  The hidden shift problem is a natural place to look for new separations
between classical and quantum models of computation. One advantage of this
problem is its flexibility, since it can be defined for a whole range of
functions and a whole range of underlying groups. In a way, this distinguishes
it from the hidden subgroup problem where more stringent requirements about the
existence of a periodic subgroup have to be made. And yet, the hidden shift
problem proves to be rich enough to capture interesting features of problems of
algebraic, geometric, and combinatorial flavor. We present a quantum algorithm
to identify the hidden shift for any Boolean function. Using Fourier analysis
for Boolean functions we relate the time and query complexity of the algorithm
to an intrinsic property of the function, namely its minimum influence. We show
that for randomly chosen functions the time complexity of the algorithm is
polynomial. Based on this we show an average case exponential separation
between classical and quantum time complexity. A perhaps interesting aspect of
this work is that, while the extremal case of the Boolean hidden shift problem
over so-called bent functions can be reduced to a hidden subgroup problem over
an abelian group, the more general case studied here does not seem to allow
such a reduction.


  In this paper we establish a Gaffney type inequality, in $W^{\ell,p}$-Sobolev
spaces, for differential forms on sub-Riemannian contact manifolds without
boundary, having bounded geometry (hence, in particular, we have in mind
non-compact manifolds). Here $p\in]1,\infty[$ and $\ell=1,2$ depending on the
order of the differential form we are considering. The proof relies on the
structure of the Rumin's complex of differential forms in contact manifolds, on
a Sobolev-Gaffney inequality proved by Baldi-Franchi in the setting of the
Heisenberg groups and on some geometric properties that can be proved for
sub-Riemannian contact manifolds with bounded geometry.


  The ability to shape health behaviors of large populations automatically,
across wearable types and disease conditions at scale has tremendous potential
to improve global health outcomes. We designed and implemented an AI driven
platform for digital algorithmic nudging, enabled by a Graph-Neural Network
(GNN) based Recommendation System, and granular health behavior data from
wearable fitness devices. Here we describe the efficacy results of this
platform with its capabilities of personalized and contextual nudging to
$n=84,764$ individuals over a 12-week period in Singapore. We statistically
validated that participants in the target group who received such AI optimized
daily nudges increased daily physical activity like step count by 6.17% ($p =
3.09\times10^{-4}$) and weekly minutes of Moderate to Vigorous Physical
Activity (MVPA) by 7.61% ($p = 1.16\times10^{-2}$), compared to matched
participants in control group who did not receive any nudges. Further, such
nudges were very well received, with a 13.1% of nudges sent being opened (open
rate), and 11.7% of the opened nudges rated useful compared to 1.9% rated as
not useful thereby demonstrating significant improvement in population level
engagement metrics.


  We propose a statistical method to test whether two phylogenetic trees with
given alignments are significantly incongruent. Our method compares the two
distributions of phylogenetic trees given by the input alignments, instead of
comparing point estimations of trees. This statistical approach can be applied
to gene tree analysis for example, detecting unusual events in genome evolution
such as horizontal gene transfer and reshuffling. Our method uses difference of
means to compare two distributions of trees, after embedding trees in a vector
space. Bootstrapping alignment columns can then be applied to obtain p-values.
To compute distances between means, we employ a "kernel trick" which speeds up
distance calculations when trees are embedded in a high-dimensional feature
space, e.g. splits or quartets feature space. In this pilot study, first we
test our statistical method's ability to distinguish between sets of gene trees
generated under coalescence models with species trees of varying dissimilarity.
We follow our simulation results with applications to various data sets of
gophers and lice, grasses and their endophytes, and different fungal genes from
the same genome. A companion toolkit, {\tt Phylotree}, is provided to
facilitate computational experiments.


  It is well known that N-body simulations of ultralight bosons show the
formation of a solitonic dark matter core in the innermost part of the halo.
The scale length of such a soliton depends on the inverse of the mass of the
boson. On the other hand, the orbital motion of stars in the Galactic Center
depends on the distribution of matter whether be it baryonic or dark, providing
an excellent probe for the gravitational field of the region. In this Letter we
propose the S-stars in the Galactic Center as a new observational tool,
complementary to other astrophysical systems, to narrow down the range of
allowed values for an ultralight dark matter candidate boson mass. We built
mock catalogs mirroring the forthcoming astrometric and spectroscopic
observations of S2, and we used a MCMC analysis to predict the accuracy down to
which the mass of an ultralight boson may be bounded, and we showed that, once
complementary constraints are considered, this analysis will help to restrict
the allowed range of the boson mass. Our analysis forecasts the bound on the
mass of an ultralight boson to be $< 10^{-19}$ eV at the 95% of confidence
level.


  The phase structure of hot gauge theories with dynamical matter fields is
reexamined in the canonical ensemble with respect to triality. Since this
ensemble implies a projection to the zero triality sector of the theory we
introduce a proper quantity which is able to reveal a critical behaviour of the
theory with fundamental quarks. We discuss the properties of both the
chromoelectric and chromomagnetic sectors of the theory and show while electric
charges carrying a unit of Z(N) charge are screened at high temperatures by
dynamical matter loops, this is not the case for the Z(N) magnetic flux. An
order parameter is constructed to probe the realization of local discrete Z(N)
symmetry in the magnetic sector. We argue it can be used to detect a
deconfinement phase being defined in terms of the screening mechanism as a
phase of unscreened Z(N) flux. It may be detectable at long range via the
Aharonov-Bohm effect. We discuss the possible phase structure of QCD in this
approach.


  We develop techniques to construct a series of sparse polyhedral
approximations of the semidefinite cone. Motivated by the semidefinite (SD)
bases proposed by Tanaka and Yoshise (2018), we propose a simple expansion of
SD bases so as to keep the sparsity of the matrices composing it. We prove that
the polyhedral approximation using our expanded SD bases contains the set of
all diagonally dominant matrices and is contained in the set of all scaled
diagonally dominant matrices. We also prove that the set of all scaled
diagonally dominant matrices can be expressed using an infinite number of
expanded SD bases. We use our approximations as the initial approximation in
cutting plane methods for solving a semidefinite relaxation of the maximum
stable set problem. It is found that the proposed methods with expanded SD
bases are significantly more efficient than methods using other existing
approximations or solving semidefinite relaxation problems directly.


  Measurements of the $c$-axis penetration depth $\lambda_c$ in the cuprates
reveal a low-temperature $T$ dependence which is inconsistent with simple
models of coupling between the CuO$_2$ layers. In this paper, we examine
whether a model based on {\it incoherent} hopping between the layers can
account for this low-$T$ behavior. We compute $\lambda_c$ directly from linear
response theory and compare our results with recent experimental measurements
on $\rm YBa_2u_3O_{7-\delta}$ as a function of temperature and doping. We find
that the data can be reproduced within this model providing the inter-layer
scattering is anisotropic and the pairing is $d$-wave. In addition, our
calculations demonstrate that $1/\lambda_c^2$ is proportional to the $c$-axis
critical current, which seems to be a generic feature in weakly coupled layered
superconductors.


  The Chinese numerical string corpus, serves as a valuable resource for
speaker verification, particularly in financial transactions. Researches
indicate that in short speech scenarios, text-dependent speaker verification
(TD-SV) consistently outperforms text-independent speaker verification (TI-SV).
However, TD-SV potentially includes the validation of text information, that
can be negatively impacted by reading rhythms and pauses. To address this
problem, we propose an end-to-end speaker verification system that enhances
TD-SV by decoupling speaker and text information. Our system consists of a text
embedding extractor, a speaker embedding extractor and a fusion module. In the
text embedding extractor, we employ an enhanced Transformer and introduce a
triple loss including text classification loss, connectionist temporal
classification (CTC) loss and decoder loss; while in the speaker embedding
extractor, we create a multi-scale pooling method by combining sliding window
attentive statistics pooling (SWASP) with attentive statistics pooling (ASP).
To mitigate the scarcity of data, we have recorded a publicly available Chinese
numerical corpus named SHALCAS22A (hereinafter called SHAL), which can be
accessed on Open-SLR. Moreover, we employ data augmentation techniques using
Tacotron2 and HiFi-GAN. Our method achieves an equal error rate (EER)
performance improvement of 49.2% on Hi-Mia and 75.0% on SHAL, respectively.


  We present a comparison of various approximations to self-consistency in the
GW method, including the one-shot G0W0 method, different quasiparticle
self-consistency schemes, and the fully self-consistent GW (scGW) approach. To
ensure an unbiased and equitable comparison, we have implemented all the
schemes with the same underlying Matsubara formalism, while employing Gaussian
orbitals to describe the system. Aiming to assess and compare different GW
schemes, we analyze band gaps in semiconductors and insulators, as well as
ionization potentials in molecules. Our findings reveal that for solids, the
different self-consistency schemes perform very similarly. However, for
molecules, full self-consistency outperforms all other approximations, i.e.,
the one-shot and quasiparticle self-consistency GW schemes. Our work highlights
the importance of implementation details when comparing different GW methods.
By employing state-of-the-art fully self-consistent, finite temperature GW
calculations, we have successfully addressed discrepancies in the existing
literature regarding its performance. Our results also indicate that when
stringent thresholds are imposed, the scGW method consistently yields accurate
results.


  Recognizing facts is the most fundamental step in making judgments, hence
detecting events in the legal documents is important to legal case analysis
tasks. However, existing Legal Event Detection (LED) datasets only concern
incomprehensive event types and have limited annotated data, which restricts
the development of LED methods and their downstream applications. To alleviate
these issues, we present LEVEN a large-scale Chinese LEgal eVENt detection
dataset, with 8,116 legal documents and 150,977 human-annotated event mentions
in 108 event types. Not only charge-related events, LEVEN also covers general
events, which are critical for legal case understanding but neglected in
existing LED datasets. To our knowledge, LEVEN is the largest LED dataset and
has dozens of times the data scale of others, which shall significantly promote
the training and evaluation of LED methods. The results of extensive
experiments indicate that LED is challenging and needs further effort.
Moreover, we simply utilize legal events as side information to promote
downstream applications. The method achieves improvements of average 2.2 points
precision in low-resource judgment prediction, and 1.5 points mean average
precision in unsupervised case retrieval, which suggests the fundamentality of
LED. The source code and dataset can be obtained from
https://github.com/thunlp/LEVEN.


