  Deep learning nowadays offers expert-level and sometimes even
super-expert-level performance, but achieving such performance demands massive
annotated data for training (e.g., Google's proprietary CXR Foundation Model
(CXR-FM) was trained on 821,544 labeled and mostly private chest X-rays
(CXRs)). Numerous datasets are publicly available in medical imaging but
individually small and heterogeneous in expert labels. We envision a powerful
and robust foundation model that can be trained by aggregating numerous small
public datasets. To realize this vision, we have developed Ark, a framework
that accrues and reuses knowledge from heterogeneous expert annotations in
various datasets. As a proof of concept, we have trained two Ark models on
335,484 and 704,363 CXRs, respectively, by merging several datasets including
ChestX-ray14, CheXpert, MIMIC-II, and VinDr-CXR, evaluated them on a wide range
of imaging tasks covering both classification and segmentation via fine-tuning,
linear-probing, and gender-bias analysis, and demonstrated our Ark's superior
and robust performance over the SOTA fully/self-supervised baselines and
Google's proprietary CXR-FM. This enhanced performance is attributed to our
simple yet powerful observation that aggregating numerous public datasets
diversifies patient populations and accrues knowledge from diverse experts,
yielding unprecedented performance yet saving annotation cost. With all codes
and pretrained models released at GitHub.com/JLiangLab/Ark, we hope that Ark
exerts an important impact on open science, as accruing and reusing knowledge
from expert annotations in public datasets can potentially surpass the
performance of proprietary models trained on unusually large data, inspiring
many more researchers worldwide to share codes and datasets to build open
foundation models, accelerate open science, and democratize deep learning for
medical imaging.


  We show that there is a constraint on the parameter space of two Higgs
doublet models that comes from the existence of the stable vortex-domain wall
systems. The constraint is quite universal in the sense that it depends on only
two combinations of Lagrangian parameters and does not depend on how fermions
couple to two Higgs fields. Numerical solutions of field configurations of
domain wall-vortex system are obtained, which provide a basis for further
quantitative study of cosmology which involve such topological objects.


  Data mining has been widely used to identify potential customers for a new
product or service. In this article is done a study of previous work relating
to the application of data mining methodologies for software projects,
specifically for direct marketing projects. Several data sets of demographic
and historical customer purchases data available for evaluation of algorithms
in this area, some of them very new and current are described. The main
contribution of this paper is the proposal of a platform for distributed data
stream processing for the processes of targeting customers and building
predictive models required response; thus facilitating several of the
functional requirements for development environments.


  Using differential techniques, we compute the Jacquet module of the locally
analytic vectors of irreducible admissible unitary representations of
GL_2(\qp). This gives a direct proof of some results of Colmez, leading to a
proof of conjectures by Berger, Breuil and Emerton.


  In this paper, we take a significant step towards real-world applicability of
monocular neural avatar reconstruction by contributing InstantAvatar, a system
that can reconstruct human avatars from a monocular video within seconds, and
these avatars can be animated and rendered at an interactive rate. To achieve
this efficiency we propose a carefully designed and engineered system, that
leverages emerging acceleration structures for neural fields, in combination
with an efficient empty space-skipping strategy for dynamic scenes. We also
contribute an efficient implementation that we will make available for research
purposes. Compared to existing methods, InstantAvatar converges 130x faster and
can be trained in minutes instead of hours. It achieves comparable or even
better reconstruction quality and novel pose synthesis results. When given the
same time budget, our method significantly outperforms SoTA methods.
InstantAvatar can yield acceptable visual quality in as little as 10 seconds
training time.


  The massless degrees of freedom of type 0 NS5-branes are derived. A
non-chiral, purely bosonic spectrum is found in both type 0A and 0B. This
non-chirality is confirmed by a one-loop computation in the bulk. Some puzzles
concerning type 0B S-duality are pointed out in this context. An interpretation
of the spectra in terms of ``type 0 little strings'' is proposed.


  Motivation. Digital commons is an emerging phenomenon and of increasing
importance, as we enter a digital society. Open data is one example that makes
up a pivotal input and foundation for many of today's digital services and
applications. Ensuring sustainable provisioning and maintenance of the data,
therefore, becomes even more important. Aim. We aim to investigate how such
provisioning and maintenance can be collaboratively performed in the community
surrounding a common. Specifically, we look at Open Data Ecosystems (ODEs), a
type of community of actors, openly sharing and evolving data on a
technological platform. Method. We use Elinor Ostrom's design principles for
Common Pool Resources as a lens to systematically analyze the governance of
earlier reported cases of ODEs using a theory-oriented software engineering
framework. Results. We find that, while natural commons must regulate
consumption, digital commons such as open data maintained by an ODE must
stimulate both use and data provisioning. Governance needs to enable such
stimulus while also ensuring that the collective action can still be
coordinated and managed within the frame of available maintenance resources of
a community. Subtractability is, in this sense, a concern regarding the
resources required to maintain the quality and value of the data, rather than
the availability of data. Further, we derive empirically-based recommended
practices for ODEs based on the design principles by Ostrom for how to design a
governance structure in a way that enables a sustainable and collaborative
provisioning and maintenance of the data. Conclusion. ODEs are expected to play
a role in data provisioning which democratize the digital society and enables
innovation from smaller commercial actors. Our empirically based guidelines
intend to support this development.


  In this paper we describe the well studied process of renormalization of
quadratic polynomials from the point of view of their natural extensions. In
particular, we describe the topology of the inverse limit of infinitely
renormalizable quadratic polynomials and prove that when they satisfy a-priori
bounds, the topology is rigid modulo its combinatorics.


  The structure of the $(\sqrt{5}\times\sqrt{5})R26.6^\circ$ reconstruction of
LaAlO$_3$ (001) has been determined using transmission electron diffraction
combined with direct methods. The structure is relatively simple, consisting of
a lanthanum oxide termination with one lanthanum cation vacancy per surface
unit cell. The electronic structure is unusual since a fractional number of
holes or atomic occupancies per surface unit cell are required to achieve
charge neutrality. Density functional calculations indicate that the charge
compensation mechanism occurs by means of highly delocalized holes. The surface
contains no oxygen vacancies and with a better than 99% confidence level, the
holes are not filled with hydrogen. The reconstruction can be understood in
terms of expulsion of the more electropositive cation from the surface followed
by an increased covalency between the remaining surface lanthanum atoms and
adjacent oxygen atoms.


  Fog computing emerged as a promising paradigm to address the challenges of
processing and managing data generated by the Internet of Things (IoT). Load
balancing (LB) plays a crucial role in Fog computing environments to optimize
the overall system performance. It requires efficient resource allocation to
improve resource utilization, minimize latency, and enhance the quality of
service for end-users. In this work, we improve the performance of
privacy-aware Reinforcement Learning (RL) agents that optimize the execution
delay of IoT applications by minimizing the waiting delay. To maintain privacy,
these agents optimize the waiting delay by minimizing the change in the number
of queued requests in the whole system, i.e., without explicitly observing the
actual number of requests that are queued in each Fog node nor observing the
compute resource capabilities of those nodes. Besides improving the performance
of these agents, we propose in this paper a lifelong learning framework for
these agents, where lightweight inference models are used during deployment to
minimize action delay and only retrained in case of significant environmental
changes. To improve the performance, minimize the training cost, and adapt the
agents to those changes, we explore the application of Transfer Learning (TL).
TL transfers the knowledge acquired from a source domain and applies it to a
target domain, enabling the reuse of learned policies and experiences. TL can
be also used to pre-train the agent in simulation before fine-tuning it in the
real environment; this significantly reduces failure probability compared to
learning from scratch in the real environment. To our knowledge, there are no
existing efforts in the literature that use TL to address lifelong learning for
RL-based Fog LB; this is one of the main obstacles in deploying RL LB solutions
in Fog systems.


  Convolutional neural networks (CNNs) have been shown to achieve optimal
approximation and estimation error rates (in minimax sense) in several function
classes. However, previous analyzed optimal CNNs are unrealistically wide and
difficult to obtain via optimization due to sparse constraints in important
function classes, including the H\"older class. We show a ResNet-type CNN can
attain the minimax optimal error rates in these classes in more plausible
situations -- it can be dense, and its width, channel size, and filter size are
constant with respect to sample size. The key idea is that we can replicate the
learning ability of Fully-connected neural networks (FNNs) by tailored CNNs, as
long as the FNNs have \textit{block-sparse} structures. Our theory is general
in a sense that we can automatically translate any approximation rate achieved
by block-sparse FNNs into that by CNNs. As an application, we derive
approximation and estimation error rates of the aformentioned type of CNNs for
the Barron and H\"older classes with the same strategy.


  We provide achievability as well as converse results for the degrees of
freedom region of a MIMO $X$ channel, i.e., a system with two transmitters, two
receivers, each equipped with multiple antennas, where independent messages
need to be conveyed over fixed channels from each transmitter to each receiver.
With M=1 antennas at each node, we find that the total (sum rate) degrees of
freedom are bounded above and below as $1 \leq\eta_X^\star \leq {4/3}$. If
$M>1$ and channel matrices are non-degenerate then the precise degrees of
freedom $\eta_X^\star = {4/3}M$. Simple zero forcing without dirty paper
encoding or successive decoding, suffices to achieve the ${4/3}M$ degrees of
freedom. With equal number of antennas at all nodes, we explore the increase in
degrees of freedom when some of the messages are made available to a
transmitter or receiver in the manner of cognitive radio. With a cognitive
transmitter we show that the number of degrees of freedom $\eta = {3/2}M$ (for
$M>1$) on the MIMO $X$ channel. The same degrees of freedom are obtained on the
MIMO $X$ channel with a cognitive receiver as well. In contrast to the $X$
channel result, we show that for the MIMO \emph{interference} channel, the
degrees of freedom are not increased even if both the transmitter and the
receiver of one user know the other user's message. However, the interference
channel can achieve the full $2M$ degrees of freedom if \emph{each} user has
either a cognitive transmitter or a cognitive receiver. Lastly, if the channels
vary with time/frequency then the $X$ channel with single antennas $(M=1)$ at
all nodes has exactly 4/3 degrees of freedom with no shared messages and
exactly 3/2 degrees of freedom with a cognitive transmitter or a cognitive
receiver.


