  Prediction of time-to-event data often suffers from rare event rates, small
sample sizes, high dimensionality and low signal-to-noise ratios. Incorporating
published prediction models from large-scale studies is expected to improve the
performance of prognosis prediction on internal individual-level time-to-event
data. However, existing integration approaches typically assume that underlying
distributions from the external and internal data sources are similar, which is
often invalid. To account for challenges including heterogeneity, data sharing,
and privacy constraints, we propose a discrete failure time modeling procedure,
which utilizes a discrete hazard-based Kullback-Leibler discriminatory
information measuring the discrepancy between the published models and the
internal dataset. Simulations show the advantage of the proposed method
compared with those solely based on the internal data or published models. We
apply the proposed method to improve prediction performance on a kidney
transplant dataset from a local hospital by integrating this small-scale
dataset with published survival models obtained from the national transplant
registry.


  The thermodynamic properties of the Zr-Nb alloy are investigated at
temperatures below 890 K with ab initio calculations. The solution energies of
the bcc Nb-rich and hcp Zr-rich solid solutions obtained within the framework
of density functional theory are in good agreement with experimental data,
although insufficient for a quantitative description of the miscibility gap,
for which non configurational entropy has to be accounted for. Whereas
electronic free energies can be neglected, we show, using the harmonic
approximation and the density functional perturbation theory, that both
solution free energies are strongly modified by the contribution related to
atomic vibrations. Considering this vibrational free energy leads to a good
description of the phase diagram.


  Vision Transformers (ViTs) is emerging as an alternative to convolutional
neural networks (CNNs) for visual recognition. They achieve competitive results
with CNNs but the lack of the typical convolutional inductive bias makes them
more data-hungry than common CNNs. They are often pretrained on JFT-300M or at
least ImageNet and few works study training ViTs with limited data. In this
paper, we investigate how to train ViTs with limited data (e.g., 2040 images).
We give theoretical analyses that our method (based on parametric instance
discrimination) is superior to other methods in that it can capture both
feature alignment and instance similarities. We achieve state-of-the-art
results when training from scratch on 7 small datasets under various ViT
backbones. We also investigate the transferring ability of small datasets and
find that representations learned from small datasets can even improve
large-scale ImageNet training.


  We consider a variant of the classical Bin Packing Problem, called Fully
Dynamic Bin Packing. In this variant, items of a size in $(0,1]$ must be packed
in bins of unit size. In each time step, an item either arrives or departs from
the packing. An algorithm for this problem must maintain a feasible packing
while only repacking a bounded number of items in each time step.
  We develop an algorithm which repacks only a constant number of items per
time step and, unlike previous work, does not rely on bundling of small items
which allowed those solutions to move an unbounded number of small items as
one. Our algorithm has an asymptotic approximation ratio of roughly $1.3871$
which is complemented by a lower bound of Balogh et al., resulting in a tight
approximation ratio for this problem. As a direct corollary, we also close the
gap to the lower bound of the Relaxed Online Bin Packing Problem in which only
insertions of items occur.


  The linearity and parallelism of Regge trajectories is combined with a
hyperfine splitting relation in multiplet to study charmonium spectrum. It is
found that predictions to the spectrum of 1D multiplet could be made once
another 1D state is confirmed. The newly observed X(3872), Y(3940), X(3940),
Y(4260) and Z(3930) are studied within the charmonium framework.


  Deep reinforcement learning algorithms that learn policies by trial-and-error
must learn from limited amounts of data collected by actively interacting with
the environment. While many prior works have shown that proper regularization
techniques are crucial for enabling data-efficient RL, a general understanding
of the bottlenecks in data-efficient RL has remained unclear. Consequently, it
has been difficult to devise a universal technique that works well across all
domains. In this paper, we attempt to understand the primary bottleneck in
sample-efficient deep RL by examining several potential hypotheses such as
non-stationarity, excessive action distribution shift, and overfitting. We
perform thorough empirical analysis on state-based DeepMind control suite (DMC)
tasks in a controlled and systematic way to show that high temporal-difference
(TD) error on the validation set of transitions is the main culprit that
severely affects the performance of deep RL algorithms, and prior methods that
lead to good performance do in fact, control the validation TD error to be low.
This observation gives us a robust principle for making deep RL efficient: we
can hill-climb on the validation TD error by utilizing any form of
regularization techniques from supervised learning. We show that a simple
online model selection method that targets the validation TD error is effective
across state-based DMC and Gym tasks.


  The metric-affine gravity provides a useful framework for analyzing
gravitational dynamics since it treats metric tensor and affine connection as
fundamentally independent variables. In this work, we show that, a
metric-affine gravity theory composed of the invariants formed from
non-metricity, torsion and curvature tensors can be decomposed into a theory of
scalar, vector and tensor fields. These fields are natural candidates for the
ones needed by various cosmological and other phenomena. Indeed, we show that
the model accommodates TeVeS gravity (relativistic modified gravity theory),
vector inflation, and aether-like models. Detailed analyses of these and other
phenomena can lead to a standard metric-affine gravity model encoding scalars,
vectors and tensors.


  We present a Ritz-Galerkin discretization on sparse grids using pre-wavelets,
which allows to solve elliptic differential equations with variable
coefficients for dimension $d=2,3$ and higher dimensions $d>3$. The method
applies multilinear finite elements. We introduce an efficient algorithm for
matrix vector multiplication using a Ritz-Galerkin discretization and
semi-orthogonality.
  This algorithm is based on standard 1-dimensional restrictions and
prolongations, a simple pre-wavelet stencil, and the classical operator
dependent stencil for multilinear finite elements. Numerical simulation results
are presented for a 3-dimensional problem on a curvilinear bounded domain and
for a 6-dimensional problem with variable coefficients.
  Simulation results show a convergence of the discretization according to the
approximation properties of the finite element space. The condition number of
the stiffness matrix can be bounded below $10$ using a standard diagonal
preconditioner.


  The paper provides a simpler method for proving a delicate inequality that
was used by Achlioptis and Naor to establish asymptotic concentration for
chromatic numbers of Erdos-Renyi random graphs. The simplifications come from
two new ideas. The first involves a sharpened form of a piece of statistical
folklore regarding goodness-of-fit tests for two-way tables of Poisson counts
under linear conditioning constraints. The second idea takes the form of a new
inequality that controls the extreme tails of the distribution of a quadratic
form in independent Poissons random variables.


  We present a formalism to calculate the probability distribution function of
a scalar field coarse-grained over some spatial scales with a Gaussian filter
at finite temperature. As an application, we investigate the role of
subcritical fluctuations in the electroweak phase transition in the minimal
standard model. It is concluded that the universe was in a mixed state of true
and false vacua already at the critical temperature.


  We find a new exact solution to Einstein field equations that represents a
cosmological wormhole embedded in a flat Friedmann-Lema\^itre-Robertson-Walker
universe. The new metric is a generalization of a previous cosmological
wormhole solution found by Kim. We explicitly show that the flaring out
condition is satisfied at the throat at all cosmic times; in addition, the null
energy condition is violated at the throat regardless of the background
cosmological model; thus, the spacetime geometry presented here describes a
wormhole coupled to the cosmic dynamics that exists at all cosmic times and
whose throat remains open in any cosmological model.


  Access to essential medication is a primary right of every individual in all
developed, developing and underdeveloped countries. This can be fulfilled by
pharmaceutical supply chains (PSC) in place which will eliminate the boundaries
between different organizations and will equip them to work collectively to
make medicines reach even the remote corners of the globe. Due to multiple
entities, which are geographically widespread, being involved and very complex
goods and economic flows, PSC is very difficult to audit and resolve any issues
involved. This has given rise to many issues, including increased threats of
counterfeiting, inaccurate information propagation throughout the network
because of data fragmentation, lack of customer confidence and delays in
distribution of medication to the place in need. Hence, there is a strong need
for robust PSC which is transparent to all parties involved and in which the
whole journey of medicine from manufacturer to consumer can be tracked and
traced easily. This will not only build safety for the consumers, but will also
help manufacturers to build confidence among consumers and increase sales. In
this article, a novel Distributed Ledger Technology (DLT) based transparent
supply chain architecture is proposed and a proof-of-concept is implemented.
Efficiency and scalability of the proposed architecture is evaluated and
compared with existing solutions.


  The thermoelectric transport through a molecular bridge is discussed, with an
emphasis on the effects of inelastic processes of the transport electrons
caused by the coupling to the vibrational modes of the molecule. In particular
it is found that when the molecule is strongly coupled to a thermal bath of its
own, which may be at a temperature different from those of the electronic
reservoirs, a heat current between the molecule and the electrons can be
converted into electric current. Expressions for the transport coefficients
governing this conversion and similar ones are derived, and a possible scenario
for increasing their magnitudes is outlined.


  We apply Guo and Wang's relaxed belief propagation (BP) method to the
estimation of a random vector from linear measurements followed by a
componentwise probabilistic measurement channel. Relaxed BP uses a Gaussian
approximation in standard BP to obtain significant computational savings for
dense measurement matrices. The main contribution of this paper is to extend
the relaxed BP method and analysis to general (non-AWGN) output channels.
Specifically, we present detailed equations for implementing relaxed BP for
general channels and show that relaxed BP has an identical asymptotic large
sparse limit behavior as standard BP, as predicted by the Guo and Wang's state
evolution (SE) equations. Applications are presented to compressed sensing and
estimation with bounded noise.


  The set of degenerate ground states of an arbitrary nonabelian topologically
massive gauge theory is shown to be in one-to-one correspondence with the
Hilbert space of the associated pure Chern-Simons theory. (Paper is being
withdrawn: original conclusion is incorrect for the nonabelian case. For a
correct treatment, see M. Asorey, S. Carlip, and F. Falceto, hep-th/9304081.)


