  Intention is an important and challenging concept in AI. It is important
because it underlies many other concepts we care about, such as agency,
manipulation, legal responsibility, and blame. However, ascribing intent to AI
systems is contentious, and there is no universally accepted theory of
intention applicable to AI agents. We operationalise the intention with which
an agent acts, relating to the reasons it chooses its decision. We introduce a
formal definition of intention in structural causal influence models, grounded
in the philosophy literature on intent and applicable to real-world machine
learning systems. Through a number of examples and results, we show that our
definition captures the intuitive notion of intent and satisfies desiderata
set-out by past work. In addition, we show how our definition relates to past
concepts, including actual causality, and the notion of instrumental goals,
which is a core idea in the literature on safe AI agents. Finally, we
demonstrate how our definition can be used to infer the intentions of
reinforcement learning agents and language models from their behaviour.


  Generating explanations for reinforcement learning (RL) is challenging as
actions may produce long-term effects on the future. In this paper, we develop
a novel framework for explainable RL by learning a causal world model without
prior knowledge of the causal structure of the environment. The model captures
the influence of actions, allowing us to interpret the long-term effects of
actions through causal chains, which present how actions influence
environmental variables and finally lead to rewards. Different from most
explanatory models which suffer from low accuracy, our model remains accurate
while improving explainability, making it applicable in model-based learning.
As a result, we demonstrate that our causal model can serve as the bridge
between explainability and learning.


  Using Random Matrix Theory we set out to compute the microscopic correlators
of the Euclidean Dirac operator in four dimensions. In particular we consider:
the chiral Orthogonal Ensemble (chOE), corresponding to a Yang-Mills theory
with two colors and fermions in the fundamental representation, and the chiral
Symplectic Ensemble (chSE), corresponding to any number of colors and fermions
in the adjoint representation. In both cases we deal with an arbitrary number
of massive fermions. We use a recent method proposed by H. Widom for deriving
closed formulas for the scalar kernels from which all spectral correlation
functions of the chGOE and chGSE can be determined. Moreover, we obtain
complete analytic expressions of such correlators in the double microscopic
limit, extending previously known results of four-dimensional QCD at beta=1 and
beta=4 to the general case with N_f flavors, with arbitrary quark masses and
arbitrary topological charge.


  Estimating the probability of rare channel conditions is a central challenge
in ultra-reliable wireless communication, where random events, such as deep
fades, can cause sudden variations in the channel quality. This paper proposes
a sample-efficient framework for predicting the statistics of such events by
utilizing spatial dependency between channel measurements acquired from various
locations. The proposed framework combines radio maps with non-parametric
models and extreme value theory (EVT) to estimate rare-event channel statistics
under a Bayesian formulation. The framework can be applied to a wide range of
problems in wireless communication and is exemplified by rate selection in
ultra-reliable communications. Notably, besides simulated data, the proposed
framework is also validated with experimental measurements. The results in both
cases show that the Bayesian formulation provides significantly better results
in terms of throughput compared to baselines that do not leverage measurements
from surrounding locations. It is also observed that the models based on EVT
are generally more accurate in predicting rare-event statistics than
non-parametric models, especially when only a limited number of channel samples
are available. Overall, the proposed methods can significantly reduce the
number of measurements required to predict rare channel conditions and
guarantee reliability.


  Stable Diffusion (SD) customization approaches enable users to personalize SD
model outputs, greatly enhancing the flexibility and diversity of AI art.
However, they also allow individuals to plagiarize specific styles or subjects
from copyrighted images, which raises significant concerns about potential
copyright infringement. To address this issue, we propose an invisible
data-free universal adversarial watermark (DUAW), aiming to protect a myriad of
copyrighted images from different customization approaches across various
versions of SD models. First, DUAW is designed to disrupt the variational
autoencoder during SD customization. Second, DUAW operates in a data-free
context, where it is trained on synthetic images produced by a Large Language
Model (LLM) and a pretrained SD model. This approach circumvents the necessity
of directly handling copyrighted images, thereby preserving their
confidentiality. Once crafted, DUAW can be imperceptibly integrated into
massive copyrighted images, serving as a protective measure by inducing
significant distortions in the images generated by customized SD models.
Experimental results demonstrate that DUAW can effectively distort the outputs
of fine-tuned SD models, rendering them discernible to both human observers and
a simple classifier.


  This article deals with quantitative error analysis resulting from
ellipsometric data obtained from measurement on curved surfaces including the
influence of non-collimated beams. Numerical model based on the combination of
geometrical and wave optics is restricted to the example of single dielectric
layer deposited on the substrate with complex index of refraction. Three
methods for averaging measurable ellipsometric data are compared.


  Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices.


  We have carried out extensive VLT-FORS1 observations covering a fair fraction
of the intermediate metallicity globular cluster NGC 6712 in the V and R bands.
We derive accurate colour-magnitude diagrammes (CMD) and luminosity functions
(LFs) of the cluster main sequence (MS) for four overlapping fields extending
from the centre of the cluster out to a radius of ~10', well beyond the nominal
tidal radius, and for a control field at ~42' distance. The LFs extend from the
cluster turn-off (TO) at M_R 4 to the point at which the incompleteness drops
below 50% (corresponding to R 23 or M_R 7.5) for most fields studied. Cluster
stars become indistinguishable from field stars at r 5'. The shape of the
cluster's LF and its variation with distance from the centre in these ranges
are well described by a standard multi-mass static model having the following
parameters: core radius r_c=1', half-light radius r_hl=1.8', tidal radius
r_t=5.2', concentration ratio c=0.7, and a power-law global mass function (MF)
with index alpha 0.9 for masses smaller than 0.8 Msolar, i.e. for all detected
MS stars, and alpha -2.35 for evolved objects. The MF obtained in this way is
consistent with that found in a preliminary investigation of this cluster with
the VLT Test Camera and confirms that this is the only globular cluster known
so far for which the global MF drops with decreasing mass below the TO.
Possible reasons for this unique characteristic are discussed with the most
likely associated with its extreme vulnerability to tidal disruption.


  A complete solution describing a binary system constituted by two unequal
counter-rotating black holes with a massless strut inbetween is presented. It
is expressed in terms of four arbitrary parameters: the half length of the two
rods representing the black hole horizons sigma1 and sigma2, the total mass M,
and the relative distance R between the centers of the horizons. The explicit
form of this solution in terms of physical parameters, i.e., the Komar masses
M1 and M2, the Komar angular momenta per unit mass a1 and a2, having a1 and a2
opposite signs, and the coordinate distance R, led us to a 4-parameter subclass
of solutions in which a set of five physical parameters satisfy a simple
algebraic relation. Moreover, the interaction force, provided by the strut,
between the black holes results to be of same form as it is for the static
double-Schwarzschild case.


  The radiative corrections to the pure leptonic decay $D_s{\longrightarrow}
{\ell}{{\nu}}_{\ell}$ up-to one-loop order is presented. We find the virtual
photon loop corrections to $D_s{\longrightarrow} {\tau}{{\nu}}_{\tau}$ is
negative and the corresponding branching ratio is larger than $3.51\times
10^{-3}$. Considering the possible experimental resolutions, our prediction of
the radiative decay $D_s{\longrightarrow} {\tau}{{\nu}}_{\tau}\gamma$ is not so
large as others, and the best channel to determine the $V_{cs}$ or $f_{D_s}$ is
$D_s{\longrightarrow} {\mu}{{\nu}}_{\mu}{\gamma}$. How to cancel the infrared
divergences appearing in the loop calculations, and the radiative decay
$D_s{\longrightarrow} {\ell}{{\nu}}_{\ell}{\gamma}$ is shown precisely. It is
emphasized that the radiative decay may be separated properly and may compare
with measurements directly as long as the theoretical `softness' of the photon
corresponds to the experimental resolutions.


  The $\gamma$-ray emission of star-forming (SF) galaxies is attributed to
hadronic interactions of cosmic ray (CR) protons with the interstellar gas and
contributions from CR electrons via bremsstrahlung and inverse Compton (IC)
scattering. The relative importance of these processes in different galaxy
types is still unclear. We model these processes in three-dimensional
magneto-hydrodynamical (MHD) simulations of the formation of isolated galactic
discs using the moving-mesh code AREPO, including dynamically coupled CR
protons and adopting different CR transport models. We calculate steady-state
CR spectra and also account for the emergence of secondary electrons and
positrons. This allows us to produce detailed $\gamma$-ray maps, luminosities
and spectra of our simulated galaxies at different evolutionary stages. Our
simulations with anisotropic CR diffusion and a low CR injection efficiency at
supernovae (SNe, $\zeta_{\mathrm{SN}}=0.05$) can successfully reproduce the
observed far infrared (FIR)-$\gamma$-ray relation. Starburst galaxies are close
to the calorimetric limit, where CR protons lose most of their energy due to
hadronic interactions and hence, their $\gamma$-ray emission is dominated by
neutral pion decay. However, in low SF galaxies, the increasing diffusive
losses soften the CR proton spectra due to energy-dependent diffusion, and
likewise steepen the pionic $\gamma$-ray spectra. In turn, IC emission hardens
the total spectra and can contribute up to $\sim40$ per cent of the total
luminosity in low SF galaxies. Furthermore, in order to match the observed
$\gamma$-ray spectra of starburst galaxies, we require a weaker energy
dependence of the CR diffusion coefficient, $D\propto{E}^{0.3}$, in comparison
to Milky Way-like galaxies.


  This paper formulates and evaluates a series of multi-unit measures of
directional association, building on the pairwise {\Delta}P measure, that are
able to quantify association in sequences of varying length and type of
representation. Multi-unit measures face an additional segmentation problem:
once the implicit length constraint of pairwise measures is abandoned,
association measures must also identify the borders of meaningful sequences.
This paper takes a vector-based approach to the segmentation problem by using
18 unique measures to describe different aspects of multi-unit association. An
examination of these measures across eight languages shows that they are stable
across languages and that each provides a unique rank of associated sequences.
Taken together, these measures expand corpus-based approaches to association by
generalizing across varying lengths and types of representation.


