  Learning signed distance functions (SDFs) from 3D point clouds is an
important task in 3D computer vision. However, without ground truth signed
distances, point normals or clean point clouds, current methods still struggle
from learning SDFs from noisy point clouds. To overcome this challenge, we
propose to learn SDFs via a noise to noise mapping, which does not require any
clean point cloud or ground truth supervision for training. Our novelty lies in
the noise to noise mapping which can infer a highly accurate SDF of a single
object or scene from its multiple or even single noisy point cloud
observations. Our novel learning manner is supported by modern Lidar systems
which capture multiple noisy observations per second. We achieve this by a
novel loss which enables statistical reasoning on point clouds and maintains
geometric consistency although point clouds are irregular, unordered and have
no point correspondence among noisy observations. Our evaluation under the
widely used benchmarks demonstrates our superiority over the state-of-the-art
methods in surface reconstruction, point cloud denoising and upsampling. Our
code, data, and pre-trained models are available at
https://github.com/mabaorui/Noise2NoiseMapping/


  This paper investigates the resilient control, analysis, recovery, and
operation of mobile robot networks in time-varying formation tracking under
deception attacks on global positioning. Local and global tracking control
algorithms are presented to ensure redundancy of the mobile robot network and
to retain the desired functionality for better resilience. Lyapunov stability
analysis is utilized to show the boundedness of the formation tracking error
and the stability of the network under various attack modes. A performance
index is designed to compare the efficiency of the proposed formation tracking
algorithms in situations with or without positioning attacks. Subsequently, a
communication-free decentralized cooperative localization approach based on
extended information filters is presented for positioning estimate recovery
where the identification of the positioning attacks is based on
Kullback-Leibler divergence. A gain-tuning resilient operation is proposed to
strategically synthesize the formation control and cooperative localization for
accurate and rapid system recovery from positioning attacks. The proposed
methods are tested using both numerical simulation and experimental validation
with a team of quadrotors.


  There is a multitude of novel generative models for open-domain
conversational systems; however, there is no systematic evaluation of different
systems. Systematic comparisons require consistency in experimental design,
evaluation sets, conversational systems and their outputs, and statistical
analysis. We lay out a protocol for the evaluation of conversational models
using head-to-head pairwise comparison. We analyze ten recent models that claim
state-of-the-art performance using a paired head-to-head performance
(win-loss-tie) on five evaluation datasets. Our findings show that DialoGPT and
Blender are superior systems using Bradley-Terry model and TrueSkill ranking
methods. These findings demonstrate the feasibility of our protocol to evaluate
conversational agents and evaluation sets. Finally, we make all code and
evaluations publicly available for researchers to compare their model to other
state-of-the-art dialog models.


  We consider a proper holomorphic map form D to G domains in C^n and show that
it induces a unitary isomorphism between the Bergman space A^2(G) and some
subspace of A^2(D). Using this isomorphism we construct orthogonal projection
onto that subspace and we derive Bells transformation formula for the Bergman
kernel under proper holomorphic mappings. As a consequence of the formula we
get that the tetrablock is not a Lu Qi-Keng domain.


  In this paper, we study the Ricci flow on a closed manifold of dimension $n
\ge 4$ and finite time interval $[0,T)~(T < \infty)$ on which the scalar
curvature are uniformly bounded. We prove that if such flow of dimension $4 \le
n \le 7$ has finite time singularities, then every blow-up sequence of a
locally Type I singularity has certain property. Here, locally Type I
singularity is what Buzano and Di-Matteo defined.


  Tau leap schemes were originally designed for the efficient time stepping of
discrete state and continuous in time Markov processes arising in stochastic
chemical kinetics. Previous convergence results on tau leaping schemes have
been restricted to systems that remain in a bounded subdomain (which may depend
on the initial condition) or satisfy global Lipschitz conditions on
propensities. This paper extends the convergence results to fairly general tau
leap schemes applied to unbounded systems that possess certain moment growth
bounds. Specifically, we prove a weak convergence result, which shows order $q$
convergence of all moments under certain form of moment growth bound
assumptions on the stochastic chemical system and the tau leap method, as well
as polynomial bound assumption on the propensity functions. The results are
stated for a general class of Markov processes with $\integ^N$ as their state
space.


  The temperature dependencies of specific heat and spin susceptibility of a
coupled $d_{x^2-y^2} +id_{xy}$ superconductor in the presence of a weak
$d_{xy}$ component are investigated in the tight-binding model (1) on square
lattice and (2) on a lattice with orthorhombic distortion. As the temperature
is lowered past the critical temperature $T_c$, first a less ordered
$d_{x^2-y^2}$ superconductor is created, which changes to a more ordered
$d_{x^2-y^2} +id_{xy}$ superconductor at $T_{c1} (<T_c)$. This manifests in two
second order phase transitions identified by two jumps in specific heat at
$T_c$ and $T_{c1}$. The temperature dependencies of the superconducting
observables exhibit a change from power-law to exponential behavior as
temperature is lowered below $T_{c1}$ and confirm the new phase transition.


  Large Language Models (LLMs) are versatile, yet they often falter in tasks
requiring deep and reliable reasoning due to issues like hallucinations,
limiting their applicability in critical scenarios. This paper introduces a
rigorously designed framework for creating LLMs that effectively anchor
knowledge and employ a closed-loop reasoning process, enhancing their
capability for in-depth analysis. We dissect the framework to illustrate the
contribution of each component to the LLMs' performance, offering a theoretical
assurance of improved reasoning under well-defined assumptions.


  For the first time, we investigate the non-perturbative dynamics of single
field inflation with a departure from slow-roll. Using simulations, we find
that oscillatory features in the potential can drastically alter the course of
inflation, with major phenomenological implications. In certain cases, the
entire Universe gets trapped in a forever inflating de Sitter state. In others,
only some regions get stuck in a false vacuum, offering an alternative channel
for primordial black hole formation. Analogous to the flap of a butterfly,
these results show that small-scale phenomena can have profound consequences on
the evolution of the entire Universe. This demonstrates the necessity of a
non-perturbative approach in the exploration of the small-scale physics of
inflation, particularly in the regime relevant for gravitational-wave
astronomy.


  A one-dimensional reaction-diffusion model consisting of two species of
particles and vacancies on a ring is introduced. The number of particles in one
species is conserved while in the other species it can fluctuate because of
creation and annihilation of particles. It has been shown that the model
undergoes a continuous phase transition from a phase where the currents of
different species of particles are equal to another phase in which they are
different. The total density of particles and also their currents in each phase
are calculated exactly.


  In radio interferometry, observed visibilities are intrinsically sampled at
some interval in time and frequency. Modern interferometers are capable of
producing data at very high time and frequency resolution; practical limits on
storage and computation costs require that some form of data compression be
imposed. The traditional form of compression is a simple averaging of the
visibilities over coarser time and frequency bins. This has an undesired side
effect: the resulting averaged visibilities "decorrelate", and do so
differently depending on the baseline length and averaging interval. This
translates into a non-trivial signature in the image domain known as
"smearing", which manifests itself as an attenuation in amplitude towards
off-centre sources. With the increasing fields of view and/or longer baselines
employed in modern and future instruments, the trade-off between data rate and
smearing becomes increasingly unfavourable. In this work we investigate
alternative approaches to low-loss data compression. We show that averaging of
the visibility data can be treated as a form of convolution by a boxcar-like
window function, and that by employing alternative baseline-dependent window
functions a more optimal interferometer smearing response may be induced. In
particular, we show improved amplitude response over a chosen field of
interest, and better attenuation of sources outside the field of interest. The
main cost of this technique is a reduction in nominal sensitivity; we
investigate the smearing vs. sensitivity trade-off, and show that in certain
regimes a favourable compromise can be achieved. We show the application of
this technique to simulated data from the Karl G. Jansky Very Large Array (VLA)
and the European Very-long-baseline interferometry Network (EVN).


  We develop a probabilistic characterisation of trajectorial expansion rates
in non-autonomous stochastic dynamical systems that can be defined over a
finite time interval and used for the subsequent uncertainty quantification in
Lagrangian (trajectory-based) predictions. These expansion rates are quantified
via certain divergences (pre-metrics) between probability measures induced by
the laws of the stochastic flow associated with the underlying dynamics. We
construct scalar fields of finite-time divergence/expansion rates, show their
existence and space-time continuity for general stochastic flows. Combining
these divergence rate fields with our 'information inequalities' derived in
allows for quantification and mitigation of the uncertainty in path-based
observables estimated from simplified models in a way that is amenable to
algorithmic implementations, and it can be utilised in information-geometric
analysis of statistical estimation and inference, as well as in a data-driven
machine/deep learning of coarse-grained models. We also derive a link between
the divergence rates and finite-time Lyapunov exponents for probability
measures and for path-based observables.


  A constructive proof is given to the fact that any ergodic Markov chain can
be realized as a random walk subject to a synchronizing road coloring.
Redundancy (ratio of extra entropy) in such a realization is also studied.


  A model is presented to explain the normal mode features of dust particles in
a planar zigzag crystal chain for the first and second neighbors. The degrees
of freedom of particles are the longitudinal and transverse displacements in
plane coupled by the first and second neighbor harmonic forces in
two-dimensions (2D). The constant electric force duded to the electrodes to
keep the zigzag structure is calculated. The coupling between transverse and
longitudinal dust-lattice (DL) modes is derived. The latter is considered due
to the energy of the electrostatic (Yukawa) potential. Moreover coupled
(acoustic and optical) and decoupled (longitudinal and transverse) branches of
dust lattice modes for different lattice parameters and structures are studied.
Propagation of the longitudinal and acoustic modes is found to be strictly
dependent on the value of the distance between the two chains; below that value
mode may not propagate Finally it is shown that the frequencies of the acoustic
(optical) branches increase (decrease) with increasing the distance between the
two chains.


