  We declare that we are living in the quantum 4-dimensional Minkowski
space-time with the force-fields gauge-group structure $SU_c(3) \times SU_L(2)
\times U(1) \times SU_f(3)$ built-in from the very beginning. From this overall
background, we see the lepton world, which has the symmetry characterized by
$SU_L(2) \times U(1) \times SU_f(3)$ - the lepton world is also called "the
atomic world". From the overall background, we also see the quark world, which
experiences the well-known $(123)$ symmetry, i.e., $SU_c(3) \times SU_L((2)
\times U(1)$. The quark world is also called "the nuclear world".
  The $3^\circ\,K$ cosmic microwave background (CMB) in our Universe provides
the evidence of that "the force-fields gauge-group structure was built-in from
the very beginning". The CMB is almost uniform, to the level of one part in
$10^5$, reflecting the massless of the photons.
  The lepton world is dimensionless in the 4-dimensional Minkowski space-time.
That is, all couplings are dimensionless. The quark world is also dimensionless
in the 4-dimensional Minkowski space-time. Apart from the "ignition" term, the
gauge and Higgs sector, i.e., the overall background, is also dimensionless.
Thus, apart the "ignition" term, our world as a whole is dimensionless in the
4-dimensional Minkowski space-time - that is, it is the characteristic of the
quantum 4-dimensional Minkowski space-time.


  We map spin ladders with $n_l$ legs and couplings $J'$ across all rungs and
$J(1 \pm \gamma)$ along the legs, staggered in both directions, to a sigma
model. Setting its $\theta = (2m+1)\pi$ (where it is known to be gapless), we
locate the critical curves in the $\gamma$ versus ${J'\over J}$ plane at each
$n_l$, and spin $S$. The phase diagram is rich and has some surprises: when two
gapped chains are suitably coupled, the combination becomes gapless. With $n_l,
\gamma $ and $J'/J$ to control, the prospects for experimentally observing any
one of these equivalent transitions seem bright. We discuss the order
parameters and the behavior of holes in the RVB description.


  We report on variations in important X-ray emission lines in a series of
Chandra grating spectra of the supermassive colliding wind binary star Eta
Carinae, including key phases around the X-ray minimum/periastron passage in
2003.5. The X-rays arise from the collision of the slow, dense wind of Eta Car
with the fast, low-density wind of an otherwise hidden companion star. The
X-ray emission lines provide the only direct measure of the flow dynamics of
the companion's wind along the wind-wind collision zone. We concentrate here on
the silicon and sulfur lines, which are the strongest and best resolved lines
in the X-ray spectra. Most of the line profiles can be adequately fit with
symmetric Gaussians with little significant skewness. Both the silicon and
sulfur lines show significant velocity shifts and correlated increases in line
widths through the observations. The R = forbidden-to-intercombination ratio
from the Si XIII and S XV triplets is near or above the low-density limit in
all observations, suggesting that the line-forming region is >1.6 stellar radii
from the companion star. We show that simple geometrical models cannot
simultaneously fit both the observed centroid variations and changes in line
width as a function of phase. We show that the observed profiles can be fitted
with synthetic profiles with a reasonable model of the emissivity along the
wind-wind collision boundary. We use this analysis to help constrain the line
formation region as a function of orbital phase, and the orbital geometry.


  We consider the Nambu and Hamiltonian representations of Rayleigh-Benard
convection with a nonlinear thermal heating effect proportional to the Eckert
number (Ec). The model we use is an extension of the classical Lorenz-63 model
with 4 kinematic and 6 thermal degrees of freedom. The conservative parts of
the dynamical equations which include all nonlinearities satisfy Liouville's
theorem and permit a conserved Hamiltonian H for arbitrary Ec. For Ec=0 two
independent conserved Casimir functions exist, one of these is associated with
unavailable potential energy and is also present in the Lorenz-63 truncation.
This Casimir C is used to construct a Nambu representation of the conserved
part of the dynamical system. The thermal heating effect can be represented
either by a second canonical Hamiltonian or as a gradient (metric) system using
the time derivative of the Casimir. The results demonstrate the impact of
viscous heating in the total energy budget and in the Lorenz energy cycle for
kinetic and available potential energy.


  Employing the Foldy-Wouthuysen transformation it is demonstrated
straightforwardly that the first and second Chern numbers are equal to the
coefficients of the 2+1 and 4+1 dimensional Chern-Simons actions which are
generated by the massive Dirac fermions coupled to the Abelian gauge fields. A
topological insulator model in 2+1 dimensions is discussed and by means of a
dimensional reduction approach the 1+1 dimensional descendant of the 2+1
dimensional Chern-Simons theory is presented. Field strength of the Berry gauge
field corresponding to the 4+1 dimensional Dirac theory is explicitly derived
through the Foldy-Wouthuysen transformation. Acquainted with it the second
Chern numbers are calculated for specific choices of the integration domain. A
method is proposed to obtain 3+1 and 2+1 dimensional descendants of the
effective field theory of the 4+1 dimensional time reversal invariant
topological insulator theory. Inspired by the spin Hall effect in graphene, a
hypothetical model of the time reversal invariant spin Hall insulator in 3+1
dimensions is proposed.


  \emph{Optimal Transport} (OT) has emerged as an important computational tool
in machine learning and computer vision, providing a geometrical framework for
studying probability measures. OT unfortunately suffers from the curse of
dimensionality and requires regularization for practical computations, of which
the \emph{entropic regularization} is a popular choice, which can be
'unbiased', resulting in a \emph{Sinkhorn divergence}. In this work, we study
the convergence of estimating the 2-Sinkhorn divergence between \emph{Gaussian
processes} (GPs) using their finite-dimensional marginal distributions. We show
almost sure convergence of the divergence when the marginals are sampled
according to some base measure. Furthermore, we show that using $n$ marginals
the estimation error of the divergence scales in a dimension-free way as
$\mathcal{O}\left(\epsilon^ {-1}n^{-\frac{1}{2}}\right)$, where $\epsilon$ is
the magnitude of entropic regularization.


  Multi-physical inversion plays a critical role in geophysics. It has been
widely used to infer various physical properties~(such as velocity and
conductivity). Among those inversion problems, some are explicitly governed by
partial differential equations~(PDEs), while others are not. Without explicit
governing equations, conventional multi-physical inversion techniques will not
be feasible and data-driven inversion requires expensive full labels. To
overcome this issue, we develop a new data-driven multi-physics inversion
technique with extremely weak supervision. Our key finding is that the pseudo
labels can be constructed by learning the local relationship among geophysical
properties at very sparse well-logging locations. We explore a multi-physics
inversion problem from two distinct measurements~(seismic and EM data) to three
geophysical properties~(velocity, conductivity, and CO$_2$ saturation). Our
results show that we are able to invert for properties without explicit
governing equations. Moreover, the label data on three geophysical properties
can be significantly reduced by 50 times~(from 100 down to only 2 locations).


  Bulk-boundary correspondence, connecting the bulk topology and the edge
states, is an essential principle of the topological phases. However, the
bulk-boundary correspondence is broken down in general non-Hermitian systems.
In this paper, we construct one-dimensional non-Hermitian Su-Schrieffer-Heeger
model with periodic driving that exhibits non-Hermitian skin effect: all the
eigenstates are localized at the boundary of the systems, whether the bulk
states or the zero and the $\pi$ modes. To capture the topological properties,
the non-Bloch winding numbers are defined by the non-Bloch periodized evolution
operators based on the generalized Brillouin zone. Furthermore, the
non-Hermitian bulk-boundary correspondence is established: the non-Bloch
winding numbers ($W_{0,\pi}$) characterize the edge states with quasienergies
$\epsilon=0, \pi$. In our non-Hermitian system, a novel phenomenon can emerge
that the robust edge states can appear even when the Floquet bands are
topological trivial with zero non-Bloch band invariant, which is defined in
terms of the non-Bloch effective Hamiltonian. We also show that the relation
between the non-Bloch winding numbers ($W_{0,\pi}$) and the non-Bloch band
invariant ($\mathcal{W}$): $\mathcal{W}= W_{0}- W_{\pi}$.


  Online unsupervised detection of anomalies is crucial to guarantee the
correct operation of cyber-physical systems and the safety of humans
interacting with them. State-of-the-art approaches based on deep learning via
neural networks achieve outstanding performance at anomaly recognition,
evaluating the discrepancy between a normal model of the system (with no
anomalies) and the real-time stream of sensor time series. However, large
training data and time are typically required, and explainability is still a
challenge to identify the root of the anomaly and implement predictive
maintainance. In this paper, we use causal discovery to learn a normal causal
graph of the system, and we evaluate the persistency of causal links during
real-time acquisition of sensor data to promptly detect anomalies. On two
benchmark anomaly detection datasets, we show that our method has higher
training efficiency, outperforms the accuracy of state-of-the-art neural
architectures and correctly identifies the sources of >10 different anomalies.
The code is at https://github.com/Isla-lab/causal_anomaly_detection.


  This research presents a method that utilizes explainability techniques to
amplify the performance of machine learning (ML) models in forecasting the
quality of milling processes, as demonstrated in this paper through a
manufacturing use case. The methodology entails the initial training of ML
models, followed by a fine-tuning phase where irrelevant features identified
through explainability methods are eliminated. This procedural refinement
results in performance enhancements, paving the way for potential reductions in
manufacturing costs and a better understanding of the trained ML models. This
study highlights the usefulness of explainability techniques in both explaining
and optimizing predictive models in the manufacturing realm.


  Diffuse gliomas are highly infiltrative tumors whose early diagnosis and
follow-up usually rely on magnetic resonance imaging (MRI). However, the
limited sensitivity of this technique makes it impossible to directly assess
the extent of the glioma cell invasion, leading to sub-optimal treatment
planing. Reaction-diffusion growth models have been proposed for decades to
extrapolate glioma cell infiltration beyond margins visible on MRI and predict
its spatial-temporal evolution. These models nevertheless require an initial
condition, that is the tumor cell density values at every location of the brain
at diagnosis time. Several works have proposed to relate the tumor cell density
function to abnormality outlines visible on MRI but the underlying assumptions
have never been verified so far. In this work we propose to verify these
assumptions by stereotactic histological analysis of a non-operated brain with
glioblastoma using a tailored 3D-printed slicer. Cell density maps are computed
from histological slides using a deep learning approach. The density maps are
then registered to a postmortem MR image and related to an MR-derived geodesic
distance map to the tumor core. The relation between the edema outlines visible
on T2 FLAIR MRI and the distance to the core is also investigated. Our results
suggest that (i) the previously suggested exponential decrease of the tumor
cell density with the distance to the tumor core is not unreasonable but (ii)
the edema outlines may in general not correspond to a cell density iso-contour
and (iii) the commonly adopted tumor cell density value at these outlines is
likely overestimated. These findings highlight the limitations of using
conventional MRI to derive glioma cell density maps and point out the need of
validating other methods to initialize reaction-diffusion growth models and
make them usable in clinical practice.


