  The intrinsic structure of binary fields poses a challenging complexity
problem from both hardware and software point of view. Motivated by
applications to modern cryptography, we describe some simple techniques aimed
at performing computations over binary fields using systems with limited
resources. This is particularly important when such computations must be
carried out by means of very small and simple machines. The algorithms
described in the present paper provide an increased efficiency in computations,
when compared to the previously known algorithms for the arithmetic over prime
fields.


  Quantum devices such as spin qubits have been extensively investigated in
electrostatically confined quantum dots using high-quality semiconductor
heterostructures like GaAs and Si. Here, we present the first demonstration of
electrostatically forming the quantum dots in ZnO heterostructures. Through the
transport measurement, we uncover the distinctive signature of the Kondo effect
independent of the even-odd electron number parity, which contrasts with the
typical behavior of the Kondo effect in GaAs. By analyzing temperature and
magnetic field dependences, we find that the absence of the even-odd parity in
the Kondo effect is not straightforwardly interpreted by the considerations
developed for conventional semiconductors. We propose that, based on the unique
parameters of ZnO, electron correlation likely plays a fundamental role in this
observation. Our study not only clarifies the physics of correlated electrons
in the quantum dot but also holds promise for applications in quantum devices,
leveraging the unique features of ZnO.


  We study relative precompleteness in the context of the theory of numberings,
and relate this to a notion of lowness. We introduce a notion of divisibility
for numberings, and use it to show that for the class of divisible numberings,
lowness and relative precompleteness coincide with being computable.
  We also study the complexity of Skolem functions arising from Arslanov's
completeness criterion with parameters. We show that for suitably divisible
numberings, these Skolem functions have the maximal possible Turing degree. In
particular this holds for the standard numberings of the partial computable
functions and the c.e. sets.


  Entropy modeling is a key component for high-performance image compression
algorithms. Recent developments in autoregressive context modeling helped
learning-based methods to surpass their classical counterparts. However, the
performance of those models can be further improved due to the underexploited
spatio-channel dependencies in latent space, and the suboptimal implementation
of context adaptivity. Inspired by the adaptive characteristics of the
transformers, we propose a transformer-based context model, named
Contextformer, which generalizes the de facto standard attention mechanism to
spatio-channel attention. We replace the context model of a modern compression
framework with the Contextformer and test it on the widely used Kodak,
CLIC2020, and Tecnick image datasets. Our experimental results show that the
proposed model provides up to 11% rate savings compared to the standard
Versatile Video Coding (VVC) Test Model (VTM) 16.2, and outperforms various
learning-based models in terms of PSNR and MS-SSIM.


  There exist a correspondence between black holes in non linear
electrodynamics (NLED) and gravitational collapse of homogeneous dust with
semi-classical corrections in the strong curvature regime that to our knowledge
has not been noticed until now. We discuss the nature of such correspondence
and explore what insights may be gained from considering black holes in NLED in
the context of semi-classical dust collapse and vice-versa.


  Fashion is a large and fast-changing industry. Foreseeing the upcoming
fashion trends is beneficial for fashion designers, consumers, and retailers.
However, fashion trends are often perceived as unpredictable due to the
enormous amount of factors involved into designers' subjectivity. In this
paper, we propose a fashion trend prediction framework and design neural
network models to leverage structured fashion runway show data, learn the
fashion collection embedding, and further train RNN/LSTM models to capture the
designers' style evolution. Our proposed framework consists of (1) a runway
embedding learning model that uses fashion runway images to learn every
season's collection embedding, and (2) a next-season fashion design prediction
model that leverage the concept of designer style and trend to predict
next-season design given designers. Through experiments on a collected dataset
across 32 years of fashion shows, our framework can achieve the best
performance of 78.42% AUC on average and 95% for an individual designer when
predicting the next season's design.


  The compression quality losses of depth sequences determine quality of view
synthesis in free-viewpoint video. The depth map intra prediction in 3D
extensions of the HEVC applies intra modes with auxiliary depth modeling modes
(DMMs) to better preserve depth edges and handle motion discontinuities.
Although such modes enable high efficiency compression, but at the cost of very
high encoding complexity. Skipping conventional intra coding modes and DMMs in
depth coding limits practical applicability of the HEVC for 3D display
applications. In this paper, we introduce a novel low-complexity scheme for
depth video compression based on low-rank tensor decomposition and HEVC intra
coding. The proposed scheme leverages spatial and temporal redundancy by
compactly representing the depth sequence as a high-order tensor. Tensor
factorization into a set of factor matrices following CANDECOMP PARAFAC (CP)
decomposition via alternating least squares give a low-rank approximation of
the scene geometry. Further, compression of factor matrices with HEVC intra
prediction support arbitrary target accuracy by flexible adjustment of bitrate,
varying tensor decomposition ranks and quantization parameters. The results
demonstrate proposed approach achieves significant rate gains by efficiently
compressing depth planes in low-rank approximated representation. The proposed
algorithm is applied to encode depth maps of benchmark Ballet and Breakdancing
sequences. The decoded depth sequences are used for view synthesis in a
multi-view video system, maintaining appropriate rendering quality.


  Entropy and the second law of thermodynamcs were discovered through study of
the behaviour of gases in confined spaces. The related techniques developed in
the kinetic theory of gases have failed to resolve the apparent conflict
between the time-reversal symmetry of all known laws of nature and the
existence of arrows of time that at all times and everywhere in the universe
all point in the same direction. I will argue that the failure may due to
unconscious application to the universe of the conceptual framework developed
for confined systems. If, as seems plausible, the universe is an unconfined
system, new concepts are needed.


  The electron localization function (ELF) is a universal measure of electron
localization that allows for, e.g., an effective characterization of physical
bonds in molecular and solid state systems. In the context of the widely used
Kohn-Sham density-functional theory (KS-DFT) and its generalizations, ELF is
given in terms of the single-particle electron density as well as the
non-interacting kinetic energy density (KED) of the KS system. Starting from
the notion of an edge electron gas put forth by Kohn and Mattsson, we here use
an \emph{exactly soluble}, strongly correlated few-electron model of a
harmonically confined electron gas in order to parameterize the
positive-definite non-interacting KS KED in terms of the density and its
reduced second-order gradients. We arrive at a simple, yet generally applicable
functional approximation to ELF expressed in the electron density and its
derivatives. To demonstrate the validity of our approach, we use the obtained
parameterization to perform topological analysis of the bonds in solid Al and
Si, and to study physical and chemical adsorption of graphene on a Ni surface.
We find that while the expression does not provide a quantitatively accurate
approximation of absolute ELF values, the most essential qualitative features
are captured. Hence, the expression is useful in contexts where the electron
density is available, but not the KS orbitals or the KED, and one desires a
qualitative picture of the electron localization that mimics ELF.


  Vessel navigation is difficult in restricted waterways and in the presence of
static and dynamic obstacles. This difficulty can be attributed to the
high-level decisions taken by humans during these maneuvers, which is evident
from the fact that 85% of the reported marine accidents are traced back to
human errors. Artificial intelligence-based methods offer us a way to eliminate
human intervention in vessel navigation. Newer methods like Deep Reinforcement
Learning (DRL) can optimize multiple objectives like path following and
collision avoidance at the same time while being computationally cheaper to
implement in comparison to traditional approaches. Before addressing the
challenge of collision avoidance along with path following, the performance of
DRL-based controllers on the path following task alone must be established.
Therefore, this study trains a DRL agent using Proximal Policy Optimization
(PPO) algorithm and tests it against a traditional PD controller guided by an
Integral Line of Sight (ILOS) guidance system. The Krisco Container Ship (KCS)
is chosen to test the different controllers. The ship dynamics are
mathematically simulated using the Maneuvering Modelling Group (MMG) model
developed by the Japanese. The simulation environment is used to train the deep
reinforcement learning-based controller and is also used to tune the gains of
the traditional PD controller. The effectiveness of the controllers in the
presence of wind is also investigated.


  Classical vortex solutions in various two-Higgs systems are studied. The
systems we consider include the standard model with two Higgs doublets, in
which case the vortex appears as part of a string-like object. The Higgs
potentials contain several different couplings in general and the spontaneous
symmetry breaking involves with two different vacuum expectation values. In
particular it is shown that the existence of such a solution in general
requires a specific ratio of the two Higgs vacuum expectation values, i.e.
$\tan\beta$, and some inequalities between different Higgs couplings. This
ratio can be determined in terms of the couplings in the Higgs potential. The
Higgs masses are also computed in this case. (1+2)-d solutions are topological
so that they are topologically stable and the Bogomol'nyi bound is saturated
for some couplings. Some comments on the stabilization of (1+3)-d solutions are
also given. Thus, as long as such a defect can be formed in the early universe,
stable or not, $\tan\beta$ is no longer an independent free parameter in the
theory.


  We discuss secure computation of modular sum when multiple access channel
from distinct players $A_1, \ldots, A_c$ to a third party (Receiver) is given.
Then, we define the secure modulo sum capacity as the supremum of the
transmission rate of modulo sum without information leakage of other
information. We derive its useful lower bound, which is numerically calculated
under a realistic model that can be realizable as a Gaussian multiple access
channel (MAC).


  If a one-variable function is sufficiently smooth, then the limit position of
secant lines its graph is a tangent line. By analogy, one would expect that the
limit position of secant planes of a two-variable smooth function is a plane
tangent to its graph. Amazingly, this is not necessarily true, even when the
function is a simple polynomial. Despite this paradox, we show that some
analogies with the one-variable case still hold in the multi-variable context,
provided we use a particular vector product: the Clifford's geometric one.


  We propose a statistical method for clustering of multivariate longitudinal
data into homogeneous groups. This method relies on a time-varying extension on
the classical K-means algorithm, where a multivariate vector autoregressive
model is additionally assumed for modeling the evolution of clusters' centroids
over time. We base the inference on a least squares specification of the model
and coordinate descent algorithm. To illustrate our work, we consider a
longitudinal dataset on human development. Three variables are modeled, namely
life expectancy, education and gross domestic product.