  The electronic transport through a triple quantum dot molecule attached in
parallel to leads in presence of a magnetic flux is studied. Analytical
expressions of the linear conductance and density of states for the molecule in
equilibrium at zero temperature are obtained. As a consequence of quantum
interference, the conductance exhibits in general a Breit--Wigner and two Fano
resonances, the positions and widths of which are controlled by the magnetic
field. Every two flux quanta, there is an inversion of roles of the bonding and
antibonding states. For particular values of the magnetic flux and dot-lead
couplings, one or even both Fano resonances collapse and bound states in the
continuum (BIC's) are formed. The line broadenings of the molecular states are
examined as a function of the Aharonov--Bohm phase around the condition for the
formation of BIC's, finding resonances extremely narrow and robust against
variations of the magnetic field.


  Over the past few years there is a growing interest in the learning-based
self driving system. To ensure safety, such systems are first developed and
validated in simulators before being deployed in the real world. However, most
of the existing driving simulators only contain a fixed set of scenes and a
limited number of configurable settings. That might easily cause the
overfitting issue for the learning-based driving systems as well as the lack of
their generalization ability to unseen scenarios. To better evaluate and
improve the generalization of end-to-end driving, we introduce an open-ended
and highly configurable driving simulator called PGDrive, following a key
feature of procedural generation. Diverse road networks are first generated by
the proposed generation algorithm via sampling from elementary road blocks.
Then they are turned into interactive training environments where traffic flows
of nearby vehicles with realistic kinematics are rendered. We validate that
training with the increasing number of procedurally generated scenes
significantly improves the generalization of the agent across scenarios of
different traffic densities and road networks. Many applications such as
multi-agent traffic simulation and safe driving benchmark can be further built
upon the simulator. To facilitate the joint research effort of end-to-end
driving, we release the simulator and pretrained models at
https://decisionforce.github.io/pgdrive


  Big data collection practices using Internet of Things (IoT) pervasive
technologies are often privacy-intrusive and result in surveillance, profiling,
and discriminatory actions over citizens that in turn undermine the
participation of citizens to the development of sustainable smart cities.
Nevertheless, real-time data analytics and aggregate information from IoT
devices open up tremendous opportunities for managing smart city
infrastructures. The privacy-enhancing aggregation of distributed sensor data,
such as residential energy consumption or traffic information, is the research
focus of this paper. Citizens have the option to choose their privacy level by
reducing the quality of the shared data at a cost of a lower accuracy in data
analytics services. A baseline scenario is considered in which IoT sensor data
are shared directly with an untrustworthy central aggregator. A grouping
mechanism is introduced that improves privacy by sharing data aggregated first
at a group level compared as opposed to sharing data directly to the central
aggregator. Group-level aggregation obfuscates sensor data of individuals, in a
similar fashion as differential privacy and homomorphic encryption schemes,
thus inference of privacy-sensitive information from single sensors becomes
computationally harder compared to the baseline scenario. The proposed system
is evaluated using real-world data from two smart city pilot projects. Privacy
under grouping increases, while preserving the accuracy of the baseline
scenario. Intra-group influences of privacy by one group member on the other
ones are measured and fairness on privacy is found to be maximized between
group members with similar privacy choices. Several grouping strategies are
compared. Grouping by proximity of privacy choices provides the highest privacy
gains. The implications of the strategy on the design of incentives mechanisms
are discussed.


  State space models have long played an important role in signal processing.
The Gaussian case can be treated algorithmically using the famous Kalman
filter. Similarly since the 1970s there has been extensive application of
Hidden Markov models in speech recognition with prediction being the most
important goal. The basic theoretical work here, in the case $X$ and $Y$ finite
(small) providing both algorithms and asymptotic analysis for inference is that
of Baum and colleagues. During the last 30-40 years these general models have
proved of great value in applications ranging from genomics to finance.
  Unless the $X,Y$ are jointly Gaussian or $X$ is finite and small the problem
of calculating the distributions discussed and the likelihood exactly are
numerically intractable and if $Y$ is not finite asymptotic analysis becomes
much more difficult. Some new developments have been the construction of
so-called ``particle filters'' (Monte Carlo type) methods for approximate
calculation of these distributions (see Doucet et al. [4]) for instance and
general asymptotic methods for analysis of statistical methods in HMM [2] and
other authors.
  We will discuss these methods and results in the light of exponential mixing
properties of the conditional (posterior) distribution of $(X_1,X_2,...)$ given
$(Y_1,Y_2,...)$ already noted by Baum and Petrie and recent work of the authors
Bickel, Ritov and Ryden, Del Moral and Jacod, Douc and Matias.


  The paper focuses on the accuracy improvement of geometric and elasto-static
calibration of industrial robots. It proposes industry-oriented performance
measures for the calibration experiment design. They are based on the concept
of manipulator test-pose and referred to the end-effector location accuracy
after application of the error compensation algorithm, which implements the
identified parameters. This approach allows the users to define optimal
measurement configurations for robot calibration for given work piece location
and machining forces/torques. These performance measures are suitable for
comparing the calibration plans for both simple and complex trajectories to be
performed. The advantages of the developed techniques are illustrated by an
example that deals with machining using robotic manipulator.


  This comprehensive study conducts an in-depth analysis of existing COVID-19
ontologies, scrutinizing their objectives, classifications, design
methodologies, and domain focal points. The study is conducted through a
dual-stage approach, commencing with a systematic review of relevant literature
and followed by an ontological assessment utilizing a parametric methodology.
Through this meticulous process, twenty-four COVID-19 Ontologies (CovOs) are
selected and examined. The findings highlight the scope, intended purpose,
granularity of ontology, modularity, formalism, vocabulary reuse, and extent of
domain coverage. The analysis reveals varying levels of formality in ontology
development, a prevalent preference for utilizing OWL as the representational
language, and diverse approaches to constructing class hierarchies within the
models. Noteworthy is the recurrent reuse of ontologies like OBO models (CIDO,
GO, etc.) alongside CODO. The METHONTOLOGY approach emerges as a favored design
methodology, often coupled with application-based or data-centric evaluation
methods. Our study provides valuable insights for the scientific community and
COVID-19 ontology developers, supplemented by comprehensive ontology metrics.
By meticulously evaluating and documenting COVID-19 information-driven
ontological models, this research offers a comparative cross-domain
perspective, shedding light on knowledge representation variations. The present
study significantly enhances understanding of CovOs, serving as a consolidated
resource for comparative analysis and future development, while also
pinpointing research gaps and domain emphases, thereby guiding the trajectory
of future ontological advancements.


  This paper describes a set of comparative experiments for the problem of
automatically filtering unwanted electronic mail messages. Several variants of
the AdaBoost algorithm with confidence-rated predictions [Schapire & Singer,
99] have been applied, which differ in the complexity of the base learners
considered. Two main conclusions can be drawn from our experiments: a) The
boosting-based methods clearly outperform the baseline learning algorithms
(Naive Bayes and Induction of Decision Trees) on the PU1 corpus, achieving very
high levels of the F1 measure; b) Increasing the complexity of the base
learners allows to obtain better ``high-precision'' classifiers, which is a
very important issue when misclassification costs are considered.


  We present a comprehensive construction of scalar, vector and tensor
harmonics on maximally symmetric three-dimensional spaces. Our formalism relies
on the introduction of spin-weighted spherical harmonics and a generalized
helicity basis which, together, are ideal tools to decompose harmonics into
their radial and angular dependencies. We provide a thorough and self-contained
set of expressions and relations for these harmonics. Being general, our
formalism also allows to build harmonics of higher tensor type by recursion
among radial functions, and we collect the complete set of recursive relations
which can be used. While the formalism is readily adapted to computation of CMB
transfer functions, we also collect explicit forms of the radial harmonics
which are needed for other cosmological observables. Finally, we show that in
curved spaces, normal modes cannot be factorized into a local angular
dependence and a unit norm function encoding the orbital dependence of the
harmonics, contrary to previous statements in the literature.


  When a strong electric field is applied to a colloidal suspension, it may
cause an aggregation of the suspended particles in response to the field. In
the case of a rotating field, the electrorotation (ER) spectrum can be modified
further due to the local field effects arising from the many-particle system.
To capture the local field effect, we invoke the Maxwell-Garnett approximation
for the dielectric response. The hydrodynamic interactions between the
suspended particles can also modify the spin friction, which is a key to
determine the angular velocity of ER. By invoking the spectral representation
approach, we derive the analytic expressions for the characteristic frequency
at which the maximum angular velocity of ER occurs. From the numerical
caculation, we find that there exist two sub-dispersions in the ER spectrum.
However, the two characteristic frequencies are so close that the two peaks
actually overlap and become a single broad peak. We report a detailed
investigation of the dependence of the characteristic frequency and the
dispersion strength of ER on various material parameters.


  We report an analytical study of the vibrational spectrum of the simplest
model of jamming, the soft perceptron. We identify two distinct classes of soft
modes. The first kind of modes are related to isostaticity and appear only in
the close vicinity of the jamming transition. The second kind of modes instead
are present everywhere in the glass phase and are related to the hierarchical
structure of the potential energy landscape. Our results highlight the
universality of the spectrum of normal modes in disordered systems, and open
the way towards a detailed analytical understanding of the vibrational spectrum
of low-temperature glasses.


  Let $q$ be a large prime number, $a$ be any integer, $\epsilon$ be a fixed
small positive quantity. Friedlander and Shparlinksi \cite{FSh} have shown that
there exists a positive integer $n\ll q^{5/2+\epsilon}$ such that $\phi(n)$
falls into the residue class $a \pmod q.$ Here, $\phi(n)$ denotes Euler's
function. In the present paper we improve this bound to $n\ll q^{2+\epsilon}.$


  Asgard/NOTT is an ERC-funded project hosted at KU Leuven and is part of a new
visitor instrumental suite, called Asgard, under preparation for the Very Large
Telescope Interferometer (VLTI). Leveraging nulling capabilities and the long
VLTI baselines, it is optimized for high-contrast imaging of the snow line
region around young nearby main-sequence stars. This will enable the
characterization of the atmosphere of young giant exoplanets and warm/hot
exozodiacal dust with spectroscopy in the L'-band (3.5-4.0$\mu$m). In this
work, we present the first lab assembly of the instrument done at KU Leuven and
the technical solutions to tackle the challenge of performing nulling in the
mid-infrared despite the thermal background. The opto-mechanical design of the
warm optics and the injection system for the photonic chip are described. The
alignment procedure used to assemble the system is also presented. Finally, the
first experimental results, including fringes and null measurements, are given
and confirm the adequacy of the bench to test and optimize the Asgard/NOTT
instrument.


