  Numerous 6D pose estimation methods have been proposed that employ end-to-end
regression to directly estimate the target pose parameters. Since the visible
features of objects are implicitly influenced by their poses, the network
allows inferring the pose by analyzing the differences in features in the
visible region. However, due to the unpredictable and unrestricted range of
pose variations, the implicitly learned visible feature-pose constraints are
insufficiently covered by the training samples, making the network vulnerable
to unseen object poses. To tackle these challenges, we proposed a novel
geometric constraints learning approach called Geo6D for direct regression 6D
pose estimation methods. It introduces a pose transformation formula expressed
in relative offset representation, which is leveraged as geometric constraints
to reconstruct the input and output targets of the network. These reconstructed
data enable the network to estimate the pose based on explicit geometric
constraints and relative offset representation mitigates the issue of the pose
distribution gap. Extensive experimental results show that when equipped with
Geo6D, the direct 6D methods achieve state-of-the-art performance on multiple
datasets and demonstrate significant effectiveness, even with only 10% amount
of data.


  This article reviews recent experimental investigations on two binary
Fe-chalcogenides: FeSe and Fe$_{1+y}$Te. The main focus is on synthesis, single
crystal growth, chemical composition, as well as on the effect of excess iron
on structural, magnetic, and transport properties of these materials. The
structurally simplest Fe-based superconductor Fe$_{1+x}$Se with a critical
temperature $T_c \approx$ 8.5 K undergoes a tetragonal to orthorhombic phase
transition at a temperature $T_s \approx$ 87 K. No long-range magnetic order is
observed down to the lowest measured temperature in Fe$_{1+x}$Se. On the other
hand, isostructural Fe$_{1+y}$Te displays a complex interplay of magnetic and
structural phase transitions in dependence on the tuning parameter such as
excess amount of Fe or pressure, but it becomes a superconductor only when Te
is substituted by a sufficient amount of Se. We summarize experimental evidence
for different competing interactions and discuss related open questions.


  Data-driven methods for modelling purposes in fluid mechanics are a promising
alternative given the continuous increase of both computational power and
data-storage capabilities. Highly non-linear flows including turbulence and
reaction are challenging to model, and accurate closures for the unresolved
terms in large eddy simulations of such flows are difficult to obtain. In this
study, we investigate the use of artificial neural networks for modelling an
important unclosed term namely the unresolved stress tensor, in a highly
demanding turbulent and reacting flow, which additionally includes mean shear.
The performance of the neural network-based modelling approach is conducted a
priori following a coarsened mesh approach, and compared against the
predictions of eight other classic models in the literature, which include both
static and dynamic formulations.


  Recent advances in media generation techniques have made it easier for
attackers to create forged images and videos. State-of-the-art methods enable
the real-time creation of a forged version of a single video obtained from a
social network. Although numerous methods have been developed for detecting
forged images and videos, they are generally targeted at certain domains and
quickly become obsolete as new kinds of attacks appear. The method introduced
in this paper uses a capsule network to detect various kinds of spoofs, from
replay attacks using printed images or recorded videos to computer-generated
videos using deep convolutional neural networks. It extends the application of
capsule networks beyond their original intention to the solving of inverse
graphics problems.


  Active feature acquisition (AFA) agents, crucial in domains like healthcare
where acquiring features is often costly or harmful, determine the optimal set
of features for a subsequent classification task. As deploying an AFA agent
introduces a shift in missingness distribution, it's vital to assess its
expected performance at deployment using retrospective data. In a companion
paper, we introduce a semi-offline reinforcement learning (RL) framework for
active feature acquisition performance evaluation (AFAPE) where features are
assumed to be time-dependent. Here, we study and extend the AFAPE problem to
cover static feature settings, where features are time-invariant, and hence
provide more flexibility to the AFA agents in deciding the order of the
acquisitions. In this static feature setting, we derive and adapt new inverse
probability weighting (IPW), direct method (DM), and double reinforcement
learning (DRL) estimators within the semi-offline RL framework. These
estimators can be applied when the missingness in the retrospective dataset
follows a missing-at-random (MAR) pattern. They also can be applied to
missing-not-at-random (MNAR) patterns in conjunction with appropriate existing
missing data techniques. We illustrate the improved data efficiency offered by
the semi-offline RL estimators in synthetic and real-world data experiments
under synthetic MAR and MNAR missingness.


  Large populations of cataclysmic variables (CVs) in globular clusters have
long been predicted, but the number of absolutely certain cluster CVs known in
globulars is still less than 10. HST and Chandra observers have recently found
dozens of cataclysmic variable candidates in several populous globular
clusters. Confirmation and characterization of these candidates are extremely
difficult, thus identification of unambiguous CVs remains important. We have
searched all archival HST images of the dense globular cluster M80 for erupting
dwarf novae (DN), and to check the outburst behaviors of two very blue objects
first identified a decade ago. Two new erupting dwarf novae were found in 8
searched epochs, making M80 a record holder for erupting DN. The quiescent
classical nova in M80 varies by no more than a few tenths of a magnitude on
timescales of minutes to years, and a similar faint, blue object varies by a
similar amount. Simulations and completeness tests indicate that there are at
most 3 erupting DN like SS Cyg and at most 9 U Gem-like DN in M80. Either this
very dense cluster contains about an order of magnitude fewer CVs than theory
predicts, or most M80 CVs are extremely faint and/or erupt very infrequently
like WZ Sge. We have detected a sequence of 54 objects running parallel to the
main sequence and several tenths of a magnitude bluewards of it. These blue
objects are significantly more centrally concentrated than the main sequence
stars, but not as centrally concentrated as the blue stragglers. We suggest
that these objects are white dwarf--red dwarf binaries, and that some are the
faint CV population of M80.


  We report structural, magnetic and dielectric properties of layered
perovskite materials LnBaCuFeO5 (Ln = La and Lu). LaBaCuFeO5 shows magnetic
cluster glass behavior below 60 K owing to the competing ferromagnetic and
antiferromagnetic exchange interactions. Glassy dynamics of electric dipoles
has also been observed in the vicinity of the magnetic glass transition
temperature. The presence of significant coupling between spin and polar
degrees of freedom results in the multiglass feature in LaBaCuFeO5. The
LuBaCuFeO5 compound undergoes YBaCuFeO5 like commensurate to incommensurate
antiferromagnetic transition at 175 K. Large magnetic irreversibility below 17
K in this compound suggests the presence of strong spin anisotropy. In
addition, in this compound the interaction between the dipoles is not strong
enough, which results in the absence of glassy dynamics of electric dipoles.
The contrasting behavior of two compounds is possibly due to variation in the
ferromagnetic and antiferromagnetic interactions along c-axis, which is the
manifestation of structural modification arising out of the difference in the
ionic radii of La and Lu.


  With the plentiful information available in the Gaia BP/RP spectra, there is
significant scope for applying discriminative models to extract stellar
atmospheric parameters and abundances. We describe an approach to leverage an
`Uncertain Neural Network' model trained on APOGEE data to provide high-quality
predictions with robust estimates for per-prediction uncertainty. We report
median formal uncertainties of 0.068 dex, 69.1K, 0.14 dex, 0.031 dex, 0.040
dex, and 0.029 dex for [Fe/H], $T_\mathrm{eff}$, $\log g$, [C/Fe], [N/Fe], and
[$\alpha$/M] respectively. We validate these predictions against our APOGEE
training data, LAMOST, and Gaia GSP-Phot stellar parameters, and see a strong
correlation between our predicted parameters and those derived from these
surveys. We investigate the information content of the spectra by considering
the `attention' our model pays to different spectral features compared to
expectations from synthetic spectra calculations. Our model's predictions are
applied to the Gaia dataset, and we produce a publicly available catalogue of
our model's predictions.


  In homotopy type theory (HoTT), all constructions are necessarily stable
under homotopy equivalence. This has shortcomings: for example, it is believed
that it is impossible to define a type of semi-simplicial types. More
generally, it is difficult and often impossible to handle towers of coherences.
To address this, we propose a 2-level theory which features both strict and
weak equality. This can essentially be represented as two type theories: an
"outer" one, containing a strict equality type former, and an "inner" one,
which is some version of HoTT. Our type theory is inspired by Voevosky's
suggestion of a homotopy type system (HTS) which currently refers to a range of
ideas. A core insight of our proposal is that we no not need any form of
equality reflection in order to achieve what HTS was suggested for. Instead,
having unique identity proofs in the outer type theory is sufficient, and it
also has the meta-theoretical advantage of not breaking decidability of type
checking. The inner theory can be an easily justifiable extensions of HoTT,
allowing the construction of "infinite structures" which are considered
impossible in plain HoTT. Alternatively, we can set the inner theory to be
exactly the current standard formulation of HoTT, in which case our system can
be thought of as a type-theoretic framework for working with "schematic"
definitions in HoTT. As demonstrations, we define semi-simplicial types and
formalise constructions of Reedy fibrant diagrams.


  We estimate Majorana CP phases for a given flavor neutrino mass matrix
(M_\nu) consistent with the bi-pair neutrino mixing, which is recently proposed
to describe neutrino mixings given by \sin\theta_{13}=0 for the reactor
neutrino mixing, \sin^2\theta_{12} = 1-1/\sqrt{2} for the solar neutrino mixing
and either \sin^2\theta_{23} = \tan^2\theta_{12} or
\sin^2\theta_{23}=1-\tan^2\theta_{12} for the atmospheric neutrino mixing.
Sizes of Majorana CP phases are evaluated so as to generate the observed baryon
asymmetry in the universe via a leptogenesis scenario within the framework of
the minimal seesaw model, where M_\nu satisfies det(M_\nu)=0 and one active
Majorana CP phase (\phi) is present. Assuming the normal mass hierarchy for
light neutrinos and one zero texture for a 3X2 Dirac neutrino mass matrix, we
find that \phi lies in the region of 0.69<|\phi|<0.92 [rad], which is converted
into allowed regions of \alpha=arg(M_{e\mu}) and \beta=arg(M_{e\tau}), where
M_{ij} (i,j=e,\mu,\tau) denote the i-j matrix element of M_\nu. The phases
\alpha and \beta turn out to satisfy 0.31<|\alpha|<0.40 [rad] and
-1.25<\beta<-0.32 [rad]. The approximate numerical equality of |\phi|\approx
2|\alpha| is consistent with our theoretical estimation of \phi=\phi_2-\phi_3
for \phi_2=-(\alpha+\beta) and \phi_3\approx \alpha-\beta valid for the normal
mass hierarchy. We also find the following scaling property:
(M'^{\mu\mu}-M'^{ee}/t^2_{12})/M'^{\mu\tau}=M'^{\mu\tau}/(M'^{\tau\tau}-M'^{ee}/t^2_{12})=-M'^{e\tau}/M'^{e\mu}
(t^2_{12}=\tan^2\theta_{12}=\sqrt{2}-1), where M'^{ij} stands for M_{ij}
evaluated on the basis of the Particle Data Group's phase convention.


