  We investigate the non-equilibrium dynamics of the symmetry-resolved R\'enyi
entropies in a one-dimensional gas of non-interacting spinless fermions by
means of quantum generalised hydrodynamics, which recently allowed to obtain
very accurate results for the total entanglement in inhomogeneous quench
settings. Although our discussion is valid for any quench setting accessible
with quantum generalised hydrodynamics, we focus on the case of a quantum gas
initially prepared in a bipartite fashion and subsequently let evolve unitarily
with a hopping Hamiltonian. For this system, we characterise the
symmetry-resolved R\'enyi entropies as function of time $t$ and of the
entangling position $x$ along the inhomogeneous profile. We observe an
asymptotic logarithmic growth of the charged moments at half system and an
asymptotic restoration of equipartition of entropy among symmetry sectors with
deviations which are proportional to the square of the inverse of the total
entropy.


  High-fidelity 3D scene reconstruction from monocular videos continues to be
challenging, especially for complete and fine-grained geometry reconstruction.
The previous 3D reconstruction approaches with neural implicit representations
have shown a promising ability for complete scene reconstruction, while their
results are often over-smooth and lack enough geometric details. This paper
introduces a novel neural implicit scene representation with volume rendering
for high-fidelity online 3D scene reconstruction from monocular videos. For
fine-grained reconstruction, our key insight is to incorporate geometric priors
into both the neural implicit scene representation and neural volume rendering,
thus leading to an effective geometry learning mechanism based on volume
rendering optimization. Benefiting from this, we present MonoNeuralFusion to
perform the online neural 3D reconstruction from monocular videos, by which the
3D scene geometry is efficiently generated and optimized during the on-the-fly
3D monocular scanning. The extensive comparisons with state-of-the-art
approaches show that our MonoNeuralFusion consistently generates much better
complete and fine-grained reconstruction results, both quantitatively and
qualitatively.


  Nearest-neighbor methods have become popular in statistics and play a key
role in statistical learning. Important decisions in nearest-neighbor methods
concern the variables to use (when many potential candidates exist) and how to
measure the dissimilarity between units. The first decision depends on the
scope of the application while second depends mainly on the type of variables.
Unfortunately, relatively few options permit to handle mixed-type variables, a
situation frequently encountered in practical applications. The most popular
dissimilarity for mixed-type variables is derived as the complement to one of
the Gower's similarity coefficient. It is appealing because ranges between 0
and 1, being an average of the scaled dissimilarities calculated variable by
variable, handles missing values and allows for a user-defined weighting scheme
when averaging dissimilarities. The discussion on the weighting schemes is
sometimes misleading since it often ignores that the unweighted "standard"
setting hides an unbalanced contribution of the single variables to the overall
dissimilarity. We address this drawback following the recent idea of
introducing a weighting scheme that minimizes the differences in the
correlation between each contributing dissimilarity and the resulting weighted
Gower's dissimilarity. In particular, this note proposes different approaches
for measuring the correlation depending on the type of variables. The
performances of the proposed approaches are evaluated in simulation studies
related to classification and imputation of missing values.


  Autonomous driving has attracted significant attention from both academia and
industries, which is expected to offer a safer and more efficient driving
system. However, current autonomous driving systems are mostly based on a
single vehicle, which has significant limitations which still poses threats to
driving safety. Collaborative perception with connected and autonomous vehicles
(CAVs) shows a promising solution to overcoming these limitations. In this
article, we first identify the challenges of collaborative perception, such as
data sharing asynchrony, data volume, and pose errors. Then, we discuss the
possible solutions to address these challenges with various technologies, where
the research opportunities are also elaborated. Furthermore, we propose a
scheme to deal with communication efficiency and latency problems, which is a
channel-aware collaborative perception framework to dynamically adjust the
communication graph and minimize latency, thereby improving perception
performance while increasing communication efficiency. Finally, we conduct
experiments to demonstrate the effectiveness of our proposed scheme.


  The backreaction of the Lorentz force on the alpha-effect is studied in the
limit of small magnetic and fluid Reynolds numbers, using the first order
smoothing approximation (FOSA) to solve both the induction and momentum
equations. Both steady and time dependent forcings are considered. In the low
Reynolds number limit, the velocity and magnetic fields can be expressed
explicitly in terms of the forcing function. The nonlinear alpha-effect is then
shown to be expressible in several equivalent forms in agreement with
formalisms that are used in various closure schemes. On the one hand, one can
express alpha completely in terms of the helical properties of the velocity
field as in traditional FOSA, or, alternatively, as the sum of two terms, a
so-called kinetic alpha-effect and an oppositely signed term proportional to
the helical part of the small scale magnetic field. These results hold for both
steady and time dependent forcing at arbitary strength of the mean field
(abridged).