  Telerobotic systems must adapt to new environmental conditions and deal with
high uncertainty caused by long-time delays. As one of the best alternatives to
human-level intelligence, Reinforcement Learning (RL) may offer a solution to
cope with these issues. This paper proposes to integrate RL with the Model
Mediated Teleoperation (MMT) concept. The teleoperator interacts with a
simulated virtual environment, which provides instant feedback. Whereas
feedback from the real environment is delayed, feedback from the model is
instantaneous, leading to high transparency. The MMT is realized in combination
with an intelligent system with two layers. The first layer utilizes Dynamic
Movement Primitives (DMP) which accounts for certain changes in the avatar
environment. And, the second layer addresses the problems caused by uncertainty
in the model using RL methods. Augmented reality was also provided to fuse the
avatar device and virtual environment models for the teleoperator. Implemented
on DLR's Exodex Adam hand-arm haptic exoskeleton, the results show RL methods
are able to find different solutions when changes are applied to the object
position after the demonstration. The results also show DMPs to be effective at
adapting to new conditions where there is no uncertainty involved.


  We investigate global solutions to the Euler-alignment system in $d$
dimensions with unidirectional flows and strongly singular communication
protocols $\phi(x) = |x|^{-(d+\alpha)}$ for $\alpha \in (0,2)$. Our paper
establishes global regularity results in both the subcritical regime
$1<\alpha<2$ and the critical regime $\alpha=1$. Notably, when $\alpha=1$, the
system exhibits a critical scaling similar to the critical quasi-geostrophic
equation. To achieve global well-posedness, we employ a novel method based on
propagating the modulus of continuity. Our approach introduces the concept of
simultaneously propagating multiple moduli of continuity, which allows us to
effectively handle the system of two equations with critical scaling.
Additionally, we improve the regularity criteria for solutions to this system
in the supercritical regime $0<\alpha<1$.


  We employed micro-focused Brillouin light scattering to study the
amplification of the thermal spin wave eigenmodes by means of a pure spin
current, generated by the spin-Hall effect, in a transversely magnetized
Pt(4nm)/NiFe(4nm)/SiO2(5nm) layered nanowire with lateral dimensions 500x2750
nm2. The frequency and the cross section of both the center (fundamental) and
the edge spin wave modes have been measured as a function of the intensity of
the injected dc electric current. The frequency of both modes exhibits a clear
redshift while their cross section is greatly enhanced on increasing the
intensity of the injected dc. A threshold-like behavior is observed for a value
of the injected dc of 2.8 mA. Interestingly an additional mode, localized in
the central part of the nanowire, appears at higher frequency on increasing the
intensity of the injected dc above the threshold value. Micromagnetic
simulations were used to quantitatively reproduce the experimental results and
to investigate the complex non-linear dynamics induced by the spin-Hall effect,
including the modification of the spatial profile of the spin wave modes and
the appearance of the extra mode above the threshold.


  We study the dynamics of the Nambu-Goto membranes with cohomogeneity one
symmetry, i.e., the membranes whose trajectories are foliated by homogeneous
surfaces. It is shown that the equation of motion reduces to a geodesic
equation on a certain manifold, which is constructed from the original
spacetime and Killing vector fields thereon. A general method is presented for
classifying the symmetry of cohomogeneity one membranes in a given spacetime.
The classification is completely carried out in Minkowski spacetime. We analyze
one of the obtained classes in depth and derive an exact solution.


  We provide a simple macroscopic analysis of the four-dimensional effective
supergravity of the Ho\v{r}ava-Witten M-theory which is expanded in powers of
$\kappa^{2/3}/\rho V^{1/3}$ and $\kappa^{2/3}\rho/V^{2/3}$ where $\kappa^2$,
$V$ and $\rho$ denote the eleven-dimensional gravitational coupling, the
Calabi-Yau volume and the eleventh length respectively. Possible higher order
terms in the K\"ahler potential are identified and matched with the heterotic
string corrections. In the context of this M-theory expansion, we analyze the
soft supersymmetry-breaking terms under the assumption that supersymmetry is
spontaneously broken by the auxiliary components of the bulk moduli
superfields. It is examined how the pattern of soft terms changes when one
moves from the weakly coupled heterotic string limit to the M-theory limit.


  We analyze numerically the correspondence between the mechanical action,
experienced by a spherical microparticle, and the internal energy flows as well
as spatial and polarization inhomogeneity of the light field incident on the
particle. The inhomogeneous incident field is modelled by superposition of two
plane waves, the mechanical action is calculated via the Mie theory for
dielectric and conducting particles of different sizes and optical properties.
It is shown that both spin and orbital components of the field momentum can
produce the mechanical action, which can thus be used for experimental study of
the internal energy flows in light beams. However, exact value and even
direction of the force applied to a particle depends on many details of the
field-particle interaction. Besides, forces that are not associated with any
sort of the energy flow (we attribute them to the gradient force owing to the
inhomogeneous intensity and to the dipole force emerging due to inhomogeneous
polarization) can strongly modify the observed mechanical action. The results
should be taken into account in experiments employing the motion of probing
particles suspended within optical fields. We suppose that situations where an
optical field exerts the polarization-dependent mechanical action on particles
can be treated as a new form of the spin-orbit interaction of light.


  Let R be a regular, local and F-finite ring defined over a field of finite
characteristic. Let I be an ideal of height c with normal quotient $A=R/I$. It
is shown that the local cohomology module H^c_I(R) contains a unique simple
D_R--submodule L(A,R). This should be viewed as a finite characteristic analog
of the Kashiwara--Brylinski D_R--module in characteristic zero which
corresponds to the intersection cohomology complex via the Riemann--Hilbert
correspondence. Besides the existence of L(A,R), more importantly, we give its
construction as a certain dual of the tight closure of zero in $H^d_m(A)$. We
obtain a precise D_R--simplicity criterion for H^c_I(R), namely H^c_I(R) is
D_R--simple if and only if the tight closure of zero in H^d_m(A) is Frobenius
nilpotent, in particular this is the case if A is F--rational.
  Furthermore, the techniques developed imply a result in tight closure theory,
saying that the parameter test module commutes with completion.


  Discontinuous magnetic multilayers [CoFe/Al2O3] are studied by use of
magnetometry, susceptometry and numeric simulations. Soft ferromagnetic
Co80Fe20 nanoparticles are embedded in a diamagnetic insulating a-Al2O3 matrix
and can be considered as homogeneously magnetized superspins exhibiting
randomness of size (viz. moment), position and anisotropy. Lacking
intra-particle core-surface ordering, generic freezing processes into
collective states rather than individual particle blocking are encountered.
With increasing particle density one observes first superspin glass and then
superferromagnetic domain state behavior. The phase diagram resembles that of a
dilute disordered ferromagnet. Criteria for the identification of the
individual phases are given.


  The expected event rate of lensed gravitational wave sources scales with the
merger rate at redshift $z\geq 1$, where the optical depth for lensing is high.
It is commonly assumed that the merger rate of the astrophysical compact
objects is closely connected with the star formation rate, which peaks around
redshift $z\sim 2$. However, a major source of uncertainty is the delay time
between the formation and merger of compact objects. We explore the impact of
delay time on the lensing event rate. We show that as the delay time increases,
the peak of the merger rate of gravitational wave sources gets deferred to a
lower redshift. This leads to a reduction in the event rate of the lensed
events which are detectable by the gravitational wave detectors. We show that
for a delay time of around $10$ Gyr or larger, the lensed event rate can be
less than one per year for the design sensitivity of LIGO/Virgo. We also
estimate the merger rate for lensed sub-threshold for different delay time
scenarios, finding that for larger delay times the number of lensed
sub-threshold events is reduced, whereas for small-delay time models they are
significantly more frequent. This analysis shows for the first time that
lensing is a complementary probe to explore different formation channels of
binary systems by exploiting the lensing event rate from the well-detected
events and sub-threshold events which are measurable using the network of
gravitational wave detectors.


  An analytical method is advanced for constructing interpolation formulae for
complicated problems of statistical mechanics, in which just a few terms of
asymptotic expansions are available. The method is based on the self-similar
approximation theory, being its variant where control functions are defined
from asymptotic crossover conditions. Several examples from statistical physics
demonstrate that the suggested method results in rather simple and surprisingly
accurate formulae.


  Customized medical prompts enable Large Language Models (LLM) to effectively
address medical dialogue summarization. The process of medical reporting is
often time-consuming for healthcare professionals. Implementing medical
dialogue summarization techniques presents a viable solution to alleviate this
time constraint by generating automated medical reports. The effectiveness of
LLMs in this process is significantly influenced by the formulation of the
prompt, which plays a crucial role in determining the quality and relevance of
the generated reports. In this research, we used a combination of two distinct
prompting strategies, known as shot prompting and pattern prompting to enhance
the performance of automated medical reporting. The evaluation of the automated
medical reports is carried out using the ROUGE score and a human evaluation
with the help of an expert panel. The two-shot prompting approach in
combination with scope and domain context outperforms other methods and
achieves the highest score when compared to the human reference set by a
general practitioner. However, the automated reports are approximately twice as
long as the human references, due to the addition of both redundant and
relevant statements that are added to the report.


  We discuss an active phase field crystal (PFC) model that describes a mixture
of active and passive particles. First, a microscopic derivation from dynamical
density functional theory (DDFT) is presented that includes a systematic
treatment of the relevant orientational degrees of freedom. Of particular
interest is the construction of the nonlinear and coupling terms. This allows
for interesting insights into the microscopic justification of phenomenological
constructions used in PFC models for active particles and mixtures, the
approximations required for obtaining them, and possible generalizations.
Second, the derived model is investigated using linear stability analysis and
nonlinear methods. It is found that the model allows for a rich nonlinear
behavior with states ranging from steady periodic and localized states to
various time-periodic states. The latter include standing, traveling, and
modulated waves corresponding to spatially periodic and localized traveling,
wiggling, and alternating peak patterns and their combinations.