  While the Anomaly flow was originally motivated by string theory, its zero
slope case is potentially of considerable interest in non-Kahler geometry, as
it is a flow of conformally balanced metrics whose stationary points are
precisely Kahler metrics. We establish its convergence on Kahler manifolds for
suitable initial data. We also discuss its relation to some current problems in
complex geometry.


  We present a first application of Whosglad method to the components A and B
of the 16 Cygni system. The method was developed to provide a comprehensive
analysis of stellar oscillation spectra. It defines new seismic indicators
which are as uncorrelated and precise as possible and hold detailed information
about stellar interiors. Such indicators, as illustrated in the present paper,
may be used to generate stellar models via forward seismic modeling. Finally,
seismic constraints retrieved by the method provide realistic stellar
parameters.


  Link prediction for knowledge graphs aims to predict missing connections
between entities. Prevailing methods are limited to a transductive setting and
hard to process unseen entities. The recent proposed subgraph-based models
provided alternatives to predict links from the subgraph structure surrounding
a candidate triplet. However, these methods require abundant known facts of
training triplets and perform poorly on relationships that only have a few
triplets. In this paper, we propose Meta-iKG, a novel subgraph-based
meta-learner for few-shot inductive relation reasoning. Meta-iKG utilizes local
subgraphs to transfer subgraph-specific information and learn transferable
patterns faster via meta gradients. In this way, we find the model can quickly
adapt to few-shot relationships using only a handful of known facts with
inductive settings. Moreover, we introduce a large-shot relation update
procedure to traditional meta-learning to ensure that our model can generalize
well both on few-shot and large-shot relations. We evaluate Meta-iKG on
inductive benchmarks sampled from NELL and Freebase, and the results show that
Meta-iKG outperforms the current state-of-the-art methods both in few-shot
scenarios and standard inductive settings.


  We point out that the similarities in N = 1 supersymmetric SO, SP gauge
theories can be explained by using the trick of extrapolating the groups to the
negative dimensions. One of the advantages of this trick is that anomaly
matching is automatically satisfied.


  We propose a resolution of the discrepancy between the proton yield predicted
by the statistical hadronization approach and data on hadron production in
ultra-relativistic nuclear collisions at the LHC. Applying the S-matrix
formulation of statistical mechanics to include pion-nucleon interactions, we
reexamine their contribution to the proton yield, taking resonance widths and
the presence of nonresonant correlations into account. The effect of
multi-pion-nucleon interactions is estimated using lattice QCD results on the
baryon-charge susceptibility. We show that a consistent implementation of these
features in the statistical hadronization model, leads to a reduction of the
predicted proton yield, which then quantitatively matches data of the ALICE
collaboration for Pb-Pb collisions at the LHC.


  Universal style transfer aims to transfer arbitrary visual styles to content
images. Existing feed-forward based methods, while enjoying the inference
efficiency, are mainly limited by inability of generalizing to unseen styles or
compromised visual quality. In this paper, we present a simple yet effective
method that tackles these limitations without training on any pre-defined
styles. The key ingredient of our method is a pair of feature transforms,
whitening and coloring, that are embedded to an image reconstruction network.
The whitening and coloring transforms reflect a direct matching of feature
covariance of the content image to a given style image, which shares similar
spirits with the optimization of Gram matrix based cost in neural style
transfer. We demonstrate the effectiveness of our algorithm by generating
high-quality stylized images with comparisons to a number of recent methods. We
also analyze our method by visualizing the whitened features and synthesizing
textures via simple feature coloring.


  Let $I$ be a graded ideal of $K[x_1,\ldots,x_n]$ generated by homogeneous
polynomials of a same degree $d$, and assume that $I$ has linear quotients. In
this note, we use Horseshoe Lemma to give a relatively direct inductive
construction of a minimal free resolution of $I$, which is called a $d$-linear
resolution.


  We re-consider Leadbetter's extremal index for stationary sequences. It has
interpretation as reciprocal of the expected size of an extremal cluster above
high thresholds. We focus on heavy-tailed time series, in particular on
regularly varying stationary sequences, and discuss recent research in extreme
value theory for these models. A regularly varying time series has multivariate
regularly varying finite-dimensional distributions. Thanks to results by Basrak
and Segers we have explicit representations of the limiting cluster structure
of extremes, leading to explicit expressions of the limiting point process of
exceedances and the extremal index as a summary measure of extremal clustering.
The extremal index appears in various situations which do not seem to be
directly related, like the convergence of maxima and point processes. We
consider different representations of the extremal index which arise from the
considered context. We discuss the theory and apply it to a regularly varying
AR(1) process and the solution to an affine stochastic recurrence equation


  In this paper, we study the relationship between the radius $r$ and the
attachment number $a$ of a tetravalent graph admitting a half-arc-transitive
group of automorphisms. These two parameters were first introduced in~[{\em
J.~Combin.~Theory Ser.~B} {73} (1998), 41--76], where among other things it was
proved that $a$ always divides $2r$. Intrigued by the empirical data from the
census~[{\em Ars Math.\ Contemp.} {8} (2015)] of all such graphs of order up to
1000 we pose the question of whether all examples for which $a$ does not divide
$r$ are arc-transitive. We prove that the answer to this question is positive
in the case when $a$ is twice an odd number. In addition, we completely
characterize the tetravalent graphs admitting a half-arc-transitive group with
$r = 3$ and $a=2$, and prove that they arise as non-sectional split $2$-fold
covers of line graphs of $2$-arc-transitive cubic graphs.


  We analyse the chemical properties of three z~8 galaxies behind the galaxy
cluster SMACS J0723.3-7327, observed as part of the Early Release Observations
programme of the James Webb Space Telescope (JWST). Exploiting [O III]4363
auroral line detections in NIRSpec spectra, we robustly apply the direct Te
method for the very first time at such high redshift, measuring metallicities
ranging from extremely metal poor (12+log(O/H)~7) to about one-third solar. We
also discuss the excitation properties of these sources, and compare them with
local strong-line metallicity calibrations. We find that none of the considered
diagnostics match simultaneously the observed relations between metallicity and
strong-line ratios for the three sources, implying that a proper re-assessment
of the calibrations may be needed at these redshifts. On the mass-metallicity
plane, the two galaxies at z~7.6 (log(M*/M_sun) = 8.1, 8.7) have metallicities
that are consistent with the extrapolation of the mass-metallicity relation at
z~2-3, while the least massive galaxy at z~8.5 (log(M*/M_sun) = 7.8) shows
instead a significantly lower metallicity . The three galaxies show different
level of offset relative to the Fundamental Metallicity Relation, with two of
them (at z~7.6) being marginally consistent, while the z~8.5 source deviating
significantly, being probably far from the smooth equilibrium between gas
flows, star formation and metal enrichment in place at later epochs.


  Non-speech emotion recognition has a wide range of applications including
healthcare, crime control and rescue, and entertainment, to name a few.
Providing these applications using edge computing has great potential, however,
recent studies are focused on speech-emotion recognition using complex
architectures. In this paper, a non-speech-based emotion recognition system is
proposed, which can rely on edge computing to analyse emotions conveyed through
non-speech expressions like screaming and crying. In particular, we explore
knowledge distillation to design a computationally efficient system that can be
deployed on edge devices with limited resources without degrading the
performance significantly. We comprehensively evaluate our proposed framework
using two publicly available datasets and highlight its effectiveness by
comparing the results with the well-known MobileNet model. Our results
demonstrate the feasibility and effectiveness of using edge computing for
non-speech emotion detection, which can potentially improve applications that
rely on emotion detection in communication networks. To the best of our
knowledge, this is the first work on an edge-computing-based framework for
detecting emotions in non-speech audio, offering promising directions for
future research.


  Estimating the heightmaps of buildings and vegetation in single remotely
sensed images is a challenging problem. Effective solutions to this problem can
comprise the stepping stone for solving complex and demanding problems that
require 3D information of aerial imagery in the remote sensing discipline,
which might be expensive or not feasible to require. We propose a task-focused
Deep Learning (DL) model that takes advantage of the shadow map of a remotely
sensed image to calculate its heightmap. The shadow is computed efficiently and
does not add significant computation complexity. The model is trained with
aerial images and their Lidar measurements, achieving superior performance on
the task. We validate the model with a dataset covering a large area of
Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest Lidar
dataset. Our work suggests that the proposed DL architecture and the technique
of injecting shadows information into the model are valuable for improving the
heightmap estimation task for single remotely sensed imagery.


  Kirchhoff showed that the number of spanning trees of a graph is the spectral
determinant of the combinatorial Laplacian divided by the number of vertices;
we reframe this result in the quantum graph setting. We prove that the spectral
determinant of the Laplace operator on a finite connected metric graph with
standard (Neummann-Kirchhoff) vertex conditions determines the number of
spanning trees when the lengths of the edges of the metric graph are
sufficiently close together. To obtain this result, we analyze an equilateral
quantum graph whose spectrum is closely related to spectra of discrete graph
operators and then use the continuity of the spectral determinant under
perturbations of the edge lengths.


  This paper concerns the composite problem of minimizing the sum of a twice
continuously differentiable function $f$ and a nonsmooth convex function. For
this class of nonconvex and nonsmooth problems, by leveraging a practical
inexactness criterion and a novel selection strategy for iterates, we propose
an inexact $q$-order regularized proximal Newton method for $q\in[2,3]$, which
becomes an inexact cubic regularization (CR) method for $q=3$. We prove that
the whole iterate sequence converges to a stationary point for the KL objective
function; and when the objective function has the KL property of exponent
$\theta\in(0,\frac{q-1}{q})$, the convergence has a local $Q$-superlinear rate
of order $\frac{q-1}{\theta q}$. In particular, under a local H\"{o}lderian
error bound of order $\gamma\in(\frac{1}{q-1},1]$ on a second-order stationary
point set, we show that the iterate and objective value sequences converge to a
second-order stationary point and a second-order stationary value,
respectively, with a local $Q$-superlinear rate of order $\gamma(q\!-\!1)$,
specified as the $Q$-quadratic rate for $q=3$ and $\gamma=1$. This is the first
practical inexact CR method with $Q$-quadratic convergence rate for nonconvex
composite optimization. We validate the efficiency of the CR method with
ZeroFPR as the inner solver by applying it to composite optimization problems
with highly nonlinear $f$.