  We develop a model of tax evasion based on the Ising model. We augment the
model using an appropriate enforcement mechanism that may allow policy makers
to curb tax evasion. With a certain probability tax evaders are subject to an
audit. If they get caught they behave honestly for a certain number of periods.
Simulating the model for a range of parameter combinations, we show that tax
evasion may be controlled effectively by using punishment as an enforcement
mechanism.


  We have observed the linear polarization of 450 micron continuum emission
from the Galactic center, using a new polarimetric detector system that is
operated on a 2 m telescope at the South Pole. The resulting polarization map
extends ~ 170 pc along the Galactic plane and ~ 30 pc in Galactic latitude, and
thus covers a significant fraction of the central molecular zone. Our map shows
that this region is permeated by large-scale toroidal magnetic fields. We
consider our results together with radio observations that show evidence for
poloidal fields in the Galactic center, and with Faraday rotation observations.
We compare all of these observations with the predictions of a magnetodynamic
model for the Galactic center that was proposed in order to explain the
Galactic Center Radio Lobe as a magnetically driven gas outflow. We conclude
that the observations are basically consistent with the model.


  In this paper, we introduce a new type of generalized neural network where
neurons and synapses maintain multiple states. We show that classical
gradient-based backpropagation in neural networks can be seen as a special case
of a two-state network where one state is used for activations and another for
gradients, with update rules derived from the chain rule. In our generalized
framework, networks have neither explicit notion of nor ever receive gradients.
The synapses and neurons are updated using a bidirectional Hebb-style update
rule parameterized by a shared low-dimensional "genome". We show that such
genomes can be meta-learned from scratch, using either conventional
optimization techniques, or evolutionary strategies, such as CMA-ES. Resulting
update rules generalize to unseen tasks and train faster than gradient descent
based optimizers for several standard computer vision and synthetic tasks.


  Employing the Polyakov extended Nambu-Jona-Lasinio model, we determine the
net-baryon number fluctuations of magnetized three-flavor quark matter. We show
that the magnetic field changes the nature of the strange quark transition from
crossover to first-order at low temperatures. In fact, the strange quark
undergoes multiple first-order phase transitions and several critical end
points emerge in the phase diagram.


  Varying one of the governing parameters of a dynamical system may lead to a
critical transition, where the new stable state is undesirable. In some cases,
there is only a limited range of the bifurcation parameter that corresponds to
that unwanted attractor, while the system runs problem-less otherwise. In this
study, we present experimental results regarding a thermoacoustic system
subject to two consecutive and mirrored supercritical Hopf bifurcations: the
system exhibits high amplitude thermoacoustic limit cycles for intermediate
values of the bifurcation parameter. Changing quickly enough the bifurcation
parameter, it was possible to dodge the unwanted limit cycles. A low-order
model of the complex thermoacoustic system was developed, in order to describe
this interesting transient dynamics. It was afterward used to assess the risk
of exceeding an oscillation amplitude threshold as a function of the rate of
change of the bifurcation parameter.


  In this paper, we address the numerical solution to the multimarginal optimal
transport (MMOT) with pairwise costs. MMOT, as a natural extension from the
classical two-marginal optimal transport, has many important applications
including image processing, density functional theory and machine learning, but
yet lacks efficient and exact numerical methods. The popular
entropy-regularized method may suffer numerical instability and blurring
issues. Inspired by the back-and-forth method introduced by Jacobs and
L\'{e}ger, we investigate MMOT problems with pairwise costs. First, such
problems have a graphical representation and we prove equivalent MMOT problems
that have a tree representation. Second, we introduce a noval algorithm to
solve MMOT on a rooted tree, by gradient based method on the dual formulation.
Last, we obtain accurate solutions which can be used for the
regularization-free applications.


  We studied the pairing instabilities in K$_x$Fe$_{2-y}$Se$_2$ using a two
stage functional renormalization group (FRG) method. Our results suggest the
leading and subleading pairing symmetries are nodeless $d_{x^2-y^2}$ and nodal
extended $s$ respectively. In addition, despite having no Fermi surfaces we
find the buried hole bands make important contributions to the final effective
interaction. From the bandstructure, spin susceptibility and the FRG results we
conclude that the low energy effective interaction in K$_x$Fe$_{2-y}$Se$_2$ is
well described by a $J_1-J_2$ model with dominant nearest-neighbor
antiferromagnetic interaction $J_1$ (at least as far as the superconducting
pairing is concerned). In the end we briefly mention several obvious
experiments to test whether the pairing symmetry is indeed $d_{x^2-y^2}$.


  Anomaly detection (AD) aims at detecting abnormal samples that deviate from
the expected normal patterns. Generally, it can be trained merely on normal
data, without a requirement for abnormal samples, and thereby plays an
important role in the recognition of rare diseases and health screening in the
medical domain. Despite the emergence of numerous methods for medical AD, we
observe a lack of a fair and comprehensive evaluation, which causes ambiguous
conclusions and hinders the development of this field. To address this problem,
this paper builds a benchmark with unified comparison. Seven medical datasets
with five image modalities, including chest X-rays, brain MRIs, retinal fundus
images, dermatoscopic images, and histopathology whole slide images, are
curated for extensive evaluation. Thirty typical AD methods, including
reconstruction and self-supervised learning-based methods, are involved in
comparison of image-level anomaly classification and pixel-level anomaly
segmentation. Furthermore, for the first time, we formally explore the effect
of key components in existing methods, clearly revealing unresolved challenges
and potential future directions. The datasets and code are available at
\url{https://github.com/caiyu6666/MedIAnomaly}.


  As large language models have demonstrated impressive performance in many
domains, recent works have adopted language models (LMs) as controllers of
visual modules for vision-and-language tasks. While existing work focuses on
equipping LMs with visual understanding, we propose two novel
interpretable/explainable visual programming frameworks for text-to-image (T2I)
generation and evaluation. First, we introduce VPGen, an interpretable
step-by-step T2I generation framework that decomposes T2I generation into three
steps: object/count generation, layout generation, and image generation. We
employ an LM to handle the first two steps (object/count generation and layout
generation), by finetuning it on text-layout pairs. Our step-by-step T2I
generation framework provides stronger spatial control than end-to-end models,
the dominant approach for this task. Furthermore, we leverage the world
knowledge of pretrained LMs, overcoming the limitation of previous
layout-guided T2I works that can only handle predefined object classes. We
demonstrate that our VPGen has improved control in counts/spatial
relations/scales of objects than state-of-the-art T2I generation models.
Second, we introduce VPEval, an interpretable and explainable evaluation
framework for T2I generation based on visual programming. Unlike previous T2I
evaluations with a single scoring model that is accurate in some skills but
unreliable in others, VPEval produces evaluation programs that invoke a set of
visual modules that are experts in different skills, and also provides
visual+textual explanations of the evaluation results. Our analysis shows that
VPEval provides a more human-correlated evaluation for skill-specific and
open-ended prompts than widely used single model-based evaluation. We hope that
our work encourages future progress on interpretable/explainable generation and
evaluation for T2I models.


  We show the numerical simulation result for the mass anomalous dimension of
the SU($3$) gauge theory coupled to $N_f = 12$ fundamental fermions. We use two
independent methods, namely the step scaling method and the hyperscaling method
of the Dirac mode number, to determine the anomalous dimension in the vicinity
of the infrared fixed point of the theory. We show the continuum extrapolations
keeping the renormalized coupling constant as a reference in both analyses.
  Furthermore, some recent works seems to suggest the lower boundary of the
conformal window of the SU($3$) gauge theory exists between $N_f=8$ and $10$.
We also briefly report our new project, in which the numerical simulation of
the SU($3$) gauge theory coupled to $N_f=9$ fundamental fermions has been
performed.


  The Sagnac time delay and the corresponding Sagnac phase shift, for
relativistic matter and electromagnetic beams counter-propagating in a rotating
interferometer, are deduced on the ground of relativistic kinematics. This
purely kinematical approach allows to explain the ''universality'' of the
effect, namely the fact that the Sagnac time difference does not depend on the
physical nature of the interfering beams. The only prime requirement is that
the counter-propagating beams have the same velocity with respect to any
Einstein synchronized local co-moving inertial frame.


  We propose and theoretically analyze an experiment where displacement sensing
of an optically levitated nanoparticle in front of a surface can be used to
measure the induced dipole-dipole interaction between the nanoparticle and its
thermal image. This is achieved by using a surface that is transparent to the
trapping light but reflective to infrared radiation, with a reflectivity that
can be time modulated. This dipole-dipole interaction relies on the thermal
radiation emitted by a silica nanoparticle having sufficient temporal coherence
to correlate the reflected radiation with the thermal fluctuations of the
dipole. The resulting force is orders of magnitude stronger than the thermal
gradient force and it strongly depends on the internal temperature of the
nanoparticle for a particle-to-surface distance greater than two micrometers.
We argue that it is experimentally feasible to use displacement sensing of a
levitated nanoparticle in front of a surface as an internal thermometer.
Experimental access to the internal physics of a levitated nanoparticle in
vacuum is crucial to understand the limitations that decoherence poses to
current efforts devoted to prepare a nanoparticle in a macroscopic quantum
superposition state.


  We study the {\em quantum} decay of D0-branes in two-dimensional 0B string
theory. The quantum nature of the branes provides a natural cut-off for the
closed string emission rate. We find exact quantum mechanical wavefunctions for
the decaying branes and show how one can include the effects of the Fermi sea
for any string coupling (Fermi energy).


  This paper presents a Domain Specific Language (DSL) for generically
describing cyber attacks, agnostic to specific system-under-test(SUT). The
creation of the presented DSL is motivated by an automotive use case. The
concepts of the DSL are generic such thatattacks on arbitrary systems can be
addressed.The ongoing trend to improve the user experience of vehicles with
connected services implies an enhanced connectivity as well asremote accessible
interface opens potential attack vectors. This might also impact safety and the
proprietary nature of potential SUTs.Reusing tests of attack vectors to
industrialize testing them on multiple SUTs mandates an abstraction mechanism
to port an attackfrom one system to another. The DSL therefore generically
describes attacks for the usage with a test case generator (and
executionenvironment) also described in this paper. The latter use this
description and a database with SUT-specific information to generateattack
implementations for a multitude of different (automotive) SUTs.