  Automated multi-document extractive text summarization is a widely studied
research problem in the field of natural language understanding. Such
extractive mechanisms compute in some form the worthiness of a sentence to be
included into the summary. While the conventional approaches rely on human
crafted document-independent features to generate a summary, we develop a
data-driven novel summary system called HNet, which exploits the various
semantic and compositional aspects latent in a sentence to capture document
independent features. The network learns sentence representation in a way that,
salient sentences are closer in the vector space than non-salient sentences.
This semantic and compositional feature vector is then concatenated with the
document-dependent features for sentence ranking. Experiments on the DUC
benchmark datasets (DUC-2001, DUC-2002 and DUC-2004) indicate that our model
shows significant performance gain of around 1.5-2 points in terms of ROUGE
score compared with the state-of-the-art baselines.


  Spatial transcriptomics (ST) enables interrogating the molecular composition
of tissue with ever-increasing resolution, depth, and sensitivity. However,
costs, rapidly evolving technology, and lack of standards have constrained
computational methods in ST to narrow tasks and small cohorts. In addition, the
underlying tissue morphology as reflected by H&E-stained whole slide images
(WSIs) encodes rich information often overlooked in ST studies. Here, we
introduce HEST-1k, a collection of 1,108 spatial transcriptomic profiles, each
linked to a WSI and metadata. HEST-1k was assembled using HEST-Library from 131
public and internal cohorts encompassing 25 organs, two species (Homo Sapiens
and Mus Musculus), and 320 cancer samples from 25 cancer types. HEST-1k
processing enabled the identification of 1.5 million expression--morphology
pairs and 60 million nuclei. HEST-1k is tested on three use cases: (1)
benchmarking foundation models for histopathology (HEST-Benchmark), (2)
biomarker identification, and (3) multimodal representation learning. HEST-1k,
HEST-Library, and HEST-Benchmark can be freely accessed via
https://github.com/mahmoodlab/hest.


  The thermal boundary resistance of Si/Ge interfaces as been determined using
approach-to-equilibrium molecular dynamics simulations. Assuming a reciprocal
linear dependence of the thermal boundary resistance, a length-independent bulk
thermal boundary resistance could be extracted from the calculation resulting
in a value of 3.76x10$^{-9}$ m$^2$ K/W for a sharp Si/Ge interface and thermal
transport from Si to Ge. Introducing an interface with finite thickness of 0.5
nm consisting of a SiGe alloy, the bulk thermal resistance slightly decreases
compared to the sharp Si/Ge interface. Further growth of the boundary leads to
an increase in the bulk thermal boundary resistance. When the heat flow is
inverted (Ge to Si), the thermal boundary resistance is found to be higher.
From the differences in the thermal boundary resistance for different heat flow
direction, the rectification factor of the Si/Ge has been determined and is
found to significantly decrease when the sharp interface is moderated by
introduction of a SiGe alloy in the boundary layer.


  In this paper, we propose a simple but effective method for fast image
segmentation. We re-examine the locality-preserving character of spectral
clustering by constructing a graph over image regions with both global and
local connections. Our novel approach to build graph connections relies on two
key observations: 1) local region pairs that co-occur frequently will have a
high probability to reside on a common object; 2) spatially distant regions in
a common object often exhibit similar visual saliency, which implies their
neighborship in a manifold. We present a novel energy function to efficiently
conduct graph partitioning. Based on multiple high quality partitions, we show
that the generated eigenvector histogram based representation can automatically
drive effective unary potentials for a hierarchical random field model to
produce multi-class segmentation. Sufficient experiments, on the BSDS500
benchmark, large-scale PASCAL VOC and COCO datasets, demonstrate the
competitive segmentation accuracy and significantly improved efficiency of our
proposed method compared with other state of the arts.


  Estimating nested expectations is an important task in computational
mathematics and statistics. In this paper we propose a new Monte Carlo method
using post-stratification to estimate nested expectations efficiently without
taking samples of the inner random variable from the conditional distribution
given the outer random variable. This property provides the advantage over many
existing methods that it enables us to estimate nested expectations only with a
dataset on the pair of the inner and outer variables drawn from the joint
distribution. We show an upper bound on the mean squared error of the proposed
method under some assumptions. Numerical experiments are conducted to compare
our proposed method with several existing methods (nested Monte Carlo method,
multilevel Monte Carlo method, and regression-based method), and we see that
our proposed method is superior to the compared methods in terms of efficiency
and applicability.


  With millimeter wave bands emerging as a strong candidate for 5G cellular
networks, next-generation systems may be in a unique position where spectrum is
plentiful. To assess the potential value of this spectrum, this paper derives
scaling laws on the per mobile downlink feasible rate with large bandwidth and
number of nodes, for both Infrastructure Single Hop (ISH) and Infrastructure
Multi-Hop (IMH) architectures. It is shown that, for both cases, there exist
\emph{critical bandwidth scalings} above which increasing the bandwidth no
longer increases the feasible rate per node. These critical thresholds coincide
exactly with the bandwidths where, for each architecture, the network
transitions from being degrees-of-freedom-limited to power-limited. For ISH,
this critical bandwidth threshold is lower than IMH when the number of users
per base station grows with network size. This result suggests that multi-hop
transmissions may be necessary to fully exploit large bandwidth degrees of
freedom in deployments with growing number of users per cell.


  Matrix completion focuses on recovering a matrix from a small subset of its
observed elements, and has already gained cumulative attention in computer
vision. Many previous approaches formulate this issue as a low-rank matrix
approximation problem. Recently, a truncated nuclear norm has been presented as
a surrogate of traditional nuclear norm, for better estimation to the rank of a
matrix. The truncated nuclear norm regularization (TNNR) method is applicable
in real-world scenarios. However, it is sensitive to the selection of the
number of truncated singular values and requires numerous iterations to
converge. Hereby, this paper proposes a revised approach called the double
weighted truncated nuclear norm regularization (DW-TNNR), which assigns
different weights to the rows and columns of a matrix separately, to accelerate
the convergence with acceptable performance. The DW-TNNR is more robust to the
number of truncated singular values than the TNNR. Instead of the iterative
updating scheme in the second step of TNNR, this paper devises an efficient
strategy that uses a gradient descent manner in a concise form, with a
theoretical guarantee in optimization. Sufficient experiments conducted on real
visual data prove that DW-TNNR has promising performance and holds the
superiority in both speed and accuracy for matrix completion.


  Testing plays a crucial role in software development, and it is essential for
software engineering students to receive proper testing education. However,
motivating students to write tests and use automated testing during software
development can be challenging. To address this issue and enhance student
engagement in testing when they write code, we propose to incentivize students
to test more by gamifying continuous integration. For this we use Gamekins, a
tool that is seamlessly integrated into the Jenkins continuous integration
platform and uses game elements based on commits to the source code repository:
Developers can earn points by completing test challenges and quests generated
by Gamekins, compete with other developers or teams on a leaderboard, and
receive achievements for their test-related accomplishments. In this paper, we
present our integration of Gamekins into an undergraduate-level course on
software testing. We observe a correlation between how students test their code
and their use of Gamekins, as well as a significant improvement in the accuracy
of their results compared to a previous iteration of the course without
gamification. As a further indicator of how this approach improves testing
behavior, the students reported enjoyment in writing tests with Gamekins.


  The development of the first generation of commercial quantum computers is
based on superconductive qubits and trapped ions respectively. Other
technologies such as semiconductor quantum dots, neutral ions and photons could
in principle provide an alternative to achieve comparable results in the medium
term. It is relevant to evaluate if one or more of them is potentially more
effective to address scalability to millions of qubits in the long term, in
view of creating a universal quantum computer. We review an all-electrical
silicon spin qubit, that is the double quantum dot hybrid qubit, a quantum
technology which relies on both solid theoretical grounding on one side, and
massive fabrication technology of nanometric scale devices by the existing
silicon supply chain on the other.


  I present a highly biased and skewed summary of IAU Symposium 314, "Young
Stars and Planets Near the Sun," held in Atlanta. This summary includes
takeaway thoughts about the rapidly evolving state of the field, as well as
crowd-sourced predictions for progress over the next ~10 years. We predict the
elimination of 1-2 of the currently recognized young moving groups, the
addition of 3 or more new moving groups within 100 pc, the continued lack of a
predictive theory of stellar mass, robust measurements of the gas and dust
content of circumstellar disks, and an ongoing struggle to achieve a consensus
definition for a planet.


  Informing Additive Manufacturing (AM) technology adoption decisions, this
paper investigates the relationship between build volume capacity utilisation
and efficient technology operation in an inter-process comparison of the costs
of manufacturing a complex component used in the packaging industry.
Confronting the reported costs of a conventional machining and welding pathway
with an estimator of the costs incurred through an AM route utilising Direct
Metal Laser Sintering (DMLS), we weave together four aspects: optimised
capacity utilisation, ancillary process steps, the effect of build failure, and
design adaptation. Recognising that AM users can fill unused machine capacity
with other, potentially unrelated, geometries, we posit a characteristic of
'fungible' build capacity. This aspect is integrated in the cost estimation
framework through computational build volume packing, drawing on a basket of
sample geometries. We show that the unit cost in mixed builds at full capacity
is lower than in builds limited to a single type of geometry; in our study this
results in a mean unit cost overstatement of 157%. The estimated manufacturing
costs savings from AM adoption range from 36% to 46%. Additionally, we indicate
that operating cost savings resulting from design adaptation are likely to far
outweigh the manufacturing cost advantage.