  I offer a simple and useful formula for the resolvent of a small rank
perturbation of large matrices. I discuss applications of this formula, in
particular, to analytical and numerical solving of difference boundary value
problems. I present examples connected with such problems for the difference
Laplacian and estimate numerical efficiency of the corresponding algorithms.


  We investigate existence, uniqueness and approximation of solutions to
stochastic delay differential equations (SDDEs) under Carath\'eodory-type drift
coefficients. Moreover, we also assume that both drift $f=f(t,x,z)$ and
diffusion $g=g(t,x,z)$ coefficient are Lipschitz continuous with respect to the
space variable $x$, but only H\"older continuous with respect to the delay
variable $z$. We provide a construction of randomized Euler scheme for
approximation of solutions of Carath\'eodory SDDEs, and investigate its upper
error bound. Finally, we report results of numerical experiments that confirm
our theoretical findings.


  Disentangling the radio flux contribution from star formation (SF) and
active-galactic-nuclei (AGN) activity is a long-standing problem in
extragalactic astronomy, since at frequencies of $\lesssim$ 10 GHz, both
processes emit synchrotron radiation. We present in this work the general
objectives of the PARADIGM Project, a multi-instrument concept to explore
star-formation and mass assembly of galaxies. We introduce two novel general
approaches for a detailed multiscale study of the radio emission in local
U/LIRGs. In this work, we use archival interferometric data from the Very Large
Array (VLA) centred at ~ 6 GHz (C band) and present new observations from the
e-Multi-Element Radio-Linked Interferometer Network (e-MERLIN) for UGC5101,
VV705, VV250 and UGC8696. Using our image decomposition methods, we robustly
disentangle the radio emission into distinct components by combining
information from the two interferometric arrays. We use e-MERLIN as a probe of
the core-compact radio emission (AGN or starburst) at ~ 20 pc scales, and as a
probe of nuclear diffuse emission, at scales ~ 100 - 200 pc. With VLA, we
characterise the source morphology and the flux density on scales from 200 pc
up to and above 1 kpc. As a result, we find deconvolved and convolved sizes for
nuclear regions from ~ 10 pc to ~ 200 pc. At larger scales, we find sizes of
1.5 - 2 kpc for diffuse structures (with effective sizes of ~ 300 - 400 pc). We
demonstrate that the radio emission from nuclear extended structures (~ 100 pc)
can dominate over core-compact components, providing a significant fraction of
the total multiscale SF output. We establish a multiscale radio tracer for star
formation by combining information from different instruments. Consequently,
this work sets a starting point to potentially correct for overestimations of
AGN fractions and underestimates of SF activity.


  We use the elliptic regulator to recover some identities between Mahler
measures involving certain families of genus 2 curves that were conjectured by
Boyd and proven by Bertin and Zudilin by differentiating the Mahler measures
and using hypergeometric identities. Since our proofs involve the regulator,
they yield light into the expected relation of each Mahler measure to special
values of $L$-functions of certain elliptic curves.


  The basic aim of physics studies at the LHC is to unravel the mechanism
responsible for the spontaneous symmetry breaking in the Standard Model (SM).
  In the currently accepted theoretical picture, this translates into finding
`direct' experimental evidence for the Higgs sector. TeV scale supersymmetry
(SUSY) provides a very attractive solution to the `naturalness' problem that
theories with elementary scalar fields have. Hence in this talk I will
summarise the physics potential of the LHC for searching for Higgs and
Supersymmetry as well as for measurement of the parameters of the Higgs sector
and the SUSY model. Theories with localised gravity (and large extra
dimensions) give a credible option to have Standard Model without the attendant
`naturalness' problems. I will therefore also summarise the potential of LHC to
probe these `large' extra dimensions.


  In this paper we investigate what kind of manifolds arise as the total spaces
of iterated $S^1$-bundles. A real Bott tower studied in \cite{CMO}, \cite{KM}
and \cite{KN} is an example of an iterated $S^1$-bundle. We show that the total
space of an iterated $S^1$-bundle is homeomorphic to an infra-nilmanifold. A
real Bott manifold, which is the total space of a real Bott tower, provides an
example of a closed flat Riemannian manifold. We also show that real Bott
manifolds are the only closed flat Riemannian manifolds obtained from iterated
$\bbr{P}^1$-bundles. Finally we classify the homeomorphism types of the total
spaces of iterated $S^1$-bundles in dimension 3.