  This article suggests that deterministic Gradient Descent, which does not use
any stochastic gradient approximation, can still exhibit stochastic behaviors.
In particular, it shows that if the objective function exhibit multiscale
behaviors, then in a large learning rate regime which only resolves the
macroscopic but not the microscopic details of the objective, the deterministic
GD dynamics can become chaotic and convergent not to a local minimizer but to a
statistical distribution. A sufficient condition is also established for
approximating this long-time statistical limit by a rescaled Gibbs
distribution. Both theoretical and numerical demonstrations are provided, and
the theoretical part relies on the construction of a stochastic map that uses
bounded noise (as opposed to discretized diffusions).


  We characterize, in terms of $X$, extensional dimension of the Stone-\v{C}ech
corona $\beta X \setminus X$ of locally compact and Lindel\"{o}f space $X$. The
non-Lindel\"{o}f case case is also settled in terms of extending proper maps
with values in $I^{\tau}\setminus L$, where $L$ is a finite complex. Further,
for a finite complex $L$, an uncountable cardinal $\tau$ and a $Z_{\tau}$-set
$X$ in the Tychonov cube $I^{\tau}$ we find necessary and sufficient condition,
in terms of $I^{\tau}\setminus X$, for $X$ to be in the class
$\operatorname{AE}([L])$. We also introduce a concept of a proper absolute
extensor and characterize the product $[0,1)\times I^{\tau}$ as the only
locally compact and Lindel\"{o}f proper absolute extensor of weight $\tau >
\omega$ which has the same pseudocharacter at each point.


  We propose a model for supernovae Ia explosions based on a phase transition
to a supersymmetric state which becomes the active trigger for the deflagration
starting the explosion in an isolated sub-Chandrasekhar white dwarf star. With
two free parameters we fit the rate and several properties of type Ia
supernovae and address the gap in the supermassive black hole mass
distribution. One parameter is a critical density fit to about $3 \cdot 10^7$
g/cc while the other has the units of a space time volume and is found to be of
order $0.05\,$ Gyr $R_E^3$ where $R_E$ is the earth radius. The model involves
a phase transition to an exact supersymmetry in a small core of a dense star.


  We propose probabilistic representations for inverse Stein operators (i.e.
solutions to Stein equations) under general conditions; in particular we deduce
new simple expressions for the Stein kernel. These representations allow to
deduce uniform and non-uniform Stein factors (i.e. bounds on solutions to Stein
equations) and lead to new covariance identities expressing the covariance
between arbitrary functionals of an arbitrary {univariate} target in terms of a
weighted covariance of the derivatives of the functionals. Our weights are
explicit, easily computable in most cases, and expressed in terms of objects
familiar within the context of Stein's method. Applications of the
Cauchy-Schwarz inequality to these weighted covariance identities lead to sharp
upper and lower covariance bounds and, in particular, weighted Poincar\'e
inequalities. Many examples are given and, in particular, classical variance
bounds due to Klaassen, Brascamp and Lieb or Otto and Menz are corollaries.
Connections with more recent literature are also detailed.


  Capturing spatiotemporal dynamics is an essential topic in video recognition.
In this paper, we present learnable higher-order operations as a generic family
of building blocks for capturing spatiotemporal dynamics from RGB input video
space. Similar to higher-order functions, the weights of higher-order
operations are themselves derived from the data with learnable parameters.
Classical architectures such as residual learning and network-in-network are
first-order operations where weights are directly learned from the data.
Higher-order operations make it easier to capture context-sensitive patterns,
such as motion. Self-attention models are also higher-order operations, but the
attention weights are mostly computed from an affine operation or dot product.
Learnable higher-order operations can be more generic and flexible.
Experimentally, we show that on the task of video recognition, our higher-order
models can achieve results on par with or better than the existing
state-of-the-art methods on Something-Something (V1 and V2), Kinetics and
Charades datasets.


  We demonstrate a combination of computational tools and experimental 4D-STEM
methods to image the local magnetic moment in antiferromagnetic Fe$_2$As with 6
angstrom spatial resolution. Our techniques utilize magnetic diffraction peaks,
common in antiferromagnetic materials, to create imaging modes that directly
visualize the magnetic lattice. Using this approach, we show that
center-of-mass analysis can determine the local magnetization component in the
plane perpendicular to the path of the electron beam. Moreover, we develop
Magnstem, a quantum mechanical electron scattering simulation code, to model
electron scattering of an angstrom-scale probe from magnetic materials. Using
these tools, we identify optimal experimental conditions for separating weak
magnetic signals from the much stronger interactions of an angstrom-scale probe
with electrostatic potentials. Our techniques should be useful for
characterizing the local magnetic order in systems such in thin films,
interfaces, and domain boundaries of antiferromagnetic materials, which are
difficult to probe with existing methods.


  Within vehicles, the Controller Area Network (CAN) allows efficient
communication between the electronic control units (ECUs) responsible for
controlling the various subsystems. The CAN protocol was not designed to
include much support for secure communication. The fact that so many critical
systems can be accessed through an insecure communication network presents a
major security concern. Adding security features to CAN is difficult due to the
limited resources available to the individual ECUs and the costs that would be
associated with adding the necessary hardware to support any additional
security operations without overly degrading the performance of standard
communication. Replacing the protocol is another option, but it is subject to
many of the same problems. The lack of security becomes even more concerning as
vehicles continue to adopt smart features. Smart vehicles have a multitude of
communication interfaces would an attacker could exploit to gain access to the
networks. In this work we propose a security framework that is based on
physically unclonable functions (PUFs) and lightweight cryptography (LWC). The
framework does not require any modification to the standard CAN protocol while
also minimizing the amount of additional message overhead required for its
operation. The improvements in our proposed framework results in major
reduction in the number of CAN frames that must be sent during operation. For a
system with 20 ECUs for example, our proposed framework only requires 6.5% of
the number of CAN frames that is required by the existing approach to
successfully authenticate every ECU.


  Saturn's C ring contains multiple spiral patterns that appear to be density
waves driven by periodic gravitational perturbations. In other parts of
Saturn's rings, such waves are generated by Lindblad resonances with Saturn's
various moons, but most of the wave-like C-ring features are not situated near
any strong resonance with any known moon. Using stellar occultation data
obtained by the Visual and Infrared Mapping Spectrometer (VIMS) onboard the
Cassini spacecraft, we investigate the origin of six unidentified C-ring waves
located between 80,900 and 87,200 km from Saturn's center. By measuring
differences in the waves' phases among the different occultations, we are able
to determine both the number of arms in each spiral pattern and the speeds at
which these patterns rotate around the planet. We find that all six of these
waves have between 2 and 4 arms and pattern speeds between 1660 degrees/day and
1861 degrees/day. These speeds are too large to be attributed to any satellite
resonance. Instead they are comparable to the predicted pattern speeds of waves
generated by low-order normal-mode oscillations within the planet [Marley &
Porco 1993, Icarus 106, 508]. The precise pattern speeds associated with these
waves should therefore provide strong constraints on Saturn's internal
structure. Furthermore, we identify multiple waves with the same number of arms
and very similar pattern speeds, indicating that multiple m=3 and m=2 sectoral
(l=m) modes may exist within the planet.


  Face modification systems using deep learning have become increasingly
powerful and accessible. Given images of a person's face, such systems can
generate new images of that same person under different expressions and poses.
Some systems can also modify targeted attributes such as hair color or age.
This type of manipulated images and video have been coined Deepfakes. In order
to prevent a malicious user from generating modified images of a person without
their consent we tackle the new problem of generating adversarial attacks
against such image translation systems, which disrupt the resulting output
image. We call this problem disrupting deepfakes. Most image translation
architectures are generative models conditioned on an attribute (e.g. put a
smile on this person's face). We are first to propose and successfully apply
(1) class transferable adversarial attacks that generalize to different
classes, which means that the attacker does not need to have knowledge about
the conditioning class, and (2) adversarial training for generative adversarial
networks (GANs) as a first step towards robust image translation networks.
Finally, in gray-box scenarios, blurring can mount a successful defense against
disruption. We present a spread-spectrum adversarial attack, which evades blur
defenses. Our open-source code can be found at
https://github.com/natanielruiz/disrupting-deepfakes.


  The Brenier optimal map and the Knothe-Rosenblatt rearrangement are two
instances of a transport map, that is to say a map sending one measure onto
another. The main interest of the former is that it solves the
Monge-Kantorovich optimal transport problem, while the latter is very easy to
compute, being given by an explicit formula. A few years ago, Carlier,
Galichon, and Santambrogio showed that the Knothe rearrangement could be seen
as the limit of the Brenier map when the quadratic cost degenerates. In this
paper, we prove that on the torus (to avoid boundary issues), when all the data
are smooth, the evolution is also smooth, and is entirely determined by a PDE
for the Kantorovich potential (which determines the map), with a subtle initial
condition. The proof requires the use of the Nash-Moser inverse function
theorem. This result generalizes the ode discovered by Carlier, Galichon, and
Santambrogio when one measure is uniform and the other is discrete, and could
pave to way to new numerical methods for optimal transportation.


  Many graph neural networks have been developed to learn graph representations
in either Euclidean or hyperbolic space, with all nodes' representations
embedded in a single space. However, a graph can have hyperbolic and Euclidean
geometries at different regions of the graph. Thus, it is sub-optimal to
indifferently embed an entire graph into a single space. In this paper, we
explore and analyze two notions of local hyperbolicity, describing the
underlying local geometry: geometric (Gromov) and model-based, to determine the
preferred space of embedding for each node. The two hyperbolicities'
distributions are aligned using the Wasserstein metric such that the calculated
geometric hyperbolicity guides the choice of the learned model hyperbolicity.
As such our model Joint Space Graph Neural Network (JSGNN) can leverage both
Euclidean and hyperbolic spaces during learning by allowing node-specific
geometry space selection. We evaluate our model on both node classification and
link prediction tasks and observe promising performance compared to baseline
models.


