$_y$})$
and fluctuations for electronic recoil events in liquid xenon at discrete
energies between 186 eV and 33.2 keV. The average electric field applied across
the liquid xenon target is 180 V/cm. The data are obtained using low energy
$^{127}$Xe electron capture decay events from the 95.0-day first run from LUX
(WS2013) in search of Weakly Interacting Massive Particles (WIMPs). The
sequence of gamma-ray and X-ray cascades associated with $^{127}$I
de-excitations produces clearly identified 2-vertex events in the LUX detector.
We observe the K- (binding energy, 33.2 keV), L- (5.2 keV), M- (1.1 keV), and
N- (186 eV) shell cascade events and verify that the relative ratio of observed
events for each shell agrees with calculations. The N-shell cascade analysis
includes single extracted electron (SE) events and represents the lowest-energy
electronic recoil $\textit{in situ}$ measurements that have been explored in
liquid xenon.


  The Balloon-borne Large Aperture Submillimeter Telescope (BLAST) operated
successfully during a 250-hour flight over Antarctica in December 2006
(BLAST06). As part of the calibration and pointing procedures, the red
hypergiant star VY CMa was observed and used as the primary calibrator. Details
of the overall BLAST06 calibration procedure are discussed. The 1-sigma
absolute calibration is accurate to 10, 12, and 13% at the 250, 350, and 500
micron bands, respectively. The errors are highly correlated between bands
resulting in much lower error for the derived shape of the 250-500 micron
continuum. The overall pointing error is <5" rms for the 36, 42, and 60" beams.
The performance of the optics and pointing systems is discussed.


  The endpoint Strichartz estimate $\| e^{it\Delta} f \|_{L^2_t L^\infty_x(\R
\times \R^2)} \lesssim \|f\|_{L^2_x(\R^2)}$ is known to be false by the work of
Montgomery-Smith, despite being only ``logarithmically far'' from being true in
some sense. In this short note we show that (in sharp constrast to the
$L^p_{t,x}$ Strichartz estimates) the situation is not improved by passing to a
bilinear setting; more precisely, if $P, P'$ are non-trivial smooth Fourier
cutoff multipliers then we show that the bilinear estimate $$\| (e^{it\Delta} P
f) (e^{it\Delta} P' g) \|_{L^2_t L^\infty_x(\R \times \R^2)} \lesssim
\|f\|_{L^2_x(\R^2)} \|g\|_{L^2_x(\R^2)} $$ fails even when $P$, $P'$ have
widely separated supports.


  We employ gravitational-wave radiometry to map the gravitational waves
stochastic background expected from a variety of contributing mechanisms and
test the assumption of isotropy using data from Advanced LIGO's first observing
run. We also search for persistent gravitational waves from point sources with
only minimal assumptions over the 20 - 1726 Hz frequency band. Finding no
evidence of gravitational waves from either point sources or a stochastic
background, we set limits at 90% confidence. For broadband point sources, we
report upper limits on the gravitational wave energy flux per unit frequency in
the range $F_{\alpha,\Theta}(f) < (0.1 - 56) \times 10^{-8}$ erg cm$^{-2}$
s$^{-1}$ Hz$^{-1}$ (f/25 Hz)$^{\alpha-1}$ depending on the sky location
$\Theta$ and the spectral power index $\alpha$. For extended sources, we report
upper limits on the fractional gravitational wave energy density required to
close the Universe of $\Omega(f,\Theta) < (0.39-7.6) \times 10^{-8}$ sr$^{-1}$
(f/25 Hz)$^\alpha$ depending on $\Theta$ and $\alpha$. Directed searches for
narrowband gravitational waves from astrophysically interesting objects
(Scorpius X-1, Supernova 1987 A, and the Galactic Center) yield median
frequency-dependent limits on strain amplitude of $h_0 <$ (6.7, 5.5, and 7.0)
$\times 10^{-25}$ respectively, at the most sensitive detector frequencies
between 130 - 175 Hz. This represents a mean improvement of a factor of 2
across the band compared to previous searches of this kind for these sky
locations, considering the different quantities of strain constrained in each
case.