  Graphical structures estimated by causal learning algorithms from time series
data can provide misleading causal information if the causal timescale of the
generating process fails to match the measurement timescale of the data.
Existing algorithms provide limited resources to respond to this challenge, and
so researchers must either use models that they know are likely misleading, or
else forego causal learning entirely. Existing methods face up-to-four distinct
shortfalls, as they might 1) require that the difference between causal and
measurement timescales is known; 2) only handle very small number of random
variables when the timescale difference is unknown; 3) only apply to pairs of
variables; or 4) be unable to find a solution given statistical noise in the
data. This research addresses these challenges. Our approach combines
constraint programming with both theoretical insights into the problem
structure and prior information about admissible causal interactions to achieve
multiple orders of magnitude in speed-up. The resulting system maintains
theoretical guarantees while scaling to significantly larger sets of random
variables (>100) without knowledge of timescale differences. This method is
also robust to edge misidentification and can use parametric connection
strengths, while optionally finding the optimal solution among many possible
ones.


  We construct natural symbolic representations of intrinsically ergodic, but
not necessarily expansive, principal algebraic actions of countably infinite
amenable groups and use these representations to find explicit generating
partitions (up to null-sets) for such actions


  Hierarchical reinforcement learning has demonstrated significant success at
solving difficult reinforcement learning (RL) tasks. Previous works have
motivated the use of hierarchy by appealing to a number of intuitive benefits,
including learning over temporally extended transitions, exploring over
temporally extended periods, and training and exploring in a more semantically
meaningful action space, among others. However, in fully observed, Markovian
settings, it is not immediately clear why hierarchical RL should provide
benefits over standard "shallow" RL architectures. In this work, we isolate and
evaluate the claimed benefits of hierarchical RL on a suite of tasks
encompassing locomotion, navigation, and manipulation. Surprisingly, we find
that most of the observed benefits of hierarchy can be attributed to improved
exploration, as opposed to easier policy learning or imposed hierarchical
structures. Given this insight, we present exploration techniques inspired by
hierarchy that achieve performance competitive with hierarchical RL while at
the same time being much simpler to use and implement.


  Using recent results on the concentration of the largest eigenvalue and
maximal vertex degree of large random graphs, we show that the infinite
sequence of Erd\H os-R\'enyi random graphs $G(n,\rho_n/n)$ such that
$\rho_n/\log n$ infinitely increases as $n\to\infty$ verifies a version of the
graph theory Riemann Hypothesis.


  The paper establishes the strong convergence rates of a spatio-temporal full
discretization of the stochastic wave equation with nonlinear damping in
dimension one and two. We discretize the SPDE by applying a spectral Galerkin
method in space and a modified implicit exponential Euler scheme in time. The
presence of the super-linearly growing damping in the underlying model brings
challenges into the error analysis. To address these difficulties, we first
achieve upper mean-square error bounds, and then obtain mean-square convergence
rates of the considered numerical solution. This is done without requiring the
moment bounds of the full approximations. The main result shows that, in
dimension one, the scheme admits a convergence rate of order $\tfrac12$ in
space and order $1$ in time. In dimension two, the error analysis is more
subtle and can be done at the expense of an order reduction due to an
infinitesimal factor. Numerical experiments are performed and confirm our
theoretical findings.


  Consider a drawing of a graph $G$ in the plane such that crossing edges are
coloured differently. The minimum number of colours, taken over all drawings of
$G$, is the classical graph parameter "thickness". By restricting the edges to
be straight, we obtain the "geometric thickness". By further restricting the
vertices to be in convex position, we obtain the "book thickness". This paper
studies the relationship between these parameters and treewidth.
  Our first main result states that for graphs of treewidth $k$, the maximum
thickness and the maximum geometric thickness both equal $\lceil{k/2}\rceil$.
This says that the lower bound for thickness can be matched by an upper bound,
even in the more restrictive geometric setting. Our second main result states
that for graphs of treewidth $k$, the maximum book thickness equals $k$ if $k
\leq 2$ and equals $k+1$ if $k \geq 3$. This refutes a conjecture of Ganley and
Heath [Discrete Appl. Math. 109(3):215-221, 2001]. Analogous results are proved
for outerthickness, arboricity, and star-arboricity.


  The effective approach is applied to the analysis of inflationary
magnetogenesis. Rather than assuming a particular underlying description, all
the generally covariant terms potentially appearing with four space-time
derivatives in the effective action have been included and weighted by
inflaton-dependent couplings. The higher derivatives are suppressed by the
negative powers of a typical mass scale whose specific values ultimately depend
on the tensor to scalar ratio. During a quasi-de Sitter stage the corresponding
corrections always lead to an asymmetry between the hypermagnetic and the
hyperelectric susceptibilities. After presenting a general method for the
estimate of the gauge power spectra, the obtained results are illustrated for
generic models and also in the case of some non-generic scenarios where either
the inflaton has some extra symmetry or the higher-order terms are potentially
dominant.


  We have obtained integrated and nuclear spectra, as well as U, B, R surface
photometry, for a representative sample of 196 nearby galaxies. These galaxies
span the entire Hubble sequence in morphological type, as well as a wide range
of luminosities (M_B=-14 to -22). Here we present the spectrophotometry for
these galaxies. The selection of the sample and the U, B, R surface photometry
is described in a companion paper (Paper I). Our goals for the project include
measuring the current star formation rates and metallicities of these galaxies,
and elucidating their star formation histories, as a function of luminosity and
morphology. We thereby extend the work of Kennicutt (1992a) to lower luminosity
systems. We anticipate that our study will be useful as a benchmark for studies
of galaxies at high redshift.
  We describe the observing, data reduction and calibration techniques, and
demonstrate that our spectrophotometry agrees well with that of Kennicutt. The
spectra span the range 3550--7250 A at a resolution (FWHM) of ~6 A, and have an
overall relative spectrophotometric accuracy of +/- 6 per cent. We present a
spectrophotometric atlas of integrated and nuclear rest-frame spectra, as well
as tables of equivalent widths and synthetic colors.
  We study the correlations of galaxy properties determined from the spectra
and images. Our findings include: (1) galaxies of a given morphological class
display a wide range of continuum shapes and emission line strengths if a broad
range of luminosities are considered, (2) emission line strengths tend to in-
crease and continua tend to get bluer as the luminosity decreases, and (3) the
scatter on the general correlation between nuclear and integrated H_alpha
emission line strengths is large.


  There is no agreed definition of intelligence, so it is problematic to simply
ask whether brains, swarms, computers, or other systems are intelligent or not.
To compare the potential intelligence exhibited by different cognitive systems,
I use the common approach used by artificial intelligence and artificial life:
Instead of studying the substrate of systems, let us focus on their
organization. This organization can be measured with information. Thus, I apply
an informationist epistemology to describe cognitive systems, including brains
and computers. This allows me to frame the usefulness and limitations of the
brain-computer analogy in different contexts. I also use this perspective to
discuss the evolution and ecology of intelligence.


  Economists are often interested in estimating averages with respect to
distributions of unobservables, such as moments of individual fixed-effects, or
average partial effects in discrete choice models. For such quantities, we
propose and study posterior average effects (PAE), where the average is
computed conditional on the sample, in the spirit of empirical Bayes and
shrinkage methods. While the usefulness of shrinkage for prediction is
well-understood, a justification of posterior conditioning to estimate
population averages is currently lacking. We show that PAE have minimum
worst-case specification error under various forms of misspecification of the
parametric distribution of unobservables. In addition, we introduce a measure
of informativeness of the posterior conditioning, which quantifies the
worst-case specification error of PAE relative to parametric model-based
estimators. As illustrations, we report PAE estimates of distributions of
neighborhood effects in the US, and of permanent and transitory components in a
model of income dynamics.


  Jet finding algorithms, as they are used in $e^+ e^-$ and hadron collisions,
are reviewed and compared. It is suggested that a successive combination style
algorithm, similar to that used in $e^+ e^-$ physics, might be useful also in
hadron collisions, where cone style algorithms have been used previously.


  The propulsion of mammalian spermatozoa relies on the spontaneous periodic
oscillation of their flagella. These oscillations are driven internally by the
coordinated action of ATP-powered dynein motors that exert sliding forces
between microtubule doublets, resulting in bending waves that propagate along
the flagellum and enable locomotion. We present an integrated chemomechanical
model of a freely swimming spermatozoon that uses a sliding-control model of
the axoneme capturing the two-way feedback between motor kinetics and elastic
deformations while accounting for detailed fluid mechanics around the moving
cell. We develop a robust computational framework that solves a boundary
integral equation for the passive sperm head alongside the slender-body
equation for the deforming flagellum described as a geometrically nonlinear
internally actuated Euler-Bernoulli beam, and captures full hydrodynamic
interactions. Nonlinear simulations are shown to produce spontaneous
oscillations with realistic beating patterns and trajectories, which we analyze
as a function of sperm number and motor activity. Our results indicate that the
swimming velocity does not vary monotonically with dynein activity, but instead
displays two maxima corresponding to distinct modes of swimming, each
characterized by qualitatively different waveforms and trajectories. Our model
also provides an estimate for the efficiency of swimming, which peaks at low
sperm number.


  We performed high resolution numerical simulations of homogenous and
isotropic compressible turbulence, with an average 3D Mach number close to 0.3.
We study the statistical properties of intermittency for velocity, density and
entropy. For the velocity field, which is the primary quantity that can be
compared to the isotropic incompressible case, we find no statistical
differences in its behavior in the inertial range due either to the slight
compressibility or to the different dissipative mechanism. For the density
field, we find evidence of ``front-like'' structures, although no shocks are
produced by the simulation.


  Spectral properties of the Schroedinger operator $A_{\lambda} = -\Delta
+\lambda V$ on regular metric trees are studied. It is shown that as $\lambda$
goes to zero the behavior of the negative eigenvalues of $A_{\lambda}$ depends
on the global structure of the tree.


  We define a new class of pseudo effect algebras, called kite pseudo effect
algebras, which is connected not necessarily with partially ordered groups, but
rather with generalized pseudo effect algebras where the greatest element is
not guaranteed. Starting even with a commutative generalized pseudo effect
algebra, we can obtain a non-commutative pseudo effect algebra. We show how
such kite pseudo effect algebras are tied with different types of the Riesz
Decomposition Properties. We find conditions when kite pseudo effect algebras
have the least non-trivial normal ideal.