  Henri Deslandres initiated imaging spectroscopy of the solar atmosphere in
1892 at Paris observatory. He invented, concurrently with George Hale in
Kenwood (USA) but quite independently, the spectroheliograph designed for
monochromatic imagery of the Sun. Deslandres developed two kinds of
spectrographs: the ''spectroh{\'e}liographe des formes'', i.e. the narrow
bandpass instrument to reveal chromospheric structures (such as filaments,
prominences, plages and active regions); and the ''spectroh{\'e}liographe des
vitesses'', i.e. the ''section'' spectroheliograph to record line profiles of
cross sections of the Sun, in order to measure the Dopplershifts of dynamic
features. Deslandres moved to Meudon in 1898 with his instruments and improved
the spectral and spatial resolutions, leading to the large quadruple
spectroheliograph in 1908 developed with Lucien d'Azambuja. CaII K systematic
observations started at this date and were followed in 1909 by H$\alpha$ with
two dedicated 3-metres spectroheliographs. The observing service was organized
by d'Azambuja who also intensively used the large 7-metres spectroheliograph
for his research and thesis (1930). This paper summarizes fifty years of
research by Mr and Mrs d'Azambuja, who explored various photospheric and
chromospheric lines, performing special spectroheliograms with the high
dispersion 7-metres instrument. They also observed intensively filaments and
prominences (memoir published in 1948) and recorded rare solar activity events
with the two 3-metres spectroheliographs, during the first half of the
twentieth century.


  Accurate segmentation of anatomical structures and pathological regions in
medical images is crucial for diagnosis, treatment planning, and disease
monitoring. While the Segment Anything Model (SAM) and its variants have
demonstrated impressive interactive segmentation capabilities on image types
not seen during training without the need for domain adaptation or retraining,
their practical application in volumetric 3D medical imaging workflows has been
hindered by the lack of a user-friendly interface. To address this challenge,
we introduce FastSAM-3DSlicer, a 3D Slicer extension that integrates both 2D
and 3D SAM models, including SAM-Med2D, MedSAM, SAM-Med3D, and FastSAM-3D.
Building on the well-established open-source 3D Slicer platform, our extension
enables efficient, real-time segmentation of 3D volumetric medical images, with
seamless interaction and visualization. By automating the handling of raw image
data, user prompts, and segmented masks, FastSAM-3DSlicer provides a
streamlined, user-friendly interface that can be easily incorporated into
medical image analysis workflows. Performance evaluations reveal that the
FastSAM-3DSlicer extension running FastSAM-3D achieves low inference times of
only 1.09 seconds per volume on CPU and 0.73 seconds per volume on GPU, making
it well-suited for real-time interactive segmentation. Moreover, we introduce
an uncertainty quantification scheme that leverages the rapid inference
capabilities of FastSAM-3D for practical implementation, further enhancing its
reliability and applicability in medical settings. FastSAM-3DSlicer offers an
interactive platform and user interface for 2D and 3D interactive volumetric
medical image segmentation, offering a powerful combination of efficiency,
precision, and ease of use with SAMs. The source code and a video demonstration
are publicly available at https://github.com/arcadelab/FastSAM3D_slicer.


  In the current LHC run, an accurate understanding of Standard Model processes
is extremely important. Processes including electroweak gauge bosons serve as
standard candles for SM measurements, and equally constitute important
backgrounds for Beyond-the-Standard Model (BSM) searches. We present here the
next-to-leading order (NLO) QCD virtual contributions to W+W- + jet in an
analytic format obtained through unitarity methods. We present results for the
full process using the Monte Carlo event generator MCFM, and discuss total as
well as differential cross-sections for the LHC with 14 TeV center-of-mass
energy, as well as a future 100 TeV proton-proton machine.


  Anomalous magnetic properties of the double perovskite ruthenates compound
Sr2YRuO6 are reported here. Magnetization measurements as a function of
temperature in low magnetic fields show clear evidence for two components of
magnetic order (TM1 ~ 32K and TM2 ~ 27K) aligned opposite to each other with
respect to the magnetic field direction even though only Ru5+moments can order
magnetically in this compound. The second component of the magnetic order at
TM2 ~ 27K results only in a magnetization reversal, and not in the negative
magnetization when the magnetization is measured in the field cooled (FC) mode.
Isothermal magnetization (M-H) measurements show hysteresis with maximum
coercivity (Hc) and remnant magnetization (Mr) at T ~ 27 K, corroborating the
presence of the two oppositely aligned magnetic moments, each with a
ferromagnetic component. The two components of magnetic ordering are further
confirmed by the double peak structure in the heat capacity measurements. These
anomalous properties have significance to some of the earlier results obtained
for the Cu-substituted superconducting Sr2YRu1-xCuxO6 compounds.


  Whistler mode chorus waves are quasi-coherent electromagnetic emissions with
frequency chirping. Various models have been proposed to understand the
chirping mechanism, which is a long-standing problem in space plasmas. Based on
analysis of effective wave growth rate and electron phase space dynamics in a
self-consistent particle simulation, we propose here a phenomenological model
called the "Trap-Release-Amplify" (TaRA) model for chorus. In this model, phase
space structures of correlated electrons are formed by nonlinear wave particle
interactions, which mainly occur in the downstream. When released from the wave
packet in the upstream, these electrons selectively amplify new emissions which
satisfy the phase-locking condition to maximize wave power transfer, leading to
frequency chirping. The phase-locking condition at the release point gives a
frequency chirping rate that is fully consistent with the one by Helliwell in
case of a nonuniform background magnetic field. The nonlinear wave particle
interaction part of the TaRA model results in a chirping rate that is
proportional to wave amplitude, a conclusion originally reached by Vomvoridis
et al. Therefore, the TaRA model unifies two different results from seemingly
unrelated studies. Furthermore, the TaRA model naturally explains fine
structures of chorus waves, including subpackets and bandwidth, and their
evolution through dynamics of phase-trapped electrons. Finally, we suggest that
this model could be applied to explain other related phenomena, including
frequency chirping of chorus in a uniform background magnetic field and of
electromagnetic ion cyclotron waves in the magnetosphere.


  We introduce and analyze an efficient family of linear feedback shift
registers (LFSR's) with maximal period. This family is word-oriented and is
suitable for implementation in software, thus provides a solution to a recent
challenge posed in FSE '94. The classical theory of LFSR's is extended to
provide efficient algorithms for generation of irreducible and primitive LFSR's
of this new type.


  Email messages have been an important mode of communication, not only for
work, but also for social interactions and marketing. When messages have time
sensitive information, it becomes relevant for the sender to know what is the
expected time within which the email will be read by the recipient. In this
paper we use a survival analysis framework to predict the time to open an email
once it has been received. We use the Cox Proportional Hazards (CoxPH) model
that offers a way to combine various features that might affect the event of
opening an email. As an extension, we also apply a mixture model (MM) approach
to CoxPH that distinguishes between recipients, based on a latent state of how
prone to opening the messages each individual is. We compare our approach with
standard classification and regression models. While the classification model
provides predictions on the likelihood of an email being opened, the regression
model provides prediction of the real-valued time to open. The use of survival
analysis based methods allows us to jointly model both the open event as well
as the time-to-open. We experimented on a large real-world dataset of marketing
emails sent in a 3-month time duration. The mixture model achieves the best
accuracy on our data where a high proportion of email messages go unopened.


  Scholarly article impact reflects the significance of academic output
recognised by academic peers, and it often plays a crucial role in assessing
the scientific achievements of researchers, teams, institutions and countries.
It is also used for addressing various needs in the academic and scientific
arena, such as recruitment decisions, promotions, and funding allocations. This
article provides a comprehensive review of recent progresses related to article
impact assessment and prediction. The~review starts by sharing some insight
into the article impact research and outlines current research status. Some
core methods and recent progress are presented to outline how article impact
metrics and prediction have evolved to consider integrating multiple networks.
Key techniques, including statistical analysis, machine learning, data mining
and network science, are discussed. In particular, we highlight important
applications of each technique in article impact research. Subsequently, we
discuss the open issues and challenges of article impact research. At the same
time, this review points out some important research directions, including
article impact evaluation by considering Conflict of Interest, time and
location information, various distributions of scholarly entities, and rising
stars.


  Recent detections of the starburst galaxies M82 and NGC 253 by gamma-ray
telescopes suggest that galaxies rapidly forming massive stars are more
luminous at gamma-ray energies compared to their quiescent relatives. Building
upon those results, we examine a sample of 69 dwarf, spiral, and luminous and
ultraluminous infrared galaxies at photon energies 0.1-100 GeV using 3 years of
data collected by the Large Area Telescope (LAT) on the \textit{Fermi Gamma-ray
Space Telescope} (\textit{Fermi}). Measured fluxes from significantly detected
sources and flux upper limits for the remaining galaxies are used to explore
the physics of cosmic rays in galaxies. We find further evidence for
quasi-linear scaling relations between gamma-ray luminosity and both radio
continuum luminosity and total infrared luminosity which apply both to
quiescent galaxies of the Local Group and low-redshift starburst galaxies
(conservative $P$-values $\lesssim0.05$ accounting for statistical and
systematic uncertainties). The normalizations of these scaling relations
correspond to luminosity ratios of $\log(L_{0.1-100 \rm{GeV}}/L_{1.4 \rm{GHz}})
= 1.7 \pm 0.1_{\rm (statistical)} \pm 0.2_{\rm (dispersion)}$ and
$\log(L_{0.1-100 \rm{GeV}}/L_{8-1000 \mu\rm{m}}) = -4.3 \pm 0.1_{\rm
(statistical)} \pm 0.2_{\rm (dispersion)}$ for a galaxy with a star formation
rate of 1 $M_{\odot}$ yr$^{-1}$, assuming a Chabrier initial mass function.
Using the relationship between infrared luminosity and gamma-ray luminosity,
the collective intensity of unresolved star-forming galaxies at redshifts
$0<z<2.5$ above 0.1 GeV is estimated to be 0.4-2.4 $\times 10^{-6}$ ph
cm$^{-2}$ s$^{-1}$ sr$^{-1}$ (4-23% of the intensity of the isotropic diffuse
component measured with the LAT). We anticipate that $\sim10$ galaxies could be
detected by their cosmic-ray induced gamma-ray emission during a 10-year
\textit{Fermi} mission.


  Suppose that $\xi^{(n)}_1,\xi^{(n)}_2,...,\xi^{(n)}_n$ are i.i.d with
$P(\xi^{(n)}_i=1)=p_n=1-P(\xi^{(n)}_i=0)$. Let $U^{(n)}$ and $W^{(n)}$ be the
longest length of arithmetic progressions and of arithmetic progressions mod
$n$ relative to $\xi^{(n)}_1,\xi^{(n)}_2,..., \xi^{(n)}_n$ respectively.
Firstly, the asymptotic distributions of $U^{(n)}$ and $W^{(n)}$ are given.
Simultaneously, the errors are estimated by using Chen-Stein method. Next, the
almost surely limits are discussed when all $p_n$ are equal and when considered
on a common probability space. Finally, we consider the case that
$\lim_{n\to\infty}p_n=0$ and $\lim_{n\to\infty}{np_n}=\infty$. We prove that as
$n$ tends to $\infty$, the probability that $U^{(n)}$ takes two numbers and
$W^{(n)}$ takes three numbers tends to 1.