  We are concerned with the behaviour of stochastic systems with inputs and
outputs, and how this might relate to the pursuit of a goal. We model this
using what we term transducers, which are a mathematical object that captures
only the external behaviour of such a system and not its internal state. We
present a framework for reasoning about the optimality of such a process, when
it is coupled to a 'teleo-environment' consisting of another transducer that
also embodies a success criterion. We find that (globally) optimal transducers
have a property closely related to Bellman's theorem: a transducer that is
optimal in one time step will again be optimal in the next time step, but with
respect to a different environment (obtained from the original one by a
modified version of Bayesian filtering). We also consider bounded rationality
and its relationship to constrained optimality, which in our framework means
optimal within some subset of all transducers. We describe a condition that is
sufficient for such a subset to have this Bellman property. Additionally, we
show that a policy is deterministic if and only if there exists a
teleo-envionment for which it is uniquely optimal among the set of all
transducers; this is at least conceptually related to classical representation
theorems from decision theory. This need not hold for constrained subsets; we
give an example of this related to the so-called absent-minded driver problem.
All of the formalism is defined using coinduction, following the style proposed
by Czajka [9].


  Data poisoning aims to compromise a machine learning based software component
by contaminating its training set to change its prediction results for test
inputs. Existing methods for deciding data-poisoning robustness have either
poor accuracy or long running time and, more importantly, they can only certify
some of the truly-robust cases, but remain inconclusive when certification
fails. In other words, they cannot falsify the truly-non-robust cases. To
overcome this limitation, we propose a systematic testing based method, which
can falsify as well as certify data-poisoning robustness for a widely used
supervised-learning technique named k-nearest neighbors (KNN). Our method is
faster and more accurate than the baseline enumeration method, due to a novel
over-approximate analysis in the abstract domain, to quickly narrow down the
search space, and systematic testing in the concrete domain, to find the actual
violations. We have evaluated our method on a set of supervised-learning
datasets. Our results show that the method significantly outperforms
state-of-the-art techniques, and can decide data-poisoning robustness of KNN
prediction results for most of the test inputs.


  In this paper, we study the singular sets of $F$-subharmonic functions $u:
B_{2}(0^{n})\rightarrow\mathbf{R}$, where $F$ is a subequation. The singular
set $\mathcal{S}(u)\subset B_{2}(0^{n})$ has a stratification
$\mathcal{S}^{0}(u)\subset\mathcal{S}^{1}(u)\subset\cdots\subset\mathcal{S}^{k}(u)\subset\cdots\subset\mathcal{S}(u)$,
where $x\in\mathcal{S}^{k}(u)$ if no tangent function to $u$ at $x$ is
$(k+1)$-homogeneous. We define the quantitative stratification
$\mathcal{S}_{\eta,r}^{k}(u)$ and
$\mathcal{S}_{\eta}^{k}(u)=\cap_{r}\mathcal{S}_{\eta,r}^{k}(u)$.
  When homogeneity of tangents holds for $F$, we prove that
$dim_{H}\mathcal{S}^{k}(u)\leq k$ and $\mathcal{S}(u)=\mathcal{S}^{n-p}(u)$,
where $p$ is the Riesz characteristic of $F$. And for the top quantitative
stratification $\mathcal{S}_{\eta}^{n-p}(u)$, we have the Minkowski estimate
$\text{Vol}(B_{r}(\mathcal{S}_{\eta}^{n-p}(u)\cap B_{1}(0^{n})))\leq
C\eta^{-1}(\int_{B_{1+r}(0^{n})}\Delta u)r^{p}$.
  When uniqueness of tangents holds for $F$, we show that $S_{\eta}^{k}(u)$ is
$k$-rectifiable, which implies $\mathcal{S}^{k}(u)$ is $k$-rectifiable.
  When strong uniqueness of tangents holds for $F$, we introduce the
monotonicity condition and the notion of $F$-energy. By using refined covering
argument, we obtain a definite upper bound on the number of $\{\Theta(u,x)\geq
c\}$ for $c>0$, where $\Theta(u,x)$ is the density of $F$-subharmonic function
$u$ at $x$.
  Geometrically determined subequations $F(\mathbb{G})$ is a very important
kind of subequation (when $p=2$, homogeneity of tangents holds for
$F(\mathbb{G})$; when $p>2$, uniqueness of tangents holds for $F(\mathbb{G})$).
By introducing the notion of $\mathbb{G}$-energy and using quantitative
differentation argument, we obtain the Minkowski estimate of quantitative
stratification $\text{Vol}(B_{r}(\mathcal{S}_{\eta,r}^{k}(u))\cap
B_{1}(0^{n}))\leq Cr^{n-k-\eta}$.


  The Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state can be found in layered
d-wave superconductors such as high $T_c$ cuprates and $\kappa$-(ET)$_2$ salts
in a planar magnetic field. We show the superconducting order parameter forms
two dimensional crystalline lattice in the FFLO state for d-wave
superconductors. Also the quasiparticle density of states and the
thermodynamics of FFLO state are constructed. Therefore STM or NMR will provide
a definitive test for the existence of the FFLO state.