  We propose a multi-view network for text classification. Our method
automatically creates various views of its input text, each taking the form of
soft attention weights that distribute the classifier's focus among a set of
base features. For a bag-of-words representation, each view focuses on a
different subset of the text's words. Aggregating many such views results in a
more discriminative and robust representation. Through a novel architecture
that both stacks and concatenates views, we produce a network that emphasizes
both depth and width, allowing training to converge quickly. Using our
multi-view architecture, we establish new state-of-the-art accuracies on two
benchmark tasks.


  In the last years researchers in the field of intelligent transportation
systems have made several efforts to extract valuable information from social
media streams. However, collecting domain-specific data from any social media
is a challenging task demanding appropriate and robust classification methods.
In this work we focus on exploring geo-located tweets in order to create a
travel-related tweet classifier using a combination of bag-of-words and word
embeddings. The resulting classification makes possible the identification of
interesting spatio-temporal relations in S\~ao Paulo and Rio de Janeiro.


  The halo of the Milky Way provides unique elemental abundance and kinematic
information on the first objects to form in the Universe, which can be used to
tightly constrain models of galaxy formation and evolution. Although the halo
was once considered a single component, evidence for its dichotomy has slowly
emerged in recent years from inspection of small samples of halo objects. Here
we show that the halo is indeed clearly divisible into two broadly overlapping
structural components -- an inner and an outer halo -- that exhibit different
spatial density profiles, stellar orbits and stellar metallicities (abundances
of elements heavier than helium). The inner halo has a modest net prograde
rotation, whereas the outer halo exhibits a net retrograde rotation and a peak
metallicity one-third that of the inner halo. These properties indicate that
the individual halo components probably formed in fundamentally different ways,
through successive dissipational (inner) and dissipationless (outer) mergers
and tidal disruption of proto-Galactic clumps.


  We show how total variation regularization of images in arbitrary dimensions
can be approximately performed by applying appropriate shrinkage to some Haar
wavelets coefficients. The approach works directly on the wavelet coefficients
and is therefore suited for the application on large volumes from computed
tomography.


  We consider the emergence of edge states in a finite optical lattice and show
that the boundaries of the lattice play a decisive role for their location in
the corresponding energy spectrum. We introduce a simple parametrisation of the
boundaries of the optical lattice and demonstrate the existence of an optimal
choice of the values of the parameters which lead to an approximate restoration
of chiral symmetry. A crucial property of this optimization is the suppression
of tunneling between next-nearest neighboring wells of the lattice. This in
turn allows the mapping of the optical lattice set-up to a finite SSH model.
The topological character of the emerging edge states is discussed.


  We study the dynamics of an active Brownian particle with a nonlinear
friction function located in a spatial cubic potential. For strong but finite
damping, the escape rate of the particle over the spatial potential barrier
shows a nonmonotonic dependence on the noise intensity. We relate this behavior
to the fact that the active particle escapes from a limit cycle rather than
from a fixed point and that a certain amount of noise can stabilize the sojourn
of the particle on this limit cycle.


  Future Higgs factories can reach impressive precision on Higgs property
measurements. In this paper, instead of conventional focus of Higgs precision
in certain interaction bases, we explored its sensitivity to new physics models
at the electron-positron colliders. In particular, we studied two categories of
new physics models, Standard Model (SM) with a real scalar singlet extension,
and Two Higgs Double Model (2HDM) as examples of weakly-interacting models,
Minimal Composite Higgs Model (MCHM) and three typical patterns of the more
general operator counting for strong interacting models as examples of strong
dynamics. We performed a global fit to various Higgs search channels to obtain
the 95% C.L. constraints on the model parameter space. In the SM with a singlet
extension, we obtained the limits on the singlet-doublet mixing angle
sin(theta), as well as the more general Wilson coefficients of the induced
higher dimensional operators. In the 2HDM, we analyzed tree level effects in
tan(beta) vs. cos(beta-alpha) plane, as well as the one-loop contributions from
the heavy Higgs bosons in the alignment limit to obtain the constraints on
heavy Higgs masses for different types of 2HDM. In strong dynamics models, we
obtained lower limits on the strong dynamics scale. In addition, once
deviations of Higgs couplings are observed, they can be used to distinguish
different models. We also compared the sensitivity of various future Higgs
factories, namely Circular Electron Positron Collider (CEPC), Future Circular
Collider (FCC)-ee and International Linear Collider (ILC).


  Smoothing is an essential tool in many NLP tasks, therefore numerous
techniques have been developed for this purpose in the past. One of the most
widely used smoothing methods are the Kneser-Ney smoothing (KNS) and its
variants, including the Modified Kneser-Ney smoothing (MKNS), which are widely
considered to be among the best smoothing methods available. Although when
creating the original KNS the intention of the authors was to develop such a
smoothing method that preserves the marginal distributions of the original
model, this property was not maintained when developing the MKNS.
  In this article I would like to overcome this and propose such a refined
version of the MKNS that preserves these marginal distributions while keeping
the advantages of both previous versions. Beside its advantageous properties,
this novel smoothing method is shown to achieve about the same results as the
MKNS in a standard language modelling task.


  Generative models, especially diffusion models (DMs), have achieved promising
results for generating feature-rich geometries and advancing foundational
science problems such as molecule design. Inspired by the recent huge success
of Stable (latent) Diffusion models, we propose a novel and principled method
for 3D molecule generation named Geometric Latent Diffusion Models (GeoLDM).
GeoLDM is the first latent DM model for the molecular geometry domain, composed
of autoencoders encoding structures into continuous latent codes and DMs
operating in the latent space. Our key innovation is that for modeling the 3D
molecular geometries, we capture its critical roto-translational equivariance
constraints by building a point-structured latent space with both invariant
scalars and equivariant tensors. Extensive experiments demonstrate that GeoLDM
can consistently achieve better performance on multiple molecule generation
benchmarks, with up to 7\% improvement for the valid percentage of large
biomolecules. Results also demonstrate GeoLDM's higher capacity for
controllable generation thanks to the latent modeling. Code is provided at
\url{https://github.com/MinkaiXu/GeoLDM}.


  Purpose: The set of linear attenuation coefficients that belong to materials
in the human body is commonly assumed to be spanned by two basis functions in
the range of clinical x-ray energies, even though there is evidence that the
dimensionality of this set is greater than two. It has not yet been clear that
the use of a third basis function could be beneficial in absence of contrast
agents.
  Approach: In this work, the choice of the number of basis functions used in
the basis decomposition method is studied for the task of producing an image
where a third material is separated from a background of two other materials,
in a case where none of the materials have a K-edge in the range of considered
x-ray energies (20-140 keV). The case of separating iron from mixtures of liver
and adipose tissue is studied with a simulated phantom which incorporates
random and realistic tissue variability.
  Results: Inclusion of a third basis function improves the quantitative
estimate of iron concentration by several orders of magnitude in terms of mean
squared error in the resulting image.
  Conclusions: The inclusion of a third basis function in the basis
decomposition is essential for the studied imaging task and could have
potential application for quantitative estimation of iron concentration from
material decomposed images.


  In a recent work, Blanc, Le Bris, and Lions defined a notion of
increment-stationarity for random point sets, which allowed them to prove the
existence of a thermodynamic limit for two-body potential energies on such
point sets (under the additional assumption of ergodicity), and to introduce a
variant of stochastic homogenization for increment-stationary coefficients.
Whereas stationary random point sets are increment-stationary, it is not clear
a priori under which conditions increment-stationary random point sets are
stationary. In the present contribution, we give a characterization of the
equivalence of both notions of stationarity based on elementary PDE theory in
the probability space. This allows us to give conditions on the decay of a
covariance function associated with the random point set, which ensure that
increment-stationary random point sets are stationary random point sets up to a
random translation with bounded second moment in dimensions $d>2$. In
dimensions $d=1$ and $d=2$, we show that such sufficient conditions cannot
exist.


  Gibrat's law predicts that the standard deviation of the growth rate of a
node's degree is constant. On the other hand, the preferential attachment(PA)
indicates that such standard deviation decreases with initial degree as a power
law of exponent $-0.5$. While both models have been applied to Internet
modeling, this inconsistency requires the verification of their validation.
Therefore we empirically study the fluctuation of Internet of three different
time intervals(daily, monthly and yearly). We find a crossover transition from
PA model to Gibrat's law, which has never been reported. Specifically
Gibrat-law starts from small degree region and extends gradually with the
increase of the observed period. We determine the validated periods for both
models and find that the correlation between internal links has large
contribution to the emergence of Gibrat law. These findings indicate neither PA
nor Gibrat law is applicable to the actual Internet, which requires a more
complete model theory.


  We use the observational Hubble parameter data (OHD), both the latest
observational dataset (Stern et al. 2010, referred to as SJVKS) and the
simulated datasets, to constrain Lema\^{\i}tre-Tolman-Bondi (LTB) void models.
The necessity of the consistency check on OHD itself in the LTB cosmology is
stressed. Three voids are chosen as test models and are constrained using the
Union2 dataset of SN Ia as well as OHD. Despite their different
parametrization, the results from our test models show some indicating
similarities, e.g., the best-fit voids obtained from OHD are all considerably
broader than those from SN Ia. Due to the small size of the SJVKS dataset, the
constraints are not conclusive. The constraining power of the future OHD
observations are therefore investigated, through a Figure of Merit (FoM)
analysis based on the Monte Carlo simulated data. We found that, in the case
that the future OHD become more consistent with SN Ia, the results from the
test models are almost unanimous: 1) as many as 32 OHD data points at the
SJVKS-like uncertainty level are needed to give a higher FoM than the Union2
dataset of SN Ia; 2) precise observation helps reduce this required number; 3)
increasing the survey depth does not always increase the FoM. On the other
hand, if the future OHD and the Union2 dataset keep favor different voids, in a
similar manner as they do at present, the 1{\sigma} confidence regions obtained
from the two probes should finally separate. We test this conjecture and found
that, the minimum observational requirement (the size of the dataset, the
uncertainty level and the survey depth) for this inconsistency to emerge
depends strongly on the void model.