  We present an efficient method of inclusion of the core-valence correlations
into the configuration interaction (CI) calculations. These correlations take
place in the core area where the potential of external electrons is
approximately constant. A constant potential does not change the core electron
wave functions and Green's functions. Therefore, all operators describing
interaction of $M$ valence electrons and $N-M$ core electrons (the core part of
the Hartree-Fock Hamiltonian
  $V^{N-M}$, the correlation potential $\hat\Sigma_1({\bf r},{\bf r'},E)$ and
the screening of interaction between valence electrons by the core electrons
$\hat\Sigma_2$) may be calculated with all
  $M$ valence electrons removed. This allows one to avoid subtraction diagrams
which make accurate inclusion of the core-valence correlations for $M>2$
prohibitively complicated. Then the CI Hamiltonian for $M$ valence electrons is
calculated using orbitals in complete $V^{N}$ potential (the mean field
produced by all electrons); $\hat\Sigma_1$ + $\hat\Sigma_2$ are added to the CI
Hamiltonian to account for the core-valence correlations. We calculate
$\hat\Sigma_1$ and $\hat\Sigma_2$ using many-body perturbation theory in which
dominating classes of diagrams are included in all orders.
  We use neutral Xe I and all positive ions up to Xe VIII as a testing ground.
We found that the core electron density for all these systems is practically
the same. Therefore, we use the same $\hat\Sigma_1$ and $\hat\Sigma_2$ to build
the CI Hamiltonian in all these systems ($M=1,2,3,4,5,6,7,8$). Good agreement
with experiment for energy levels and Land\'{e} factors is demonstrated for all
cases from Xe I to Xe VIII.


  Fair representation learning transforms user data into a representation that
ensures fairness and utility regardless of the downstream application. However,
learning individually fair representations, i.e., guaranteeing that similar
individuals are treated similarly, remains challenging in high-dimensional
settings such as computer vision. In this work, we introduce LASSI, the first
representation learning method for certifying individual fairness of
high-dimensional data. Our key insight is to leverage recent advances in
generative modeling to capture the set of similar individuals in the generative
latent space. This enables us to learn individually fair representations that
map similar individuals close together by using adversarial training to
minimize the distance between their representations. Finally, we employ
randomized smoothing to provably map similar individuals close together, in
turn ensuring that local robustness verification of the downstream application
results in end-to-end fairness certification. Our experimental evaluation on
challenging real-world image data demonstrates that our method increases
certified individual fairness by up to 90% without significantly affecting task
utility.


  In the last decades the exploitation of silver nanoparticles in novel
antibacterial and detection devices have risen to prominence for their
well-known specific interaction with bacteria. The vast majority of studies
focus on the investigation over the mechanism of action underpinning bacterial
eradication, while little efforts have been devoted to the modification of
silver optical properties upon interaction with bacteria. Specifically, given
the characteristic localized surface plasmon resonance of silver
nanostructures, which is sensitive to changes in the charge carrier density or
in the dielectric environment, these systems can offer a handle in the
detection of bacteria pathogens. In this review, we present the state of art of
the research activity on the interaction of silver nanoparticles with bacteria,
with emphasis on the modification of their optical properties. This may indeed
lead to easy color reading of bacterial tests and pave the way to the
development of nanotechnology silver based bacterial detection.


  We extend the confined-density-dependent-mass (CDDM) model to include isospin
dependence of the equivalent quark mass. Within the
confined-isospin-density-dependent-mass (CIDDM) model, we study the quark
matter symmetry energy, the stability of strange quark matter, and the
properties of quark stars. We find that including isospin dependence of the
equivalent quark mass can significantly influence the quark matter symmetry
energy as well as the properties of strange quark matter and quark stars. While
the recently discovered large mass pulsars PSR J1614-2230 and PSR J0348+0432
with masses around $2M_{\odot}$ cannot be quark stars within the CDDM model,
they can be well described by quark stars in the CIDDM model. In particular,
our results indicate that the two-flavor $u$-$d$ quark matter symmetry energy
should be at least about twice that of a free quark gas or normal quark matter
within conventional Nambu-Jona-Lasinio model in order to describe the PSR
J1614-2230 and PSR J0348+0432 as quark stars.


  We report about direct measurements of heat capacity of Majorana
quasiparticles in superfluid ${^3}$He-B which appear near the surface of the
experimental bolometer on the coherence length ${\xi}$. Two bolometers with
different surface-to-volume ratios were used which allows us to have different
calibrated contributions from Majorana quasiparticles to the ${^3}$He heat
capacity. Estimations of possible impact of ${^3}$He layers adsorbed on the
walls of the bolometer have been done.


  We address the problem of {\it adaptivity} in the framework of reproducing
kernel Hilbert space (RKHS) regression. More precisely, we analyze estimators
arising from a linear regularization scheme $g_\lam$. In practical
applications, an important task is to choose the regularization parameter
$\lam$ appropriately, i.e. based only on the given data and independently on
unknown structural assumptions on the regression function. An attractive
approach avoiding data-splitting is the {\it Lepskii Principle} (LP), also
known as the {\it Balancing Principle} is this setting. We show that a modified
parameter choice based on (LP) is minimax optimal adaptive, up to
$\log\log(n)$. A convenient result is the fact that balancing in $L^2(\nu)-$
norm, which is easiest, automatically gives optimal balancing in all stronger
norms, interpolating between $L^2(\nu)$ and the RKHS. An analogous result is
open for other classical approaches to data dependent choices of the
regularization parameter, e.g. for Hold-Out.


  We consider non-local energy forms of fractional Laplace type on quasicircles
and prove that they can be approximated by similar energy forms on polygonal
curves. The approximation is in terms of generalized Mosco convergence along a
sequence of varying Hilbert spaces. The domains of the energy forms are the
natural trace spaces, and we focus on the case of quasicircles of Hausdorff
dimension greater than one. The jump in Hausdorff dimension results in a
mismatch of fractional orders, which we compensate by a suitable choice of
kernels. We provide approximations of quasidiscs by polygonal
$(\varepsilon,\infty)$-domains with common parameter $\varepsilon>0$ and show
convergence results for superpositions of Dirichlet integrals and non-local
boundary energy forms.


  Millimeter Waves (mmW) and sub-THz frequencies are the candidate bands for
the upcoming Sixth Generation (6G) of communication systems. The use of
collimated beams at mmW/sub-THz to compensate for the increased path and
penetration loss arises the need for a seamless Beam Management (BM),
especially for high mobility scenarios such as the Vehicle-to-Infrastructure
(V2I) one. Recent research advances in Integrated Sensing and Communication
(ISAC) indicate that equipping the network infrastructure, e.g., the Base
Station (BS), with either a stand-alone radar or sensing capabilities using
optimized waveforms, represents the killer technology to facilitate the BM.
However, radio sensing should accurately localize the Vehicular Equipment
(VE)'s antenna, which is not guaranteed in general. Differently, employing side
information from VE's onboard positioning sensors might overcome this
limitation at the price of an increased control signaling between VE and BS.
This paper provides a pragmatic comparison between radar-assisted and
position-assisted BM for mmW V2I systems in a typical urban scenario in terms
of BM training time and beamforming gain loss due to a wrong BM decision.
Simulation results, supported by experimental evidence, show that the point
target approximation of a traveling VE does not hold in practical V2I scenarios
with radar-equipped BS. Therefore, the true antenna position has a residual
uncertainty that is independent of radar's resolution and implies 50\,\% more
BM training time on average. Moreover, there is not a winning technology for BM
between BS-mounted radar and VE's onboard positioning systems. They provide
complementary performance, depending on position, although outperforming blind
BM techniques compared to conventional blind methods. Thus, we propose to
optimally combine radar and positioning information in a multi-technology
integrated BM solution.


  In this paper we show that a free electromagnetic field living in Minkowski
spacetime generates an effective Weitzenbock or an effective Lorentzian
spacetime whose properties aredetermined in details. These results are possible
because we found using the Clifford bundle formalism the noticeable result that
the energy-momentum densities of a free electromagnetic field are sources of
the Hodge duals of exact 2-form fields which satisfy Maxwell like equations.


  We study the boundary traces of Newton-Sobolev, Hajlasz-Sobolev, and BV
(bounded variation) functions. Assuming less regularity of the domain than is
usually done in the literature, we show that all of these function classes
achieve the same "boundary values", which in particular implies that the trace
spaces coincide provided that they exist. Many of our results seem to be new
even in Euclidean spaces but we work in a more general complete metric space
equipped with a doubling measure and supporting a Poincare inequality.


  Ultraluminous supersoft X-ray sources (ULSSS) exhibit supersoft spectra with
blackbody temperatures of 50-100 eV and bolometric luminosities above $10^{39}$
erg s$^{-1}$, and are possibly intermediate mass black holes (IMBHs) of
$\ge10^3 M_\odot$ or massive white dwarfs that are progenitors of type Ia
supernovae. In this letter we report our optical studies of such a source in
M81, M81-ULS1, with HST archive observations. M81-ULS1 is identified with a
point-like object, the spectral energy distribution of which reveals a blue
component in addition to the companion of an AGB star. The blue component is
consistent with the power-law as expected from the geometrically-thin accretion
disk around an IMBH accretor, but inconsistent with the power-law as expected
from the X-ray irradiated flared accretion disk around a white dwarf accretor.
This result is strong evidence that M81-ULS1 is an IMBH instead of a white
dwarf.