  There are many networks in real life which exist as form of Scale-free
networks such as World Wide Web, protein-protein inter action network, semantic
networks, airline networks, interbank payment networks, etc. If we want to
analyze these networks, it is really necessary to understand the properties of
scale-free networks. By using the properties of scale free networks, we can
identify any type of anomalies in those networks. In this research, we proposed
a methodology in a form of an algorithm to predict hidden links and missing
nodes in scale-free networks where we combined a generator of random networks
as a source of train data, on one hand, with artificial neural networks for
supervised classification, on the other, we aimed at training the neural
networks to discriminate between different subtypes of scale-free networks and
predicted the missing nodes and hidden links among (present and missing) nodes
in a given scale-free network. We chose Bela Bollobas's directed scale-free
random graph generation algorithm as a generator of random networks to generate
a large set of scale-free network's data.


  Power system planning problems become computationally intractable if one
accounts for all uncertain operating scenarios. Consequently, one selects a
subset of scenarios that are representative of likely/extreme operating
conditions, e.g. heavy summer, heavy winter, light summer, and so on. However,
such an approach may not be able to accurately capture the dependencies that
exist between renewable generation (RG) and system load in RG-rich power
systems. This paper proposes the use of fast dynamic time warping (FDTW) and
fuzzy c-means++ (FCM++) clustering to account for key statistical properties of
load and RG for scenario generation for power system planning problems. Case
studies using a U.S. power network, and comparison with existing scenario
generation techniques demonstrate the benefits of the proposed approach.


  The benefits of ultrasound are its ease-of-use and its ability to precisely
deliver energy in opaque and complex media. However, most materials responsive
to ultrasound show a weak response, requiring the use of high powers, which are
associated with undesirable streaming, cavitation, or temperature rise. These
effects hinder response control and may even cause damage to the medium where
the ultrasound is applied. Moreover, materials that are currently in use rely
on all-or-nothing effects, limiting the ability to fine-tune the response of
the material on the fly. For these reasons, there is a need for materials that
can respond to low intensity ultrasound with programmable responses. Here it is
demonstrated that antibubbles are a low-intensity-ultrasound-responsive
material system that can controllably release a payload using acoustic
pressures in the kPa range. Varying their size and composition tunes the
release pressure, and the response can be switched between a single release and
stepwise release across multiple ultrasound pulses. Observations using confocal
and high-speed microscopy revealed different ways that can lead to release.
These findings lay the groundwork to design antibubbles that controllably
respond to low-intensity ultrasound, opening a wide range of applications
ranging from ultrasound-responsive material systems to carriers for targeted
delivery.


  Rigel (beta Ori, B8 Ia) is a nearby blue supergiant displaying alpha Cyg type
variability, and is one of the nearest type-II supernova progenitors. As such
it is an excellent test bed to study the internal structure of pre
core-collapse stars. In this study, for the first time, we present 28 days of
high precision MOST photometry and over 6 years of spectroscopic monitoring. We
report nineteen significant pulsation modes of SNR>4.6 from radial velocities,
with variability time scales ranging from 1.21 to 74.7 days, which are
associated with high order low degree gravity modes. While the radial velocity
variations show a degree of correlation with the flux changes, there is no
clear interplay between the equivalent widths of different metallic and Halpha
lines.


  With the availability of large-scale surveys like Kepler and TESS, there is a
pressing need for automated methods to classify light curves according to known
classes of variable stars. We introduce a new algorithm for classifying light
curves that compares 7000 time-series features to find those which most
effectively classify a given set of light curves. We apply our method to Kepler
light curves for stars with effective temperatures in the range 6500--10,000K.
We show that the sample can be meaningfully represented in an interpretable
five-dimensional feature space that separates seven major classes of light
curves (delta Scuti stars, gamma Doradus stars, RR Lyrae stars, rotational
variables, contact eclipsing binaries, detached eclipsing binaries, and
non-variables). We achieve a balanced classification accuracy of 82% on an
independent test set of Kepler stars using a Gaussian mixture model classifier.
We use our method to classify 12,000 Kepler light curves from Quarter 9 and
provide a catalogue of the results. We further outline a confidence heuristic
based on probability density with which to search our catalogue, and extract
candidate lists of correctly-classified variable stars.


  It is shown that the high-Delta m^2 part of the large mixing angle MSW
solution of the solar neutrino problem is disfavored by the Super-Kamiokande
data for low-energy upward-going events. A quantitative bound is obtained in
the three-neutrino scheme with a negligibly small element U_{e3} of the
neutrino mixing matrix, as indicated by the result of the CHOOZ long-baseline
antinu_e->antinu_e oscillation experiment.


  This study presents the detection of a high-frequency Quasi-Periodic
Oscillation (QPO) in the Seyfert galaxy NGC 1365, based on observational data
obtained by the XMM-Newton in January 2004. Utilizing the Weighted Wavelet
Z-transform (WWZ) and Lomb-Scargle Periodogram (LSP) methods, a QPO signal was
identified at a frequency of 2.19 * 10^-4 Hz (4566 s), with a confidence level
of 3.6 sigma. The signal was notably absent in the lower 0.2-1.0 keV energy
band, with the primary contribution emerging from the 2.0-10.0 keV band, where
the confidence level reached 3.9 sigma. Spectral analysis shows that there are
multiple absorption and emission lines in the high-energy band (> 6 keV). The
correlation between the QPO frequency (f_QPO) and the mass of NGC 1365 central
black hole (M_BH) aligns with the established logarithmic trend observed across
black holes, indicating the QPO is of high frequency. This discovery provides
new clues for studying the generation mechanism of QPO in Seyfert galaxies,
which helps us understand the accretion process around supermassive black holes
and the characteristics of strong gravitational fields in active galactic
nuclei.


  We investigate cosmological dynamics and screening mechanism of the
Dirac-Born-Infeld (DBI) Galileon model. The model has been divided into two
regimes, one has positive signs in front of scalar field kinetic terms
so-called the DBI galileon, another one has negative signs and it is dubbed as
the DBIonic galileon. We find de Sitter solution and evolution of the Universe
starting from radiation dominated era to late-time accelerated expansion in the
DBI galileon model without the presence of potential term. In one of the
attractors, the ghost and Laplacian instabilities vanishes for the whole
evolution. We find mixing of screening mechanisms between the Vainshtein
mechanism and the DBIonic screening mechanism in the DBIonic galileon model, in
which a scale changing between these two mechanisms depends on a mass of a
source.


  We investigate the resonantly enhanced tunneling dynamics of ultracold bosons
loaded on a tilted 1-D optical lattice, which can be used to simulate a chain
of Ising spins and associated quantum phase transitions. The center of mass
motion after a sudden tilt both at commensurate and incommensurate fillings is
obtained via analytic, time-dependent exact diagonalization and density matrix
renormalization group methods (adaptive t-DMRG). We identify a maximum in the
amplitude of the center of mass oscillations at the quantum critical point of
the effective spin system. For the dynamics of incommensurate systems, which
cannot be mapped to a spin model, we develop an analytical approach in which
the time evolution is obtained by projecting onto resonant families of small
clusters. We compare the results of this approach at low fillings to the exact
time evolution and find good agreement even at filling factors as large as 2/3.
Using this projection onto small clusters, we propose a controllable transport
scheme applicable in the context of Atomtronic devices on optical lattices
(`slinky scheme').


  We prove a new minimax theorem connecting the worst-case Bayesian regret and
minimax regret under partial monitoring with no assumptions on the space of
signals or decisions of the adversary. We then generalise the
information-theoretic tools of Russo and Van Roy (2016) for proving Bayesian
regret bounds and combine them with the minimax theorem to derive minimax
regret bounds for various partial monitoring settings. The highlight is a clean
analysis of `non-degenerate easy' and `hard' finite partial monitoring, with
new regret bounds that are independent of arbitrarily large game-dependent
constants. The power of the generalised machinery is further demonstrated by
proving that the minimax regret for k-armed adversarial bandits is at most
sqrt{2kn}, improving on existing results by a factor of 2. Finally, we provide
a simple analysis of the cops and robbers game, also improving best known
constants.


  Every artinian quotient of $K[x,y]$ has the strong Lefschetz property if $K$
is a field of characteristic zero or is an infinite field whose characteristic
is greater than the regularity of the quotient. We improve this bound in the
case of monomial ideals. Using this we classify when both bounds are sharp.
Moreover, we prove that the artinian quotient of a monomial ideal in $K[x,y]$
always has the strong Lefschetz property, regardless of the characteristic of
the field, exactly when the ideal is lexsegment. As a consequence we describe a
family of non-monomial complete intersections that always have the strong
Lefschetz property.


  Since the seminal work of Venkatakrishnan et al. in 2013, Plug & Play (PnP)
methods have become ubiquitous in Bayesian imaging. These methods derive
Minimum Mean Square Error (MMSE) or Maximum A Posteriori (MAP) estimators for
inverse problems in imaging by combining an explicit likelihood function with a
prior that is implicitly defined by an image denoising algorithm. The PnP
algorithms proposed in the literature mainly differ in the iterative schemes
they use for optimisation or for sampling. In the case of optimisation schemes,
some recent works guarantee the convergence to a fixed point, albeit not
necessarily a MAP estimate. In the case of sampling schemes, to the best of our
knowledge, there is no known proof of convergence. There also remain important
open questions regarding whether the underlying Bayesian models and estimators
are well defined, well-posed, and have the basic regularity properties required
to support these numerical schemes. To address these limitations, this paper
develops theory, methods, and provably convergent algorithms for performing
Bayesian inference with PnP priors. We introduce two algorithms: 1) PnP-ULA
(Unadjusted Langevin Algorithm) for Monte Carlo sampling and MMSE inference;
and 2) PnP-SGD (Stochastic Gradient Descent) for MAP inference. Using recent
results on the quantitative convergence of Markov chains, we establish detailed
convergence guarantees for these two algorithms under realistic assumptions on
the denoising operators used, with special attention to denoisers based on deep
neural networks. We also show that these algorithms approximately target a
decision-theoretically optimal Bayesian model that is well-posed. The proposed
algorithms are demonstrated on several canonical problems such as image
deblurring, inpainting, and denoising, where they are used for point estimation
as well as for uncertainty visualisation and quantification.


