  The exotic $\beta$-delayed proton emission is calculated in $^{11}$Be from
first principles using chiral two- and three-nucleon forces. To investigate the
unexpectedly-large branching ratio measured in [PRL 123, 082501 (2019)] we
calculate the proposed $(1/2^+,1/2)$ proton resonance in $^{11}$B using the
no-core shell model with continuum. This calculation helps to address whether
this enhancement is caused by unknown dark decay modes or an unobserved proton
resonance. We report a branching ratio of $b_p = (1.3\pm0.5)\times10^{-6}$,
suggesting that its unexpectedly-large value is caused by an unobserved proton
resonance in $^{11}$B.


  We use large amounts of unlabeled video to learn models for visual tracking
without manual human supervision. We leverage the natural temporal coherency of
color to create a model that learns to colorize gray-scale videos by copying
colors from a reference frame. Quantitative and qualitative experiments suggest
that this task causes the model to automatically learn to track visual regions.
Although the model is trained without any ground-truth labels, our method
learns to track well enough to outperform the latest methods based on optical
flow. Moreover, our results suggest that failures to track are correlated with
failures to colorize, indicating that advancing video colorization may further
improve self-supervised visual tracking.


  Shapiro's notations for natural numbers, and the associated desideratum of
acceptability - the property of a notation that all recursive functions are
computable in it - is well-known in philosophy of computing. Computable
structure theory, however, although capable of fully reconstructing Shapiro's
approach, seems to be off philosophers' radar. Based on the case study of
natural numbers with standard order, we make initial steps to reconcile these
two perspectives. First, we lay the elementary conceptual groundwork for the
reconstruction of Shapiro's approach in terms of computable structures and
show, on a few examples, how results pertinent to the former can inform our
understanding of the latter. Secondly, we prove a new result, inspired by
Shapiro's notion of acceptability, but also relevant for computable structure
theory. The result explores the relationship between the classical notion of
degree spectrum of a computable function on the structure in question -
specifically, having all c.e. degrees as a spectrum - and our ability to
compute the (image of the) successor from the (image of the) function in any
computable copy of the structure. The latter property may be otherwise seen as
relativized acceptability of every notation for the structure.


  Using a combination of general-relativistic magnetohydrodynamics simulations
and ray tracing of synchrotron emission, we study the effect of modest (24
degrees) misalignment between the black hole spin and plasma angular momentum,
focusing on the variability of total flux, image centroids, and image sizes. We
consider both millimeter and infrared (IR) observables motivated by Sagittarius
A* (Sgr A*), though our results apply more generally to optically thin flows.
For most quantities, tilted accretion is more variable, primarily due to a
significantly hotter and denser "coronal" region well off the disk midplane. We
find (1) a 150% increase in millimeter light curve variability when adding tilt
to the flow; (2) the tilted image centroid in the millimeter shifts on a scale
of 3.7 microarcseconds over 28 hours (5000 gravitational times) for some
electron temperature models; (3) tilted disk image diameters in the millimeter
can be 10% larger (52 versus 47 microarcseconds) than those of aligned disks at
certain viewing angles; (4) the tilted models produce significant IR flux,
similar to that seen in Sgr A*, with comparable or even greater variability
than observed; (5) for some electron models, the tilted IR centroid moves by
more than 50 microarcseconds over several hours, in a similar fashion to the
centroid motion detected by the GRAVITY interferometer.


  We introduce and demonstrate the usage of the origin-fate map (OFM) as a tool
for the detailed investigation of phase space transport in reactant-product
type systems. For these systems, which exhibit clearly defined start and end
states, it is possible to build a comprehensive picture of the lobe dynamics by
considering backward and forward integration of sets of initial conditions to
index their origin and fate. We illustrate the method and its utility in the
study of a two degrees of freedom caldera potential with four exits,
demonstrating that the OFM not only recapitulates results from classical
manifold theory, but even provides more detailed information about complex lobe
structures. The OFM allows the detection of dynamically significant transitions
caused by the creation of new lobes, and is also able to guide the prediction
of the position of unstable periodic orbits (UPOs). Further, we compute the OFM
on the periodic orbit dividing surface (PODS) associated with the transition
state of a caldera entrance, which allows for a powerful analysis of reactive
trajectories. The intersection of the manifolds corresponding to this UPO with
other manifolds in the phase space results in the appearance of lobes on the
PODS, which are directly classified by the OFM. This allows computations of
branching ratios and the exploration of a fractal cascade of lobes as the
caldera is stretched, which results in fluctuations in the branching ratio and
chaotic selectivity. The OFM is found to be a simple and very useful tool with
a vast range of descriptive and quantitative applications.


  A Van Vleck transformation U=e^g; g=-g^+, to an effective Hamiltonian changes
an orthonormal basis in the zeroth order eigenspace Omega_0 to one in the
subspace Omega of the corresponding exact eigenvectors. The canonical U_c=e^g_c
is the only where g is odd. Joergensen's, theorem of uniqueness reveals that
U_c equals U_c'=P(P_0PP_0)^(-1/2)+Q(Q_0QQ_0)^(-1/2) where P_0/P project on
Omega_0/Omega, and where Q_0=1-P_0 and Q=1-P. By Klein's theorem of uniqueness,
u_c=P(P_0PP_0) is the mapping which changes an orthonormal basis in Omega_0
minimally.
  In the present paper, Klein's theorem is developed, proven by simple geometry
and also as a direct consequence of Joergensen's. It is shown that U_c' equals
|S|^(-1)S=S|S|^(-1) where S=PP_0+QQ_0 satisfies SS^+=S^+S=|S|^2. These
commutations simplify earlier proofs, lead to g_c in terms of P_0 and P and to
a series of geometrical interpretations, all easily illustrated in the
elementary case where Omega_0 and Omega are 2-dimensional planes in the
3-dimensional space. Thus U_c: Omega_0-->Omega is the reflection in the plane
Omega_m between Omega_0 and Omega as well as a rotation around their line of
intersection.


  Over the past years there has been quite a lot of activity in the algebraic
community about using algebraic methods for providing support to model-driven
software engineering. The aim of this workshop is to gather researchers working
on the development and application of algebraic methods to provide rigorous
support to model-based software engineering. The topics relevant to the
workshop are all those related to the use of algebraic methods in software
engineering, including but not limited to: formally specifying and verifying
model-based software engineering concepts and related ones (MDE, UML, OCL, MOF,
DSLs, ...); tool support for the above; integration of formal and informal
methods; and theoretical frameworks (algebraic, rewriting-based, category
theory-based, ...). The workshop's main goal is to examine, discuss, and relate
the existing projects within the algebraic community that address common
open-issues in model-driven software engineering.


  Planting a solution into the random RB model, which is a prototype of random
constraint satisfaction problem (CSP) with growing domains, can generate very
hard satisfiable CSP benchmarks. We study the solution space structure of the
planted RB model. With constraint density growing, we find that this model goes
through four phase transitions. In the replica symmetric phase, what we call
the independent phase transition occurs, after which the planted cluster
(cluster containing the planted solution) is separated from the giant cluster.
Then the solutions except that in the planted cluster go through the same
clustering phase transition and the same satisfiability phase transition as the
random RB model. The planted cluster goes through the isolated phase
transition, after which the planted cluster contains only one solution. This
phase diagram provides strong evidence that this model can generate very hard
satisfiable CSP benchmarks. For over constraint instances (where the constraint
density is very large), we find that the configuration space has only a single
energy valley, which makes the instances tractable. Experiments using Belief
Propagation confirm the locations of the clustering, satisfiability (by
configurations outside the planted cluster), and isolated phase transition
points.


  This paper addresses trust issues created from the ubiquity of black box
algorithms and surrogate explainers in Explainable Intrusion Detection Systems
(X-IDS). While Explainable Artificial Intelligence (XAI) aims to enhance
transparency, black box surrogate explainers, such as Local Interpretable
Model-Agnostic Explanation (LIME) and SHapley Additive exPlanation (SHAP), are
difficult to trust. The black box nature of these surrogate explainers makes
the process behind explanation generation opaque and difficult to understand.
To avoid this problem, one can use transparent white box algorithms such as
Rule Extraction (RE). There are three types of RE algorithms: pedagogical,
decompositional, and eclectic. Pedagogical methods offer fast but untrustworthy
white-box explanations, while decompositional RE provides trustworthy
explanations with poor scalability. This work explores eclectic rule
extraction, which strikes a balance between scalability and trustworthiness. By
combining techniques from pedagogical and decompositional approaches, eclectic
rule extraction leverages the advantages of both, while mitigating some of
their drawbacks. The proposed Hybrid X-IDS architecture features eclectic RE as
a white box surrogate explainer for black box Deep Neural Networks (DNN). The
presented eclectic RE algorithm extracts human-readable rules from hidden
layers, facilitating explainable and trustworthy rulesets. Evaluations on
UNSW-NB15 and CIC-IDS-2017 datasets demonstrate the algorithm's ability to
generate rulesets with 99.9% accuracy, mimicking DNN outputs. The contributions
of this work include the hybrid X-IDS architecture, the eclectic rule
extraction algorithm applicable to intrusion detection datasets, and a thorough
analysis of performance and explainability, demonstrating the trade-offs
involved in rule extraction speed and accuracy.


  We present a high order immersed finite element (IFE) method for solving the
elliptic interface problem with interface-independent meshes. The IFE functions
developed here satisfy the interface conditions exactly and they have optimal
approximation capabilities. The construction of this novel IFE space relies on
a nonlinear transformation based on the Frenet-Serret frame of the interface to
locally map it into a line segment, and this feature makes the process of
constructing the IFE functions cost-effective and robust for any degree. This
new class of immersed finite element functions is locally conforming with the
usual weak form of the interface problem so that they can be employed in the
standard interior penalty discontinuous Galerkin scheme without additional
penalties on the interface. Numerical examples are provided to showcase the
convergence properties of the method under $h$ and $p$ refinements.


  We present an exact collapsing solution to 2+1 gravity with a negative
cosmological constant minimally coupled to a massless scalar field, which
exhibits physical properties making it a candidate critical solution. We
discuss its global causal structure and its symmetries in relation with those
of the corresponding continously self-similar solution derived in the
$\Lambda=0$ case. Linear perturbations on this background lead to approximate
black hole solutions. The critical exponent is found to be $\gamma = 2/5$.