  Non-photonic electrons (NPE), produced by semileptonic decays of D and B
mesons, are good probes to study the properties of hot and dense medium created
in relativistic heavy ion collisions at RHIC. Studies of heavy quark production
in p+p collisions can test the validity of perturbative QCD. They also provide
a baseline to study the effects of nuclear matter on the production of heavy
quarks in heavy ion collisions. In this paper, we present recent results of NPE
spectra measured in p+p collisions at $\sqrt{s}=200$ GeV in mid-rapidity. We
also report NPE nuclear modification factor $R_{AA}$ and elliptic flow $v_2$ in
Au+Au collisions at $\sqrt{s_{NN}}=200$ GeV.


  The quantum entanglement is an important feature of many protocols in the
field of quantum computing. In this paper we evaluate a level of entanglement
in short qutrit chains. This evaluation is carried out with use of the CCNR
criterion and the concurrence measure. We also present some explicit formulae
describing the values of CCNR criterion and concurrence for exemplary short
spin chains. Utilizing the obtained results, we indicate that analyzing the
level of entanglement allows to detect the noise or deviation in the transfer
process, in comparison to the perfect transfer where only operation realizing
transfer is present.


  By means of computer simulations and solution of the equations of the Mode
Coupling Theory (MCT), we investigate the role of the intramolecular barriers
on several dynamic aspects of non-entangled polymers. The investigated dynamic
range extends from the caging regime characteristic of glass-formers to the
relaxation of the chain Rouse modes. We review our recent work on this
question, provide new results and critically discuss the limitations of the
theory. Solutions of the MCT for the structural relaxation reproduce
qualitative trends of simulations for weak and moderate barriers. However a
progressive discrepancy is revealed as the limit of stiff chains is approached.
This disagreement does not seem related with dynamic heterogeneities, which
indeed are not enhanced by increasing barrier strength. It is not connected
either with the breakdown of the convolution approximation for three-point
static correlations, which retains its validity for stiff chains. These
findings suggest the need of an improvement of the MCT equations for polymer
melts. Concerning the relaxation of the chain degrees of freedom, MCT provides
a microscopic basis for time scales from chain reorientation down to the caging
regime. It rationalizes, from first principles, the observed devations from the
Rouse model on increasing the barrier strength. These include anomalous scaling
of relaxation times, long-time plateaux, and non-monotonous wavelength
dependence of the mode correlators.


  We review recent studies on the measures of zero temperature quantum
correlations namely, the quantum entanglement (concurrence) and discord present
in the final state of a transverse XY spin chain following a quench through
quantum critical points; the aim of these studies is to explore the scaling of
the above quantities as a function of the quench rate. A comparative study
between the concurrence and the quantum discord shows that their behavior is
qualitatively the same though there are quantitative differences. For the
present model, the scaling of both the quantities are given by the scaling of
the density of the defect present in the final state though one can not find a
closed form expression for the discord. We also extend our study of quantum
discord to a transverse Ising chain in the presence of a three spin
interaction. Finally, we present a study of the dynamical evolution of quantum
discord and concurrence when two central qubits, initially prepared in a Werner
state, are coupled to the environmental XY spin chain which is driven through
quantum critical points. The qualitative behavior of quantum discord and
concurrence are found to be similar as that of the decoherence factor.


  We study the complexity of computing the mixed-integer hull
$\operatorname{conv}(P\cap\mathbb{Z}^n\times\mathbb{R}^d)$ of a polyhedron $P$.
Given an inequality description, with one integer variable, the mixed-integer
hull can have exponentially many vertices and facets in $d$. For $n,d$ fixed,
we give an algorithm to find the mixed integer hull in polynomial time. Given
$P=\operatorname{conv}(V)$ and $n$ fixed, we compute a vertex description of
the mixed-integer hull in polynomial time and give bounds on the number of
vertices of the mixed integer hull.


  This paper focuses on tackling the problem of temporal language localization
in videos, which aims to identify the start and end points of a moment
described by a natural language sentence in an untrimmed video. However, it is
non-trivial since it requires not only the comprehensive understanding of the
video and sentence query, but also the accurate semantic correspondence capture
between them. Existing efforts are mainly centered on exploring the sequential
relation among video clips and query words to reason the video and sentence
query, neglecting the other intra-modal relations (e.g., semantic similarity
among video clips and syntactic dependency among the query words). Towards this
end, in this work, we propose a Multi-modal Interaction Graph Convolutional
Network (MIGCN), which jointly explores the complex intra-modal relations and
inter-modal interactions residing in the video and sentence query to facilitate
the understanding and semantic correspondence capture of the video and sentence
query. In addition, we devise an adaptive context-aware localization method,
where the context information is taken into the candidate moments and the
multi-scale fully connected layers are designed to rank and adjust the boundary
of the generated coarse candidate moments with different lengths. Extensive
experiments on Charades-STA and ActivityNet datasets demonstrate the promising
performance and superior efficiency of our model.


  Business processes evolve over time to adapt to changing business
environments. This requires continuous monitoring of business processes to gain
insights into whether they conform to the intended design or deviate from it.
The situation when a business process changes while being analysed is denoted
as Concept Drift. Its analysis is concerned with studying how a business
process changes, in terms of detecting and localising changes and studying the
effects of the latter. Concept drift analysis is crucial to enable early
detection and management of changes, that is, whether to promote a change to
become part of an improved process, or to reject the change and make decisions
to mitigate its effects. Despite its importance, there exists no comprehensive
framework for analysing concept drift types, affected process perspectives, and
granularity levels of a business process. This article proposes the CONcept
Drift Analysis in Process Mining (CONDA-PM) framework describing phases and
requirements of a concept drift analysis approach. CONDA-PM was derived from a
Systematic Literature Review (SLR) of current approaches analysing concept
drift. We apply the CONDA-PM framework on current approaches to concept drift
analysis and evaluate their maturity. Applying CONDA-PM framework highlights
areas where research is needed to complement existing efforts.


  The boundary integral method is an efficient approach for solving
time-harmonic acoustic obstacle scattering problems. The main computational
task is the evaluation of an oscillatory boundary integral at each
discretization point of the boundary. This paper presents a new fast algorithm
for this task in two dimensions. This algorithm is built on top of directional
low-rank approximations of the scattering kernel and uses oscillatory Chebyshev
interpolation and local FFTs to achieve quasi-linear complexity. The algorithm
is simple, fast, and kernel-independent. Numerical results are provided to
demonstrate the effectiveness of the proposed algorithm.


  Properties of group coherent states can be derived "effectively" without
knowing full wave functions. The procedure is detailed in this article as an
example of general methods for effective constraints. The role of constraints
in the present context is played by a Casimir condition that puts states within
an irreducible representation of a Lie group (or, equivalently, on a
quantization of a co-adjoint orbit of the dual Lie algebra). Simplifications
implied by a Casimir condition, compared with general first-class constraints,
allows one to show that the correct number of degrees of freedom is obtained
after imposing the condition. When combined with conditions to saturate
uncertainty relations, moments of group coherent states can be derived. A
detailed example in quantum cosmology (cosmic forgetfulness) illustrates the
usefulness of the methods.


  Given a blackbox for f, a smooth real scalar function of d real variables,
one wants to estimate the gradient of f at a given point with n bits of
precision. On a classical computer this requires a minimum of d+1 blackbox
queries, whereas on a quantum computer it requires only one query regardless of
d. The number of bits of precision to which f must be evaluated matches the
classical requirement in the limit of large n.


  We study dephasing in an electronic Mach-Zehnder (MZ) interferometer based on
quantum Hall (QH) edge states by a micromiter-sized Ohmic contact embedded in
one of its arms. We find that at the filling factor $\nu=1$, as well as in the
case where an Ohmic contact is connected to an MZ interfeoremter by a quantum
point contact (QPC) that transmits only one electron channel, the phase
coherence may not be fully suppressed. Namely, if the voltage bias $\Delta \mu$
and the temperature $T$ are small compared to the charging energy of the Ohmic
contact $E_C$, the free fermion picture is manifested, and the visibility
saturates at its maximum value. At large biases, $\Delta \mu \gg E_C$, the
visibility decays in a power-law manner.


  Zero-shot learning has received increasing interest as a means to alleviate
the often prohibitive expense of annotating training data for large scale
recognition problems. These methods have achieved great success via learning
intermediate semantic representations in the form of attributes and more
recently, semantic word vectors. However, they have thus far been constrained
to the single-label case, in contrast to the growing popularity and importance
of more realistic multi-label data. In this paper, for the first time, we
investigate and formalise a general framework for multi-label zero-shot
learning, addressing the unique challenge therein: how to exploit multi-label
correlation at test time with no training data for those classes? In
particular, we propose (1) a multi-output deep regression model to project an
image into a semantic word space, which explicitly exploits the correlations in
the intermediate semantic layer of word vectors; (2) a novel zero-shot learning
algorithm for multi-label data that exploits the unique compositionality
property of semantic word vector representations; and (3) a transductive
learning strategy to enable the regression model learned from seen classes to
generalise well to unseen classes. Our zero-shot learning experiments on a
number of standard multi-label datasets demonstrate that our method outperforms
a variety of baselines.


  We investigate vacuum entanglement harvesting in the presence of a zero mode.
We show that, for a variety of detector models and couplings (namely,
Unruh-DeWitt qubit and harmonic oscillator detectors, amplitude and derivative
coupling), the results are strongly dependent on the state of the zero mode,
revealing an ambiguity in studies of entanglement harvesting with Neumann or
periodic boundary conditions, or in general in spacetimes with toroidal
topologies.


  We determine the lowest bound-state pole of the density-density correlator in
the scalar Wick-Cutkosky model where two equal-mass constituents interact via
the exchange of mesons. This is done by employing the worldline representation
of field theory together with a variational approximation as in Feynman's
treatment of the polaron. Unlike traditional methods based on the
Bethe-Salpeter equation, self-energy and vertex corrections are (approximately)
included as are crossed diagrams. Only vacuum-polarization effects of the heavy
particles are neglected. The well-known instability of the model due to
self-energy effects leads to large qualitative and quantitative changes
compared to traditional approaches which neglect them. We determine numerically
the critical coupling constant above which no real solutions of the variational
equations exist anymore and show that it is smaller than in the one-body case
due to an induced instability. The width of the bound state above the critical
coupling is estimated analytically.