  The generalization with respect to domain shifts, as they frequently appear
in applications such as autonomous driving, is one of the remaining big
challenges for deep learning models. Therefore, we propose an intra-source
style augmentation (ISSA) method to improve domain generalization in semantic
segmentation. Our method is based on a novel masked noise encoder for StyleGAN2
inversion. The model learns to faithfully reconstruct the image preserving its
semantic layout through noise prediction. Random masking of the estimated noise
enables the style mixing capability of our model, i.e. it allows to alter the
global appearance without affecting the semantic layout of an image. Using the
proposed masked noise encoder to randomize style and content combinations in
the training set, ISSA effectively increases the diversity of training data and
reduces spurious correlation. As a result, we achieve up to $12.4\%$ mIoU
improvements on driving-scene semantic segmentation under different types of
data shifts, i.e., changing geographic locations, adverse weather conditions,
and day to night. ISSA is model-agnostic and straightforwardly applicable with
CNNs and Transformers. It is also complementary to other domain generalization
techniques, e.g., it improves the recent state-of-the-art solution RobustNet by
$3\%$ mIoU in Cityscapes to Dark Z\"urich.


  Dimension-reduction homogenization results for thin films have been obtained
under hypotheses of periodicity or almost-periodicity of the energies in the
directions of the mid-plane of the film. In this note we consider thin films,
obtained as sections of a periodic medium with a mid-plane that may be
incommensurate; that is, not containing periods other than $0$. A geometric
almost-periodicity argument similar to the cut-and-project argument used for
quasicrystals allows to prove a general homogenization result.


  We are grateful to all discussants for their invaluable comments,
suggestions, questions, and contributions to our article. We have attentively
reviewed all discussions with keen interest. In this rejoinder, our objective
is to address and engage with all points raised by the discussants in a
comprehensive and considerate manner. Consistently, we identify the
discussants, in alphabetical order, as follows: CJK for Cronie, Jansson, and
Konstantinou, DS for Stoyan, GP for Grabarnik and Pommerening, MRS for
Myllym\"aki, Rajala, and S\"arkk\"a, and MCvL for van Lieshout throughout this
rejoinder.


  Many real-world tasks such as classification of digital histopathology images
and 3D object detection involve learning from a set of instances. In these
cases, only a group of instances or a set, collectively, contains meaningful
information and therefore only the sets have labels, and not individual data
instances. In this work, we present a permutation invariant neural network
called Memory-based Exchangeable Model (MEM) for learning set functions. The
MEM model consists of memory units that embed an input sequence to high-level
features enabling the model to learn inter-dependencies among instances through
a self-attention mechanism. We evaluated the learning ability of MEM on various
toy datasets, point cloud classification, and classification of lung whole
slide images (WSIs) into two subtypes of lung cancer---Lung Adenocarcinoma, and
Lung Squamous Cell Carcinoma. We systematically extracted patches from lung
WSIs downloaded from The Cancer Genome Atlas~(TCGA) dataset, the largest public
repository of WSIs, achieving a competitive accuracy of 84.84\% for
classification of two sub-types of lung cancer. The results on other datasets
are promising as well, and demonstrate the efficacy of our model.


  A major focus of much current research in gravitation theory is on
understanding how radiation reaction drives the evolution of a binary system,
particularly in the extreme mass ratio limit. Such research is of direct
relevance to gravitational-wave sources for space-based detectors (such as
LISA). We present here a study of the radiative evolution of circular (i.e.,
constant Boyer-Lindquist coordinate radius), non-equatorial Kerr black hole
orbits. Recent theorems have shown that, at least in an adiabatic evolution,
such orbits evolve from one circular configuration into another, changing only
their radius and inclination angle. This constrains the system's evolution in
such a way that the change in its Carter constant can be deduced from knowledge
of gravitational wave fluxes propagating to infinity and down the black hole's
horizon. Thus, in this particular case, a local radiation reaction force is not
needed. In accordance with post-Newtonian weak-field predictions, we find that
inclined orbits radiatively evolve to larger inclination angles (although the
post-Newtonian prediction overestimates the rate of this evolution in the
strong field by a factor $\lesssim 3$). We also find that the gravitational
waveforms emitted by these orbits are rather complicated, particularly when the
hole is rapidly spinning, as the radiation is influenced by many harmonics of
the orbital frequencies.


  Intermediate mass galaxies (logM(Msun)>10) at z~0.6 are the likeliest
progenitors of the present-day numerous population of spirals. There is growing
evidence that they have evolved rapidly since the last 6 to 8 Gyr ago, and
likely have formed a significant fraction of their stellar mass, often showing
perturbed morphologies and kinematics. We have gathered a representative sample
of 88 such galaxies and have provided robust estimates of their gas phase
metallicity. For doing so, we have used moderate spectral resolution
spectroscopy at VLT/FORS2 with unprecedented high S/N allowing to remove biases
coming from interstellar absorption lines and extinction to establish robust
values of R23=([OII]3727 + [OIII]4959,5007)/Hbeta. We definitively confirm that
the predominant population of z~0.6 starbursts and luminous IR galaxies (LIRGs)
are on average, two times less metal rich than the local galaxies at a given
stellar mass. We do find that the metal abundance of the gaseous phase of
galaxies is evolving linearly with time, from z=1 to z=0 and after comparing
with other studies, from z=3 to z=0. Combining our results with the reported
evolution of the Tully Fisher relation, we do find that such an evolution
requires that ~30% of the stellar mass of local galaxies have been formed
through an external supply of gas, thus excluding the close box model. Distant
starbursts & LIRGs have properties (metal abundance, star formation efficiency
& morphologies) similar to those of local LIRGs. Their underlying physics is
likely dominated by gas infall probably through merging or interactions. Our
study further supports the rapid evolution of z~0.4-1 galaxies. Gas exchanges
between galaxies is likely the main cause of this evolution.


  Greg Hjorth and Simon Thomas proved that the classification problem for
torsion-free abelian groups of finite rank \emph{strictly increases} in
complexity with the rank. Subsequently, Thomas proved that the complexity of
the classification problems for $p$-local torsion-free abelian groups of fixed
rank $n$ are \emph{pairwise incomparable} as $p$ varies. We prove that if
$3\leq m<n$ and $p,q$ are distinct primes, then the complexity of the
classification problem for $p$-local torsion-free abelian groups of rank $m$ is
again incomparable with that for $q$-local torsion-free abelian groups of rank
$n$.


  Displaying near-real-time traffic information is a useful feature of digital
navigation maps. However, most commercial providers rely on
privacy-compromising measures such as deriving location information from
cellphones to estimate traffic. The lack of an open-source traffic estimation
method using open data platforms is a bottleneck for building sophisticated
navigation services on top of OpenStreetMap (OSM). We propose a deep
learning-based Complex Event Processing (CEP) method that relies on publicly
available video camera streams for traffic estimation. The proposed framework
performs near-real-time object detection and objects property extraction across
camera clusters in parallel to derive multiple measures related to traffic with
the results visualized on OpenStreetMap. The estimation of object properties
(e.g. vehicle speed, count, direction) provides multidimensional data that can
be leveraged to create metrics and visualization for congestion beyond commonly
used density-based measures. Our approach couples both flow and count measures
during interpolation by considering each vehicle as a sample point and their
speed as weight. We demonstrate multidimensional traffic metrics (e.g. flow
rate, congestion estimation) over OSM by processing 22 traffic cameras from
London streets. The system achieves a near-real-time performance of 1.42
seconds median latency and an average F-score of 0.80.


  Instrumental variable (IV) methods are widely used for estimating average
treatment effects in the presence of unmeasured confounders. However, the
capability of existing IV procedures, and most notably the two-stage residual
inclusion (2SRI) procedure recommended for use in nonlinear contexts, to
account for unmeasured confounders in the Cox proportional hazard model is
unclear. We show that instrumenting an endogenous treatment induces an
unmeasured covariate, referred to as an individual frailty in survival analysis
parlance, which if not accounted for leads to bias. We propose a new procedure
that augments 2SRI with an individual frailty and prove that it is consistent
under certain conditions. The finite sample-size behavior is studied across a
broad set of conditions via Monte Carlo simulations. Finally, the proposed
methodology is used to estimate the average effect of carotid endarterectomy
versus carotid artery stenting on the mortality of patients suffering from
carotid artery disease. Results suggest that the 2SRI-frailty estimator
generally reduces the bias of both point and interval estimators compared to
traditional 2SRI.


  With the growing number of published functional magnetic resonance imaging
(fMRI) studies, meta-analysis databases and models have become an integral part
of brain mapping research. Coordinate-based meta-analysis (CBMA) databases are
built by automatically extracting both coordinates of reported peak activations
and term associations using natural language processing (NLP) techniques.
Solving term-based queries on these databases make it possible to obtain
statistical maps of the brain related to specific cognitive processes. However,
with tools like Neurosynth, only singleterm queries lead to statistically
reliable results. When solving richer queries, too few studies from the
database contribute to the statistical estimations. We design a probabilistic
domain-specific language (DSL) standing on Datalog and one of its probabilistic
extensions, CP-Logic, for expressing and solving rich logic-based queries. We
encode a CBMA database into a probabilistic program. Using the joint
distribution of its Bayesian network translation, we show that solutions of
queries on this program compute the right probability distributions of voxel
activations. We explain how recent lifted query processing algorithms make it
possible to scale to the size of large neuroimaging data, where state of the
art knowledge compilation (KC) techniques fail to solve queries fast enough for
practical applications. Finally, we introduce a method for relating studies to
terms probabilistically, leading to better solutions for conjunctive queries on
smaller databases. We demonstrate results for two-term conjunctive queries,
both on simulated meta-analysis databases and on the widely-used Neurosynth
database.