  Explainable Artificial Intelligence (XAI) research gained prominence in
recent years in response to the demand for greater transparency and trust in AI
from the user communities. This is especially critical because AI is adopted in
sensitive fields such as finance, medicine etc., where implications for
society, ethics, and safety are immense. Following thorough systematic
evaluations, work in XAI has primarily focused on Machine Learning (ML) for
categorization, decision, or action. To the best of our knowledge, no work is
reported that offers an Explainable Reinforcement Learning (XRL) method for
trading financial stocks. In this paper, we proposed to employ SHapley Additive
exPlanation (SHAP) on a popular deep reinforcement learning architecture viz.,
deep Q network (DQN) to explain an action of an agent at a given instance in
financial stock trading. To demonstrate the effectiveness of our method, we
tested it on two popular datasets namely, SENSEX and DJIA, and reported the
results.


  An exact solution representing black holes in an expanding universe is found.
The black holes are maximally charged and the universe is expanding with
arbitrary equation of state. It is an exact solution of the
Einstein-scalar-Maxwell system, in which we have two Maxwell-type U(1) fields
coupled to the scalar field. The potential of the scalar field is an
exponential. We find a regular horizon, which depends on one parameter (the
ratio of the energy density of U(1) fields to that of the scalar field). The
horizon is static because of the balance on the horizon between gravitational
attractive force and U(1) repulsive force acting on the scalar field. We also
calculate the black hole temperature.


  The orbital basis is natural when one needs to calculate the effect of local
interactions or to unravel the role of orbital physics in the response to
external probes. In systems with nonsymmorphic point groups, such as the
iron-based superconductors, we show that symmetries that emerge in observable
response functions at certain wave vectors are absent from generalized
susceptibilities calculated with tight-binding Hamiltonians in the orbital
basis. Such symmetries are recovered only when the generalized susceptibilities
are embeded back to the continuum using appropriate matrix elements between
basis states. This is illustrated with the case of LiFeAs and is further
clarified using a minimal tight-binding Hamiltonian with non-symmorphic space
group.


  We consider low-distortion embeddings for subspaces under \emph{entrywise
nonlinear transformations}. In particular we seek embeddings that preserve the
norm of all vectors in a space $S = \{y: y = f(x)\text{ for }x \in Z\}$, where
$Z$ is a $k$-dimensional subspace of $\mathbb{R}^n$ and $f(x)$ is a nonlinear
activation function applied entrywise to $x$. When $f$ is the identity, and so
$S$ is just a $k$-dimensional subspace, it is known that, with high
probability, a random embedding into $O(k/\epsilon^2)$ dimensions preserves the
norm of all $y \in S$ up to $(1\pm \epsilon)$ relative error. Such embeddings
are known as \emph{subspace embeddings}, and have found widespread use in
compressed sensing and approximation algorithms. We give the first
low-distortion embeddings for a wide class of nonlinear functions $f$. In
particular, we give additive $\epsilon$ error embeddings into $O(\frac{k\log
(n/\epsilon)}{\epsilon^2})$ dimensions for a class of nonlinearities that
includes the popular Sigmoid SoftPlus, and Gaussian functions. We strengthen
this result to give relative error embeddings under some further restrictions,
which are satisfied e.g., by the Tanh, SoftSign, Exponential Linear Unit, and
many other `soft' step functions and rectifying units. Understanding embeddings
for subspaces under nonlinear transformations is a key step towards extending
random sketching and compressing sensing techniques for linear problems to
nonlinear ones. We discuss example applications of our results to improved
bounds for compressed sensing via generative neural networks.


  Climate models are essential for assessing the impact of greenhouse gas
emissions on our changing climate and the resulting increase in the frequency
and severity of natural disasters. Despite the widespread acceptance of climate
models produced by the Coupled Model Intercomparison Project (CMIP), they still
face challenges in accurately predicting climate extremes, which pose most
significant threats to both people and the environment. To address this
limitation and improve predictions of natural disaster risks, we introduce
Extreme Model Output Statistics (X-MOS). This approach utilizes deep regression
techniques to precisely map CMIP model outputs to real measurements obtained
from weather stations, which results in a more accurate analysis of the XXI
climate extremes. In contrast to previous research, our study places a strong
emphasis on enhancing the estimation of the tails of future climate parameter
distributions. The latter supports decision-makers, enabling them to better
assess climate-related risks across the globe.


  Bitmap indexes are commonly used in databases and search engines. By
exploiting bit-level parallelism, they can significantly accelerate queries.
However, they can use much memory, and thus we might prefer compressed bitmap
indexes. Following Oracle's lead, bitmaps are often compressed using run-length
encoding (RLE). Building on prior work, we introduce the Roaring compressed
bitmap format: it uses packed arrays for compression instead of RLE. We compare
it to two high-performance RLE-based bitmap encoding techniques: WAH (Word
Aligned Hybrid compression scheme) and Concise (Compressed `n' Composable
Integer Set). On synthetic and real data, we find that Roaring bitmaps (1)
often compress significantly better (e.g., 2 times) and (2) are faster than the
compressed alternatives (up to 900 times faster for intersections). Our results
challenge the view that RLE-based bitmap compression is best.


  In three dimensions, Kerr-de Sitter spacetime as a solution of Einstein
gravity with positive cosmological constant has a single cosmological horizon.
The flat-space limit (zero cosmological constant limit) of this spacetime is
well-defined and yields the flat-space cosmological solution which is a
significant spacetime in the context of flat-space holography. In this paper,
we calculate the free energy of this spacetime and compare it with the free
energy of the three-dimensional de Sitter spacetime. We investigate which one
of these two spacetimes will dominate in the semi-classical approximation for
estimating the partition function. It is shown that for the same temperature of
cosmological horizon of two spacetimes this is the de Sitter spacetime which is
always dominant. Hence, contrary to asymptotically flat and asymptotically AdS
spacetimes, there is no phase transition in three dimensional de Sitter
gravity.


  We propose a bosonic quantum breakdown Hubbard model, which generalizes the
Bose-Hubbard model by adding an asymmetric breakdown interaction turning one
boson into two between adjacent sites. We show that the ground state undergoes
a first-order phase transition from Mott insulator to breakdown condensate as
the breakdown interaction increases. When the normal hopping is zero, this
model has a global exponential U(1) symmetry, which is spontaneously broken by
the breakdown condensate, while the Mott-condensate transition remains first
order. Surprisingly, the breakdown condensate with spontaneously broken
exponential U(1) symmetry does not have a gapless Goldstone mode, which
invalidates the Mermin-Wagner theorem and leads to stable spontaneous symmetry
breaking in 1D. Moreover, we show that the model at non-equilibrium exhibits a
dynamical transition from dielectric phase to breakdown phase with respect to
the breakdown interaction. Our results show that quantum systems with
exponential symmetry can exhibit novel unexpected properties.


  Space-time is one of the most essential, yet most mysterious concepts in
physics. In quantum mechanics it is common to understand time as a marker of
instances of evolution and define states around all the space but at one time;
while in general relativity space-time is taken as a combinator, curved around
mass. Here we present a unified approach on both space and time in quantum
theory, and build quantum states across spacetime instead of only on spatial
slices. We no longer distinguish measurements on the same system at different
times with measurements on different systems at one time and construct
spacetime states upon these measurement statistics. As a first step towards
non-relativistic quantum field theory, we consider how to approach this in the
continuous-variable multi-mode regime. We propose six possible definitions for
spacetime states in continuous variables, based on four different measurement
processes: quadratures, displaced parity operators, position measurements and
weak measurements. The basic idea is to treat different instances of time as
different quantum modes. They are motivated by the pseudo-density matrix
formulation among indefinite causal structures and the path integral formalism.
We show that these definitions lead to desirable properties, and raise the
differences and similarities between spatial and temporal correlations. An
experimental proposal for tomography is presented, construing the operational
meaning of the spacetime states.


  We present a realisation of high bandwidth instrumentation at cryogenic
temperatures and for dilution refrigerator operation that possesses advantages
over methods using radio-frequency single electron transistor or transimpedance
amplifiers. The ability for the low temperature electronics to carry out faster
measurements than with room temperature electronics is investigated by the use
of a phosphorous-doped single-electron transistor. A single-shot technique is
successfully implemented and used to observe the real time decay of a quantum
state. A discussion on various measurement strategies is presented and the
consequences on electron heating and noise are analysed.


  Spin interactions in relativistic quark-antiquark system in magnetic field is
considered in the framework of the relativistic Hamiltonian, derived from the
QCD path integral. The formalism allows to separate spin-dependent terms from
the basic spin-independent interaction, contained in the Wilson loop, and
producing confining and gluon exchange interaction. As a result one obtains
relativistic spin-spin interaction $V_{ss}$, generalizing its nonrelativistic
analog. It is shown, that in large magnetic field $eB, V_{ss}$ modifies and
produces hyperfine shifts which grow linearly with $eB$ and preclude the use of
perturbation theory. We also show, that tensor forces for $eB \neq 0$ are
active in all meson states, but do not grow with $eB$


  X-ray emission from stars much more massive than the Sun was discovered only
35 years ago. Such stars drive fast stellar winds where shocks can develop, and
it is commonly assumed that the X-rays emerge from the shock-heated plasma.
Many massive stars additionally pulsate. However, hitherto it was neither
theoretically predicted nor observed that these pulsations would affect their
X-ray emission. Here we report the discovery of pulsating X-rays from the
massive B-type star Xi1 Canis Majoris. This star is a variable of beta Cephei
type and has a strong magnetic field. Our observations with the XMM-Newton
telescope reveal X-ray pulsations with the same period as the fundamental
stellar pulsation. This discovery challenges our understanding of stellar winds
from massive stars, their X-ray emission, and their magnetism.