  Engineering atomic-scale defects is crucial for realizing wafer-scale,
single-crystalline transition metal dichalcogenide monolayers for electronic
devices. However, connecting atomic-scale defects to larger morphologies poses
a significant challenge. Using electron microscopy and atomistic simulations,
we provide insights into WS2 crystal growth mechanisms, providing a direct link
between synthetic conditions and the microstructure. Dark-field TEM imaging of
coalesced monolayer WS2 films illuminates defect arrays that atomic-resolution
STEM imaging identifies as translational grain boundaries. Imaging reveals the
films to have nearly a single orientation with imperfectly stitched domains.
Through atomic-resolution imaging and ReaxFF reactive force field-based
molecular dynamics simulations, we observe two types of translational mismatch
and discuss their atomic structures and origin. Our results indicate that the
mismatch results from relatively fast growth rates. Through statistical
analysis of >1300 facets, we demonstrate that the macrostructural features are
constructed from nanometer-scale building blocks, describing the system across
sub-{\AA}ngstrom to multi-micrometer length scales.


  In this work, we develop a novel efficient quadrature and sparse grid based
polynomial interpolation method to price American options with multiple
underlying assets. The approach is based on first formulating the pricing of
American options using dynamic programming, and then employing static sparse
grids to interpolate the continuation value function at each time step. To
achieve high efficiency, we first transform the domain from $\mathbb{R}^d$ to
$(-1,1)^d$ via a scaled tanh map, and then remove the boundary singularity of
the resulting multivariate function over $(-1,1)^d$ by a bubble function and
simultaneously, to significantly reduce the number of interpolation points. We
rigorously establish that with a proper choice of the bubble function, the
resulting function has bounded mixed derivatives up to a certain order, which
provides theoretical underpinnings for the use of sparse grids. Numerical
experiments for American arithmetic and geometric basket put options with the
number of underlying assets up to 16 are presented to validate the
effectiveness of the approach.


  We study the dynamics of states perturbatively expanded about a harmonic
system of loop quantum cosmology, exhibiting a bounce. In particular, the
evolution equations for the first and second order moments of the system are
analyzed. These moments back-react on the trajectories of the expectation
values of the state and hence alter the energy density at the bounce. This
analysis is performed for isotropic loop quantum cosmology coupled to a scalar
field with a small but non-zero constant potential, hence in a regime in which
the kinetic energy of matter dominates. Analytic restrictions on the existence
of dynamical coherent states and the meaning of semi-classicality within these
systems are discussed. A numerical investigation of the trajectories of states
that remain semi-classical across the bounce demonstrates that, at least for
such states, the bounce persists and that its properties are similar to the
standard case, in which the moments of the states are entirely neglected.
However the bounce density does change, implying that a quantum bounce may not
be guaranteed to happen when the potential is no longer negligible.


  This paper explores the challenges in evaluating machine learning (ML) models
for continuous health monitoring using wearable devices beyond conventional
metrics. We state the complexities posed by real-world variability, disease
dynamics, user-specific characteristics, and the prevalence of false
notifications, necessitating novel evaluation strategies. Drawing insights from
large-scale heart studies, the paper offers a comprehensive guideline for
robust ML model evaluation on continuous health monitoring.


  X-ray and radio data recently acquired as part of a project to study Cyg
OB2#9 are used to constrain physical models of the binary system, providing
in-depth knowledge about the wind-wind collision and the thermal, and
non-thermal, emission arising from the shocks. We use a three-dimensional,
adaptive mesh refinement simulation (including wind acceleration, radiative
cooling, and the orbital motion of the stars) to model the gas dynamics of the
wind-wind collision. The simulation output is used as the basis for radiative
transfer calculations considering the thermal X-ray emission and the
thermal/non-thermal radio emission. To obtain good agreement with the X-ray
observations, our initial mass-loss rate estimates require a down-shift by a
factor of roughly 7.7 to $6.5\times10^{-7}$ and $7.5\times10^{-7}$ solar mass
per year for the primary and secondary star, respectively. Furthermore, the low
gas densities and high shock velocities in Cyg OB2#9 are suggestive of unequal
electron and ion temperatures, and the X-ray analysis indicates that an
(immediately post-shock) electron-ion temperature ratio of $\simeq 0.1$ is also
required. The radio emission is dominated by (non-thermal) synchrotron
emission. A parameter space exploration provides evidence against models
assuming equipartition between magnetic and relativistic energy densities.
However, fits of comparable quality can be attained with models having stark
contrasts in the ratio of magnetic-to-relativistic energy densities. The radio
models also reveal a subtle effect whereby inverse Compton cooling leads to an
increase in emissivity as a result of the synchrotron characteristic frequency
being significantly reduced. Finally, using the results of the radio analysis,
we estimate the surface magnetic field strengths to be $\approx 0.3-52\;$G.
(Abridged)