  We present analysis results for a nearby galaxy cluster Abell 1631 at
$z~=~0.046$ using the X-ray observatory Suzaku. This cluster is categorized as
a low X-ray surface brightness cluster. To study the dynamical state of the
cluster, we conduct four-pointed Suzaku observations and investigate physical
properties of the Mpc-scale hot gas associated with the A1631 cluster for the
first time. Unlike relaxed clusters, the X-ray image shows no strong peak at
the center and an irregular morphology. We perform spectral analysis and
investigate the radial profiles of the gas temperature, density, and entropy
out to approximately 1.5~Mpc in the east, north, west, and south directions by
combining with the XMM-Newton data archive. The measured gas density in the
central region is relatively low (${\rm a~few} \times~10^{-4}~{\rm cm^{-3}}$)
at the given temperature ($\sim2.9~{\rm keV}$) compared with X-ray-selected
clusters. The entropy profile and value within the central region
($r<0.1~r_{200}$) are found to be flatter and higher ($\gtrsim400~ {\rm
keV~cm}^2$). The observed bolometric luminosity is approximately three times
lower than that expected from the luminosity-temperature relation in previous
studies for relaxed clusters. These features are also observed in another low
surface brightness cluster, Abell 76. The spatial distributions of galaxies and
the hot gas appear to be different. The X-ray luminosity is relatively lower
than that expected from the velocity dispersion. A post-merger scenario may
explain the observed results.


  We present a Chandra study of mass profiles in 7 elliptical galaxies, of
which 3 have galaxy-scale and 4 group-scale halos demarcated at 1E13Msun. These
represent the best available data for nearby objects with comparable X-ray
luminosities. We measure ~flat mass-to-light (M/L) profiles within an optical
half-light radius (Reff), rising by an order of magnitude at ~10Reff, which
confirms the presence of dark matter (DM). The data indicate hydrostatic
equilibrium, which is also supported by agreement with studies of stellar
kinematics in elliptical galaxies. The data are well-fitted by a model
comprising an NFW DM profile and a baryonic component following the optical
light. The distribution of DM halo concentration parameters (c) versus Mvir
agrees with LCDM predictions and our observations of bright groups.
Concentrations are slightly higher than expected, which is most likely a
selection effect. Omitting the stellar mass drastically increases c, possibly
explaining large concentrations found by some past observers. The stellar M/LK
agree with population synthesis models, assuming a Kroupa IMF. Allowing
adiabatic compression (AC) of the DM halo by baryons made M/L more discrepant,
casting some doubt on AC. Our best-fitting models imply total baryon fractions
\~0.04--0.09, consistent with models of galaxy formation incorporating strong
feedback. The groups exhibit positive temperature gradients, consistent with
the "Universal" profiles found in other groups and clusters, whereas the
galaxies have negative gradients, suggesting a change in the evolutionary
history of the systems around Mvir=1E13 Msun.


  We present a new Bitcoin coin selection algorithm, "coin selection with
leverage", which aims to improve upon cost savings than that of standard
knapsack like approaches. Parameters to the new algorithm are available to be
tuned at the users discretion to address other goals of coin selection. Our
approach naturally fits as a replacement for the standard knapsack ingredient
of full coin selection procedures.


  We compare and combine likelihood functions of the cosmological parameters
Omega_m, h and sigma_8, from peculiar velocities, CMB and type Ia supernovae.
These three data sets directly probe the mass in the Universe, without the need
to relate the galaxy distribution to the underlying mass via a "biasing"
relation. We include the recent results from the CMB experiments BOOMERANG and
MAXIMA-1. Our analysis assumes a flat Lambda CDM cosmology with a
scale-invariant adiabatic initial power spectrum and baryonic fraction as
inferred from big-bang nucleosynthesis. We find that all three data sets agree
well, overlapping significantly at the 2 sigma level. This therefore justifies
a joint analysis, in which we find a joint best fit point and 95 per cent
confidence limits of Omega_m=0.28 (0.17,0.39), h=0.74 (0.64,0.86), and
sigma_8=1.17 (0.98,1.37). In terms of the natural parameter combinations for
these data sigma_8 Omega_m^0.6 = 0.54 (0.40,0.73), Omega_m h = 0.21
(0.16,0.27). Also for the best fit point, Q_rms-ps = 19.7 muK and the age of
the universe is 13.2 Gyr.


  Recently there have been several historical changes in electricity networks
that necessitate the development of Demand Side Management (DSM). The main
objective of DSM is to achieve an aggregated consumption pattern that is
efficient in terms of energy cost reduction, welfare maximization and
satisfaction of network constraints. This is generally envisaged by encouraging
electricity use at low-peak times. In this paper, we consider a system with
strategic, price anticipating consumers with private preferences that choose
their electricity consumption patterns so as to maximize their own benefit. In
this context, we take on the problem of coordinating the consumers' consumption
behavior without sacrificing their welfare (Quality of Experience). In order to
tackle this problem, we draw on concepts of indirect mechanism design and
propose a DSM architecture that is able to fulfill specific system-wide
constraints (e.g. energy cost reduction) and simultaneously achieve welfare
that is very close to optimal. The proposed billing rule preserves both the
budget-balance and the individual rationality properties. According to our
evaluation, the proposed DSM architecture achieves a close to optimal
allocation (1%-3% gap), compared to an "optimal" system that would use central
optimization of user loads without user consensus or protection of their
privacy


  Social media platforms are rife with politically charged discussions.
Therefore, accurately deciphering and predicting partisan biases using Large
Language Models (LLMs) is increasingly critical. In this study, we address the
challenge of understanding political bias in digitized discourse using LLMs.
While traditional approaches often rely on finetuning separate models for each
political faction, our work innovates by employing a singular,
instruction-tuned LLM to reflect a spectrum of political ideologies. We present
a comprehensive analytical framework, consisting of Partisan Bias Divergence
Assessment and Partisan Class Tendency Prediction, to evaluate the model's
alignment with real-world political ideologies in terms of stances, emotions,
and moral foundations. Our findings reveal the model's effectiveness in
capturing emotional and moral nuances, albeit with some challenges in stance
detection, highlighting the intricacies and potential for refinement in NLP
tools for politically sensitive contexts. This research contributes
significantly to the field by demonstrating the feasibility and importance of
nuanced political understanding in LLMs, particularly for applications
requiring acute awareness of political bias.


  The resonance energy transfer (RET) from tryptophan present in bovine serum
albumin (BSA) to two dimensional (2D) nanomaterials has been reported. In these
bio-nano systems, BSA molecules act as efficient donors while 2D van der Waals
nanosheets of graphene, molybdenum disulfide (Mo$S_2$) and tungsten disulfide
(W$S_2$) act as acceptors. The fluorescence emission from tryptophan arises at
350 nm, in presence of 2D nanosheets, the fluorescence intensity of BSA
decreases. The time resolved fluorescence study shows that there is decrease in
the fluorescence lifetime of BSA molecules in presence of 2D nanosheets. The
decrease in fluorescence lifetime of BSA confirms the energy transfer
phenomenon in bio-nano interactions. This study is important for finding the
bio-compatibility of 2D nanomaterials in biological applications.


  Weather forecasting is one of the cornerstones of meteorological work. In
this paper, we present a new benchmark dataset named Weather2K, which aims to
make up for the deficiencies of existing weather forecasting datasets in terms
of real-time, reliability, and diversity, as well as the key bottleneck of data
quality. To be specific, our Weather2K is featured from the following aspects:
1) Reliable and real-time data. The data is hourly collected from 2,130 ground
weather stations covering an area of 6 million square kilometers. 2)
Multivariate meteorological variables. 20 meteorological factors and 3
constants for position information are provided with a length of 40,896 time
steps. 3) Applicable to diverse tasks. We conduct a set of baseline tests on
time series forecasting and spatio-temporal forecasting. To the best of our
knowledge, our Weather2K is the first attempt to tackle weather forecasting
task by taking full advantage of the strengths of observation data from ground
weather stations. Based on Weather2K, we further propose Meteorological Factors
based Multi-Graph Convolution Network (MFMGCN), which can effectively construct
the intrinsic correlation among geographic locations based on meteorological
factors. Sufficient experiments show that MFMGCN improves both the forecasting
performance and temporal robustness. We hope our Weather2K can significantly
motivate researchers to develop efficient and accurate algorithms to advance
the task of weather forecasting. The dataset can be available at
https://github.com/bycnfz/weather2k/.


  The NASA Astrophysics Data System (ADS) is the primary Digital Library portal
for researchers in astronomy and astrophysics. Over the past 30 years, the ADS
has gone from being an astronomy-focused bibliographic database to an open
digital library system supporting research in space and (soon) earth sciences.
This paper describes the evolution of the ADS system, its capabilities, and the
technological infrastructure underpinning it.
  We give an overview of the ADS's original architecture, constructed primarily
around simple database models. This bespoke system allowed for the efficient
indexing of metadata and citations, the digitization and archival of full-text
articles, and the rapid development of discipline-specific capabilities running
on commodity hardware. The move towards a cloud-based microservices
architecture and an open-source search engine in the late 2010s marked a
significant shift, bringing full-text search capabilities, a modern API, higher
uptime, more reliable data retrieval, and integration of advanced
visualizations and analytics.
  Another crucial evolution came with the gradual and ongoing incorporation of
Machine Learning and Natural Language Processing algorithms in our data
pipelines. Originally used for information extraction and classification tasks,
NLP and ML techniques are now being developed to improve metadata enrichment,
search, notifications, and recommendations. we describe how these computational
techniques are being embedded into our software infrastructure, the challenges
faced, and the benefits reaped.
  Finally, we conclude by describing the future prospects of ADS and its
ongoing expansion, discussing the challenges of managing an interdisciplinary
information system in the era of AI and Open Science, where information is
abundant, technology is transformative, but their trustworthiness can be
elusive.


  In this work we apply point canonical transformations to solve some classes
of nonautonomous nonlinear Schr\"{o}dinger equation namely, those which possess
specific cubic and quintic - time and space dependent - nonlinearities. In this
way we generalize some procedures recently published which resort to an ansatz
to the wavefunction and recover a time and space independent nonlinear equation
which can be solved explicitly. The method applied here allow us to find wide
localized (in space) soliton solutions to the nonautonomous nonlinear
Schr\"{o}dinger equation, which were not presented before. We also generalize
the external potential which traps the system and the nonlinearities terms.


  We observed the J = 9-8 and 16-15 rotational transitions of the normal
species and five 13C isotopologues of HC5N to study its formation mechanisms
toward the cyanopolyyne peak in Taurus Molecular Cloud-1, with the 45-m radio
telescope of Nobeyama Radio Observatory. We detected the five 13C isotopologues
with high signal-to-noise ratios between 12 and 20, as well as the normal
species. The abundance ratios of the five 13C isotopologues of HC5N are found
to be 1.00:0.97:1.03:1.05:1.16 (0.19) for
[H13CCCCCN]:[HC13CCCCN]:[HCC13CCCN]:[HCCC13CCN]:[HCCCC13CN]. We do not find any
significant differences among the five 13C isotopologues. The averaged
[HC5N]/[13C isotopologues] abundance ratio is determined to be 94 (6), which is
slightly higher than the local interstellar elemental 12C/13C ratio of 60-70.
Possible formation pathways are discussed on the basis of these results.