  The difference in formation process between binary stars and planetary
systems is reflected in their composition as well as their orbital
architecture, particularly orbital eccentricity as a function of orbital
period. It is suggested here that this difference can be used as an
observational criterion to distinguish between brown dwarfs and planets.
Application of the orbital criterion suggests that with three possible
exceptions, all of the recently-discovered substellar companions discovered to
date may be brown dwarfs and not planets. These criterion may be used as a
guide for interpretation of the nature of sub-stellar mass companions to stars
in the future.


  A complete characterization of parabolic self-maps of finite shift is given
in terms of their Herglotz's representation. This improves a previous result
due to Contreras, D\'iaz-Madrigal, and Pommerenke. We also derive some
consequences for the rate of convergence of these functions to their
Denjoy-Wolff point, improving a related result of Kourou, Theodosiadis, and
Zarvalis for the continuous setting.


  Classical geometric and topological operations on polyhedra, maps and
polytopes often give rise to structures with the same symmetry group as the
original one, but with more flags. In this paper we introduce the notion of
voltage operations on maniplexes, as a way to generalize such operations. Our
technique provides a way to study classical operations in a graph theory
setting. In fact, voltage operations can be applied to symmetry type graphs,
and more generally to n-valent properly n-edge colored graphs. We focus on
studying the interactions between voltage operations and the symmetries of the
operated object, and show that they can be potentially used to build maniplexes
with prescribed symmetry type graphs. Moreover, a complete characterization of
when an operation can be seen as a voltage operation is given.


  We study parametric inference for diffusion processes when observations occur
nonsynchronously and are contaminated by market microstructure noise. We
construct a quasi-likelihood function and study asymptotic mixed normality of
maximum-likelihood- and Bayes-type estimators based on it. We also prove the
local asymptotic normality of the model and asymptotic efficiency of our
estimator when the diffusion coefficients are constant and noise follows a
normal distribution. We conjecture that our estimator is asymptotically
efficient even when the latent process is a general diffusion process. An
estimator for the quadratic covariation of the latent process is also
constructed. Some numerical examples show that this estimator performs better
compared to existing estimators of the quadratic covariation.


  Properties of transition metal (TM) doped single wall (8,0) SiC nanotube is
investigated using first principles density functional theory as implemented
within quantum espresso code. The properties studied are electronic, optical,
and hydrogen storage prospect, while the transition metals used in the doping
are Iron, Manganese, and Cobalt. The outcomes show that ferromagnetic ordering
better describes the magnetic order within the doping process. The dopings
result in half-metallic property. Hybridization between TM-3d and C-2p near
Fermi-level region contributes to occurrence of the half-metallicity property.
In addition, optoelectronics character and hydrogen storage capacity of the
tube have appeared to be changed compared to that of the pristine. Dopings have
appeared to result in an expanded range of optoelectronics applications ranging
from photovoltaic effects of far infrared to visible lights. Furthermore, the
tube appears to show a potential for hydrogen energy storage.


  We present the design of a new functional programming language, MLTS, that
uses the lambda-tree syntax approach to encoding bindings appearing within data
structures. In this approach, bindings never become free nor escape their
scope: instead, binders in data structures are permitted to move to binders
within programs. The design of MLTS includes additional sites within programs
that directly support this movement of bindings. In order to formally define
the language's operational semantics, we present an abstract syntax for MLTS
and a natural semantics for its evaluation. We shall view such natural
semantics as a logical theory within a rich logic that includes both *nominal
abstraction* and the *nabla-quantifier*: as a result, the natural semantics
specification of MLTS can be given a succinct and elegant presentation. We
present a typing discipline that naturally extends the typing of core ML
programs and we illustrate the features of MLTS by presenting several examples.
An on-line interpreter for MLTS is briefly described.


  Causal inference is one of the hallmarks of human intelligence. While the
field of CausalNLP has attracted much interest in the recent years, existing
causal inference datasets in NLP primarily rely on discovering causality from
empirical knowledge (e.g., commonsense knowledge). In this work, we propose the
first benchmark dataset to test the pure causal inference skills of large
language models (LLMs). Specifically, we formulate a novel task Corr2Cause,
which takes a set of correlational statements and determines the causal
relationship between the variables. We curate a large-scale dataset of more
than 200K samples, on which we evaluate seventeen existing LLMs. Through our
experiments, we identify a key shortcoming of LLMs in terms of their causal
inference skills, and show that these models achieve almost close to random
performance on the task. This shortcoming is somewhat mitigated when we try to
re-purpose LLMs for this skill via finetuning, but we find that these models
still fail to generalize -- they can only perform causal inference in
in-distribution settings when variable names and textual expressions used in
the queries are similar to those in the training set, but fail in
out-of-distribution settings generated by perturbing these queries. Corr2Cause
is a challenging task for LLMs, and would be helpful in guiding future research
on improving LLMs' pure reasoning skills and generalizability. Our data is at
https://huggingface.co/datasets/causalnlp/corr2cause. Our code is at
https://github.com/causalNLP/corr2cause.


  This paper is devoted to establish sufficient conditions under which a
transcendental meromorphic function has no unbounded Fatou components and to
extend some results for entire functions to meromorphic functions. Actually, we
shall mainly discuss non-existence of unbounded wandering domains of a
meromorphic function. The case for a composition of finitely many meromorphic
function with at least one of them being transcendental can be also
investigated in the argument of this paper.


  In Kondo insulator samarium hexaboride SmB$_6$, strong correlation and band
hybridization lead to an insulating gap and a diverging resistance at low
temperature. The resistance divergence ends at about 5 Kelvin, a behavior
recently demonstrated to arise from the surface conductance. However, questions
remain whether and where a topological surface state exists. Quantum
oscillations have not been observed to map the Fermi surface. We solve the
problem by resolving the Landau Level quantization and Fermi surface topology
using torque magnetometry. The observed Fermi surface suggests a two
dimensional surface state on the (101) plane. Furthermore, the tracking of the
Landau Levels in the infinite magnetic field limit points to -1/2, which
indicates a 2D Dirac electronic state.


  We present a comprehensive analysis of hadron production in e+e- collisions
at different center-of-mass energies in the framework of the statistical model
of the hadron resonance gas. The model is formulated in the canonical ensemble
with exact conservation of all relevant quantum numbers. The parameters of the
underlying model were determined using a fit to the average multiplicities of
the latest measurements at $\sqrt{s}$ = 10, 29-35, 91 and 130-200 GeV. The
results demonstrate that, within the accuracy of the experiments, none of the
data sets is satisfactorily described with this approach, calling into question
the notion that particle production in e+e- collisions is thermal in origin.


  This note is a short summary of the workshop on "Energy and time measurements
with high-granular silicon devices" that took place on the 13/6/16 and the
14/6/16 at DESY/Hamburg in the frame of the first AIDA-2020 Annual Meeting.
This note tries to put forward trends that could be spotted and to emphasise in
particular open issues that were addressed by the speakers.


  For every Banach space $(Y,\|\cdot\|_Y)$ that admits an equivalent uniformly
convex norm we prove that there exists $c=c(Y)\in (0,\infty)$ with the
following property. Suppose that $n\in \mathbb{N}$ and that $X$ is an
$n$-dimensional normed space with unit ball $B_X$. Then for every $1$-Lipschitz
function $f:B_X\to Y$ and for every $\varepsilon\in (0,1/2]$ there exists a
radius $r\ge\exp(-1/\varepsilon^{cn})$, a point $x\in B_X$ with $x+rB_X\subset
B_X$, and an affine mapping $\Lambda:X\to Y$ such that
$\|f(y)-\Lambda(y)\|_Y\le \varepsilon r$ for every $y\in x+rB_X$.


  Separability problem is a long-standing tough issue in quantum information
theory. In this paper, we propose a general method to detect entanglement via
arbitrary measurement $\boldsymbol{X}$, by which several novel criteria are
established. The new criteria are found incorporate many of the prevailing ones
in the literatures. Our method is applicable as well to the steering detection,
which possesses a merit of ignoring the knowledge of involved quantum states. A
concept of measurement orbit, which plays an important role in the detection of
entanglement and steering, is introduced, which enlightens our understanding of
uncertainty relation. Moreover, an extension of symmetric informationally
complete positive operator-valued measures (SIC-POVM), viz. symmetric complete
measurements (SCM), is proposed, and employed to reconstruct the quantum state
analytically.


  The connected sum construction, which takes as input Gorenstein rings and
produces new Gorenstein rings, can be considered as an algebraic analogue for
the topological construction having the same name. We determine the graded
Betti numbers for connected sums of graded Artinian Gorenstein algebras. Along
the way, we find the graded Betti numbers for fiber products of graded rings;
an analogous result was obtained in the local case by Geller. We relate the
connected sum construction to the doubling construction, which also produces
Gorenstein rings. Specifically, we show that a connected sum of doublings is
the doubling of a fiber product ring.


  Doping via electrostatic gating is a powerful and widely used technique to
tune the electron densities in layered materials. The microscopic details of
how these setups affect the layered material are, however, subtle and call for
careful theoretical treatments. Using semiconducting monolayers of transition
metal dichalcogenides (TMDs) as prototypical systems affected by electrostatic
gating, we show that the electronic and optical properties change indeed
dramatically when the gating geometry is properly taken into account. This
effect is implemented by a self-consistent calculation of the Coulomb
interaction between the charges in different sub-layers within the
tight-binding approximation. Thereby we consider both, single- and double-sided
gating. Our results show that, at low doping levels of $10^{13}$ cm$^{-2}$, the
electronic bands of monolayer TMDs shift rigidly for both types of gating, and
subsequently undergo a Lifshitz transition. When approaching the doping level
of $10^{14}$ cm$^{-2}$, the band structure changes dramatically, especially in
the case of single-sided gating where we find that monolayer \ce{MoS2} and
\ce{WS2} become indirect gap semiconductors. The optical conductivities
calculated within linear response theory also show clear signatures of these
doping-induced band structure renormalizations. Our numerical results based on
light-weighted tight-binding models indicate the importance of electronic
screening in doped layered structures, and pave the way for further
understanding gated super-lattice structures formed by mutlilayers with
extended Moir\'{e} pattern.


  We study theoretically spin pumping in bilayers consisting of superconductors
and antiferromagnetic insulators. We consider both compensated and
uncompensated interfaces and include both the regular scattering channel and
the Umklapp scattering channel. We find that at temperatures close to the
critical temperatures and precession frequencies much lower than the gap, the
spin-current is enhanced in superconductors as compared to normal metals.
Otherwise, the spin-current is suppressed. The relevant precession frequencies
where the spin-current in SC/AFI is enhanced compared to NM/AFI is much lower
than the typical resonance frequencies of antiferromagnets, which makes the
detection of this effect experimentally challenging. A possible solution lies
in the shifting of the resonance frequency by a static magnetic field.