  We consider the flotation of deformable, non-wetting drops on a liquid
interface. We consider the deflection of both the liquid interface and the
droplet itself in response to the buoyancy forces, density difference and the
various surface tensions within the system. Our results suggest new insight
into a range of phenomena in which such drops occur, including Leidenfrost
droplets and floating liquid marbles. In particular, we show that the floating
state of liquid marbles is very sensitive to the tension of the
particle-covered interface and suggest that this sensitivity may make such
experiments a useful assay of the properties of these complex interfaces.


  Resonant magnetic perturbations (RMP) have extensively been demonstrated as a
plausible technique for mitigating or suppressing large edge localized modes
(ELMs). Associated with this is a substantial amount of theory and modelling
efforts during recent years. Various models describing the plasma response to
the RMP fields have been proposed in the literature, and are briefly reviewed
in this work. Despite their simplicity, linear response models can provide
alternative criteria, than the vacuum field based criteria, for guiding the
choice of the coil configurations to achieve the best control of ELMs. The role
of the edge peeling response to the RMP fields is illustrated as a key
indicator for the ELM mitigation in low collisionality plasmas, in various
tokamak devices.


  This paper concentrates on the understanding of interlocutors' emotions
evoked in conversational utterances. Previous studies in this literature mainly
focus on more accurate emotional predictions, while ignoring model robustness
when the local context is corrupted by adversarial attacks. To maintain
robustness while ensuring accuracy, we propose an emotion recognizer augmented
by a full-attention topic regularizer, which enables an emotion-related global
view when modeling the local context in a conversation. A joint topic modeling
strategy is introduced to implement regularization from both representation and
loss perspectives. To avoid over-regularization, we drop the constraints on
prior distributions that exist in traditional topic modeling and perform
probabilistic approximations based entirely on attention alignment. Experiments
show that our models obtain more favorable results than state-of-the-art
models, and gain convincing robustness under three types of adversarial
attacks.


  It is shown that a quasi two dimensional (layered) Heisenberg antiferromagnet
with fully frustrated interplane couplings ({\it e.g.} on a body-centered
tetragonal lattice) generically exhibits two thermal phase transitions with
lowering temperature -- an upper transition at $T_{TO}$ (``order from disorder
without order'') in which the lattice point-group symmetry is spontaneously
broken, and a lower N\'{e}el transition at $T_{N}$ at which spin-rotation
symmetry is broken. Although this is the same sequence of transitions observed
in La$_2$CuO$_4$, in the Heisenberg model (without additional lattice degrees
of freedom) $(T_{TO}-T_N) /T_N$ is much smaller than is observed. The model may
apply to the bilayer cuprate La$_2$CaCuO$_6$, in which the transitions are
nearly coincident.


  We construct some examples of analytic solutions of the low energy (i.e.
tree-level) string cosmological effective action. We work with the ``minimal''
field content (i.e. graviton and dilaton) in the absence of any dilaton
potential. Provided the metric is sufficiently inhomogeneous we find solutions
whose curvature invariants are bounded and everywhere defined in time and
space. The dilaton coupling and its associated energy density are regular and
homogeneous. A phase of growing (and non-singular) dilaton coupling compatible
with the regularity of the curvature invariants without the addition of higher
curvature (or higher genus) corrections to the tree-level effective action. We
discuss the symmetries of the obtained solutions.


  We consider an analogue of the Kac random walk on the special orthogonal
group $SO(N)$, in which at each step a random rotation is performed in a
randomly chosen 2-plane of $\bR^N$. We obtain sharp asymptotics for the rate of
convergence in total variance distance, establishing a cut-off phenomenon in
the large $N$ limit. In the special case where the angle of rotation is
deterministic this confirms a conjecture of Rosenthal \cite{Rosenthal}. Under
mild conditions we also establish a cut-off for convergence of the walk to
stationarity under the $L^2$ norm. Depending on the distribution of the
randomly chosen angle of rotation, several surprising features emerge. For
instance, it is sometimes the case that the mixing times differ in the total
variation and $L^2$ norms. Our estimates use an integral representation of the
characters of the special orthogonal group together with saddle point analysis.


  The main uncertainty in current determinations of the power spectrum
normalization, sigma_8, from abundances of X-ray luminous galaxy clusters
arises from the calibration of the mass-temperature relation. We use our weak
lensing mass determinations of 30 clusters from the hitherto largest sample of
clusters with lensing masses, combined with X-ray temperature data from the
literature, to calibrate the normalization of this relation at a temperature of
8 keV, M_{500c,8 keV}=(8.7 +/- 1.6) h^{-1} 10^{14} M_sun. This normalization is
consistent with previous lensing-based results based on smaller cluster
samples, and with some predictions from numerical simulations, but higher than
most normalizations based on X-ray derived cluster masses. Assuming the
theoretically expected slope alpha=3/2 of the mass-temperature relation, we
derive sigma_8 = 0.88 +/-0.09 for a spatially-flat LambdaCDM universe with
Omega_m = 0.3. The main systematic errors on the lensing masses result from
extrapolating the cluster masses beyond the field-of-view used for the
gravitational lensing measurements, and from the separation of
cluster/background galaxies, contributing each with a scatter of 20%. Taking
this into account, there is still significant intrinsic scatter in the
mass-temperature relation indicating that this relation may not be very tight,
at least at the high mass end. Furthermore, we find that dynamically relaxed
clusters are 75 +/-40% hotter than non-relaxed clusters.


  The next generation of gravitational wave (gw) detectors is expected to fully
enter into the quantum regime of force and displacement detection. With this
aim, it is important to scale up the experiments on opto-mechanical effects
from the microscopic regime to large mass systems and test the schemes that
should be applied to reach the quantum regime of detection. In this work we
present the experimental characterization of a prototype of massive gw
detector, composed of two oscillators with a mass of the order of the kg, whose
distance is read by a high finesse optical cavity. The mechanical response
function is measured by exciting the oscillators though modulated radiation
pressure. We demonstrate two effects crucial for the next generation of
massive, cryogenic gw detectors (DUAL detectors): a) the reduction of the
contribution of 'local' susceptibility thanks to an average over a large
interrogation area. Such effect is measured on the photo-thermal response
thanks to the first implementation of a folded-Fabry-Perot cavity; b) the
'back-action reduction' due to negative interference between acoustic modes.
Moreover, we obtain the active cooling of an oscillation mode through radiation
pressure, on the described mechanical device which is several orders of
magnitude heavier than previously demonstrated radiation-pressure cooled
systems.


  In this paper, we study the exact controllability and stabilization of a
system of two wave equations coupled by velocities with an internal, local
control acting on only one equation. We distinguish two cases. In the first
one, when the waves propagate at the same speed: using a frequency domain
approach combined with multiplier technique, we prove that the system is
exponentially stable when the coupling region satisfies the geometric control
condition GCC. Following a result of Haraux ([11]), we establish the main
indirect observability inequality. This results leads, by the HUM method, to
prove that the total system is exactly controllable by means of locally
distributed control. In the second case, when the waves propagate at different
speed, we establish an exponential decay rate in the weak energy space.
Consequently, the system is exactly controllable using a result of [11].


  Let X be a closed subscheme and let HF(X,-) and hp(X,-) denote, respectively,
the Hilbert function and the Hilbert polynomial of X. We say that X has
bipolynomial Hilbert function if HF(X,d)=min{hp(P^n,d),hp(X,d)} for every
non-negative integer d. We show that if X consists of a plane and generic
lines, then X has bipolynomial Hilbert function. We also conjecture that
generic configurations of non-intersecting linear spaces have bipolynomial
Hilbert function.


  In modern video coding standards, block-based inter prediction is widely
adopted, which brings high compression efficiency. However, in natural videos,
there are usually multiple moving objects of arbitrary shapes, resulting in
complex motion fields that are difficult to compactly represent. This problem
has been tackled by more flexible block partitioning methods in the Versatile
Video Coding (VVC) standard, but the more flexible partitions require more
overhead bits to signal and still cannot be made arbitrary shaped. To address
this limitation, we propose an object segmentation-assisted inter prediction
method (SAIP), where objects in the reference frames are segmented by some
advanced technologies. With a proper indication, the object segmentation mask
is translated from the reference frame to the current frame as the
arbitrary-shaped partition of different regions without any extra signal. Using
the segmentation mask, motion compensation is separately performed for
different regions, achieving higher prediction accuracy. The segmentation mask
is further used to code the motion vectors of different regions more
efficiently. Moreover, segmentation mask is considered in the joint
rate-distortion optimization for motion estimation and partition estimation to
derive the motion vector of different regions and partition more accurately.
The proposed method is implemented into the VVC reference software, VTM version
12.0. Experimental results show that the proposed method achieves up to 1.98%,
1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common
test sequences, under the Low-delay P, Low-delay B, and Random Access
configurations, respectively.


  We review the present knowledge for $\alpha_s$, the fundamental coupling
underlying the interactions of quarks and gluons in QCD. The dependence of
$\alpha_s(Q^2)$ on momentum transfer $Q$ encodes the underlying dynamics of
hadron physics -from color confinement in the infrared domain to asymptotic
freedom at short distances. We review constraints on $\alpha_s(Q^2)$ at high
$Q^2$, as predicted by perturbative QCD, and its analytic behavior at small
$Q^2$, based on models of nonperturbative dynamics. In the introductory part of
this review, we explain the phenomenological meaning of $\alpha_s$, the reason
for its running, and the challenges facing a complete understanding of its
analytic behavior in the infrared domain. In the second, more technical, part
of the review, we discuss the behavior of $\alpha_s(Q^2)$ in the high $Q^2$
domain of QCD. We review how $\alpha_s$ is defined, including its
renormalization scheme dependence, the definition of its renormalization scale,
the utility of effective charges, as well as Commensurate Scale Relations which
connect the various definitions of $\alpha_s$ without renormalization-scale
ambiguity. We also report recent measurements and theoretical analyses which
have led to precise QCD predictions at high energy. In the last part of the
review, we discuss the challenge of understanding the analytic behavior
$\alpha_s(Q^2)$ in the infrared domain. We also review important methods for
computing $\alpha_s$, including lattice QCD, the Schwinger-Dyson equations, the
Gribov-Zwanziger analysis and light-front holographic QCD. After describing
these approaches and enumerating their conflicting predictions, we discuss the
origin of these discrepancies and how to remedy them. Our aim is not only to
review the advances in this difficult area, but also to suggest what could be
an optimal definition of $\alpha_s$ in order to bring better unity to the
subject.