  Absence of the Dirac cone due to a strong band hybridization is revealed to
be a common feature for epitaxial silicene on metal substrates according to our
first-principles calculations for silicene on Ir, Cu, Mg, Au, Pt, Al, and Ag
substrates. The destroyed Dirac cone of silicene, however, can be effectively
restored with linear or parabolic dispersion by intercalating alkali metal
atoms between silicene and the metal substrates, offering an opportunity to
study the intriguing properties of silicene without further transfer of
silicene from the metal substrates.


  The Maxwell-Smoluchowski (MS) theory of gas diffusion is revisited here in
the context of gas transport in straight channels in the Knudsen regime of
large mean free path. This classical theory is based on a phenomenological
model of gas-surface interaction that posits that a fraction $\vartheta$ of
molecular collisions with the channel surface consists of diffuse collisions,
i.e., the direction of post-collision velocities is distributed according to
the Knudsen Cosine Law, and a fraction $1-\vartheta$ undergoes specular
reflection. From this assumption one obtains the value
$\mathcal{D}=\frac{2-\vartheta}{\vartheta}\mathcal{D}_K$ for the
self-diffusivity constant, where $\mathcal{D}_K$ is a reference value
corresponding to $\vartheta=1$. In this paper we show that $\vartheta$ can be
expressed in terms of micro- and macro-geometric parameters for a model
consisting of hard spheres colliding elastically against a rigid surface with
prescribed microgeometry. Our refinement of the MS theory is based on the
observation that the classical surface scattering operator associated to the
microgeometry has a canonical velocity space diffusion approximation by a
generalized Legendre differential operator whose spectral theory is known
explicitly. More specifically, starting from an explicit description of the
effective channel surface microgeometry -- a concept which incorporates both
the actual surface microgeometry and the molecular radius -- and using this
operator approximation, we show that $\vartheta$ can be resolved into easily
obtained geometric parameters.


  The rapid development of aspect-based sentiment analysis (ABSA) within recent
decades shows great potential for real-world society. The current ABSA works,
however, are mostly limited to the scenario of a single text piece, leaving the
study in dialogue contexts unexplored. To bridge the gap between fine-grained
sentiment analysis and conversational opinion mining, in this work, we
introduce a novel task of conversational aspect-based sentiment quadruple
analysis, namely DiaASQ, aiming to detect the quadruple of
target-aspect-opinion-sentiment in a dialogue. We manually construct a
large-scale high-quality DiaASQ dataset in both Chinese and English languages.
We deliberately develop a neural model to benchmark the task, which advances in
effectively performing end-to-end quadruple prediction, and manages to
incorporate rich dialogue-specific and discourse feature representations for
better cross-utterance quadruple extraction. We hope the new benchmark will
spur more advancements in the sentiment analysis community.


  In remote sensing images, the presence of thick cloud accompanying cloud
shadow is a high probability event, which can affect the quality of subsequent
processing and limit the scenarios of application. Hence, removing the thick
cloud and cloud shadow as well as recovering the cloud-contaminated pixels is
indispensable to make good use of remote sensing images. In this paper, a novel
thick cloud removal method for remote sensing images based on temporal
smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed.
The basic idea of TSSTO is that the thick cloud and cloud shadow are not only
sparse but also smooth along the horizontal and vertical direction in images
while the clean images are smooth along the temporal direction between images.
Therefore, the sparsity norm is used to boost the sparsity of the cloud and
cloud shadow, and unidirectional total variation (UTV) regularizers are applied
to ensure the unidirectional smoothness. This paper utilizes alternation
direction method of multipliers to solve the presented model and generate the
cloud and cloud shadow element as well as the clean element. The cloud and
cloud shadow element is purified to get the cloud area and cloud shadow area.
Then, the clean area of the original cloud-contaminated images is replaced to
the corresponding area of the clean element. Finally, the reference image is
selected to reconstruct details of the cloud area and cloud shadow area using
the information cloning method. A series of experiments are conducted both on
simulated and real cloud-contaminated images from different sensors and with
different resolutions, and the results demonstrate the potential of the
proposed TSSTO method for removing cloud and cloud shadow from both qualitative
and quantitative viewpoints.


  The renormalization group equations of the Yang-Mills theory are examined in
the non-critical string theory according to the framework of the holography.
Under a simple ansatz for the tachyon, we could find several interesting
solutions which are classified by the value of the tachyon potential at the
vacuum. We show two typical, asymptotic-free solutions which are different in
their infrared behaviors. For both types of solutions, we could obtain
quark-confinement potential from the Wilson-loop. The stability of the tachyon
and the ZigZag symmetry are also discussed for these solutions.


  Chain of calculations which have to be performed to predict any kind of
signal in a deep underwater/ice neutrino detector necessarily includes the
lepton propagation through thick layers of matter, as neutrino can be observed
only by means of leptons (muons, first of all, due to their large ranges) that
are generated in $\nu N \to l N$ interactions. Thus, the muon propagation plays
a key role when analyzing data and it is important to understand clearly how
transportation part of simulation chain contributes to total inaccuracy of
final results. Here we consider sources of uncertainties that appear in Monte
Carlo algorithms for simulation of muon transport. The trivial but effective
test is proposed to measure the propagation algorithm accuracy. The test is
applied to three MC muon transport codes (PROPMU, MUSIC, MUM) and results are
reported.


  We study the impact of the effect of multinucleon interactions in the
reconstruction of the neutrino energy on the fit of the MiniBooNE data in terms
of neutrino oscillations. We obtain some improvement of the fit of the
MiniBooNE low-energy excess in the framework of two-neutrino oscillations and a
shift of the allowed region in the $\sin^2 2\vartheta$--$\Delta{m}^2$ plane
towards smaller values of $\sin^2 2\vartheta$ and larger values of
$\Delta{m}^2$. However this effect is not enough to solve the problem of the
appearance-disappearance tension in the global fit of short-baseline neutrino
oscillation data.


  First we summarize the quark-level linear $\sigma$ model compositeness
conditions and verify that indeed $m_\sigma = 2 m_q$ when $m_\pi = 0$ and
$N_c=3$, rather than in the $N_c\to\infty$ limit, as is sometimes suggested.
Later we show that this compositeness picture also predicts a chiral symmetry
restoration temperature $T_c = 2f_\pi$, where $f_\pi$ is the pion decay
constant. We contrast this self-consistent Z=0 compositeness analysis with
prior studies of the compositeness problem.


  Hybrid electrolyte materials comprising polymer-ionic salt matrixes embedded
with garnet particles constitute a promising class of materials for the
realization of all-solid-state batteries. In addition to providing solutions to
the safety issues inherent to current liquid electrolytes, hybrid polymer
electrolytes offer advantages over other solid-state electrolytes. This is
because their functional properties such as ionic conductivity, electrochemical
stability, mechanical and thermal properties can be tailored to a particular
application by independently optimizing the properties of the constituent
materials. Thereby, providing a rational approach to solving bottlenecks
currently preventing solid-state electrolytes from practical implementation
into battery devices. This review starts with a survey of solid-state
electrolytes, focusing on their materials and ion transport limitations. Next,
we summarize the current understanding of transport mechanisms in composite
polymer electrolytes (CPEs) with the purpose of identifying materials solutions
for further improving their properties. The overall goal of the review is to
foster heightened research interest in these hybrid structures to rapidly
advance development of future all-solid-state battery devices.


  We theoretically investigate the Cooper-pair symmetry to be realized in
hole-doped monolayer MoS$_2$ by solving linearized BCS gap equations on the
three-orbital attractive Hubbard-like model in the presence of the atomic
spin-orbit coupling. In hole-doped monolayer MoS$_2$, both spin-orbit coupling
and the multi-orbital effects are more prominent than those of electron-doped
system. Near the valence band edge, the Fermi surfaces are composed of three
different types of hole pockets, namely, one mainly consisting of the almost
spin-degenerate $|d_{z^{2}}\rangle$ orbital near $\Gamma$ point, and the others
of the spin-split upper and lower bands near ${\rm K}$ and ${\rm K}'$ points
arising from the $|d_{x^{2}-y^{2}}\rangle$ and $|d_{xy}\rangle$ orbitals. The
number of relevant Fermi pockets increases with increase of the doping. At very
low doping, the upper split bands of $|d_{x^{2}-y^{2}}\rangle$ and
$|d_{xy}\rangle$ are concerned, yielding extremely low $T_{\rm c}$ due to small
density of states of the split bands. For further doping, the conventional
spin-singlet state (SS) appears in the $\Gamma$ pocket, which has a mixture of
the spin-triplet (orbital-singlet) (ST-OS) and spin-singlet (orbital-triplet)
(SS-OT) states in the K and K$'$ pockets. The ratio of the mixture depends on
the relative strength of the interactions, and the sign of the exchange
interactions. Moderately strong ferromagnetic exchange interactions even lead
to the pairing state with the dominant ST-OS state over the conventional SS
one. With these observations, we expect that the fascinating pairing with
relatively high $T_{\rm c}$ emerges at high doping that involves all the three
Fermi pockets.


  We introduce CORTEX, an algorithmic framework designed for large-scale brain
simulation. Leveraging the computational capacity of the Fugaku Supercomputer,
CORTEX maximizes available problem size and processing performance. Our primary
innovation, Indegree Sub-Graph Decomposition, along with a suite of parallel
algorithms, facilitates efficient domain decomposition by segmenting the global
graph structure into smaller, identically structured sub-graphs. This
segmentation allows for parallel processing of synaptic interactions without
inter-process dependencies, effectively eliminating data racing at the thread
level without necessitating mutexes or atomic operations. Additionally, this
strategy enhances the overlap of communication and computation. Benchmark tests
conducted on spiking neural networks, characterized by biological parameters,
have demonstrated significant enhancements in both problem size and simulation
performance, surpassing the capabilities of the current leading open-source
solution, the NEST Simulator. Our work offers a powerful new tool for the field
of neuromorphic computing and understanding brain function.