  FlameFinder is a deep metric learning (DML) framework designed to accurately
detect flames, even when obscured by smoke, using thermal images from
firefighter drones during wildfire monitoring. Traditional RGB cameras struggle
in such conditions, but thermal cameras can capture smoke-obscured flame
features. However, they lack absolute thermal reference points, leading to
false positives.To address this issue, FlameFinder utilizes paired thermal-RGB
images for training. By learning latent flame features from smoke-free samples,
the model becomes less biased towards relative thermal gradients. In testing,
it identifies flames in smoky patches by analyzing their equivalent
thermal-domain distribution. This method improves performance using both
supervised and distance-based clustering metrics.The framework incorporates a
flame segmentation method and a DML-aided detection framework. This includes
utilizing center loss (CL), triplet center loss (TCL), and triplet cosine
center loss (TCCL) to identify optimal cluster representatives for
classification. However, the dominance of center loss over the other losses
leads to the model missing features sensitive to them. To address this
limitation, an attention mechanism is proposed. This mechanism allows for
non-uniform feature contribution, amplifying the critical role of cosine and
triplet loss in the DML framework. Additionally, it improves interpretability,
class discrimination, and decreases intra-class variance. As a result, the
proposed model surpasses the baseline by 4.4% in the FLAME2 dataset and 7% in
the FLAME3 dataset for unobscured flame detection accuracy. Moreover, it
demonstrates enhanced class separation in obscured scenarios compared to VGG19,
ResNet18, and three backbone models tailored for flame detection.


  Revised spectroscopic parameters for the HF molecule and a new CN line list
in the 2.3 mu region have been recently available, allowing a revision of the F
content in AGB stars. AGB carbon stars are the only observationally confirmed
sources of fluorine. Nowadays there is not a consensus on the relevance of AGB
stars in its Galactic chemical evolution. The aim of this article is to better
constrain the contribution of these stars with a more accurate estimate of
their fluorine abundances. Using new spectroscopic tools and LTE spectral
synthesis, we redetermine fluorine abundances from several HF lines in the
K-band in a sample of Galactic and extragalactic AGB carbon stars of spectral
types N, J and SC spanning a wide range of metallicities. On average, the new
derived fluorine abundances are systematically lower by 0.33 dex with respect
to previous determinations. This may derive from a combination of the lower
excitation energies of the HF lines and the larger macroturbulence parameters
used here as well as from the new adopted CN line list. Yet, theoretical
nucleosynthesis models in AGB stars agree with the new fluorine determinations
at solar metallicities. At low metallicities, an agreement between theory and
observations can be found by handling in a different way the
radiative/convective interface at the base of the convective envelope. New
fluorine spectroscopic measurements agree with theoretical models at low and at
solar metallicity. Despite this, complementary sources are needed to explain
its observed abundance in the solar neighbourhood.


  It is well known that in every inverse semigroup the binary operation and the
unary operation of inversion satisfy the following three identities: [\quad
x=(xx')x \qquad \quad (xx')(y'y)=(y'y)(xx') \qquad \quad (xy)z=x(yz"). ] The
goal of this note is to prove the converse, that is, we prove that an algebra
of type $<2,1>$ satisfying these three identities is an inverse semigroup and
the unary operation coincides with the usual inversion on such semigroups.


  Partition identities are often statements asserting that the set $\mathcal
P_X$ of partitions of $n$ subject to condition $X$ is equinumerous to the set
$\mathcal P_Y$ of partitions of $n$ subject to condition $Y$. A Beck-type
identity is a companion identity to $|\mathcal P_X|=|\mathcal P_Y|$ asserting
that the difference $b(n)$ between the number of parts in all partitions in
$\mathcal P_X$ and the number of parts in all partitions in $\mathcal P_Y$
equals a $c|\mathcal P_{X'}|$ and also $c|\mathcal P_{Y'}|$, where $c$ is some
constant related to the original identity, and $X'$, respectively $Y'$, is a
condition on partitions that is a very slight relaxation of condition $X$,
respectively $Y$. A second Beck-type identity involves the difference $b'(n)$
between the total number of different parts in all partitions in $\mathcal P_X$
and the total number of different parts in all partitions in $\mathcal P_Y$. We
extend these results to Beck-type identities accompanying all identities given
by Euler pairs of order $r$ (for any $r\geq 2$). As a consequence, we obtain
many families of new Beck-type identities. We give analytic and bijective
proofs of our results.


  The complete fusion of $^9$Be with $^{144}$Sm and $^{208}$Pb targets is
calculated in the coupled-channels approach. The calculations include couplings
between the 3/2$^-$, 5/2$^-$, and 7/2$^-$ states in the $K=3/2$ ground state
rotational band of $^9$Be. It is shown that the $B(E2)$ values for the
excitation of these states are accurately described in the rotor model. The
interaction of the strongly deformed $^9$Be nucleus with a spherical target is
calculated using the double-folding technique and the effective M3Y
interaction, which is supplemented with a repulsive term that is adjusted to
optimize the fit to the data for the $^{144}$Sm target. The complete fusion is
described by in-going-wave boundary conditions. The decay of the unbound
excited states in $^9$Be is considered explicitly in the calculations by using
complex excitation energies. The model gives an excellent account of the
complete fusion (CF) data for $^9$Be+$^{144}$Sm, and the cross sections for the
decay of the excited states are in surprisingly good agreement with the
incomplete fusion (ICF) data. Similar calculations for $^9$Be+$^{208}$Pb
explain the total fusion data at high energies but fail to explain the CF data,
which are suppressed by 20%, and the calculated cross section for the decay of
excited states is a factor of three smaller than the ICF data at high energies.
Possible reasons for these discrepancies are discussed.


  Let $\tau$ be a strongly $(n,p;a,c)$ regular graph,such that $0<c<p<n-1,$ $A$
his matrix of adjacency and let ${\cal V}_{n}$ be the Euclidean space spanned
by the powers of $A$ over the reals where the scallar product $\bullet|\bullet$
is defined by $x|y={trace}(x \cdot y).$ In this work ones proves that ${\cal
V}_{n}$ is an Euclidean Jordan algebra of rank 3 when one introduces in ${\cal
V}_{n}$ the usual product of matrices. In this Euclidean Jordan algebra one
defines the modulus of a matrix, and afterwards one defines $|A|^x \forall x\in
\mathbb{R}.$ Working inside the Euclidean Jordan algebra ${\cal V}_{n}$ and
making use of the properties of $|A|^x$ one defines the generalized krein
parameters of the strongly $(n,p;a,c)$ regular graph $\tau$ and finally one
presents necessary conditions over the parameters and the spectra of the $\tau$
strongly $(n,p;a,c)$ regular graph.


  This paper presents the measurements of $\pi^{\pm}$, $\rm{K}^{\pm}$, $\rm{p}$
and $\bar{\rm{p}}$ transverse momentum ($p_{\rm{T}}$) spectra as a function of
charged-particle multiplicity density in proton-proton (pp) collisions at
$\sqrt{s}$ = 13 TeV with the ALICE detector at the LHC. Such study allows us to
isolate the center-of-mass energy dependence of light-flavour particle
production. The measurements reported here cover a $p_{\rm{T}}$ range from 0.1
GeV/$c$ to 20 GeV/$c$ and are done in the rapidity interval $|y|<0.5$. The
$p_{\rm{T}}$-differential particle ratios exhibit an evolution with
multiplicity, similar to that observed in pp collisions at $\sqrt{s}$ = 7 TeV,
which is qualitatively described by some of the hydrodynamical and
pQCD-inspired models discussed in this paper. Furthermore, the
$p_{\rm{T}}$-integrated hadron-to-pion yield ratios measured in pp collisions
at two different center-of-mass energies are consistent when compared at
similar multiplicities. This also extends to strange and multistrange hadrons,
suggesting that, at LHC energies, particle hadrochemistry scales with particle
multiplicity the same way under different collision energies and colliding
systems.


  Despite their rarity, massive stars dominate the ecology of galaxies via
their strong, radiatively-driven winds throughout their lives and as supernovae
in their deaths. However, their evolution and subsequent impact on their
environment can be significantly affected by the presence of a magnetic field.
While recent studies indicate that about 7% of OB stars in the Milky Way host
strong, stable, organised (fossil) magnetic fields at their surfaces, little is
known about the fields of very massive stars, nor the magnetic properties of
stars outside our Galaxy. We aim to continue searching for strong magnetic
fields in a diverse set of massive and very massive stars (VMS) in the Large
and Small Magellanic Clouds (LMC/SMC), and we evaluate the overall capability
of FORS2 to usefully search for and detect stellar magnetic fields in
extra-galactic environments. We have obtained FORS2 spectropolarimetry of a
sample of 41 stars, which principally consist of spectral types B, O, Of/WN,
WNh, and classical WR stars in the LMC and SMC. Four of our targets are Of?p
stars; one of them was just recently discovered. Each spectrum was analysed to
infer the longitudinal magnetic field. No magnetic fields were formally
detected in our study, although Bayesian statistical considerations suggest
that the Of?p star SMC159-2 is magnetic with a dipolar field of the order of
2.4 to 4.4kG. In addition, our first constraints of magnetic fields in VMS
provide interesting insights into the formation of the most massive stars in
the Universe.


  In this work has been proposed a numerical scheme with the aim of simulate
the coalescence process between water drops immersed in a continuous phase
(n-heptane). This numerical scheme is based in the Finite Volume method and two
different values for the initial velocity of the drops were chosen. Depending
of the initial velocity of collision some scenarios emerge, such as: permanent
coalescence, formation of satellite drops etc. For some snap shots the
streamlines are calculated for the different process of permanent coalescence.
These streamlines allow the understanding of the dynamics of the droplets
immersed on the n-heptane phase. The model used for the surface tension is
presented and its effects on the dynamics of coalescence of the droplets are
showed, which after some time tends to the spherical form.