  To quantify the role of radio jets for Intra-Night Optical Variability (INOV)
in Radio-Loud Narrow-Line Seyfert 1 (RLNLSy1) galaxies, we report the first
systematic comparative INOV study of 23 RLNLSy1 galaxies, with 15 RLNLSy1s
having confirmed detection of jets (jetted) and the remaining 8 RLNLSy1s having
no detection of jets (non-jetted) based on their Very Long Baseline Array
observations. We have monitored these two samples, respectively, in 37 and 16
sessions of a minimum 3-hour duration each. Based upon F$^{\eta}$-test at 99\%
confidence level with a typical INOV amplitude ($\psi$) detection threshold of
$>$ 3\%, we find the INOV duty cycles of 12\% for the sample of jetted
RLNLSy1s, however, none of the sources showed INOV in the sample of non-jetted
RLNLSy1s. Among the jetted RLNLSy1s, we find that the Duty Cycle (DC) for
jetted $\gamma$-ray detected ($\gamma$-ray) RLNLSy1s is found to be 34\% in
contrast to null INOV detection in the case of non-$\gamma$-ray RLNLSy1s. It
suggests that instead of the mere presence of a jet, relativistic beaming plays
a significant role for INOV in the case of low-luminous high accreting AGNs
such as NLSy1s in which dilution of the AGN's non-thermal optical emission by
the (much steadier) optical emission contributed by the nuclear accretion disc
is quite likely. Our study of jetted $\gamma$-ray RLNLSy1s shows more frequent
INOV detection for sources with higher apparent jet speed. Further, our results
also suggest that among the NLSy1s, only jetted $\gamma$-ray RNLSy1 galaxies DC
approaches blazar like DC.


  In a recent letter, Zvyagin calculates the density of states for 4f electrons
coupled to a conduction band in the framework of the Bethe ansatz (BA) solution
for the degenerate Anderson model. It is claimed that the results qualitatively
disagree with the results obtained for the same model but using a variational
approach. Even the high energy feature in the f-spectral function near the
4f-level energy ef, i.e. the ``normal'' ionization peak (NIP), is argued to be
qualitatively different in the two approaches. In the following we point out
that this is not the case.


  We offer a digital signature scheme using Boolean automorphisms of a
multivariate polynomial algebra over integers. Verification part of this scheme
is based on the approximation of the number of zeros of a multivariate Boolean
function.


  This paper introduces a stochastic AC-OPF (SOPF) for the flexibility
management of electric vehicle (EV) charging pools in distribution networks
under uncertainty. The SOPF considers discrete utility functions from charging
pools as a compensation mechanism for eventual energy not served to their
charging tasks. An application of the proposed SOPF is described where a
distribution system operator (DSO) requires flexibility to each charging pool
in a day-ahead time frame, minimizing the cost for flexibility while
guaranteeing technical limits. Flexibility areas are defined for each charging
pool and calculated as a function of a risk parameter involving the solution's
uncertainty. Results show that all players can benefit from this approach,
i.e., the DSO obtains a risk-aware solution, while charging pools/tasks
perceive a reduction in the total energy payment due to flexibility services.


  We consider a setup in which the channel from Alice to Bob is less noisy than
the channel from Eve to Bob. We show that there exist encoding and decoding
which accomplish error correction and authentication simultaneously; that is,
Bob is able to correctly decode a message coming from Alice and reject a
message coming from Eve with high probability. The system does not require any
secret key shared between Alice and Bob, provides information theoretic
security, and can safely be composed with other protocols in an arbitrary
context.


  Recent experiments in the integer quantum Hall regime seem to find direct
transitions from a quantum Hall state with Hall conductance $\sigma_{xy} = n
e^2/h $ with integer $n > 1$, to an insulating state in weak magnetic fields.
We study this issue using a variation of the tight-binding lattice model for
non-interacting electrons. Although quantum Hall transitions with change in
Hall conductance $n e^2/ h$ with $n > 1$ do exist in our model for special
tuning of parameters, they generically split into quantum Hall transitions with
the Hall conductance changing by $e^2 / h$ at each transition. This suggests
that a generic multiple step quantum Hall transition is incompatible with a
non-interacting electron picture.


  We investigate the superfluid state of repulsively interacting
three-component (color) fermionic atoms in optical lattices. When the
anisotropy of the three repulsive interactions is strong, atoms of two of the
three colors form Cooper pairs and atoms of the third color remain a Fermi
liquid. An effective attractive interaction is induced by density fluctuations
of the third-color atoms. This superfluid state is stable against changes in
filling close to half filling. We determine the phase diagrams in terms of
temperature, filling, and the anisotropy of the repulsive interactions.


  Stream media content caching is a key enabling technology to promote the
value chain of future urban vehicular networks. Nevertheless, the high mobility
of vehicles, intermittency of information transmissions, high dynamics of user
requests, limited caching capacities and extreme complexity of business
scenarios pose an enormous challenge to content caching and distribution in
vehicular networks. To tackle this problem, this paper aims to design a novel
edge-computing-enabled hierarchical cooperative caching framework. Firstly, we
profoundly analyze the spatio-temporal correlation between the historical
vehicle trajectory of user requests and construct the system model to predict
the vehicle trajectory and content popularity, which lays a foundation for
mobility-aware content caching and dispatching. Meanwhile, we probe into
privacy protection strategies to realize privacy-preserved prediction model.
Furthermore, based on trajectory and popular content prediction results,
content caching strategy is studied, and adaptive and dynamic resource
management schemes are proposed for hierarchical cooperative caching networks.
Finally, simulations are provided to verify the superiority of our proposed
scheme and algorithms. It shows that the proposed algorithms effectively
improve the performance of the considered system in terms of hit ratio and
average delay, and narrow the gap to the optimal caching scheme comparing with
the traditional schemes.


  We give several bounds for $sdepth_S(I+J)$, $sdepth_S(I\cap J)$,
$sdepth_S(S/(I+J))$, $sdepth_S(S/(I\cap J))$, $sdepth_S(I:J)$ and
$sdepth_S(S/(I:J))$ where $I,J\subset S=K[x_1,...,x_n]$ are monomial ideals.
Also, we give several equivalent forms of Stanley Conjecture for $I$ and $S/I$,
where $I\subset S$ is a monomial ideal.


  A non-vanishing vacuum expectation value for an antisymmetric tensor field
leads to the violation of Lorentz invariance, controlled by the dimension (-2)
parameter, theta_{mu nu}. We assume that the zeroth order term in
theta-expansion represents the Standard Model and study the effects induced by
linear terms in theta_{mu nu}. If coupling to theta_{mu nu} is realized in
strongly interacting sector of the theory, the clock comparison experiments
place the limit on the possible size of this background at the level of
1/\sqrt{theta}>~5X10^{14} GeV. If the interaction with theta_{mu nu} is
initially present only in the QED sector, this limit can be relaxed to
10^{11}-10^{12} GeV level. The strength of these limits obviates the
inferiority of collider physics with regard to experimental checks of Lorentz
invariance. Limits of similar strength are expected to hold in the case of
mixed non-commutativity between four-dimensional and extra-dimensional
coordinates. We also show that in certain models mixed non-commutativity can be
interpreted as 4d CPT-violating background.


  We investigate the effects of the UV background radiation on galaxy formation
by using the semi-analytic model including the photoionization process. The
semi-analytic model is based on Cole et al. (1994) and we use almost the same
parameters of their `fiducial' model. We find that the UV background mainly
affects the formation of dwarf galaxies. Because of the suppression of star
formation, the number density of small objects corresponding to dwarf galaxies
decreases compared to the case of no UV radiation when the UV background exists
until the present epoch. When the UV background vanishes at a low redshift, the
number density of small objects is hardly changed but the colour becomes bluer,
compared to the case of no UV radiation, because stars are newly formed after
the UV background vanishes. On the other hand, the UV radiation hardly affects
massive galaxies. This is because the massive galaxies are formed by mergers of
small galaxies.


  We present BPFroid -- a novel dynamic analysis framework for Android that
uses the eBPF technology of the Linux kernel to continuously monitor events of
user applications running on a real device. The monitored events are collected
from different components of the Android software stack: internal kernel
functions, system calls, native library functions, and the Java API framework.
As BPFroid hooks these events in the kernel, a malware is unable to trivially
bypass monitoring. Moreover, using eBPF doesn't require any change to the
Android system or the monitored applications. We also present an analytical
comparison of BPFroid to other malware detection methods and demonstrate its
usage by developing novel signatures to detect suspicious behavior that are
based on it. These signatures are then evaluated using real apps. We also
demonstrate how BPFroid can be used to capture forensic artifacts for further
investigation. Our results show that BPFroid successfully alerts in real time
when a suspicious behavioral signature is detected, without incurring a
significant runtime performance overhead.


  We report on the study of extremely high energy photons, pions and etas,
produced in intermediate energy heavy-ion collisions. Possibility of imaging
the final-state phase space in these collisions by the Bose-Einstein
correlations for photons is critically examined.


  Seesaw models with leptonic symmetries allow right-handed (RH) neutrino
masses at the electroweak scale, or even lower, at the same time having large
Yukawa couplings with the Standard Model leptons, thus yielding observable
effects at current or near-future lepton-flavour-violation (LFV) experiments.
These models have been previously considered also in connection to low-scale
leptogenesis, but the combination of observable LFV and successful leptogenesis
has appeared to be difficult to achieve unless the leptonic symmetry is
embedded into a larger one. In this paper, instead, we follow a different route
and consider a possible connection between large LFV rates and Dark Matter
(DM). We present a model in which the same leptonic symmetry responsible for
the large Yukawa couplings guarantees the stability of the DM candidate,
identified as the lightest of the RH neutrinos. The spontaneous breaking of
this symmetry, caused by a Majoron-like field, also provides a mechanism to
produce the observed relic density via the decays of the latter. The
phenomenological implications of the model are discussed, finding that large
LFV rates, observable in the near-future $\mu \to e$ conversion experiments,
require the DM mass to be in the keV range. Moreover, the active-neutrino
coupling to the Majoron-like scalar field could be probed in future detections
of supernova neutrino bursts.