  It is well known that a majorana mass induces a (small) transition magnetic
moment. The converse is also true; in this paper we estimate the loop
contribution of transition magnetic moments $[\mu]_{\alpha \beta}$ to the
neutrino mass matrix $[m]_{\alpha \beta}$. We show that for hierarchical
neutrino masses, the contribution of $[\mu]_{e \tau}$ to $[m]_{e \tau}$ can
exceed the experimental value of $[m]_{e \tau}$.


  Topological materials which are also superconducting are of great current
interest, since they may exhibit a non-trivial topologically-mediated
superconducting phase. Although there have been many reports of pressure-tuned
or chemical-doping-induced superconductivity in a variety of topological
materials, there have been few examples of intrinsic, ambient pressure
superconductivity in a topological system having a stoichiometric composition.
Here, we report that the pure intermetallic CaSn3 not only exhibits topological
fermion properties but also has a superconducting phase at 1.178 K under
ambient pressure. The topological fermion properties, including the nearly zero
quasi-particle mass and the non-trivial Berry phase accumulated in cyclotron
motions, were revealed from the de Haas-van Alphen (dHvA) quantum oscillation
studies of this material. Although CaSn3 was previously reported to be
superconducting at 4.2K, our studies show that the superconductivity at 4.2K is
extrinsic and caused by Sn on the degraded surface, whereas its intrinsic bulk
superconducting transition occurs at 1.178 K. These findings make CaSn3 a
promising candidate for exploring new exotic states arising from the interplay
between non-trivial band topology and superconductivity, e.g. topological
superconductivity


  We consider an S=1 Heisenberg chain with strong exchange (Delta) and
single--ion uniaxial anisotropy (D) in a magnetic field (B) along the symmetry
axis. The low energy spectrum is described by an effective S=1/2 XXZ model that
acts on two different low energy sectors for a given window of fields. The
vacuum of each sector exhibits Ising-like antiferromagnetic ordering that
coexists with the finite spin stiffness obtained from the exact solution of the
effective XXZ model. In this way, we demonstrate the existence of a spin
supersolid phase. We also compute the full Delta-B quantum phase diagram by
means of a quantum Monte Carlo simulation.


  An enigmatic group of objects, unabsorbed Seyfert 2s may have intrinsically
weak broad line regions, obscuration in the line of sight to the BLR but not to
the X-ray corona, or so much obscuration that the X-ray continuum is completely
suppressed and the observed spectrum is actually scattered into the line of
sight from nearby material. NGC 3660 has been shown to have weak broad
optical/near infrared lines, no obscuration in the soft X-ray band, and no
indication of "changing look" behavior. The only previous hard X-ray detection
of this source by Beppo-SAX seemed to indicate that the source might harbor a
heavily obscured nucleus. However, our analysis of a long-look Suzaku
observation of this source shows that this is not the case, and that this
source has a typical power law X-ray continuum with normal reflection and no
obscuration. We conclude that NGC 3660 is confirmed to have no unidentified
obscuration and that the anomolously high Beppo-SAX measurement must be due to
source confusion or similar, being inconsistent with our Suzaku measurements as
well as non-detections from Swift-BAT and RXTE.


  We present a far-ultraviolet (FUV) study of hot stellar populations in the
second parameter pair globular clusters (GCs) M3 and M13, as a part of the
Globular cluster UVIT Legacy Survey program (GlobULeS). We use observations
made with F148W and F169M filters of the Ultraviolet Imaging Telescope (UVIT)
onboard {\em AstroSat} along with ground-based data (UBVRI filters), {\em
Hubble Space Telescope (HST)} GC catalogue, and {\em Gaia} EDR3 catalogue.
Based on the FUV-optical colour-magnitude diagrams, we classify the sources
into the horizontal branch (HB) stars, post-HB stars, and hot white dwarfs
(WDs) in both the GCs. The comparison of synthetic and observed colours of the
observed HB stars suggests that the mass-loss at the red giant branch (RGB) and
He spread in both clusters have a simultaneous effect on the different HB
distributions detected in M3 and M13, such that, HB stars of M13 require a
larger spread in He (${\rm 0.247-0.310}$) than those of M3 (${\rm Y=
0.252-0.266}$). The evolutionary status of HB stars, post-HB stars, and WDs are
studied using SED fit parameters and theoretical evolutionary tracks on the H-R
diagram. We found that the observed post-HB stars have evolved from zero-age HB
(ZAHB) stars of the mass range $0.48-0.55$ \Msun\ in M3 and M13. We detect 24
WD candidates in each cluster having ${\rm \log(L_{bol}/L_\odot)}$ in the range
$-0.8$ to $+0.6$ and ${\rm \log(T_{eff}/K)}$ in the range of 4.2 to 5.0.
Placing the WDs on the H-R diagram and comparing them with models suggest that
M13 has a population of low-mass WDs, probably originating from binary
evolution.


  We adapt the classical Q-method based on a reddening-free parameter
constructed from three passband magnitudes to thwe filter set of IPHAS survey
and combine it with the maximum-likelihood-based cluster parameter estimator by
Naylor and Jeffries (2006) to determine the extinction, heliocentric distances,
and ages of young open clusters using Halpha and ri data. Themethod is also
adapted for the case of signific ant variations of extinction across the
cluster rfield. Our technique is validated by comparing the colour excesses,
disdtances, and ages determined in this study with the most bona fide values
reported for the 18 well-studied young open clusters in the past, and a fairly
good agreement is found between our extinction and distance estimnates and
earlier published results. although our age estimates are not very consistent
with those published by other authors. We also show that individual extinction
values can be determined rather accurately for stars with (r-i)0>0.1. Our
results open up a prospect fpr determining a uniform set of parameters for
northern clusters based on homogeneous photometric data, and for searching for
new, hitherto undiscovered open clusters.


  We investigate the pseudoscalar (PS) meson ($\pi$ and $K$) quasi-distribution
amplitude (QDA), which is supposed to be an asymptotic analog to the meson
distribution amplitude (DA) $\phi_{\pi,K}(x)$ in the limit of the large
longitudinal PS-meson momentum, i.e. $p_3\to\infty$, in the nonperturbative
region. For this purpose, we employ the nonlocal chiral-quark model (NLChQM) in
the light-front formalism with a minimal Fock-state for the mesons $\sim
q\bar{q}$ at the low-energy scale parameter of the model $\Lambda\sim1$ GeV. As
a trial, we extract the transverse-momentum distribution amplitude (TMDA) from
the light-front wave function within the model, and convert it to QDA with help
of the virtuality-distribution amplitude. By doing that, we derive an
analytical expression for the nonperturbative QDA with the current-quark mass
correction up to $\mathcal{O}(\Delta m_q)$. Numerically, we confirm that the
obtained TMDA reproduces the experimental data for the photon-pion transition
form factor $F_{\gamma\gamma^*\pi^0}(Q^2)$ at the low-$Q^2$ qualitatively well.
We also observe that the obtained QDA approaches to DA as $p_3$ increases,
showing the symmetric and asymmetric curves with respect to $x$ for the pion
and kaon, respectively, due to the current-quark mass difference $m_{u,d}\ll
m_s$. Assigning $\xi\equiv2x-1$, the moments $\langle\xi^n\rangle_{\pi,K}$ are
computed, using the pion and kaon QDAs, and there appear only a few percent
deviations in the moments for $p_3\gtrsim30\Lambda$ in comparison to the values
calculated directly from DAs. It turns out that the higher moments are more
sensitive to the change of $p_3$, whereas the lower ones depend less on it.


  A major challenge in Explainable AI is in correctly interpreting activations
of hidden neurons: accurate interpretations would help answer the question of
what a deep learning system internally detects as relevant in the input,
demystifying the otherwise black-box nature of deep learning systems. The state
of the art indicates that hidden node activations can, in some cases, be
interpretable in a way that makes sense to humans, but systematic automated
methods that would be able to hypothesize and verify interpretations of hidden
neuron activations are underexplored. This is particularly the case for
approaches that can both draw explanations from substantial background
knowledge, and that are based on inherently explainable (symbolic) methods.
  In this paper, we introduce a novel model-agnostic post-hoc Explainable AI
method demonstrating that it provides meaningful interpretations. Our approach
is based on using a Wikipedia-derived concept hierarchy with approximately 2
million classes as background knowledge, and utilizes OWL-reasoning-based
Concept Induction for explanation generation. Additionally, we explore and
compare the capabilities of off-the-shelf pre-trained multimodal-based
explainable methods.
  Our results indicate that our approach can automatically attach meaningful
class expressions as explanations to individual neurons in the dense layer of a
Convolutional Neural Network. Evaluation through statistical analysis and
degree of concept activation in the hidden layer show that our method provides
a competitive edge in both quantitative and qualitative aspects compared to
prior work.


  Can we train a machine to detect if another machine has understood a concept?
In principle, this is possible by conducting tests on the subject of that
concept. However we want this procedure to be done by avoiding direct
questions. In other words, we would like to isolate the absolute meaning of an
abstract idea by putting it into a class of equivalence, hence without adopting
straight definitions or showing how this idea "works" in practice. We discuss
the metaphysical implications hidden in the above question, with the aim of
providing a plausible reference framework.


  Quantum field theory predicts the vacuum to exhibit a non-linear response to
strong electromagnetic fields. This fundamental tenet has remained
experimentally challenging and is yet to be tested in the laboratory. We
present proof of concept and detailed theoretical analysis of an experimental
setup for precision measurements of the quantum vacuum signal generated by the
collision of a brilliant x-ray probe with a high-intensity pump laser. The
signal features components polarised parallel and perpendicularly to the
incident x-ray probe. Our proof-of-concept measurements show that the
background can be efficiently suppressed by many orders of magnitude which
should not only facilitate a detection of the perpendicularly polarised
component of non-linear vacuum response, but even make the parallel polarised
component experimentally accessible for the first time. Remarkably, the angular
separation of the signal from the intense x-ray probe enables precision
measurements even in presence of pump fluctuations and alignment jitter. This
provides direct access to the low-energy constants governing light-by-light
scattering.