  During the development of the security subsystem of modern information
systems, a problem of the joint implementation of several access control models
arises quite often. Traditionally, a request for the user's access to resources
is granted in case of simultaneous access permission by all active security
policies. When there is a conflict between the decisions of the security
policies, the issue of granting access remains open. The proposed method of
combining multiple security policies is based on the decision support
algorithms and provides a response to the access request, even in case of
various decisions of active security policies. To construct combining algorithm
we determine a number of weight coefficients, use a weighted sum of the
clearance levels of individual security policies and apply the analytic
hierarchy process. The weight coefficients are adjustable parameters of the
algorithm and allow administrator to manage the impact of the various security
rules flexibly.


  We develop a semi-analytic method for determining the phase-space population
of tidal debris along the orbit of a disrupting satellite galaxy and illustrate
its use with a number of applications. We use this method to analyze Zhao's
proposal that the microlensing events towards the Large Magellanic Cloud (LMC)
might be explained by an appropriately placed tidal streamer, and find that his
scenarios lead either to unacceptably high overdensities (10 -- 100%) in faint
star counts (apparent magnitudes 17.5 -- 20.5) away from the Galactic plane or
short timescales for the debris to disperse (10^8 years). We predict that the
tidal streamers from the LMC and the Sagittarius dwarf galaxy currently extend
over more than $2\pi$ in azimuth along their orbits. Assuming that each
satellite has lost half of its primordial mass, we find that the streamers will
have overdensities in faint star counts of 10 -- 100% and < 1% respectively,
and conclude that this mass loss rate is unlikely for the LMC, but possible for
Sagittarius. If the Galaxy has accreted one hundred $10^5-10^6 M_{\odot}$
objects (comparable to its current population of globular clusters) at
distances of 20 -- 100 kpc during its lifetime then 10% of the sky will now be
covered by tidal streamers.


  Let $\Omega \subset \mathbb{C}^m$ be an open, connected and bounded set and
$\mathcal{A}(\Omega)$ be a function algebra of holomorphic functions on
$\Omega$. In this article we study quotient Hilbert modules obtained from
submodules, consisting of functions in $\mathcal{M}$ vanishing to order $k$
along a smooth irreducible complex analytic set $\mathcal{Z}\subset\Omega$ of
codimension at least $2$, of a quasi-free Hilbert module, $\mathcal{M}$. Our
motive is to investigate unitary invariants of such quotient modules. We
completely determine unitary equivalence of aforementioned quotient modules and
relate it to geometric invariants of a Hermitian holomorphic vector bundles.
Then, as an application, we characterize unitary equivalence classes of
weighted Bergman modules over $\mathcal{A}(\mathbb{D}^m)$ in terms of those of
quotient modules arising from the submodules of functions vanishing to order
$2$ along the diagonal in $\mathbb{D}^m$.


  A fast algorithm for the approximation of a low rank LU decomposition is
presented. In order to achieve a low complexity, the algorithm uses sparse
random projections combined with FFT-based random projections. The asymptotic
approximation error of the algorithm is analyzed and a theoretical error bound
is presented. Finally, numerical examples illustrate that for a similar
approximation error, the sparse LU algorithm is faster than recent
state-of-the-art methods. The algorithm is completely parallelizable that
enables to run on a GPU. The performance is tested on a GPU card, showing a
significant improvement in the running time in comparison to sequential
execution.


  The homology of an unknown subspace of Euclidean space can be determined from
the intrinsic \v{C}ech complex of a sample of points in the subspace, without
reference to the ambient Euclidean space. More precisely, given a subspace $X$
of Euclidean space and a sample $A$ of points in $X$, we give conditions for
the homology of $X$ to be isomorphic to a certain persistent homology group of
the intrinsic \v{C}ech complex.


  Real-time machine learning has recently attracted significant interest due to
its potential to support instantaneous learning, adaptation, and decision
making in a wide range of application domains, including self-driving vehicles,
intelligent transportation, and industry automation. We investigate real-time
ML in a federated edge intelligence (FEI) system, an edge computing system that
implements federated learning (FL) solutions based on data samples collected
and uploaded from decentralized data networks. FEI systems often exhibit
heterogenous communication and computational resource distribution, as well as
non-i.i.d. data samples, resulting in long model training time and inefficient
resource utilization. Motivated by this fact, we propose a time-sensitive
federated learning (TS-FL) framework to minimize the overall run-time for
collaboratively training a shared ML model. Training acceleration solutions for
both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous
coordination (TS-FL-ASC) are investigated. To address straggler effect in
TS-FL-SC, we develop an analytical solution to characterize the impact of
selecting different subsets of edge servers on the overall model training time.
A server dropping-based solution is proposed to allow slow-performance edge
servers to be removed from participating in model training if their impact on
the resulting model accuracy is limited. A joint optimization algorithm is
proposed to minimize the overall time consumption of model training by
selecting participating edge servers, local epoch number. We develop an
analytical expression to characterize the impact of staleness effect of
asynchronous coordination and straggler effect of FL on the time consumption of
TS-FL-ASC. Experimental results show that TS-FL-SC and TS-FL-ASC can provide up
to 63% and 28% of reduction, in the overall model training time, respectively.


  Small cells are a cost-effective way to reliably expand network coverage and
provide significantly increased capacity for end users. The ultra-high
bandwidth available at millimeter (mmWave) and Terahertz (THz) frequencies can
effectively realize short-range wireless access links in small cells enabling
potential uses cases such as driver-less cars, data backhauling and
ultra-high-definition infotainment services. This paper describes a new
software defined network (SDN) framework for vehicles equipped with
transceivers capable of dynamically switching between THz and mmWave bands. We
present a novel SDN controlled admission policy that preferentially handoffs
between the mmWave and THz small cells, accommodates asymmetric uplink/downlink
traffic, performs error recovery and handles distinct link states that arise
due to motion along practical vehicular paths. We then analytically derive the
resulting capacity of such a small cell network by accounting for the channel
characteristics unique to both these spectrum bands, relative distance and the
contact times between a given transceiver pair. We then formulate the optimal
procedure for scheduling multiple vehicles at a given infrastructure tower,
with regards to practical road congestion scenarios. The search for the optimal
schedule is shown to be a NP-hard problem. Hence, we design a
computationally-feasible polynomial-time scheduling algorithm that runs at the
SDN controller and compare its performance against the optimal procedure and
random access. Additionally, we present a simulation-based case study for the
use case of data center backhauling in Boston city to showcase the benefits of
our approach.


  Federated learning (FL) is a promising strategy for performing
privacy-preserving, distributed learning with a network of clients (i.e., edge
devices). However, the data distribution among clients is often non-IID in
nature, making efficient optimization difficult. To alleviate this issue, many
FL algorithms focus on mitigating the effects of data heterogeneity across
clients by introducing a variety of proximal terms, some incurring considerable
compute and/or memory overheads, to restrain local updates with respect to the
global model. Instead, we consider rethinking solutions to data heterogeneity
in FL with a focus on local learning generality rather than proximal
restriction. To this end, we first present a systematic study informed by
second-order indicators to better understand algorithm effectiveness in FL.
Interestingly, we find that standard regularization methods are surprisingly
strong performers in mitigating data heterogeneity effects. Based on our
findings, we further propose a simple and effective method, FedAlign, to
overcome data heterogeneity and the pitfalls of previous methods. FedAlign
achieves competitive accuracy with state-of-the-art FL methods across a variety
of settings while minimizing computation and memory overhead. Code is available
at https://github.com/mmendiet/FedAlign


  The p_trans-distribution for the diphoton production at the LHC is calculated
in the modified Randall-Sundrum model with a small curvature of the space-time
kappa, and 5-dimensional Planck scale M_5 in the TeV region. The discovery
limits on M_5 are obtained to be 9.4 TeV and 11.6 TeV for the integrated
luminosities 30 fb^(-1) and 100 fb^(-1), respectively. These limits do not
depend on kappa provided it is much smaller than M_5. The account of graviton
widths is a crucial points of our calculations.


  The \textit{$k$-weak-dynamic number} of a graph $G$ is the smallest number of
colors we need to color the vertices of $G$ in such a way that each vertex $v$
of degree $d(v)$ sees at least $\rm{min}\{k,d(v)\}$ colors on its neighborhood.
We use reducible configurations and list coloring of graphs to prove that all
planar graphs have 3-weak-dynamic number at most 6.


  Let $p$ be a monic hyperbolic polynomial and let $H$ be the Bezoutian matrix
of $p$ and $p'$. Then $H$ symmetrizes the Sylvester matrix associated with $p$.
This fact is observed by E.Jannelli. We give a simple proof of this fact and at
the same time show that the family of Bezoutian matrices of Nuij approximation
of $p$ gives quasi-symmetrizers introduced by S.Spagnolo. A relation connecting
$H$with the symmetrizer which was used by J.Leray for strictly hyperbolic
polynomial is given.


  Superconductivity in doped quantum paramagnets has been a subject of long
theoretical inquiry. In this work we report a density matrix renormalization
group study of lightly doped $t$-$J$ models on the square lattice (doped hole
densities $\delta = 1/12$ and 1/8) with parameters for which previous studies
have suggested that the undoped system in 2D is either a quantum spin liquid or
a valence bond crystal. Our studies are performed on cylinders with width up to
8. Ground-state correlations are found to be nearly identical for the ``doped
quantum spin liquid'' and ``doped valence bond crystal''. Upon increasing the
cylinder width from 4 to 8, we observed a significant strengthening of the
quasi-long-range superconducting correlations, and a dramatic suppression of
any ``competing'' charge-density-wave order. Extrapolating from the observed
behavior of the width 8 cylinders, we speculate that the system has a nodeless
d-wave superconducting ground-state in the 2D limit.