  Sharp asymptotic for norms of Fourier projections on two-point homogeneous
manifolds (the real sphere, the real, complex and quaternionic projective
spaces and the Cayley elliptic plain) are established. These results extend
sharp asymptotic estimates found by Fejer on the circle (in 1910) and then by
Gronwall on the two-dimensional sphere (in 1914). As an application of these
results we give solution of the problem of Kolmogorov on sharp asymptotic for
the rate of convergence of Fourier sums on a wide range of sets of multiplier
operators.


  We propose a conceptual frame to interpret the prolate differential operator,
which appears in Communication Theory, as an entropy operator; indeed, we write
its expectation values as a sum of terms, each subject to an entropy reading by
an embedding suggested by Quantum Field Theory. This adds meaning to the
classical work by Slepian et al. on the problem of simultaneously concentrating
a function and its Fourier transform, in particular to the ``lucky accident"
that the truncated Fourier transform commutes with the prolate operator. The
key is the notion of entropy of a vector of a complex Hilbert space with
respect to a real linear subspace, recently introduced by the author by means
of the Tomita-Takesaki modular theory of von Neumann algebras. We consider a
generalization of the prolate operator to the higher dimensional case and show
that it admits a natural extension commuting with the truncated Fourier
transform; this partly generalizes the one-dimensional result by Connes to the
effect that there exists a natural selfadjoint extension to the full line
commuting with the truncated Fourier transform.


  We carried out optical high-dispersion spectroscopic monitoring of the Be
disk in a Be/X-ray binary A 0535+262/V725 Tau from 2009 to 2012, covering two
giant outbursts and several normal outbursts. This monitoring was performed in
order to investigate variabilities of the Be disk due to the interaction with
the neutron star in recent X-ray active phase from 2008 to 2011. Such
variabilities give a clue to uncleared detailed mechanism for very bright X-ray
outbursts, which are unique to some Be/X-ray binaries with relatively wide and
eccentric orbit. In the previous letter (Moritani et al. 2011), a brief
overview of line profile variabilities around the 2009 giant outburst was given
and the possibility of a warped Be disk was discussed. In this paper, a full
analysis of the Halpha line profiles as well as other line profiles is carried
out. A bright blue component, or blue "shoulder", showing up after periastron
indicates the presence of a dense gas stream toward the neutron star, which is
associated with observed outbursts. We re-analyze the Halpha line profiles
before 2009 (down to 2005) in order to investigate the variability of the the
disk structure in the innermost region, which seems to have detached from the
Be star surface by 2008. A redshifted enhanced component is remarkable in all
emission lines observed around the 2009 giant outburst, occasionally forming a
triple peak. These features indicate that the Be disk was warped in X-ray
active phase. We estimate the position of the warped region from fitting the
radial velocity of the redshifted enhanced component of Halpha, and find that
it was very close to the periastron when two giant outbursts in 2009 and 2011
and a bright normal outburst in 2010 March occurred. These facts strongly
suggest that the warped Be disk triggered these giant outbursts.


  The field of biomedical research has witnessed a significant increase in the
accumulation of vast amounts of textual data from various sources such as
scientific literatures, electronic health records, clinical trial reports, and
social media. However, manually processing and analyzing these extensive and
complex resources is time-consuming and inefficient. To address this challenge,
biomedical text mining, also known as biomedical natural language processing,
has garnered great attention. Community challenge evaluation competitions have
played an important role in promoting technology innovation and
interdisciplinary collaboration in biomedical text mining research. These
challenges provide platforms for researchers to develop state-of-the-art
solutions for data mining and information processing in biomedical research. In
this article, we review the recent advances in community challenges specific to
Chinese biomedical text mining. Firstly, we collect the information of these
evaluation tasks, such as data sources and task types. Secondly, we conduct
systematic summary and comparative analysis, including named entity
recognition, entity normalization, attribute extraction, relation extraction,
event extraction, text classification, text similarity, knowledge graph
construction, question answering, text generation, and large language model
evaluation. Then, we summarize the potential clinical applications of these
community challenge tasks from translational informatics perspective. Finally,
we discuss the contributions and limitations of these community challenges,
while highlighting future directions in the era of large language models.


  Measles is a significant source of global disease burden and child mortality.
Measles vaccination through routine immunization (RI) programs in high-burden
settings remains a challenge due to poor health care infrastructure and access.
Supplementary immunization activities (SIA) in the form of vaccination
campaigns are therefore implemented to prevent measles outbreaks by reducing
the size of the susceptible population. The SIA efficacy, defined as the
fraction of susceptible population immunized by an SIA, is a critical metric
for assessing campaign effectiveness. We propose a discrete-time hidden Markov
model for estimating SIA efficacy and forecasting future incidence trends using
reported measles incidence data. Our approach extends the time-series
susceptible-infected-recovered (TSIR) framework by adding a model component to
capture the impact of SIAs on the susceptible population. It also accounts for
under-reporting and its associated uncertainty via a two-stage estimation
procedure with uncertainty propagation. The proposed model can be used to
estimate the underlying susceptible population dynamics, assess how many
susceptible people were immunized by past SIAs, and forecast incidence trends
in the future under various hypothetical SIA scenarios. We examine model
performance via simulations under various levels of under-reporting, and apply
the model to analyze monthly reported measles incidence in Benin from 2012 to
2018.


  We present a technique for deriving lower bounds for incidences with
hypersurfaces in ${\mathbb R}^d$ with $d\ge 4$. These bounds apply to a large
variety of hypersurfaces, such as hyperplanes, hyperspheres, paraboloids, and
hypersurfaces of any degree.
  Beyond being the first non-trivial lower bounds for various incidence
problems, our bounds show that some of the known upper bounds for incidence
problems in ${\mathbb R}^d$ are tight up to an extra $\varepsilon$ in the
exponent. Specifically, for every $m$, $d\ge 4$, and $\varepsilon>0$ there
exist $m$ points and $n$ hypersurfaces in ${\mathbb R}^d$ (where $n$ depends on
$m$) with no $K_{2,\frac{d-1}{\varepsilon}}$ in the incidence graph and
$\Omega\left(m^{(2d-2)/(2d-1)}n^{d/(2d-1)-\varepsilon} \right)$ incidences.
Moreover, we provide improved lower bounds for the case of no $K_{s,s}$ in the
incidence graph, for large constants $s$.
  Our analysis builds upon ideas from a recent work of Bourgain and Demeter on
discrete Fourier restriction to the four- and five-dimensional spheres.
Specifically, it is based on studying the additive energy of the integer points
in a truncated paraboloid.


  We present a new algorithm that can reconstruct the full distributions of
metric components within the class of spherically symmetric dust universes that
may include a cosmological constant. The algorithm is capable of confronting
this class of solutions with arbitrary data and opens a new observational
window to determine the value of the cosmological constant. In this work we use
luminosity and age data to constrain the geometry of the universe up to a
redshift of $z = 1.75$. We show that, although current data are perfectly
compatible with homogeneous models of the universe, simple radially
inhomogeneous void models that are sometimes used as alternative explanations
for the apparent acceleration of the late time universe cannot yet be ruled
out. In doing so we reconstruct the density of cold dark matter out to $z =
1.75$ and derive constraints on the metric components when the universe was
10.5 Gyr old within a comoving volume of approximately 1 Gpc$^{3}$.


  Experiments using polarized $^{3}He$ atom beams to search for short range
spin dependent forces are proposed. High intensity, high polarization, small
beam size $^{3}He$ atom beams have been successfully produced and used in
surface science researches. By incorporating background reduction designs as
combination shielding by $\mu$-metal and superconductor and double beam pathes,
the precision of spin rotation angle per unit length could be improved by a
factor of $\sim10^{4}$. By this precision, in combination with using a high
density and low magnetic susceptibility sample source mass,and reversing one
beam path if necessary, sensitivities on three different types of spin
dependent interactions could be possibly improved by as much as $\sim10^{2}$ to
$\sim10^{8}$ over the current experiments at the millimeter range.


  Boolean Satisfiability solvers have gone through dramatic improvements in
their performances and scalability over the last few years by considering
symmetries. It has been shown that by using graph symmetries and generating
symmetry breaking predicates (SBPs) it is possible to break symmetries in
Conjunctive Normal Form (CNF). The SBPs cut down the search space to the
nonsymmetric regions of the space without affecting the satisfiability of the
CNF formula. The symmetry breaking predicates are created by representing the
formula as a graph, finding the graph symmetries and using some symmetry
extraction mechanism (Crawford et al.). Here in this paper we take one
non-trivial CNF and explore its symmetries. Finally, we generate the SBPs and
adding it to CNF we show how it helps to prune the search tree, so that SAT
solver would take short time. Here we present the pruning procedure of the
search tree from scratch, starting from the CNF and its graph representation.
As we explore the whole mechanism by a non-trivial example, it would be easily
comprehendible. Also we have given a new idea of generating symmetry breaking
predicates for breaking symmetry in CNF, not derived from Crawford's
conditions. At last we propose a backtrack SAT solver with inbuilt SBP
generator.


  We present a novel approach to achieve spatial variations in the degree of
non-covalent functionalization of twisted bilayer graphene (tBLG). The tBLG
with twist angles varying between ~ 5{\deg} and 7{\deg} was non-covalently
functionalized with 1,4,5,8,9,11-hexaazatriphenylenehexacarbonitrile (HATCN)
molecules. Our results show a correlation between the degree of
functionalization and the twist angle of tBLG. This correlation was determined
through Raman spectroscopy, where areas with larger twist angles exhibited a
lower HATCN peak intensity compared to areas with smaller twist angles. We
suggest that the HATCN adsorption follows the moir\'e pattern of tBLG by
avoiding AA-stacked areas and attach predominantly to areas with a local
AB-stacking order of tBLG, forming an overall ABA-stacking configuration. This
is supported by density functional theory (DFT) calculations. Our work
highlights the role of the moir\'e lattice in controlling the non-covalent
functionalization of tBLG. Our approach can be generalized for designing
nanoscale patterns on two-dimensional (2D) materials using moir\'e structures
as a template.


  For many years, astronomers have promised that the study of pulsating white
dwarfs would ultimately lead to useful information about the physics of matter
under extreme conditions of temperature and pressure. We can now make good on
that promise. Using observational data from the Whole Earth Telescope and a new
analysis method employing a genetic algorithm, we empirically determine the
central oxygen abundance of the helium-atmosphere variable white dwarf GD 358.
We use this value, combined with detailed evolutionary calculations of the
internal chemical profiles to place constraints on the 12C(alpha,gamma)16O
nuclear reaction cross-section.