  We studied the correction of the quantum tunneling radiation of fermions with
spin $1/2$ in Kerr Anti-de-Sitter black hole. First, the dynamic equation of
spin $1/2$ fermions was corrected using Lorentz's violation theory. Second, the
new expressions of the fermions quantum tunneling rate, the Hawking temperature
of the black hole and the entropy of the black hole were obtained according to
the corrected fermions dynamic equation. At last, some comments are made on the
results of our work.


  We study various inequalities for numerical radius and Berezin number of a
bounded linear operator on a Hilbert space. It is proved that the numerical
radius of a pure two-isometry is 1 and the Crawford number of a pure
two-isometry is 0. In particular, we show that for any
scalar-valuednon-constant inner function $\theta$, the numerical radius and the
Crawford number of a Toeplitz operator $T_{\theta}$ on a Hardy space is 1 and
0, respectively. It is also shown that numerical radius is multiplicative for a
class of isometries and sub-multiplicative for a class of commutants of a
shift. We have illustrated these results with some concrete examples. Finally,
some Hardy-type inequalities for Berezin number of certain class of operators
are established with the help of the classical Hardy's inequality.


  We study the rolling of the Chaplygin ball in $\mathbb R^n$ over a fixed
$(n-1)$--dimensional sphere without slipping and without slipping and twisting.
The problems can be naturally considered within a framework of appropriate
modifications of the L+R and LR systems -- well known systems on Lie groups
groups with an invariant measure. In the case of the rolling without slipping
and twisting, we describe the $SO(n)$-Chaplygin reduction to $S^{n-1}$ and
prove the Hamiltonization of the reduced system for a special inertia operator.


  After reviewing the context in which Euclidean propagation is useful we
compare and contrast Euclidean and Lorentzian Maxwell-Einstein theory and give
some examples of Euclidean solutions.


  Let $G = (V,E)$ be a finite simple undirected graph without isolated
vertices. A bijective map $f: V \cup E \rightarrow \{1,2, \dots, |V|+ |E| \}$
is called total local antimagic labeling if for each edge $uv \in E, w(u) \ne
w(v)$, where $w(v)$ is a weight of a vertex $v$ defined by $w(v) = \sum_{x \in
NT(v)} f(x)$, where $NT(u) = N(u) \cup \{uv: uv\in E\}$ is the total open
neighborhood of a vertex $u$. Further, $f$ is called super vertex total local
antimagic labeling or super edge total local antimagic labeling if $f(V) =
\{1,2, \dots, |V|\}$ or $f(E) = \{1,2, \dots, |E|\}$, respectively. The
labeling $f$ induces a proper vertex coloring of $G$. The super vertex (edge)
total local antimagic chromatic number of a graph $G$ is the minimum number of
colors used overall colorings of $G$ induced by super vertex (edge) total local
antimagic labeling of $G$. In this paper, we have calculated the super vertex
(edge) total local antimagic chromatic number of some families of graphs.


  We explore the experimental implications of a recent theory of the quantum
dynamics of vortices in two-dimensional superfluids proximate to Mott
insulators. The theory predicts modulations in the local density of states in
the regions over which the vortices execute their quantum zero point motion. We
use the spatial extent of such modulations in scanning tunnelling microscopy
measurements on the vortex lattice of BSCCO to estimate the inertial mass of a
point vortex. We discuss other, more direct, experimental signatures of the
vortex dynamics.


  Recent results from BaBar and Belle experiments on $B \bar B$ mixing and
$\sin 2\phi_1$ are presented. Accuracy of $\Delta m_d$ measurements has reached
1.2%. Higher order effects within the Standard Model or possible new physics
effect that might appear in the $B \bar B$ mixing through non-zero $\Delta
\Gamma/\Gamma$, CP violation, or $CPT$ violation have been explored. The BaBar
and Belle results on $\sin 2\phi_1$ from the $b \to c \bar{c} s$ modes are in
good agreement with each other and a combined result with an accuracy of 8% is
in good agreement with a global CKM fit. A simple average of the $\sin 2\phi_1$
values that were measured in the penguin-loop dominated decay modes, $\phi
K_S$, $\eta^{\prime} K_S$, and $K^+ K^- K_S$, shows about 2.5$\sigma$ deviation
from the Standard Model.


  The cosmic infrared background from galaxies should have a minimum
fluctuation of the order of 10\% on angular scales probed by the DIRBE (Diffuse
Infrared Background Experiment) on board COBE (Cosmic Background Explorer). We
present the theoretical basis for the analysis and discuss the all-sky
all-wavelengths survey that we conducted. We further show that a linear
combination of maps at different wavelengths can greatly reduce the
fluctuations produced by foreground stars in the near-IR, while not eliminating
the fluctuations of the background from high redshift galaxies. The method is
potentially very powerful, especially at wavelengths in the mid- to far-IR
where the foreground is bright but smooth.


  We clarify the origin of the strikingly different spectroscopic properties of
the chemically similar compounds NaOsO$_3$ and LiOsO$_3$. Our first-principle
many-body analysis demonstrates that the highly sensitive physics of these two
materials is controlled by their proximity to an adjacent Hund's-Mott
insulating phase. In mildly correlated $5d$ oxides, this physics is triggered
by the {\sl cooperative} action of intraorbital repulsion and Hund's exchange
in half-filled $t_{2g}$ outer-shells. Small material-specific details hence
result in an extremely sharp change of the electronic mobility, explaining the
surprisingly different properties of the paramagnetic high-temperature phases
of the two compounds.


  This survey summarizes the results discussed in a talk at "Bielefeld Geometry
& Topology Days" held at Bielefeld University in July 2015. We are interested
in quantitative and qualitative properties of Bott-Chern cohomology. We
announce new results obtained in [D. Angella, N. Tardini, Quantitative and
qualitative cohomological properties for non-K\"ahler manifolds,
arXiv:1507.07108] jointly with Nicoletta Tardini. In particular, we prove an
upper bound of the dimensions of Bott-Chern cohomology in terms of Hodge
numbers. We also introduce a notion, called Schweitzer qualitative property,
which encodes the existence of a non-degenerate pairing in Bott-Chern
cohomology, like the Poincar\'e duality for the de Rham cohomology. We show
that this property characterizes the $\partial\overline\partial$-Lemma.


  Vision State Space Models (VSSMs), a novel architecture that combines the
strengths of recurrent neural networks and latent variable models, have
demonstrated remarkable performance in visual perception tasks by efficiently
capturing long-range dependencies and modeling complex visual dynamics.
However, their robustness under natural and adversarial perturbations remains a
critical concern. In this work, we present a comprehensive evaluation of VSSMs'
robustness under various perturbation scenarios, including occlusions, image
structure, common corruptions, and adversarial attacks, and compare their
performance to well-established architectures such as transformers and
Convolutional Neural Networks. Furthermore, we investigate the resilience of
VSSMs to object-background compositional changes on sophisticated benchmarks
designed to test model performance in complex visual scenes. We also assess
their robustness on object detection and segmentation tasks using corrupted
datasets that mimic real-world scenarios. To gain a deeper understanding of
VSSMs' adversarial robustness, we conduct a frequency analysis of adversarial
attacks, evaluating their performance against low-frequency and high-frequency
perturbations. Our findings highlight the strengths and limitations of VSSMs in
handling complex visual corruptions, offering valuable insights for future
research and improvements in this promising field. Our code and models will be
available at https://github.com/HashmatShadab/MambaRobustness.


  The Random Phase Approximation (RPA) for total energies has previously been
shown to provide a qualitatively correct description of static correlation in
molecular systems, where density functional theory (DFT) with local functionals
are bound to fail. This immediately poses the question of whether the RPA is
also able to capture the correct physics of strongly correlated solids such as
Mott insulators. Due to strong electron localization, magnetic interactions in
such systems are dominated by superexchange, which in the simplest picture can
be regarded as the analogue of static correlation for molecules. In the present
work we investigate the performance of the RPA for evaluating both
superexchange and direct exchange interactions in the magnetic solids NiO, MnO,
Na3Cu2SbO6, Sr2CuO3, Sr2CuTeO6, and a monolayer of CrI3, which are chosen to
represent a broad variety of magnetic interactions. It is found that the RPA
can accurately correct the large errors introduced by Hartree-Fock -
independent of the input orbitals used for the perturbative expansion. However,
in most cases, accuracies similar to RPA can be obtained with DFT+U, which is
significantly simpler from a computational point of view.


  In the course of the energy transition, the expansion of generation and
consumption will change, and many of these technologies, such as PV systems,
electric cars and heat pumps, will influence the power flow, especially in the
distribution grids. Scalable methods that can make decisions for each grid
connection are needed to enable congestion-free grid operation in the
distribution grids. This paper presents a novel end-to-end approach to
resolving congestion in distribution grids with deep reinforcement learning.
Our architecture learns to curtail power and set appropriate reactive power to
determine a non-congested and, thus, feasible grid state. State-of-the-art
methods such as the optimal power flow (OPF) demand high computational costs
and detailed measurements of every bus in a grid. In contrast, the presented
method enables decisions under sparse information with just some buses
observable in the grid. Distribution grids are generally not yet fully
digitized and observable, so this method can be used for decision-making on the
majority of low-voltage grids. On a real low-voltage grid the approach resolves
100\% of violations in the voltage band and 98.8\% of asset overloads. The
results show that decisions can also be made on real grids that guarantee
sufficient quality for congestion-free grid operation.


  We discuss a new class of exoplanets that appear to be emitting a tail of
dusty effluents. These disintegrating planets are found close to their host
stars and have very hot, and likely molten, surfaces. The properties of the
dust should provide a direct probe of the constituent material of these rocky
bodies.


  Via molecular dynamics simulations, we study the kinetics in a phase
separating active matter model. Quantitative results for the isotropic
bicontinuous pattern formation, its growth and aging, studied, respectively,
via the two-point equal-time density-density correlation function, the average
domain length and the two-time density autocorrelation function, are presented.
Both the correlation functions exhibit basic scaling properties, implying
self-similarity in the pattern dynamics, for which the average domain size
exhibits a power-law growth in time. The equal-time correlation has a short
distance behavior that provides reasonable agreement of the corresponding
structure factor tail with the Porod law. The autocorrelation decay is a
power-law in the average domain size. Apart from these basic similarities, the
quantitative behavior of the above mentioned observables are found to be vastly
different from those of the corresponding passive limit of the model which also
undergoes phase separation. The functional forms of these have been quantified.
An exceptionally rapid growth in the active system occurs due to fast coherent
motion of the particles, mean-squared-displacements of which exhibit multiple
scaling regimes, including a long time ballistic one.