  We present results from studies of high temperature QCD with two flavors of
Kogut-Susskind quarks on $16^3\times 8$ lattices at a quark mass of
$am_q=0.00625$ and on $24^3\times 12$ lattices at quark masses $am_q=0.008$ and
0.016. The value of the crossover temperature is consistent with that obtained
on coarser lattices and/or at larger quark masses. Results are presented for
the chiral order parameter and for the baryon number susceptibility.


  In this paper we present a modification to the recently proposed transmission
F-scan technique, the differential F-scan technique. In differential F-scan
technique the programmed focal distance in the electronic-tunable lens
oscillates, allowing the light detector of the setup to record a signal
proportional to the derivative of the signal recorded with an F-scan. As for
the differential Z-scan a background-free signal is obtained, but also the
optical setup is simplified and the available laser power is double. We also
present and validate a new fitting-procedure protocol that increments the
accuracy of the technique. Finally, we show that fitting a signal from
differential F-scan or the derivate of the signal of transmission F-scan is
more accurate than simply fitting the signal from F-scan directly. Results from
two-photon absorption at 790 nm of CdS, ZeSe and CdSe are presented.


  Context. The near-Earth orbital space is shared by natural objects and space
debris that can be temporarily captured in geocentric orbits. Short-term
natural satellites are often called mini-moons. Reflectance spectroscopy can
determine the true nature of transient satellites because the spectral
signatures of spacecraft materials and near-Earth asteroids (NEAs) are
different. The recently discovered object 2022 NX1 follows an Earth-like orbit
that turns it into a recurrent but ephemeral Earth companion. It has been
suggested that 2022 NX1 could have an artificial origin or be lunar ejecta.
  Aims. Here, we use reflectance spectroscopy and N-body simulations to
determine the nature and actual origin of 2022 NX1.
  Methods. We carried out an observational study of 2022 NX1, using the OSIRIS
camera spectrograph at the 10.4 m Gran Telescopio Canarias, to derive its
spectral class. N-body simulations were also performed to investigate how it
reached NEA space.
  Results. The reflectance spectrum of 2022 NX1 is neither compatible with an
artificial origin nor lunar ejecta; it is also different from the V type of the
only other mini-moon with available spectroscopy, 2020 CD3. The visible
spectrum of 2022 NX1 is consistent with that of a K-type asteroid, although it
could also be classified as an Xk type. Considering typical values of the
similar albedo of both K-type and Xk-type asteroids and its absolute magnitude,
2022 NX1 may have a size range of 5 to 15 m. We confirm that 2022 NX1 inhabits
the rim of Earth's co-orbital space, the 1:1 mean-motion resonance, and
experiences recurrent co-orbital engagements of the horseshoe-type and
mini-moon events.
  Conclusions. The discovery of 2022 NX1 confirms that mini-moons can be larger
than a few meters and also that they belong to a heterogeneous population in
terms of surface composition.


  Perturbative QCD formula for inclusive energy spectra of heavy quarks from
heavy quark initiated jets which takes into account collinear and/or soft
logarithms in all orders, the exact first order result and two-loop effects is
applied to distributions of heavy flavoured hadrons in the framework of the
LPHD concept. Fits to experimentally measured charm and bottom mean energy
losses result in $\alpha_{\MSbar}(M_Z)=0.125\pm 0.003\pm 0.004$ and
$(2\GeV)^{-1}\int_0^{2\GeV} dk \alpha_s^{eff}(k)= 0.18\pm 0.01\pm 0.02$ with
$\alpha_s^{eff}$ an infrared finite effective QCD coupling.


  This paper describes the local density inequality approach to getting upper
bounds for sphere packing densities in R^n. This approach was first suggested
by L. Fejes-Toth in 1956 to prove the Kepler conjecture that the densest sphere
packing in R^3 is the "cannonball packing". The approaches of L. Fejes-Toth,
W.-Y. Hsiang, and T. Hales to the Kepler conjecture are each based on
(different) local density inequalities. Recently T. Hales and S. P. Ferguson
have presented extensive details carrying out a modified version of the
otrignal Hales approach to prove the Kepler conjecture. We describe the
particular local density inequality undelying the Hales and Ferguson approach
and sketch some features of their proof.