  Three integrable models - the deformed Heisenberg, Landau - Lifschits and
Ishimori magnets are written in terms of the stereographic projection. The
Hamiltonians of these models are obtained and certain questions related to the
existence of exact solutions are studied. In particular, the stability of
solitions is studied for the Heisenberg marnet, simplest stationary solutions
are obtained for the Landau - Lifschits magnet, and Hamiltonians and
topological charges are calculated for several known solutions of the Ishimori
model


  Photoacoustic (PA) technology can provide information on both the physical
structure and chemical composition of bone, showing great potential in bone
assessment. However, due to the complex composition and porous structure of
cancellous bone, the PA signals generated and propagated in cancellous bone are
complex and difficult to be directly used in cancellous bone analysis. In this
paper, a photoacoustic differential attenuation spectrum (PA-DAS) method is
proposed. By eliminating the PA spectrum of the optical absorption sources, the
propagation attenuation characteristics of cancellous bone are studied
theoretically and experimentally. An analytical solution for the propagation
attenuation of broadband ultrasound waves in cancellous bone is given by
applying high-frequency and viscous corrections to Biot's theory. An
experimental system of PA-DAS with an eccentric excitation differential
detection system is established to obtain the PA-DAS of cancellous bone and its
acoustic propagation characteristic on the rabbit osteoporosis model. The
PA-DAS quantization parameter slope is further extracted to quantify the
attenuation of high and low frequency components. The results show that the
PA-DAS can distinguish osteoporotic bone from normal bone, enabling
quantitative assessment of bone mineral density and the diagnosis of
osteoporosis.


  There has been a recent explosion in applications for dialogue interaction
ranging from direction-giving and tourist information to interactive story
systems. Yet the natural language generation (NLG) component for many of these
systems remains largely handcrafted. This limitation greatly restricts the
range of applications; it also means that it is impossible to take advantage of
recent work in expressive and statistical language generation that can
dynamically and automatically produce a large number of variations of given
content. We propose that a solution to this problem lies in new methods for
developing language generation resources. We describe the ES-Translator, a
computational language generator that has previously been applied only to
fables, and quantitatively evaluate the domain independence of the EST by
applying it to personal narratives from weblogs. We then take advantage of
recent work on language generation to create a parameterized sentence planner
for story generation that provides aggregation operations, variations in
discourse and in point of view. Finally, we present a user evaluation of
different personal narrative retellings.


  Astrophysical and cosmological arguments and observations give us the most
restrictive constraints on neutrino masses, electromagnetic couplings, and
other properties. Conversely, massive neutrinos would contribute to the cosmic
dark-matter density and would play an important role for the formation of
structure in the universe. Neutrino oscillations may well solve the solar
neutrino problem, and can have a significant impact on supernova physics.


  The depletion interactions between two colloidal plates or between two
colloidal spheres, induced by interacting polymers in a good solvent, are
calculated theoretically and by computer simulations. A simple analytical
theory is shown to be quantitatively accurate for case of two plates. A related
depletion potential is derived for two spheres; it also agrees very well with
direct computer simulations. Theories based on ideal polymers show important
deviations with increasing polymer concentration: They overestimate the range
of the depletion potential between two plates or two spheres at all densities,
with the largest relative change occurring in the dilute regime. They
underestimate the well depth at contact for the case of two plates, but
overestimate it for two spheres. Depletion potentials are also calculated using
a coarse graining approach which represents the polymers as ``soft colloids'':
good agreement is found in the dilute regime. Finally, the effect of the
polymers on colloid-colloid osmotic virial coefficients is related to phase
behavior of polymer-colloid mixtures.


  We consider an automatic overload control for two large service systems
modeled as multi-server queues, such as call centers. We assume that the two
systems are designed to operate independently, but want to help each other
respond to unexpected overloads. The proposed overload control automatically
activates sharing (sending some customers from one system to the other) once a
ratio of the queue lengths in the two systems crosses an activation threshold
(with ratio and activation threshold parameters for each direction). To prevent
harmful sharing, sharing is allowed in only one direction at any time. In this
paper, we are primarily concerned with ensuring that the system recovers
rapidly after the overload is over, either (i) because the two systems return
to normal loading or (ii) because the direction of the overload suddenly shifts
in the opposite direction. To achieve rapid recovery, we introduce lower
thresholds for the queue ratios, below which one-way sharing is released. As a
basis for studying the complex dynamics, we develop a new six-dimensional fluid
approximation for a system with time-varying arrival rates, extending a
previous fluid approximation involving a stochastic averaging principle. We
conduct simulations to confirm that the new algorithm is effective for
predicting the system performance and choosing effective control parameters.
The simulation and the algorithm both show that the system can experience an
inefficient nearly-periodic behavior, corresponding to an oscillating
equilibrium (congestion collapse), if the sharing is strongly inefficient and
the control parameters are set inappropriately.


  X-ray observations of galaxy clusters have shown that the intra-cluster gas
has iron abundances of about one third of the solar value. These observations
also show that part (if not all) of the intra-cluster gas metals were produced
within the member galaxies. We present a systematic analysis of 20 galaxy
clusters to explore the connection between the iron mass and the total
luminosity of early-type and late-type galaxies, and of the brightest cluster
galaxies (BCGs). From our results, the intra-cluster medium (ICM) iron mass
seems to correlate better with the luminosity of the BCGs than with that of the
red and blue galaxy populations. As the BCGs cannot produce alone the observed
amount of iron, we suggest that ram-pressure plus tidal stripping act together
to enhance, at the same time, the BCG luminosities and the iron mass in the
ICM. Through the analysis of the iron yield, we have also estimated that SN Ia
are responsible for more than 50% of the total iron in the ICM. This result
corroborates the fact that ram-pressure contributes to the gas removal from
galaxies to the inta-cluster medium, being very efficient for clusters in the
temperature range 2 < kT (keV)< 10


  With the Komar mass formula we calculate the electromagnetic energy for a
charged Kerr black hole in a uniform magnetic field. We find that the total
electromagnetic energy takes the minimum when the Kerr black hole possesses a
non-zero net charge $Q = 2\xi B_0 J_H$ where $B_0$ is the strength of the
magnetic field, $J_H$ is the angular momentum of the black hole, $\xi$ is a
dimensionless parameter determined by the spin of the black hole.


  We study a three-dimensional Anderson-Hubbard model under the coexistence of
short-range interaction and diagonal disorder within the Hartree-Fock
approximation. We show that the density of states at the Fermi energy is
suppressed in the metallic phases near the metal-insulator transition as a
proximity effect of the soft Hubbard gap in the insulating phases. The
transition to the insulator is characterized by a vanishing DOS in contrast to
formation of a quasiparticle peak at the Fermi energy obtained by the dynamical
mean field theory in pure systems. Furthermore, we show that there exist frozen
spin moments in the paramagnetic metal.


  We investigate the effect of $Z_2$ magnetic monopoles and vortices on the
finite temperature deconfinement phase transition in the fundamental - adjoint
SU(2) lattice gauge theory. In the limit of complete suppression of the $Z_2$
monopoles, the mixed action for the SU(2) theory in its Villain form is shown
to be self-dual under the exchange of the fundamental and adjoint couplings. By
further suppressing the $Z_2$ vortices we show that the extended model reduces
to the Wilson action with a modified coupling. The universality of the SU(2)
deconfinement phase transition with the Ising model is therefore expected to
remain intact in the entire plane of the fundamental-adjoint couplings in the
continuum limit. The self-duality arguments related to the suppression of $Z_2$
monopoles are also applicable to the Villain form of mixed action for the SU(N)
theory with $Z_{N}$ magnetic monopoles.


  The XCAT phantom is a realistic 4D digital torso phantom that is widely used
in imaging and therapy research. However, lung mass is not conserved between
respiratory phases of the phantom, making detailed dosimetric simulations and
dose accumulation unphysical. A framework is developed to correct this issue by
enforcing local mass conservation in the XCAT lung. Dose calculations are
performed to assess the implications of neglecting mass conservation, and to
demonstrate an application of the phantom to calculate the accumulated
delivered dose in an irregularly breathing patient. Monte Carlo methods are
used to simulate conventional and SBRT treatment delivery. The spatial
distribution of the lung dose was qualitatively changed by the use of mass
conservation; however the corresponding DVH did not change significantly.
Comparison of the delivered dose with 4DCT-based predictions shows similar lung
metric results, however dose differences of 10% can be seen in some spatial
regions. Using this tool to simulate patient treatments reveals differences
between the planned dose and the calculated delivered dose for the full
treatment. The software is freely available from the authors.


  It is demonstrated that the inherent structure of Boris pusher may result in
the accumulation of errors in numerical integration when estimating the works
of electric fields on particles. This, in turn, leads to an incorrect
estimations of particle acceleration mechanisms. A method of error correction,
reducing error to the acceptable levels of $<10\%$, is implemented. Numerical
integration error in question is most pronounced in scenarios where substantial
number of electrons are injected into the accelerating structure through wave
breaking of plasma waves i.e. in near-critical density plasma.


  In this paper, we propose a novel approach for unsupervised domain
adaptation, that relates notions of optimal transport, learning probability
measures and unsupervised learning. The proposed approach, HOT-DA, is based on
a hierarchical formulation of optimal transport, that leverages beyond the
geometrical information captured by the ground metric, richer structural
information in the source and target domains. The additional information in the
labeled source domain is formed instinctively by grouping samples into
structures according to their class labels. While exploring hidden structures
in the unlabeled target domain is reduced to the problem of learning
probability measures through Wasserstein barycenter, which we prove to be
equivalent to spectral clustering. Experiments on a toy dataset with
controllable complexity and two challenging visual adaptation datasets show the
superiority of the proposed approach over the state-of-the-art.


  Energy-parity objectives combine $\omega$-regular with quantitative
objectives of reward MDPs. The controller needs to avoid to run out of energy
while satisfying a parity objective.
  We refute the common belief that, if an energy-parity objective holds
almost-surely, then this can be realised by some finite memory strategy. We
provide a surprisingly simple counterexample that only uses coB\"uchi
conditions.
  We introduce the new class of bounded (energy) storage objectives that, when
combined with parity objectives, preserve the finite memory property. Based on
these, we show that almost-sure and limit-sure energy-parity objectives, as
well as almost-sure and limit-sure storage parity objectives, are in
$\mathit{NP}\cap \mathit{coNP}$ and can be solved in pseudo-polynomial time for
energy-parity MDPs.