  The light-front dynamics (LFD) of the scalar field model theory is analyzed
to solve the two-body bound-state problem. The light-front two-body bound-state
equation is extended to the full LFD kernel including the ladder, cross-ladder,
stretched-box, and particle-antiparticle creation/annihilation effects to study
the contributions of higher Fock-states. The light-front two-body equation is
also modified by the term corresponding to the self-energy corrections and
counter-terms. Using the variational principle, we obtain the numerical result
of the binding energy B versus the coupling constant \alpha\ for various mass
ratios of the constituent particles including the cases of non-zero exchange
particle mass. We also discuss the correlation between the mass spectrum and
the corresponding bound-state wavefunction.


  We prove that categories enriched in the Thomason model structure admit a
model structure that is Quillen equivalent to the Bergner model structure on
simplicial categories, providing a new model for (infinity,1)-categories. Along
the way, we construct model structures on modules and monoids in the Thomason
model structure and prove that any model structure on the category of small
categories that has the same weak equivalences as the Thomason model structure
is not a cartesian model structure.


  The paper is devoted to the study of combinatorial determinacy properties of
a family of substitution complexes consisting of quadrilaterals glued
side-to-side with each other. These properties are useful in constructing
algebraic structures with a finite number of defining relations. In particular,
this method was used when constructing a finitely presented infinite
nisemigroup satisfying the identity x^9 =0. This construction responds to the
problem of L. N. Shevrin and M. V. Sapir. In this paper, we investigate the
possibility of coloring the entire sequence of complexes into a finite number
of colors, in which the property of weak determinism is fulfilled: if the
colors of the three vertices of a certain quadrilateral are known, then the
color of the fourth side is uniquely determined, except in some cases of a
special arrangement of the quadrilateral.


  B[e] supergiants and yellow hypergiants share a number of common properties
regarding their circumstellar environments. Using the forbidden [O I] and [Ca
II] lines as disk tracers, we suggest the presence of a Keplerian disk or ring
around the yellow hypergiant V509 Cas and confirm the pole-on inner disk around
V1302 Aql. These findings indicate a change in mass-loss behavior from
spherical in cooler yellow hypergiants to axisymmetric in the hotter ones
during the passage through the Yellow Void. The accumulation of material in the
equatorial plane reminds of the disks of B[e] supergiants, supporting the
suggestion that yellow hypergiants might appear as B[e] supergiants after they
reach the blue edge of the yellow instability domain.


  For an arbitrary prime $p$ we prove that the proportion of entries divisible
by $p$ in certain columns of the character table of the symmetric group $S_n$
tends to 1 as $n\to\infty$. This is done by finding lower bounds on the number
of $k$-cores, for $k$ large enough with respect to $n$.


  Using the quark-meson coupling (QMC) model, we study the density dependence
of the quark and gluon condensates in nuclear matter. We show that the change
of the quark condensate is mainly driven by the scalar field in the medium and
that the reduction of the quark condensate is suppressed at high density, even
in the mean-field approximation. The gluon condensate decreases by 4 - 6 % at
nuclear saturation density. We also give a simple relationship between the
change of the quark condensate and that of a hadron mass in the medium.


  Reinforcement Learning (RL) is a powerful framework to address the
discrepancy between loss functions used during training and the final
evaluation metrics to be used at test time. When applied to neural Machine
Translation (MT), it minimises the mismatch between the cross-entropy loss and
non-differentiable evaluation metrics like BLEU. However, the suitability of
these metrics as reward function at training time is questionable: they tend to
be sparse and biased towards the specific words used in the reference texts. We
propose to address this problem by making models less reliant on such metrics
in two ways: (a) with an entropy-regularised RL method that does not only
maximise a reward function but also explore the action space to avoid peaky
distributions; (b) with a novel RL method that explores a dynamic unsupervised
reward function to balance between exploration and exploitation. We base our
proposals on the Soft Actor-Critic (SAC) framework, adapting the off-policy
maximum entropy model for language generation applications such as MT. We
demonstrate that SAC with BLEU reward tends to overfit less to the training
data and performs better on out-of-domain data. We also show that our dynamic
unsupervised reward can lead to better translation of ambiguous words.


  Convolutional Neural Networks (CNNs) have achieved comparable error rates to
well-trained human on ILSVRC2014 image classification task. To achieve better
performance, the complexity of CNNs is continually increasing with deeper and
bigger architectures. Though CNNs achieved promising external classification
behavior, understanding of their internal work mechanism is still limited. In
this work, we attempt to understand the internal work mechanism of CNNs by
probing the internal representations in two comprehensive aspects, i.e.,
visualizing patches in the representation spaces constructed by different
layers, and visualizing visual information kept in each layer. We further
compare CNNs with different depths and show the advantages brought by deeper
architecture.


  We present an online catalog of distance determinations for $\rm 6036$ K
giants, most of which are members of the Milky Way's stellar halo. Their
medium-resolution spectra from SDSS/SEGUE are used to derive metallicities and
rough gravity estimates, along with radial velocities. Distance moduli are
derived from a comparison of each star's apparent magnitude with the absolute
magnitude of empirically calibrated color-luminosity fiducials, at the observed
$(g-r)_0$ color and spectroscopic [Fe/H]. We employ a probabilistic approach
that makes it straightforward to properly propagate the errors in
metallicities, magnitudes, and colors into distance uncertainties. We also fold
in ${\it prior}$ information about the giant-branch luminosity function and the
different metallicity distributions of the SEGUE K-giant targeting
sub-categories. We show that the metallicity prior plays a small role in the
distance estimates, but that neglecting the luminosity prior could lead to a
systematic distance modulus bias of up to 0.25 mag, compared to the case of
using the luminosity prior. We find a median distance precision of $16\%$, with
distance estimates most precise for the least metal-poor stars near the tip of
the red-giant branch. The precision and accuracy of our distance estimates are
validated with observations of globular and open clusters. The stars in our
catalog are up to 125 kpc distant from the Galactic center, with 283 stars
beyond 50 kpc, forming the largest available spectroscopic sample of distant
tracers in the Galactic halo.


  Dillon observed that an APN function $F$ over $\mathbb{F}_2^{n}$ with $n$
greater than $2$ must satisfy the condition $\{F(x) + F(y) + F(z) + F(x + y +
z) \,:\, x,y,z \in\mathbb{F}_2^n\}= \mathbb{F}_2^n$. Recently, Taniguchi (2023)
generalized this condition to functions defined from $\mathbb{F}_2^n$ to
$\mathbb{F}_2^m$, with $m>n$, calling it the D-property. Taniguchi gave some
characterizations of APN functions satisfying the D-property and provided some
families of APN functions from $\mathbb{F}_2^n$ to $\mathbb{F}_2^{n+1}$
satisfying this property. In this work, we further study the D-property for
$(n,m)$-functions with $m\ge n$. We give some combinatorial bounds on the
dimension $m$ for the existence of such functions. Then, we characterize the
D-property in terms of the Walsh transform and for quadratic functions we give
a characterization of this property in terms of the ANF. We also give a
simplification on checking the D-property for quadratic functions, which
permits to extend some of the APN families provided by Taniguchi. We further
focus on the class of the plateaued functions, providing conditions for the
D-property.


  Domain adaptation for visual recognition has undergone great progress in the
past few years. Nevertheless, most existing methods work in the so-called
closed-set scenario, assuming that the classes depicted by the target images
are exactly the same as those of the source domain. In this paper, we tackle
the more challenging, yet more realistic case of open-set domain adaptation,
where new, unknown classes can be present in the target data. While, in the
unsupervised scenario, one cannot expect to be able to identify each specific
new class, we aim to automatically detect which samples belong to these new
classes and discard them from the recognition process. To this end, we rely on
the intuition that the source and target samples depicting the known classes
can be generated by a shared subspace, whereas the target samples from unknown
classes come from a different, private subspace. We therefore introduce a
framework that factorizes the data into shared and private parts, while
encouraging the shared representation to be discriminative. Our experiments on
standard benchmarks evidence that our approach significantly outperforms the
state-of-the-art in open-set domain adaptation.


  We determine the semi-regular subgroups of the 2-transitive permutation
groups PGL(2,n), PSL(2,n), PGU(3,n), PSU(3,n), Sz(n) and Ree(n) with n a
suitable power of a prime number p.


  Given a constant $k>1$ and a real valued function $K$ on the hyperbolic plane
$\mathbb H^2$, we study the problem of finding, for any $\epsilon\approx 0$, a
closed and embedded curve $u^\epsilon $ in $\mathbb H^2$ having geodesic
curvature $k+\epsilon K(u^\epsilon)$ at each point.


  We study magnetotransport in a rare earth doped topological insulator,
Sm0.1Sb1.9Te3 single crystals, under magnetic fields up to 14 T. It is found
that that the crystals exhibit Shubnikov de Haas oscillations in their
magneto-transport behaviour at low temperatures and high magnetic fields. The
SdH oscillations result from the mixed contributions of bulk and surface
states. We also investigate the SdH oscillations in different orientations of
the magnetic field, which reveals a three dimensional Fermi surface topology.
By fitting the oscillatory resistance with the Lifshitz Kosevich theory, we
draw a Landau-level fan diagram that displays the expected nontrivial phase. In
addition, the density functional theory calculations shows that Sm doping
changes the crystal structure and electronic structure compared with pure
Sb2Te3. This work demonstrates that rare earth doping is an effective way to
manipulate the Fermi surface of topological insulators. Our results hold
potential for the realization of exotic topological effects in magnetic
topological insulators.