  With X-ray radiation protection and dose management constantly gaining
interest in interventional radiology, novel procedures often undergo
prospective dose studies using anthropomorphic phantoms to determine expected
dose values. Due to inherent uncertainties, these dose values might not allow
for patient-specific risk assessment. Therefore, first the aim of this study is
to quantify the influence of these parameters on local X-ray dose to evaluate
their relevance in the assessment of patient-specific organ doses. Second, this
knowledge further enables validating a simulation approach, which allows
employing physiological material models and patient-specific geometries.
Phantom dosimetry experiments using MOSFET dosimeters were conducted
reproducing imaging scenarios in prostatic arterial embolization (PAE).
Associated organ-equivalent dose of prostate, bladder, colon and skin was
determined. Dose deviation induced by possible small displacements of the
patient was reproduced by moving the X-ray source. Dose deviation induced by
material differences was investigated by analyzing two different commonly used
phantoms. We reconstructed the experiments using Monte Carlo (MC) simulations,
a reference male geometry, and different material properties. Overall, MC
simulated organ dose values are in accordance with the measured ones for the
majority of cases. Marginal displacements of X-ray source relative to the
phantoms lead to deviations of 6% to 135% in organ dose values, while skin dose
remains relatively constant. Regarding the impact of phantom material
composition, underestimation of internal organ dose values by 12% to 20% is
prevalent in all simulated phantoms. Skin dose, however, can be estimated with
low deviation of 1% to 8% at least for two materials. Prospective reference
dose studies might not extend to precise patient-specific dose assessment.


  We investigate a dynamical magnetoelectric effect due to a magnetic resonance
in helical spin structures through the coupling between magnetization and
electric polarization via a spin current mechanism. We show that the magnon has
both the dynamical magnetic moment $\Delta M^\omega$ and the electric moment
$\Delta P^\omega$ ($\perp \Delta M^\omega$), i.e., a dynamical toroidal moment,
under external magnetic fields, and thus it is named the {\em toroidalmagnon}.
The toroidalmagnon exists in most conical spin structures owing to the
generality of the spin current mechanism. In the absorption of electromagnetic
waves, the toroidalmagnon excitation process generally induces a nonreciprocal
directional dichroism as a consequence of an interference of the magnetic and
electric responses.


  Online game forums are popular to most of game players. They use it to
communicate and discuss the strategy of the game, or even to make friends.
However, game forums also contain abusive and harassment speech, disturbing and
threatening players. Therefore, it is necessary to automatically detect and
remove cyberbullying comments to keep the game forum clean and friendly. We use
the Cyberbullying dataset collected from World of Warcraft (WoW) and League of
Legends (LoL) forums and train classification models to automatically detect
whether a comment of a player is abusive or not. The result obtains 82.69% of
macro F1-score for LoL forum and 83.86% of macro F1-score for WoW forum by the
Toxic-BERT model on the Cyberbullying dataset.


  We discuss and compare different approaches to include gluon transverse
momenta for heavy quark-antiquark pair and meson production. The results are
illustrated with the help of different unintegrated gluon distributions (UGDF)
from the literature. We compare results obtained with on-shell and off-shell
matrix elements and kinematics. The results are compared with recent
experimental results of the CDF collaboration.


  The growth of magnesium on ruthenium has been studied by low-energy electron
microscopy (LEEM) and scanning tunneling microscopy (STM). In LEEM, a
layer-by-layer growth is observed except in the first monolayer, where the
completion of the first layer in inferred by a clear peak in electron
reflectivity. Desorption from the films is readily observable at 400 K.
Real-space STM and low-energy electron diffraction confirm that sub-monolayer
coverage presents a moir\'e pattern with a 1.2 nm periodicity, which evolves
with further Mg deposition by compressing the Mg layer to a 2.2 nm periodicity.
Layer-by-layer growth is followed in LEEM up to 10 ML. On films several ML
thick a substantial density of stacking faults are observed by dark-field
imaging on large terraces of the substrate, while screw dislocations appear in
the stepped areas. The latter are suggested to result from the mismatch in
heights of the Mg and Ru steps. Quantum size effect oscillations in the
reflected LEEM intensity are observed as a function of thickness, indicating an
abrupt Mg/Ru interface.


  We construct a new catalog of extragalactic X-ray binaries (XRBs) by matching
the latest Chandra source catalog with local galaxy catalogs. Our XRB catalog
contains 4430 XRBs hosted by 237 galaxies within ~130 Mpc. As XRBs dominate the
X-ray activity in galaxies, the catalog enables us to study the correlations
between the total X-ray luminosity of a galaxy $L_{X,\rm tot}$, star formation
rate $\dot{\rho}_\star$, and stellar mass $M_\star$. As previously reported,
$L_{X,\rm tot}$ is correlated with $\dot{\rho}_\star$ and $M_\star$. In
particular, we find that there is a fundamental plane in those three parameters
as $\log L_{X,\rm tot}={38.80^{+0.09}_{-0.12}}+\log(\dot{\rho}_\star + \alpha
M_\star)$, where $\alpha = {(3.36\pm1.40)\times10^{-11}}\ {\rm yr^{-1}}$. In
order to investigate this relation, we construct a phenomenological binary
population synthesis model. We find that the high mass XRB and low mass XRB
fraction in formed compact object binary systems is ~9% and ~0.04%,
respectively. Utilizing the latest XMM-Newton, and Swift X-ray source catalog
data sets, additional XRB candidates are also found resulting in 5757 XRBs
hosted by 311 galaxies.


  Floating point operations are fast, but require continuous effort on the part
of the user in order to ensure that the results are correct. This burden can be
shifted away from the user by providing a library of exact analysis in which
the computer handles the error estimates. Previously, we [Krebbers/Spitters
2011] provided a fast implementation of the exact real numbers in the Coq proof
assistant. Our implementation improved on an earlier implementation by O'Connor
by using type classes to describe an abstract specification of the underlying
dense set from which the real numbers are built. In particular, we used dyadic
rationals built from Coq's machine integers to obtain a 100 times speed up of
the basic operations already. This article is a substantially expanded version
of [Krebbers/Spitters 2011] in which the implementation is extended in the
various ways. First, we implement and verify the sine and cosine function.
Secondly, we create an additional implementation of the dense set based on
Coq's fast rational numbers. Thirdly, we extend the hierarchy to capture order
on undecidable structures, while it was limited to decidable structures before.
This hierarchy, based on type classes, allows us to share theory on the
naturals, integers, rationals, dyadics, and reals in a convenient way. Finally,
we obtain another dramatic speed-up by avoiding evaluation of termination
proofs at runtime.


  The discovery of topological phases introduces new perspectives and platforms
for various interesting physics originally investigated in quantum context and
then, on an equal footing, in classic wave systems, such as photonics,
acoustics and mechanics. As a characteristic feature, nontrivial Fermi arcs,
connecting between topologically distinct Fermi surfaces, play vital roles in
the clarification of Dirac and Weyl semimetals and have been observed in
quantum materials very recently. However, in classical systems, no direct
experimental observation of Fermi arcs in the momentum space has been reported
so far. Here, using near-field scanning measurements, we show the observation
of a photonic Fermi arcs connecting topologically distinct bulk states in
chiral hyperbolic metamaterials. To verify the topological nature of this
system, we further observe backscattering-immune propagation of nontrivial
surface wave across a three-dimension physical step. Our results demonstrate
metamaterial approach towards topological photonics and offer a deeper
understanding of topological phases in three-dimension classical systems.


  We present the general expression of helicity amplitudes for generic
multi-body particle decays characterised by multiple decay chains. This is
achieved by addressing for the first time the issue of the matching of final
particle spin states among different decay chains in full generality for
generic multi-body decays, proposing a method able to match the exact
definition of spin states relative to the decaying particle ones. We stress the
importance of our result by showing that one of the matching method used in the
literature is incorrect, leading to amplitude models violating rotational
invariance. The results presented are therefore relevant for performing
numerous amplitude analysis, notably those searching for exotic structures like
pentaquarks.


  We study the physical properties of the ionized gas in local disks using the
sample of 38 nearby $\sim10^{8.5-11.2}$M$_\odot$ Star-Forming Main Sequence
(SFMS) galaxies observed so far as part of the MUSE Atlas of Disks (MAD).
Specifically, we use all strong emission lines in the MUSE wavelength range
4650-9300 \AA\ to investigate the resolved ionized gas properties on $\sim$100
pc scales. This spatial resolution enables us to disentangle HII regions from
the Diffuse Ionized Gas (DIG) in the computation of gas metallicities and Star
Formation Rates (SFRs) of star forming regions.
  The gas metallicities generally decrease with radius. The metallicity of the
HII regions is on average $\sim$0.1 dex higher than that of the DIG, but the
metallicity radial gradient in both components is similar. The mean
metallicities within the inner galaxy cores correlate with the total stellar
mass of the galaxies. On our <100 pc scales, we find two correlations
previously reported at kpc scales: a spatially resolved Mass-Metallicity
Relation (RMZR) and a spatially resolved SFMS (RSFMS). We find no secondary
dependency of the RMZR with the SFR density. We find that both resolved
relations have a local origin, as they do not depend on the total stellar mass.
The observational results of this paper are consistent with the inside-out
scenario for the growth of galactic disks.


  We discuss the linearization of Einstein equations in the presence of a
cosmological constant, by expanding the solution for the metric around a flat
Minkowski space-time. We demonstrate that one can find consistent solutions to
the linearized set of equations for the metric perturbations, in the Lorentz
gauge, which are not spherically symmetric, but they rather exhibit a
cylindrical symmetry. We find that the components of the gravitational field
satisfying the appropriate Poisson equations have the property of ensuring that
a scalar potential can be constructed, in which both contributions, from
ordinary matter and $\Lambda > 0$, are attractive. In addition, there is a
novel tensor potential, induced by the pressure density, in which the effect of
the cosmological constant is repulsive. We also linearize the Schwarzschild-de
Sitter exact solution of Einstein's equations (due to a generalization of
Birkhoff's theorem) in the domain between the two horizons. We manage to
transform it first to a gauge in which the 3-space metric is conformally flat
and, then, make an additional coordinate transformation leading to the Lorentz
gauge conditions. We compare our non-spherically symmetric solution with the
linearized Schwarzschild-de Sitter metric, when the latter is transformed to
the Lorentz gauge, and we find agreement. The resulting metric, however, does
not acquire a proper Newtonian form in terms of the unique scalar potential
that solves the corresponding Poisson equation. Nevertheless, our solution is
stable, in the sense that the physical energy density is positive.


  Linear spin wave theory shows that ring exchange induces a quantum disordered
region in the phase diagram of the title model. Spin wave spectra show that
this is a direct manifestation of competing classical orders. A spin liquid is
found in the `Goldilocks zone' of frustration, where the quantum fluctuations
are large enough to cause strong competition between different classical
orderings but not strong enough to stabilize spiral order. We note that the
spin liquid phases of $\kappa$-(BEDT-TTF)${_2}X$ and $Y$[Pd(dmit)$_2$]$_2$ are
found in this Goldilocks zone.