  We show that geometric scaling is satisfied to good accuracy in the forward
region of d+Au collisions at RHIC. Scaling violations do show up, however, at
mid-rapidity, and the anomalous dimension of the small-x gluon distribution
evolves to near its DGLAP limit for transverse momenta of a few GeV. This
represents a first consistency check of RHIC deuteron-nucleus and HERA DIS
phenomenology, and of the universality of the underlying Color Glass Condensate
(CGC) theory, which describes both phenomena. It also reconciles successful
leading-twist LO and NLO perturbative QCD computations of mid-rapidity particle
production with small-x evolution. Finally, we introduce a new parameterization
for the anomalous dimension of the small-x gluon distribution which properly
reproduces known theoretical limits at large rapidity, at large virtuality, and
on the saturation boundary, and still fits the available data from d+Au
collisions at RHIC. We find indications that sub-asymptotic terms in the
rapidity-evolution of the anomalous dimension are large.


  The discovery of noncentrosymmetric superconductors, such as CePt$_3$Si, and
chiral superconductors, such as Sr$_2$RuO$_4$, calls for experimental methods
to identify the presence of spin-triplet pairing. We here demonstrate a method
which accomplishes this in an appealingly simple manner: a spin-sensitive
proximity effect in a ferromagnet$\mid$triplet superconductor bilayer. It is
shown how the orientation of the field can be used to unambiguously distinguish
between different spin-triplet states. Moreover, the proximity effect becomes
long-ranged in spite of the presence of an exchange field and even without any
magnetic inhomogeneities, in contrast to conventional S$\mid$F junctions. Our
results can be verified by STM-spectroscopy and could be useful as a tool to
characterize the pairing state in unconventional superconducting materials.


  We study the recently proposed AdS$_7$/CFT$_6$ dualities for a class of 6d
$\mathcal{N} = (1,0)$ theories that flow on the tensor branch to long linear
quiver gauge theories. We find a precise agreement in the symmetries and in the
spectrum of charged states between the 6d SCFTs and their conjectured AdS$_7$
duals. We also confirm a recent conjecture that a discrete $S_N$ symmetry
relating the baryons in the quiver theories is in fact gauged.


  Delivery of content to mobile devices gains increasing importance in
industrial environments to support employees in the field. An important
application are e-mail push services like the fashionable Blackberry. These
systems are facing security challenges regarding data transport to, and storage
of the data on the end user equipment. The emerging Trusted Computing
technology offers new answers to these open questions.


  We calculate the q q correlation function associated to transverse target
lepton-nucleon inclusive deep-inelastic scattering by direct evaluation of the
corresponding matrix element. We use the electromagnetic impact parameter
potential for a transversely polarized nucleon to define the q q correlation
function for each avor in terms of the field components of that avor. The
results are compared with the two existing models for the q q correlator. Using
the calculated q q correlation function, we estimated the transverse target SSA
in IDIS process.


  The effects due to soft-photon emission (and the related virtual corrections)
in multi-body decays of B, D, and K mesons are analysed. We present analytic
expressions for the universal O(alpha) correction factors which can be applied
to all multi-body decay modes where a tight soft-photon energy cut in the
decaying-particle rest-frame is applied. All-order resummations valid in the
limit of small and large velocities of the final-state particles are also
discussed. The phenomenological implications of these correction factors in the
distortion of Dalitz-plot distributions of K -> 3 pi decays are briefly
analysed.


  The effect of electronic current on the atomic motion still poses many open
questions, and several mechanisms are at play. Recently there has been focus on
the importance of the current-induced non-conservative forces (NC) and
Berry-phase derived forces (BP) regarding the stability of molecular-scale
contacts. Systems based on molecules bridging electrically gated graphene
electrodes may offer an interesting test-bed for these effects.
  We employ a semi-classical Langevin approach in combination with DFT
calculations to study the current-induced vibrational dynamics of an atomic
carbon chain connecting electrically gated graphene electrodes. This
illustrates how the device stability can be predicted solely from the modes
obtained from the Langevin equation including the current induced forces. We
point out that the gate offers control of the current independent of bias
voltage which can be used to explore current-induced vibrational instabilities
due the NC/BP forces. Furthermore, using tight-binding and the Brenner
potential we illustrate how Langevin-type molecular dynamics can be performed
including the Joule heating effect for the carbon chain systems. Molecular
dynamics including current-induced forces enables an energy redistribution
mechanism among the modes, mediated by anharmonic interactions, which is found
to be vital in the description of the electronic heating.
  We have developed a semi-classical Langevin equation approach which can be
used to explore current-induced dynamics and instabilities. We find
instabilities at experimentally relevant bias and gate voltages for the carbon
chain system.