  Katz & Zingale (2019, KZ19) recently studied a one-dimensional test problem,
intended to mimic the process of detonation ignition in head-on collisions of
two carbon--oxygen (CO) white dwarfs. They do not obtain ignition of a
detonation in pure CO compositions unless the temperature is artificially
increased or 5% He is included. In both of these cases they obtain converged
ignition only for spatial resolutions better than 0.1 km, which are beyond the
capability of multidimensional simulations. This is in a contradiction with the
claims of Kushnir et al. (2013, K13), that a convergence to $\sim10\%$ is
achieved for a resolution of a few km. Using Eulerian and Lagrangian codes we
show that a converged and resolved ignition is obtained for pure CO in this
test problem without the need for He or increasing the temperature. The two
codes agree to within 1% and convergence is obtained at resolutions of several
km. We calculate the case that includes He and obtain a similar slow
convergence, but find that it is due to a boundary numerical artifact that can
(and should) be avoided. Correcting the boundary conditions allows convergence
with resolution of $\sim10\,\textrm{km}$ in an agreement with the claims of
K13. It is likely that the slow convergence obtained by KZ19 in this case is
because of a similar boundary numerical artifact, but we are unable to verify
this. KZ19 further recommended to avoid the use of the burning limiter
introduced by K13. We show that their recommendation is not justified.


  We report the application of time- and polarization-resolved
photoluminescence up-conversion spectroscopy to the study of spin capture and
energy relaxation in positively and negatively charged, as well as neutral,
InAs self-assembled quantum dots. When compared to the neutral dots, we find
that carrier capture and relaxation to the ground state is much faster in the
highly charged dots, suggesting that electron-hole scattering dominates this
process. The long spin lifetime, short capture time, and high radiative
efficiency of the positively charged dots, indicates that these structures are
superior to both quantum well and neutral quantum dot light-emitting diode
(LED) spin detectors for spintronics applications.


  The free interface separating an exterior, viscous fluid from an intrusive
conduit of buoyant, less viscous fluid is known to support strongly nonlinear
solitary waves due to a balance between viscosity-induced dispersion and
buoyancy-induced nonlinearity. The overtaking, pairwise interaction of weakly
nonlinear solitary waves has been classified theoretically for the Korteweg-de
Vries equation and experimentally in the context of shallow water waves, but a
theoretical and experimental classification of strongly nonlinear solitary wave
interactions is lacking. The interactions of large amplitude solitary waves in
viscous fluid conduits, a model physical system for the study of
one-dimensional, truly dissipationless, dispersive nonlinear waves, are
classified. Using a combined numerical and experimental approach, three classes
of nonlinear interaction behavior are identified: purely bimodal, purely
unimodal, and a mixed type. The magnitude of the dispersive radiation due to
solitary wave interactions is quantified numerically and observed to be beyond
the sensitivity of our experiments, suggesting that conduit solitary waves
behave as "physical solitons." Experimental data are shown to be in excellent
agreement with numerical simulations of the reduced model. Experimental movies
are available with the online version of the paper.


  Popularity of the use of free software in the IT industry is much higher than
its popular use in educational activities. Disadvantages of free software and
problems of its implementation in the educational process is a limiting factor
for its use in the education system, however, openness, accessibility and
functionality are the main factors for the introduction of free software in the
educational process. Nevertheless, for future teachers of mathematics, physics
and informatics free software is designed as well as possible because of the
specificity of its creation, and therefore, there is a question of the system
analysis of the possibilities of using open source software in e-learning for
future teachers of mathematics, physics and computer science.


  We present a numerical study of the shape taken by a spherical elastic
surface when the volume it encloses is decreased. For the range of 2D
parameters where such surface may modelize a thin shell of an isotropic elastic
material, the mode of deformation that develops a single depression is
investigated in detail. It first occurs via buckling from sphere toward an
axisymmetric dimple, followed by a second buckling where the depression loses
its axisymmetry, by folding along portions of meridians. We could exhibit
unifying master curves for the relative volume variation at which first and
second buckling occur, and clarify the role of the Poisson's ratio. After the
second buckling, the number of folds and inner pressure are investigated,
allowing to infer shell features from mere observation and/or knowledge of
external constraints.


  Nuclear reactions in stars are difficult to measure directly in the
laboratory at the small astrophysical energies. In recent years indirect
methods with rare isotopes have been developed and applied to extract
low-energy astrophysical cross sections.


  Vision-Large-Language-Models (Vision-LLMs) are increasingly being integrated
into autonomous driving (AD) systems due to their advanced visual-language
reasoning capabilities, targeting the perception, prediction, planning, and
control mechanisms. However, Vision-LLMs have demonstrated susceptibilities
against various types of adversarial attacks, which would compromise their
reliability and safety. To further explore the risk in AD systems and the
transferability of practical threats, we propose to leverage typographic
attacks against AD systems relying on the decision-making capabilities of
Vision-LLMs. Different from the few existing works developing general datasets
of typographic attacks, this paper focuses on realistic traffic scenarios where
these attacks can be deployed, on their potential effects on the
decision-making autonomy, and on the practical ways in which these attacks can
be physically presented. To achieve the above goals, we first propose a
dataset-agnostic framework for automatically generating false answers that can
mislead Vision-LLMs' reasoning. Then, we present a linguistic augmentation
scheme that facilitates attacks at image-level and region-level reasoning, and
we extend it with attack patterns against multiple reasoning tasks
simultaneously. Based on these, we conduct a study on how these attacks can be
realized in physical traffic scenarios. Through our empirical study, we
evaluate the effectiveness, transferability, and realizability of typographic
attacks in traffic scenes. Our findings demonstrate particular harmfulness of
the typographic attacks against existing Vision-LLMs (e.g., LLaVA, Qwen-VL,
VILA, and Imp), thereby raising community awareness of vulnerabilities when
incorporating such models into AD systems. We will release our source code upon
acceptance.


  We summarize recent results on the evolution of unpolarized parton densities
and deep-inelastic structure functions in massless perturbative QCD. Due to
last year's extension of the integer-moment calculations of the three-loop
splitting functions, the NNLO evolution of the parton distributions can now be
performed reliably at momentum fractions x >= 10^-4, facilitating a
considerably improved theoretical accuracy of their extraction from data on
deep-inelastic scattering. The NNLO corrections are not dominated, at relevant
values of x, by their leading small-x terms. At large x the splitting-function
series converges very rapidly, hence, employing results on the three-loop
coefficient functions, the structure functions can be analysed at N^3LO for x >
10^-2. The resulting values for alpha_s do not significantly change beyond
NNLO, their renormalization scale dependence reaches about +-1% at N^3LO.


  Fermionic Monte Carlo calculations with continuous auxiliary fields often
encounter infinite variance problem from fermionic observables. This issue
renders the estimation of observables unreliable, even with an infinite number
of samples. In this work, we show that the infinite variance problem stems from
the fermionic determinant. Also, we propose an approach to address this problem
by employing a reweighting method that utilizes the distribution from an extra
time-slice. Two strategies to compute the reweighting factor are explored: one
involves truncating and analytically calculating the reweighting factor, while
the other employs a secondary Monte Carlo estimation. With Hubbard model as a
testbed, we demonstrate that utilizing the sub-Monte Carlo estimation, coupled
with an unbiased estimator, offers a solution that effectively mitigates the
infinite variance problem at a minimal additional cost.


  A generalized approach to decompose the compressible Navier-Stokes equations
into an equivalent set of coupled equations for flow and acoustics is
introduced. As a significant extension to standard hydrodynamic/acoustic
splitting methods, the approach provides the essential coupling terms, which
account for the feedback from the acoustics to the flow. A unique simplified
version of the split equation system with feedback is derived that conforms to
the compressible Navier-Stokes equations in the subsonic flow regime, where the
feedback reduces to one additional term in the flow momentum equation. Subsonic
simulations are conducted for flow-acoustic feedback cases using a
scale-resolving run-time coupled hierarchical Cartesian mesh solver, which
operates with different explicit time step sizes for incompressible flow and
acoustics. The first simulation case focuses on the tone of a generic flute.
With the major flow-acoustic feedback term included, the simulation yields the
tone characteristics in agreement with reference results from K\"uhnelt based
on Lattice-Boltzmann simulation. On the contrary, the standard hybrid
hydrodynamic/acoustic method with the feedback-term switched off lacks the
proper tone. As the second simulation case, a thick plate in a duct is studied
at various low Mach numbers around the Parker-beta-mode resonance. The
simulations reveal the flow-acoustic feedback mechanism in very good agreement
with experimental data of Welsh et al. Simulations and theoretical
considerations reveal that the feedback term does not reduce the stable
convective flow based time step size of the flow equations.


  We prove that in presence of $L^2$ Gaussian estimates, so-called
Davies-Gaffney estimates, on-diagonal upper bounds imply precise off-diagonal
Gaussian upper bounds for the kernels of analytic families of operators on
metric measure spaces.


  In this paper, we present extensions of the exact simulation algorithm
introduced by Beskos et al. (2006). First, a modification in the order in which
the simulation is done accelerates the algorithm. In addition, we propose a
truncated version of the modified algorithm. We obtain a control of the bias of
this last version, exponentially small in function of the truncation parameter.
Then, we extend it to more general drift functions. Our main result is an
unbiased algorithm to approximate the two first derivatives with respect to the
initial condition \(x\) of quantities with the form \(\mathbb{E}\Psi(X_T^x)\).
We describe it in details in dimension 1 and also discuss its multi-dimensional
extensions for the evaluation of \(\mathbb{E}\Psi(X_T^x)\). Finally, we apply
the algorithm to the CIR process and perform numerical tests to compare it with
classical approximation procedures.


  We analyze the zero-temperature behavior of the XY Edwards-Anderson spin
glass model on a square lattice. A newly developed algorithm combining exact
ground-state computations for Ising variables embedded into the planar spins
with a specially tailored evolutionary method, resulting in the genetic
embedded matching (GEM) approach, allows for the computation of numerically
exact ground states for relatively large systems. This enables a thorough
re-investigation of the long-standing questions of (i) extensive degeneracy of
the ground state and (ii) a possible decoupling of spin and chiral degrees of
freedom in such systems. The new algorithm together with appropriate choices
for the considered sets of boundary conditions and finite-size scaling
techniques allows for a consistent determination of the spin and chiral
stiffness scaling exponents.


  The symbol is used to describe the Springer correspondence for the classical
groups by Lusztig. We refine the explanation that the $S$-duality maps of the
rigid surface operators are symbol preserving maps. And we find that the maps
$X_S$ and $Y_S$ used in the construction of $S$-duality maps are essentially
the same. We clear up cause of the mismatch problem of the total number of the
rigid surface operators between the $B_n$ and $C_n$ theories. And we construct
all the $B_n/C_n$ rigid surface operators which can not have a dual. A
classification of the problematic surface operators is made.