  Recently, the first author classified finite groups obtained as automorphism
groups of smooth plane curves of degree $d \ge 4$ into five types. He gave an
upper bound of the order of the automorphism group for each types. For one of
them, the type (a-ii), that is given by $\max \left\{ 2 d (d - 2), 60 d
\right\}$. In this article, we shall construct typical examples of smooth plane
curve $C$ by applying the method of Galois points, whose automorphism group has
order $60 d$. In fact, we determine the structure of the automorphism group of
those curves.


  In an attempt to reconcile recent spectral data with predictions of the
standard cooling flow model, it has been suggested that the metals in the
intracluster medium (ICM) might be distributed inhomogeneously on small scales.
We investigate the possible consequences of such a situation within the
framework of the cooling flow scenario. Using the standard isobaric cooling
flow model, we study the ability of such metallicity variations to
preferentially suppress low-temperature line emission in cooling flow spectra.
We then use simple numerical simulations to investigate the temporal and
spatial evolution of the ICM when the metals are distributed in such a fashion.
Simulated observations are used to study the constraints real data can place on
conditions in the ICM. The difficulty of ruling out abundance variations on
small spatial scales with current observational limits is emphasized. We find
that a bimodal distribution of metals may give rise to interesting effects in
the observed abundance profile, in that apparent abundance gradients with
central abundance drops and off-centre peaks, similar to those seen recently in
some clusters, are produced. Different elements behave in different fashion as
governed by the temperature dependence of their equivalent widths. Our overall
conclusion is that, whilst this process alone seems unlikely to be able to
account for the sharp reduction in low temperature emission lines seen in
current spectral data, a contribution at some level is possible and difficult
to rule out. The possibility of small-scale metallicity variations should be
considered when analysing high resolution cluster X-ray spectra.


  Diethyl sulfoxide (DESO) is far less known than its shorter-alkyl-chain
homolog, dimethyl sulfoxide. Although the field of senior dialkyl sulfoxides
does not currently exhibit an explosive growth, new fundamental and applied
research works routinely appear from a few research groups in the world.
Recently, the tetraethylammonium diethylsulfoxidopentachloroplatinate complex
compound was synthesized containing the DESO molecule as a neutral ligand. In
the present paper, we use a systematic computational method to rationalize the
mentioned synthetic achievement in coordination chemistry. We show that only up
to two DESO molecules may exist in the platinum (IV) complex ion, whereas all
higher contents of DESO are thermodynamically unstable and the sterical factor
plays an important role in their instabilities. Structural analysis of the
tetraethylammonium diethylsulfoxidopentachloroplatinate ion pair reveals its
rather strong cation-anion coordination and for the first time explains an
experimentally derived high melting point. The reported results are expected to
inspire experimental efforts to extend the universe of senior sulfoxides as
neutral organic ligands in d-metal complexes.


  The chemical abundances of the very metal-poor double-enhanced stars are
excellent information for setting new constraints on models of neutron-capture
processes at low metallicity. These stars are known as s+r stars, since they
show enhancements of both s-process and r-process elements. The observed
abundance ratios for the double-enhanced stars can be explained by those of
stars that were polluted by an AGB star and subsequently accreted very
significant amounts of r-process material out of an AIC (accretion-induced
collapse) or Type 1.5 supernova. In this paper we present for the first time an
attempt to fit the elemental abundances observed in the s- and r-rich, very
metal-poor stars using a parametric model and suggest a new concept of
component coefficients to describe the contributions of the individual
neutron-capture processes to double-enhanced stars. We find that the abundance
ratios of these stars are best fitted by enrichments of s- and r-process
material. The overlap factor in the AGB stars where the observed s-process
elements were produced lies between 0.1 and 0.81. Taking into account the
dependence of the initial-final mass relations on metallicity, this wide range
of values could possibly be explained by a wide range of core-mass values of
AGB stars at low metallicity. The component coefficient of the r-process is
strongly correlated with the component coefficient of the s-process for the
double-enhanced stars. This is significant evidence that the r-process material
in double-enhanced stars comes from an AIC or Type 1.5 supernova.


  We present and discuss a new method to extract parton distribution functions
from hard scattering processes based on an alternative type of neural network,
the Self-Organizing Map. Quantitative results including a detailed treatment of
uncertainties are presented within a Next to Leading Order analysis of
inclusive electron proton deep inelastic scattering data.


  We show that eigenvector centrality exhibits localization phenomena on
networks that can be easily partitioned by the removal of a vertex cut set, the
most extreme example being networks with a cut vertex. Three distinct types of
localization are identified in these structures. One is related to the
well-established hub node localization phenomenon and the other two are
introduced and characterized here. We gain insights into these problems by
deriving the relationship between eigenvector centrality and Katz centrality.
This leads to an interpretation of the principal eigenvector as an
approximation to more robust centrality measures which exist in the full span
of an eigenbasis of the adjacency matrix.


  We present a new, model-independent method to reconstruct the temporal
evolution of the speed of light $c(z)$ using astronomical observations. After
validating our pipeline using mock datasets, we apply our method to the latest
BAO and supernovae observations, and reconstruct $c(z)$ in the redshift range
of $z\in[0,1.5]$. We find no evidence of a varying speed of light, although we
see some interesting features of $\Delta c(z)$, the fractional difference
between $c(z)$ and $c_0$ (the speed of light in SI), \eg, $\Delta c(z)<0$ and
$\Delta c(z)>0$ at $0.2\lesssim z\lesssim0.5$ and $0.8\lesssim z\lesssim1.3$,
respectively, although the significance of these features is currently far
below statistical importance.


  A nonthermal particle acceleration mechanism involving the interaction of a
charged particle with multiple magnetic islands is proposed. The original Fermi
acceleration model, which assumes randomly distributed magnetic clouds moving
at random velocity $V_c$ in the interstellar medium, is known to be of
second-order acceleration of $O(V_c/c)^2$ owing to the combination of head-on
and head-tail collisions. In this letter, we reconsider the original Fermi
model by introducing multiple magnetic islands during reconnect ion instead of
magnetic clouds. We discuss that the energetic particles have a tendency to be
distributed outside the magnetic islands, and they mainly interact with
reconnection outflow jets. As a result, the acceleration efficiency becomes
first-order of $O(V_A/c)$, where $V_A$ and $c$ are the Alfv\'en velocity and
the speed of light, respectively.


  Given a pointed 4-ended tangle $T \subset D^3$, there are two Khovanov
theoretic tangle invariants, $\unicode{1044}_1(T)$ from [arXiv:1910.1458] and
$L_T$ from [arXiv:1808.06957], which are twisted complexes over the Fukaya
category of the boundary 4-punctured sphere $(S^2,4\text{pt})=\partial (D^3,
T)$. We prove that these two invariants are the same.


  We propose Remote Attestation with TOCTOU Avoidance (RATA): a provably secure
approach to address the RA TOCTOU problem. With RATA, even malware that erases
itself before execution of the next RA, can not hide its ephemeral presence.
RATA targets hybrid RA architectures (implemented as Hardware/Software
co-designs), which are aimed at low-end embedded devices. We present two
alternative techniques - RATAa and RATAb - suitable for devices with and
without real-time clocks, respectively. Each is shown to be secure and
accompanied by a publicly available and formally verified implementation. Our
evaluation demonstrates low hardware overhead of both techniques. Compared with
current RA architectures - that offer no TOCTOU protection - RATA incurs no
extra runtime overhead. In fact, RATA substantially reduces computational costs
of RA execution.


  In this paper, we present a comprehensive proof concerning the regularity of
critical points for the spline energy functional on Riemannian manifolds, even
for the general higher-order case. Although this result is widely acknowledged
in the literature, a detailed proof was previously absent. Our proof relies on
a generalization of the DuBois-Reymond Lemma. Furthermore, we establish the
existence of minimizers for the spline energy functional in cases where
multiple interpolation points are prescribed alongside just one velocity.


  We search for possible pulsar TeV halos among the very-high-energy (VHE)
sources reported in different VHE surveys, among which in particular we use the
results from the first Large High Altitude Air Shower Observatory (LHAASO)
catalog of $\gamma$-ray sources. Six candidates are found. They share similar
properties of containing a middle-aged, gamma-ray--bright pulsar in their
positional error circles (the respective pulsars are J0248+6021, J0359+5414,
J0622+3749, J0633+0632, J2006+3102, and J2238+5903), being in a rather clean
field without any common Galactic VHE-emitting supernova remnants or (bright)
pulsar wind nebulae (PWNe), and showing an absence of any gamma-ray emissions
in 0.1--500\,GeV after removing the pulsars' emissions. Combining these
candidates with several reported (candidate) TeV halos, we obtain the
relationships between their luminosity at 50\,TeV ($L_{\rm 50TeV}$) and the
corresponding pulsars' spin-down energy ($\dot{E}$), which are $L_{\rm
50TeV}\sim \dot{E}^{0.9}$ and $L_{\rm 50TeV}/\dot{E}\sim 6.4\times 10^{-4}$.
The relationships are nearly identical to previously reported ones. We probe
possible connections between the extension sizes of the VHE sources and the
pulsars' ages, and find a weak older-and-smaller trend. By comparing to the VHE
detection results for PWNe, it is clear that the (candidate) TeV halos have
hard emissions by either having their power-law indices be smaller than 2 in
1--25\,TeV or by only being detected in 25--100\,TeV. In addition, we also
consider seven other VHE sources as possible TeV halos based on the results
from different studies of them, but they do not fit cleanly with the properties
listed above, indicating their potentially complex nature.