  Mathematical world knowledge is a fundamental component of Wikidata. However,
to date, no expertly curated knowledge graph has focused specifically on
contemporary mathematics. Addressing this gap, the Mathematical Research Data
Initiative (MaRDI) has developed a comprehensive knowledge graph that links
multimodal research data in mathematics. This encompasses traditional research
data items like datasets, software, and publications and includes semantically
advanced objects such as mathematical formulas and hypotheses. This paper
details the abilities of the MaRDI knowledge graph, which is based on Wikibase,
leading up to its inaugural public release, codenamed Bravo, available on
https://portal.mardi4nfdi.de.


  It is a common method for proving weak convergence of a sequence of
time-homogeneous Markov processes towards a time-homogeneous Markov process
first to show convergence of the corresponding infinitesimal generators and
then to check some additional conditions. The aim of the present paper is to
investigate convergence properties of discrete infinitesimal generators of
appropriately scaled random step functions formed from a multi-type continuous
state and continuous time branching process with immigration. We also present a
convergence result for usual infinitesimal generators of the branching
processes in question appropriately normalized.


  For chaotic scattering on quantum graphs, the semiclassical approximation is
exact. We use this fact and employ supersymmetry, the colour-flavour
transformation, and the saddle-point approximation to calculate the exact
expression for the lowest and asymptotic expressions in the Ericson regime for
all higher correlation functions of the scattering matrix. Our results agree
with those available from the random-matrix approach to chaotic scattering. We
conjecture that our results hold universally for quantum-chaotic scattering.


  In these lectures, I examine the effect of the meson factory $\pi$N data on
the current algebra/PCAC program which describes chiral symmetry breaking in
this system. After historical remarks on the current algebra/PCAC versus chiral
Lagrangians approaches to chiral symmetry, and description of the need for
$\pi$N amplitudes with virtual (off-mass-shell) pions in nuclear force models
and other nuclear physics problems, I begin with kinematics and isospin aspects
of the invariant amplitudes. A detailed introduction to the hadronic vector and
axial-vector currents and the hypothesis of partially conserved axial-vector
currents (PCAC) follows. I review and test against contemporary data the PCAC
predictions of the Goldberger-Treiman relation, and the Adler consistency
condition for a $\pi$N amplitude. Then comes a detailed description of the
current algebra Ward-Takahashi identities in the chiral limit and a brief
account of the on-shell current algebra Ward-Takahashi identities. The latter
identities form the basis of so-called current algebra models of $\pi$N
scattering. I then test these models against the contemporary empirical $\pi$N
amplitudes extrapolated into the subthreshold region via dispersion relations.
The scale and the t dependence of the "sigma term" is determined by the recent
data.


  In this article we present the application of classical and quantum-classical
hybrid anomaly detection schemes to explore exotic configuration with anomalous
features. We consider the Anderson model as a prototype where we define two
types of anomalies - a high conductance in presence of strong impurity and low
conductance in presence of weak impurity - as a function of random impurity
distribution. Such anomalous outcome constitutes an imperceptible fraction of
the data set and is not a part of the training process. These exotic
configurations, which can be a source of rich new physics, usually remain
elusive to conventional classification or regression methods and can be tracked
only with a suitable anomaly detection scheme. We also present a systematic
study of the performance of the classical and the quantum-classical hybrid
anomaly detection method and show that the inclusion of a quantum circuit
significantly enhances the performance of anomaly detection which we quantify
with suitable performance metrics. Our approach is quite generic in nature and
can be used for any system that relies on a large number of parameters to find
their new configurations which can hold exotic new features.


  This paper addresses the problem of finite horizon constrained robust optimal
control for nonlinear systems subject to norm-bounded disturbances. To this
end, the underlying uncertain nonlinear system is decomposed based on a
first-order Taylor series expansion into a nominal system and an error
(deviation) described as an uncertain linear time-varying system. This
decomposition allows us to leverage System Level Synthesis to jointly optimize
an affine error feedback, a nominal nonlinear trajectory, and, most
importantly, a dynamic linearization error over-bound used to ensure robust
constraint satisfaction for the nonlinear system. The proposed approach thereby
results in less conservative planning compared with state-of-the-art
techniques. We demonstrate the benefits of the proposed approach to control the
rotational motion of a rigid body subject to state and input constraints.


  The exemplar-free class incremental learning requires classification models
to learn new class knowledge incrementally without retaining any old samples.
Recently, the framework based on parallel one-class classifiers (POC), which
trains a one-class classifier (OCC) independently for each category, has
attracted extensive attention, since it can naturally avoid catastrophic
forgetting. POC, however, suffers from weak discriminability and comparability
due to its independent training strategy for different OOCs. To meet this
challenge, we propose a new framework, named Discriminative and Comparable
One-class classifiers for Incremental Learning (DisCOIL). DisCOIL follows the
basic principle of POC, but it adopts variational auto-encoders (VAE) instead
of other well-established one-class classifiers (e.g. deep SVDD), because a
trained VAE can not only identify the probability of an input sample belonging
to a class but also generate pseudo samples of the class to assist in learning
new tasks. With this advantage, DisCOIL trains a new-class VAE in contrast with
the old-class VAEs, which forces the new-class VAE to reconstruct better for
new-class samples but worse for the old-class pseudo samples, thus enhancing
the comparability. Furthermore, DisCOIL introduces a hinge reconstruction loss
to ensure the discriminability. We evaluate our method extensively on MNIST,
CIFAR10, and Tiny-ImageNet. The experimental results show that DisCOIL achieves
state-of-the-art performance.


  Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations
(ABSs) to provide wireless connectivity for ground users (GUs) in various
emergency scenarios. However, it is a NP-hard problem with exponential
complexity in $M$ and $N$, in order to maximize the coverage rate of $M$ GUs by
jointly placing $N$ ABSs with limited coverage range. The problem is further
complicated when the coverage range becomes irregular due to site-specific
blockages (e.g., buildings) on the air-ground channel, and/or when the GUs are
moving. To address the above challenges, we study a multi-ABS movement
optimization problem to maximize the average coverage rate of mobile GUs in a
site-specific environment. The Spatial Deep Learning with Multi-dimensional
Archive of Phenotypic Elites (SDL-ME) algorithm is proposed to tackle this
challenging problem by 1) partitioning the complicated ABS movement problem
into ABS placement sub-problems each spanning finite time horizon; 2) using an
encoder-decoder deep neural network (DNN) as the emulator to capture the
spatial correlation of ABSs/GUs and thereby reducing the cost of interaction
with the actual environment; 3) employing the emulator to speed up a
quality-diversity search for the optimal placement solution; and 4) proposing a
planning-exploration-serving scheme for multi-ABS movement coordination.
Numerical results demonstrate that the proposed approach significantly
outperforms the benchmark Deep Reinforcement Learning (DRL)-based method and
other two baselines in terms of average coverage rate, training time and/or
sample efficiency. Moreover, with one-time training, our proposed method can be
applied in scenarios where the number of ABSs/GUs dynamically changes on site
and/or with different/varying GU speeds, which is thus more robust and flexible
compared with conventional DRL-based methods.


  We consider equilibrium statistics for high Reynolds number isotropic
turbulence in an incompressible flow driven by steady forcing at the largest
scale. Motivated by shell model observations, we develop a similarity theory
for the inertial range from clearly stated assumptions. In the right variables,
the theory is scaling invariant, but in traditional variables it shows
anomalous scaling. We obtain the underlying probability density function, the
scaling exponents, and the coefficients for the structure functions. An
inertial range length scale also emerges.


  Although tremendous strides have been made in face detection, one of the
remaining open challenges is to achieve real-time speed on the CPU as well as
maintain high performance, since effective models for face detection tend to be
computationally prohibitive. To address this challenge, we propose a novel face
detector, named FaceBoxes, with superior performance on both speed and
accuracy. Specifically, our method has a lightweight yet powerful network
structure that consists of the Rapidly Digested Convolutional Layers (RDCL) and
the Multiple Scale Convolutional Layers (MSCL). The RDCL is designed to enable
FaceBoxes to achieve real-time speed on the CPU. The MSCL aims at enriching the
receptive fields and discretizing anchors over different layers to handle faces
of various scales. Besides, we propose a new anchor densification strategy to
make different types of anchors have the same density on the image, which
significantly improves the recall rate of small faces. As a consequence, the
proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU
for VGA-resolution images. Moreover, the speed of FaceBoxes is invariant to the
number of faces. We comprehensively evaluate this method and present
state-of-the-art detection performance on several face detection benchmark
datasets, including the AFW, PASCAL face, and FDDB. Code is available at
https://github.com/sfzhang15/FaceBoxes


  Quantum Chromodynamics (QCD) is the fundamental theory of strong
interactions. It describes the behavior of quarks and gluons which are the
smallest known constituents of nuclear matter. The difficulties in solving the
theory at low energies in the strongly interacting, non-perturbative regime
have left unanswered many important questions in QCD, such as the nature of
confinement or the mechanism of hadronization. In these lectures oriented
towards the students we introduce two classes of dualities that attempt to
reproduce many of the features of QCD, while making the treatment at strong
coupling more tractable: (1) the AdS/CFT correspondence between a specific
class of string theories and a conformal field theory and (2) an effective
low-energy theory of QCD dual to classical QCD on a curved conformal
gravitational background. The hope is that by applying these dualities to the
evaluation of various properties of the strongly-interacting matter produced in
heavy ion collisions one can understand how QCD behaves at strong coupling. We
give an outline of the applications, with emphasis on two transport
coefficients of QCD matter -- shear and bulk viscosities.


  Suppose n >= 2. We show that there is no integer v >= 1 such that for all
commutative rings R with identity, every element of the subring J(2^n,R) of R
generated by 2^n-th powers can be written in the form \pm f_1^{2^n} \pm \cdots
\pm f_v^{2^n} for some f_1,...,f_v \in R and some choice of signs.