  It is generally believed that Veselago's criterion for negative refraction
cannot be fulfilled in natural materials. However, considering imaginary parts
of the permittivity ({\epsilon}) and permeability ({\mu}) and for metals at not
too high frequencies the general condition for negative refraction becomes
extremely simple: Re({\mu}) < 0 --> Re(n) < 0. Here we demonstrate
experimentally that in such natural metals as pure Co and FeCo alloy the
negative values of the refractive index are achieved close to the frequency of
the ferromagnetic resonance. Large values of the negative refraction can be
obtained at room temperature and they can easily be tuned in moderate magnetic
fields.


  Analyzing how interrelated ideas flow within and between multiple social
groups helps understand the propagation of information, ideas, and thoughts on
social media. The existing dynamic text analysis work on idea flow analysis is
mostly based on the topic model. Therefore, when analyzing the reasons behind
the flow of ideas, people have to check the textual data of the ideas, which is
annoying because of the huge amount and complex structures of these texts. To
solve this problem, we propose a concept-based dynamic visual text analytics
method, which illustrates how the content of the ideas change and helps users
analyze the root cause of the idea flow. We use concepts to summarize the
content of the ideas and show the flow of concepts with the flow lines. To
ensure the stability of the flow lines, a constrained t-SNE projection
algorithm is used to display the change of concepts over time and the
correlation between them. In order to better convey the anomalous change of the
concepts, we propose a method to detect the time periods with anomalous change
of concepts based on anomaly detection and highlight them. A qualitative
evaluation and a case study on real-world Twitter datasets demonstrate the
correctness and effectiveness of our visual analytics method.


  We present results from thirteen cosmological simulations that explore the
parameter space of the "Evolution and Assembly of GaLaxies and their
Environments" (EAGLE) simulation project. Four of the simulations follow the
evolution of a periodic cube L = 50 cMpc on a side, and each employs a
different subgrid model of the energetic feedback associated with star
formation. The relevant parameters were adjusted so that the simulations each
reproduce the observed galaxy stellar mass function at z = 0.1. Three of the
simulations fail to form disc galaxies as extended as observed, and we show
analytically that this is a consequence of numerical radiative losses that
reduce the efficiency of stellar feedback in high-density gas. Such losses are
greatly reduced in the fourth simulation - the EAGLE reference model - by
injecting more energy in higher density gas. This model produces galaxies with
the observed size distribution, and also reproduces many galaxy scaling
relations. In the remaining nine simulations, a single parameter or process of
the reference model was varied at a time. We find that the properties of
galaxies with stellar mass <~ M* (the "knee" of the galaxy stellar mass
function) are largely governed by feedback associated with star formation,
while those of more massive galaxies are also controlled by feedback from
accretion onto their central black holes. Both processes must be efficient in
order to reproduce the observed galaxy population. In general, simulations that
have been calibrated to reproduce the low-redshift galaxy stellar mass function
will still not form realistic galaxies, but the additional requirement that
galaxy sizes be acceptable leads to agreement with a large range of
observables.


  We propose a theory based on the $A_5$ discrete group which successfully
accounts for lepton masses and mixings. In the proposed model, the neutrino
masses arise from an inverse seesaw mechanism. The leptonic mixing consists in
a perturbed golden ratio mixing pattern with a strong correlation arising
between the leptonic mixing observables, which can be expressed as
$\theta_{13}$ predicting the other mixing angles and the Dirac CP phase.


  Visual question answering (VQA) is a task that combines both the techniques
of computer vision and natural language processing. It requires models to
answer a text-based question according to the information contained in a
visual. In recent years, the research field of VQA has been expanded. Research
that focuses on the VQA, examining the reasoning ability and VQA on scientific
diagrams, has also been explored more. Meanwhile, more multimodal feature
fusion mechanisms have been proposed. This paper will review and analyze
existing datasets, metrics, and models proposed for the VQA task.


  The first integrated implosion experiment of three-axis cylindrical hohlraum
(TACH) was accomplished at the SGIII laser facility. 24 laser beams of the
SGIII laser facility were carefully chosen and quasi-symmetrically injected
into the TACH, in which a highly symmetric radiation filed was generated with a
peak radiation temperature of ~190eV. Driven by the radiation field, the
neutron yield of a deuterium gas filled capsule reached ~1e9, and the
corresponding yield over clean (YOC) was ~40% for a convergence ratio (Cr) of
~17. The X-ray self-emission image of imploded capsule cores was nearly round,
and the backscatter fraction of laser beams was less than 1.25%. This
experiment preliminarily demonstrated the major performance of TACH, such as
the robustness of symmetry, and a laser plasma instability (LPI) behavior
similar to that of the outer ring of traditional cylindrical hohlraum.