  Let $A$ and $B$ be Banach algebras, $\theta: A\to B$ be a continuous Banach
algebra homomorphism and $I$ be a closed ideal in $B$. Then the direct sum of
$A$ and $I$ with respect to $\theta$, denoted $A\bowtie^{\theta}I$, with a
special product becomes a Banach algebra which is called the amalgamated Banach
algebra. In this paper, among other things, we compute the topological centre
of $A\bowtie^{\theta}I$ in terms of that of $A$ and $I$. Using this, we provide
a characterization of the Arens regularity of $A\bowtie^{\theta}I$. Then we
determine the character space of $A\bowtie^{\theta}I$ in terms of that of $A$
and $I$. Moreover, we study the weak amenability of $A\bowtie^{\theta}I$.


  Creating multiple-choice questions to assess reading comprehension of a given
article involves generating question-answer pairs (QAPs) on the main points of
the document. We present a learning scheme to generate adequate QAPs via
meta-sequence representations of sentences. A meta sequence is a sequence of
vectors comprising semantic and syntactic tags. In particular, we devise a
scheme called MetaQA to learn meta sequences from training data to form pairs
of a meta sequence for a declarative sentence (MD) and a corresponding
interrogative sentence (MIs). On a given declarative sentence, a trained MetaQA
model converts it to a meta sequence, finds a matched MD, and uses the
corresponding MIs and the input sentence to generate QAPs. We implement MetaQA
for the English language using semantic-role labeling, part-of-speech tagging,
and named-entity recognition, and show that trained on a small dataset, MetaQA
generates efficiently over the official SAT practice reading tests a large
number of syntactically and semantically correct QAPs with over 97\% accuracy.


  Consider a Markov chain $\{X_n\}_{n\ge 0}$ with an ergodic probability
measure $\pi$. Let $\Psi$ a function on the state space of the chain, with
$\alpha$-tails with respect to $\pi$, $\alpha\in (0,2)$. We find sufficient
conditions on the probability transition to prove convergence in law of
$N^{1/\alpha}\sum_n^N \Psi(X_n)$ to a $\alpha$-stable law. ``Martingale
approximation'' approach and ``coupling'' approach give two different sets of
conditions. We extend these results to continuous time Markov jump processes
$X_t$, whose skeleton chain satisfies our assumptions. If waiting time between
jumps has finite expectation, we prove convergence of $N^{-1/\alpha}\int_0^{Nt}
V(X_s) ds$ to a stable process. In the case of waiting times with infinite
average, we prove convergence to a Mittag-Leffler process.


  Quantum technology has grown out of quantum information theory and now
provides a valuable tool that researchers from numerous fields can add to their
toolbox of research methods. To date, various systems have been exploited to
promote the application of quantum information processing. The systems that can
be used for quantum technology include superconducting circuits, ultra-cold
atoms, trapped ions, semiconductor quantum dots, and solid-state spins and
emitters. In this review, we will discuss the state of the art on material
platforms for spin-based quantum technology, with a focus on the progress in
solid-state spins and emitters in several leading host materials, including
diamond, silicon carbide, boron nitride, silicon, two-dimensional
semiconductors, and other materials. We will highlight how first-principles
calculations can serve as an exceptionally robust tool for finding the novel
defect qubits and single photon emitters in solids, through detailed
predictions of the electronic, magnetic and optical properties.


  Video summarization has become an increasingly important task in the field of
computer vision due to the vast amount of video content available on the
internet. In this project, we propose a new method for natural language query
based joint video summarization and highlight detection using multi-modal
transformers. This approach will use both visual and audio cues to match a
user's natural language query to retrieve the most relevant and interesting
moments from a video. Our approach employs multiple recent techniques used in
Vision Transformers (ViTs) to create a transformer-like encoder-decoder model.
We evaluated our approach on multiple datasets such as YouTube Highlights and
TVSum to demonstrate the flexibility of our proposed method.


  Symmetries play a distinctive role at the high temperature phase transition
in QCD. Therefore the spectrum of screening masses has been investigated with
emphasis on taste breaking. Although taste violation is an UV effect the
relevant operators could be temperature dependent. We have studied the meson
screening masses in the temperature range between 140 MeV to 550 MeV. The
computation has been performed using dynamical N_f = 2+1 gauge field
configurations generated with the p4 staggered action. For temperatures below
the transition an agreement with the prediction of staggered chiral
perturbation theory has been found and no temperature effect can be observed on
the taste violation. Above the transition the taste splitting still shows an
O(a^2) behavior but with a temperature dependent slope.


  In this contribution, typical friction driven structures are summarized and
presented considering the mechanical structures and operation principles of
different types of piezoelectric motors. A two degree-of-freedom dynamic model
with one unilateral frictional contact is built for one of the friction driven
structures. Different contact regimes and the transitions between them are
identified and analyzed. Numerical simulations are conducted to find out
different operation modes of the system concerning the sequence of contact
regimes in one steady state period. The influences of parameters on the
operation modes and corresponding steady state characteristics are also
explored. Some advice are then given in terms of the design of friction driven
structures and piezoelectric motors.


  We present three different methods to estimate error bars on the predictions
made using a neural network. All of them represent lower bounds for the
extrapolation errors. For example, we did not include an analysis on robustness
against small perturbations of the input data.
  At first, we illustrate the methods through a simple toy model, then, we
apply them to some realistic cases related to nuclear masses. By using
theoretical data simulated either with a liquid-drop model or a Skyrme energy
density functional, we benchmark the extrapolation performance of the neural
network in regions of the Segr\`e chart far away from the ones used for the
training and validation. Finally, we discuss how error bars can help
identifying when the extrapolation becomes too uncertain and thus unreliable


  We present explicit analytic, twice-differentiable expressions for the
temperature-dependent anisotropic step line tension and step stiffness for the
two principal surfaces of face-centered-cubic crystals, the square {001} and
the hexagonal {111}. These expressions improve on simple expressions that are
valid only for low temperatures and away from singular orientations. They are
well suited for implementation into numerical methods such as finite-element
simulation of step evolution.


  In this paper, we study the near-field radiative energy, linear-momentum, and
angular-momentum transfer from a current-biased graphene to nanoparticles. The
electric current through the graphene sheet induces nonequilibrium
fluctuations, causing energy and momentum transfer even in the absence of a
temperature difference. The inherent spin-momentum locking of graphene surface
plasmons leads to an in-plane torque perpendicular to the direction of the
electric current. In the presence of a temperature difference, the energy
transfer is greatly enhanced while the lateral force and torque remain within
the same order. Our work explores the potential of utilizing current-biased
graphene to manipulate nanoparticles.


  The energy spectrum of the two-dimensional t-J model in a perpendicular
magnetic field is investigated. The density of states at the Fermi level as a
function of the inverse magnetic field $\frac{1}{B}$ reveals oscillations in
the range of hole concentrations $0.08<x<0.18$. In the used approximation
zero-field Fermi surfaces are large for these $x$, and oscillation frequencies
conform with such Fermi surfaces. However, the amplitude of these oscillations
is modulated with a frequency which is smaller by an order of magnitude. The
appearance of this modulation is related to van Hove singularities in the
Landau subbands, which traverse the Fermi level with changing $B$. The
singularities are connected with bending the Landau subbands due to strong
electron correlations. The frequency of the modulation is of the same order of
magnitude as the quantum oscillation frequency observed in underdoped cuprates.


  We study finite volume effects in heavy quark systems in the framework of
heavy meson chiral perturbation theory for full, quenched, and partially
quenched QCD. A novel feature of this investigation is the role played by the
scales Delta and delta_s, where Delta is the mass difference between the
heavy-light vector and pseudoscalar mesons of the same quark content, and
delta_s is the mass difference due to light flavour SU(3) breaking. The primary
conclusion of this work is that finite volume effects arising from the
propagation of Goldstone particles in the effective theory can be altered by
the presence of these scales. Since Delta varies significantly with the heavy
quark mass, these volume effects can be amplified in both heavy and light quark
mass extrapolations (interpolations). As an explicit example, we present
results for B parameters of neutral B meson mixing matrix elements and
heavy-light decay constants to one-loop order in finite volume heavy meson
chiral perturbation theory for full, quenched, and N_f=2+1 partially quenched
QCD. Our calculation shows that for high-precision determinations of the
phenomenologically interesting SU(3) breaking ratios, finite volume effects are
significant in quenched and not negligible in partially quenched QCD, although
they are generally small in full QCD.


  We propose a new representation of the nonlinear sigma model that exhibits a
manifest duality between flavor and kinematics. The fields couple exclusively
through cubic Feynman vertices which also serve as the structure constants of
an underlying kinematic algebra. The action is invariant under a combination of
internal and spacetime symmetries whose conservation equations imply
flavor-kinematics duality, ensuring that all Feynman diagrams satisfy kinematic
Jacobi identities. Substituting flavor for kinematics, we derive a new cubic
action for the special Galileon theory. In this picture, the vanishing soft
behavior of amplitudes is a byproduct of the Weinberg soft theorem.


  The DEEP-South photometric census of small Solar System bodies produces
massive time-series data of variable, transient or moving objects as a
by-product. To fully investigate unexplored variable phenomena, we present an
application of multi-aperture photometry and FastBit indexing techniques for
faster access to a portion of the DEEP-South year-one data. Our new pipeline is
designed to perform automated point source detection, robust high-precision
photometry and calibration of non-crowded fields which have overlap with
previously surveyed areas. In this paper, we show some examples of
catalog-based variability searches to find new variable stars and to recover
targeted asteroids. We discover 21 new periodic variables with period ranging
between 0.1 and 31 days, including four eclipsing binary systems (detached,
over-contact, and ellipsoidal variables), one white dwarf/M dwarf pair
candidate, and rotating variable stars. We also recover astrometry ($<\pm$1--2
arcsec level accuracy) and photometry of two targeted near-earth asteroids,
2006 DZ169 and 1996 SK, along with the small- ($\sim$0.12 mag) and relatively
large-amplitude ($\sim$0.5 mag) variations of their dominant rotational signals
in $R$-band.


  We propose a foundation model for soccer, which is able to predict subsequent
actions in a soccer match from a given input sequence of actions. As a proof of
concept, we train a transformer architecture on three seasons of data from a
professional soccer league. We quantitatively and qualitatively compare the
performance of this transformer architecture to two baseline models: a Markov
model and a multi-layer perceptron. Additionally, we discuss potential
applications of our model. We provide an open-source implementation of our
methods at https://github.com/danielhocevar/Foundation-Model-for-Soccer.