  This paper concerns a variational representation formula for Wiener
functionals. Let $B=\{ B_{t}\} _{t\ge 0}$ be a standard $d$-dimensional
Brownian motion. Bou\'e and Dupuis (1998) showed that, for any bounded
measurable functional $F(B)$ of $B$ up to time $1$, the expectation
$\mathbb{E}\!\left[ e^{F(B)}\right] $ admits a variational representation in
terms of drifted Brownian motions. In this paper, with a slight modification of
insightful reasoning by Lehec (2013) allowing also $F(B)$ to be a functional of
$B$ over the whole time interval, we prove that the Bou\'e--Dupuis formula
holds true provided that both $e^{F(B)}$ and $F(B)$ are integrable, relaxing
conditions in earlier works. We also show that the formula implies the
exponential hypercontractivity of the Ornstein--Uhlenbeck semigroup in
$\mathbb{R}^{d}$, and hence, due to their equivalence, implies the logarithmic
Sobolev inequality in the $d$-dimensional Gaussian space.


  Sparse representation systems that encode the signal architecture have had an
exceptional impact on sampling and compression paradigms. Remarkable examples
are multi-scale directional systems, which, similar to our vision system,
encode the underlying architecture of natural images with sparse features.
Inspired by this philosophy, the present study introduces a representation
system for acoustic waves in 2D space-time, referred to as the boostlet
transform, which encodes sparse features of natural acoustic fields with the
Poincar\'e group and isotropic dilations. Continuous boostlets,
$\psi_{a,\theta,\tau}(\varsigma) = a^{-1} \psi \left(D_a^{-1}
B_\theta^{-1}(\varsigma-\tau)\right) \in L^2(\mathbb{R}^2)$, are spatiotemporal
functions parametrized with dilations $a > 0$, Lorentz boosts $\theta \in
\mathbb{R}$, and translations $\smash{\tau \in \mathbb{R}^2}$ in space--time.
The admissibility condition requires that boostlets are supported away from the
acoustic radiation cone, i.e., have phase velocities other than the speed of
sound, resulting in a peculiar scaling function. The continuous boostlet
transform is an isometry for $L^2(\mathbb{R}^2)$, and a sparsity analysis with
experimentally measured fields indicates that boostlet coefficients decay
faster than wavelets, curvelets, wave atoms, and shearlets. The uncertainty
principles and minimizers associated with the boostlet transform are derived
and interpreted physically.


  We investigate valley exciton dynamics in MoSe2 monolayers in polarization-
and time-resolved photoluminescence (PL) spectroscopy at 4K. Following
circularly polarized laser excitation, we record a low circular polarization
degree of the PL of typically $\leq5\%$. This is about 10 times lower than the
polarization induced under comparable conditions in MoS2 and WSe2 monolayers.
The evolution of the exciton polarization as a function of excitation laser
energy and power is monitored in PL excitation (PLE) experiments. Fast PL
emission times are recorded for both the neutral exciton of $\leq3$ ps and for
the charged exciton (trion) of 12 ps.


  The phase noise and frequency stability measurements of 1 GHz, 100 MHz, and
10 MHz signals are presented which have been synthesized from microwave
cryogenic sapphire oscillators using ultra-low-vibration pulse-tube cryocooler
technology. We present the measured data using independent cryogenic
oscillators for the 100 MHz and 10 MHz synthesized signals, whereas previously
we only estimated the expected results based on residual phase noise
measurements, when only one cryogenic oscillator was available. In addition we
present the design of a 1 GHz synthesizer using a Crystek voltage controlled
oscillator phase locked to 1 GHz output derived from a cryogenic sapphire
oscillator.


  Success stories of applied machine learning can be traced back to the
datasets and environments that were put forward as challenges for the
community. The challenge that the community sets as a benchmark is usually the
challenge that the community eventually solves. The ultimate challenge of
reinforcement learning research is to train real agents to operate in the real
environment, but until now there has not been a common real-world RL benchmark.
In this work, we present a prototype real-world environment from OffWorld Gym
-- a collection of real-world environments for reinforcement learning in
robotics with free public remote access. Close integration into existing
ecosystem allows the community to start using OffWorld Gym without any prior
experience in robotics and takes away the burden of managing a physical
robotics system, abstracting it under a familiar API. We introduce a navigation
task, where a robot has to reach a visual beacon on an uneven terrain using
only the camera input and provide baseline results in both the real environment
and the simulated replica. To start training, visit https://gym.offworld.ai


  We present a novel twistor formulation of the ten-dimensional massless
superparticle. This formulation is based on the introduction of pure spinor
variables through a field redefinition of another model for the superparticle,
and in the new description we find that the super-Pauli-Lubanski three-form
naturally arises as a constraint. Quantization is studied in detail for both
models and they are shown to correctly describe the $D = 10$ super-Yang-Mills
states.


  Injecting a sufficiently large energy density into an isolated many-particle
system prepared in a state with long-range order will lead to the melting of
the order over time. Detailed information about this process can be derived
from the quantum mechanical probability distribution of the order parameter. We
study this process for the paradigmatic case of the spin-1/2 Heisenberg XXZ
chain. We determine the full quantum mechanical distribution function of the
staggered subsystem magnetization as a function of time after a quantum quench
from the classical N\'eel state. This provides a detailed picture of how the
N\'eel order melts and reveals the existence of an interesting regime at
intermediate times that is characterized by a very broad probability
distribution.


  This article classifies Knutsen K3 surfaces all of whose hyperplane sections
are irreducible and reduced. As an application, this gives infinite families of
K3 surfaces of Picard number two whose general hyperplane sections are
Brill-Noether general curves.


  Recommending Chemical Compounds of interest to a particular researcher is a
poorly explored field. The few existent datasets with information about the
preferences of the researchers use implicit feedback. The lack of Recommender
Systems in this particular field presents a challenge for the development of
new recommendations models. In this work, we propose a Hybrid recommender model
for recommending Chemical Compounds. The model integrates
collaborative-filtering algorithms for implicit feedback (Alternating Least
Squares (ALS) and Bayesian Personalized Ranking(BPR)) and semantic similarity
between the Chemical Compounds in the ChEBI ontology (ONTO). We evaluated the
model in an implicit dataset of Chemical Compounds, CheRM. The Hybrid model was
able to improve the results of state-of-the-art collaborative-filtering
algorithms, especially for Mean Reciprocal Rank, with an increase of 6.7% when
comparing the collaborative-filtering ALS and the Hybrid ALS_ONTO.


  We have analyzed the X-ray spectra of the microquasar GRS 1915+105, as
observed with the PCA on the Rossi XTE, during periods of stable weak emission,
outbursts and rapid flaring. We find that the complicated X-ray intensity curve
of this source can be described by the rapid removal and replenishment of
matter forming the inner part of an optically thick accretion disk, probably
caused by a thermal-viscous instability analogous to that operating in dwarf
novae, but here driven by the Lightman-Eardley instability. We find that the
mass accretion rate in quiescence is 10^-6 solar masses per year. Only a small
fraction of the energy liberated by accretion is emitted as radiation. We
suggest that most of this energy is advected into the black hole in the
high-viscosity state of the outburst cycle.


  We use up to fourth order cumulants of net strangeness fluctuations and their
correlations with net baryon number fluctuations to extract information on the
strange meson and baryon contribution to the low temperature hadron resonance
gas, the dissolution of strange hadronic states in the crossover region of the
QCD transition and the quasi-particle nature of strange quark contributions to
the high temperature quark-gluon plasma phase.


  In recent years,constructive field techniques and the method of
renormalization group around extended singularities have been applied to the
weak coupling regime of the Anderson Model. It has allowed to clarify the
relationship between this model and the theory of random matrices. We review
this situation and the current program to analyze in detail the density of
states and Green's functions of this model using the supersymmetric formalism.


  Adversarial robustness has emerged as an important topic in deep learning as
carefully crafted attack samples can significantly disturb the performance of a
model. Many recent methods have proposed to improve adversarial robustness by
utilizing adversarial training or model distillation, which adds additional
procedures to model training. In this paper, we propose a new training paradigm
called Guided Complement Entropy (GCE) that is capable of achieving
"adversarial defense for free," which involves no additional procedures in the
process of improving adversarial robustness. In addition to maximizing model
probabilities on the ground-truth class like cross-entropy, we neutralize its
probabilities on the incorrect classes along with a "guided" term to balance
between these two terms. We show in the experiments that our method achieves
better model robustness with even better performance compared to the commonly
used cross-entropy training objective. We also show that our method can be used
orthogonal to adversarial training across well-known methods with noticeable
robustness gain. To the best of our knowledge, our approach is the first one
that improves model robustness without compromising performance.


  We present a new class of stochastic, geometrically-driven optimization
algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous
manifolds obtained from the action of the rotation group $SO(d)$. We
theoretically and experimentally demonstrate that our methods can be applied in
various fields of machine learning including deep, convolutional and recurrent
neural networks, reinforcement learning, normalizing flows and metric learning.
We show an intriguing connection between efficient stochastic optimization on
the orthogonal group and graph theory (e.g. matching problem, partition
functions over graphs, graph-coloring). We leverage the theory of Lie groups
and provide theoretical results for the designed class of algorithms. We
demonstrate broad applicability of our methods by showing strong performance on
the seemingly unrelated tasks of learning world models to obtain stable
policies for the most difficult $\mathrm{Humanoid}$ agent from
$\mathrm{OpenAI}$ $\mathrm{Gym}$ and improving convolutional neural networks.


  We study a chain of four interacting rotors (rotators) connected at both ends
to stochastic heat baths at different temperatures. We show that for
non-degenerate interaction potentials the system relaxes, at a stretched
exponential rate, to a non-equilibrium steady state (NESS). Rotors with high
energy tend to decouple from their neighbors due to fast oscillation of the
forces. Because of this, the energy of the central two rotors, which interact
with the heat baths only through the external rotors, can take a very long time
to dissipate. By appropriately averaging the oscillatory forces, we estimate
the dissipation rate and construct a Lyapunov function. Compared to the chain
of length three (considered previously by C. Poquet and the current authors),
the new difficulty with four rotors is the appearance of resonances when both
central rotors are fast. We deal with these resonances using the rapid
thermalization of the two external rotors.