  Semantic networks provide a useful tool to understand how related concepts
are retrieved from memory. However, most current network approaches use
pairwise links to represent memory recall patterns. Pairwise connections
neglect higher-order associations, i.e. relationships between more than two
concepts at a time. These higher-order interactions might covariate with (and
thus contain information about) how similar concepts are along psycholinguistic
dimensions like arousal, valence, familiarity, gender and others. We overcome
these limits by introducing feature-rich cognitive hypergraphs as quantitative
models of human memory where: (i) concepts recalled together can all engage in
hyperlinks involving also more than two concepts at once (cognitive hypergraph
aspect), and (ii) each concept is endowed with a vector of psycholinguistic
features (feature-rich aspect). We build hypergraphs from word association data
and use evaluation methods from machine learning features to predict concept
concreteness. Since concepts with similar concreteness tend to cluster together
in human memory, we expect to be able to leverage this structure. Using word
association data from the Small World of Words dataset, we compared a pairwise
network and a hypergraph with N=3586 concepts/nodes. Interpretable artificial
intelligence models trained on (1) psycholinguistic features only, (2)
pairwise-based feature aggregations, and on (3) hypergraph-based aggregations
show significant differences between pairwise and hypergraph links.
Specifically, our results show that higher-order and feature-rich hypergraph
models contain richer information than pairwise networks leading to improved
prediction of word concreteness. The relation with previous studies about
conceptual clustering and compartmentalisation in associative knowledge and
human memory are discussed.


  Deep neural networks based on reinforcement learning (RL) for solving
combinatorial optimization (CO) problems are developing rapidly and have shown
a tendency to approach or even outperform traditional solvers. However,
existing methods overlook an important distinction: CO problems differ from
other traditional problems in that they focus solely on the optimal solution
provided by the model within a specific length of time, rather than considering
the overall quality of all solutions generated by the model. In this paper, we
propose Leader Reward and apply it during two different training phases of the
Policy Optimization with Multiple Optima (POMO) model to enhance the model's
ability to generate optimal solutions. This approach is applicable to a variety
of CO problems, such as the Traveling Salesman Problem (TSP), the Capacitated
Vehicle Routing Problem (CVRP), and the Flexible Flow Shop Problem (FFSP), but
also works well with other POMO-based models or inference phase's strategies.
We demonstrate that Leader Reward greatly improves the quality of the optimal
solutions generated by the model. Specifically, we reduce the POMO's gap to the
optimum by more than 100 times on TSP100 with almost no additional
computational overhead.


  The recent high energy electron and positron flux observed by the DAMPE
experiment indicates possible excess events near 1.4 TeV. Such an excess may be
evidence of dark matter annihilations or decays in a dark matter subhalo that
is located close to the solar system. We give here an analysis of this excess
from annihilations of Dirac fermion dark matter which is charged under a new
$U(1)_X$ gauge symmetry. The interactions between dark matter and the standard
model particles are mediated the $U(1)_X$ gauge boson. We show that dark matter
annihilations from a local subhalo can explain the excess with the canonical
thermal annihilation cross section. We further discuss the constraints from the
relic density, from the dark matter direct detection, from the dark matter
indirect detection, from the cosmic microwave background, and from the particle
colliders.


  This paper addresses finite sample stability properties of sequential Monte
Carlo methods for approximating sequences of probability distributions. The
results presented herein are applicable in the scenario where the start and end
distributions in the sequence are fixed and the number of intermediate steps is
a parameter of the algorithm. Under assumptions which hold on non-compact
spaces, it is shown that the effect of the initial distribution decays
exponentially fast in the number of intermediate steps and the corresponding
stochastic error is stable in \mathbb{L}_{p} norm.


  Given arbitrary monomial ideals $I$ and $J$ in polynomial rings $A$ and $B$
over a field $K$, we investigate the Stanley depth of powers of the sum $I+J$,
and their quotient rings, in $A\otimes_K B$ in terms of those of $I$ and $J$.
Our results can be used to study the asymptotic behavior of the Stanley depth
of powers of a monomial ideal. For instance, we solved the case of monomial
complete intersection.


  While the origin of r-process nuclei remains a long-standing mystery, recent
spectroscopic studies of extremely metal-poor stars in the Galactic halo
strongly suggest that it is associated with core-collapse supernovae. In this
article, an overview of the recent theoretical studies of the r-process is
presented with a special emphasis on the astrophysical scenarios related to
core-collapse supernovae. We also review a recent progress of the Galactic
chemical evolution studies as well as of the spectroscopic studies of extremely
metal-poor halo stars, which provide us important clues to better understanding
of the astrophysical r-process site.


  We give a method to construct stable vector bundles whose rank divides the
degree over curves of genus bigger than one. The method complements the one
given by Newstead. Finally, we make some systematic remarks and observations in
connection with rationality of moduli spaces of stable vector bundles.


  We introduce a hybrid metaphor for the visualization of the reconciliations
of co-phylogenetic trees, that are mappings among the nodes of two trees. The
typical application is the visualization of the co-evolution of hosts and
parasites in biology. Our strategy combines a space-filling and a node-link
approach. Differently from traditional methods, it guarantees an unambiguous
and `downward' representation whenever the reconciliation is time-consistent
(i.e., meaningful). We address the problem of the minimization of the number of
crossings in the representation, by giving a characterization of planar
instances and by establishing the complexity of the problem. Finally, we
propose heuristics for computing representations with few crossings.


  Using direct numerical simulations (DNS) we verify that in the kinematic
regime, a turbulent helical dynamo grows in such a way that the magnetic energy
spectrum remains to high precision shape-invariant, i.e., at each wavenumber
$k$ the spectrum grows with the same growth rate. Signatures of large-scale
dynamo action can be identified through the excess of magnetic energy at small
$k$, of one of the two oppositely polarized constituents. Also a suitably
defined planar average of the magnetic field can be chosen such that its rms
value isolates the strength of the mean field. However, these different means
of analysis suggest that the strength of the large-scale field diminishes with
increasing magnetic Reynolds number ${\rm Re}_{\rm M}$ like ${\rm Re}_{\rm
M}^{-1/2}$ for intermediate values and like ${\rm Re}_{\rm M}^{-3/4}$ for
larger ones. Both an analysis from the Kazantsev model including helicity and
the DNS show that this arises due to the magnetic energy spectrum still peaking
at resistive scales, even when helicity is present. As expected, the amplitude
of the large-scale field increases with increasing fractional helicity,
enabling us to determine the onset of large scale dynamo action and
distinguishing it from that of the small-scale dynamo. Our DNS show that,
contrary to earlier results for smaller scale separation (only 1.5 instead of
now 4), the small-scale dynamo can still be excited at magnetic Prandtl numbers
of 0.1 and only moderate values of the magnetic Reynolds numbers ($\sim 160$).


  We provide a set of exact solutions in field theory of scalar fields with
$Z_2$ symmetry that involve Jacobi elliptic functions. These solutions have the
interesting property to provide massive waves even if one starts from a
massless equation. We analyze them classically providing also exact solutions
to the corresponding equations for the Green functions needed to completely
solve them in a strong coupling limit. This is accomplished using a functional
expansion into powers of the current. It is shown that the spontaneous breaking
of the $Z_2$ symmetry is due to the existence of a zero mode that persists also
in the case of the Higgs mechanism. In this latter case, the zero mode seems to
play a role similar to the Goldstone boson in the breaking of a continuous
symmetry and so, it should be important at lower momenta as a long range
excitation.


  We give an unified framework to solve rough differential equations. Based on
flows, our approach unifies the former ones developed by Davie, Friz-Victoir
and Bailleul. The main idea is to build a flow from the iterated product of an
almost flow which can be viewed as a good approximation of the solution at
small time. In this second article, we give some tractable conditions under
which the limit flow is Lipschitz continuous and its links with uniqueness of
solutions of rough differential equations. We also give perturbation formulas
on almost flows which link the former constructions.


  Olfaction, often overlooked in cultural heritage studies, holds profound
significance in shaping human experiences and identities. Examining historical
depictions of olfactory scenes can offer valuable insights into the role of
smells in history. We show that a transfer-learning approach using weakly
labeled training data can remarkably improve the classification of fragrant
spaces and, more generally, artistic scene depictions. We fine-tune
Places365-pre-trained models by querying two cultural heritage data sources and
using the search terms as supervision signal. The models are evaluated on two
manually corrected test splits. This work lays a foundation for further
exploration of fragrant spaces recognition and artistic scene classification.
All images and labels are released as the ArtPlaces dataset at
https://zenodo.org/doi/10.5281/zenodo.11584328.


  Visual reconstruction algorithms are an interpretive tool that map brain
activity to pixels. Past reconstruction algorithms employed brute-force search
through a massive library to select candidate images that, when passed through
an encoding model, accurately predict brain activity. Here, we use conditional
generative diffusion models to extend and improve this search-based strategy.
We decode a semantic descriptor from human brain activity (7T fMRI) in voxels
across most of visual cortex, then use a diffusion model to sample a small
library of images conditioned on this descriptor. We pass each sample through
an encoding model, select the images that best predict brain activity, and then
use these images to seed another library. We show that this process converges
on high-quality reconstructions by refining low-level image details while
preserving semantic content across iterations. Interestingly, the
time-to-convergence differs systematically across visual cortex, suggesting a
succinct new way to measure the diversity of representations across visual
brain areas.