  With the ever-growing size of pretrained models (PMs), fine-tuning them has
become more expensive and resource-hungry. As a remedy, low-rank adapters
(LoRA) keep the main pretrained weights of the model frozen and just introduce
some learnable truncated SVD modules (so-called LoRA blocks) to the model.
While LoRA blocks are parameter-efficient, they suffer from two major problems:
first, the size of these blocks is fixed and cannot be modified after training
(for example, if we need to change the rank of LoRA blocks, then we need to
re-train them from scratch); second, optimizing their rank requires an
exhaustive search and effort. In this work, we introduce a dynamic low-rank
adaptation (DyLoRA) technique to address these two problems together. Our
DyLoRA method trains LoRA blocks for a range of ranks instead of a single rank
by sorting the representation learned by the adapter module at different ranks
during training. We evaluate our solution on different natural language
understanding (GLUE benchmark) and language generation tasks (E2E, DART and
WebNLG) using different pretrained models such as RoBERTa and GPT with
different sizes. Our results show that we can train dynamic search-free models
with DyLoRA at least 4 to 7 times (depending to the task) faster than LoRA
without significantly compromising performance. Moreover, our models can
perform consistently well on a much larger range of ranks compared to LoRA.


  Given a hyperelliptic curve $C$ of genus $g$ over a number field $K$ and a
Weierstrass model $\mathscr{C}$ of $C$ over the ring of integers ${\mathcal
O}_K$ (i.e. the hyperelliptic involution of $C$ extends to $\mathscr{C}$ and
the quotient is a smooth model of ${\mathbb P}^1_K$ over ${\mathcal O}_K$), we
give necessary and sometimes sufficient conditions for $\mathscr{C}$ to be
defined by a global Weierstrass equation. In particular, if $C$ has everywhere
good reduction, we prove that it is defined by a global Weierstrass equation
with invertible discriminant if the class number $h_K$ is prime to $2(2g+1)$,
confirming a conjecture of M. Sadek.


  It is shown that every separable reflexive Banach space is a quotient of a
reflexive Hereditarily Indecomposable space, which yields that every separable
reflexive Banach is isomorphic to a subspace of a reflexive Indecomposable
space. Furthermore, every separable reflexive Banach space is a quotient of a
reflexive complementably $\ell_p$ saturated space with $1<p<\infty$ and of a
$c_0$ saturated space.


  Diagnosis of adverse neonatal outcomes is crucial for preterm survival since
it enables doctors to provide timely treatment. Machine learning (ML)
algorithms have been demonstrated to be effective in predicting adverse
neonatal outcomes. However, most previous ML-based methods have only focused on
predicting a single outcome, ignoring the potential correlations between
different outcomes, and potentially leading to suboptimal results and
overfitting issues. In this work, we first analyze the correlations between
three adverse neonatal outcomes and then formulate the diagnosis of multiple
neonatal outcomes as a multi-task learning (MTL) problem. We then propose an
MTL framework to jointly predict multiple adverse neonatal outcomes. In
particular, the MTL framework contains shared hidden layers and multiple
task-specific branches. Extensive experiments have been conducted using
Electronic Health Records (EHRs) from 121 preterm neonates. Empirical results
demonstrate the effectiveness of the MTL framework. Furthermore, the feature
importance is analyzed for each neonatal outcome, providing insights into model
interpretability.


  We investigate the electromagnetic form factors of the nucleon in the
framework of basis light front quantization. We compute the form factors using
the light front wavefunctions obtained by diagonalizing the effective
Hamiltonian consisting of the holographic QCD confinement potential, the
longitudinal confinement, and a one-gluon exchange interaction with fixed
coupling. The electromagnetic radii of the nucleon are also computed.


  Non-resonant ferromagnetic x-ray diffraction has been used to separate the
spin and orbital contribution to the magnetization density of the proposed
zero-moment ferromagnet $Sm_{0.982}Gd_{0.018} Al_{2}$. The alignment of the
spin and orbital moments relative to the net magnetization shows a sign
reversal at 84K, the compensation temperature. Below this temperature the
orbital moment is larger than the spin moment, and vice versa above it. This
result implies that the compensation mechanism is driven by the different
temperature dependencies of the $4f$ spin and orbital moments. Specific heat
data indicate that the system remains ferromagnetically ordered throughout.


  We consider the entanglement entropy for a sub-system in d+1 dimensional
SU(N) lattice gauge theory. The 1+1 gauge theory is treated exactly and shows
trivial behavior. Gauge theories in higher dimensions are treated within
Migdal-Kadanoff approximation. We consider the gauge theory in the confinement
phase. We demonstrate the existence of a non-analytical change from the short
distance to long distance form in the entanglement entropy in such systems
(d>2) reminiscent of a phase transition. The transition is manifested in
nontrivial change in the RG flow of the character expansion coefficients
defining the partition function.


  The experimental and phenomenological status of high energy photoproduction
is reviewed. Topics covered include the structure of the photon, production of
jets, heavy flavours and prompt photons, rapidity gaps, energy flow and
underlying events. The results are placed in the context of the current
understanding of QCD, with particular application to present and future hadron
and lepton colliders.


  We characterize the set of properties of Boolean-valued functions on a finite
domain $\mathcal{X}$ that are testable with a constant number of samples.
Specifically, we show that a property $\mathcal{P}$ is testable with a constant
number of samples if and only if it is (essentially) a $k$-part symmetric
property for some constant $k$, where a property is {\em $k$-part symmetric} if
there is a partition $S_1,\ldots,S_k$ of $\mathcal{X}$ such that whether
$f:\mathcal{X} \to \{0,1\}$ satisfies the property is determined solely by the
densities of $f$ on $S_1,\ldots,S_k$. We use this characterization to obtain a
number of corollaries, namely: (i) A graph property $\mathcal{P}$ is testable
with a constant number of samples if and only if whether a graph $G$ satisfies
$\mathcal{P}$ is (essentially) determined by the edge density of $G$. (ii) An
affine-invariant property $\mathcal{P}$ of functions $f:\mathbb{F}_p^n \to
\{0,1\}$ is testable with a constant number of samples if and only if whether
$f$ satisfies $\mathcal{P}$ is (essentially) determined by the density of $f$.
(iii) For every constant $d \geq 1$, monotonicity of functions $f : [n]^d \to
\{0, 1\}$ on the $d$-dimensional hypergrid is testable with a constant number
of samples.


  We study D-branes on Calabi-Yau manifolds, carrying charges which are torsion
elements of the K-theory. Interesting physics ensues when we follow these
branes into nongeometrical phases of the compactification. On the level of
K-theory, we determine the monodromies of the group of charges as we circle
singular loci in the closed string moduli space. Going beyond K-theory, we
discuss the stability of torsion D-branes as a function of the K\"ahler moduli.
When the fundamental group of the Calabi-Yau is nonabelian, we find evidence
for new threshold bound states of BPS branes. In a two-parameter example, we
compare our results with computations in the Gepner model. Our study of the
torsion D-branes in the compactification of [FHSV] sheds light on the physics
of that model. In particular, we develop a proposal for the group of allowed
D-brane charges in the presence of discrete RR fluxes.


  In this article, we will present an NLP application in text classifying
process using the content-based router. The ultimate goal throughout this
article is to predict the event described by a legal ad from the plain text of
the ad. This problem is purely a supervised problem that will involve the use
of NLP techniques and conventional modeling methodologies through the use of
the Multi-label Residual Convolutional Neural Network for text classification.
We will explain the approach put in place to solve the problem of classified
ads, the difficulties encountered and the experimental results.


  We study increasing subsequences (IS) for an ensemble of sequences given by
permutation of numbers {1,2,...,n}. We consider a Boltzmann ensemble at
temperature T. Thus each IS appears with the corresponding Boltzmann
probability where the energy is the negative length -l of the IS. For T -> 0,
only ground states, i.e. longest IS (LIS) contribute, also called Ulam's
problem. We introduce an algorithm which allows us to directly sample IS in
perfect equilibrium in polynomial time, for any given sequence and any
temperature. Thus, we can study very large sizes. We obtain averages for the
first and second moments of number of IS as function of $n$ and confirm
analytical predictions. Furthermore, we analyze for low temperature $T$ the
sampled ISs by computing the distribution of overlaps and performing
hierarchical cluster analyses. In the thermodynamic limit the distribution of
overlaps stays broad and the configuration landscape remains complex. Thus,
Ulam's problem exhibits replica symmetry breaking. This means it constitutes a
model with complex behavior which can be studied numerically exactly in a
highly efficient way, in contrast to other RSB-showing models, like spin
glasses or NP-hard optimization problems, where no fast exact algorithms are
known.


  A one-dimensional two-state number-conserving cellular automaton (NCCA) is a
cellular automaton whose states are 0 or 1 and where cells take states 0 and 1
and updated their states by the rule which keeps overall sum of states
constant. It can be regarded as a kind of particle based modeling of physical
systems and has another intuitive representation, motion representation, based
on the movement of each particle. We introduced a kind of hierarchical
interpretation of motion representations to understand the necessary pattern
size to each motion. We show any NCCA of its neighborhood size n can be
hierarchically represented by NCCAs of their neighborhood size from n-1 to 1.


  We present a probabilistic model for natural images which is based on
Gaussian scale mixtures and a simple multiscale representation. In contrast to
the dominant approach to modeling whole images focusing on Markov random
fields, we formulate our model in terms of a directed graphical model. We show
that it is able to generate images with interesting higher-order correlations
when trained on natural images or samples from an occlusion based model. More
importantly, the directed model enables us to perform a principled evaluation.
While it is easy to generate visually appealing images, we demonstrate that our
model also yields the best performance reported to date when evaluated with
respect to the cross-entropy rate, a measure tightly linked to the average
log-likelihood.