  Massive present-day early-type (elliptical and lenticular) galaxies probably
gained the bulk of their stellar mass and heavy elements through intense,
dust-enshrouded starbursts - that is, increased rates of star formation - in
the most massive dark matter halos at early epochs. However, it remains unknown
how soon after the Big Bang such massive starburst progenitors exist. The
measured redshift distribution of dusty, massive starbursts has long been
suspected to be biased low in redshift owing to selection effects, as confirmed
by recent findings of systems out to redshift z~5. Here we report the
identification of a massive starburst galaxy at redshift 6.34 through a
submillimeter color-selection technique. We unambiguously determined the
redshift from a suite of molecular and atomic fine structure cooling lines.
These measurements reveal a hundred billion solar masses of highly excited,
chemically evolved interstellar medium in this galaxy, which constitutes at
least 40% of the baryonic mass. A "maximum starburst" converts the gas into
stars at a rate more than 2,000 times that of the Milky Way, a rate among the
highest observed at any epoch. Despite the overall downturn of cosmic star
formation towards the highest redshifts, it seems that environments mature
enough to form the most massive, intense starbursts existed at least as early
as 880 million years after the Big Bang.


  With the rise of social media, people can now form relationships and
communities easily regardless of location, race, ethnicity, or gender. However,
the power of social media simultaneously enables harmful online behavior such
as harassment and bullying. Cyberbullying is a serious social problem, making
it an important topic in social network analysis. Machine learning methods can
potentially help provide better understanding of this phenomenon, but they must
address several key challenges: the rapidly changing vocabulary involved in
cyber- bullying, the role of social network structure, and the scale of the
data. In this study, we propose a model that simultaneously discovers
instigators and victims of bullying as well as new bullying vocabulary by
starting with a corpus of social interactions and a seed dictionary of bullying
indicators. We formulate an objective function based on participant-vocabulary
consistency. We evaluate this approach on Twitter and Ask.fm data sets and show
that the proposed method can detect new bullying vocabulary as well as victims
and bullies.


  In the present paper the new multiplier transformations
$\mathrm{{\mathcal{J}% }}_{p}^{\delta }(\lambda ,\mu ,l)$ $(\delta ,l\geq
0,\;\lambda \geq \mu \geq 0;\;p\in \mathrm{% }%\mathbb{N} )}$ of multivalent
functions is defined. Making use of the operator $\mathrm{%
{\mathcal{J}}}_{p}^{\delta }(\lambda ,\mu ,l),$ two new subclasses $\mathcal{%
P}_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p)$ and $\widetilde{\mathcal{P}}%
_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p)$\textbf{\ }of multivalent analytic
functions are introduced and investigated in the open unit disk. Some
interesting relations and characteristics such as inclusion relationships,
neighborhoods, partial sums, some applications of fractional calculus and
quasi-convolution properties of functions belonging to each of these subclasses
$\mathcal{P}_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p)$ and
$\widetilde{\mathcal{P}}_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p)$ are
investigated. Relevant connections of the definitions and results presented in
this paper with those obtained in several earlier works on the subject are also
pointed out.


  We propose a scheme of continuous tunable THz emission based on dipolaritons
--- mixtures of strongly interacting cavity photons and direct excitons, where
the latter are coupled to indirect excitons via tunnelling. We investigate the
property of multistability under continuous wave (CW) pumping, and the
stability of the solutions. We establish the conditions of parametric
instability, giving rise to oscillations in density between the direct exciton
and indirect modes under CW pumping. In this way we achieve continuous and
tunable emission in the THz range in a compact single-crystal device. We show
that the emission frequency can be tuned in a certain range by varying an
applied electric field and pumping conditions. Finally, we demonstrate the
dynamic switching between different phases in our system, allowing rapid
control of THz radiation.


  Dynamics of a system in general depends on its initial state and how the
system is driven, but in many-body systems the memory is usually averaged out
during evolution. Here, interacting quantum systems without external
relaxations are shown to retain long-time memory effects in steady states. To
identify memory effects, we first show quasi-steady state currents form in
finite, isolated Bose and Fermi Hubbard models driven by interaction imbalance
and they become steady-state currents in the thermodynamic limit. By comparing
the steady state currents from different initial states or ramping rates of the
imbalance, long-time memory effects can be quantified. While the memory effects
of initial states are more ubiquitous, the memory effects of switching
protocols are mostly visible in interaction-induced transport in lattices. Our
simulations suggest the systems enter a regime governed by a generalized Fick's
law and memory effects lead to initial-state dependent diffusion coefficients.
We also identify conditions for enhancing memory effects and discuss possible
experimental implications.


  We study the Cauchy problem for an evolution equation of Schr\"odinger type.
The Hamiltonian is the Weyl quantization of a real homogeneous quadratic form
with a pseudodifferential perturbation of negative order from Shubin's class.
We prove that the propagator is a Fourier integral operator of Shubin type of
order zero. Using results for such operators and corresponding Lagrangian
distributions, we study the propagator and the solution, and derive phase space
estimates for them.


  In the current paper we prove simplicity for the spectrum of Lyapunov
exponents for triangle sequence and Selmer algorithm in dimension 3. We
introduce a strategy that can be applied for a wide class of Markovian MCF.


  Overlap functions were introduced as class of bivariate aggregation functions
on [0, 1] to be applied in image processing. This paper has as main objective
to present appropriates definitions of overlap functions considering the scope
of lattices and introduced a more general definition, called of quasi-overlaps,
which arise of abolishes the continuity condition. In addition, are
investigated the main properties of (quasi-)overlaps on bounded lattices,
namely, convex sum, migrativity, homogeneity, idempotency and cancellation law.
Moreover, we make a characterization of Archimedian overlaps.


  In this paper the non-linear wave equation with a spatial inhomogeneity is
considered. The inhomogeneity splits the unbounded spatial domain into three or
more intervals, on each of which the non-linear wave equation is homogeneous.
In such setting, there often exist multiple stationary fronts. In this paper we
present a necessary and sufficient stability criterion in terms of the length
of the middle interval(s) and the energy associated with the front in these
interval(s). To prove this criterion, it is shown that critical points of the
length function and zeros of the linearisation have the same order.
Furthermore, the Evans function is used to identify the stable branch. The
criterion is illustrated with an example which shows the existence of
bi-stability: two stable fronts, one of which is non-monotonic. The Evans
function also give a sufficient instability criterion in terms of the
derivative of the length function.


  We consider approximating distributions within the framework of optimal mass
transport and specialize to the problem of clustering data sets. Distances
between distributions are measured in the Wasserstein metric. The main problem
we consider is that of approximating sample distributions by ones with sparse
support. This provides a new viewpoint to clustering. We propose different
relaxations of a cardinality function which penalizes the size of the support
set. We establish that a certain relaxation provides the tightest convex lower
approximation to the cardinality penalty. We compare the performance of
alternative relaxations on a numerical study on clustering.


  Despite the amount of savings available and the money supply managed by
financial institutions, significant market failures and the failure of carbon
pricing strategies prevent sufficient financing of the transition, notably
through bank credit. Aware of the links between natural, monetary and
productive aggregates, we propose the development of ''eco-systemic''
prudential policies by exposing the interdependence between macro, micro and
environmental prudential measures. These would be based on a reorientation of
corporate accounting standards towards the concept of socio-environmental
solvency, notably the CARE-TDL model (integration of human and natural capital
alongside financial capital on the liabilities side of the balance sheet). In
an ecosystemic framework, this solvency of virtuous companies would compensate
in accounting terms for the lack of financial solvency. The State would then be
the guarantor in order to facilitate their access to financing, also reduced by
Basel III and Solvency II. This policy develops a system of reallocation of
financing capacities from non-virtuous companies to the most virtuous ones with
public guarantees, aiming to reduce the debt ratio while increasing green
investments, with monetary policies of rates but also of volumes and ratios
differentiated according to the types of assets and the greening of bank
balance sheets, and finally forms of public-private partnership. Facilitating
the financing of green companies would green capital but increase it, partly
neutralising the positive environmental impact. It is therefore necessary to
limit the credit expansion of ''brown'' companies. This would reduce risky
operations and favour less leveraged investments more connected to the real
economy, reducing systemic financial risk. -- The Agenda 2030 Policy Briefs
series (PoCFiN Kedge Business School - SDSN France - Institut Rousseau)
mobilises economists and practitioners to identify an agenda of economic and
financial reforms to achieve the 2030 Agenda, at territorial, national and
supranational levels. These are published after peer review.


  Prior efforts in light-weight model development mainly centered on CNN and
Transformer-based designs yet faced persistent challenges. CNNs adept at local
feature extraction compromise resolution while Transformers offer global reach
but escalate computational demands $\mathcal{O}(N^2)$. This ongoing trade-off
between accuracy and efficiency remains a significant hurdle. Recently, state
space models (SSMs), such as Mamba, have shown outstanding performance and
competitiveness in various tasks such as language modeling and computer vision,
while reducing the time complexity of global information extraction to
$\mathcal{O}(N)$. Inspired by this, this work proposes to explore the potential
of visual state space models in light-weight model design and introduce a novel
efficient model variant dubbed EfficientVMamba. Concretely, our EfficientVMamba
integrates a atrous-based selective scan approach by efficient skip sampling,
constituting building blocks designed to harness both global and local
representational features. Additionally, we investigate the integration between
SSM blocks and convolutions, and introduce an efficient visual state space
block combined with an additional convolution branch, which further elevate the
model performance. Experimental results show that, EfficientVMamba scales down
the computational complexity while yields competitive results across a variety
of vision tasks. For example, our EfficientVMamba-S with $1.3$G FLOPs improves
Vim-Ti with $1.5$G FLOPs by a large margin of $5.6\%$ accuracy on ImageNet.
Code is available at: \url{https://github.com/TerryPei/EfficientVMamba}.


  Market and user characteristics of mobile apps make their release management
different from proprietary software products and web services. Despite the
wealth of information regarding users' feedback on an app, an in-depth analysis
of app releases is difficult due to the inconsistency and uncertainty of the
information. To better understand and potentially improve app release
processes, we analyze major, minor, and patch releases for releases following
semantic versioning. In particular, we were interested in finding out the
difference between marketed and not-marketed releases. Our results show that,
in general, major, minor, and patch releases have significant differences in
the release cycle duration, nature, and change velocity. We also observed that
there is a significant difference between marketed and non-marketed mobile app
releases in terms of cycle duration, nature and the extent of changes, and the
number of opened and closed issues.