  By rigorously formalizing the Einstein-Podolsky-Rosen (EPR) argument, and
Bohr's reply, one can appreciate that both arguments were technically correct.
Their opposed conclusions about the completeness of quantum mechanics hinged
upon an explicit difference in their criteria for when a measurement on Alice's
system can be regarded as not disturbing Bob's system. The EPR criteria allow
their conclusion (incompletness) to be reached by establishing the physical
reality of just a single observable $q$ (not a conjugate pair $q$ and $p$), but
I show that Bohr's definition of disturbance prevents the EPR chain of
reasoning from establishing even this. Moreover, I show that Bohr's definition
is intimately related to the asymmetric concept of quantum discord from quantum
information theory: if and only if the joint state has no Alice-discord, she
can measure any observable without disturbing (in Bohr's sense) Bob's system.
Discord can be present even when systems are unentangled, and this has
implications for our understanding of the historical development of notions of
quantum nonlocality.


  Stabilizing a heterotic string vacuum with a large expectation value of the
dilaton and simultaneously breaking low-energy supersymmetry is a long-standing
problem of string phenomenology. We reconsider these issues in light of the
recent developments in F-theory.


  A computational framework integrating thermodynamics, kinetics, and
mechanical stress calculations is developed to study supercritical CO2 induced
corrosion in model Fe-based MA956 and Ni-based H214 alloys. Empirical models
parametrized using experimental data show surface oxidation and sub-surface
carburization for a wide range of thermodynamic conditions (800-1200 {\deg}C,
1-250 bar). CALPHAD simulations based on empirical models demonstrate higher
carburization resistance in H214 compared to MA956 below 900 {\deg}C and
through-thickness carburization in both alloys at higher temperatures. Finite
element modeling reveals enhanced volumetric misfit induced stresses at oxide
and carbide interfaces, and its critical dependence on the carbide chemistry
and concentration.


  Recent advances have shown the capability of Fully Convolutional Neural
Networks (FCN) to model cost functions for motion planning in the context of
learning driving preferences purely based on demonstration data from human
drivers. While pure learning from demonstrations in the framework of Inverse
Reinforcement Learning (IRL) is a promising approach, we can benefit from well
informed human priors and incorporate them into the learning process. Our work
achieves this by pretraining a model to regress to a manual cost function and
refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When
injecting prior knowledge as pretraining for the network, we achieve higher
robustness, more visually distinct obstacle boundaries, and the ability to
capture instances of obstacles that elude models that purely learn from
demonstration data. Furthermore, by exploiting these human priors, the
resulting model can more accurately handle corner cases that are scarcely seen
in the demonstration data, such as stairs, slopes, and underpasses.


  Regularisation allows one to handle ill-posed inverse problems. Here we focus
on discrete unfolding problems. The properties of the results are characterised
by the consistency between measurements and unfolding result and by the
posterior response matrix. We introduce a novel regularisation scheme based on
a discrete-valued penalty function and compare its performance to that of a
simple cutoff-regularisation. The discrete-valued penalty function does not
require a regularisation parameter that needs to be adjusted on a case-by-case
basis. In toy studies very satisfactory results are obtained.


  A charged holostar is an exact solution of the Einstein field equations. Its
interior matter distribution rho = 1 / (8 pi r^2) is singularity free with an
overall string equation of state. It has a boundary membrane of tangential
pressure (but no mass-energy) situated roughly a Planck coordinate distance
outside of the outer horizon of the RN-solution with the same mass and charge.
  The geometric mass Mg = M + r0/2 of a charged holostar is always larger than
its charge. r0 is a Planck size correction to the gravitational mass M with r0
2 r_Pl. For a large holostar this condition is practically identical to the
classical condition M >= Q. Whereas RN solutions with M < Q are possible, a
charged holostar with Mg > Q doesn't exist.
  The total charge Q is derived by the proper integral over the interior charge
density, which is attributed to the charged massive particles. The interior
energy density splits into an electromagnetic and a "matter" contribution. Both
contributions are proportional to 1/r^2. The ratio of electro-magnetic to total
energy density rho_em / rho = 4 pi Q^2/A is constant throughout the whole
interior. It is related to the dimensionless ratio of the exterior conserved
quantities Q^2/A (or alternatively Q/M_g). An extremely charged holostar has a
surface area A = 4 pi Q^2, so that its interior energy density consists
entirely out of electromagnetic energy.
  A large holostar can be regarded as the classical analogue of a loop quantum
gravity (LQG) spin-network state. The Immirzi parameter is determined: g = s
/(pi \sqrt{3}), where s is the mean entropy per particle. g is larger by a
factor of ~4.8 than the LQG-result. An explanation for the discrepancy is
given.


  We describe the methodology to include nonlinear evolution, including tidal
effects, in the computation of subhalo distribution properties in both cold
(CDM) and warm (WDM) dark matter universes. Using semi-analytic modeling, we
include effects from dynamical friction, tidal stripping, and tidal heating,
allowing us to dynamically evolve the subhalo distribution. We calibrate our
nonlinear evolution scheme to the CDM subhalo mass function in the Aquarius
N-body simulation, producing a subhalo mass function within the range of
simulations. We find tidal effects to be the dominant mechanism of nonlinear
evolution in the subhalo population. Finally, we compute the subhalo mass
function for $m_\chi=1.5$ keV WDM including the effects of nonlinear evolution,
and compare radial number densities and mass density profiles of subhalos in
CDM and WDM models. We show that all three signatures differ between the two
dark matter models, suggesting that probes of substructure may be able to
differentiate between them.


  Maximally supersymmetric spacetime algebras in eleven-dimensions, which are
the isometry superalgebras of Minkowski space, AdS_7 x S^4, AdS_4 x S^7 and
pp-wave background, are related by Inonu-Wigner contractions. The
super-AdS_{4(7)} x S^{7(4)} algebras allow to introduce two contraction
parameters, the one for the flat limit to the super-Poincare algebra and the
other for a Penrose limit to the super-pp-wave algebra. Under these
contractions supersymmetries are maintained because the Jacobi identity of
three supercharges holds for any values of contraction parameters.


  To illustrate the complementarity of the linear collider and astrophysics
bounds on the light (MeV-scale mass) dark matter (DM), we study the constraints
on the magnetic dipole DM from the DM-electron interactions at the proposed
International Linear Collider (ILC) and in supernova (SN) 1987A. We in
particular focus on the $e^+ e^-$ annihilation which is the common process for
producing DM pairs both at the ILC and in the SN. We estimate the bounds on the
DM magnetic dipole moment from the mono-photon signals at the ILC and also from
the energy loss rate due to the freely streaming DM produced in the SN. The SN
bounds can be more stringent than those from the ILC by as much as a factor
${\cal O}(10^5)$ for a DM mass below $10^2$ MeV. For larger DM masses, on the
other hand, SN rapidly loses its sensitivity and the collider constraints can
complement the SN constraints.


  We present results from a sensitive high-resolution X-ray observation of the
nearby active galaxy Centaurus A (NGC 5128) with the ROSAT HRI. The 65~ksec
X-ray image clearly distinguishes different components of the X-ray emission
from Cen A: the nucleus and the jet, the diffuse galaxy halo, and a number of
individual sources associated with the galaxy. The luminosity of the nucleus
increased by a factor of two compared to an earlier ROSAT observation in 1990.
The high spatial resolution of the ROSAT HRI shows that most of the knots in
the jet are extended both along and perpendicular to the jet axis. We report
the detection of a new X-ray feature, at the opposite side of the X-ray jet
which is probably due to compression of hot interstellar gas by the expanding
southwestern inner radio lobe.


  Hove, Mo, and Sudbo [Phys. Rev. Lett. 85, 2368 (2000)] derived a simple
connection, $\eta + D_H = 2$, between the anomalous scaling dimension $\eta$ of
the U(1) universality class order parameter and the Hausdorff dimension $D_H$
of critical loops in loop representations of U(1) models. We show that the
above relation is wrong and establish a correct relation that contains a new
critical exponent.


  We introduce twin neural network (TNN) regression. This method predicts
differences between the target values of two different data points rather than
the targets themselves. The solution of a traditional regression problem is
then obtained by averaging over an ensemble of all predicted differences
between the targets of an unseen data point and all training data points.
Whereas ensembles are normally costly to produce, TNN regression intrinsically
creates an ensemble of predictions of twice the size of the training set while
only training a single neural network. Since ensembles have been shown to be
more accurate than single models this property naturally transfers to TNN
regression. We show that TNNs are able to compete or yield more accurate
predictions for different data sets, compared to other state-of-the-art
methods. Furthermore, TNN regression is constrained by self-consistency
conditions. We find that the violation of these conditions provides an estimate
for the prediction uncertainty.


  We investigate the orbital evolution of a system of N mutually interacting
stars on initially circular orbits around the dominating central mass. We
include perturbative influence of a distant axisymmetric source and an extended
spherical potential. In particular, we focus on the case when the secular
evolution of orbital eccentricities is suppressed by the spherical
perturbation. By means of standard perturbation methods, we derive
semi-analytic formulae for the evolution of normal vectors of the individual
orbits. We find its two qualitatively different modes. Either the orbits
interact strongly and, under such circumstances, they become dynamically
coupled, precessing synchronously in the potential of the axisymmetric
perturbation. Or, if their mutual interaction is weaker, the orbits precess
independently, interchanging periodically their angular momentum, which leads
to oscillations of inclinations. We argue that these processes may have been
fundamental for the evolution of the disc of young stars orbiting the
supermassive black hole in the centre of the Milky Way.


  We have obtained new images of the protoplanetary disk orbiting TW Hya in
visible, total intensity light with the Space Telescope Imaging Spectrograph
(STIS) on the Hubble Space Telescope (HST), using the newly commissioned BAR5
occulter. These HST/STIS observations achieved an inner working angle
$\sim$0.2\arcsec, or 11.7~AU, probing the system at angular radii coincident
with recent images of the disk obtained by ALMA and in polarized intensity
near-infrared light. By comparing our new STIS images to those taken with STIS
in 2000 and with NICMOS in 1998, 2004, and 2005, we demonstrate that TW Hya's
azimuthal surface brightness asymmetry moves coherently in position angle.
Between 50~AU and 141~AU we measure a constant angular velocity in the
azimuthal brightness asymmetry of 22.7$^\circ$~yr$^{-1}$ in a counter-clockwise
direction, equivalent to a period of 15.9~yr assuming circular motion. Both the
(short) inferred period and lack of radial dependence of the moving shadow
pattern are inconsistent with Keplerian rotation at these disk radii. We
hypothesize that the asymmetry arises from the fact that the disk interior to
1~AU is inclined and precessing due to a planetary companion, thus partially
shadowing the outer disk. Further monitoring of this and other shadows on
protoplanetary disks potentially opens a new avenue for indirectly observing
the sites of planet formation.