  Exact particle-like static, spherically and/or cylindrically symmetric
solutions to the equations of interacting scalar and electromagnetic field
system have been obtained within the scope of general relativity. In
particular, we considered Freedman-Robertson-Walker (FRW) space-time as an
external homogenous and isotropic gravitational field whereas the inhomogenous
and isotropic Universe is given by the G$\ddot o$del model. The solutions
obtained have been thoroughly studied for different types of interaction term.
It has been shown that in FRW space-time equations with different interaction
terms may have stable solutions while within the scope of G$\ddot o$del model
only the droplet-like configurations may be stable, if they are located in the
region where $g^{00}>0$.


  Understanding how attitudes towards the Climate Emergency vary can hold the
key to driving policy changes for effective action to mitigate climate related
risk. The Oil and Gas industry account for a significant proportion of global
emissions and so it could be speculated that there is a relationship between
Crude Oil Futures and sentiment towards the Climate Emergency. Using Latent
Dirichlet Allocation for Topic Modelling on a bespoke Twitter dataset, this
study shows that it is possible to split the conversation surrounding the
Climate Emergency into 3 distinct topics. Forecasting Crude Oil Futures using
Seasonal AutoRegressive Integrated Moving Average Modelling gives promising
results with a root mean squared error of 0.196 and 0.209 on the training and
testing data respectively. Understanding variation in attitudes towards climate
emergency provides inconclusive results which could be improved using
spatial-temporal analysis methods such as Density Based Clustering (DBSCAN).


  Automatic depression detection from conversational data has gained
significant interest in recent years. The DAIC-WOZ dataset, interviews
conducted by a human-controlled virtual agent, has been widely used for this
task. Recent studies have reported enhanced performance when incorporating
interviewer's prompts into the model. In this work, we hypothesize that this
improvement might be mainly due to a bias present in these prompts, rather than
the proposed architectures and methods. Through ablation experiments and
qualitative analysis, we discover that models using interviewer's prompts learn
to focus on a specific region of the interviews, where questions about past
experiences with mental health issues are asked, and use them as discriminative
shortcuts to detect depressed participants. In contrast, models using
participant responses gather evidence from across the entire interview.
Finally, to highlight the magnitude of this bias, we achieve a 0.90 F1 score by
intentionally exploiting it, the highest result reported to date on this
dataset using only textual information. Our findings underline the need for
caution when incorporating interviewers' prompts into models, as they may
inadvertently learn to exploit targeted prompts, rather than learning to
characterize the language and behavior that are genuinely indicative of the
patient's mental health condition.


  We deal with homogeneous Besov and Triebel-Lizorkin spaces in the setting of
a doubling metric measure space in the presence of a non-negative self-adjoint
operator whose heat kernel has Gaussian localization and the Markov property.
The class of almost diagonal operators on the associated sequence spaces is
developed and it is shown that this class is an algebra. The boundedness of
almost diagonal operators is utilized for establishing smooth molecular and
atomic decompositions for the above homogeneous Besov and Triebel-Lizorkin
spaces. Spectral multipliers for these spaces are established as well.


  We present the results of a coordinated campaign conducted with the Murchison
Widefield Array (MWA) to shadow Fast Radio Bursts (FRBs) detected by the
Australian Square Kilometre Array Pathfinder (ASKAP) at 1.4 GHz, which resulted
in simultaneous MWA observations of seven ASKAP FRBs. We de-dispersed the $24$
$\times$ $1.28$ MHz MWA images across the $170-200$ MHz band taken at 0.5
second time resolution at the known dispersion measures (DMs) and arrival times
of the bursts and searched both within the ASKAP error regions (typically
$\sim$ $10$ arcmin $\times$ $10$ arcmin), and beyond ($4$ deg $\times$ $4$
deg). We identified no candidates exceeding a $5\sigma$ threshold at these DMs
in the dynamic spectra. These limits are inconsistent with the mean fluence
scaling of $\alpha=-1.8 \pm 0.3$ (${\cal F}_\nu \propto \nu^\alpha$, where
$\nu$ is the observing frequency) that is reported for ASKAP events, most
notably for the three high fluence (${\cal F}_{1.4\,{\rm GHz}} \gtrsim 100$ Jy
ms) FRBs 171020, 180110 and 180324. Our limits show that pulse broadening alone
cannot explain our non-detections, and that there must be a spectral turnover
at frequencies above 200 MHz. We discuss and constrain parameters of three
remaining plausible spectral break mechanisms: free-free absorption, intrinsic
spectral turn-over of the radiative processes, and magnification of signals at
ASKAP frequencies by caustics or scintillation. If free-free absorption were
the cause of the spectral turnover, we constrain the thickness of the absorbing
medium in terms of the electron temperature, $T$, to $< 0.03$ $(T/10^4
K)^{-1.35}$ pc for FRB 171020.


  In this paper, we obtain an explicit formula for the Chern character of a
locally abelian parabolic bundle in terms of its constituent bundles. Several
features and variants of parabolic structures are discussed. Parabolic bundles
arising from logarithmic connections form an important class of examples. As an
application, we consider the situation when the local monodromies are
semi-simple and are of finite order at infinity. In this case the parabolic
Chern classes of the associated locally abelian parabolic bundle are deduced to
be zero in the rational Deligne cohomology in degrees $\geq 2$.


  The XYZ model is an integrable spin chain which has an infinite set of
conserved charges, but it lacks a global $U(1)$-symmetry. We consider the
current operators, which describe the flow of the conserved quantities in this
model. We derive an exact result for the current mean values, valid for any
eigenstate in a finite volume with periodic boundary conditions. This result
can serve as a basis for studying the transport properties of this model within
Generalized Hydrodynamics.


  A vortex in a Bose-Einstein condensate generates the optical analog of the
Aharonov-Bohm effect when illuminated with slow light. In contrast to the
original Aharonov-Bohm effect the vortex will exchange forces with the light
that lead to a measurable motion of the vortex.


  We consider an inverse source problem in the stationary radiating transport
through a two dimensional absorbing and scattering medium. Of specific
interest, the exiting radiation is measured on an arc. The attenuation and
scattering properties of the medium are assumed known. For scattering kernels
of finite Fourier content in the angular variable, we show how to
quantitatively recover the part of the isotropic sources restricted to the
convex hull of the measurement arc. The approach is based on the Cauchy problem
with partial data for a Beltrami-like equation associated with $A$-analytic
maps in the sense of Bukhgeim, and extends authors' previous work to this
specific partial data case. The robustness of the method is demonstrated by the
results of several numerical experiments.


  The signature is an infinite graded sequence of statistics known to
characterise a stream of data up to a negligible equivalence class. It is a
transform which has previously been treated as a fixed feature transformation,
on top of which a model may be built. We propose a novel approach which
combines the advantages of the signature transform with modern deep learning
frameworks. By learning an augmentation of the stream prior to the signature
transform, the terms of the signature may be selected in a data-dependent way.
More generally, we describe how the signature transform may be used as a layer
anywhere within a neural network. In this context it may be interpreted as a
pooling operation. We present the results of empirical experiments to back up
the theoretical justification. Code available at
https://github.com/patrick-kidger/Deep-Signature-Transforms.


  We consider radiation generated by an electron travelling parallel to a
planar rectangular silicon grating: Smith-Purcell radiation to the vacuum side,
internal Smith-Purcell radiation into the dielectric, and Cherenkov radiation
into the dielectric. Internal Smith-Purcell radiation dominates over the other
two radiation mechanisms in the range where conventional Smith-Purcell
radiation is forbidden. This observation may lead to improved design of
contactless particle beam monitors.


  The first row of the Cabibbo-Cobayashi-Maskawa (CKM) matrix shows a
discrepancy of $\sim $$3\,\sigma$ with unitarity, known as the "Cabibbo Angle
Anomaly" (CAA). This tension can be explained via modified $W$ couplings to
leptons, but in order to consistently assess the validity of this solution, a
global fit including the electroweak (EW) precision constraints and the low
energy tests of lepton flavour universality (LFU) is necessary. Performing such
a fit for gauge-invariant dimension-6 operators, we find that even when
assuming LFU, including the CKM elements $V_{us}$ and $V_{ud}$ into the
electroweak fit has a relevant impact, shifting the best fit point
significantly. Vector-like leptons (VLLs) are prime candidates for a
corresponding UV completion since they can affect the $W$ and $Z$ couplings to
leptons already at tree-level. Studying each pattern of new physics (NP) given
by the six possible representations of VLLs (under the SM gauge group) we find
that any single representation describes experimental data at most slighlty
better than the SM hypothesis. However, allowing for several representations of
VLLs at the same time, we find a simple scenario consisting of a singlet, $N$,
coupling to electrons and a triplet, $\Sigma_1$, coupling to muons which not
only explains the CAA but also improves the electroweak fit in such a way that
its best fit point is preferred by more than $4\,\sigma$ with respect to the
SM.


  We define a Wigner distribution function for a one-dimensional finite quantum
system, in which the position and momentum operators have a finite
(multiplicity-free) spectrum. The distribution function is thus defined on
discrete phase-space, i.e. on a finite discrete square grid. These discrete
Wigner functions possess a number of properties similar to the Wigner function
for a continuous quantum system such as the quantum harmonic oscillator. As an
example, we consider the so-called su(2) oscillator model in dimension 2j+1,
which is known to tend to the canonical oscillator when j tends to infinity. In
particular, we compare plots of our discrete Wigner functions for the su(2)
oscillator with the well known plots of Wigner functions for the canonical
quantum oscillator. This comparison supports our approach to discrete Wigner
functions.


  Let $K$ be a number field and $E/K$ be an elliptic curve with no $2$-torsion
points. In the present article we give lower and upper bounds for the
$2$-Selmer rank of $E$ in terms of the $2$-torsion of a narrow class group of a
certain cubic extension of $K$ attached to $E$. As an application, we prove
(under mild hypotheses) that a positive proportion of prime conductor quadratic
twists of $E$ have the same $2$-Selmer group.