  We consider an Ising spin-chain in a random transverse magnetic field and
compute the zero temperature wave vector and frequency dependent dynamic
structure factor numerically by using Jordan-Wigner transformation. Two types
of distributions of magnetic fields are introduced. For a rectangular
distribution, a dispersing branch is observed, and disorder tends to broaden
the dispersion peak and close the excitation gap. For a binary distribution, a
non-dispersing branch at almost zero energy is recovered. We discuss the
relationship of our work to the neutron scattering measurement in
$\mathrm{LiHoF_4}$.


  The LBV/WR system HD 5980 contains a short-period, eccentric binary system
with interacting stellar winds. In this paper we summarize results from model
calculations of the tidal flows on the LBV component showing that energy
dissipation rates, E-dot, associated with turbulent viscosity are orbital-phase
dependent as well as variable over the stellar surface. We speculate that if
E-dot contributes towards driving mass-loss, the strongest wind-wind
interaction effects may occur after periastron passage. In addition, the model
suggests the presence of stronger outflows localized at polar angles theta~30
--50 degrees during part of the orbital cycle. Thus, the analysis of wind-wind
interactions in this system requires that models be revised to incorporate
non-stationary and asymmetric wind structures.


  The automatic identification system (AIS) reports vessels' static and dynamic
information, which are essential for maritime traffic situation awareness.
However, AIS transponders can be switched off to hide suspicious activities,
such as illegal fishing, or piracy. Therefore, this paper uses real world AIS
data to analyze the possibility of successful detection of various anomalies in
the maritime domain. We propose a multi-class artificial neural network
(ANN)-based anomaly detection framework to classify intentional and
non-intentional AIS on-off switching anomalies. The multi-class anomaly
framework captures AIS message dropouts due to various reasons, e.g., channel
effects or intentional one for carrying illegal activities. We extract
position, speed, course and timing information from real world AIS data, and
use them to train a 2-class (normal and anomaly) and a 3-class (normal, power
outage and anomaly) anomaly detection models. Our results show that the models
achieve around 99.9% overall accuracy, and are able to classify a test sample
in the order of microseconds.


  We give a survey of results relating the restricted holonomy of a Riemannian
spin manifold with lower bounds on the spectrum of its Dirac operator, giving a
new proof of a result originally due to Kirchberg.


  Following the new description of an oriented full transformation on a finite
chain given recently by Higgins and Vernitski in "Orientation-preserving and
orientation-reversing mappings: a new description", Semigroup Forum 104 (2022),
509--514, in this short note we present a refinement of this description which
is extendable to partial transformations and to injective partial
transformations.


  A parameterized supernova synthetic-spectrum code is used to study line
identifications in the photospheric-phase spectra of the peculiar Type Ia SN
1991T, and to extract some constraints on the composition structure of the
ejected matter. The inferred composition structure is not like that of any
hydrodynamical model for Type Ia supernovae. Evidence that SN 1991T was
overluminous for an SN Ia is presented, and it is suggested that this peculiar
event probably was a substantially super-Chandrasekhar explosion that resulted
from the merger of two white dwarfs.


  Motivated by lubrication problems, we consider a micropolar uid ow in a 2D
domain with a rough and free boundary. We assume that the thickness and the
roughness are both of order 0 < " << 1. We prove the existence and uniqueness
of a solution of this problem for any value of " and we establish some a priori
estimates. Then we use the two-scale convergence technique to derive the limit
problem when " tends to zero. Moreover we show that the limit velocity and
micro-rotation elds are uniquely determined via auxiliary well-posed problems
and the limit pressure is given as the unique solution of a Reynolds equation.


  This paper proposes an end-to-end Efficient Re-parameterizationResidual
Attention Network(ERRA-Net) to directly restore the nonhomogeneous hazy image.
The contribution of this paper mainly has the following three aspects: 1) A
novel Multi-branch Attention (MA) block. The spatial attention mechanism better
reconstructs high-frequency features, and the channel attention mechanism
treats the features of different channels differently. Multi-branch structure
dramatically improves the representation ability of the model and can be
changed into a single path structure after re-parameterization to speed up the
process of inference. Local Residual Connection allows the low-frequency
information in the nonhomogeneous area to pass through the block without
processing so that the block can focus on detailed features. 2) A lightweight
network structure. We use cascaded MA blocks to extract high-frequency features
step by step, and the Multi-layer attention fusion tail combines the shallow
and deep features of the model to get the residual of the clean image finally.
3)We propose two novel loss functions to help reconstruct the hazy image
ColorAttenuation loss and Laplace Pyramid loss. ERRA-Net has an impressive
speed, processing 1200x1600 HD quality images with an average runtime of 166.11
fps. Extensive evaluations demonstrate that ERSANet performs favorably against
the SOTA approaches on the real-world hazy images.


  We consider a curve $\alpha=\alpha(s)$ in Minkowski 3-space $E_1^3$ and
denote by $\{T,N,B}$ the Frenet frame of $\alpha$. We say that $\alpha$ is a
slant helix if there exists a fixed direction $U$ of $E_1^3$ such that the
function $<N(s),U>$ is constant. In this work we give characterizations of
slant helices in terms of the curvature and torsion of $\alpha$.


  Routing, as a basic phenomena, by itself, has got umpteen scopes to analyse,
discuss and arrive at an optimal solution for the technocrats over years.
Routing is analysed based on many factors; few key constraints that decide the
factors are communication medium, time dependency, information source nature.
Parametric routing has become the requirement of the day, with some kind of
adaptation to the underlying network environment. Satellite constellations,
particularly LEO satellite constellations have become a reality in operational
to have a non-breaking voice/data communication around the world.Routing in
these constellations has to be treated in a non conventional way, taking their
network geometry into consideration. One of the efficient methods of
optimization is putting Neural Networks to use. Few Artificial Neural Network
models are very much suitable for the adaptive control mechanism, by their
nature of network arrangement. One such efficient model is Hopfield Network
model.
  This paper is an attempt to design a framework for the Hopfield Network based
adaptive routing phenomena in satellite constellations.


  Linear optical elements are pivotal instruments in the manipulation of
classical and quantum states of light. The vast progress in integrated quantum
photonic technology enables the implementation of large numbers of such
elements on chip while providing interferometric stability. As a trade-off
these structures face the intrinsic challenge of characterizing their optical
transformation as individual optical elements are not directly accessible. Thus
the unitary transformation needs to be reconstructed from a dataset generated
with having access to the input and output ports of the device only. Here we
present a novel approach to unitary reconstruction that significantly improves
upon existing approaches. We compare its performance to several approaches via
numerical simulations for networks up to 14 modes. We show that an adapted
version of our approach allows to recover all mode-dependent losses and to
obtain highest reconstruction fidelities under such conditions.


  We study the correlations of the shear signal between triplets of sources in
the Canada-France-Hawaii Lensing Survey (CFHTLenS) to probe cosmological
parameters via the matter bispectrum. In contrast to previous studies, we
adopted a non-Gaussian model of the data likelihood which is supported by our
simulations of the survey. We find that for state-of-the-art surveys, similar
to CFHTLenS, a Gaussian likelihood analysis is a reasonable approximation,
albeit small differences in the parameter constraints are already visible. For
future surveys we expect that a Gaussian model becomes inaccurate. Our
algorithm for a refined non-Gaussian analysis and data compression is then of
great utility especially because it is not much more elaborate if simulated
data are available. Applying this algorithm to the third-order correlations of
shear alone in a blind analysis, we find a good agreement with the standard
cosmological model: $\Sigma_8$=$\sigma_8$ $(\Omega_{\rm
m}/0.27)^{0.64}$=$0.79^{+0.08}_{-0.11}$ for a flat $\Lambda\rm CDM$ cosmology
with $h=0.7\pm0.04$ ($68\%$ credible interval). Nevertheless our models provide
only moderately good fits as indicated by $\chi^2/{\rm dof}=2.9$, including a
$20\%$ r.m.s. uncertainty in the predicted signal amplitude. The models cannot
explain a signal drop on scales around 15 arcmin, which may be caused by
systematics. It is unclear whether the discrepancy can be fully explained by
residual PSF systematics of which we find evidence at least on scales of a few
arcmin. Therefore we need a better understanding of higher-order correlations
of cosmic shear and their systematics to confidently apply them as cosmological
probes.


  Mean motion resonances (MMRs) can lead either to chaotic or regular motion.
We report on a numerical experiment showing that even in one of the most
chaotic regions of the Solar System - the region of the giant planets, there
are numerous bands where MMRs can stabilize orbits of small bodies in a time
span comparable to their lifetimes. Two types of temporary stabilization were
observed: short period ($\sim10^{4}$ years) when a body was in a MMR with only
one planet and long period (over $10^{5}$ years) when a body is located in
overlapping MMRs with two or three planets.
  The experiment showed that the Main Belt region can be enriched by cometary
material in its pre-active state due to temporary resonant interactions between
small bodies and giant planets.


  Semiconducting transition metal dichalcogenides (TMDs) have emerged as
materials that can be used to realize two-dimensional (2D) crystals possessing
rather unique transport and optical properties. Most research has so far
focused on sulfur and selenium compounds, while tellurium-based materials
attracted little attention so far. As a first step in the investigation of
Te-based semiconducting TMDs in this context, we have studied MoTe$_2$ crystals
with thicknesses above 4 nm, focusing on surface transport and a quantitative
determination of the gap structure. Using ionic-liquid gated transistors, we
show that ambipolar transport at the surface of the material is reproducibly
achieved, with hole and electron mobility values between 10 and 30 cm$^2$/Vs at
room temperature. The gap structure is determined through three different
techniques: ionic-liquid gated transistors and scanning tunneling spectroscopy,
that allow the measurement of the indirect gap ($E_{ind}$), and optical
transmission spectroscopy on crystals of different thickness, that enables the
determination of both the direct ($E_{dir}$) and the indirect gap. We find that
at room temperature $E_{ind}$ = 0.88 eV and $E_{dir}$ = 1.02 eV. Our results
suggest that thin MoTe$_2$ layers may exhibit a transition to a direct gap
before mono-layer thickness. They should also drastically extend the range of
direct gaps accessible in 2D semiconducting TMDs.