  In IEEE 802.11 based wireless networks adding more access points does not
always guarantee an increase of network capacity. In some cases, additional
access points may contribute to degrade the aggregated network throughput as
more interference is introduced.
  This paper characterizes the power interference in CSMA/CA based networks
consisting of nodes using directional antenna. The severity of the interference
is quantized via an improved form of the Attacking Case metric as the original
form of this metric was developed for nodes using omnidirectional antenna.
  The proposed metric is attractive because it considers nodes using
directional or omnidirectional antenna, and it enables the quantization of
interference in wireless networks using multiple transmission power schemes.
The improved Attacking Case metric is useful to study the aggregated throughput
of IEEE 802.11 based networks; reducing Attacking Case probably results in an
increase of aggregated throughput. This reduction can be implemented using
strategies such as directional antenna, transmit power control, or both.


  The physics of long-range interacting quantum systems is currently living
through a renaissance driven by the fast progress in quantum simulators. In
these systems many paradigms of statistical physics do not apply and also the
universal long-wavelength physics gets substantially modified by the presence
of long-ranged forces. Here we explore the low-energy excitations of several
long-range interacting quantum systems, including spin models and interacting
Bose gases, in the ordered phase associated with the spontaneous breaking of
U(1) and SU(2) symmetries. Instead of the expected Goldstone modes, we find
three qualitatively different regimes, depending on the range of the
interaction. In one of these regimes the Goldstone modes are gapped, via a
generalization of the Higgs mechanism. Moreover, we show how this effect is
realized in current experiments with ultracold atomic gases in optical
cavities.


  Recently, some studies started to unveil the wealthy of interactions that
occur between groups of nodes when looking at the small scale of interactions
taking place in complex networks. Such findings claim for a new systematic
methodology to quantify, at node level, how a dynamics is being influenced (or
differentiated) by the structure of the underlying system. Here we define a new
measure that, based on dynamical characteristics obtained for a large set of
initial conditions, compares the dynamical behavior of the nodes present in the
system. Through this measure we find that the geographic and Barab\'asi-Albert
models have high capacity for generating networks that exhibit groups of nodes
with distinct dynamics compared to the rest of the network. The application of
our methodology is illustrated with respect of two real systems. In the first
we use the neuronal network of the nematode Caenorhabditis elegans to show that
the interneurons of the ventral cord of the nematode presents a very large
dynamical differentiation when compared to the rest of the network. The second
application concerns the SIS epidemic model on an airport network, where we
quantify how different the distribution of infection times of high and low
degree nodes can be, when compared to the expected value for the network.


  Backdoor poisoning attacks pose a well-known risk to neural networks.
However, most studies have focused on lenient threat models. We introduce
Silent Killer, a novel attack that operates in clean-label, black-box settings,
uses a stealthy poison and trigger and outperforms existing methods. We
investigate the use of universal adversarial perturbations as triggers in
clean-label attacks, following the success of such approaches under
poison-label settings. We analyze the success of a naive adaptation and find
that gradient alignment for crafting the poison is required to ensure high
success rates. We conduct thorough experiments on MNIST, CIFAR10, and a reduced
version of ImageNet and achieve state-of-the-art results.


  We review current and new estimates of the eclipsing binary YZ Cassiopeiae
metallicity (Z). Since the individual components cover a quite large range of
mass (1.35-2.31 Solar masses), YZ Cas is potentially one of the best stellar
laboratories to understand the structure and evolution of 1 to 2 Solar masses
stars. The derivation of Z from IUE spectra, as well as from photometric
indices, provides the chemical composition of the atmosphere (Z_{atmospheric}),
while the fit of evolutionary tracks provides the initial chemical composition
(Z_{initial}). While a disagreement is expected between Z_{atmospheric} and
Z_{initial} because the primary component is an Am star (one expects
Z_{atmospheric} to be larger), we find some unexpected discrepancy between
atmospheric determinations of Z for this star.


  Due to large fluctuations in cellular environments, transfer of information
in biological processes without regulation is inherently error-prone. The
mechanistic details of error-reducing mechanisms in biological copying
processes have been a subject of active research; however, how error reduction
of a process is balanced with its thermodynamic cost and dynamical properties
remain largely unexplored. Here, we study the error reducing strategies in
light of the recently discovered thermodynamic uncertainty relation (TUR) that
sets a physical bound to the cost-precision trade-off relevant in general
dissipative processes. We found that the two representative copying processes,
DNA replication by the exonuclease-deficient T7 DNA polymerase and mRNA
translation by the \textit{E. coli} ribosome, reduce the error rates to
biologically acceptable levels while also optimizing the processes close to the
physical limit dictated by TUR.


  The majority of multi-agent system (MAS) implementations aim to optimise
agents' policies with respect to a single objective, despite the fact that many
real-world problem domains are inherently multi-objective in nature.
Multi-objective multi-agent systems (MOMAS) explicitly consider the possible
trade-offs between conflicting objective functions. We argue that, in MOMAS,
such compromises should be analysed on the basis of the utility that these
compromises have for the users of a system. As is standard in multi-objective
optimisation, we model the user utility using utility functions that map value
or return vectors to scalar values. This approach naturally leads to two
different optimisation criteria: expected scalarised returns (ESR) and
scalarised expected returns (SER). We develop a new taxonomy which classifies
multi-objective multi-agent decision making settings, on the basis of the
reward structures, and which and how utility functions are applied. This allows
us to offer a structured view of the field, to clearly delineate the current
state-of-the-art in multi-objective multi-agent decision making approaches and
to identify promising directions for future research. Starting from the
execution phase, in which the selected policies are applied and the utility for
the users is attained, we analyse which solution concepts apply to the
different settings in our taxonomy. Furthermore, we define and discuss these
solution concepts under both ESR and SER optimisation criteria. We conclude
with a summary of our main findings and a discussion of many promising future
research directions in multi-objective multi-agent systems.


  In this paper we discuss the optimal convergence of a standard adaptive
scheme based on mixed finite element approximation to the solution of the
eigenvalue problem associated with the Stokes equations. The proofs of the
quasi-orthogonality and the discrete reliability are presented. Our numerical
experiments confirm the efficacy of the proposed adaptive scheme.


  We consider a linearised inverse conductivity problem for electromagnetic
waves in a three dimensional bounded domain at a high time-harmonic frequency.
Increasing stability bounds for the conductivity coefficient in the full
Maxwell system and in a simplified transverse electric mode are derived. These
bounds contain a Lipschitz term with a factor growing polynomially in terms of
the frequency, a Holder term, and a logarithmic term which decays with respect
to the frequency as a power. To validate this increasing stability numerically,
we propose a reconstruction algorithm aiming at the recovery of sufficiently
many Fourier modes of the conductivity. A numerical evidence sheds light on the
influence of the growing frequency and confirms the improved resolution at
higher frequencies.


  When traveling in the heliosphere, Galactic cosmic rays (GCRs) are subjected
to the solar modulation effect, a quasiperiodical change of their intensity
caused by the 11-year cycle of solar activity. Here we investigate the
association of solar activity and cosmic radiation over five solar cycles, from
1965 to 2020, using a collection of multichannel data from neutron monitors,
space missions, and solar observatories. In particular, we focus on the time
lag between the monthly sunspot number and the GCR flux variations. We show
that the modulation lag is subjected to a 22-year periodical variation, ranging
from about 2 to 14 months and following the polarity cycle of the Sun's
magnetic field. We also show that the lag is remarkably decreasing with
increasing energy of the GCR particles. These results reflect the interplay of
basic physics phenomena that cause the GCR modulation effect: the drift motion
of charged particles in the interplanetary magnetic field, the latitudinal
dependence of the solar wind, the energy dependence of their residence time in
the heliosphere. Based on this interpretation, we end up with a global
effective formula for the modulation lag and testable predictions for the flux
evolution of cosmic particles and antiparticles over the solar cycle.


  We investigated Sr$_3$Ru$_2$O$_7$, a quantum critical metal that shows a
metamagnetic quantum phase transition and electronic nematicity, through
density functional calculations. These predict a ferromagnetic ground state in
contrast to the experimentally observed paramagnetism, raising the question of
competing magnetic states and associated fluctuations that may suppress
magnetic order. We did a search to identify such low energy
antiferromagnetically ordered metastable states. We find that the lowest energy
antiferromagnetic state has a striped order. This corresponds to the E-type
order that has been shown to be induced by Mn alloying. We also note
significant transport anisotropy in this E-type ordered state. These results
are discussed in relation to experimental observations.


  We consider a definition of the QCD running coupling constant $\alpha(\mu)$
related to Wilson loops of size $r{\times}t$ with arbitrary fixed $t/r$. The
schemes defined by these couplings are very close to the $\overline{\rm MS}$
scheme for all values of $t/r$; in the $t/r\to\infty$ limit, the ``$q\bar q$
force'' scheme is recovered. We propose a definition of correlation length,
also related to Wilson loops, which can be applied to the Monte Carlo
evaluation of $\alpha(\mu)$ up to very large momentum scales by use of
finite-size scaling techniques.


  We describe a method to create and store scalable and long-lived entangled
spin-squeezed states within a manifold of many-body cavity dark states using
collective emission of light from multilevel atoms inside an optical cavity. We
show that the system can be tuned to generate squeezing in a dark state where
it will be immune to superradiance. We also show more generically that
squeezing can be generated using a combination of superradiance and coherent
driving in a bright state, and subsequently be transferred via single-particle
rotations to a dark state where squeezing can be stored. Our findings, readily
testable in current optical cavity experiments with alkaline-earth-like atoms,
can open a path for dissipative generation and storage of metrologically useful
states in optical transitions.


  In this letter, we present an approach for learning human driving behavior,
without relying on specific model structures or prior distributions, in a
mixed-traffic environment where connected and automated vehicles (CAVs) coexist
with human-driven vehicles (HDVs). We employ conformal prediction to obtain
theoretical safety guarantees and use real-world traffic data to validate our
approach. Then, we design a controller that ensures effective merging of CAVs
with HDVs with safety guarantees. We provide numerical simulations to
illustrate the efficacy of the control approach.