  Hubble (HST) spectroscopic transit observations of the temperate sub-Neptune
K2-18b were interpreted as the presence of water vapour with potential water
clouds. 1D modelling studies also predict the formation of water clouds at some
conditions. However, such models cannot predict the cloud cover, driven by
atmospheric dynamics and thermal contrasts, and thus their real impact on
spectra. The main goal of this study is to understand the formation,
distribution and observational consequences of water clouds on K2-18b and other
temperate sub-Neptunes. We simulated the atmospheric dynamics, water cloud
formation and spectra of K2-18b for H2-dominated atmosphere using a 3D GCM. We
analysed the impact of atmospheric composition (with metallicity from 1*solar
to 1000*solar), concentration of cloud condensation nuclei and planetary
rotation rate. Assuming that K2-18b has a synchronous rotation, we show that
the atmospheric circulation in the upper atmosphere essentially corresponds to
a symmetric day-to-night circulation. This regime preferentially leads to cloud
formation at the substellar point or at the terminator. Clouds form for
metallicity >100*solar with relatively large particles. For 100-300*solar
metallicity, the cloud fraction at the terminators is small with a limited
impact on transit spectra. For 1000*solar metallicity, very thick clouds form
at the terminator. The cloud distribution appears very sensitive to the
concentration of CCN and to the planetary rotation rate. Fitting HST transit
data with our simulated spectra suggests a metallicity of ~100-300*solar. In
addition, we found that the cloud fraction at the terminator can be highly
variable, leading to a potential variability in transit spectra. This effect
could be common on cloudy exoplanets and could be detectable with multiple
transit observations.


  We study quantum mechanics problem described by the Schr\"{o}dinger equation
with Kapitza pendulum potential, that is the asymmetric double-well potential
on the circle. For the oscillatory states spatially localize around the two
stable saddle positions of the potential, we obtain the perturbative
eigenvalues and corresponding piecewise wavefunctions. The spectrum is computed
by extending the angle coordinate to the complex plane so that the quantization
condition is formulated as contour integral along a path extending in the
imaginary direction. Quantum tunneling between the wells is computed.


  We develop a tractable model for studying strategic interactions between
learning algorithms. We uncover a mechanism responsible for the emergence of
algorithmic collusion. We observe that algorithms periodically coordinate on
actions that are more profitable than static Nash equilibria. This novel
collusive channel relies on an endogenous statistical linkage in the
algorithms' estimates which we call spontaneous coupling. The model's
parameters predict whether the statistical linkage will appear, and what market
structures facilitate algorithmic collusion. We show that spontaneous coupling
can sustain collusion in prices and market shares, complementing experimental
findings in the literature. Finally, we apply our results to design algorithmic
markets.


  The contribution presents an analysis of a rate-independent non-local damage
model, recently proposed by (Mielke and Roubicek, 2006). An analytical as well
as numerical solution of a simple one-dimensional bifurcation problem is
performed, demonstrating that, for the elementary localization test, the model
is free of pathological features.


  Here we introduce the concept of classical input - quantum output (C-Q)
non-signalling boxes, a generalisation of the classical input - classical
output (C-C) non-signalling boxes. We argue that studying such objects leads to
a better understanding of the relation between quantum nonlocality and
non-locality beyond quantum mechanics. The main issue discussed in the paper is
whether there exist 'genuine' C-Q boxes or all C-Q boxes can be built from
objects already known, namely C-C boxes acting on pre-shared entangled quantum
particles. We show that large classes of C-Q boxes are non-genuine. In
particular, we show that all bi-partite C-Q boxes with outputs that are pure
states are non-genuine. We also present various strategies for addressing the
general problem, i.e. for multi-partite C-Q boxes which output mixed states,
whose answer is still open. Finally, we show that even some very simple
non-genuine C-Q boxes require large amounts of C-C nonlocal correlations in
order to simulate them.


  CO plays an important role in interstellar molecular clouds, both as a
coolant, and as a diagnostic molecule. However, a proper evaluation of the
cooling rate due to CO requires a determination of the populations of many
levels, the spontaneous and stimulated radiative de-excitation rates between
these levels, and the transfer of the emitted multi-line radiation;
additionally, this must be done for three isotopologues. It would be useful to
have a simple analytic formulation that avoided these complications; this could
then be used in situations where CO plays an important role as a coolant, but
the details of this role are not the main concern. We derive such a formulation
here, by first considering the two asymptotic forms that obtain in the limits
of (a) low volume-density and optical depth, and (b) high volume-density and
optical depth. These forms are then combined in such a way as to fit the
detailed numerical results from Goldsmith \& Langer (1978), which cover low
temperatures, and a range of physical conditions where the interplay of thermal
and sub-thermal excitation, optical-depth effects, and the contributions from
rare isotopologues, are all important. The fit is obtained using the
Metropolis-Hastings method, and reproduces the results of GL78 well. It is a
purely local and analytic function of state --- specifically a function of the
density, $\rho$, isothermal sound speed, $a$, CO abundance, $X_{_{\rm CO}}$,
and velocity divergence, $\nabla\cdot{\boldsymbol\upsilon}$. As an application,
we consider the cooling layer following a slow steady non-magnetic planar
J-shock. We show that, if the post-shock cooling is dominated by CO and its
isotopologues, the thickness of the post-shock cooling layer is very small and
approximately independent of the pre-shock velocity, $\upsilon_o$, or pre-shock
isothermal sound speed, $a_o$.


  In this paper, we consider the degenerate and singular oscillatory integral
operator with a singular kernel which is not a Calder\'{o}n-Zygmund kernel and
satisfies suitable size and derivative conditions related to a real parameter
$\mu$. For any given homogeneous polynomial phases, except monomial phases, of
degreee $n$, we give the range of $p$ for which the sharp decay rate
$-\frac{1-\mu}{n}$ on $L^2$ spaces can be preserved on $L^p$ spaces.


  We consider the corresponding Christoffel-Minkowski problem for curvature
measures. The existence of star-shaped $(n-k)$-convex bodies with prescribed
$k$-th curvature measures ($k>0$) has been a longstanding problem. This is
settled in this paper through the establishment of a crucial $C^2$ a priori
estimate for the corresponding curvature equation on $\mathbb S^n$.


  We investigate harmonic maps from weighted graphs into metric spaces that
locally admit unique centers of gravity, like Alexandrov spaces with upper
curvature bounds. We prove an existence result by constructing an iterative
geometric process that converges to such maps, called harmonic nets for short.


  Replicability is a fundamental quality of scientific discoveries: we are
interested in those signals that are detectable in different laboratories,
study populations, across time etc. Unlike meta-analysis which accounts for
experimental variability but does not guarantee replicability, testing a
partial conjunction (PC) null aims specifically to identify the signals that
are discovered in multiple studies. In many contemporary applications, e.g.,
comparing multiple high-throughput genetic experiments, a large number $M$ of
PC nulls need to be tested simultaneously, calling for a multiple comparisons
correction. However, standard multiple testing adjustments on the $M$ PC
$p$-values can be severely conservative, especially when $M$ is large and the
signals are sparse. We introduce AdaFilter, a new multiple testing procedure
that increases power by adaptively filtering out unlikely candidates of PC
nulls. We prove that AdaFilter can control FWER and FDR as long as data across
studies are independent, and has much higher power than other existing methods.
We illustrate the application of AdaFilter with three examples: microarray
studies of Duchenne muscular dystrophy, single-cell RNA sequencing of T cells
in lung cancer tumors and GWAS for metabolomics.


  Time-evolving or temporal graphs gain more and more popularity when studying
the behavior of complex networks. In this context, the multistage view on
computational problems is among the most natural frameworks. Roughly speaking,
herein one studies the different (time) layers of a temporal graph (effectively
meaning that the edge set may change over time, but the vertex set remains
unchanged), and one searches for a solution of a given graph problem for each
layer. The twist in the multistage setting is that the solutions found must not
differ too much between subsequent layers. We relax on this already established
notion by introducing a global instead of the local budget view studied so far.
More specifically, we allow for few disruptive changes between subsequent
layers but request that overall, that is, summing over all layers, the degree
of change is moderate. Studying several classical graph problems (both NP-hard
and polynomial-time solvable ones) from a parameterized complexity angle, we
encounter both fixed-parameter tractability and parameterized hardness results.
Somewhat surprisingly, we find that sometimes the global multistage versions of
NP-hard problems such as Vertex Cover turn out to be computationally more
tractable than the ones of polynomial-time solvable problems such as Matching.


  In this paper we introduce a new modification of the Jacobi-Perron algorithm
in three dimensional case and prove its periodicity for the case of
totally-real conjugate cubic vectors. This provides an answer in the
totally-real case to the question son algebraic periodicity for cubic
irrationalities posed in 1849 by Ch.~Hermite.


  We used the flux calibrated images through the Broad Band Filter Imager and
Stokes Polarimeter data obtained with the Solar Optical Telescope onboard the
Hinode spacecraft to study the properties of bright points in and around the
sunspots. The well isolated bright points were selected and classified as
umbral dot, peripheral umbral dot, penumbral grains and G-band bright point
depending on their location. Most of the bright points are smaller than about
150 km. The larger points are mostly associated with the penumbral features.
The bright points are not uniformly distributed over the umbra but
preferentially located around the penumbral boundary and in the fast decaying
parts of umbra. The color temperature of the bright points, derived using the
continuum irradiance, are in the range of 4600 K to 6600 K with cooler ones
located in the umbra. The temperature increases as a function of distance from
the center to outside. The G-band, CN-band and CaII H flux of the bright points
as a function of their blue band brightness increase continuously in a
nonlinear fashion unlike their red and green counterpart. This is consistent
with a model in which the localized heating of the flux tube deplete the
molecular concentration resulting the reduced opacity which leads to the
exposition of deeper and hotter layers. The scatter in CaII H irradiance is
higher compared to the G-band and CN-band irradiance. The light curve of the
bright points show that the enhanced brightness at these locations last for
about 15 to 60 minutes with the least contrast for the points out side the
spot. The umbral dots near the penumbral boundary are associated with elongated
filamentary structures.


  We present an approximation scheme for support vector machine models that use
an RBF kernel. A second-order Maclaurin series approximation is used for
exponentials of inner products between support vectors and test instances. The
approximation is applicable to all kernel methods featuring sums of kernel
evaluations and makes no assumptions regarding data normalization. The
prediction speed of approximated models no longer relates to the amount of
support vectors but is quadratic in terms of the number of input dimensions. If
the number of input dimensions is small compared to the amount of support
vectors, the approximated model is significantly faster in prediction and has a
smaller memory footprint. An optimized C++ implementation was made to assess
the gain in prediction speed in a set of practical tests. We additionally
provide a method to verify the approximation accuracy, prior to training models
or during run-time, to ensure the loss in accuracy remains acceptable and
within known bounds.