  A well-known drawback of state-of-the-art machine-learning interatomic
potentials is their poor ability to extrapolate beyond the training domain. For
small-scale problems with tens to hundreds of atoms this can be solved by using
active learning which is able to select atomic configurations on which a
potential attempts extrapolation and add them to the ab initio-computed
training set. In this sense an active learning algorithm can be viewed as an
on-the-fly interpolation of an ab initio model. For large-scale problems,
possibly involving tens of thousands of atoms, this is not feasible because one
cannot afford even a single density functional theory computation with such a
large number of atoms.
  This work marks a new milestone toward fully automatic ab initio-accurate
large-scale atomistic simulations. We develop an active learning algorithm that
identifies local subregions of the simulation region where the potential
extrapolates. Then the algorithm constructs periodic configurations out of
these local, non-periodic subregions, sufficiently small to be computable with
plane-wave density functional theory codes, in order to obtain accurate ab
initio energies. We benchmark our algorithm on the problem of the screw
dislocation motion in bcc tungsten and show that our algorithm reaches ab
initio accuracy, down to typical magnitudes of numerical noise in DFT codes. We
show that our algorithm reproduces material properties such as core structure,
Peierls barrier, and Peierls stress. This unleashes new capabilities for
computational materials science toward applications which have currently been
out of scope if approached solely by ab initio methods.


  To select a subset of samples or "winners" from a population of candidates,
order sampling [Rosen 1997] and the k-unit Myerson auction [Myerson 1981] share
a common scheme: assign a (random) score to each candidate, then select the k
candidates with the highest scores. We study a generalization of both order
sampling and Myerson's allocation rule, called winner-selecting dice. The
setting for winner-selecting dice is similar to auctions with feasibility
constraints: candidates have random types drawn from independent prior
distributions, and the winner set must be feasible subject to certain
constraints. Dice (distributions over scores) are assigned to each type, and
winners are selected to maximize the sum of the dice rolls, subject to the
feasibility constraints. We examine the existence of winner-selecting dice that
implement prescribed probabilities of winning (i.e., an interim rule) for all
types.
  Our first result shows that when the feasibility constraint is a matroid,
then for any feasible interim rule, there always exist winner-selecting dice
that implement it. Unfortunately, our proof does not yield an efficient
algorithm for constructing the dice. In the special case of a 1-uniform
matroid, i.e., only one winner can be selected, we give an efficient algorithm
that constructs winner-selecting dice for any feasible interim rule.
Furthermore, when the types of the candidates are drawn in an i.i.d.~manner and
the interim rule is symmetric across candidates, unsurprisingly, an algorithm
can efficiently construct symmetric dice that only depend on the type but not
the identity of the candidate.


  We study the problem of online learning in competitive settings in the
context of two-sided matching markets. In particular, one side of the market,
the agents, must learn about their preferences over the other side, the firms,
through repeated interaction while competing with other agents for successful
matches. We propose a class of decentralized, communication- and
coordination-free algorithms that agents can use to reach to their stable match
in structured matching markets. In contrast to prior works, the proposed
algorithms make decisions based solely on an agent's own history of play and
requires no foreknowledge of the firms' preferences. Our algorithms are
constructed by splitting up the statistical problem of learning one's
preferences, from noisy observations, from the problem of competing for firms.
We show that under realistic structural assumptions on the underlying
preferences of the agents and firms, the proposed algorithms incur a regret
which grows at most logarithmically in the time horizon. Our results show that,
in the case of matching markets, competition need not drastically affect the
performance of decentralized, communication and coordination free online
learning algorithms.