  Metallic nanocavity lasers provide important technological advancement
towards even smaller integrable light sources. They give access to widely
unexplored lasing physics in which the distinction between different
operational regimes, like those of thermal or a coherent light emission,
becomes increasingly challenging upon approaching a device with a near-perfect
spontaneous-emission coupling factor $\beta$. In fact, quantum-optical studies
have to be employed to reveal a transition to coherent emission in the
intensity fluctuation behavior of nanolasers when the input-output
characteristic appears thresholdless for $\beta = 1$ nanolasers. Here, we
identify a new indicator for lasing operation in high-$\beta$ lasers by showing
that stimulated emission can give rise to a lineshape anomaly manifesting as a
transition from a Lorentzian to a Gaussian component in the emission linewidth
that dominates the spectrum above the lasing threshold.


  We investigate the dynamics of gravitational field and particles in a
generalized framework of a Lorentz tangent bundle. By variating an appropriate
action for each case, we obtain generalized forms of paths and generalized
field equations for a Sasaki type metric. We show that Stokes theorem is
modified with respect to general relativity due to local anisotropy and the
presence of a nonlinear connection which induces an adapted basis in our space.


  Dissipative Kerr soliton microcombs in microresonators has enabled
fundamental advances in chip scale precision metrology, communication,
spectroscopy, and parallel signal processing. Here we demonstrate polarization
diverse soliton transitions and deterministic switching dynamics of a self
stabilized microcomb in a strongly coupled dispersion-managed microresonator
driven with a single pump laser. The switching dynamics are induced by the
differential thermorefractivity between coupled transverse magnetic and
transverse electric supermodes during the forward backward pump detunings. The
achieved large soliton existence range and deterministic transitions benefit
from the switching dynamics, leading to the cross polarized soliton microcomb
formation when driven in the transverse magnetic supermode of the single
resonator. Resultantly the pump laser always exists at the effective blue
detuning of the transverse magnetic resonance, fundamentally mitigating the
thermal destabilization barrier and improving accessibility of the soliton
formation regime. Subsequently and secondly, we demonstrate two distinct
polarization diverse soliton formation routes arising from chaotic or
periodically modulated waveforms via pump power selection. The generated self
stabilized supermode microcomb features an extraordinarily large soliton
existence range, a variety of soliton state transitions with well defined pump
laser tuning, high pump microcomb conversion efficiency, and low repetition
rate phase noise.


  Proper orthogonal decomposition (POD) allows reduced-order modeling of
complex dynamical systems at a substantial level, while maintaining a high
degree of accuracy in modeling the underlying dynamical systems. Advances in
machine learning algorithms enable learning POD-based dynamics from data and
making accurate and fast predictions of dynamical systems. In this paper, we
leverage the recently proposed heavy-ball neural ODEs (HBNODEs) [Xia et al.
NeurIPS, 2021] for learning data-driven reduced-order models (ROMs) in the POD
context, in particular, for learning dynamics of time-varying coefficients
generated by the POD analysis on training snapshots generated from solving full
order models. HBNODE enjoys several practical advantages for learning POD-based
ROMs with theoretical guarantees, including 1) HBNODE can learn long-term
dependencies effectively from sequential observations and 2) HBNODE is
computationally efficient in both training and testing. We compare HBNODE with
other popular ROMs on several complex dynamical systems, including the von
K\'{a}rm\'{a}n Street flow, the Kurganov-Petrova-Popov equation, and the
one-dimensional Euler equations for fluids modeling.


  The study of multiplicity distributions of identified particles in terms of
their higher moments is at the focus of contemporary experimental and
theoretical studies. In a thermalized system, combinations of these moments are
directly related to the Equation of State (EoS). The ultimate goal of the
experimental measurements in relativistic nuclear collisions is, by systematic
comparison to QCD and QCD inspired calculations, to probe the dynamics of
genuine phase transitions between a hadron gas and the quark-gluon plasma.
However, the comparison between experiment and theory is far from trivial,
because several non-dynamical effects on fluctuations need to be controlled
prior to a meaningful comparison to theoretical predictions. In this report we
present quantitative estimates for these non-dynamical contributions using the
Canonical Ensemble (CE) formulation of statistical mechanics. Together with
analytical formulas we provide also results from Monte Carlo (MC) simulations
within the CE and compare our predictions with the corresponding measurements
from the STAR experiment.


  The direct detection and characterization of planetary and substellar
companions at small angular separations is a rapidly advancing field. Dedicated
high-contrast imaging instruments deliver unprecedented sensitivity, enabling
detailed insights into the atmospheres of young low-mass companions. In
addition, improvements in data reduction and PSF subtraction algorithms are
equally relevant for maximizing the scientific yield, both from new and
archival data sets. We aim at developing a generic and modular data reduction
pipeline for processing and analysis of high-contrast imaging data obtained
with pupil-stabilized observations. The package should be scalable and robust
for future implementations and in particular well suitable for the 3-5 micron
wavelength range where typically (ten) thousands of frames have to be processed
and an accurate subtraction of the thermal background emission is critical.
PynPoint is written in Python 2.7 and applies various image processing
techniques, as well as statistical tools for analyzing the data, building on
open-source Python packages. The current version of PynPoint has evolved from
an earlier version that was developed as a PSF subtraction tool based on PCA.
The architecture of PynPoint has been redesigned with the core functionalities
decoupled from the pipeline modules. Modules have been implemented for
dedicated processing and analysis steps, including background subtraction,
frame registration, PSF subtraction, photometric and astrometric measurements,
and estimation of detection limits. The pipeline package enables end-to-end
data reduction of pupil-stabilized data and supports classical dithering and
coronagraphic data sets. As an example, we processed archival VLT/NACO L' and
M' data of beta Pic b and reassessed the planet's brightness and position with
an MCMC analysis, and we provide a derivation of the photometric error budget.


  Merging is, in general, a challenging task for both human drivers and
autonomous vehicles, especially in dense traffic, because the merging vehicle
typically needs to interact with other vehicles to identify or create a gap and
safely merge into. In this paper, we consider the problem of autonomous vehicle
control for forced merge scenarios. We propose a novel game-theoretic
controller, called the Leader-Follower Game Controller (LFGC), in which the
interactions between the autonomous ego vehicle and other vehicles with a
priori uncertain driving intentions is modeled as a partially observable
leader-follower game. The LFGC estimates the other vehicles' intentions online
based on observed trajectories, and then predicts their future trajectories and
plans the ego vehicle's own trajectory using Model Predictive Control (MPC) to
simultaneously achieve probabilistically guaranteed safety and merging
objectives. To verify the performance of LFGC, we test it in simulations and
with the NGSIM data, where the LFGC demonstrates a high success rate of 97.5%
in merging.


  This is an exposition of the results on Geometric crystals and the associated
Kashiwara crystal bases (presented by the first author in RIMS, August 2004)


  This paper introduces the way of the embedding of spinning particle quantum
mechanically. Schr\"odinger equation on its submanifold obtains the gauge field
as spin connection, and it reduces to the ones obtained by Ohnuki and Kitakado
when we consider $S^2$ in $R^3$. PACS numbers: 03.65


  We prove that all Gibbs states of the q-state nearest neighbor Potts model on
Z^2 below the critical temperature are convex combinations of the q pure
phases; in particular, they are all translation invariant. To achieve this
goal, we consider such models in large finite boxes with arbitrary boundary
condition, and prove that the center of the box lies deeply inside a pure phase
with high probability. Our estimate of the finite-volume error term is of
essentially optimal order, which stems from the Brownian scaling of fluctuating
interfaces. The results hold at any supercritical value of the inverse
temperature.


  We compute the Dixmier trace of Hankel operator on the weighted Bergman
space. The same result of the Bergman space has been shown by Englis and
Rochberg.


  We investigate fluxbrane solutions to the Einstein-antisymmetric form-dilaton
theory in arbitrary space-time dimensions for a transverse space of cylindrical
topology $S^k\times R^n$, corresponding to smeared and unsmeared solutions. A
master equation for a single metric function is derived. This is a non-linear
second-order ordinary differential equation admitting an analytic solution,
singular at the origin, which serves as an attractor for globally regular
solutions, whose existence is demonstrated numerically. For all fluxbranes of
different levels of smearing the metric function diverges at infinity as the
same power of the radial coordinate except for the maximally smeared case,
where a global solution is known in closed form and can be obtained
algebraically using U-duality. The particular cases of F6 and F3 fluxbranes in
D=11 supergravity and fluxbranes in IIA, IIB supergravities are discussed.


  We study the light emission from a Ag(111) surface when the bias voltage on a
scanning tunneling microscope (STM) junction is ramped into the field emission
regime. Above the vacuum level, scanning tunneling spectroscopy (STS) shows a
series of well defined resonances associated with the image states of the
surface, which are Stark shifted due to the electric field provided by the STM
tip. We present photon--energy resolved measurements that unambiguously show
that the mechanism for light emission is the radiative decay of surface
localized plasmons excited by the electrons that tunnel inelastically into the
Stark shifted image states. Our work illustrates the effect of the tip radius
both in the STS spectrum and the light emission maps by repeating the
experiment with different tips.


  ICARUS is the largest liquid argon TPC detector ever built (~600 ton LAr
mass). It operates underground at the LNGS laboratory in Gran Sasso. It has
been smoothly running since summer 2010, collecting data with the CNGS beam and
with cosmics. Liquid argon TPCs are really "electronic bubble chamber"
providing a completely uniform imaging and calorimetry with unprecedented
accuracy on massive volumes. ICARUS is internationally considered as a
milestone towards the realization of the next generation of massive detectors
(~tens of ktons) for neutrino and rare event physics. Results will be presented
on the data collected during 2010 with the detector at LNGS.


  We have studied the sub-picosecond quasiparticle dynamics in the perovskite
manganite La0.7Ca0.3MnO3 and the layered manganite La1.4Sr1.6Mn2O7 using
ultrafast optical spectroscopy. We found that for T > TC, initial relaxation
proceeds on the time scale of several hundred femtoseconds and corresponds to
the redressing of a photoexcited electron to its polaronic ground state. The
temperature and dimensionality dependence of this polaron redressing time
provides insight into the relationship between polaronic motion and spin
dynamics on a sub-picosecond time scale. We also observe a crossover to a more
conventional electron-phonon relaxation in the ferromagnetic metallic phase
below Tc.