  Supervised 3D reconstruction has witnessed a significant progress through the
use of deep neural networks. However, this increase in performance requires
large scale annotations of 2D/3D data. In this paper, we explore inexpensive 2D
supervision as an alternative for expensive 3D CAD annotation. Specifically, we
use foreground masks as weak supervision through a raytrace pooling layer that
enables perspective projection and backpropagation. Additionally, since the 3D
reconstruction from masks is an ill posed problem, we propose to constrain the
3D reconstruction to the manifold of unlabeled realistic 3D shapes that match
mask observations. We demonstrate that learning a log-barrier solution to this
constrained optimization problem resembles the GAN objective, enabling the use
of existing tools for training GANs. We evaluate and analyze the manifold
constrained reconstruction on various datasets for single and multi-view
reconstruction of both synthetic and real images.


  It has been a long-standing debate that why quantum mechanics uses complex
numbers but not only real numbers. To address this topic, in recent years, the
imaginarity theory has been developed in the way of quantum resource theory.
However, the existing imaginarity theory mainly focuses on the quantum systems
with finite dimensions. Gaussian states are widely used in many fields of
quantum physics, but they are in the quantum systems with infinite dimensions.
In this paper we establish a resource theory of imaginarity for bosonic
Gaussian states. To do so, under the Fock basis, we determine the real Gaussian
states and real Gaussian channels in terms of the means and covariance matrices
of Gaussian states. Also, we provide two imaginary measures for Gaussian states
based on the fidelity.


  In this addendum to Phys. Rev. D 88, no. 1, 013009 (2013), we give results
for the electroweak Sudakov corrections in gauge-boson production at large
transverse momentum p_T at proton colliders. In order for the results to be
easily usable, we provide a simple and accurate parameterization of the
corrections as a function of p_T and the center-of-mass energy \sqrt{s}.
Additionally, we also discuss the dependence of the electroweak corrections on
the rapidity of the produced boson, and comment on the complications that arise
in the photon-production case due to isolation requirements


  This paper presents the design, objectives, experiences, and results of an
international cooperation project funded by the European Commission in the
context of the Erasmus Intensive Programme (IP, for short) designed to improve
students' curricula. An IP is a short programme of study (minimum 2 weeks) that
brings together university students and staff from at least three countries in
order to encourage efficient and multinational teaching of specialist topics,
which might otherwise not be taught at all. This project lasted for 6 years,
covering two different editions, each one with three year duration. This
project lasted for 6 years, covering two different editions, each one with
three year duration. The first edition, named SAVRO (Simulation and Virtual
Reality in Robotics for Industrial Assembly Processes) was held in the period
2008-2010, with the participation of three Universities, namely the Universitat
Politecnica de Valencia (Spain), acting as IP coordinator, the Technische
Universitat Kaiserslautern (Germany), and the Universita degli Studi di Salerno
(Italy). The Universite de Reims Champagne-Ardenne (France) participated as a
new partner in the subsequent edition (2011-2013) of the IP, renamed as HUMAIN
(Human-Machine Interaction). Both editions of the teaching project were
characterized by the same objectives and organizational aspects, aiming to
provide educational initiatives based on active teaching through collaborative
works between international institutions, involving industrial partners too.
The aim of the paper is to illustrate the best practices that characterized the
organization of our experience as well as to present some general
recommendations and suggestions on how to devise computing academic curricula.


  Original paper from Mirebeau et al. PRL 83, 628, 1999 Comment from W. Bao et
al (cond-mat/0008042, 2 Aug 2000 submitted to PRL, updated on 1 sep 2000


  A brief survey is given on the current status of evaluating thermal
production of photons from a strongly interacting medium. Emphasis is put on
recent progress in assessing equilibrium emission rates in both hadronic and
quark-gluon matter. We also give an update on the status of comparing
theoretical calculations with experimental data from heavy-ion collisions at
the SPS, as well as prospects for RHIC. Finally, applications of photon rate
calculations to colorsuperconducting quark matter are discussed.


  Consider many instances of an arbitrary quadripartite pure state of four
quantum systems ABCD. Alice holds the AC part of each state, Bob holds B, while
D represents all other parties correlated with ABC. Alice is required to
redistribute the C systems to Bob while asymptotically preserving the overall
purity. We prove that this is possible using Q qubits of communication and E
ebits of shared entanglement between Alice and Bob, provided that Q geq
I(C;D|B)/2 and Q+E geq H(C|B), proving the optimality of the Luo-Devetak outer
bound. The optimal qubit rate provides the first known operational
interpretation of quantum conditional mutual information. We also show how our
protocol leads to a fully operational proof of strong subadditivity and uncover
a general organizing principle, in analogy to thermodynamics, that underlies
the optimal rates.


  Elastic Optical Networks (EON) emerge as a viable solution to supply the
current growing demand for bandwidth. With the application of multi-core fibers
(MCF) in EON links, it is possible to increase the availability of spectral
resources. An EON network with MCF enables Space-Division Multiplexing (SDM),
allowing the use of more resources in the fibers and increasing the capacity of
attending circuit requests. However, the use of SDM brings some problems of
interference between the circuits of a fiber, with greater emphasis on
crosstalk interference. In this paper, some important concepts around EON are
presented, along with the characterization of SDM technology. The Routing,
Modulation, Spectrum and Core Allocation (RMSCA) problem is also characterized,
and some solutions currently found in the literature are cited. After, the
impact of crosstalk interference is discussed, and which elements are
responsible for its occurrence. The paper is concludes with an evaluation of
the state of the art, and the discrimination of the main points found from the
study of papers related to the SDM-EON scenario.


  In this paper, we formulate a Nonlinear Model Predictive Control (NMPC) to
plan appropriate social distancing measures (and relaxations) in order to
mitigate the COVID-19 pandemic effects, considering the contagion development
in Brazil. The NMPC strategy is designed upon an adapted data-driven
Susceptible-Infected-Recovered-Deceased (SIRD) contagion model, which takes
into account the effects of social distancing. Furthermore, the adapted SIRD
model includes time-varying auto-regressive contagion parameters, which
dynamically converge according to the stage of the pandemic. This new model is
identified through a three-layered procedures, with analytical regressions,
Least-Squares optimization runs and auto-regressive model fits. The data-driven
model is validated and shown to adequately describe the contagion curves over
large forecast horizons. In this model, control input is defined as finitely
parametrized values for social distancing guidelines, which directly affect the
transmission and infection rates of the SARS-CoV-2 virus. The NMPC strategy
generates piece-wise constant quarantine guidelines which can be
relaxed/strengthen as each week passes. The implementation of the method is
pursued through a search mechanism, since the control is finitely parametrized
and, thus, there exist a finite number of possible control sequences.
Simulation essays are shown to illustrate the results obtained with the
proposed closed-loop NMPC strategy, which is able to mitigate the number of
infections and progressively loosen social distancing measures. With respect to
an "open-loop"/no control condition, the number of deaths still could be
reduced in up to 30 %. The forecast preview an infection peak to September 2nd,
2020, which could lead to over 1.5 million deaths if no coordinate health
policy is enacted. The framework serves as guidelines for possible public
health policies in Brazil.


  A simple, non-invasive method using Raman spectroscopy for the estimation of
the thickness of graphene layers grown epitaxially on silicon carbide (SiC) is
presented, enabling simultaneous determination of thickness, grain size and
disorder using the spectra. The attenuation of the substrate Raman signal due
to the graphene overlayer is found to be dependent on the graphene film
thickness deduced from X-ray photoelectron spectroscopy and transmission
electron microscopy of the surfaces. We explain this dependence using an
absorbing overlayer model. This method can be used for mapping graphene
thickness over a region and is capable of estimating thickness of multilayer
graphene films beyond that possible by XPS and Auger electron spectroscopy
(AES).


  Friction measurements in the range of several meters per second are still of
great interests. With the atomic force microscopy (AFM), the oscilaltion
situation of the quartz crystal resonators of 3MHz resonance frequency are
studied. And the oscillation speed could reach up to several m/s. Then the
friction measurements are carried out on the Fischer Pattern, which is prepared
on the quartz crystal resonator. In this article, we present how to measure the
oscillation speeds of the quartz crystal resonator and the friction
measurements under different designs. Calibration method and the calculation
methods are also discussed in details. Although there are errors in the AFM
measurements due to the setup itself and the oscillating quartz which could
highly affect the sharpness of the cantilever tip, the results indicate that
the local friction coefficients with oscillation are higher than that without
oscillation.


  A new approach to effective theory of quarks in the instanton vacuum is
presented. Exact equations for the quark propagator and Lagrangian are derived
which contain contributions of all quark modes with known coefficients. The
resulting effective Lagrangian differs from the standard one and resembles that
of the Nambu-Jona-Lasinio model.


  Liver cancer has high morbidity and mortality rates in the world. Multi-phase
CT is a main medical imaging modality for detecting/identifying and diagnosing
liver tumors. Automatically detecting and classifying liver lesions in CT
images have the potential to improve the clinical workflow. This task remains
challenging due to liver lesions' large variations in size, appearance, image
contrast, and the complexities of tumor types or subtypes. In this work, we
customize a multi-object labeling tool for multi-phase CT images, which is used
to curate a large-scale dataset containing 1,631 patients with four-phase CT
images, multi-organ masks, and multi-lesion (six major types of liver lesions
confirmed by pathology) masks. We develop a two-stage liver lesion detection
pipeline, where the high-sensitivity detecting algorithms in the first stage
discover as many lesion proposals as possible, and the lesion-reclassification
algorithms in the second stage remove as many false alarms as possible. The
multi-sensitivity lesion detection algorithm maximizes the information
utilization of the individual probability maps of segmentation, and the
lesion-shuffle augmentation effectively explores the texture contrast between
lesions and the liver. Independently tested on 331 patient cases, the proposed
model achieves high sensitivity and specificity for malignancy classification
in the multi-phase contrast-enhanced CT (99.2%, 97.1%, diagnosis setting) and
in the noncontrast CT (97.3%, 95.7%, screening setting).