  We present a new framework for self-supervised representation learning by
formulating it as a ranking problem in an image retrieval context on a large
number of random views (augmentations) obtained from images. Our work is based
on two intuitions: first, a good representation of images must yield a
high-quality image ranking in a retrieval task; second, we would expect random
views of an image to be ranked closer to a reference view of that image than
random views of other images. Hence, we model representation learning as a
learning to rank problem for image retrieval. We train a representation encoder
by maximizing average precision (AP) for ranking, where random views of an
image are considered positively related, and that of the other images
considered negatives. The new framework, dubbed S2R2, enables computing a
global objective on multiple views, compared to the local objective in the
popular contrastive learning framework, which is calculated on pairs of views.
In principle, by using a ranking criterion, we eliminate reliance on
object-centric curated datasets. When trained on STL10 and MS-COCO, S2R2
outperforms SimCLR and the clustering-based contrastive learning model, SwAV,
while being much simpler both conceptually and at implementation. On MS-COCO,
S2R2 outperforms both SwAV and SimCLR with a larger margin than on STl10. This
indicates that S2R2 is more effective on diverse scenes and could eliminate the
need for an object-centric large training dataset for self-supervised
representation learning.


  The frequency of low-mass pre-main sequence binary systems is significantly
lower in the Trapezium Cluster than in Taurus-Auriga. We investigate if this
difference can be explained through stellar encounters in dense clusters. To
this effect, a range of possible models of the well observed Trapezium Cluster
are calculated using Aarseth's direct N-body code, which treats binaries
accurately. The results are confronted with observational constraints. The
range of models include clusters in virial equilibrium, expanding clusters as a
result of instantaneous mass loss, as well as collapsing clusters. In all cases
the primordial binary proportion is larger than 50 per cent, with initial
period distributions as observed in Taurus-Auriga and the Galactic field.
  It is found that the expanding model, with an initial binary population as in
the Galactic field, is most consistent with the observational constraints. This
raises the possibility that the primordial group of OB stars may have expelled
the cluster gas roughly 50 000 yr ago. The cluster's bulk expansion rate is
thus a key observable that needs to be determined. The other models demonstrate
that the rapidly decreasing binary proportion, its radial dependence and the
form of the period distribution, together with structural and kinematical data,
are very useful diagnostics on the present and past dynamical state of a young
cluster. In particular, kinematical cooling from the disruption of wide
binaries is seen for the first time.


  If G is a finitely generated group, and A an algebraic group, then Hom(G,A)
is a possibly reducible algebraic variety denoted by R_A(G). Here we define the
profile function, P_d(R_A(G)), of the representation variety of G over A to be
P_d(R_A(G))=(N_d(R_A(G)),...,N_0(R_A(G))), where N_i(R_A(G)) stands for the
number of irreducible components of R_A(G) of dimension i, where 0\leq i\leq d,
and d=Dim(R_A(G)). We then use this invariant in the study of fg groups and
prove various results. In particular, we show that if G an orientable surface
group of genus g\geq 1, then P_d(R_{SL(2,C)}(G))\neq P_d(R_{PSL(2,C)}(G)). We
also show that the same holds for G a torus knot group with presentation
<x,y;x^p=y^t> where both p,t are greater than 2, and that the same also holds
when G is a the fundamental group of a compact non-orientable surface of genus
g\geq 3. Further, we show that if a group G can be n+1 generated, and presented
by <x_1,...,x_n,y ; W=y^p>, where W is a non-trivial word in F_n=<x_1,...,x_n>,
and A=PSL(2, C), that then Dim(R_{A}(G)) is equal to Max{3n, Dim(R_{A}(G'))+2
\} \leq 3n+1, where G'=<x_1,...,x_n; W=1>. We also give a condition
guaranteeing that the resulting algebraic variety is reducible.


  In order to observe nearly simultaneous emission from Gamma-ray Bursts
(GRBs), the Robotic Optical Transient Search Experiment (ROTSE) receives
triggers via the GRB Coordinates Network (GCN). Since beginning operations in
March, 1998, ROTSE has also taken useful data for 10 SGR events: 8 from SGR
1900+14 and 2 from SGR 1806-20. We have searched for new or variable sources in
the error regions of these SGRs and no optical counterparts were observed.
Limits are in the range m_ROTSE ~ 12.5 - 15.5 during the period 20 seconds to 1
hour after the observed SGR events.


  The aim of the Karlsruhe Tritium Neutrino experiment (KATRIN) is the direct
(model-independent) measurement of the neutrino mass. For that purpose a
windowless gaseous tritium source is used, with a tritium throughput of 40
g/day. In order to reach the design sensitivity of 0.2 eV/c^{2} (90% C.L.) the
key parameters of the tritium source, i.e. the gas inlet rate and the gas
composition, have to be stabilized and monitored at the 0.1% level (1 sigma).
Any small change of the tritium gas composition will manifest itself in
non-negligible effects on the KATRIN measurements; therefore, Laser Raman
spectroscopy (LARA) is the method of choice for the monitoring of the gas
composition because it is a non-invasive and fast in-line measurement
technique. In these proceedings, the requirements of KATRIN for statistical and
systematical uncertainties of this method are discussed. An overview of the
current performance of the LARA system in regard to precision will be given. In
addition, two complementary approaches of intensity calibration are presented.


  This article gives the perturbative NNLO results for the moments of the most
commonly used event shape variables associated to three-jet events in
electron-positron annihilation: Thrust, heavy jet mass, wide jet broadening,
total jet broadening, C parameter and the Durham three-to-two jet transition
variable.


  Bayesian coresets approximate a posterior distribution by building a small
weighted subset of the data points. Any inference procedure that is too
computationally expensive to be run on the full posterior can instead be run
inexpensively on the coreset, with results that approximate those on the full
data. However, current approaches are limited by either a significant run-time
or the need for the user to specify a low-cost approximation to the full
posterior. We propose a Bayesian coreset construction algorithm that first
selects a uniformly random subset of data, and then optimizes the weights using
a novel quasi-Newton method. Our algorithm is a simple to implement, black-box
method, that does not require the user to specify a low-cost posterior
approximation. It is the first to come with a general high-probability bound on
the KL divergence of the output coreset posterior. Experiments demonstrate that
our method provides significant improvements in coreset quality against
alternatives with comparable construction times, with far less storage cost and
user input required.


  It is well understood that dynamic instability is among the primary drivers
of forecast uncertainty in chaotic, physical systems. Data assimilation
techniques have been designed to exploit this phenomena, reducing the effective
dimension of the data assimilation problem to the directions of rapidly growing
errors. Recent mathematical work has, moreover, provided formal proofs of the
central hypothesis of the Assimilation in the Unstable Subspace methodology of
Anna Trevisan and her collaborators: for filters and smoothers in perfect,
linear, Gaussian models, the distribution of forecast errors asymptotically
conforms to the unstable-neutral subspace. Specifically, the column span of the
forecast and posterior error covariances asymptotically align with the span of
backward Lyapunov vectors with non-negative exponents.
  Earlier mathematical studies have focused on perfect models, and this current
work now explores the relationship between dynamical instability, the precision
of observations and the evolution of forecast error in linear models with
additive model error. We prove bounds for the asymptotic uncertainty,
explicitly relating the rate of dynamical expansion, model precision and
observational accuracy. Formalizing this relationship, we provide a novel,
necessary criterion for the boundedness of forecast errors. Furthermore, we
numerically explore the relationship between observational design, dynamical
instability and filter boundedness. Additionally, we include a detailed
introduction to the Multiplicative Ergodic Theorem and to the theory and
construction of Lyapunov vectors.


  Benders decomposition with adaptive oracles was proposed to solve large-scale
optimisation problems with a column bounded block-diagonal structure, where
subproblems differ on the right-hand side and cost coefficients. Adaptive
Benders reduces computational effort significantly by iteratively building
inexact cutting planes and valid upper and lower bounds. However, Adaptive
Benders and standard Benders may suffer severe oscillation when solving a
multi-region investment planning problem. Therefore, we propose stabilising
Adaptive Benders with the level set method and adaptively selecting the
subproblems to solve per iteration for more accurate information. Furthermore,
we propose a dynamic level set method to improve the robustness of stabilised
Adaptive Benders by adjusting the level set per iteration. We compare
stabilised Adaptive Benders with the unstabilised versions of Adaptive Benders
with one subproblem solved per iteration and standard Benders on a multi-region
long-term power system investment planning problem with short-term and
long-term uncertainty. The problem is formulated as multi-horizon stochastic
programming. Four algorithms were implemented to solve linear programming with
up to 1 billion variables and 4.5 billion constraints. The computational
results show that: a) for a 1.00% convergence tolerance, the proposed
stabilised method is up to 113.7 times faster than standard Benders and 2.14
times faster than unstabilised Adaptive Benders; b) for a 0.10% convergence
tolerance, the proposed stabilised method is up to 45.5 times faster than
standard Benders and unstabilised Adaptive Benders cannot solve the largest
instance to convergence tolerance due to severe oscillation and c) dynamic
level set method makes stabilisation more robust.


  Non-critical String Cosmologies are offered as an alternative to Standard Big
Bang Cosmology. The new features encompassed within the dilaton dependent
non-critical terms affect the dynamics of the Universe\'s evolution in an
unconventional manner being in agreement with the cosmological data.
Non-criticality is responsible for a late transition to acceleration at
redshifts z=0.2. The role of the uncoupled rolling dilaton to relic abundance
calculations is discussed. The uncoupled rolling dilaton dilutes the neutralino
relic densities in supersymmetric theories by factors of ten, relaxing
considerably the severe WMAP Dark Matter constraints, while at the same time
leaves almost unaffected the baryon density in agreement with primordial
Nucleosynthesis.


  The global solutions in critical spaces to the multi-dimensional compressible
viscoelastic flows are considered. The global existence of the Cauchy problem
with initial data close to an equilibrium state is established in Besov spaces.
Using uniform estimates for a hyperbolic-parabolic linear system with
convection terms, we prove the global existence in the Besov space which is
invariant with respect to the {scaling} of the associated equations. Several
important estimates are achieved, including a smoothing effect on the velocity,
and the $L^1-$decay of the density and deformation gradient.