  FLASH is a new treatment modality that requires optimization of dose, dose
rate, and LET. Here we validate these three quantities under FLASH conditions,
which includes the quantum uncertainty in the time-dependent instantaneous dose
rate (IDR) curves and LET spectra that underlie the newly proposed integrated
optimization framework.
  Measurements of dose, IDR, and LET were performed at the Emory Proton Therapy
Center using a FLASH proton beam with a nominal energy of 250 MeV and a 3D
printed ridge filter. Because 3D printing resin is made from a proprietary
chemical formula, we developed a method for realistically characterizing and
modeling the material in simulations. Absolute dose in 3D space was measured
using a 2D MatriXX PT detector as well as by a novel 4D multi-layer strip
ionization chamber (MLSIC), which also simultaneously measures IDR. Further
timing data was measured in the secondary beam by detecting prompt gammas using
a Minipix Timepix3; a second detector, Advapix Timepix3, was used to measure
LET. To account for the quantum mechanical nature of particle transport, we
developed a technique for detecting individual protons within a high flux
primary beam, which was necessary for properly measuring LET spectra.
  TOPAS simulations agreed with measurement, with absolute dose typically
having a gamma passing rate of at least 95% (3 mm/3% criteria). Likewise, IDR
and LET showed good agreement, with averaged IDR values agreeing within 0.3%
with fluctuations on the order of 10%, and LET distributions overlapping by at
least 85% and showing an increase in high LET components (greater than 4
keV/um) with increasing depth.
  As LET and FLASH optimization continues to grow in popularity, measuring IDR,
LET, and dose will become even more important, and we expect that the methods
described here will prove to be useful tools in radiotherapy treatment planning
and QA.


  In time series forecasting, effectively disentangling intricate temporal
patterns is crucial. While recent works endeavor to combine decomposition
techniques with deep learning, multiple frequencies may still be mixed in the
decomposed components, e.g., trend and seasonal. Furthermore, frequency domain
analysis methods, e.g., Fourier and wavelet transforms, have limitations in
resolution in the time domain and adaptability. In this paper, we propose
D-PAD, a deep-shallow multi-frequency patterns disentangling neural network for
time series forecasting. Specifically, a multi-component decomposing (MCD)
block is introduced to decompose the series into components with different
frequency ranges, corresponding to the "shallow" aspect. A
decomposition-reconstruction-decomposition (D-R-D) module is proposed to
progressively extract the information of frequencies mixed in the components,
corresponding to the "deep" aspect. After that, an interaction and fusion (IF)
module is used to further analyze the components. Extensive experiments on
seven real-world datasets demonstrate that D-PAD achieves the state-of-the-art
performance, outperforming the best baseline by an average of 9.48% and 7.15%
in MSE and MAE, respectively.


  Subbarrier alpha transfer reaction 13C(6Li,d)17O(6.356 MeV, 1/2+) at 3.6 MeV
is analyzed with a alpha + d + 13C three-body model, and the asymptotic
normalization coefficient (ANC) for alpha + 13C --> 17O(6.356 MeV, 1/2+), which
essentially determines the reaction rate of 13C(alpha,n)16O, is extracted.
Breakup effects of 6Li in the initial channel and those of 17O in the final
channel are investigated with the continuum-discretized coupled-channels method
(CDCC). The former is found to have a large back-coupling to the elastic
channel, while the latter turns out significantly small. The transfer cross
section calculated with Born approximation to the transition operator,
including breakup states of 6Li, gives (C_{alpha 13C}{17O*})^2 =1.03 \pm 0.29
fm^{-1}. This result is consistent with the value obtained by the previous DWBA
calculation.


  In this work, a new approach has been developed to obtain numerical solution
of linear Volterra type integral equations by obtaining asymptotic
approximation to solutions. Using the classical Bernoulli polynomials, a set of
orthonormal polynomials have been derived, and these orthonormal polynomials
have been used to form an operational matrix of integration which is has been
implemented to find numerical or exact solution of non-singular Volterra
integral equations. Two linear Volterra integral and two convolution integral
equations of second kind have been solved to demonstrate the effectiveness of
present method. Obtained approximate solutions have been compared with the
exact solutions for numerical values. High degree of accuracy of numerical
solutions has established the credibility of the present method.


  Based on classical nucleation theory, we propose a couple of theoretical
models for the nucleation of polymer crystallization, i.e. one for a single
chain system (Model S) and the other for a multi-chain system (Model M). In
these models, we assume that the nucleus is composed of tails, loops and a
cylindrical ordered region, and we evaluate the conformation entropy explicitly
by introducing a transfer matrix. Using these two models, we evaluate the
occurrence probability of critical nucleus as a function of the polymer chain
stiffness. We found that the critical nucleus in Model M is easier to occur
than in Model S because, for semi-flexible chains, the nucleus in Model M can
grow by adding a new polymer chain into the nucleus rather than to diminish the
loop and tail parts as in the case of Model S.