  Our goal is to build classification models using a combination of free-text
and structured data. To do this, we represent structured data by text
sentences, DataWords, so that similar data items are mapped into the same
sentence. This permits modeling a mixture of text and structured data by using
only text-modeling algorithms. Several examples illustrate that it is possible
to improve text classification performance by first running extraction tools
(named entity recognition), then converting the output to DataWords, and adding
the DataWords to the original text -- before model building and classification.
This approach also allows us to produce explanations for inferences in terms of
both free text and structured data.


  Inspired by the strong correlation between the Label Smoothing
Regularization(LSR) and Knowledge distillation(KD), we propose an algorithm
LsrKD for training boost by extending the LSR method to the KD regime and
applying a softer temperature. Then we improve the LsrKD by a Teacher
Correction(TC) method, which manually sets a constant larger proportion for the
right class in the uniform distribution teacher. To further improve the
performance of LsrKD, we develop a self-distillation method named Memory-replay
Knowledge Distillation (MrKD) that provides a knowledgeable teacher to replace
the uniform distribution one in LsrKD. The MrKD method penalizes the KD loss
between the current model's output distributions and its copies' on the
training trajectory. By preventing the model learning so far from its
historical output distribution space, MrKD can stabilize the learning and find
a more robust minimum. Our experiments show that LsrKD can improve LSR
performance consistently at no cost, especially on several deep neural networks
where LSR is ineffectual. Also, MrKD can significantly improve single model
training. The experiment results confirm that the TC can help LsrKD and MrKD to
boost training, especially on the networks they are failed. Overall, LsrKD,
MrKD, and their TC variants are comparable to or outperform the LSR method,
suggesting the broad applicability of these KD methods.


  We investigate optimal channel assignment algorithms that maximize per node
throughput in dense multichannel multi-radio (MC-MR) wireless networks.
Specifically, we consider an MC-MR network where all nodes are within the
transmission range of each other. This situation is encountered in many
real-life settings such as students in a lecture hall, delegates attending a
conference, or soldiers in a battlefield. In this scenario, we show that
intelligent assignment of the available channels results in a significantly
higher per node throughput. We first propose a class of channel assignment
algorithms, parameterized by T (the number of transceivers per node), that can
achieve $\Theta(1/N^{1/T})$ per node throughput using $\Theta(TN^{1-1/T})$
channels. In view of practical constraints on $T$, we then propose another
algorithm that can achieve $\Theta(1/(\log_2 N)^2)$ per node throughput using
only two transceivers per node. Finally, we identify a fundamental relationship
between the achievable per node throughput, the total number of channels used,
and the network size under any strategy. Using analysis and simulations, we
show that our algorithms achieve close to optimal performance at different
operating points on this curve. Our work has several interesting implications
on the optimal network design for dense MC-MR wireless networks.


  In this paper, we have studied the bouncing behavior of the cosmological
models formulated at the background of the Hubble function in the F(R, G)
theory of gravity, where R and G denote the Ricci scalar and Gauss-Bonnet
invariant. The actions of bouncing cosmology are studied under consideration of
different viable models and can resolve the difficulty of singularity in
standard Big-Bang cosmology. Both models show bouncing behavior and satisfy the
bouncing cosmological properties. Models based on dynamical, deceleration, and
energy conditions indicate the accelerating behavior at the late evolution
time. Phantom at the bounce epoch is analogous to a quintessence behavior.
Finally, we formulate the perturbed evolution equations and investigate the
stability of two bouncing solutions.


  The Metis coronagraph is one of the remote sensing instruments hosted on
board the ESA/NASA Solar Orbiter mission. Metis is devoted to carry out the
first simultaneous imaging of the solar corona in both visible light (VL) and
ultraviolet (UV). High-energy particles penetrate spacecraft materials and may
limit the performance of on-board instruments. A study of galactic cosmic-ray
(GCR) tracks observed in the first VL images gathered by Metis during the
commissioning phase for a total of 60 seconds of exposure time is presented
here. A similar analysis is planned for the UV channel. A prediction of the GCR
flux up to hundreds of GeV is made here for the first part of the Solar Orbiter
mission to study the Metis coronagraph performance. GCR model predictions are
compared to observations gathered on board Solar Orbiter by the EPD/HET
experiment in the range 10 MeV-100 MeV in the summer 2020 and with previous
measurements. Estimated cosmic-ray fluxes above 70 MeV n$^{-1}$ have been also
parameterized and used for Monte Carlo simulations aiming at reproducing the
cosmic-ray track observations in the Metis coronagraph VL images. The same
parameterizations can also be used to study the performance of other detectors.
By comparing observations of cosmic-ray tracks in the Metis VL images with
FLUKA Monte Carlo simulations of cosmic-ray interactions in the VL detector, it
is found that cosmic rays fire a fraction of the order of 10$^{-4}$ of the
whole image pixel sample. Therefore, cosmic rays do not affect sensibly the
quality of Metis VL images. It is also found that the overall efficiency for
cosmic-ray identification in the Metis VL images is approximately equal to the
contribution of Z$>$2 particles. As a result, the Metis coronagraph may play
the role of a proton monitor for long-term GCR variations during the overall
mission duration.


  In lexical semantics, full-sentence segmentation and segment labeling of
various phenomena are generally treated separately, despite their
interdependence. We hypothesize that a unified lexical semantic recognition
task is an effective way to encapsulate previously disparate styles of
annotation, including multiword expression identification / classification and
supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence
tagger and evaluate its performance along various axes of annotation. As the
label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally
evaluate how well the model generalizes to those test sets, finding that it
approaches or surpasses existing models despite training only on STREUSLE. Our
work also establishes baseline models and evaluation metrics for integrated and
accurate modeling of lexical semantics, facilitating future work in this area.


  For a given finite group $G$ consisting of morphisms and antimorphisms of a
free monoid $\mathcal{A}^*$, we study infinite words with language closed under
the group $G$. We focus on the notion of $G$-richness which describes words
rich in generalized palindromic factors, i.e., in factors $w$ satisfying
$\Theta(w) = w$ for some antimorphism $\Theta \in G$. We give several
equivalent descriptions which are generalizations of know characterizations of
rich words (in the terms of classical palindromes) and show two examples of
$G$-rich words.


  Jet production at hadron colliders is a benchmark process to probe the
dynamics of the strong interaction and the structure of the colliding hadrons.
One of the most basic jet production observables is the single jet inclusive
cross section, which is obtained by summing all jets that are observed in an
event. Our recent computation of next-to-next-to-leading order (NNLO) QCD
contributions to single jet inclusive observables uncovered large corrections
in certain kinematical regions, which also resulted in a sizeable ambiguity on
the appropriate choice of renormalization and factorization scales. We now
perform a detailed investigation of the infrared sensitivity of the different
ingredients to the single jet inclusive cross section. We show that the
contribution from the second jet, ordered in transverse momentum $p_{T}$, in
the event is particularly sensitive to higher order effects due to implicit
restrictions on its kinematics. By investigating the second-jet transverse
momentum distribution, we identify large-scale cancellations between different
kinematical event configurations, which are aggravated by certain types of
scale choice. Taking perturbative convergence and stability as selection
criteria enables us to single out the total partonic transverse energy
$\hat{H}_{T}$ and twice the individual jet transverse momentum $2\,p_{T}$ (with
which $\hat{H}_{T}$ coincides in Born kinematics) as the most appropriate
scales in the perturbative description of single jet inclusive production.


  Two recent observations regarding the halo of M33 seem to contradict each
other. First, the star clusters in the halo of M33 exhibit an age range of 5 to
7 Gyr suggesting a formation scenario that involves the chaotic fragmentation
and accretion of dwarf satellites. In contrast, deep photometric searches for
the resultant tidal tails and stellar streams in the vicinity of M33 have
turned up nothing significant. In this contribution, we have tried to reconcile
these apparently disparate observations. We suggest that M33 is situated within
a 'superhalo' which contains many other dwarf spheroidal and dwarf irregular
galaxies that are satellites of M31. In such a scenario, the tidal field of M31
could have disrupted and/or diluted the leftover tails and streams leaving
little to be detected in the present day.


  We show that the peculiar surface abundance patterns of Carbon Enhanced Metal
Poor (CEMP) stars has been inherited from material having been processed by H-
and He-burning phases in a previous generation of stars (hereafter called the
``Source Stars''). In this previous generation, some mixing must have occurred
between the He- and the H-burning regions in order to explain the high observed
abundances of nitrogen. In addition, it is necessary to postulate that a very
small fraction of the carbon-oxygen core has been expelled (either by winds or
by the supernova explosion). Therefore only the most outer layers should have
been released by the Source Stars. Some of the CEMP stars may be He-rich if the
matter from the Source Star is not too much diluted with the InterStellar
Medium (ISM). Those stars formed from nearly pure ejecta would also be Li-poor.


  The nonlinear propagation of two-dimensional (2D) quantum ion-acoustic waves
(QIAWs) is studied in a quantum electron-ion plasma. By using a 2D quantum
hydrodynamic model and the method of multiple scales, a new set of coupled
nonlinear partial differential equations is derived which governs the slow
modulation of the 2D QIAW packets. The oblique modulational instability (MI) is
then studied by means of a corresponding nonlinear Schroedinger equation
derived from the coupled nonlinear partial differential equations. It is shown
that the quantum parameter H (proportional to $\hbar$), associated with the
Bohm potential, shifts the MI domains around the $k\theta$-plane, where $k$ is
the carrier wave number and $\theta$ is the angle of modulation. In particular,
the ion-acoustic wave (IAW), previously known to be stable under parallel
modulation in classical plasmas, is shown to be unstable in quantum plasmas.
The growth rate of the MI is found to be quenched by the obliqueness of
modulation. The modulation of 2D QIAW packets along $\mathbf{k}$ is shown to be
described by a set of Davey-Stewartson-like equations. The latter can be
studied for the 2D wave collapse in dense plasmas. The predicted results, which
could be important to look for stable wave propagation in laboratory
experiments as well as in dense astrophysical plasmas, thus generalize the
theory of MI of IAW propagations both in classical and quantum electron-ion
plasmas.