  This paper presents an efficient method for updating particles in a particle
filter (PF) to address the position estimation problem when dealing with
sharp-peaked likelihood functions derived from multiple observations.
Sharp-peaked likelihood functions commonly arise from millimeter-accurate
distance observations of carrier phases in the global navigation satellite
system (GNSS). However, when such likelihood functions are used for particle
weight updates, the absence of particles within the peaks leads to all particle
weights becoming zero. To overcome this problem, in this study, a
straightforward and effective approach is introduced for updating particles
when dealing with sharp-peaked likelihood functions obtained from multiple
observations. The proposed method, termed as the multiple update PF, leverages
prior knowledge regarding the spread of distribution for each likelihood
function and conducts weight updates and resampling iteratively in the particle
update process, prioritizing the likelihood function spreads. Experimental
results demonstrate the efficacy of our proposed method, particularly when
applied to position estimation utilizing GNSS pseudorange and carrier phase
observations. The multiple update PF exhibits faster convergence with fewer
particles when compared to the conventional PF. Moreover, vehicle position
estimation experiments conducted in urban environments reveal that the proposed
method outperforms conventional GNSS positioning techniques, yielding more
accurate position estimates.


  We use a Lagrangian perspective to show the limiting absorption principle on
Riemannian scattering, i.e. asymptotically conic, spaces, and their
generalizations. More precisely we show that, for non-zero spectral parameter,
the `on spectrum', as well as the `off-spectrum', spectral family is Fredholm
in function spaces which encode the Lagrangian regularity of generalizations of
`outgoing spherical waves' of scattering theory, and indeed this persists in
the `physical half plane'.


  It is proven that the volume of an infinitesimally flexible polyhedron in
$R^3$ is a multiple root of its volume polynomial.


  We present a new explicit construction of onesided bipartite lossless
expanders of constant degree, with arbitrary constant ratio between the sizes
of the two vertex sets. Our construction is simpler to state and analyze than
the only prior construction of Capalbo, Reingold, Vadhan, and Wigderson (2002),
and achieves improvements in some parameters.
  We construct our lossless expanders by imposing the structure of a
constant-sized lossless expander "gadget" within the neighborhoods of a large
bipartite spectral expander; similar constructions were previously used to
obtain the weaker notion of unique-neighbor expansion. Our analysis simply
consists of elementary counting arguments and an application of the expander
mixing lemma.


  Despite numerous advances in the field and a seemingly ever-increasing amount
of investment, we are still some years away from seeing a production quantum
computer in action. However, it is possible to make some educated guesses about
the operational difficulties and challenges that may be encountered in
practice. We can be reasonably confident that the early machines will be
hybrid, with the quantum devices used in an apparently similar way to current
accelerators such as FPGAs or GPUs. Compilers, libraries and the other tools
relied upon currently for development of software will have to evolve/be
reinvented to support the new technology, and training courses will have to be
rethought completely rather than ``just'' updated alongside them.
  The workloads we are likely to see making best use of these hybrid machines
will initially be few, before rapidly increasing in diversity as we saw with
the uptake of GPUs and other new technologies in the past. This will again be
helped by the increase in the number of supporting libraries and development
tools, and by the gradual re-development of existing software, to make use of
the new quantum devices.
  Unfortunately, at present the problem of error correction is still largely
unsolved, although there have been many advances. Quantum computation is very
sensitive to noise, leading to frequent errors during execution.
  Quantum calculations, although asymptotically faster than their equivalents
in ``traditional'' HPC, still take time, and while the profiling tools and
programming approaches will have to change drastically, many of the skills
honed in the current HPC industry will not suddenly become obsolete, but
continue to be useful in the quantum era.


  This paper investigates the fundamental tradeoff between cache size and
download time in the (H;r;M;N) combination network, where a server with N files
is connected to H relays (without caches) and each of the K:=\binom{H}{r} users
(with caches of size M files) is connected to a different subset of r relays.
Existing schemes fall within two categories: either use the uncoded symmetric
cache placement originally proposed for the shared-link model and design
delivery phase dependent on the network topology, or effectively divide the
combination network into H independent shared-link networks each serving
\binom{H-1}{r-1} users; in either case, the placement phase is independent of
network topology. In this paper, a novel strategy is proposed where the coded
cache placement is dependent on network topology. The proposed scheme is shown
to be information theoretically optimal for large cache size. In addition, when
not exactly optimal, the proposed scheme can also outperform existing schemes.


  This paper discusses nonlinear proportional-integral (PI) current control
with anti-windup of reluctance synchronous machines (RSMs) for which the flux
linkage maps are known. The nonlinear controller design is based on the tuning
rule "Magnitude Optimum criterion" [1]. Due to the nonlinear flux linkage, the
current dynamics of RSMs are highly nonlinear and, so, the parameters of the PI
controllers and the disturbance compensation feedforward control must be
adjusted online (e.g., for each sampling instant). The theoretical results are
illustrated and validated by simulation and measurement results.


  Aims: We employ archival Spitzer slit-scan observations of the HH211 outflow
in order to investigate its warm gas content, assess the jet mass flux in the
form of H2 and probe for the existence of an embedded atomic jet. Methods:
Detected molecular and atomic lines are interpreted by means of emission line
diagnostics and an existing grid of molecular shock models. The physical
properties of the warm gas are compared against other molecular jet tracers and
to the results of a similar study towards the L1448-C outflow. Results: We have
detected and mapped the v=0-0 S(0) - S(7) H2 lines and fine-structure lines of
S, Fe+, and Si+. H2 is detected down to 5" from the source and is characterized
by a "cool" T~300K and a "warm" T~1000 K component, with an extinction Av ~ 8
mag. The amount of cool H2 towards the jet agrees with that estimated from CO
assuming fully molecular gas. The warm component is well fitted by C-type
shocks with a low beam filling factor ~ 0.01-0.04 and a mass-flux similar to
the cool H2. The fine-structure line emission arises from dense gas with
ionization fraction ~0.5 - 5 x 10e-3, suggestive of dissociative shocks. Line
ratios to sulfur indicate that iron and silicon are depleted compared to solar
abundances by a factor ~10-50. Conclusions: Spitzer spectral mapping
observations reveal for the first time a cool H$_2$ component towards the CO
jet of HH211 consistent with the CO material being fully molecular and warm at
~ 300 K. The maps also reveal for the first time the existence of an embedded
atomic jet in the HH211 outflow that can be traced down to the central source
position. Its significant iron and silicon depletion excludes an origin from
within the dust sublimation zone around the protostar. The momentum-flux seems
insufficient to entrain the CO jet, although current uncertainties on jet speed
and shock conditions are too large for a definite conclusion.


  In this paper the phase structure of dense quark matter has been investigated
in the presence of baryon, isospin and chiral isospin chemical potentials in
the framework of (3+1)-dimensional Nambu--Jona-Lasinio model with two quark
flavors. It has been shown that in the large-$N_{c}$ limit ($N_{c}$ is the
number of colors of quarks) there exists a duality correspondence between the
chiral symmetry breaking phase and the charged pion condensation one. The key
conclusion of our studies is the fact that chiral isospin chemical potential
generates charged pion condensation in dense quark matter with isotopic
asymmetry.


  We demonstrate a new method to coherently excite and control the quantum spin
states of an atomic Bose gas using parametric excitation of the collective spin
by time varying the relative strength of the Zeeman and spin-dependent
collisional interaction energies at multiples of the natural frequency of the
system. Compared to the usual single-particle quantum control techniques used
to excite atomic spins (e.g. Rabi oscillations using rf or microwave fields),
the method demonstrated here is intrinsically many-body, requiring
inter-particle interactions. While parametric excitation of a classical system
is ineffective from the ground state, we show that in our quantum system,
parametric excitation from the quantum ground state leads to the generation of
quantum squeezed states.


  Serving systems for Large Language Models (LLMs) are often optimized to
improve quality of service (QoS) and throughput. However, due to the lack of
open-sourced LLM serving workloads, these systems are frequently evaluated
under unrealistic workload assumptions. Consequently, performance may degrade
when these systems are deployed in real-world scenarios. This work presents
BurstGPT, an LLM serving workload with 5.29 million traces from regional Azure
OpenAI GPT services over 121 days. BurstGPT captures realistic LLM serving
characteristics through detailed tracing of: (1) Concurrency of requests: It
traces burstiness variations of requests in Azure OpenAI GPT services,
revealing diversified concurrency patterns in different services and model
types. (2) Response Lengths of requests: It traces the auto-regressive serving
processes of GPT models, showing statistical relations between requests and
their responses. (3) Failures of requests: It traces failures of conversation
and API services, showing intensive resource needs and limited resource
availability of such services in Azure. Details of the characteristics can
serve multiple purposes in LLM serving optimizations, such as system evaluation
and trace provisioning. In our demo evaluation with BurstGPT, we observe that
frequent variations in BurstGPT reveal declines in efficiency, stability, or
reliability in realistic LLM serving. We identify that the generalization of KV
cache management and request scheduling optimization is not guaranteed for
different workloads, especially when systems are poorly optimized for
unrealistic workloads. We have made the dataset publicly available to encourage
further research at https://github.com/HPMLL/BurstGPT.


  It is well known that the memory effect in flat spacetime is parametrized by
the BMS supertranslation. We investigate the relation between the memory effect
and diffeomorphism in de Sitter spacetime. We find that gravitational memory is
parametrized by a BMS-like supertranslation in the static patch of de Sitter
spacetime. We also show a diffeomorphism that corresponds to gravitational
memory in the Poincare/cosmological patch. Our method does not need to assume
the separation between the source and the detector to be small compared with
the Hubble radius, and can potentially be applicable to other FLRW universes,
as well as "ordinary memory" mediated by massive messenger particles.


  We have found experimentally that the shot noise in InAlAs-InGaAs-InAlAs
Triple-Barrier Resonant-Tunneling Diodes (TBRTD) is reduced over the 2eI
Poissonian value whenever their differential conductance is positive, and is
enhanced over 2eI when the differential conductance is negative. This behavior,
although qualitatively similar to that found in double-barrier diodes, differs
from it in important details. In TBRTDs the noise reduction is considerably
larger than predicted by a semi-classical model, and the enhancement does not
correlate with the strength of the negative differential conductance. These
results suggest an incomplete understanding of the noise properties of
multiple-barrier heterostructures.