  Credit (CVA), Debit (DVA) and Funding Valuation Adjustments (FVA) are now
familiar valuation adjustments made to the value of a portfolio of derivatives
to account for credit risks and funding costs. However, recent changes in the
regulatory regime and the increases in regulatory capital requirements has led
many banks to include the cost of capital in derivative pricing. This paper
formalises the addition of cost of capital by extending the Burgard-Kjaer
(2013) semi-replication approach to CVA and FVA to include an addition capital
term, Capital Valuation Adjustment (KVA, i.e. Kapital Valuation Adjustment to
distinguish from CVA.) The utilization of the capital for funding purposes is
also considered. The use of the semi-replication approach means that the
flexibility around the treatment of self-default is carried over into this
analysis. The paper further considers the practical calculation of KVA with
reference to the Basel II (BCBS-128) and Basel III (BCBS-189) capital regimes
and their implementation via CRD IV. The paper also assesses how KVA may be
hedged, given that any hedging transactions themselves lead to regulatory
capital requirements and hence capital costs. Finally a number of numerical
examples are presented to gauge the cost impact of KVA on vanilla derivative
products.


  It is show that one can derive a novel BPS bound for the gauged
Non-Linear-Sigma-Model (NLSM) Maxwell theory in (3+1) dimensions which can
actually be saturated. Such novel bound is constructed using Hamilton-Jacobi
equation from classical mechanics. The configurations saturating the bound
represent Hadronic layers possessing both Baryonic charge and magnetic flux.
However, unlike what happens in the more common situations, the topological
charge which appears naturally in the BPS bound is a non-linear function of the
Baryonic charge. This BPS bound can be saturated when the surface area of the
layer is quantized. The far-reaching implications of these results are
discussed. In particular, we determine the exact relation between the magnetic
flux and the Baryonic charge as well as the critical value of the Baryonic
chemical potential beyond which these configurations become thermodynamically
unstable.


  Method of active correlations is a key one to suppress radically beam
associated backgrounds in heavy ion induced complete fusion nuclear reactions.
This property is enhanced significantly when applying DSSSD detector instead of
position sensitive resistive strip detector. Example of application in the
240Pu+48Ca->114+3n reaction is presented. An edge effect between neighbor
strips is considered too. The value of the mentioned effect is measured as 15.9
percents for ER-alpha correlation chains. C++ Builder GFS2016 program package
for spectral analysis is presented. Calibration parameters (totally 352) were
obtained from natYb+48Ca nuclear reaction. New ultra intense FLNR DC280
cyclotron that is expected to be put into operation in the nearest future is
also discussed from the viewpoint of the described method developments.


  We present a calculation of the complete $O(\alpha)$ electroweak radiative
corrections to the Moller scattering process e^-e^- -> e^-e^-, including hard
bremsstrahlung contributions. We study the effects of these corrections on both
the total cross section and polarization asymmetry measured in low energy fixed
target experiments. Numerical results are presented for the experimental cuts
relevant for E-158, a fixed target e^-e^- experiment being performed at SLAC;
the effect of hard bremsstrahlung is to shift the measured polarization
asymmetry by approximately +4%. We briefly discuss the remaining theoretical
uncertainty in the prediction for the low energy Moller scattering polarization
asymmetry.


  The recently introduced Intelligent Trial-and-Error (IT&E) algorithm showed
that robots can adapt to damage in a matter of a few trials. The success of
this algorithm relies on two components: prior knowledge acquired through
simulation with an intact robot, and Bayesian optimization (BO) that operates
on-line, on the damaged robot. While IT&E leads to fast damage recovery, it
does not incorporate any safety constraints that prevent the robot from
attempting harmful behaviors. In this work, we address this limitation by
replacing the BO component with a constrained BO procedure. We evaluate our
approach on a simulated damaged humanoid robot that needs to crawl as fast as
possible, while performing as few unsafe trials as possible. We compare our new
"safety-aware IT&E" algorithm to IT&E and a multi-objective version of IT&E in
which the safety constraints are dealt as separate objectives. Our results show
that our algorithm outperforms the other approaches, both in crawling speed
within the safe regions and number of unsafe trials.


  We study the natural Poisson structure on the Lie group SU(1,1) and related
questions. In particular, we give an explicit description of the
Ginzburg-Weinstein isomorphism for the sets of admissible elements. We also
establish an analogue of Thompson's conjecture for this group.


  In recent times, there has been a growing focus on end-to-end autonomous
driving technologies. This technology involves the replacement of the entire
driving pipeline with a single neural network, which has a simpler structure
and faster inference time. However, while this approach reduces the number of
components in the driving pipeline, it also presents challenges related to
interpretability and safety. For instance, the trained policy may not always
comply with traffic rules, and it is difficult to determine the reason for such
misbehavior due to the lack of intermediate outputs. Additionally, the
successful implementation of autonomous driving technology heavily depends on
the reliable and expedient processing of sensory data to accurately perceive
the surrounding environment. In this paper, we provide penalty-based imitation
learning approach combined with cross semantics generation sensor fusion
technologies (P-CSG) to efficiently integrate multiple modalities of
information and enable the autonomous agent to effectively adhere to traffic
regulations. Our model undergoes evaluation within the Town 05 Long benchmark,
where we observe a remarkable increase in the driving score by more than 12%
when compared to the state-of-the-art (SOTA) model, InterFuser. Notably, our
model achieves this performance enhancement while achieving a 7-fold increase
in inference speed and reducing the model size by approximately 30%. For more
detailed information, including code-based resources, they can be found at
https://hk-zh.github.io/p-csg/


  We clarify the links between a recently developped long wavelength iteration
scheme of Einstein's equations, the Belinski Khalatnikov Lifchitz (BKL) general
solution near a singularity and the antinewtonian scheme of Tomita's. We
determine the regimes when the long wavelength or antinewtonian scheme is
directly applicable and show how it can otherwise be implemented to yield the
BKL oscillatory approach to a spacetime singularity. When directly applicable
we obtain the generic solution of the scheme at first iteration (third order in
the gradients) for matter a perfect fluid. Specializing to spherical symmetry
for simplicity and to clarify gauge issues, we then show how the metric behaves
near a singularity when gradient effects are taken into account.


  Ferromagnetism is an iconic example of a first-order phase transition taking
place in spatially extended systems and is characterized by hysteresis and the
formation of domain walls. In this paper we demonstrate that an extended atomic
superfluid in the presence of a coherent coupling between two internal states
exhibits a quantum phase transition from a para- to a ferromagnetic state. The
nature of the transition is experimentally assessed by looking at the phase
diagram as a function of the control parameters, at hysteresis phenomena, at
the magnetic susceptibility and the magnetization fluctuations around the
critical point. We show that the observed features are in good agreement with
mean-field calculations. Additionally, we develop experimental protocols to
deterministically generate domain walls that separate spatial regions of
opposite magnetization in the ferromagnetic state. Thanks to the enhanced
coherence properties of our atomic superfluid system compared to standard
condensed matter systems, our results open the way towards the study of
different aspects of the relaxation dynamics in isolated coherent many-body
quantum systems.


  The giant elliptical galaxy M86 in Virgo has a ~28 kpc long dust trail inside
its optical halo that points toward the nucleated dwarf elliptical galaxy, VCC
882. The trail seems to be stripped material from the dwarf. Extinction
measurements suggest that the ratio of the total gas mass in the trail to the
blue luminosity of the dwarf is about unity, which is comparable to such ratios
in dwarf irregular galaxies. The ram pressure experienced by the dwarf galaxy
in the hot gaseous halo of M86 was comparable to the internal gravitational
binding energy density of the presumed former gas disk in VCC 882. Published
numerical models of this case are consistent with the overall trail-like
morphology observed here. Three concentrations in the trail may be evidence for
the predicted periodicity of the mass loss. The evaporation time of the trail
is comparable to the trail age obtained from the relative speed of the galaxies
and the trail length. Thus the trail could be continuously formed from stripped
replenished gas if the VCC 882 orbit is bound. However, the high gas mass and
the low expected replenishment rate suggest that this is only the first
stripping event. Implications for the origin of nucleated dwarf ellipticals are
briefly discussed.


  We give counterexamples to a question of Bowditch that if a non-elementary
type-preserving representation $\rho:\pi_1(\Sigma_{g,n})\rightarrow
PSL(2;\mathbb R)$ of a punctured surface group sends every non-peripheral
simple closed curve to a hyperbolic element, then must $\rho$ be Fuchsian. The
counterexamples come from relative Euler class $\pm1$ representations of the
four-punctured sphere group. We also show that the mapping class group action
on each non-extremal component of the character space of type-preserving
representations of the four-punctured sphere group is ergodic, which confirms a
conjecture of Goldman for this case. The main tool we use is Kashaev-Penner's
lengths coordinates of the decorated character spaces.


  We present a new holographic concept, named six-pack holography (6PH), in
which we compress six off-axis holograms into a single multiplexed off-axis
hologram without loss of magnification or resolution. The multiplexed hologram
contains straight off-axis fringes with six different orientations, and can be
generated optically or digitally. We show that since the six different complex
wavefronts do not overlap in the spatial frequency domain, they can be fully
reconstructed. 6PH allows more than 50% improvement in the spatial bandwidth
consumption when compared to the best multiplexing method proposed so far. We
expect the 6PH concept to be useful for a variety of applications, such as
field-of-view multiplexing, wavelength multiplexing, temporal multiplexing,
multiplexing for super-resolution imaging, and others.


  Continuum soft robots are mechanical systems entirely made of continuously
deformable elements. This design solution aims to bring robots closer to
invertebrate animals and soft appendices of vertebrate animals (e.g., an
elephant's trunk, a monkey's tail). This work aims to introduce the control
theorist perspective to this novel development in robotics. We aim to remove
the barriers to entry into this field by presenting existing results and future
challenges using a unified language and within a coherent framework. Indeed,
the main difficulty in entering this field is the wide variability of
terminology and scientific backgrounds, making it quite hard to acquire a
comprehensive view on the topic. Another limiting factor is that it is not
obvious where to draw a clear line between the limitations imposed by the
technology not being mature yet and the challenges intrinsic to this class of
robots. In this work, we argue that the intrinsic effects are the continuum or
multi-body dynamics, the presence of a non-negligible elastic potential field,
and the variability in sensing and actuation strategies.