  We apply our technique on the dispersion of magnetic fields in molecular
clouds to high spatial resolution Submillimeter Array polarization data
obtained for Orion KL in OMC-1, IRAS 16293, and NGC 1333 IRAS 4A. We show how
one can take advantage of such high resolution data to characterize the
magnetized turbulence power spectrum in the inertial and dissipation ranges.
For Orion KL we determine that in the inertial range the spectrum can be
approximately fitted with a power law k^-(2.9\pm0.9) and we report a value of
9.9 mpc for {\lambda}_AD, the high spatial frequency cutoff presumably due to
turbulent ambipolar diffusion. For the same parameters we have \sim
k^-(1.4\pm0.4) and a tentative value of {\lambda}_AD \simeq 2.2 mpc for NGC
1333 IRAS 4A, and \sim k^-(1.8\pm0.3) with an upper limit of {\lambda}_AD < 1.8
mpc for IRAS 16293. We also discuss the application of the technique to
interferometry measurements and the effects of the inherent spatial filtering
process on the interpretation of the results.


  Clusters of galaxies are revealing themselves as powerful sources of non
thermal radiation in a wide range of wavelengths. In order to account for these
multifrequency observations equipartition of cosmic rays (CRs) with the thermal
gas in clusters of galaxies is often invoked. This condition might suggest a
dynamical role played by cosmic rays in the virialization of these large scale
structures and is now testable through gamma ray observations. We show here, in
the specific case of the Coma and Virgo clusters, for which upper limits on the
gamma ray emission exist, that equipartition implies gamma ray fluxes that are
close or even in excess of the EGRET limit, depending on the adopted model of
CR injection. We use this bound to limit the validity of the equipartition
condition. We also show that, contrary to what claimed in previous
calculations, the equipartition assumption implies gamma ray fluxes in the TeV
range which can be detectable even by currently operating gamma ray
observatories if the injection cosmic ray spectrum is flatter than $E^{-2.4}$.


  Observations of the flow on Jupiter exists essentially only for the
cloud-level, which is dominated by strong east-west jet-streams. These have
been suggested to result from dynamics in a superficial thin weather-layer, or
alternatively be a manifestation of deep interior cylindrical flows. However,
it is possible that the observed winds are indeed superficial, yet there exists
deep flow that is completely decoupled from it. To date, all models linking the
wind, via the induced density anomalies, to the gravity field, to be measured
by Juno, consider only flow that is a projection of the observed could-level
wind. Here we explore the possibility of complex wind dynamics that include
both the shallow weather-layer wind, and a deep flow that is decoupled from the
flow above it. The upper flow is based on the observed cloud-level flow and is
set to decay with depth. The deep flow is constructed to produce cylindrical
structures with variable width and magnitude, thus allowing for a wide range of
possible scenarios for the unknown deep flow. The combined flow is then related
to the density anomalies and gravitational moments via a dynamical model. An
adjoint inverse model is used for optimizing the parameters controlling the
setup of the deep and surface-bound flows, so that these flows can be
reconstructed given a gravity field. We show that the model can be used for
examination of various scenarios, including cases in which the deep flow is
dominating over the surface wind. We discuss extensively the uncertainties
associated with the model solution. The flexibility of the adjoint method
allows for a wide range of dynamical setups, so that when new observations and
physical understanding will arise, these constraints could be easily
implemented and used to better decipher Jupiter flow dynamics.


  This paper describes application of information granulation theory, on the
design of rock engineering flowcharts. Firstly, an overall flowchart, based on
information granulation theory has been highlighted. Information granulation
theory, in crisp (non-fuzzy) or fuzzy format, can take into account engineering
experiences (especially in fuzzy shape-incomplete information or superfluous),
or engineering judgments, in each step of designing procedure, while the
suitable instruments modeling are employed. In this manner and to extension of
soft modeling instruments, using three combinations of Self Organizing Map
(SOM), Neuro-Fuzzy Inference System (NFIS), and Rough Set Theory (RST) crisp
and fuzzy granules, from monitored data sets are obtained. The main underlined
core of our algorithms are balancing of crisp(rough or non-fuzzy) granules and
sub fuzzy granules, within non fuzzy information (initial granulation) upon the
open-close iterations. Using different criteria on balancing best granules
(information pockets), are obtained. Validations of our proposed methods, on
the data set of in-situ permeability in rock masses in Shivashan dam, Iran have
been highlighted.


  The differential rotation of a 1.2 $M_\odot$ zero age MS star (spectral type
F8) is computed and the results are compared with those from a similar model of
the Sun. The rotation pattern is determined by solving the Reynolds equation
including the convective energy transport. The latter is anisotropic due to the
Coriolis force causing a horizontal temperature gradient of ~ 7 K between the
poles and the equator. Comparison of the transport mechanisms of angular
momentum (the eddy viscosity, the $\Lambda$-effect and the meridional flow)
shows that for the F star the $\Lambda$-effect is the most powerful transporter
for rotation periods of 7 d or less. In the limit of very fast rotation the
$\Lambda$-effect is balanced by the meridional flow alone and the rotation is
nearly rigid. The rotation pattern found for the F star is very similar to the
solar rotation law, but the horizontal shear is about twice the solar value. As
a function of the rotation period, the total equator-pole difference of the
angular velocity has a (slight) maximum at a period of 7 d and (slowly)
vanishes in both the limiting cases of very fast and very slow rotation. A
comparison of the solar models with those for the F-type star shows a much
stronger dependence of the differential surface rotation on the stellar
luminosity rather than on the rotation rate.


  I review and improve on how two-index SU(N) gauge-fermion theories help
access salient information about the large $N$ vacuum and spectrum of QCD,
super Yang Mills and meson-meson scattering. The interplay with recent lattice
simulations will be employed to deduce the size of $1/N^2$ corrections. Through
the meson-meson scattering analysis I will honor Steven Weinberg's memory by
showing how two-index extrapolations naturally accommodate the appearance of
tetraquarks states crucial to unitarize meson-meson scattering at low energies.


  In the presence of collective flow a new model potential describing the
interaction of the hard jet with scattering centers is derived based on the
static color-screened Yukawa potential. The flow effect on jet quenching with
detailed balance is investigated in pQCD. It turns out, considering the
collective flow with velocity $v_z$ along the jet direction, the collective
flow decreases the LPM destructive interference comparing to that in the static
medium. The gluon absorption plays a more important role in the moving medium.
  The collective flow increases the energy gain from gluon absorption, however,
decreases the energy loss from gluon radiation, which is $(1 - v_z )$ times as
that in the static medium to the first order of opacity. In the presence of
collective flow, the second order in opacity correction is relatively small
compared to the first order. So that the total effective energy loss is
decreased. The flow dependence of the energy loss will affect the suppression
of high $p_T$ hadron spectrum and anisotropy parameter $v_2$ in high-energy
heavy-ion collisions.


  Low-energy spectra of 4$n$ nuclei are described with high accuracy in terms
of four-body correlated structures ("quartets"). The states of all $N\geq Z$
nuclei belonging to the $A=24$ isobaric chain are represented as a
superposition of two-quartet states, with quartets being characterized by
isospin $T$ and angular momentum $J$. These quartets are assumed to be those
describing the lowest states in $^{20}$Ne ($T_z$=0), $^{20}$F ($T_z$=1) and
$^{20}$O ($T_z$=2). We find that the spectrum of the self-conjugate nucleus
$^{24}$Mg can be well reproduced in terms of $T$=0 quartets only and that,
among these, the $J$=0 quartet plays by far the leading role in the structure
of the ground state. The same conclusion is drawn in the case of the
three-quartet $N=Z$ nucleus $^{28}$Si. As an application of the quartet
formalism to nuclei not confined to the $sd$ shell, we provide a description of
the low-lying spectrum of the proton-rich $^{92}$Pd. The results achieved
indicate that, in 4$n$ nuclei, four-body degrees of freedom are more important
and more general than usually expected.


  We investigate linear dynamical systems consisting of ordinary differential
equations with high dimensionality. Model order reduction yields alternative
systems of much lower dimensions. However, a reduced system may be unstable,
although the original system is asymptotically stable. We consider
projection-based model order reduction of Galerkin-type. A transformation of
the original system ensures that any reduced system is asymptotically stable.
This transformation requires the solution of a high-dimensional Lyapunov
inequality. We solve this problem using a specific Lyapunov equation. Its
solution can be represented as a matrix-valued integral in the frequency
domain. Consequently, quadrature rules yield numerical approximations, where
large sparse linear systems of algebraic equations have to be solved. We
analyse this approach and show a sufficient condition on the error to meet the
Lyapunov inequality. Furthermore, this technique is extended to systems of
differential-algebraic equations with strictly proper transfer functions by a
regularisation. Finally, we present results of numerical computations for
high-dimensional examples, which indicate the efficiency of this
stability-preserving method.


  Two theorems involving curl eigenfields on the 3--sphere are obtained using
angular momentum theory. Spinor hyperspherical harmonics are shown to form an
explicit, convenient basis. In particular, a spin--one vector calculus is
reviewed. An easy proof of the vanishing of `odd' eigenfields is given and
related to the sign change of fermionic spinors under 2$\pi$ rotations. The
theorem that curl eigenfields with constant norm have to be proportional to a
fundamental eigenfield (Hopf field) is also rapidly obtained. Attention is
drawn to the relevance of early work of Schr\"odinger on Maxwell theory in an
expanding universe.


  A particle-hole symmetric Anderson impurity model with a metallic host of
narrow bandwidth is studied within the framework of the local moment approach.
The resultant single-particle spectra are compared to unrestricted
Hartree-Fock, second order perturbation theory about the noninteracting limit,
and Lanczos spectra by Hofstetter and Kehrein. Rather accurate analytical
results explain the spectral evolution over almost the entire range of
interactions. These encompass, in particular, a rationale for the four-peak
structure observed in the low-energy sector of the Lanczos spectra in the
moderate-coupling regime. In weak coupling, the spectral evolution is governed
by orbital effects, while in the strong coupling Kondo limit, the model is
shown to connect smoothly to the generic Anderson impurity with a flat and
infinitely wide hybridization band.


  We consider a rotating BEC in an optical lattices in a regime which can be
mapped to the Joseohson junction arrays. In this regime, we formulate the
ground state energy in terms of vortex configuration. This method give us the
vortex lattice in the ground state in a natural way. We apply our result for an
approximation scheme of the problem which we suppose that the coupling of the
Josephson junctions are uniform. Application of method for ladder case
presented and the results compared with Monte-Carlo method numerically.We
discuss about restriction of method and suggest improvement for it.