  A recent paper arXiv:1312.4890 on multi-symplectic magnetohydrodynamics (MHD)
using Clebsch variables in an Eulerian action principle with constraints is
further extended. We relate a class of symplecticity conservation laws to a
vorticity conservation law, and provide a corrected form of the
Poincar\'e-Cartan differential form formulation of the system. We also correct
some typographical errors (omissions) in arXiv:1312.4890. We show that the
vorticity-symplecticity conservation law, that arises as a compatibility
condition on the system, expressed in terms of the Clebsch variables is
equivalent to taking the curl of the conservation form of the MHD momentum
equation. We use the Cartan-Poincar\'e form to obtain a class of differential
forms that represent the system using Cartan's geometric theory of partial
differential equations.


  The purpose of this paper is to use the votes cast at the 2019 European
elections held in United Kingdom to re-visit the analysis conducted subsequent
to its 2016 European Union referendum vote. This exercise provides a staging
post on public opinion as the United Kingdom moves to leave the European Union
during 2020. A composition data analysis in a seemingly unrelated regression
framework is adopted that respects the compositional nature of the vote
outcome; each outcome is a share that adds up to 100% and each outcome is
related to the alternatives. Contemporary explanatory data for each counting
area is sourced from the themes of socio-demographics, employment, life
satisfaction and place. The study find that there are still strong and stark
divisions in the United Kingdom, defined by age, qualifications, employment and
place. The use of a compositional analysis approach produces challenges in
regards to the interpretation of these models, but marginal plots are seen to
aid the interpretation somewhat.


  The spin orbit effect in graphene is too muted to have any observable
significance with respect to its application in spintronics. However, graphene
technology is too valuable to be rendered impotent to spin transport. In this
communication we look at the effect of adiabatically twisting a single molecule
magnet embedded in a graphene monolayer. Surprisingly, we see that pure spin
currents (zero charge current) can be generated from the system via quantum
pumping. In addition we also see spin selective current can also be pumped from
the system. The pure spin current seen is quite resilient to temperature while
disorder has a limited effect. Further the direction of these spin pumped
currents can be easily and exclusively controlled by the magnetization of the
single molecule magnet with disorder having no effect on the magnetization
control of the pumped spin currents.


  We study SUSY models in which Bino contributions solve the muon g-2 anomaly.
The contributions are enhanced by large left-right mixing of the smuons.
However, it is constrained by the vacuum stability condition of the
slepton--Higgs potential. Therefore, there are upper bounds on masses of
sleptons and Bino. When the slepton soft masses are universal, the upper bound
on the smuon mass becomes 330 (460)GeV in order to solve the g-2 anomaly at the
1 sigma (2 sigma) level. It is within the reach of LHC and ILC. If the stau is
heavier than the smuon, the bound can be as large as 1.4 (1.9)TeV. Such
non-universal slepton mass spectrum generically predicts too large LFV/CPV. We
show that the models are expected to be probed by LHC/ILC and LFV/CPV
complementarily in future.


  Wearable collaborative robots stand to assist human wearers who need fall
prevention assistance or wear exoskeletons. Such a robot needs to be able to
constantly adapt to the surrounding scene based on egocentric vision, and
predict the ego motion of the wearer. In this work, we leveraged body-mounted
cameras and sensors to anticipate the trajectory of human wearers through
complex surroundings. To facilitate research in ego-motion prediction, we have
collected a comprehensive walking scene navigation dataset centered on the
user's perspective. We then present a method to predict human motion
conditioning on the surrounding static scene. Our method leverages a diffusion
model to produce a distribution of potential future trajectories, taking into
account the user's observation of the environment. To that end, we introduce a
compact representation to encode the user's visual memory of the surroundings,
as well as an efficient sample-generating technique to speed up real-time
inference of a diffusion model. We ablate our model and compare it to
baselines, and results show that our model outperforms existing methods on key
metrics of collision avoidance and trajectory mode coverage.


  Motivated by the MIT experiment [Gorlitz {\em et al.}, Phys. Rev. Lett. {\bf
87}, 130402 (2001)], we analytically study the effect of density and phase
fluctuations on various observables in a quasi one-dimensional degenerate Bose
gases. Quantizing the Gross-Pitaevskii Hamiltonian and diagonalize it in terms
of the normal modes associated with the density and phase fluctuations of a
quasi-one dimensional Bose gas. We calculate dynamic structure factor
$S(q,\omega)$ from low-energy condensate density fluctuations and find that
there are multiple peaks in $S(q,\omega)$ for a given momentum $q$ due to the
discrete energy spectrum. These multiple peaks can be resolved by a two-photon
Bragg pulse with a long duration which transfer the momentum to the system. We
calculate the momentum transferred $ P_z(t)$ by using the phase-density
representation of the Bose order parameter. We also calculate the
single-particle density matrix, phase coherence length, and momentum
distribution by taking care of the phase fluctuations upto fourth-order term as
well as the density fluctuations. Our studies on coherence properties shows
that 1D Bose gases of MIT experiment do not form a true condensate, but it can
be obtained by a moderate changes of the current experimental parameters.


  Almost contact complex Riemannian manifolds, also known as almost contact
B-metric manifolds, are equipped with a pair of pseudo-Riemannian metrics that
are mutually associated with each other using the tensor structure. Here we
consider a special class of these manifolds, those of the Sasaki-like type.
They have an interesting geometric interpretation: the complex cone of such a
manifold is a holomorphic complex Riemannian manifold (also called a
K\"ahler-Norden manifold). The basic metric on the considered manifold is
specialized here as a soliton, i.e. has an additional curvature property such
that the metric is a self-similar solution of an intrinsic geometric flow.
Almost solitons are more general objects than solitons because they use
functions rather than constants as coefficients in the defining condition. A
$\beta$-Ricci-Bourguignon-like almost soliton ($\beta$ is a real constant) is
defined using the pair of metrics. The introduced soliton is a generalization
of some well-known (almost) solitons (such as those of Ricci, Schouten, and
Einstein), which in principle arise from a single metric rather than a pair of
metrics. The soliton potential is chosen to be pointwise collinear to the Reeb
vector field, or the Lie derivative of any B-metric along the potential to be
the same metric multiplied by a function. The resulting manifolds equipped with
the introduced almost solitons are characterized geometrically. Appropriate
examples for two types of almost solitons are constructed and the properties
obtained in the theoretical part are confirmed.


  Over a decade ago De Loera, Haws and K\"oppe conjectured that Ehrhart
polynomials of matroid polytopes have only positive coefficients and that the
coefficients of the corresponding $h^*$-polynomials form a unimodal sequence.
The first of these intensively studied conjectures has recently been disproved
by the first author who gave counterexamples in all ranks greater or equal to
three. In this article we complete the picture by showing that Ehrhart
polynomials of matroids of lower rank have indeed only positive coefficients.
Moreover, we show that they are coefficient-wise bounded by the Ehrhart
polynomials of minimal and uniform matroids. We furthermore address the second
conjecture by proving that $h^*$-polynomials of matroid polytopes of sparse
paving matroids of rank two are real-rooted and therefore have log-concave and
unimodal coefficients. In particular, this shows that the $h^*$-polynomial of
the second hypersimplex is real-rooted thereby strengthening a result of De
Loera, Haws and K\"oppe.


  The $p$-Laplacian for graphs, as well as the vertex Laplace operator and the
hyperedge Laplace operator for the general setting of oriented hypergraphs, are
generalized. In particular, both a vertex $p$-Laplacian and a hyperedge
$p$-Laplacian are defined for oriented hypergraphs, for all $p\geq 1$. Several
spectral properties of these operators are investigated.


  We characterize the soliton solutions of the nonlinear Schroedinger equation
on the half line with linearizable boundary conditions. Using an extension of
the solution to the whole line and the corresponding symmetries of the
scattering data, we identify the properties of the discrete spectrum of the
scattering problem. We show that discrete eigenvalues appear in quartets (as
opposed to pairs in the initial value problem), and we obtain explicit
relations for the norming constants associated to symmetric eigenvalues. The
apparent reflection of each soliton at the boundary of the spatial domain is
due to the presence of a "mirror" soliton, with equal amplitude and opposite
velocity, located beyond the boundary. We then calculate the position shift of
the physical solitons as a result of the nonlinear reflection. These results
provide a nonlinear analogue of the method of images that is used to solve
boundary value problems in electrostatics.


  Person re-identification has always been a hot and challenging task. This
paper introduces our solution for the re-identification track in VIPriors
Challenge 2021. In this challenge, the difficulty is how to train the model
from scratch without any pretrained weight. In our method, we show use
state-of-the-art data processing strategies, model designs, and post-processing
ensemble methods, it is possible to overcome the difficulty of data shortage
and obtain competitive results. (1) Both image augmentation strategy and novel
pre-processing method for occluded images can help the model learn more
discriminative features. (2) Several strong backbones and multiple loss
functions are used to learn more representative features. (3) Post-processing
techniques including re-ranking, automatic query expansion, ensemble learning,
etc., significantly improve the final performance. The final score of our team
(ALONG) is 96.5154% mAP, ranking first in the leaderboard.


  Recent works have demonstrated that deep learning (DL) based compressed
sensing (CS) implementation can accelerate Magnetic Resonance (MR) Imaging by
reconstructing MR images from sub-sampled k-space data. However, network
architectures adopted in previous methods are all designed by handcraft. Neural
Architecture Search (NAS) algorithms can automatically build neural network
architectures which have outperformed human designed ones in several vision
tasks. Inspired by this, here we proposed a novel and efficient network for the
MR image reconstruction problem via NAS instead of manual attempts.
Particularly, a specific cell structure, which was integrated into the
model-driven MR reconstruction pipeline, was automatically searched from a
flexible pre-defined operation search space in a differentiable manner.
Experimental results show that our searched network can produce better
reconstruction results compared to previous state-of-the-art methods in terms
of PSNR and SSIM with 4-6 times fewer computation resources. Extensive
experiments were conducted to analyze how hyper-parameters affect
reconstruction performance and the searched structures. The generalizability of
the searched architecture was also evaluated on different organ MR datasets.
Our proposed method can reach a better trade-off between computation cost and
reconstruction performance for MR reconstruction problem with good
generalizability and offer insights to design neural networks for other medical
image applications. The evaluation code will be available at
https://github.com/yjump/NAS-for-CSMRI.