  Drawing inspiration from transportation theory, in this work we introduce the
notions of "well-structured" and "stable" Gibbs states and we investigate their
implications for quantum thermodynamics and its resource theory approach via
thermal operations. It turns out that, in the quasi-classical realm, global
cyclic state transfers are impossible if and only if the Gibbs state is stable.
Moreover, using a geometric approach by studying the so-called
thermomajorization polytope we prove that any subspace in equilibrium can be
brought out of equilibrium via thermal operations. Interestingly, the case of
some subsystem being in equilibrium can be witnessed via degenerate extreme
points of the thermomajorization polytope, assuming the Gibbs state of the
system is well structured. These physical considerations are complemented by
simple new constructions for the polytope's extreme points as well as for an
important class of extremal Gibbs-stochastic matrices.


  We consider the motion of a particle described by an action that is a
functional of the Frenet-Serret [FS] curvatures associated with the embedding
of its worldline in Minkowski space. We develop a theory of deformations
tailored to the FS frame. Both the Euler-Lagrange equations and the physical
invariants of the motion associated with the Poincar\'e symmetry of Minkowski
space, the mass and the spin of the particle, are expressed in a simple way in
terms of these curvatures. The simplest non-trivial model of this form, with
the lagrangian depending on the first FS (or geodesic) curvature, is
integrable. We show how this integrability can be deduced from the Poincar\'e
invariants of the motion. We go on to explore the structure of these invariants
in higher-order models. In particular, the integrability of the model described
by a lagrangian that is a function of the second FS curvature (or torsion) is
established in a three dimensional ambient spacetime.


  In this study, we propose the polyhedral clinching auction for indivisible
goods, which has so far been studied for divisible goods. As in the divisible
setting by Goel et al. (2015), our mechanism enjoys incentive compatibility,
individual rationality, and Pareto optimality, and works with polymatroidal
environments. A notable feature for the indivisible setting is that the whole
procedure can be conducted in time polynomial of the number of buyers and
goods. Moreover, we show additional efficiency guarantees, recently established
by Sato for the divisible setting: The liquid welfare (LW) of our mechanism
achieves more than 1/2 of the optimal LW, and that the social welfare is more
than the optimal LW.


  The behavior of a surface energy $\mathcal F(E,u)$, where $E$ is a set of
finite perimeter and $u\in L^1(\partial^* E, \mathbb R_+)$ is studied. These
energies have been recently considered in the context of materials science to
derive a new model in crystal growth that takes into account the effect of
atoms freely diffusing on the surface (called adatoms), which are responsible
for morphological evolution through an attachment and detachment process.
Regular critical points, existence and uniqueness of minimizers are discussed
and the relaxation of $\mathcal F$ in a general setting under the $L^1$
convergence of sets and the vague convergence of measures is characterized.
This is part of an ongoing project aimed at an analytical study of diffuse
interface approximations of the associated evolution equations.


  The Eulerian triangle is a classical array of combinatorial numbers defined
by a linear recursion. The associated boundary problem asks one to find all
extreme nonnegative solutions to a dual recursion. Exploiting connections with
random permutations and Markov chains we show that the boundary is discrete and
explicitly identify its elements.


  The early stage of stellar evolution is characterized by a star-disc locking
mechanism. The disc-locking prevents the star to spin its rotation up, and its
timescale depends on the disc lifetime. Some mechanisms can significantly
shorten this lifetime, allowing a few stars to start spinning up much earlier
than other stars. In the present study, we aim to investigate how the
properties of the circumstellar environment can shorten the disc lifetime. We
have identified a few multiple stellar systems, composed of stars with similar
masses, which belong to associations with a known age. Since all parameters
that are responsible for the rotational evolution, with the exception of
environment properties and initial stellar rotation, are similar for all
components, we expect that significant differences among the rotation periods
can only arise from differences in the disc lifetimes. A photometric timeseries
allowed us to measure the rotation periods of each component, while
high-resolution spectra provided us with the fundamental parameters, $v\sin{i}$
and chromospheric line fluxes. The rotation periods of the components differ
significantly, and the component B, which has a closer companion C, rotates
faster than the more distant and isolated component A. We can ascribe the
rotation period difference to either different initial rotation periods or
different disc-locking phases arising from the presence of the close companion
C. In the specific case of BD$-$21 1074, the second scenario seems to be more
favored. In our hypothesis of different disc-locking phase, any planet orbiting
this star is likely formed very rapidly owing to a gravitational instability
mechanism, rather than core accretion. Only a large difference of initial
rotation periods alone could account for the observed period difference,
leaving comparable disc lifetimes.


  We study the risk performance of distributed learning for the regularization
empirical risk minimization with fast convergence rate, substantially improving
the error analysis of the existing divide-and-conquer based distributed
learning. An interesting theoretical finding is that the larger the diversity
of each local estimate is, the tighter the risk bound is. This theoretical
analysis motivates us to devise an effective maxdiversity distributed learning
algorithm (MDD). Experimental results show that MDD can outperform the existing
divide-andconquer methods but with a bit more time. Theoretical analysis and
empirical results demonstrate that our proposed MDD is sound and effective.


  In this paper we consider the following optimal stopping problem
$$V^{\omega}_{\rm A}(s) = \sup_{\tau\in\mathcal{T}}
\mathbb{E}_{s}[e^{-\int_0^\tau \omega(S_w) dw} g(S_\tau)],$$ where the process
$S_t$ is a jump-diffusion process, $\mathcal{T}$ is a family of stopping times
while $g$ and $\omega$ are fixed payoff function and discount function,
respectively. In a financial market context, if $g(s)=(K-s)^+$ or
$g(s)=(s-K)^+$ and $\mathbb{E}$ is the expectation taken with respect to a
martingale measure, $V^{\omega}_{\rm A}(s)$ describes the price of a perpetual
American option with a discount rate depending on the value of the asset
process $S_t$. If $\omega$ is a constant, the above problem produces the
standard case of pricing perpetual American options. In the first part of this
paper we find sufficient conditions for the convexity of the value function
$V^{\omega}_{\rm A}(s)$. This allows us to determine the stopping region as a
certain interval and hence we are able to identify the form of $V^{\omega}_{\rm
A}(s)$. We also prove a put-call symmetry for American options with
asset-dependent discounting. In the case when $S_t$ is a geometric L\'evy
process we give exact expressions using the so-called omega scale functions
introduced in Li and Palmowski (2018). We prove that the analysed value
function satisfies the HJB equation and we give sufficient conditions for the
smooth fit property as well. Finally, we present a few examples for which we
obtain the analytical form of the value function $V^{\omega}_{\rm A}(s)$.


  During the pull-out maneuver, Peregrine falcons were observed to adopt
specific flight configurations which are thought to offer an aerodynamic
advantage over aerial prey. Analysis of the flight trajectory of a falcon in a
controlled environment shows it experiencing load factors up to 3 and further
predictions suggest this could be increased up to almost 10g during high-speed
pull-out. This can be attributed to the high maneuverability promoted by
lift-generating vortical structures over the wing. Wind-tunnel experiments on
life-sized models together with high fidelity simulations on idealized models,
which are based on taxidermy falcons in different configurations, show that
deploying the hand-wing in a pull-out creates extra vortex-lift, similar to
that of combat aircraft with delta wings. The aerodynamic forces and the
position of aerodynamic center were calculated from Large Eddy Simulations of
the flow around the model. This allowed for an analysis of the longitudinal
static stability in a pull-out, confirming that the falcon is flying unstably
in pitch with a positive slope in the pitching moment and a trim angle of
attack of about 5$^\circ$, possibly to maximize responsiveness. The
hand-wings/primaries were seen to contribute to the augmented stability acting
as `elevons' would on a tailless blended-wing-body aircraft.


  Tidal streams in the Milky Way are sensitive probes of the population of
dark-matter subhalos predicted in cold-dark-matter (CDM) simulations. We
present a new calculus for computing the effect of subhalo fly-bys on cold
tidal streams based on the action-angle representation of streams. The heart of
this calculus is a line-of-parallel-angle approach that calculates the
perturbed distribution function of a given stream segment by undoing the effect
of all impacts. This approach allows one to compute the perturbed stream
density and track in any coordinate system in minutes for realizations of the
subhalo distribution down to 10^5 Msun, accounting for the stream's internal
dispersion and overlapping impacts. We study the properties of density and
track fluctuations with suites of simulations. The one-dimensional density and
track power spectra along the stream trace the subhalo mass function, with
higher-mass subhalos producing power only on large scales, while lower mass
subhalos cause structure on smaller scales. The time-dependence of impacts and
of the evolution of the stream after an impact gives rise to bispectra. We
further find that tidal streams are essentially corrugated sheets in the
presence of subhalo perturbations: different projections of the track all
reflect the same pattern of perturbations, facilitating their observational
measurement. We apply this formalism to density data for the Pal 5 stream and
make a first rigorous determination of 10^{+11}_{-6} dark-matter subhalos with
masses between 3x10^6 and 10^9 Msun within 20 kpc from the Galactic center
(corresponding to 1.4^{+1.6}_{-0.9} times the number predicted by CDM-only
simulations or to f_{sub}(r<20 kpc) ~ 0.2%). Improved data will allow
measurements of the subhalo mass function down to 10^5 Msun, thus definitively
testing whether dark matter clumps on the smallest scales relevant for galaxy
formation.


  Working with complex, high-level MOEA meta-models such as Multiobjec-tive
Optimization Hierarchic Genetic Strategy (MO-mHGS) with multi-deme support
usually requires dedicated implementation and configuration for each internal
(single-deme) algorithm variant. If we generalize meta-model, we can simplify
whole simulation process and bind any internal algorithm (we denote it as a
driver), without providing redundant meta-model implementations. This idea has
become a fundamental of Evogil platform. Our aim was to allow construct-ing
custom hybrid models or combine existing solutions in runtime simulation
environment. We define hybrid solution as a composition of a meta-model and a
driver (or multiple drivers). Meta-model uses drivers to perform evolutionary
calculations and process their results. Moreover, Evogil provides set of
ready-made solutions divided into two groups (multi-deme meta-models and
single-deme drivers), as well as processing tools (quality metrics, statistics
and plotting scripts), simulation management and results persistence layer.


  We present a pathwise proof of the HWI inequality which is based on en-tropic
interpolations rather than displacement ones. Unlike the latter, entropic
interpolations are regular both in space and time. Consequently, our approach
is closer to the Otto-Villani heuristics, presented in the first part of the
article [23], than the original rigorous proof presented in the second part of
[23].


  We will discuss here how structures observed in clusters of galaxies can
provide us insight on the formation and evolution of these objects. We will
focus primarily on X-ray observations and results from hydrodynamical $N$-body
simulations. This paper is based on a talk given at the School of Cosmology
Jose Plinio Baptista -- `Cosmological perturbations and Structure Formation' in
Ubu/ES, Brazil.