  Two-dimensional hexagonal boron nitride (hBN) that hosts bright
room-temperature single-photon emitters (SPEs) is a promising material platform
for quantum information applications. An important step towards the practical
application of hBN is the on-demand, position-controlled generation of SPEs.
Several strategies have been reported to achieve the deterministic creation of
hBN SPEs. However, they either rely on a substrate nanopatterning procedure
that is not compatible with integrated photonic devices or utilize a radiation
source that might cause unpredictable damage to hBN and underlying substrates.
Here, we report a radiation- and lithography-free route to deterministically
activate hBN SPEs by nanoindentation with an atomic force microscope (AFM) tip.
The method is applied to thin hBN flakes (less than 25 nm in thickness) on flat
silicon-dioxide-silicon substrates that can be readily integrated into on-chip
photonic devices. The achieved SPEs yields are above 30% by utilizing multiple
indent sizes, and a maximum yield of 36% is demonstrated for the indent size of
around 400 nm. Our results mark an important step towards the deterministic
creation and integration of hBN SPEs with photonic and plasmonic on-chip
devices.


  We investigate nonequilibrium phase transitions in classical Heisenberg spin
chains associated with spontaneous breaking of parity-time ($\mathcal{PT}$)
symmetry of the system under the action of Slonczewski spin-transfer torque
(STT) modeled by an applied \textit{imaginary} magnetic field. We reveal the
STT-driven $\mathcal{PT}$ symmetry-breaking phase transition between the
regimes of precessional and exponentially damped spin dynamics and show that
its several properties can be derived from the distribution of zeros of the
system's partition function, the approach first introduced by Yang and Lee for
studying equilibrium phase transitions in Ising spin chains. The physical
interpretation of imaginary magnetic field as describing the action of
non-conservative forces opens the possibility of direct observations of
Lee-Yang zeros in nonequilibrium physical systems.


  For a large number of real nonlinear equations, either continuous or
discrete, integrable or nonintegrable, we show that whenever a real nonlinear
equation admits a solution in terms of $\sech x$, it also admits solutions in
terms of the PT-invariant combinations $\sech x \pm i \tanh x$. Further, for a
number of real nonlinear equations we show that whenever a nonlinear equation
admits a solution in terms $\sech^2 x$, it also admits solutions in terms of
the PT-invariant combinations $\sech^2 x \pm i \sech x \tanh x$. Besides, we
show that similar results are also true in the periodic case involving Jacobi
elliptic functions.


  Cooper pair (CP) binding with both zero and nonzero center-of-mass momenta
(CMM) is studied with a set of renormalized equations assuming a short-ranged
(attractive) pairwise interfermion interaction. Expanding the associated
dispersion relation in 2D in powers of the CMM, in weak-to-moderate coupling a
term {\it linear} in the CMM dominates the pair excitation energy, while the
quadratic behavior usually assumed in Bose-Einstein (BE)-condensation studies
prevails for any coupling {\it only} in the limit of zero Fermi velocity when
the Fermi sea disappears, i.e., in vacuum. In 3D this same behavior is observed
numerically. The linear term, moreover, exhibits CP breakup beyond a threshold
CMM value which vanishes with coupling. This makes all the excited
(nonzero-CMM) BE levels with preformed CPs collapse into a single ground level
so that a BCS condensate (where only zero CMM CPs are usually allowed) appears
in zero coupling to be a special case in either 2D or 3D of the BE condensate
of linear-dispersion-relation CPs.


  Passive scalar transport in turbulent channel flow subject to spanwise system
rotation is studied by direct numerical simulations. The Reynolds number $Re =
U_b h/\nu$ is fixed at $20\,000$ and the rotation number $Ro = 2 \Omega h/U_b$
is varied from 0 to 1.2, where $U_b$ is the bulk mean velocity, $h$ the half
channel gap width and $\Omega$ the rotation rate. The scalar value is constant
but different at the two walls, leading to steady scalar transport across the
channel. The rotation causes an unstable channel side with relatively strong
turbulence and turbulent scalar transport, and a stable channel side with
relatively weak turbulence or laminar-like flow, weak turbulent scalar
transport but large scalar fluctuations and steep mean scalar gradients. The
distinct turbulent-laminar patterns observed at certain $Ro$ on the stable
channel side induce similar patterns in the scalar gradient field. The main
conclusions of the study are that rotation reduces the similarity between the
scalar and velocity field and that the Reynolds analogy for scalar-momentum
transport does not hold for rotating turbulent channel flow. This is shown by a
reduced correlation between velocity and scalar fluctuations, and a strongly
reduced turbulent Prandtl number of less than 0.2 on the unstable channel side
away from the wall at higher $Ro$. On the unstable channel side, scalar scales
become larger than turbulence scales according to spectra and the turbulent
scalar flux vector becomes more aligned with the mean scalar gradient owing to
rotation. Budgets in the governing equations of the scalar energy and scalar
fluxes are presented and discussed as well as other statistics relevant for
turbulence modelling.


  The statistical analysis and the spherical wavelet analysis of the SDSS DR7
quasars distribution and of the WMAP CMB anisotropy are performed. They
revealed the qualitative agreement between the angular power spectrum of CMB
and the angular power spectrum of the quasar distribution on the celestial
sphere. The angular correlation function and the angular power spectrum of the
quasar distribution may be described by the power laws. The large quasar groups
are discovered and they form the fractal set: the relation between their
angular size and a number of quasar groups with this size is characterized by a
power-law with fractal dimension 2.08.


  We study the influence of magnetic scattering on the Josephson critical
current, Ic, in a superconductor/ferromagnetic metal/superconductor (SFS)
junction by a tunneling Hamiltonian approach. An analytical formula of Ic is
given in the fourth order perturbation theory as regards the tunneling matrix
element. The Ic exhibits the damped oscillatory dependence on the thickness of
the ferromagnetic metal, d, and shows the transition between 0- and pi-states
with d. When the superconducting transition temperature is comparable to the
ferromagnetic Curie temperature, the period of oscillation is obviously changed
by increasing temperature, T, due to the magnetic scattering, which induces the
0-pi transition with T. The magnetic scattering provides rich variety of
Josephson effect in the SFS junction. Our results present an appropriate
condition of a superconductor and a ferromagnetic metal to control the 0- and
the pi-states.


  We give several general theorems concerning positive definite solutions of
Riemann-Hilbert problems on the real line. Furthermore, as an example, we apply
our theory to the characteristic function of a class of L\'{e}vy processes and
we find the distribution of their extrema at a given stopping time.


  We implement self-consistent microscopic calculations in order to describe
out-of-equilibrium non-local transport in normal metal-superconductor-normal
metal hybrid structures in the presence of a magnetic field and for arbitrary
interface transparencies. A four terminal setup simulating usual experimental
situations is described by means of a tight-binding model. We present results
for the self-consistent order parameter and current profiles within the sample.
These profiles illustrate a crossover from a quasi-equilibrium to a strong
non-equilibrium situation when increasing the interface transparencies and the
applied voltages. We analyze in detail the behavior of the non-local
conductance in these two different regimes. While in quasi-equilibrium
conditions this can be expressed as the difference between elastic cotunneling
and crossed Andreev transmission coefficients, in a general situation
additional contributions due to the voltage dependence of the self-consistent
order parameter have to be taken into account. The present results provide a
first step towards a self-consistent theory of non-local transport including
non-equilibrium effects and describe qualitatively a recent experiment [Phys.
Rev. Lett. 97, 237003 (2006)].


  We study spacetimes with a closed conformal Killing-Yano tensor. It is shown
that the D-dimensional Kerr-NUT-de Sitter spacetime constructed by Chen-Lu-Pope
is the only spacetime admitting a rank-2 closed conformal Killing-Yano tensor
with a certain symmetry.


  We consider Angelesco ensembles with respect to two modified Jacobi weights
on touching intervals [a,0] and [0,1], for a < 0. As a \to -1 the particles
around 0 experience a phase transition. This transition is studied in a double
scaling limit, where we let the number of particles of the ensemble tend to
infinity while the parameter a tends to -1 at a rate of order n^{-1/2}. The
correlation kernel converges, in this regime, to a new kind of universal
kernel, the Angelesco kernel K^{Ang}. The result follows from the Deift/Zhou
steepest descent analysis, applied to the Riemann-Hilbert problem for multiple
orthogonal polynomials.


  Roadmaps constructed by many sampling-based motion planners coincide, in the
absence of obstacles, with standard models of random geometric graphs (RGGs).
Those models have been studied for several decades and by now a rich body of
literature exists analyzing various properties and types of RGGs. In their
seminal work on optimal motion planning Karaman and Frazzoli (2011) conjectured
that a sampling-based planner has a certain property if the underlying RGG has
this property as well. In this paper we settle this conjecture and leverage it
for the development of a general framework for the analysis of sampling-based
planners. Our framework, which we call localization-tessellation, allows for
easy transfer of arguments on RGGs from the free unit-hypercube to spaces
punctured by obstacles, which are geometrically and topologically much more
complex. We demonstrate its power by providing alternative and (arguably)
simple proofs for probabilistic completeness and asymptotic (near-)optimality
of probabilistic roadmaps (PRMs). Furthermore, we introduce several variants of
PRMs, analyze them using our framework, and discuss the implications of the
analysis.


  Notions of robust and "classical" states for an open quantum system are
introduced and discussed in the framework of the isometric-sweeping
decomposition of trace class operators. Using the predictability sieve proposed
by Zurek, ``quasi-classical'' states are defined. A number of examples
illustrating how the ``quasi-classical'' states correspond to classical points
in phase space connected with the measuring apparatus are presented.


  Using the Hartree-Fock-Bogoliubov (HFB) self-consistent mean-field theory
with the SkM* Skyrme energy-density functional, we study nuclear structure
properties of even-even superheavy nuclei (SHN) of $Z=120$ isotopes and $N=184$
isotones. The shape of the nucleus along the lowest energy curve as a function
of the quadrupole moment $Q_{20}$ makes a sudden transition from the oblate
spheroids (biconcave discs) to the toroidal shapes, in the region of large
oblate quadrupole moments.


  Traditional ferroelectrics undergo thermally-induced phase transitions
whereby their structural symmetry increases. The associated higher-symmetry
structure is dubbed {\em paraelectric}. Ferroelectric transition metal
dichalcogenide bilayers have been recently shown to become paraelectric, but
not much has been said of the atomistic configuration of such a phase. As
discovered through numerical calculations that include molecular dynamics here,
their paraelectricity can only be ascribed to a time average of ferroelectric
phases with opposing intrinsic polarizations, whose switching requires
macroscopically large areas to slip in unison.