  Simple general formula describing the p-V-T relationships of elastic solids
is derived from theoretical considerations. The new EoS was tested to
experiments of perovskite 0-109 GPa and 293-2000 K. The parameters providing
the best fit are: Ko = 267.5 GPa, Vo = 24.284 cm3, alpha_o = 2.079x10^-5 K^-1,
the pressure derivative of Ko is 1.556 and the pressure derivative of alpha_o
is -1.098x10^-7 K^-1GPa^-1 . The root-mean-square-deviations (RMSD) of the
residuals are 0.043 cm3, 0.79 GPa, and 125 K for the molar volume, pressure,
and temperature respectively. These RMSD values are in the range of the
uncertainty of the experiments, indicating that the five parameters
semi-empirical EoS correctly describes the p-V-T relationships of perovskite.
Separating the experiments into 200 K ranges the semi-empirical EoS was
compared to the most widely used finite strain, interatomic potential, and
empirical isothermal EoSs such as the Birch-Murnaghan, the Vinet, and the
Roy-Roy respectively. Correlation coefficients, RMSDs of the residuals and
Akaike Information Criteria were used for evaluating the fitting. Based on
these fitting parameters under pure isothermal conditions the semi-empirical
p-V EoS is slightly weaker than the Birch-Murnaghan and Vinet EoSs; however,
the semi-empirical p-V-T EoS is superior in every temperature range to all of
the investigated conventional isothermal EoSs.


  We identify and correct an error in the paper "Excess Optimism: How Biased is
the Apparent Error of an Estimator Tuned by SURE?" This correction allows new
guarantees on the excess degrees of freedom--the bias in the error estimate of
Stein's unbiased risk estimate (SURE) for an estimator tuned by directly
minimizing the SURE criterion--for arbitrary SURE-tuned linear estimators.
Oracle inequalities follow as a consequence of these results for such
estimators.


  We derive the quantum master equations for heavy quark systems in a
high-temperature quark- gluon plasma in the Lindblad form. The master equations
are derived in the influence functional formalism for open quantum systems in
perturbation theory. These master equations have a wide range of applications,
such as decoherence of a heavy quarkonium and Langevin dynamics of a heavy
quark in the quark-gluon plasma. We also show the equivalence between the
quarkonium master equations in the recoilless limit and the Schroedinger
equations with stochastic potential.


  Recent findings suggest that abnormal operating conditions of equipment in
the oil and gas supply chain represent a large fraction of anthropogenic
methane emissions. Thus, effective mitigation of emissions necessitates rapid
identification and repair of sources caused by faulty equipment. In addition to
advances in sensing technology that allow for more frequent surveillance,
prompt and cost-effective identification of sources requires computational
frameworks that provide automatic fault detection. Here, we present a
changepoint detection algorithm based on a recursive Bayesian scheme that
allows for simultaneous emission rate estimation and fault detection. The
proposed algorithm is tested on a series of near-field controlled release
mobile experiments, with promising results demonstrating successful detection
(>90% success rate) of changes in the leak rate when the emission rate is
tripled after an abrupt change. Moreover, we show that the statistics of the
measurements, such as the coefficient of variation and range are good
predictors of the performance of the algorithm. Finally, we describe how this
methodology can be easily adapted to suit time-averaged concentration data
measured by stationary sensors, thus showcasing its flexibility.


  The gauge dependence of the renormalization group functions of the
Ginzburg-Landau model is investigated. The analysis is done by means of the
Ward-Takahashi identities. After defining the local superconducting order
parameter, it is shown that its exponent $\beta$ is in fact gauge independent.
This happens because in $d=3$ the Landau gauge is the only gauge having a
physical meaning, a property not shared by the four-dimensional model where any
gauge choice is possible. The analysis is done in both the context of the
$\epsilon$-expansion and in the fixed dimension approach. It is pointed out the
differences that arise in both of these approaches concerning the gauge
dependence.


  The asymptotic variance of the maximum likelihood estimate is proved to
decrease when the maximization is restricted to a subspace that contains the
true parameter value. Maximum likelihood estimation allows a systematic fitting
of covariance models to the sample, which is important in data assimilation.
The hierarchical maximum likelihood approach is applied to the spectral
diagonal covariance model with different parameterizations of eigenvalue decay,
and to the sparse inverse covariance model with specified parameter values on
different sets of nonzero entries. It is shown computationally that using
smaller sets of parameters can decrease the sampling noise in high dimension
substantially.


  We theoretically investigate the evolution of the peak height of an energy
resolved electronic wave-packets ballistically propagating along integer
quantum Hall edge channels at filling factor $\nu=2$. This is ultimately
related to the elastic scattering amplitude for the fermionic excitations
evaluated at different injection energy. We investigate this quantity assuming
a short range capacitive coupling between the edges. Moreover, we also take
into account phenomenologically the possibility of energy dissipation towards
additional degrees of freedom both linear and quadratic in the injection
energy. Comparing with recent experimental data, we rule out the
non-dissipative case as well a quadratic dependence of the dissipation,
indicating a linear energy loss rate as the best candidate to describe the
behavior of the quasi-particle peak at short enough propagation lengths.


  New superconductors, Li$_x$(C$_n$H$_{2n+3}$N)$_y$Fe$_{1-z}$Se ($n$ = 6, 8,
18), have been synthesized via the co-intercalation of linear monoamines
together with Li into FeSe. The distance between neighboring Fe layers expands
up to 55.7 {\AA} for n = 18, which is much larger than the previous record of
19 {\AA} in the FeSe-based intercalation superconductors. Tc remains saturated
at $\sim$42 K.


  We present results on both the restoration of the spontaneously broken chiral
symmetry and the effective restoration of the anomalously broken U(1)_A
symmetry in finite temperature QCD at zero chemical potential using lattice
QCD. We employ domain wall fermions on lattices with fixed temporal extent
N_\tau = 8 and spatial extent N_\sigma = 16 in a temperature range of T = 139 -
195 MeV, corresponding to lattice spacings of a \approx 0.12 - 0.18 fm. In
these calculations, we include two degenerate light quarks and a strange quark
at fixed pion mass m_\pi = 200 MeV. The strange quark mass is set near its
physical value. We also present results from a second set of finite temperature
gauge configurations at the same volume and temporal extent with slightly
heavier pion mass. To study chiral symmetry restoration, we calculate the
chiral condensate, the disconnected chiral susceptibility, and susceptibilities
in several meson channels of different quantum numbers. To study U(1)_A
restoration, we calculate spatial correlators in the scalar and pseudo-scalar
channels, as well as the corresponding susceptibilities. Furthermore, we also
show results for the eigenvalue spectrum of the Dirac operator as a function of
temperature, which can be connected to both U(1)_A and chiral symmetry
restoration via Banks-Casher relations.


  We introduce the notion of entanglement of subspaces as a measure that
quantify the entanglement of bipartite states in a randomly selected subspace.
We discuss its properties and in particular we show that for maximally
entangled subspaces it is additive. Furthermore, we show that maximally
entangled subspaces can play an important role in the study of quantum error
correction codes. We discuss both degenerate and non-degenerate codes and show
that the subspace spanned by the logical codewords of a non-degenerate code is
a 2k-totally (maximally) entangled subspace. As for non-degenerate codes, we
provide a mathematical definition in terms of subspaces and, as an example, we
analyze Shor's nine qubits code in terms of 22 mutually orthogonal subspaces.


  Advances in material fabrication techniques and growth methods have opened up
a new chapter for twistronics, in the form of twisted freestanding
three-dimensional material membranes. Through first-principles calculations
based on density functional theory, we investigate the crystal and electronic
structures of twisted bilayer BaTiO$_3$. Our findings reveal that large
stacking fault energy leads to chiral in-plane vortex pattern that was recently
observed in experiments. Moreover, we also found non-zero out-of-plane local
dipole moments, indicating that the strong interlayer interaction might offer
promising strategy to stabilize ferroelectric order in the two-dimensional
limit. Remarkably, the vortex pattern in the twisted BaTiO$_3$ bilayer support
localized electronic states with quasi-flat bands, associated with the
interlayer hybridization of oxygen $p_z$ orbitals. We found that the associated
band width reaches a minimum at $\sim$19$^{\circ}$ twisting, configuring the
largest magic angle in moir\'e systems reported so far. Further, the moir\'e
vortex pattern bears a striking resemblance to two interpenetrating Lieb
lattices and corresponding tight-binding model provides a comprehensive
description of the evolution the moir\'e bands with twist angle and reveals the
topological nature of these states.


  The $\alpha$-decay half-lives of 204 superheavy nuclei covering the range
$114 \leq Z \leq 126$ have been investigated using the relativistic mean-field
model (RMF) for NL3$^*$ parameter set. The ground state bulk properties such as
binding energy, quadrupole deformation parameter ($\beta_2$ ), and
root-mean-square charge radii for these nuclei are analyzed. Four different
semi-empirical formulae, namely, the universal decay law (UDL), the
Viola-Seaborg (VSS) formula, the modified universal decay law (MUDL), and the
modified Brown formula (MBrown), are used to obtain the $\alpha$-decay
half-lives for the considered nuclei. To examine the applicability of
relativistic mean-field model within NL3$^*$ parametrization, the
$\alpha$-decay energies, and the half-lives of a few known superheavy nuclei
within the range 102 $\leq$ Z $\leq$ 118 are calculated and the results are
compared with the experimental data along with the theoretical predictions. The
$\alpha$-decay energies ($Q$-values) are estimated from the binding energies of
the parent, and daughter from the RMF (NL3$^*$) parameter set. The calculated
results are compared with macroscopic-microscopic Finite-Range-Droplet-Model
(FRDM), Global Nuclear Mass Model (WS3, WS4), Weizsacker-Skyrme mass model
(WS$^*$) predictions, and the experimental data, wherever available. The
possible standard deviations are also estimated for experimental and various
theoretical predictions. We find a good consistency for the
experimental-to-UDL, FRDM-to-UDL, and WS4-to-UDL estimates of the decay energy
and corresponding half-life. The present analysis provides the theoretical
predictions within the microscopic model for the upcoming experiments on the
superheavy region.


  Traditional machine learning methods usually minimize a simple loss function
to learn a predictive model, and then use a complex performance measure to
measure the prediction performance. However, minimizing a simple loss function
cannot guarantee that an optimal performance. In this paper, we study the
problem of optimizing the complex performance measure directly to obtain a
predictive model. We proposed to construct a maximum likelihood model for this
problem, and to learn the model parameter, we minimize a com- plex loss
function corresponding to the desired complex performance measure. To optimize
the loss function, we approximate the upper bound of the complex loss. We also
propose impose the sparsity to the model parameter to obtain a sparse model. An
objective is constructed by combining the upper bound of the loss function and
the sparsity of the model parameter, and we develop an iterative algorithm to
minimize it by using the fast iterative shrinkage- thresholding algorithm
framework. The experiments on optimization on three different complex
performance measures, including F-score, receiver operating characteristic
curve, and recall precision curve break even point, over three real-world
applications, aircraft event recognition of civil aviation safety, in- trusion
detection in wireless mesh networks, and image classification, show the
advantages of the proposed method over state-of-the-art methods.