  The properties of prototypical examples of one-dimensional fermionic systems
undergoing a sudden quantum quench from a gapless state to a (partially) gapped
state are analyzed. By means of a Generalized Gibbs Ensemble analysis or by
numerical solutions in the interacting cases, we observe an anomalous,
non-monotonic response of steady state correlation functions as a function of
the strength of the mechanism opening the gap. In order to interpret this
result, we calculate the full dynamical evolution of these correlation
functions, which shows a freezing of the propagation of the quench information
(light cone) for large quenches. We argue that this freezing is responsible for
the non-monotonous behaviour of observables. In continuum non-interacting
models, this freezing can be traced back to a Klein-Gordon equation in the
presence of a source term. We conclude by arguing in favour of the robustness
of the phenomenon in the cases of non-sudden quenches and higher
dimensionality.


  We study a simple system of two hyperbolic semi-linear equations, inspired by
the Einstein equations. The system, which was introduced in gr-qc/0612136, is a
model for singularity formation inside black holes. We show for a particular
case of the equations that the system demonstrates a finite time blowup. The
singularity that is formed is a null singularity. Then we show that in this
particular case the singularity has features that are analogous to known
features of models of black-hole interiors - which describe the inner-horizon
instability. Our simple system may provide insight into the formation of null
singularities inside spinning or charged black holes.


  We consider GL_n(F_q)-analogues of certain factorization problems in the
symmetric group S_n: rather than counting factorizations of the long cycle (1,
2, ..., n) given the number of cycles of each factor, we count factorizations
of a regular elliptic element given the fixed space dimension of each factor.
We show that, as in S_n, the generating function counting these factorizations
has attractive coefficients after an appropriate change of basis. Our work
generalizes several recent results on factorizations in GL_n(F_q) and also uses
a character-based approach.
  As an application of our results, we compute the asymptotic growth rate of
the number of factorizations of fixed genus of a regular elliptic element in
GL_n(F_q) into two factors as n goes to infinity. We end with a number of open
questions.


  Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial
in computer vision and biometrics. In this work, we aim to extend LT-ReID
beyond pedestrian recognition to include a wider range of real-world human
activities while still accounting for cloth-changing scenarios over large time
gaps. This setting poses additional challenges due to the geometric
misalignment and appearance ambiguity caused by the diversity of human pose and
clothing. To address these challenges, we propose a new approach 3DInvarReID
for (i) disentangling identity from non-identity components (pose, clothing
shape, and texture) of 3D clothed humans, and (ii) reconstructing accurate 3D
clothed body shapes and learning discriminative features of naked body shapes
for person ReID in a joint manner. To better evaluate our study of LT-ReID, we
collect a real-world dataset called CCDA, which contains a wide variety of
human activities and clothing changes. Experimentally, we show the superior
performance of our approach for person ReID.


  This paper investigates an issue of distributed fusion estimation under
network-induced complexity and stochastic parameter uncertainties. First, a
novel signal selection method based on event-trigger is developed to handle
network-induced packet dropouts as well as packet disorders resulting from
random transmission delays, where the ${H_2}/{H_\infty }$ performance of the
system is analyzed in different noise environments. In addition, a linear delay
compensation strategy is further employed for solving the complexity
network-induced problem, which may deteriorate the system performance.
Moreover, a weighted fusion scheme is used to integrate multiple resources
through an error cross-covariance matrix. Several case studies validate the
proposed algorithm and demonstrate satisfactory system performance in target
tracking.


  We construct an analytic theory of many-body localization (MBL) in random
spin chains. The approach is based on a first quantized perspective in which
MBL is understood as a localization phenomenon on the high dimensional lattice
defined by the discrete Hilbert space of the clean system. We construct a field
theory on that lattice and apply it to discuss the stability of a weak disorder
(`Wigner-Dyson') and a strong disorder (`Poisson') phase.


  Recent studies have shown that (a) quantum effects may be sufficient to
support a wormhole throat and (b) the total amount of "exotic matter" can be
made arbitrarily small. Unfortunately, using only small amounts of exotic
matter may result in a wormhole that flares out too slowly to be traversable in
a reasonable length of time. Combined with the Ford-Roman constraints, the
wormhole may also come close to having an event horizon at the throat. This
paper examines a model that overcomes these difficulties, while satisfying the
usual traversability conditions. This model also confirms that the total amount
of exotic matter can indeed be made arbitrarily small.


  The structure of $^{22}$C plays a vital role in the new physics at subshell
closure of $N=16$ in the neutron-rich region. We study the two-neutron
correlations in the ground state of the weakly-bound Borromean nucleus $^{22}$C
sitting at the edge of the neutron-drip line and its sensitivity to ${\rm
core}$-$n$ potential. For the present study, we employ a three-body (${\rm
core}+n+n$) structure model designed for describing the Borromean system by
explicit coupling of unbound continuum states of the subsystem (${\rm
core}+n$). We use a density-independent contact-delta interaction to describe
the neutron-neutron interaction and its strength is varied to fix the binding
energy. Along with the ground-state properties of $^{22}$C, we investigate its
electric-dipole and monopole responses, discussing the contribution of various
configurations. Our results indicate more configuration mixing as compared to
the previous studies in the ground state of $^{22}$C. However, they strongly
depend upon the choice of the $^{20}{\rm C}$-$n$ potential as well as the
binding energy of $^{22}$C, which call for new precise measurements for the
low-lying continuum structure of the binary system ($^{20}{\rm C}+n$) and the
mass of $^{22}$C. These measurements will be essential to understand the
Borromean three-body system $^{22}$C with more accuracy.


  We study the precessional switching of the magnetization in microscopic spin
valve cells induced by ultra short in-plane hard axis magnetic field pulses.
Stable and highly efficient switching is monitored following pulses as short as
140 ps with energies down to 15 pJ. Multiple application of identical pulses
reversibly toggles the cell's magnetization be-tween the two easy directions.
Variations of pulse duration and amplitude reveal alter-nating regimes of
switching and non-switching corresponding to transitions from in-phase to
out-of-phase excitations of the magnetic precession by the field pulse. In the
low field limit damping becomes predominant and a relaxational reversal is
found allowing switching by hard axis fields below the in-plane anisotropy
field threshold.


  Resistive Plate Chambers (RPCs) were used for the instrumentation of the iron
flux return of the BABAR detector as a muon system. Unfortunately the
efficiency of the original RPCs degraded rapidly with time. Limited Streamer
Tubes (LSTs) were chosen for an upgrade of the barrel portion of the detector.
  The phased installation started in summer 2004 with replacing two sextants of
the BABAR barrel muon system with LSTs. The modules for the remaining four
sextants are under long-term test at SLAC and ready for installation, expected
in 2006. The modules become inaccessible once installed in BABAR, so it is
critical to select only perfectly working tubes. To accomplish this a strong QC
system was established during the prototype phase, and fully implemented
throughout pre-production and construction. To spot any damage during
transport, the final modules are subjected to comprehensive tests at SLAC
immediately after arrival and kept under long-term test till installation into
BABAR. Details of these tests and results from long-term testing will be
discussed. Since spring 2005 the PEP-II accelerator is running and BABAR is
collecting data. First experience from data taking with the LSTs will be
presented and the performance of the detector discussed.


  In this article we prove that the inclusion of the space of Engel structures
of a smooth $4$-fold into the space of full flags of its tangent bundle induces
surjections in all homotopy groups. In particular, we construct Engel
structures representing any given full flag.


  Radiology report generation (RRG) has gained increasing research attention
because of its huge potential to mitigate medical resource shortages and aid
the process of disease decision making by radiologists. Recent advancements in
RRG are largely driven by improving a model's capabilities in encoding
single-modal feature representations, while few studies explicitly explore the
cross-modal alignment between image regions and words. Radiologists typically
focus first on abnormal image regions before composing the corresponding text
descriptions, thus cross-modal alignment is of great importance to learn a RRG
model which is aware of abnormalities in the image. Motivated by this, we
propose a Class Activation Map guided Attention Network (CAMANet) which
explicitly promotes crossmodal alignment by employing aggregated class
activation maps to supervise cross-modal attention learning, and simultaneously
enrich the discriminative information. CAMANet contains three complementary
modules: a Visual Discriminative Map Generation module to generate the
importance/contribution of each visual token; Visual Discriminative Map
Assisted Encoder to learn the discriminative representation and enrich the
discriminative information; and a Visual Textual Attention Consistency module
to ensure the attention consistency between the visual and textual tokens, to
achieve the cross-modal alignment. Experimental results demonstrate that
CAMANet outperforms previous SOTA methods on two commonly used RRG benchmarks.


  With the popularity of Social Networking Services (SNS), more and more
sensitive information are stored online and associated with SNS accounts. The
obvious value of SNS accounts motivates the usage stealing problem --
unauthorized, stealthy use of SNS accounts on the devices owned/used by account
owners without any technology hacks. For example, anxious parents may use their
kids' SNS accounts to inspect the kids' social status; husbands/wives may use
their spouses' SNS accounts to spot possible affairs. Usage stealing could
happen anywhere in any form, and seriously invades the privacy of account
owners. However, there is no any currently known defense against such usage
stealing. To an SNS operator (e.g., Facebook Inc.), usage stealing is hard to
detect using traditional methods because such attackers come from the same IP
addresses/devices, use the same credentials, and share the same accounts as the
owners do.
  In this paper, we propose a novel continuous authentication approach that
analyzes user browsing behavior to detect SNS usage stealing incidents. We use
Facebook as a case study and show that it is possible to detect such incidents
by analyzing SNS browsing behavior. Our experiment results show that our
proposal can achieve higher than 80% detection accuracy within 2 minutes, and
higher than 90% detection accuracy after 7 minutes of observation time.


  We consider a boundary value problem for the Dirac equation in a smooth,
asymptotically flat Lorentzian manifold admitting a Killing field which is
timelike near and tangential to the boundary. A self-adjoint extension of the
Dirac Hamiltonian is constructed. Our results also apply to the situation that
the space-time includes horizons, where the Hamiltonian fails to be elliptic.


  Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.