  In this paper we discuss in detail computational methods and new results for
one-loop virtual corrections to N = 4 super Yang-Mills scattering amplitudes
calculated to all orders in epsilon, the dimensional regularization parameter.
It is often the case that one-loop gauge theory computations are carried out to
order epsilon^0, since higher order in epsilon contributions vanish in the
small epsilon limit. We will show, however, that the higher order contributions
are actually quite useful. In the context of maximally supersymmetric
Yang-Mills, we consider two examples in detail to illustrate our point. First
we will concentrate on computations with gluonic external states and argue that
N = 4 supersymmetry implies a simple relation between all-orders-in-epsilon
one-loop N = 4 super Yang-Mills amplitudes and the first and second stringy
corrections to analogous tree-level superstring amplitudes. For our second
example we will derive a new result for the all-orders-in-epsilon one-loop
superamplitude for planar six-particle NMHV scattering, an object which allows
one to easily obtain six-point NMHV amplitudes with arbitrary external states.
We will then discuss the relevance of this computation to the evaluation of the
ratio of the planar two-loop six-point NMHV superamplitude to the planar
two-loop six-point MHV superamplitude, a quantity which is expected to have
remarkable properties and has been the subject of much recent investigation. To
make the presentation as self-contained as possible, we extensively review the
prerequisites necessary to understand the main results of this work.


  Masked autoregressive flow (MAF) is a state-of-the-art non-parametric density
estimation technique. It is based on the idea (known as a normalizing flow)
that a simple base probability distribution can be mapped into a complicated
target distribution that one wishes to approximate, using a sequence of
bijective transformations. The denmarf package provides a scikit-learn-like
interface in Python for researchers to effortlessly use MAF for density
estimation in their applications to evaluate probability densities of the
underlying distribution of a set of data and generate new samples from the
data, on either a CPU or a GPU, as simple as "from denmarf import
DensityEstimate; de = DensityEstimate().fit(X)". The package also implements
logistic transformations to facilitate the fitting of bounded distributions.


  A major research area of Ching-Zong Wei (1949--2004) was time series models
and their applications in econometrics and engineering, to which he made many
important contributions. A conference on time series and related topics in
memory of him was held on December 12--14, 2005, at Academia Sinica in Taipei,
where he was Director of the Institute of Statistical Science from 1993 to
1999. Of the forty-two speakers at the conference, twenty contributed to this
volume. These papers are listed under the following three headings.


  The amplitude for photoproduction gamma p -> \Upsilon p is calculated in a
pQCD k_\perp-factorization approach. The total cross section for diffractive
\Upsilon's is compared to recent HERA data. The amplitude is used to predict
the cross section for exclusive ppbar \to ppbar \Upsilon(1S,2S) process in
hadronic reactions at Tevatron energies. We also included absorption effects.


  We present measurements of rotations of the optical polarization of blazars
during the second year of operation of RoboPol, a monitoring programme of an
unbiased sample of gamma-ray bright blazars specially designed for effective
detection of such events, and we analyse the large set of rotation events
discovered in two years of observation. We investigate patterns of variability
in the polarization parameters and total flux density during the rotation
events and compare them to the behaviour in a non-rotating state. We have
searched for possible correlations between average parameters of the
polarization-plane rotations and average parameters of polarization, with the
following results: (1) there is no statistical association of the rotations
with contemporaneous optical flares; (2) the average fractional polarization
during the rotations tends to be lower than that in a non-rotating state; (3)
the average fractional polarization during rotations is correlated with the
rotation rate of the polarization plane in the jet rest frame; (4) it is likely
that distributions of amplitudes and durations of the rotations have physical
upper bounds, so arbitrarily long rotations are not realised in nature.


  We propose a new method for computing the ground state properties and the
time evolution of infinite chains based on a transverse contraction of the
tensor network. The method does not require finite size extrapolation and
avoids explicit truncation of the bond dimension along the evolution. By
folding the network in the time direction prior to contraction, time dependent
expectation values and dynamic correlation functions can be computed after much
longer evolution time than with any previous method. Moreover, the algorithm we
propose can be used for the study of some non-invariant infinite chains,
including impurity models.


  Regular Path Queries (RPQs) are a type of graph query where answers are pairs
of nodes connected by a sequence of edges matching a regular expression. We
study the techniques to process such queries on a distributed graph of data.
While many techniques assume the location of each data element (node or edge)
is known, when the components of the distributed system are autonomous, the
data will be arbitrarily distributed. As the different query processing
strategies are equivalently costly in the worst case, we isolate
query-dependent cost factors and present a method to choose between strategies,
using new query cost estimation techniques. We evaluate our techniques using
meaningful queries on biomedical data.


  Fast, volumetric imaging over large scales has been a long-standing goal in
biological microscopy. Scanning techniques such as fluorescence confocal
microscopy can acquire 2D images at high resolution and high speed, but
extending the acquisition to multiple planes at different depths requires an
axial scanning mechanism that drastically reduces the acquisition speed. To
address this challenge, we report an augmented variant of confocal microscopy
where the key innovation consists to use a series of reflecting pinholes
axially distributed in the detection plane, each one probing a different depth
within the sample. As no axial scanning mechanism is involved, our technique
provides simultaneous multiplane imaging over fields of view larger than a
millimeter at video-rate. We demonstrate the general applicability of our
technique to neuronal imaging of both Caenorhabditis elegans and mouse brains
in-vivo.


  We discuss the implications of assuming a four-zero Yukawa texture for the
properties of the charged Higgs boson within the context of the general 2-Higgs
Doublet Model of Type III. We begin by presenting a detailed analysis of the
charged Higgs boson couplings with heavy quarks and the resulting pattern for
its decays. The production of charged Higgs bosons is also sensitive to the
modifications of its couplings, so that we also evaluate the resulting effects
on the top decay $t \to b H^{+}$ as well as on `direct' $c\bar{b}\to H^++c. c.$
and `indirect' $q\bar q,gg\to \bar t b H^++c. c.$ production. Significant scope
exists at the Large Hadron Collider for several $H^\pm$ production and decay
channels combined to enable one to distinguish between such a model and
alternative 2-Higgs doublet scenarios.


  We prove that the existence of infinitely many $(m_k, n_k) \in \mathbb{N}^2$
such that the difference of harmonic numbers $H_{m_k} - H_{n_k}$ approximates 1
well $$ \lim_{k \rightarrow \infty} \left| \sum_{\ell = n}^{m_k} \frac{1}{\ell}
- 1 \right|\cdot n_k^2 = 0.$$ This answers a question of Erd\H{o}s and Graham.
The construction uses asymptotics for harmonic numbers, the precise nature of
the continued fraction expansion of $e$ and a suitable rescaling of a
subsequence of convergents. We also prove a quantitative rate by appealing to
techniques of Heilbronn, Danicic, Harman, Hooley and others regarding $\min_{1
\leq n \leq N} \min_{m \in \mathbb{N}}\| n^2 \theta - m\|$.


  Spatial confounding poses a significant challenge in scientific studies
involving spatial data, where unobserved spatial variables can influence both
treatment and outcome, possibly leading to spurious associations. To address
this problem, we introduce SpaCE: The Spatial Confounding Environment, the
first toolkit to provide realistic benchmark datasets and tools for
systematically evaluating causal inference methods designed to alleviate
spatial confounding. Each dataset includes training data, true counterfactuals,
a spatial graph with coordinates, and smoothness and confounding scores
characterizing the effect of a missing spatial confounder. It also includes
realistic semi-synthetic outcomes and counterfactuals, generated using
state-of-the-art machine learning ensembles, following best practices for
causal inference benchmarks. The datasets cover real treatment and covariates
from diverse domains, including climate, health and social sciences. SpaCE
facilitates an automated end-to-end pipeline, simplifying data loading,
experimental setup, and evaluating machine learning and causal inference
models. The SpaCE project provides several dozens of datasets of diverse sizes
and spatial complexity. It is publicly available as a Python package,
encouraging community feedback and contributions.


  Duplication, whether exact or partial, is a common issue in many datasets. In
clinical notes data, duplication (and near duplication) can arise for many
reasons, such as the pervasive use of templates, copy-pasting, or notes being
generated by automated procedures. A key challenge in removing such near
duplicates is the size of such datasets; our own dataset consists of more than
10 million notes. To detect and correct such duplicates requires algorithms
that both accurate and highly scalable. We describe a solution based on
Minhashing with Locality Sensitive Hashing. In this paper, we present the
theory behind this method and present a database-inspired approach to make the
method scalable. We also present a clustering technique using disjoint sets to
produce dense clusters, which speeds up our algorithm.


  Observations of cool stars reveal dark spot-like features on their surfaces.
Compared to sunspots, starspots can be bigger or cover a larger fraction of the
stellar surface. While sunspots appear only at low latitudes, starspots are
also found in polar regions, in particular on rapidly rotating stars. Sunspots
are believed to result from the eruption of magnetic flux-tubes rising from the
deep interior of the Sun. The strong magnetic field locally reduces convective
heat transport to the solar surface. Such flux-tube models have also been
invoked to explain starspot properties. However, these models use several
simplifications and so far the generation of either sunspots or starspots has
not been demonstrated in a self-consistent simulation of stellar magnetic
convection. Here we show that direct numerical simulations of a distributed
dynamo operating in a density-stratified rotating spherical shell can
spontaneously generate cool spots. Convection in the interior of the model
produces a large scale magnetic field which interacts with near surface
granular convection leading to strong concentrations of magnetic flux and
formation of starspots. Prerequisites for the formation of sizeable
high-latitude spots in the model are sufficiently strong density stratification
and rapid rotation. Our model presents an alternate mechanism for starspot
formation by distributed dynamo action.