  We construct, for any given $ \ell = \frac{1}{2} + {\mathbb N}_0, $ the
second-order \textit{nonlinear} partial differential equations (PDEs) which are
invariant under the transformations generated by the centrally extended
conformal Galilei algebras. The generators are obtained by a coset construction
and the PDEs are constructed by standard Lie symmetry technique. It is observed
that the invariant PDEs have significant difference for $ \ell > \frac{3}{2}. $


  We consider the application of Runge-Kutta (RK) methods to gradient systems
$(d/dt)x = -\nabla V(x)$, where, as in many optimization problems, $V$ is
convex and $\nabla V$ (globally) Lipschitz-continuous with Lipschitz constant
$L$. Solutions of this system behave contractively, i.e. the Euclidean distance
between two solutions $x(t)$ and $\widetilde{x}(t)$ is a nonincreasing function
of $t$. It is then of interest to investigate whether a similar contraction
takes place, at least for suitably small step sizes $h$, for the discrete
solution. Dahlquist and Jeltsch results' imply that (1) there are explicit RK
schemes that behave contractively whenever $Lh$ is below a scheme-dependent
constant and (2) Euler's rule is optimal in this regard. We prove however, by
explicit construction of a convex potential using ideas from robust control
theory, that there exists RK schemes that fail to behave contractively for any
choice of the time-step $h$.


  Medical conditions due to acute cell injury, such as stroke and heart attack,
are of tremendous impact and have attracted huge amounts of research effort.
The biomedical research that seeks cures for these conditions has been
dominated by a qualitative, inductive mindset. Although the inductive approach
has not been effective in developing medical treatments, it has amassed enough
information to allow construction of quantitative, deductive models of acute
cell injury. In this work we develop a modeling approach by extending an
autonomous nonlinear dynamic theory of acute cell injury that offered new ways
to conceptualize cell injury but possessed limitations that decrease its
effectiveness. Here we study the global dynamics of the cell injury theory
using a nonautonomous formulation. Different from the standard scenario in
nonlinear dynamics that is determined by the steady state and fixed points of
the model equations, in this nonautonomous model with a trivial fixed point,
the system property is dominated by the transient states and the corresponding
dynamic processes. The model gives rise to four qualitative types of dynamical
patterns that can be mapped to the behavior of cells after clinical acute
injuries. The nonautonomous theory predicts the existence of a latent stress
response capacity (LSRC) possessed by injured cells. The LSRC provides a
theoretical explanation of how therapies, such as hypothermia, can prevent cell
death after lethal injuries. The nonautonomous theory of acute cell injury
provides an improved quantitative framework for understanding cell death and
recovery and lays a foundation for developing effective therapeutics for acute
injury.


  We describe rings over which every right module is almost injective. We give
a description of rings over which every simple module is a almost projective.


  Regionalization methods have long been used to estimate high return levels of
river discharges at ungauged locations on a river network. In these methods,
the recorded discharge measurements of a group of similar, gauged, stations is
used to estimate high quantiles at the target catchment that has no
observations. This group is called the region of influence and its similarity
to the ungauged location is measured in terms of physical and meteorological
catchment attributes. We develop a statistical method for estimation of high
return levels based on regionalizing the parameters of a generalized extreme
value distribution. The region of influence is chosen in an optimal way,
ensuring similarity and in-group homogeneity. Our method is applied to
discharge data from the Rhine basin in Switzerland, and its performance at
ungauged locations is compared to that of classical regionalization methods.
For gauged locations we show how our approach improves the estimation
uncertainty for long return periods by combining local measurements with those
from the region of influence.


  We calculate limits to the properties of massive, unstable neutrinos using
data from gamma-ray detectors on the Pioneer Venus Orbiter Satellite; a massive
neutrino emitted from SN1987A that decayed in flight and produced gamma rays
would be detectable by this instruments. The lack of such a signal allows us to
constrain the branching ratio to photons ($\Bg$), mass ($\mnu$), and radiative
lifetime ($\tau_\gamma = \tau/\Bg$). For low mass ($m<T\sim8\MeV$) neutrinos
decaying $\nu\rightarrow\nu'\gamma$, $\Bg<3\times 10^{-7}$, for $\mt\lesssim
10^6 \keV\sec$, and $\Bg<6\times 10^{-14} \mt/\keV\sec$ for $\mt\gtrsim 10^6
\keV\sec$; limits for high-mass neutrinos are somewhat weaker due to Boltzmann
suppression. We also calculate limits for decays that produce gamma rays
through the \brem channel, $\nu\rightarrow\nu'e^+e^-\gamma$. In the case that
neutrino mass states are nearly degenerate, $\delta m^2/m^2\ll1$, our limits
for the mode $\nu\rightarrow\nu'\gamma$ become more stringent by a factor of
$\delta m^2/m^2$, because more of the decay photons are shifted into the PVO
detector energy window.


  We show that the singularities of the twisted K\"ahler--Einstein metric
arising as the long-time solution of the K\"ahler--Ricci flow or in the
collapsed limit of Ricci-flat K\"ahler metrics is intimately related to the
holomorphic sectional curvature of the reference conical geometry. This
provides an alternative proof of the second-order estimate obtained by
Gross--Tosatti--Zhang with explicit constants appearing in the divisorial pole.


  We examine the behaviors of various models of $k$-limited automata, which
naturally extend Hibbard's [Inf. Control, vol. 11, pp. 196--238, 1967] scan
limited automata, each of which is a single-tape linear-bounded automaton
satisfying the $k$-limitedness requirement that the content of each tape cell
should be modified only during the first $k$ visits of a tape head. One central
computation model is a probabilistic $k$-limited automaton (abbreviated as a
$k$-lpa), which accepts an input exactly when its accepting states are
reachable from its initial state with probability more than 1/2 within expected
polynomial time. We also study the behaviors of one-sided-error and
bounded-error variants of such $k$-lpa's as well as the deterministic,
nondeterministic, and unambiguous models of $k$-limited automata, which can be
viewed as natural restrictions of $k$-lpa's. We discuss fundamental properties
of these machine models and obtain inclusions and separations among language
families induced by them. In due course, we study special features -- the blank
skipping property and the closure under reversal -- which are keys to the
robustness of $k$-lpa's.


  Rather than using an adaptive optics (AO) system to correct a telescope s
entire pupil, it can instead be used to more finely correct a smaller
sub-aperture. Indeed, existing AO systems can be used to correct a sub-aperture
1/3 to 1/2 the size of a 5-10 m telescope to extreme adaptive optics (ExAO)
levels. We discuss the potential performance of a clear off-axis well-corrected
sub-aperture (WCS), and describe our initial imaging results with a 1.5 m
diameter WCS on the Palomar Observatory s Hale telescope. These include
measured Strehl ratios of 0.92-0.94 in the infrared (2.17 microns), and 0.12 in
the B band, the latter allowing a binary of separation 0.34 arc sec to be
easily resolved in the blue. Such performance levels enable a variety of novel
observational modes, such as infrared ExAO, visible-wavelength AO, and
high-contrast coronagraphy. One specific application suggested by the high
Strehl ratio stability obtained (1%) is the measurement of planetary transits
and eclipses. Also described is a simple dark-hole experiment carried out on a
binary star, in which a comatic phase term was applied directly to the
deformable mirror, in order to shift the diffraction rings to one side of the
point spread function.


  We tackle the problem of discovering novel classes in an image collection
given labelled examples of other classes. We present a new approach called
AutoNovel to address this problem by combining three ideas: (1) we suggest that
the common approach of bootstrapping an image representation using the labelled
data only introduces an unwanted bias, and that this can be avoided by using
self-supervised learning to train the representation from scratch on the union
of labelled and unlabelled data; (2) we use ranking statistics to transfer the
model's knowledge of the labelled classes to the problem of clustering the
unlabelled images; and, (3) we train the data representation by optimizing a
joint objective function on the labelled and unlabelled subsets of the data,
improving both the supervised classification of the labelled data, and the
clustering of the unlabelled data. Moreover, we propose a method to estimate
the number of classes for the case where the number of new categories is not
known a priori. We evaluate AutoNovel on standard classification benchmarks and
substantially outperform current methods for novel category discovery. In
addition, we also show that AutoNovel can be used for fully unsupervised image
clustering, achieving promising results.


  In this paper, we explore the challenges inherent to Large Language Models
(LLMs) like GPT-4, particularly their propensity for hallucinations, logic
mistakes, and incorrect conclusions when tasked with answering complex
questions. The capacity of LLMs to present erroneous answers in a coherent and
semantically rigorous manner further complicates the detection of factual
inaccuracies. This issue is especially pronounced in fields that require
specialized expertise. Our work delves into these challenges, aiming to enhance
the understanding and mitigation of such errors, thereby contributing to the
improvement of LLM accuracy and reliability in scientific and other specialized
domains. Our findings reveal a non-linear relationship between the context's
relevancy and the answers' measured quality. In addition, we demonstrate that
with the correct calibration, it is possible to automate the grading procedure
-- a finding suggesting that, at least to some degree, the LLMs can be used to
self-examine the quality of their own performance. Finally, we describe an
experimental platform that can be seen as a proof-of-concept of the techniques
described in this work.


  The Milne-Eddington approximation provides an analytic and simple solution to
the radiative transfer equation. It can be easily implemented in inversion
codes that are used to fit spectro-polarimetric observations to infer average
values of the magnetic field vector and the line-of-sight velocity of the solar
plasma. However, it is in principle restricted to spectral lines formed under
local thermodynamic conditions. We show that a simple modification in the
linear source function of the Milne-Eddington approximation is sufficient to
infer relevant physical parameters from spectral lines that deviate from local
thermodynamic equilibrium. This is not a new modification for the solar
community but it has been forgotten for quite some time To check the validity
of such approximation we make use of the Mg I b2 and the Ca II lines. We first
study the influence of these new terms on the profile shape by means of the
response functions. Then, we test the performance of an inversion code
including such modification against the presence of noise. The approximation is
also tested with realistic spectral lines generated with the RH numerical
radiative transfer code. Finally, we confront the code with synthetic profiles
generated from magneto-hydrodynamic simulations.