  One key feature of ultra-reliable low-latency communications (URLLC) in 5G is
to support short packet transmission (SPT). However, the pilot overhead in SPT
for channel estimation is relatively high, especially in high Doppler
environments. In this paper, we advocate the adoption of differential
modulation to support ultra-low latency services, which can ease the channel
estimation burden and reduce the power and bandwidth overhead incurred in
traditional coherent modulation schemes. Specifically, we consider a
multi-connectivity (MC) scheme employing differential modulation to enable
URLLC services. The popular selection combining and maximal ratio combining
schemes are respectively applied to explore the diversity gain in the MC
scheme. A first-order autoregressive model is further utilized to characterize
the time-varying nature of the channel. Theoretically, the maximum achievable
rate and minimum achievable block error rate under ergodic fading channels with
PSK inputs and perfect CSI are first derived by using the non-asymptotic
information-theoretic bounds. The performance of SPT with differential
modulation and MC schemes is then analysed by characterizing the effect of
differential modulation and time-varying channels as a reduction in the
effective SNR. Simulation results show that differential modulation does offer
a significant advantage over the pilot-assisted coherent scheme for SPT,
especially in high Doppler environments.


  A review of works on associative neural networks accomplished during last
four years in the Institute of Optical Neural Technologies RAS is given. The
presentation is based on description of parametrical neural networks (PNN). For
today PNN have record recognizing characteristics (storage capacity, noise
immunity and speed of operation). Presentation of basic ideas and principles is
accentuated.


  The Large Hadron Collider provides a unique opportunity to study quantum
entanglement and violation of Bell inequalities at the highest energy available
today. In this paper, we will investigate these quantum correlations with top
quark pair production, which represents a system of two-qubits. The spacelike
separation requirement for the two causally disconnected top quarks requires
they fly relativistically away from each other, which motivates the use of the
boosted top-tagging with the semi-leptonic top pair channel. Although measuring
the spin polarization of the hadronic top quark is known to be challenging, our
study indicates that it is feasible to reconstruct the spin density matrix of
the two-qubit system using an optimal hadronic polarimeter. This is achieved
with the aid of jet substructure techniques and NN-inspired reconstruction
methods, which improve the mapping between subjets and quarks. We find that
entanglement can already be observed at more than $5\sigma$ level with existing
data, and violation of Bell inequalities may be probed above 4$\sigma$ level at
the HL-LHC with 3 ab$^{-1}$ of data.


  We prove that the K-moduli space of cubic fourfolds is identical to their GIT
moduli space. More precisely, the K-(semi/poly)stability of cubic fourfolds
coincide to the corresponding GIT stabilities, which was studied in detail by
Laza. In particular, this implies that all smooth cubic fourfolds admit
K\"ahler-Einstein metrics. Key ingredients are local volume estimates in
dimension three due to Liu-Xu, and Ambro-Kawamata's non-vanishing theorem for
Fano fourfolds.


  We report on a possible cloud-cloud collision in the DR 21 region, which we
found through molecular observations with the Nobeyama 45-m telescope. We
mapped an area of 8'x12' around the region with twenty molecular lines
including the 12CO(J=1-0) and 13CO(J=1-0) emission lines, and sixteen of them
were significantly detected. Based on the 12CO and 13CO data, we found five
distinct velocity components in the observed region, and we call molecular gas
associated with these components -42, -22, -3, 9, and 17 km/s clouds taking
after their typical radial velocities. The -3 km/s cloud is the main
filamentary cloud (31,000 Mo) associated with young massive stars such as DR21
and DR21(OH), and the 9 km/s cloud is a smaller cloud (3,400 Mo) which may be
an extension of the W75 region in the north. The other clouds are much smaller.
We found a clear anticorrelation in the distributions of the -3 and 9 km/s
clouds, and detected faint 12CO emission having intermediate velocities
bridging the two clouds at their intersection. These facts strongly indicate
that the two clouds are colliding against each other. In addition, we found
that DR21 and DR21(OH) are located in the periphery of the densest part of the
9 km/s cloud, which is consistent with results of recent numerical simulations
of cloud-cloud collisions. We therefore suggest that the -3 and 9 km/s clouds
are colliding, and that the collision induced the massive star formation in the
DR21 cloud. The interaction of the -3 and 9 km/s clouds was previously
suggested by Dickel et al. (1978), and our results strongly support their
hypothesis of the interaction.