  Quantum K-theory of a smooth projective variety at genus zero is a collection
of integers that can be assembled into a generating series $J(Q,q,t)$ that
satisfies a system of linear differential equations with respect to $t$ and
$q$-difference equations with respect to $Q$. With some mild assumptions on the
variety, it is known that the full theory can be reconstructed from its small
$J$-function $J(Q,q,0)$ which, in the case of Fano manifolds, is a
vector-valued $q$-hypergeometric function. On the other hand, for the quintic
3-fold we formulate an explicit conjecture for the small $J$-function and its
small linear $q$-difference equation expressed linearly in terms of the
Gopakumar-Vafa invariants. Unlike the case of quantum knot invariants, and the
case of Fano manifolds, the coefficients of the small linear $q$-difference
equations are not Laurent polynomials, but rather analytic functions in two
variables determined linearly by the Gopakumar-Vafa invariants of the quintic.
Our conjecture for the small $J$-function agrees with a proposal of
Jockers-Mayr.


  We examine an effect of acoustic phonon scattering on an electric
conductivity of single-component molecular conductor [Pd(dddt)$_2$] (dddt =
5,6-dihydro-1,4-dithiin-2,3-dithiolate) with a half-filled band by applying the
previous calculation in a two-dimensional model with Dirac cone [Phys. Rev. B
{\bf 98},161205 (2018)], where the electric transport by the impurity
scattering exhibits the noticeable interplay of the Dirac cone and the phonon
scattering,resulting in a maximum of the conductivity with increasing
temperature. The conductor shows a nodal line semimetal where the band crossing
of HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied
Molecular Orbital) provides a loop of Dirac points located close to the Fermi
energy followed by the density of states (DOS) similar to that of
two-dimensional Dirac cone. Using a tight-binding (TB) model
[arXiv:2008.09277], which was obtained using the crystal structure observed
from a recent X ray diffraction experiment under pressure, it is shown that the
obtained conductivity explains reasonably the anomalous behavior in
[Pd(dddt)$_2$] exhibiting almost temperature independent resistivity at finite
temperatures. This paper demonstrates a crucial role of the acoustic phonon
scattering at finite temperatures in the electric conductivity of Dirac
electrons. The present theoretical results of conductivity are compared with
those of experiments.


  Our current transportation system suffers from a number of problems in terms
of safety, mobility, and environmental sustainability. The emergence of
innovative intelligent transportation systems (ITS) technologies, and in
particular connected and automated vehicles (CAVs), provides many opportunities
to address the aforementioned issues. In this paper, we propose a hierarchical
ramp merging system that not only generates microscopic cooperative maneuvers
for CAVs on the ramp to merge into the mainline traffic flow, but also provides
controllability of the ramp inflow rate, thereby enabling macroscopic traffic
flow control. A centralized optimal control-based approach is proposed to
smooth the merging flow, improve the system-wide mobility, and decrease the
overall fuel consumption of the network. Linear quadratic trackers in both
finite horizon and receding horizon forms are developed to solve the
optimization problem in terms of path planning and sequence determination,
where a microscopic vehicle fuel consumption model is applied. Extensive
traffic simulation runs have been conducted using PTV VISSIM to evaluate the
impact of the proposed system on a segment of SR-91 E in Corona, California.
The results confirm that under the regulated inflow rate, the proposed system
can avoid potential traffic congestion and improve mobility (e.g., VMT/VHT) up
to 147%, with a 47% fuel savings compared to the conventional ramp metering and
the ramp without any control approach.


  One major challenge for the legacy measurements at the LHC is that the
likelihood function is not tractable when the collected data is
high-dimensional and the detector response has to be modeled. We review how
different analysis strategies solve this issue, including the traditional
histogram approach used in most particle physics analyses, the Matrix Element
Method, Optimal Observables, and modern techniques based on neural density
estimation. We then discuss powerful new inference methods that use a
combination of matrix element information and machine learning to accurately
estimate the likelihood function. The MadMiner package automates all necessary
data-processing steps. In first studies we find that these new techniques have
the potential to substantially improve the sensitivity of the LHC legacy
measurements.


  The Alberta Infant Motor Scale (AIMS) is a well-known assessment scheme that
evaluates the gross motor development of infants by recording the number of
specific poses achieved. With the aid of the image-based pose recognition
model, the AIMS evaluation procedure can be shortened and automated, providing
early diagnosis or indicator of potential developmental disorder. Due to
limited public infant-related datasets, many works use the SMIL-based method to
generate synthetic infant images for training. However, this domain mismatch
between real and synthetic training samples often leads to performance
degradation during inference. In this paper, we present a CNN-based model which
takes any infant image as input and predicts the coarse and fine-level pose
labels. The model consists of an image branch and a pose branch, which
respectively generates the coarse-level logits facilitated by the unsupervised
domain adaptation and the 3D keypoints using the HRNet with SMPLify
optimization. Then the outputs of these branches will be sent into the
hierarchical pose recognition module to estimate the fine-level pose labels. We
also collect and label a new AIMS dataset, which contains 750 real and 4000
synthetic infants images with AIMS pose labels. Our experimental results show
that the proposed method can significantly align the distribution of synthetic
and real-world datasets, thus achieving accurate performance on fine-grained
infant pose recognition.


  New experimental information has been recently obtained on the odd-odd
nucleus 134-I. We interpret the five observed excited states up to the energy
of ~3 MeV on the basis of a realistic shell-model calculation, and make
spin-parity assignments accordingly. A very good agreement is found between the
experimental and calculated energies.


  In the context of isogeometric analysis, we consider two discretization
approaches that make the resulting stiffness matrix nonsymmetric even if the
differential operator is self-adjoint. These are the collocation method and the
recently-developed weighted quadrature for the Galerkin discretization. In this
paper, we are interested in the solution of the linear systems arising from the
discretization of the Poisson problem using one of these approaches. In [SIAM
J. Sci. Comput. 38(6) (2016) pp. A3644--A3671], a well-established direct
solver for linear systems with tensor structure was used as a preconditioner in
the context of Galerkin isogeometric analysis, yielding promising results. In
particular, this preconditioner is robust with respect to the mesh size $h$ and
the spline degree $p$. In the present work, we discuss how a similar approach
can applied to the considered nonsymmetric linear systems. The efficiency of
the proposed preconditioning strategy is assessed with numerical experiments on
two-dimensional and three-dimensional problems.


  We show that the dynamics of automorphisms on all projective complex
manifolds X (of dimension 3, or of any dimension but assuming the Good Minimal
Model Program or Mori's Program) are canonically built up from the dynamics on
just three types of projective complex manifolds: complex tori, weak Calabi-Yau
manifolds and rationally connected manifolds. As a by-product, we confirm the
conjecture of Guedj for automorphisms on 3-dimensional projective manifolds,
and also determine pi_1(X).


  The spectra of helium-dominated white dwarf stars with hydrogen in their
atmosphere present a distinctive broad feature centered around 1160~\AA\/ in
the blue wing of the Lyman-$\alpha$ line. It is extremely apparent in WD
1425+540 recently observed with HST COS. With new theoretical line profiles
based on ab initio atomic interaction potentials we show that this feature is a
signature of a collision-induced satellite due to an asymptotically forbidden
transition. This quasi-molecular spectral satellite is crucial to understanding
the asymmetrical shape of Lyman-$\alpha$ seen in this and other white dwarf
spectra. Our previous work predicting this absorption feature was limited by
molecular potentials that were not adequate to follow the atomic interactions
with spectroscopic precision to the asymptotic limit of large separation. A new
set of potential energy curves and electronic dipole transition moments for the
lowest electronic states of the H--He system were developed to account
accurately for the behavior of the atomic interactions at all distances, from
the chemical regime within 1~\AA\/ out to where the radiating H atoms are not
significantly perturbed by their neighbors. We use a general unified theory of
collision-broadened atomic spectral lines to describe a rigorous treatment of
hydrogen Lyman-$\alpha$ with these potentials and present a new study of its
broadening by radiative collisions of hydrogen and neutral helium. These
results enable ab initio modeling of radiative transport in DBA white dwarf
atmospheres.


  A recently proposed method for regularizing chiral gauge theories
non-perturbatively is discussed in detail. The result is an effective action
which can be computed from the lattice gauge field, and which is suited for
numerical simulations.


  Hyperspectral image (HSI) classification is a hot topic in the remote sensing
community. This paper proposes a new framework of spectral-spatial feature
extraction for HSI classification, in which for the first time the concept of
deep learning is introduced. Specifically, the model of autoencoder is
exploited in our framework to extract various kinds of features. First we
verify the eligibility of autoencoder by following classical spectral
information based classification and use autoencoders with different depth to
classify hyperspectral image. Further in the proposed framework, we combine PCA
on spectral dimension and autoencoder on the other two spatial dimensions to
extract spectral-spatial information for classification. The experimental
results show that this framework achieves the highest classification accuracy
among all methods, and outperforms classical classifiers such as SVM and
PCA-based SVM.


  The tests of the constancy of the fundamental constants are tests of the
local position invariance and thus of the equivalence principle. We summarize
the various constraints that have been obtained and then describe the
connection between varying constants and extensions of general relativity. To
finish, we discuss the link with cosmology, and more particularly with the
acceleration of the Universe. We take the opportunity to summarize various
possibilities to test general relativity (but also the Copernican principle) on
cosmological scales.


  Message passing neural networks (MPNNs) operate on graphs by exchanging
information between neigbouring nodes. MPNNs have been successfully applied to
various node-, edge-, and graph-level tasks in areas like molecular science,
computer vision, natural language processing, and combinatorial optimization.
However, most MPNNs require training on large amounts of labeled data, which
can be costly and time-consuming. In this work, we explore the use of various
untrained message passing layers in graph neural networks, i.e. variants of
popular message passing architecture where we remove all trainable parameters
that are used to transform node features in the message passing step. Focusing
on link prediction, we find that untrained message passing layers can lead to
competitive and even superior performance compared to fully trained MPNNs,
especially in the presence of high-dimensional features. We provide a
theoretical analysis of untrained message passing by relating the inner
products of features implicitly produced by untrained message passing layers to
path-based topological node similarity measures. As such, untrained message
passing architectures can be viewed as a highly efficient and interpretable
approach to link prediction.