  We develop and investigate a test for jumps based on high-frequency
observations of a fractional process with an additive jump component. The Hurst
exponent of the fractional process is unknown. The asymptotic theory under
infill asymptotics builds upon extreme value theory for weakly dependent,
stationary time series and extends techniques for the semimartingale case from
the literature. It is shown that the statistic on which the test is based on
weakly converges to a Gumbel distribution under the null hypothesis of no
jumps. We prove consistency under the alternative hypothesis when there are
jumps. Moreover, we establish convergence rates for local alternatives and
consistent estimation of jump times. In the process, we show that inference on
the Hurst exponent of a rough fractional process is robust with respect to
jumps. This provides an important insight for the growing literature on rough
volatility. We demonstrate sound finite-sample properties in a simulation study
and showcase the applicability of our methods in an empirical example with a
time series of volatilities.


  P. M. Cohn showed in 1971 that given a ring $R$, to describe, up to
isomorphism, a division ring $D$ generated by a homomorphic image of $R$ is
equivalent to specifying the set of square matrices over $R$ which map to
singular matrices over $D,$ and he determined precisely the conditions that
such a set of matrices must satisfy. The present author later developed another
version of this data, in terms of closure operators on free $R$-modules.
  In this note, we examine the latter concept further, and show how an
$R$-module $M$ satisfying certain conditions can be made to induce such data.
In an appendix we make some observations on Cohn's original construction, and
note how the data it uses can similarly be induced by appropriate sorts of
$R$-modules.
  Our motivation is the longstanding question of whether, for $G$ a
right-orderable group and $k$ a field, the group algebra $kG$ must be
embeddable in a division ring. Our hope is that the right $kG$-module
$M=k((G))$ might induce a closure operator of the required sort. We re-prove a
partial result in this direction due to N. I. Dubrovin, note a plausible
generalization thereof which would give the desired embedding, and briefly
sketch some thoughts on other ways of approaching the problem.


  Additive regression trees are flexible non-parametric models and popular
off-the-shelf tools for real-world non-linear regression. In application
domains, such as bioinformatics, where there is also demand for probabilistic
predictions with measures of uncertainty, the Bayesian additive regression
trees (BART) model, introduced by Chipman et al. (2010), is increasingly
popular. As data sets have grown in size, however, the standard
Metropolis-Hastings algorithms used to perform inference in BART are proving
inadequate. In particular, these Markov chains make local changes to the trees
and suffer from slow mixing when the data are high-dimensional or the best
fitting trees are more than a few layers deep. We present a novel sampler for
BART based on the Particle Gibbs (PG) algorithm (Andrieu et al., 2010) and a
top-down particle filtering algorithm for Bayesian decision trees
(Lakshminarayanan et al., 2013). Rather than making local changes to individual
trees, the PG sampler proposes a complete tree to fit the residual. Experiments
show that the PG sampler outperforms existing samplers in many settings.


  We study the so--called ring $(p,Q)$--mappings which are the natural
generalization of quasiregular and quasi isometric mappings. It is proved that
open discrete ring $(p,Q)$--mappings are differentiable a.e. and belong to the
class $ACL$ in ${\Bbb R}^n$, $n\ge 2$, furthermore, $f\in W_{loc}^{1,1}$
provided that $Q\in L^{1}_{loc}$ and $p>n-1$


  A gauge-invariant Wigner quasi-distribution function for charged particles in
classical electromagnetic fields is derived in a rigorous way. Its relation to
the axial gauge is discussed, as well as the relation between the kinetic and
canonical momenta in the Wigner representation. Gauge-invariant quantum analogs
of Hamilton-Jacobi and Boltzmann kinetic equations are formulated for arbitrary
classical electromagnetic fields in terms of the "slashed" derivatives and
momenta, introduced for this purpose. The kinetic meaning of these slashed
quantities is discussed. We introduce gauge-invariant conditional moments and
use them to derive a kinetic momentum continuity equation. This equation
provides us with a hydrodynamic representation for quantum transport processes
and a definition of the "collision force". The hydrodynamic equation is applied
for the rotation part of the electron motion. The theory is illustrated by its
application in three examples. These are: Wigner quasi-distribution function
and equations for an electron in a magnetic field and harmonic potential;
Wigner quasi-distribution function for a charged particle in periodic systems
using the kq representation; two Wigner quasi-distribution functions for
heavy-mass polaron in an electric field.


  Denote by s_F(n) the minimal number of Fibonacci numbers needed to write n as
a sum of Fibonacci numbers. We obtain the extremal minimal and maximal orders
of magnitude of s_F(n^h)/s_F(n) for any h>= 2. We use this to show that for all
$>N_0(h) there is an n such that n is the sum of N Fibonacci numbers and n^h is
the sum of at most 130 h^2 Fibonacci numbers. Moreover, we give upper and lower
bounds on the number of n's with small and large values of s_F(n^h)/s_F(n).
This extends a problem of Stolarsky to the Zeckendorf representation of powers,
and it is in line with the classical investigation of finding perfect powers
among the Fibonacci numbers and their finite sums.


  The rate of accretion of matter from a solar-type star onto a primordial
black hole (PBH) that passes through it is calculated. The probability that a
PBH is captured into an orbit around a star in a galaxy is found. The mean
lifetime of the PBH in such an orbit and the rate of orbital captures of PBHs
in the galaxy are calculated. It is shown that this rate does not depend on the
mass of the PBH. This mechanism cannot make an appreciable contribution to the
rate of observed gamma-ray bursts. The density of PBHs in the galaxy can reach
a critical value - the density of the mass of dark matter in the galaxy.


  We aim to derive the electron temperature Te in the gas of metal-rich
star-forming galaxies, which can be obtained from their ratios of auroral lines
[O II]7320,7330 to nebular lines [O II]3727, in order to establish a more
robust mass-metallicity relationship, and compare the Te-based (O/H) abundances
with those from empirical strong-line calibrations, such as R23. We obtained 27
spectra by stacking the spectra of several hundred (even several thousand)
star-forming galaxies selected from the SDSS-DR4 in each of the 27 stellar mass
bins from log(M*) ~8.0 to 10.6 (logMsun). This "stack" method sufficiently
improves the signal-to-noise ratio of the auroral lines [O II]7320,7330, which
allow us to reliably obtain the electron temperature t2 in the low ionization
region from the ratio of [O II]7320,7330 to [O II]3727, then t3 in the high
ionization region from t2 by using a relation, and then the direct (O/H)
abundances from Te. The results show that the empirical R23 method will
overestimate the log(O/H) by 0.2 to 0.6 dex for these moderate metal-rich
galaxies. The new metal-mass relationship of the galaxies with moderate
metallicities is fitted by a linear fit (12+log(O/H) =6.223+0.231*logM*)
confirming that empirical methods significantly overestimate (O/H). We also
derived their (N/O) abundance ratios on the basis of the Te method, which are
consistent with the combination of the primary and secondary components of
nitrogen. For actual use, we re-derive the relations of 12+log(O/H)(Bay) vs.
logM* and 12+log(O/H)(Bay) vs. logR23 from the SDSS-DR4 data, which are a bit
different from those derived from DR2.


  Based on general considerations rather than model-dependent fits to specific
scenarios, we argue that an increase with energy of the positron fraction in
cosmic rays, suggested by several experiments at E>~7 GeV, most likely requires
a primary source of electron-positron pairs. We discuss the possible
alternatives, and find none of them plausible on astrophysical or particle
physics grounds. Further observational ways to test different scenarios are
discussed.


  Diffusion-based generative models have emerged as powerful tools in the realm
of generative modeling. Despite extensive research on denoising across various
timesteps and noise levels, a conflict persists regarding the relative
difficulties of the denoising tasks. While various studies argue that lower
timesteps present more challenging tasks, others contend that higher timesteps
are more difficult. To address this conflict, our study undertakes a
comprehensive examination of task difficulties, focusing on convergence
behavior and changes in relative entropy between consecutive probability
distributions across timesteps. Our observational study reveals that denoising
at earlier timesteps poses challenges characterized by slower convergence and
higher relative entropy, indicating increased task difficulty at these lower
timesteps. Building on these observations, we introduce an easy-to-hard
learning scheme, drawing from curriculum learning, to enhance the training
process of diffusion models. By organizing timesteps or noise levels into
clusters and training models with ascending orders of difficulty, we facilitate
an order-aware training regime, progressing from easier to harder denoising
tasks, thereby deviating from the conventional approach of training diffusion
models simultaneously across all timesteps. Our approach leads to improved
performance and faster convergence by leveraging benefits of curriculum
learning, while maintaining orthogonality with existing improvements in
diffusion training techniques. We validate these advantages through
comprehensive experiments in image generation tasks, including unconditional,
class-conditional, and text-to-image generation.


  Almost all remote sensing atmospheric PM2.5 estimation methods need satellite
aerosol optical depth (AOD) products, which are often retrieved from
top-of-atmosphere (TOA) reflectance via an atmospheric radiative transfer
model. Then, is it possible to estimate ground-level PM2.5 directly from
satellite TOA reflectance without a physical model? In this study, this
challenging work are achieved based on a machine learning model. Specifically,
we establish the relationship between PM2.5, satellite TOA reflectance,
observation angles, and meteorological factors in a deep learning architecture
(denoted as Ref-PM modeling). Taking the Wuhan Urban Agglomeration (WUA) as a
case study, the results demonstrate that compared with the AOD-PM modeling, the
Ref-PM modeling obtains a competitive performance, with out-of-sample
cross-validated R2 and RMSE values of 0.87 and 9.89 ug/m3 respectively. Also,
the TOA-reflectance-derived PM2.5 have a finer resolution and larger spatial
coverage than the AOD-derived PM2.5. This work updates the traditional
cognition of remote sensing PM2.5 estimation and has the potential to promote
the application in atmospheric environmental monitoring.


  Function secret sharing (FSS) scheme is a mechanism that calculates a
function f(x) for x in {0,1}^n which is shared among p parties, by using
distributed functions f_i:{0,1}^n -> G, where G is an Abelian group, while the
function f:{0,1}^n -> G is kept secret to the parties. Ohsawa et al. in 2017
observed that any function f can be described as a linear combination of the
basis functions by regarding the function space as a vector space of dimension
2^n and gave new FSS schemes based on the Fourier basis. All existing FSS
schemes are of (p,p)-threshold type. That is, to compute f(x), we have to
collect f_i(x) for all the distributed functions. In this paper, as in the
secret sharing schemes, we consider FSS schemes with any general access
structure. To do this, we observe that Fourier-based FSS schemes by Ohsawa et
al. are compatible with linear secret sharing scheme. By incorporating the
techniques of linear secret sharing with any general access structure into the
Fourier-based FSS schemes, we show Fourier-based FSS schemes with any general
access structure.