  Combining experiments on active colloids, whose propulsion velocity can be
controlled via a feedback loop, and theory of active Brownian motion, we
explore the dynamics of an overdamped active particle with a motility that
depends explicitly on the particle orientation. In this case, the active
particle moves faster when oriented along one direction and slower when
oriented along another, leading to an anisotropic translational dynamics which
is coupled to the particle's rotational diffusion. We propose a basic model of
active Brownian motion for orientation-dependent motility. Based on this model,
we obtain analytic results for the mean trajectories, averaged over the
Brownian noise for various initial configurations, and for the mean-square
displacements including their anisotropic non-Gaussian behavior. The
theoretical results are found to be in good agreement with the experimental
data. Our findings establish a methodology to engineer complex anisotropic
motilities of active Brownian particles, with potential impact in the study of
the swimming behavior of microorganisms subjected to anisotropic driving
fields.


  A search for pair-production of long-lived, heavy, singly-charged particles
has been performed with data collected by the ALEPH detector at a
centre-of-mass energy of 172 GeV. Data at \sqrt{s} = 161, 136, and 130 GeV are
also included to improve the sensitivity to lower masses. No candidate is found
in the data. A model-independent 95% confidence level upper limit on the
production cross section at 172 GeV of 0.2-0.4pb is derived for masses between
45 and 86 GeV/c^2. This cross section limit implies, assuming the MSSM, a lower
limit of 67 (69) GeV/c^2 on the mass of right- (left-) handed long-lived scalar
taus or scalar muons and of 86 GeV/c^2 on the mass of long-lived charginos.


  In this article, we construct differential modular forms for compact Shimura
curves over totally real fields bigger than rational of non-zero integral
weights that is not classical (of order zero) generalizing the construction of
Buium [8].


  In a recent paper, Baker and Kong have studied the Hausdorff dimension of the
intersection of Cantor sets with their translations. We extend their results to
more general Cantor sets. The proofs rely on the frequencies of digits in
unique expansions in non-integer bases. In relation with this, we introduce a
practical method to determine the frequency of any given finite block in
Thue--Morse type sequences.


  Regression quantiles have asymptotic variances that depend on the conditional
densities of the response variable given regressors. This paper develops a new
estimate of the asymptotic variance of regression quantiles that leads any
resulting Wald-type test or confidence region to behave as well in large
samples as its infeasible counterpart in which the true conditional response
densities are embedded. We give explicit guidance on implementing the new
variance estimator to control adaptively the size of any resulting Wald-type
test. Monte Carlo evidence indicates the potential of our approach to deliver
powerful tests of heterogeneity of quantile treatment effects in covariates
with good size performance over different quantile levels, data-generating
processes and sample sizes. We also include an empirical example. Supplementary
material is available online.


  We address how the finite frequency real conductivity $\sigma(\omega)$ in the
underdoped cuprates is affected by the pseudogap, contrasting the behavior
above and below $T_c$. The f-sum rule is analytically shown to hold. Here we
presume the pseudogap is associated with non-condensed pairs arising from
stronger-than-BCS attraction. This leads to both a Drude and a mid infrared
(MIR) peak, the latter associated with the energy needed to break pairs. These
general characteristics appear consistent with experiment. Importantly, there
is no more theoretical flexibility (phenomenology) here than in BCS theory; the
origin of the two component conductivity we find is robust.


  In this work, we show that the periodic defocusing Ablowitz-Ladik equation
can be expressed as an isospectral deformation of Floquet CMV matrices. We then
introduce a Poisson Lie group whose underlying group is a loop group and show
that the set of Floquet CMV matrices is a Coxeter dressing orbit of this
Poisson Lie group. By using the group-theoretic framework, we establish the
Liouville integrability of the equation by constructing action-angle variables,
we also solve the Hamiltonian equations generated by the commuting flows via
Riemann-Hilbert factorization problems.


  An important step in the design of autonomous systems is to evaluate the
probability that a failure will occur. In safety-critical domains, the failure
probability is extremely small so that the evaluation of a policy through Monte
Carlo sampling is inefficient. Adaptive importance sampling approaches have
been developed for rare event estimation but do not scale well to sequential
systems with long horizons. In this work, we develop two adaptive importance
sampling algorithms that can efficiently estimate the probability of rare
events for sequential decision making systems. The basis for these algorithms
is the minimization of the Kullback-Leibler divergence between a
state-dependent proposal distribution and a target distribution over
trajectories, but the resulting algorithms resemble policy gradient and
value-based reinforcement learning. We apply multiple importance sampling to
reduce the variance of our estimate and to address the issue of multi-modality
in the optimal proposal distribution. We demonstrate our approach on a control
task with both continuous and discrete actions spaces and show accuracy
improvements over several baselines.


  In general, to avoid a singularity in cosmological models involves the
introduction of exotic kind of matter fields, for example, a scalar field with
negative energy density. In order to have a bouncing solution in classical
General Relativity, violation of the energy conditions is required. In this
work, we discuss a case of the bouncing solution in the Brans-Dicke theory with
radiative fluid that obeys the energy conditions, and with no ghosts.


  With the expansion of mobile communications infrastructure, social media
usage in the Global South is surging. Compared to the Global North, populations
of the Global South have had less prior experience with social media from
stationary computers and wired Internet. Many countries are experiencing
violent conflicts that have a profound effect on their societies. As a result,
social networks develop under different conditions than elsewhere, and our goal
is to provide data for studying this phenomenon. In this dataset paper, we
present a data collection of a national Twittersphere in a West African country
of conflict. While not the largest social network in terms of users, Twitter is
an important platform where people engage in public discussion. The focus is on
Mali, a country beset by conflict since 2012 that has recently had a relatively
precarious media ecology. The dataset consists of tweets and Twitter users in
Mali and was collected in June 2022, when the Malian conflict became more
violent internally both towards external and international actors. In a
preliminary analysis, we assume that the conflictual context influences how
people access social media and, therefore, the shape of the Twittersphere and
its characteristics. The aim of this paper is to primarily invite researchers
from various disciplines including complex networks and social sciences
scholars to explore the data at hand further. We collected the dataset using a
scraping strategy of the follower network and the identification of
characteristics of a Malian Twitter user. The given snapshot of the Malian
Twitter follower network contains around seven million accounts, of which
56,000 are clearly identifiable as Malian. In addition, we present the tweets.
The dataset is available at: https://osf.io/mj2qt/


  We report on the investigation of thermal transport in non-cured silicone
composites with graphene fillers of different lateral dimensions. Graphene
fillers are comprised of few-layer graphene flakes with lateral sizes in the
range from 400 nm to 1200 nm and number of atomic planes from one to ~100. The
distribution of the lateral dimensions and thicknesses of graphene fillers has
been determined via atomic force microscopy statistics. It was found that in
the examined range of the lateral dimensions the thermal conductivity of the
composites increases with the increasing size of the graphene fillers. The
observed difference in thermal properties can be related to the average gray
phonon mean free path in graphene, which has been estimated to be around ~800
nm at room temperature. The thermal contact resistance of composites with
graphene fillers of 1200-nm lateral dimensions was also smaller than that of
composites with graphene fillers of 400-nm lateral dimensions. The effects of
the filler loading fraction and the filler size on the thermal conductivity of
the composites were rationalized within the Kanari model. The obtained results
are important for optimization of graphene fillers for applications in thermal
interface materials for heat removal from high-power-density electronics.


  A commuting family of subnormal operators need not have a commuting normal
extension. We study when a representation of an abelian semigroup can be
extended to a normal representation, and show that it suffices to extend the
set of generators to commuting normals. We also extended a result due to
Athavale to representations on abelian lattice ordered semigroups.


  The European VLBI Network (EVN) is in the process of establishing an e-VLBI
array in which the radio telescopes and the EVN correlator at JIVE are
connected in real-time, via high-speed national fibre optic networks and the
pan-European research network, GEANT. This paper reports on recent test
results, including the production of the first real-time e-VLBI astronomical
image. In a parallel and related development, the field-of-view of VLBI is also
expanding by many orders of magnitude, and the first results of deep,
wide-field surveys capable of detecting many sources simultaneously are
summarised. The detection of sources as faint as 10 microJy should soon be
possible in the era of ``Mk5'' and e-VLBI.


  In many applications a source of the black-body radiation (BBR) can be highly
anisotropic. This leads to the BBR shift that depends on tensor polarizability
and on the projection of the total angular momentum of ions and atoms in a
trap. We derived formula for the anisotropic BBR shift and performed numerical
calculations of this effect for Ca$^+$ and Yb$^+$ transitions of experimental
interest. These ions used for a design of high-precision atomic clocks,
fundamental physics tests such as search for the Lorentz invariance violation
and space-time variation of the fundamental constants, and quantum information.
Anisotropic BBR shift may be one of the major systematic effect in these
experiments.


  We introduce a general method of extending (pseudo-)metrics from X to FX,
where F is a normal functor on the category of metrizable compacta. For many
concrete instances of F, our method specializes to the known constructions.


  Let $X^1, ..., X^k$ and $Y^1, ..., Y^m$ be jointly independent copies of
random variables $X$ and $Y$, respectively. For a fixed total number $n$ of
random variables, we aim at maximising $M(k,m):= E \max \{X^1, ..., X^k, Y^1,
>..., Y^{m} \}$ in $k = n-m\ge 0$, which corresponds to maximising the expected
lifetime of an $n$-component parallel system whose components can be chosen
from two different types. We show that the lattice $\{M(k,m): k, m\ge 0\}$ is
concave, give sufficient conditions on $X$ and $Y$ for M(n,0) to be always or
ultimately maximal and derive a bound on the number of sign changes in the
sequence $M(n,0)-M(0,n)$, $n\ge 1$. The results are applied to a mixed
population of Bienayme-Galton-Watson processes, with the objective to derive
the optimal initial composition to maximise the expected time to extinction.


  Every exotic pair in 4-dimension is obtained each other by twisting a {\it
cork} or {\it plug} which are codimension 0 submanifolds embedded in the
4-manifolds. The twist was an involution on the boundary of the submanifold. We
define cork (or plug) with order $p\in {\Bbb N}\cup \{\infty\}$ and show there
exists a plug with infinite order. Furthermore we show twisting $(P,\varphi^2)$
gives to enlargements of $P$ compact exotic manifolds with boundary.