  We re-examine critically the estimates of the duration of different phases of
star formation and the lifetimes of molecular clouds, based on the ages of
T-Tauri stars, age spreads of stars in clusters, and statistics of pre-stellar
cores. We show that all available observational data are consistent with
lifetimes of molecular clouds comparable to 10 Myr, as well as with the
predictions of the theory of self-initiated, ambipolar-diffusion--controlled
star formation. We conclude that there exists no observational support for
either "young" molecular clouds or "rapid" star formation.


  A dynamic mitigation mechanism for instability growth was proposed and
discussed in the paper [Phys. Plasmas 19, 024503 (2012)]. In the present paper
the robustness of the dynamic instability mitigation mechanism is discussed
further. The results presented here show that the mechanism of the dynamic
instability mitigation is rather robust against changes in the phase, the
amplitude and the wavelength of the wobbling perturbation applied. Generally
instability would emerge from the perturbation of the physical quantity.
Normally the perturbation phase is unknown so that the instability growth rate
is discussed. However, if the perturbation phase is known, the instability
growth can be controlled by a superposition of perturbations imposed actively:
if the perturbation is induced by, for example, a driving beam axis oscillation
or wobbling, the perturbation phase could be controlled and the instability
growth is mitigated by the superposition of the growing perturbations.


  The transport of heat and charge in cuprates was measured in single crystals
of La_{2-x}Sr_xCuO_{4+\delta} (LSCO) across the doping phase diagram at low
temperatures. In underdoped LSCO, the thermal conductivity is found to decrease
with increasing magnetic field in the T goes to 0 limit, in striking contrast
to the increase observed in all superconductors, including cuprates at higher
doping. In heavily underdoped LSCO, where superconductivity can be entirely
suppressed with an applied field, we show that a novel thermal
metal-to-insulator transition takes place upon going from the superconducting
state to the field-induced normal state.


  We performed an internal-consistency test of the KiDS+VIKING-450 (KV450)
cosmic shear analysis with a colour-based split of source galaxies. Utilising
the same measurements and calibrations for both sub-samples, we inspected the
characteristics of the shear measurements and the performance of the
calibration pipelines. On the modelling side, we examined the observational
nuisance parameters, specifically those for the redshift calibration and
intrinsic alignments, using a Bayesian analysis with dedicated test parameters.
We verified that the current nuisance parameters are sufficient for the KV450
data to capture residual systematics, with slight deviations seen in the second
and the third redshift tomographic bins. Our test also showcases the degeneracy
between the inferred amplitude of intrinsic alignments and the redshift
uncertainties in low redshift tomographic bins. The test is rather insensitive
to the background cosmology and, therefore, can be implemented before any
cosmological inference is made.


  Argument Mining is defined as the task of automatically identifying and
extracting argumentative components (e.g., premises, claims, etc.) and
detecting the existing relations among them (i.e., support, attack, rephrase,
no relation). One of the main issues when approaching this problem is the lack
of data, and the size of the publicly available corpora. In this work, we use
the recently annotated US2016 debate corpus. US2016 is the largest existing
argument annotated corpus, which allows exploring the benefits of the most
recent advances in Natural Language Processing in a complex domain like
Argument (relation) Mining. We present an exhaustive analysis of the behavior
of transformer-based models (i.e., BERT, XLNET, RoBERTa, DistilBERT and ALBERT)
when predicting argument relations. Finally, we evaluate the models in five
different domains, with the objective of finding the less domain dependent
model. We obtain a macro F1-score of 0.70 with the US2016 evaluation corpus,
and a macro F1-score of 0.61 with the Moral Maze cross-domain corpus.


  A smooth hypergraph property $\mathcal{P}$ is a class of hypergraphs that is
hereditary and non-trivial, i.e., closed under induced subhypergraphs and it
contains a non-empty hypergraph but not all hypergraphs. In this paper we
examine $\mathcal{P}$-colorings of hypergraphs with smooth hypergraph
properties $\mathcal{P}$. A $\mathcal{P}$-coloring of a hypergraph $H$ with
color set $C$ is a function $\varphi:V(H) \to C$ such that $H[\varphi^{-1}(c)]$
belongs to $\mathcal{P}$ for all $c \in C$. Let $L: V(H) \to 2^C$ be a so
called list-assignment of the hypergraph $H$. Then, a
$(\mathcal{P},L)$-coloring of $H$ is a $\mathcal{P}$-coloring $\varphi$ of $H$
such that $\varphi(v) \in L(v)$ for all $v \in V(H)$. The aim of this paper is
a characterization of $(\mathcal{P},L)$-critical hypergraphs. Those are
hypergraphs $H$ such $H-v$ is $(\mathcal{P},L)$-colorable for all $v \in V(H)$
but $H$ itself is not. Our main theorem is a Gallai-type result for critical
hypergraphs, which implies a Brooks-type result for $(\mathcal{P},L)$-colorable
hypergraphs. In the last section, we prove a Gallai bound for the degree sum of
$(\mathcal{P},L)$-critical locally linear hypergraphs.


  We classify Galois objects for the dual of a group algebra of a finite group
over an arbitrary field.


  We consider static massive thin cylindrical shells (tubes) as the sources in
Einstein's equations. They correspond to $\dl$- and $\dl'$-function type
energy-momentum tensors. The corresponding metric components are found
explicitly. They are not continuous functions in general and lead to ambiguous
curvature tensor components. Nevertheless all ambiguous terms in Einstein's
equations safely cancel. The interplay between elasticity theory, geometric
theory of defects, and General Relativity is analyzed. The elasticity theory
provides a simple picture for defects creation and a new look on General
Relativity.


  We estimate the Milky Way (MW) halo properties using satellite kinematic data
including the latest measurements from Gaia DR2. With a simulation-based 6D
phase-space distribution function (DF) of satellite kinematics, we can infer
halo properties efficiently and without bias, and handle the selection function
and measurement errors rigorously in the Bayesian framework. Applying our DF
from the EAGLE simulation to 28 satellites, we obtain an MW halo mass of
$M=1.23_{-0.18}^{+0.21}\times 10^{12} M_\odot$ and a concentration of $c=9.4_{
-2.1}^{ +2.8}$ with the prior based on the $M$-$c$ relation. The inferred mass
profile is consistent with previous measurements but with better precision and
reliability due to the improved methodology and data. Potential improvement is
illustrated by combining satellite data and stellar rotation curves. Using our
EAGLE DF and best-fit MW potential, we provide much more precise estimates of
kinematics for those satellites with uncertain measurements. Compared to the
EAGLE DF, which matches the observed satellite kinematics very well, the DF
from the semi-analytical model based on the dark-matter-only simulation
Millennium II (SAM-MII) over-represents satellites with small radii and
velocities. We attribute this difference to less disruption of satellites with
small pericenter distances in the SAM-MII simulation. By varying the disruption
rate of such satellites in this simulation, we estimate a $\sim 5\%$ scatter in
the inferred MW halo mass among hydrodynamics-based simulations.


  We study causality in non-commutative quantum field theory with a space-space
non-commutativity. We employ the S-operator approach of Bogoliubov-Shirkov(BS).
We generalize the BS criterion of causality to the noncommutative theory. The
criterion to test causality leads to a nonzero difference between T*-product
and T-product as a condition of causality violation for a spacelike separation.
We discuss two examples; one in a scalar theory and one in the Yukawa theory.
In particular, in the context of a non-commutative Yukawa theory, with the
interaction Lagrangian $\bar{\psi}(x)\star\psi(x)\star\phi(x)$, is observed to
be causality violating even in case of space-space noncommutativity for which
\theta^{0i}=0.
  \


  In this work, we study solving (decoupled) forward-backward stochastic
differential equations (FBSDEs) numerically using the regression trees. Based
on the general theta-discretization for the time-integrands, we show how to
efficiently use regression tree-based methods to solve the resulting
conditional expectations. Several numerical experiments including
high-dimensional problems are provided to demonstrate the accuracy and
performance of the tree-based approach. For the applicability of FBSDEs in
financial problems, we apply our tree-based approach to the Heston stochastic
volatility model, the high-dimensional pricing problems of a Rainbow option and
an European financial derivative with different interest rates for borrowing
and lending.


  Here we explore the capabilities of NASA's 3.0 meter Infrared Telescope
Facility (IRTF) and SpeX spectrometer and the 5.08 meter Hale telescope with
the TripleSpec spectrometer with near-infrared H, K, and L-band measurements of
HD 209458b's secondary eclipse. Our IRTF/SpeX data are the first absolute
L-band spectroscopic emission measurements of any exoplanet other than the hot
Jupiter HD 189733b. Previous measurements of HD 189733b's L-band indicate
bright emission hypothesized to result from non-LTE CH$_{4}$ $\nu_{3}$
fluorescence. We do not detect a similar bright 3.3 $\mu$m feature to
~3$\sigma$, suggesting that fluorescence does not need to be invoked to explain
HD 209458b's L-band measurements. The validity of our observation and reduction
techniques, which decrease the flux variance by up to 2.8 orders of magnitude,
is reinforced by 1$\sigma$ agreement with existent $Hubble$/NICMOS and
$Spitzer$/IRAC1 observations which overlap the H, K, and L-bands, suggesting
that both IRTF/SpeX and Palomar/TripleSpec can measure an exoplanet's emission
with high precision.


  Floods are the most common and among the most severe natural disasters in
many countries around the world. As global warming continues to exacerbate sea
level rise and extreme weather, governmental authorities and environmental
agencies are facing the pressing need of timely and accurate evaluations and
predictions of flood risks. Current flood forecasts are generally based on
historical measurements of environmental variables at monitoring stations. In
recent years, in addition to traditional data sources, large amounts of
information related to floods have been made available via social media.
Members of the public are constantly and promptly posting information and
updates on local environmental phenomena on social media platforms. Despite the
growing interest of scholars towards the usage of online data during natural
disasters, the majority of studies focus exclusively on social media as a
stand-alone data source, while its joint use with other type of information is
still unexplored. In this paper we propose to fill this gap by integrating
traditional historical information on floods with data extracted by Twitter and
Google Trends. Our methodology is based on vine copulas, that allow us to
capture the dependence structure among the marginals, which are modelled via
appropriate time series methods, in a very flexible way. We apply our
methodology to data related to three different coastal locations on the South
coast of the United Kingdom (UK). The results show that our approach, based on
the integration of social media data, outperforms traditional methods in terms
of evaluation and prediction of flood events.