  We study the limit as $\e\to 0$ of the entropy solutions of the equation
$\p_t \ue + \dv_x[A(\frac{x}{\e},\ue)] =0$. We prove that the sequence $\ue$
two-scale converges towards a function $u(t,x,y)$, and $u$ is the unique
solution of a limit evolution problem. The remarkable point is that the limit
problem is not a scalar conservation law, but rather a kinetic equation in
which the macroscopic and microscopic variables are mixed. We also prove a
strong convergence result in $L^1_{\text{loc}}$.


  This article introduces and discusses the concept of entanglement detachment.
Under some circumstances, enlarging a few couplings of a Hamiltonian can
effectively detach a (possibly disjoint) block within the ground state. This
detachment is characterized by a sharp decrease in the entanglement entropy
between block and environment, and leads to an increase of the internal
correlations between the (possibly distant) sites of the block. We provide some
examples of this detachment in free fermionic systems. The first example is an
edge-dimerized chain, where the second and penultimate hoppings are increased.
In that case, the two extreme sites constitute a block which disentangles from
the rest of the chain. Further examples are given by (a) a superlattice which
can be detached from a 1D chain, and (b) a star-graph, where the extreme sites
can be detached or not depending on the presence of an external magnetic field,
in analogy with the Aharonov-Bohm effect. We characterize these detached blocks
by their reduced matrices, specially through their entanglement spectrum and
entanglement Hamiltonian.


  Sensitivity is an important complexity measure of Boolean functions. In this
paper we present properties of the minimal and maximal sensitivity of the
simplified weighted sum function. A simple close formula of the minimal
sensitivity of the simplified weighted sum function is obtained. A phenomenon
is exhibited that the minimal sensitivity of the weighted sum function is
indeed an indicator of large primes, that is, for large prime number p, the
minimal sensitivity of the weighted sum function is always equal to one.


  This paper gives a new interpretation of the virtual braid group in terms of
a strict monoidal category SC that is freely generated by one object and three
morphisms, two of the morphisms corresponding to basic pure virtual braids and
one morphism corresponding to a transposition in the symmetric group. The key
to this approach is to take pure virtual braids as primary. The generators of
the pure virtual braid group are abstract solutions to the algebraic
Yang-Baxter equation. This point of view illuminates representations of the
virtual braid groups and pure virtual braid groups via solutions to the
algebraic Yang-Baxter equation. In this categorical framework, the virtual
braid group is a natural group associated with the structure of algebraic
braiding. We then point out how the category SC is related to categories
associated with quantum algebras and Hopf algebras and with quantum invariants
of virtual links.


  We report the discovery of very high energy gamma-ray emission from the
direction of the SNR G54.1+0.3 using the VERITAS ground-based gamma-ray
observatory. The TeV signal has an overall significance of 6.8$\sigma$ and
appears point-like given the 5$^{arcminute}$ resolution of the instrument. The
integral flux above 1 TeV is 2.5% of the Crab Nebula flux and significant
emission is measured between 250 GeV and 4 TeV, well described by a power-law
energy spectrum dN/dE $\sim$ E$^{-\Gamma}$ with a photon index $\Gamma=
2.39\pm0.23_{stat}\pm0.30_{sys}$. We find no evidence of time variability among
observations spanning almost two years. Based on the location, the morphology,
the measured spectrum, the lack of variability and a comparison with similar
systems previously detected in the TeV band, the most likely counterpart of
this new VHE gamma-ray source is the PWN in the SNR G54.1+0.3. The measured
X-ray to VHE gamma-ray luminosity ratio is the lowest among all the nebulae
supposedly driven by young rotation-powered pulsars, which could indicate a
particle-dominated PWN.


  Tracy and Widom have evaluated the cumulative distribution of the largest
eigenvalue for the finite and scaled infinite GUE in terms of a PIV and PII
transcendent respectively. We generalise these results to the evaluation of
$\tilde{E}_N(\lambda;a) := \Big < \prod_{l=1}^N \chi_{(-\infty, \lambda]}^{(l)}
(\lambda - \lambda_l)^a \Big>$, where $ \chi_{(-\infty, \lambda]}^{(l)} = 1$
for $\lambda_l \in (-\infty, \lambda]$ and $ \chi_{(-\infty, \lambda]}^{(l)} =
0$ otherwise, and the average is with respect to the joint eigenvalue
distribution of the GUE, as well as to the evaluation of $F_N(\lambda;a) :=
\Big < \prod_{l=1}^N (\lambda - \lambda_l)^a \Big >$. Of particular interest
are $\tilde{E}_N(\lambda;2)$ and $F_N(\lambda;2)$, and their scaled limits,
which give the distribution of the largest eigenvalue and the density
respectively. Our results are obtained by applying the Okamoto $\tau$-function
theory of PIV and PII, for which we give a self contained presentation based on
the recent work of Noumi and Yamada. We point out that the same approach can be
used to study the quantities $\tilde{E}_N(\lambda;a)$ and $F_N(\lambda;a)$ for
the other classical matrix ensembles.


  We consider strong combinatorial principles for sigma-directed families of
countable sets in the ordering by inclusion modulo finite, e.g. P-ideals of
countable sets. We try for principles as strong as possible while remaining
compatible with CH, and we also consider principles compatible with the
existence of nonspecial Aronszajn trees. The main thrust is towards abstract
principles with game theoretic formulations. Some of these principles are
purely combinatorial, while the ultimate principles are primarily combinatorial
but also have aspects of forcing axioms.


  In the 1980s, Harada introduced a new class of algebras now called Harada
algebras. Harada algebras provides us with a rich source of Auslander's
1-Gorenstein algebras. In this paper, we have two main results about Harada
algebras. The first is the classification of modules over Harada algebras whose
projective dimension is at most one. The second is the classification of
tilting modules over Harada algebras, which is shown by giving a bijection
between tilting modules over Harada algebras and tilting modules over direct
products of upper triangular matrix algebras over $K$. A combinatorial
description of tilting modules over upper triangular matrix algebras over $K$
is known. These facts allow us to classify tilting modules over a given Harada
algebra.


  Given a monotone Lagrangian $L$ in a compact symplectic manifold $X$, we
construct a commutative diagram relating the closed-open string map $CO_\lambda
: QH^*(X) \to HH^*(\mathcal{F}(X)_\lambda)$ to a variant of the length-zero
closed-open map on $L$ incorporating $\mathbf{k}[H_1(L; \mathbb{Z})]$
coefficients, denoted $CO^0_\mathbf{L}$. The former is categorically important
but very difficult to compute, whilst the latter is geometrically natural and
amenable to calculation. We further show that, after a suitable completion,
injectivity of $CO^0_\mathbf{L}$ implies injectivity of $CO_\lambda$. Via
Sheridan's version of Abouzaid's generation criterion, this gives a powerful
tool for proving split-generation of the Fukaya category. We illustrate this by
showing that the real part of a monotone toric manifold (of minimal Chern
number at least 2) split-generates the Fukaya category in characteristic 2. We
also give a short new proof (modulo foundational assumptions in the
non-monotone case) that the Fukaya category of an arbitrary compact toric
manifold is split-generated by toric fibres.


  Within Bayesian nonparametrics, dependent Dirichlet process mixture models
provide a highly flexible approach for conducting inference about the
conditional density function. However, several formulations of this class make
either rather restrictive modelling assumptions or involve intricate algorithms
for posterior inference, thus preventing their widespread use. In response to
these challenges, we present a flexible, versatile, and computationally
tractable model for density regression based on a single-weights dependent
Dirichlet process mixture of normal distributions model for univariate
continuous responses. We assume an additive structure for the mean of each
mixture component and incorporate the effects of continuous covariates through
smooth nonlinear functions. The key components of our modelling approach are
penalised B-splines and their bivariate tensor product extension. Our proposed
method also seamlessly accommodates parametric effects of categorical
covariates, linear effects of continuous covariates, interactions between
categorical and/or continuous covariates, varying coefficient terms, and random
effects, which is why we refer our model as a Dirichlet process mixture of
normal structured additive regression models. A noteworthy feature of our
method is its efficiency in posterior simulation through Gibbs sampling, as
closed-form full conditional distributions for all model parameters are
available. Results from a simulation study demonstrate that our approach
successfully recovers true conditional densities and other regression
functionals in various challenging scenarios. Applications to a toxicology,
disease diagnosis, and agricultural study are provided and further underpin the
broad applicability of our modelling framework. An R package, DDPstar,
implementing the proposed method is publicly available at
https://bitbucket.org/mxrodriguez/ddpstar.


  In this paper we consider the monoid $\DPC_n$ of all partial isometries of a
$n$-cycle graph $C_n$. We show that $\DPC_n$ is the submonoid of the monoid of
all oriented partial permutations on a $n$-chain whose elements are precisely
all restrictions of the dihedral group of order $2n$. Our main aim is to
exhibit a presentation of $\DPC_n$. We also describe Green's relations of
$\DPC_n$ and calculate its cardinal and rank.


  We present a study of the existence, stability and bifurcation structure of
families of dark breathers in a one-dimensional uniform chain of spherical
beads under static load. A defocus- ing nonlinear Schrodinger equation (NLS) is
derived for frequencies that are close to the edge of the phonon band and is
used to construct targeted initial conditions for numerical computations.
Salient features of the system include the existence of large amplitude
solutions that bifurcate with the small amplitude solutions described by the
NLS equation, and the presence of a nonlinear instability that, to the best of
the authors knowledge, has not been observed in classical Fermi- Pasta-Ulam
lattices. Finally, it is also demonstrated that these dark breathers can be
detected in a physically realistic way by merely actuating the ends of an
initially at rest chain of beads and inducing destructive interference between
their signals.