  We construct local charts for the ramified cusps of the Hilbert modular
variety of level $\Gamma_1(\mathfrak{c},\mathfrak{n})$. This allows us,
following closely M. Rapoport and C.-L. Chai, to construct arithmetic toroidal
and minimal compactifications of these modular varieties. Some applications to
modular forms are given. See our joint work with J. Tilouine for other
applications.


  Consistency properties provided by most key-value stores can be classified
into sequential consistency and eventual consistency. The former is easier to
program with but suffers from lower performance whereas the latter suffers from
potential anomalies while providing higher performance. We focus on the problem
of what a designer should do if he/she has an algorithm that works correctly
with sequential consistency but is faced with an underlying key-value store
that provides a weaker consistency. We propose a detect-rollback based
approach: The designer identifies a correctness predicate, say $P$, and
continues to run the protocol, as our system monitors $P$. If $P$ is violated
(because of weaker consistency), the system rolls back and resumes the
computation at a state where $P$ holds.
  We evaluate this approach with graph-based applications running on the
Voldemort key-value store. Our experiments with deployment on Amazon AWS EC2
instances shows that using eventual consistency with monitoring can provide a
$50\%$ -- $80\%$ increase in throughput when compared with sequential
consistency. We also observe that the overhead of the monitoring itself was low
(typically less than $4\%$) and the latency of detecting violations was small.
In particular, in a scenario designed to intentionally cause a large number of
violations, more than $99.9\%$ of violations were detected in less than 50
milliseconds in regional networks, and in less than 3 seconds in global
networks.
  We find that for some applications, frequent rollback can cause the program
using eventual consistency to effectively \textit{stall}. We propose alternate
mechanisms for dealing with re-occurring rollbacks. Overall, for applications
considered in this paper, we find that even with rollback, eventual consistency
provides better performance than using sequential consistency.


  We argue that identifying the electroweak Higgs particle with the extra
components of the gauge field in $4+d$ dimensions provides a solution to the
hierarchy problem. The absence of ultraviolate quadratic divergences is due to
the fact that the Higgs mass is protected by an exact gauge symmetry at
energies beyond the cutoff. The idea is implemented within explicit models
which also provide a link between fermion chirality and electroweak symmetry
breaking in four dimensions.


  Models are often employed to integrate knowledge about epidemics across
scales and simulate disease dynamics. While these approaches have played a
central role in studying the mechanics underlying epidemics, we lack ways to
reliably predict how the relationship between virulence (the harm to hosts
caused by an infection) and transmission will evolve in certain virus-host
contexts. In this study, we invoke evolutionary invasion analysis -- a method
used to identify the evolution of uninvadable strategies in dynamical systems
-- to examine how the virulence-transmission dichotomy can evolve in models of
virus infections defined by different natural histories. We reveal that
peculiar ecologies drive different evolved relationships between virulence and
transmission. Specifically, we discover patterns of virulence evolution between
epidemics of various kinds (SARS-CoV-2 and hepatitis C virus) and that varying
definitions of virulence alter our predictions for how viruses will evolve. We
discuss the findings in light of contemporary conversations in the public
health sector around the possibility of predicting virus evolution and in more
extensive theoretical discussions involving virulence evolution in emerging
infectious diseases.


  Singing Voice Conversion (SVC) is a technique that enables any singer to
perform any song. To achieve this, it is essential to obtain speaker-agnostic
representations from the source audio, which poses a significant challenge. A
common solution involves utilizing a semantic-based audio pretrained model as a
feature extractor. However, the degree to which the extracted features can meet
the SVC requirements remains an open question. This includes their capability
to accurately model melody and lyrics, the speaker-independency of their
underlying acoustic information, and their robustness for in-the-wild acoustic
environments. In this study, we investigate the knowledge within classical
semantic-based pretrained models in much detail. We discover that the knowledge
of different models is diverse and can be complementary for SVC. To jointly
utilize the diverse pretrained models with mismatched time resolutions, we
propose an efficient ReTrans strategy to address the feature fusion problem.
Based on the above, we design a Singing Voice Conversion framework based on
Diverse Semantic-based Feature Fusion (DSFF-SVC). Experimental results
demonstrate that DSFF-SVC can be generalized and improve various existing SVC
models, particularly in challenging real-world conversion tasks.


  Indirect and direct CP violations have been established in K_L and B_d
decays. They have been found in two-body decay channels -- with the exception
of K_L to pi^+ pi^- e^+ e^- transitions. Evidence for direct CP asymmetry has
just appeared in LHCb data on A_{CP}(D^0 to K^+ K^-) - A_{CP}(D^0 to pi^+ pi^-)
with 3.5 sigma significance. Manifestations of New Dynamics (ND) can appear in
CP asymmetries just below experimental bounds. We discuss D^{\pm}_{(s)},
D^0/\bar D^0 and D_L/D_S transitions to 2-, 3- and 4-body final states with a
comment on predictions for inclusive vs. exclusive CP asymmetries. In
particular we discuss T asymmetries in D to h_1 h_2 l^+ l^- in analogy with K_L
to pi^+ pi^- e^+ e^- transitions due to interference between M1, internal
bremsstrahlung and possible E1 amplitudes. Such an effect depends on the
strength of CP violation originating from the ND -- as discussed here for
Little Higgs Models with T parity and non-minimal Higgs sectors -- but also in
the interferences between these amplitudes even in the Standard Model (SM).
More general lessons can be learnt for T asymmetries in non-leptonic D decays
like D to h_1h_2 h_3 h_4. Such manifestations of ND can be tested at LHCb and
other Super-Flavour Factories like the projects at KEK near Tokyo and at Tor
Vergata/Frascati near Rome.


  Ultrafast electron pulses, combined with laser-pump and electron-probe
technologies, allow for various forms of ultrafast microscopy and spectroscopy
to elucidate otherwise challenging to observe physical and chemical
transitions. However, the pursuit of simultaneous ultimate spatial and temporal
resolution has been largely subdued by the low monochromaticity of the electron
pulses and their poor phase synchronization to the optical excitation pulses.
State-of-the-art photon-driven sources have good monochromaticity but poor
phase synchronization. In contrast, field-driven photoemission has much higher
light phase synchronization, due to the intrinsic sub-cycle emission dynamics,
but poor monochromaticity. Such sources suffer from larger electron energy
spreads (3 - 100 eV) attributed to the relatively low field enhancement of the
conventional metal tips which necessitates long pump wavelengths (> 800 nm) in
order to gain sufficient ponderomotive potential to access the field-driven
regime. In this work, field-driven photoemission from ~1 nm radius carbon
nanotubes excited by a femtosecond laser at a short wavelength of 410 nm has
been realized. The energy spread of field-driven electrons is effectively
compressed to 0.25 eV outperforming all conventional ultrafast electron
sources. Our new nanotube-based ultrafast electron source opens exciting
prospects for attosecond imaging and emerging light-wave electronics.


  Emergency of superconductivity at the instabilities of antiferromagnetism
(AFM), spin/charge density waves has been widely recognized in unconventional
superconductors. In copper-oxide superconductors, spin fluctuations play a
predominant role in electron pairing with electron dopants yet composite orders
veil the nature of superconductivity for hole-doped family. However, in
electron-doped ones the ending point of AFM is still in controversy for
different probes or its sensitivity to oxygen content. Here, by carefully
tuning the oxygen content, a systematic study of Hall signal and
magnetoresistivity up to 58 Tesla on optimally doped La2-xCexCuO4+-{\delta} (x
= 0.10) thin films identifies two characteristic temperatures at 62.5+-7.5 K
and 25+-5 K. The former is quite robust whereas the latter becomes flexible
with increasing magnetic field, thereby linked to two- and three-dimensional
AFM, evident from the multidimensional phase diagram as a function of oxygen as
well as Ce dopants. Consequently, the observation of extended AFM phase in
contrast to {\mu}SR probe corroborates an elevated critical doping in field,
providing an unambiguous picture to understand the interactions between AFM and
superconductivity.


  We obtain a presentation by generators and relations for generalized Schur
algebras and their quantizations. This extends earlier results obtained in the
type A case. The presentation is compatible with Lusztig's modified form of a
quantized enveloping algebra. We show that generalized Schur algebras inherit a
canonical basis from the modified form of the quantized enveloping algebra,
that this gives them a cellular structure, and thus they are quasihereditary
over a field.


  Recent results of J.Gu and H.Jockers provide the lacking initial conditions
for the evolution method in the case of the first non-trivially colored HOMFLY
polynomials H_{[21]} for the family of twist knots. We describe this
application of the evolution method, which finally allows one to penetrate
through the wall between (anti)symmetric and non-rectangular representations
for a whole family. We reveal the necessary deformation of the differential
expansion, what, together with the recently suggested matrix model approach
gives new opportunities to guess what it could be for a generic representation,
at least for the family of twist knots.


  Chromatic dispersion is a common problem to degrade the system resolution in
optical coherence tomography (OCT). This study is to develop a deep learning
network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is
based on a redesigned UNet architecture which employs an encoder-decoder
pipeline. The input section encompasses partially compensated OCT B-scans with
individual retinal layers optimized. Corresponding output is a fully
compensated OCT B-scans with all retinal layers optimized. Two numeric
parameters, i.e., peak signal to noise ratio (PSNR) and structural similarity
index metric computed at multiple scales (MS-SSIM), were used for objective
assessment of the ADC-Net performance. Comparative analysis of training models,
including single, three, five, seven and nine input channels were implemented.
The five-input channels implementation was observed as the optimal mode for
ADC-Net training to achieve robust dispersion compensation in OCT


  In this paper we study collapsing sequences M_{i}-> X of Riemannian manifolds
with curvature bounded or bounded away from a controlled subset. We introduce a
structure over X which in an appropriate sense is dual to the N-structure of
Cheeger, Fukaya and Gromov. As opposed to the N-structure, which live over the
M_{i} themselves, this structure lives over X and allows for a convenient
notion of global convergence as well as the appropriate background structure
for doing analysis on X. This structure is new even in the case of uniformly
bounded curvature and as an application we give a generalization of Gromov's
Almost Flat Theorem and prove new Ricci pinching theorems which extend those
known in the noncollapsed setting. There are also interesting topological
consequences to the structure.