  Using the framework of soft-collinear effective theory (SCET), we factorize
and calculate $e^+e^-$ angularity distributions, including perturbative
resummation and the incorporation of a universal model for the nonperturbative
soft function. Angularities $\tau_a$ are a class of event shapes varying in
their sensitivity to the substructure of jets in the final state, controlled by
a continuous parameter $a<2$. We calculate the jet and soft functions in
factorized angularity distributions for all $a<1$ to first order in the strong
coupling $\alpha_s$ and resum large logarithms to next-to-leading logarithmic
(NLL) accuracy. We employ a universal model for the nonperturbative soft
function with a gap parameter which cancels the renormalon ambiguity in the
partonic soft function.


  Mobile notification systems play a major role in a variety of applications to
communicate, send alerts and reminders to the users to inform them about news,
events or messages. In this paper, we formulate the near-real-time notification
decision problem as a Markov Decision Process where we optimize for multiple
objectives in the rewards. We propose an end-to-end offline reinforcement
learning framework to optimize sequential notification decisions. We address
the challenge of offline learning using a Double Deep Q-network method based on
Conservative Q-learning that mitigates the distributional shift problem and
Q-value overestimation. We illustrate our fully-deployed system and demonstrate
the performance and benefits of the proposed approach through both offline and
online experiments.


  The frustrated three-leg antiferromagnetic spin-1/2 tube with a weak
interchain coupling in a magnetic field is investigated by means of Abelian
bosonization techniques. It is clearly shown that a vector chiral order and a
one-component Tomonaga-Luttinger liquid coexist in a wide magnetic-field region
from a state with a small magnetization to a nearly saturated one. The chiral
order is predicted to still survive in the intermediate plateau state. We
further predict that (even) when the strength of one bond in the three rung
couplings is decreased (increased), an Ising type quantum phase transition
takes place and the chirality vanishes (no singular phenomena occur and the
chiral order is maintained). Even without magnetic fields, the chiral order
would also be present, if the spin tube possess easy-plane anisotropy.


  When predicting a target variable $Y$ from features $X$, the prediction
$\hat{Y}$ can be performative: an agent might act on this prediction, affecting
the value of $Y$ that we eventually observe. Performative predictions are
deliberately prevalent in algorithmic decision support, where a Decision
Support System (DSS) provides a prediction for an agent to affect the value of
the target variable. When deploying a DSS in high-stakes settings (e.g.
healthcare, law, predictive policing, or child welfare screening) it is
imperative to carefully assess the performative effects of the DSS. In the case
that the DSS serves as an alarm for a predicted negative outcome, naive
retraining of the prediction model is bound to result in a model that
underestimates the risk, due to effective workings of the previous model. In
this work, we propose to model the deployment of a DSS as causal domain shift
and provide novel cross-domain identification results for the conditional
expectation $E[Y | X]$, allowing for pre- and post-hoc assessment of the
deployment of the DSS, and for retraining of a model that assesses the risk
under a baseline policy where the DSS is not deployed. Using a running example,
we empirically show that a repeated regression procedure provides a practical
framework for estimating these quantities, even when the data is affected by
sample selection bias and selective labelling, offering for a practical,
unified solution for multiple forms of target variable bias.


  The insulator-metal transition in single crystal La(5/8-y)Pr(y)Ca(3/8)MnO3
with y=0.35 was studied using synchrotron x-ray diffraction, electric
resistivity, magnetic susceptibility, and specific heat measurements. Despite
the dramatic drop in the resistivity at the insulator-metal transition
temperature Tmi, the charge-ordering (CO) peaks exhibit no anomaly at this
temperature and continue to grow below Tmi. Our data suggest then, that in
addition to the CO phase, another insulating phase is present below Tco. In
this picture, the insulator-metal transition is due to the changes within this
latter phase. The CO phase does not appear to play a major role in this
transition. We propose that a percolation-like insulator-metal transition
occurs via the growth of ferromagnetic metallic domains within the parts of the
sample that do not exhibit charge ordering. Finally, we find that the
low-temperature phase-separated state is unstable against x-ray irradiation,
which destroys the CO phase at low temperatures.


  We compute all 3-point functions of the ``universal'' scalar operators
contained in the interacting, maximally supersymmetric CFTs at large N by using
the AdS/CFT correspondence. These SCFTs are related to the low energy
description of M5, M2 and D3 branes, and the common set of universal scalars
corresponds through the AdS/CFT relation to the fluctuations of the metric and
the magnetic potential along the internal manifold. For the interacting (0,2)
SCFT_6 at large N, which is related to M5 branes, this set of scalars is
complete, while additional non-universal scalar operators are present in the
d=4, N=4 super Yang-Mills theory and in the N=8 SCFT_3, related to D3 and M2
branes, respectively.


  We introduce segmental recurrent neural networks (SRNNs) which define, given
an input sequence, a joint probability distribution over segmentations of the
input and labelings of the segments. Representations of the input segments
(i.e., contiguous subsequences of the input) are computed by encoding their
constituent tokens using bidirectional recurrent neural nets, and these
"segment embeddings" are used to define compatibility scores with output
labels. These local compatibility scores are integrated using a global
semi-Markov conditional random field. Both fully supervised training -- in
which segment boundaries and labels are observed -- as well as partially
supervised training -- in which segment boundaries are latent -- are
straightforward. Experiments on handwriting recognition and joint Chinese word
segmentation/POS tagging show that, compared to models that do not explicitly
represent segments such as BIO tagging schemes and connectionist temporal
classification (CTC), SRNNs obtain substantially higher accuracies.


  In this paper, we examine in detail the principal branches of solutions that
arise in vector discrete models with nonlinear inter-component coupling and
four wave mixing. The relevant four branches of solutions consist of two single
mode branches (transverse electric and transverse magnetic) and two mixed mode
branches, involving both components (linearly polarized and elliptically
polarized). These solutions are obtained explicitly and their stability is
analyzed completely in the anti-continuum limit (where the nodes of the lattice
are uncoupled), illustrating the supercritical pitchfork nature of the
bifurcations that give rise to the latter two, respectively, from the former
two. Then the branches are continued for finite coupling constructing a full
two-parameter numerical bifurcation diagram of their existence. Relevant
stability ranges and instability regimes are highlighted and, whenever
unstable, the solutions are dynamically evolved through direct computations to
monitor the development of the corresponding instabilities. Direct connections
to the earlier experimental work of Meier et al. [Phys. Rev. Lett. {\bf 91},
143907 (2003)] that motivated the present work are given.


  Waveform decomposition is needed as a first step in the extraction of various
types of geometric and spectral information from hyperspectral full-waveform
LiDAR echoes. We present a new approach to deal with the "Pseudo-monopulse"
waveform formed by the overlapped waveforms from multi-targets when they are
very close. We use one single skew-normal distribution (SND) model to fit
waveforms of all spectral channels first and count the geometric center
position distribution of the echoes to decide whether it contains
multi-targets. The geometric center position distribution of the
"Pseudo-monopulse" presents aggregation and asymmetry with the change of
wavelength, while such an asymmetric phenomenon cannot be found from the echoes
of the single target. Both theoretical and experimental data verify the point.
Based on such observation, we further propose a hyperspectral waveform
decomposition method utilizing the SND mixture model with: 1) initializing new
waveform component parameters and their ranges based on the distinction of the
three characteristics (geometric center position, pulse width, and
skew-coefficient) between the echo and fitted SND waveform and 2) conducting
single-channel waveform decomposition for all channels and 3) setting
thresholds to find outlier channels based on statistical parameters of all
single-channel decomposition results (the standard deviation and the means of
geometric center position) and 4) re-conducting single-channel waveform
decomposition for these outlier channels. The proposed method significantly
improves the range resolution from 60cm to 5cm at most for a 4ns width laser
pulse and represents the state-of-the-art in "Pseudo-monopulse" waveform
decomposition.


  Geometric quantum manipulation and Landau-Zener interferometry have been
separately explored in many quantum systems. In this Letter, we combine these
two approaches to study the dynamics of a superconducting phase qubit. We
experimentally demonstrate Landau-Zener interferometry based on the pure
geometric phases in this solid-state qubit. We observe the interference caused
by a pure geometric phase accumulated in the evolution between two consecutive
Landau-Zener transitions, while the dynamical phase is canceled out by a
spin-echo pulse. The full controllability of the qubit state as a function of
the intrinsically robust geometric phase provides a promising approach for
quantum state manipulation.


  We present a general multi-component density functional theory in which
electrons and nuclei are treated completely quantum mechanically, without the
use of a Born-Oppenheimer approximation. The two fundamental quantities in
terms of which our theory is formulated are the nuclear N-body density and the
electron density expressed in coordinates referring to the nuclear framework.
For these two densities coupled Kohn-Sham equations are derived and the
electron-nuclear correlation functional is analyzed in detail. The formalism is
tested on the hydrogen molecule $H_2$ and its positive ion $H_2^+$ using
several approximations for the electron-nuclear correlation functional.


  It is known that interference alignment (IA) plays an important role in
improving the degree of freedom (DoF) of multi-input and multi-output (MIMO)
systems. However, most of the traditional IA schemes suffer from the high
computational complexity and require the global and instantaneous channel state
information (CSI), both of which make them difficult to be extended to cellular
MIMO systems. To handle these issues, two new interference alignment schemes,
i.e., the retrospective interference regeneration (RIR) scheme and the
beamforming based distributed retrospective interference alignment (B-DRIA)
scheme, are proposed for cellular K-user MIMO downlink networks. For the RIR
scheme, it adopts interference elimination algorithm to erase redundant symbols
in inter-cell interference (ICI) signals, and then uses interference
regeneration algorithm to avoid secondary interference. The RIR scheme obtains
greater DoF gain than the retrospective interference alignment (RIA) scheme,
but incurs performance degradation when the transceiver antennas ratio (TAR)
approaches 1. Therefore, the B-DRIA scheme is further proposed. For the B-DRIA
scheme, the cellular beamforming matrix is introduced to eliminate the ICI, and
meanwhile distributed retrospective interference alignment algorithm is adopted
to align inter-user interference (IUI). The simulation results show that the
B-DRIA scheme obtains larger DoF than the RIR scheme locally. Specifically,
when TAR approaches 1, two schemes obtain the same DoF. While TAR approaches 2,
the DoF of the B-DRIA scheme is superior than the RIR scheme.