  We present sub-milliarcsecond observations of SiO masers in the late-type
stars IRC +10011 and Chi Cyg. We have used the NRAO Very Long Baseline Array
(VLBA) to map the 43 GHz (v=1, 2 J=1-0) and the 86 GHz (v=1, 2 J=2-1) SiO
masers. All the transitions have been imaged except the v=2 J=2-1 in IRC
+10011. We report the first VLBI map of the v=1 J=2-1 28SiO maser in IRC +10011
as well as the first VLBA images of SiO masers in an S-type Mira variable, Chi
Cyg. In this paper we have focused on the study of the relative spatial
distribution of the different observed lines. We have found that in some cases
the observational results are not reproduced by the current theoretical pumping
models, either radiative or collisional. In particular, for IRC +10011, the v=1
J=1-0 and J=2-1 28SiO lines have different spatial distributions and emitting
region sizes, the J=2-1 emission being located in an outer region of the
envelope. For Chi Cyg, the distributions also differ, but the sizes of the
masing regions are comparable. We suggest that the line overlaps between
ro-vibrational transitions of two abundant molecular species, H2O and 28SiO, is
a possible explanation for the discrepancies found between the observations and
the theoretical predictions. We have introduced this overlapping process in the
calculations of the excitation of the SiO molecule. We conclude that the line
overlaps can strongly affect the excitation of SiO and may reproduce the
unexpected observational results for the two sources studied.


  Influence maximization is the problem of selecting top $k$ seed nodes in a
social network to maximize their influence coverage under certain influence
diffusion models. In this paper, we propose a novel algorithm IRIE that
integrates a new message passing based influence ranking (IR), and influence
estimation (IE) methods for influence maximization in both the independent
cascade (IC) model and its extension IC-N that incorporates negative opinion
propagations. Through extensive experiments, we demonstrate that IRIE matches
the influence coverage of other algorithms while scales much better than all
other algorithms. Moreover IRIE is more robust and stable than other algorithms
both in running time and memory usage for various density of networks and
cascade size. It runs up to two orders of magnitude faster than other
state-of-the-art algorithms such as PMIA for large networks with tens of
millions of nodes and edges, while using only a fraction of memory comparing
with PMIA.


  We introduce generalized quantum Markov states and generalized d-Markov
chains which extend the notion quantum Markov chains on spin systems to that on
$C^*$-algebras defined by general graphs. As examples of generalized d-Markov
chains, we construct the entangled Markov fields on tree graphs. The concrete
examples of generalized d-Markov chains on Cayley trees are also investigated.


  We theoretically explore the optical flux lattices produced for ultra-cold
atoms subject to laser fields where both the atom-light coupling and the
effective detuning are spatially periodic. We analyze the geometric vector
potential and the magnetic flux it generates, as well as the accompanying
geometric scalar potential. We show how to understand the gauge-dependent
Aharonov-Bohm singularities in the vector potential, and calculate the
continuous magnetic flux through the elementary cell in terms of these
singularities. The analysis is illustrated with a square optical flux lattice.
We conclude with an explicit laser configuration yielding such a lattice using
a set of five properly chosen beams with two counterpropagating pairs (one
along the x axes and the other y axes), together with a single beam along the z
axis. We show that this lattice is not phase-stable, and identify the one
phase-difference that affects the magnetic flux. Thus armed with realistic
laser setup, we directly compute the Chern number of the lowest Bloch band to
identify the region where the non- zero magnetic flux produces a topologically
non-trivial band structure.


  We address the problem of real-time 3D object detection from point clouds in
the context of autonomous driving. Computation speed is critical as detection
is a necessary component for safety. Existing approaches are, however,
expensive in computation due to high dimensionality of point clouds. We utilize
the 3D data more efficiently by representing the scene from the Bird's Eye View
(BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs
oriented 3D object estimates decoded from pixel-wise neural network
predictions. The input representation, network architecture, and model
optimization are especially designed to balance high accuracy and real-time
efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection
benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets
we show that the proposed detector surpasses other state-of-the-art methods
notably in terms of Average Precision (AP), while still runs at >28 FPS.


  We present a very simple family of traveling salesman instances with $n$
cities where the nearest neighbor rule may produce a tour that is $\Theta(\log
n)$ times longer than an optimum solution. Our family works for the graphic,
the euclidean, and the rectilinear traveling salesman problem at the same time.
It improves the so far best known lower bound in the euclidean case and proves
for the first time a lower bound in the rectilinear case.


  Laser-based sources of ionizing radiation have attracted a considerable
attention in the last years for their broad potential applications. However the
stability and robustness of such sources is still an issue that needs to be
addressed. Aiming to solve such problems, we propose a source that uses a
liquid jet -rather than a solid- as a target for the production of X-rays.
Liquid jets offer always a clean surface for every laser shot which represent a
clear advantage over solids. In this work, we present an experimental
characterization of the X-ray emission of such targets, and study the
efficiency of the process when two temporally delayed pulses are used.
According to the obtained results, the X-ray yield is comparable with commonly
used targets.


  We present a set of second-order, time-reversible algorithms for the
isothermal (NVT) molecular-dynamics (MD) simulation of systems with mixed
hard-core/continuous potentials. The methods are generated by combining
real-time Nose' thermostats with our previously developed Collision Verlet
algorithm [Mol. Phys. 98, 309 (1999)] for constant energy MD simulation of such
systems. In all we present 5 methods, one based on the Nose'-Hoover [Phys. Rev.
A 31, 1695 (1985)] equations of motion and four based on the Nose'-Poincare'
[J.Comp.Phys., 151 114 (1999)] real-time formulation of Nose' dynamics. The
methods are tested using a system of hard spheres with attractive tails and all
correctly reproduce a canonical distribution of instantaneous temperature. The
Nose'-Hoover based method and two of the Nose'-Poincare' methods are shown to
have good energy conservation in long simulations.


  We consider planar vector field without zeroes X and study the image of the
associated Lie derivative operator LX acting on the space of smooth functions.
We show that the cokernel of LX is infinite-dimensional as soon as X is not
topologically conjugate to a constant vector field and that, if the topology of
the integral trajectories of X is ``simple enough'' (e.g. if X is polynomial)
then X is transversal to a Hamiltonian foliation. We use this fact to find a
large explicit subalgebra of the image of LX and to build an embedding of R^2
into R^4 which rectifies X. Finally we use this embedding to characterize the
functions in the image of LX.


  The \emph{$q,t$-Catalan numbers} $C_n(q,t)$ are polynomials in $q$ and $t$
that reduce to the ordinary Catalan numbers when $q=t=1$. These polynomials
have important connections to representation theory, algebraic geometry, and
symmetric functions. Haglund and Haiman discovered combinatorial formulas for
$C_n(q,t)$ as weighted sums of Dyck paths (or equivalently, integer partitions
contained in a staircase shape). This paper undertakes a combinatorial
investigation of the joint symmetry property $C_n(q,t)=C_n(t,q)$. We conjecture
some structural decompositions of Dyck objects into "mutually opposite"
subcollections that lead to a bijective explanation of joint symmetry in
certain cases. A key new idea is the construction of infinite chains of
partitions that are independent of $n$ but induce the joint symmetry for all
$n$ simultaneously. Using these methods, we prove combinatorially that for
$0\leq k\leq 9$ and all $n$, the terms in $C_n(q,t)$ of total degree
$\binom{n}{2}-k$ have the required symmetry property.


  Practical implementations of high-level languages must provide access to
libraries and system services that have APIs specified in a low-level language
(usually C). An important characteristic of such mechanisms is the
foreign-interface policy that defines how to bridge the semantic gap between
the high-level language and C. For example, IDL-based tools generate code to
marshal data into and out of the high-level representation according to user
annotations. The design space of foreign-interface policies is large and there
are pros and cons to each approach. Rather than commit to a particular policy,
we choose to focus on the problem of supporting a gamut of interoperability
policies. In this paper, we describe a framework for language interoperability
that is expressive enough to support very efficient implementations of a wide
range of different foreign-interface policies. We describe two tools that
implement substantially different policies on top of our framework and present
benchmarks that demonstrate their efficiency.


  BeppoSAX observations of Mkn501 in April 1997 (Pian et al. 1998), have
revealed an extraordinary X-ray emission from this BL Lac object, during a
phase of high activity at TeV energies, as monitored with the Whipple, HEGRA
and CAT Cerenkov telescopes. The 0.1-200 keV spectrum was hard, and the X-ray
power output peaked at or above 100 keV, 2 or 3 orders of magnitude more than
what indicated by previuos observations, while the luminosity increased by at
least a factor 20. The X-ray spectrum hardens when the source is brighter, but
variations seem limited to energies greater than 0.5 keV. All these
unprecedented spectral information pose severe constraints to all models, and
we discuss in particular how the homogenous, one-zone synchrotron self Compton
model must be modified to account for the observed properties. Other sources,
besides Mkn 501, could undergo similar flares.


  Uncovering the community structure exhibited by real networks is a crucial
step towards an understanding of complex systems that goes beyond the local
organization of their constituents. Many algorithms have been proposed so far,
but none of them has been subjected to strict tests to evaluate their
performance. Most of the sporadic tests performed so far involved small
networks with known community structure and/or artificial graphs with a
simplified structure, which is very uncommon in real systems. Here we test
several methods against a recently introduced class of benchmark graphs, with
heterogeneous distributions of degree and community size. The methods are also
tested against the benchmark by Girvan and Newman and on random graphs. As a
result of our analysis, three recent algorithms introduced by Rosvall and
Bergstrom, Blondel et al. and Ronhovde and Nussinov, respectively, have an
excellent performance, with the additional advantage of low computational
complexity, which enables one to analyze large systems.


  We propose an effective action that describe a relativistic fluid with Hall
viscosity. The construction involves a Wess-Zumino-Witten term that exists only
in (2+1) spacetime dimensions. We note that this formalism can accommodate only
a Hall viscosity which is a homogeneous function of the entropy and particle
number densities of degree one.


  In this paper we continue the study (initiated in a previous article) of
linear Koszul duality, a geometric version of the standard duality between
modules over symmetric and exterior algebras. We construct this duality in a
very general setting, and prove its compatibility with morphisms of vector
bundles and base change.


  Films of the topological insulator Bi2Se3 are grown by molecular beam epitaxy
with in-situ reflection high-energy electron diffraction. The films are shown
to be high-quality by X-ray reflectivity and diffraction and atomic-force
microscopy. Quantum interference control of photocurrents is observed by
excitation with harmonically related pulses and detected by terahertz
radiation. The injection current obeys the expected excitation irradiance
dependence, showing linear dependence on the fundamental pulse irradiance and
square-root irradiance dependence of the frequency-doubled optical pulses. The
injection current also follows a sinusoidal relative-phase dependence between
the two excitation pulses. These results confirm the third-order nonlinear
optical origins of the coherently controlled injection current. Experiments are
compared to a tight-binding band structure to illustrate the possible optical
transitions that occur in creating the injection current.