  Let $\pi$ be a Hecke-Maass cusp form for $SL(3,\mathbb Z)$ and $f$ be a
holomorphic (or Maass) Hecke form for $SL(2,\mathbb{Z})$. In this paper we
prove the following subconvex bound $$ L\left(\tfrac{1}{2}+it,\pi\times
f\right)\ll_{\pi,f,\varepsilon} (1+|t|)^{\frac{3}{2}-\frac{1}{42}+\varepsilon}.
$$


  The Multi-Carrier Code Division Multiple Access (MC-CDMA) is becoming a very
significant downlink multiple access technique for high-rate data transmission
in the fourth generation wireless communication systems. By means of efficient
resource allocation higher data rate i.e. throughput can be achieved. This
paper evaluates the performance of criteria used for group (subchannel)
allocation employed in downlink transmission, which results in throughput
maximization. Proposed algorithm gives the modified technique of sub channel
allocation in the downlink transmission of MC-CDMA systems. Simulation are
carried out for all the three combining schemes, results shows that for the
given power and BER proposed algorithm comparatively gives far better results


  Given a simplicial complex $\Delta$, we investigate how to construct a new
simplicial complex $\bar{\Delta}$ such that the corresponding monomial ideals
satisfy nice algebraic properties. We give a procedure to check the vertex
decomposability of an arbitrary hypergraph. As a consequence, we prove that
attaching non-pure skeletons at all vertices of a cycle cover of a simplicial
complex $\Delta$ results in a simplicial complex $\bar{\Delta}$ such that the
associated hypergraph $\mathcal{H}(\bar{\Delta})$ is vertex decomposable. Also,
we prove that all symbolic powers of the cover ideal of $\bar{\Delta}$ are
componentwise linear. Our work generalizes the earlier known result where
non-pure complete graphs were added to all vertices of a cycle cover of a
graph.


  Reactive synthesis deals with the automated construction of implementations
of reactive systems from their specifications. To make the approach feasible in
practice, systems engineers need effective and efficient means of debugging
these specifications.
  In this paper, we provide techniques for report-based specification
debugging, wherein salient properties of a specification are analyzed, and the
result presented to the user in the form of a report. This provides a
low-effort way to debug specifications, complementing high-effort techniques
including the simulation of synthesized implementations.
  We demonstrate the usefulness of our report-based specification debugging
toolkit by providing examples in the context of generalized reactivity(1)
synthesis.


  In this paper, we study the trade-offs of different inference approaches for
Bayesian matrix factorisation methods, which are commonly used for predicting
missing values, and for finding patterns in the data. In particular, we
consider Bayesian nonnegative variants of matrix factorisation and
tri-factorisation, and compare non-probabilistic inference, Gibbs sampling,
variational Bayesian inference, and a maximum-a-posteriori approach. The
variational approach is new for the Bayesian nonnegative models. We compare
their convergence, and robustness to noise and sparsity of the data, on both
synthetic and real-world datasets. Furthermore, we extend the models with the
Bayesian automatic relevance determination prior, allowing the models to
perform automatic model selection, and demonstrate its efficiency.


  We consider the following chemotaxis systems $$\begin{cases}u_t=\Delta
u-\chi_1\nabla(u\nabla v_1)+\chi_2\nabla(u\nabla v_2)+u(a-bu),\ \ x\in\mathbb
R^N,t>0,\\0=(\Delta-\lambda_1I)v_1+\mu_1u,\ \ x\in\mathbb
R^N,t>0,\\0=(\Delta-\lambda_2I)v_2+\mu_2u,\ \ \text{in}\ x\in\mathbb R^N,\
t>0,\\u(\cdot,0)=u_0,\ \ x\in\mathbb R^N,\end{cases}$$where $\chi_i,\
\lambda_i,\ \mu_i,\ i=1,2$ and $a,\ b$ are positive constant real numbers and
$N$ is a positive integer. Under some conditions on the parameters, we prove
the global existence and boundedness of classical solutions
$(u(x,t;u_0),v_1(x,t;u_0),v_2(x,t;u_0))$ for nonnegative, bounded, and
uniformly continuous initials $u_0(x)$. Next, we show that, for every strictly
positive initial
\,$u_0(x)$,$$\lim_{t\to\infty}\left[\|u(\cdot,t;u_0)-\frac{a}{b}\|_{\infty}+\|\lambda_1v_1(\cdot,t;u_0)-\frac{a}{b}\mu_1\|_{\infty}+\|\lambda_2v_2(\cdot,t;u_0)-\frac{a}{b}\mu_2\|_{\infty}\right]=0.$$
Finally, we explore the spreading properties of the global solutions and prove
that there are two positive numbers
$0<c^*_-(\chi_1,\mu_1,\lambda_1,\chi_2,\mu_2,\lambda_2)<c^*_+(\chi_1,\mu_1,\lambda_1,\chi_2,\mu_2,\lambda_2)$
such that for every nonegative initial $u_0(x)$ with nonempty and compact
support,
$$\lim_{t\to\infty}\left[\sup_{|x|\leq{ct}}|u(x,t;u_0)-\frac{a}{b}|+\sup_{|x|\leq
ct}|\lambda_1v_1(x,t;u_0)-\frac{a}{b}\mu_1|+\sup_{|x|\leq
ct}|\lambda_2v_2(x,t;u_0)-\frac{a}{b}\mu_2|\right]=0$$whenever $0\leq
c<c^*_-(\chi_1,\mu_1,\lambda_1,\chi_2,\mu_2,\lambda_2)$,\
and$$\lim_{t\to\infty}\left[\sup_{|x|\geq ct}|u(x,t;u_0)|+\sup_{|x|\geq ct} |
v_1(x,t;u_0)|+\sup_{|x|\geq ct}|v_2(x,t;u_0)|\right]=0$$whenever
$c>c^*_+(\chi_1,\mu_1,\lambda_1,\chi_2,\mu_2,\lambda_2)$. Furthermore we show
that$$\lim_{(\chi_1,\chi_2)\to(0,0)}c^*_-(\chi_1,\mu_1,\lambda_1,\chi_2,\mu_2,\lambda_2)=\lim_{(\chi_1,\chi_2)\to(0,0)}c^*_+(\chi_1,\mu_1,\lambda_1,\chi_2,\mu_2,\lambda_2)=2\sqrt{a}.$$


  A model for the production of quarkonium states in the midrapidity region at
RHIC and LHC energy range is presented which explores well understood
properties of QCD only. An increase of the quarkonium hadronisation time with
the initial energy leads to a gradual change of the most important phenomena
from fixed target- to collider-energies. We evaluate nuclear effects in the
quarkonium production due to medium modification of the momentum distribution
of the heavy quarks produced in the hard interactions, i.e. due to the
broadening of the transverse momentum distribution. Other nuclear effects, i.e.
nuclear shadowing and parton energy loss, are also evaluated.


  The explanation of the accelerated expansion of the Universe poses one of the
most fundamental questions in physics and cosmology today. If the acceleration
is driven by some form of dark energy, one can try to constrain the parameters
using a cosmographic approach. Our high-redshift analysis allows us to put
constraints on the cosmographic expansion up to the fifth order. It is based on
the Union2 Type Ia Supernovae (SNIa) data set, the Hubble diagram constructed
from some Gamma Ray Bursts luminosity distance indicators, and gaussian priors
on the distance from the Baryon Acoustic Oscillations (BAO), and the Hubble
constant h (these priors have been included in order to help break the
degeneracies among model parameters). To perform our statistical analysis and
to explore the probability distributions of the cosmographic parameters we use
the Markov Chain Monte Carlo Method (MCMC). We finally investigate implications
of our results for the dark energy, in particular, we focus on the
parametrization of the dark energy equation of state (EOS). Actually, a
possibility to investigate the nature of dark energy lies in measuring the dark
energy equation of state, w, and its time (or redshift) dependence at high
accuracy. However, since w(z) is not directly accessible to measurement,
reconstruction methods are needed to extract it reliably from observations.
Here we investigate different models of dark energy, described through several
parametrizations of the equation of state, by comparing the cosmographic and
the EOS series.


  Few years ago we started the investigation of microscopic Optical Potentials
(OP) in the framework of chiral effective field theories and published our
results in a series of manuscripts. Starting from the very first work, where a
microscopic OP was introduced following the multiple scattering procedure of
Watson, and then followed by more recent works, where the agreement with
experimental data and phenomenological approaches was successfully tested, we
finally arrived at a description of elastic scattering processes off non-zero
spin nuclei. Among our achievements, it is worth mentioning the partial
inclusion of three-nucleon forces, and the extension of our OP to
antiproton-nucleus elastic scattering. Despite the overall good agreement with
empirical data obtained so far, we do believe that several improvements and
upgrades of the present approach are still to be achieved. In this short essay
we would like to address some of the most relevant achievements and discuss an
interesting development that, in our opinion, is needed to further improve
microscopic OPs in order to reach in a near future the same level of accuracy
of the phenomenological ones.


  With the prevalence of applications in cloud, Database as a Service (DBaaS)
becomes a promising method to provide cloud applications with reliable and
flexible data storage services. It provides a number of interesting features to
cloud developers, however, it suffers a few drawbacks: long learning curve and
development cycle, lacking of in-depth support for NoSQL, lacking of flexible
configuration for security and privacy, and high cost models. In this paper, we
investigate these issues among current DBaaS providers and propose a novel
Trinity Model that can significantly reduce the learning curves, improve the
security and privacy, and accelerate database design and development. We
further elaborate our ongoing and future work on developing large real-world
SaaS projects using this new DBaaS model.


  Nearly K\"{a}hler and K\"{a}hler-Codazzi type manifolds are defined in a very
similar way. We prove that nearly K\"{a}hler type manifolds have sense just in
Hermitian and para-Hermitian contexts, and that K\"{a}hler-Codazzi type
manifolds reduce to K\"{a}hler type manifolds in all the four Hermitian,
para-Hermitian, Norden and product Riemannian geometries.


  The $q$-state Potts model with a long-range correlated disorder is studied by
means of large-scale Monte Carlo simulations for $q=2,4,8$ and 16. Evidence is
given of the existence of a Griffiths phase, where the thermodynamic quantities
display an algebraic Finite-Size Scaling, in a finite range of temperatures
around the self-dual point. The critical exponents are shown to depend on both
the temperature and the exponent of the algebraic decay of disorder
correlations, but not on the number of states of the Potts model. The mechanism
leading to the violation of hyperscaling relations is observed in the entire
Griffiths phase.