  Using an approach based on the techniques of FBI transforms, we give a new
simple proof of the global subelliptic estimates for non-selfadjoint
non-elliptic quadratic differential operators, under a natural averaging
condition on the Weyl symbols of the operators, established by the second
author. The loss of the derivatives in the subelliptic estimates depends
directly on algebraic properties of the Hamilton maps of the quadratic symbols.
Using the FBI point of view, we also give accurate smoothing estimates of
Gelfand-Shilov type for the associated heat semigroup in the limit of small
times.


  Astrophysical S-factors at zero energy for the direct nuclear capture
reactions $^{3}{\rm He}(\alpha, \gamma)^{7}{\rm Be}$, $^{3}{\rm H}(\alpha,
\gamma)^{7}{\rm Li}$ and $^{7}{\rm Be}(p, \gamma)^{8}{\rm B}$ are estimated
within the framework of two-body potential cluster model on the basis of
extranuclear capture approximation of D. Baye and E. Brainis. The values of
S(0)-factors have been calculated using two different potential models for each
process, which were adjusted to the binding energies and empirical values of
the asymptotical normalization coefficients from the literature. New values of
S(0)-factors have been obtained.


  Antagonistic salts are composed of hydrophilic and hydrophobic ions. In a
mixture solvent (water-oil) such ion pairs are preferentially attracted to
water or oil, giving rise to a coupling between the charge density and the
composition. First, they form a large electric double layer at a water-oil
interface, reducing the surface tension and producing mesophases. Here, the
cations and anions are loosely bound by the Coulomb attraction across the
interface on the scale of the Debye screening length. Second, on solid
surfaces, hydrophilic (hydrophobic) ions are trapped in a water-rich (oil-rich)
adsorption layer, while those of the other species are expelled from the layer.
This yields a solvation mechanism of local charge separation near a solid. In
particular, near the solvent criticality, disturbances around solid surfaces
can become oscillatory in space. In mesophases, we calculate periodic
structures, which resemble those in experiments.


  Spin torque majority gates are modeled and several regimes of magnetization
switching (some leading to failure) are discovered. The switching speed and
noise margins are determined for STMGs and an adder based on it. With switching
time of 3ns at current of 80uA, the adder computational throughput is
comparable to that of a CMOS adder.


  This paper examines the problem of locating outlier columns in a large,
otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and
inference approach and establish theoretical guarantees for its performance;
our results show that accurate outlier identification is achievable using very
few linear summaries of the original data matrix -- as few as the squared rank
of the low-rank component plus the number of outliers, times constant and
logarithmic factors. We demonstrate the performance of our approach
experimentally in two stylized applications, one motivated by robust
collaborative filtering tasks, and the other by saliency map estimation tasks
arising in computer vision and automated surveillance, and also investigate
extensions to settings where the data are noisy, or possibly incomplete.


  Herein we report on the thermal expansions and temperature-dependent crystal
structures of select ternary carbide MAX phases in the Ti-Al-C phase diagram in
the 100-1000 deg C temperature range. A bulk sample containing 38 wt.%
Ti5Al2C3, "523"), $32 wt.% Ti2AlC ("211"), 18 wt.% Ti3AlC2 ("312"), and 12 wt.%
(Ti{0.5}Al{0.5})Al is studied by Rietveld analysis of high-temperature neutron
diffraction data. We also report on the same for a single-phase sample of
Ti$_3$AlC$_2$ for comparison. The thermal expansions of all the MAX phases
studied are higher in the $c$ direction than in the $a$ direction. The bulk
expansion coefficients - 9.3 x 10^-6 for Ti5Al2C3, 9.2 x 10^-6 for Ti2AlC, and
9.0 x 10^-6 for Ti3AlC2 - are comparable within one standard deviation of each
other. In Ti5Al2C3, the dimensions of the Ti-C octahedra for the 211-like and
312-like regions are comparable to the Ti-C octahedra in Ti2AlC and Ti3AlC2,
respectively. The isotropic mean-squared atomic displacement parameters are
highest for the Al atoms in all three phases, and the values predicted from
first-principles phonon calculations agree well with those measured.


  The inert Zee model is an extension of the Zee model for neutrino masses.
This new model explains the dark matter relic abundance, generates a one-loop
neutrino masses and forbids tree-level Higgs-mediated flavor changing neutral
currents. Although the dark matter phenomenology of the model is similar to
that of the inert doublet model, the presence of new vector-like fermions opens
the lepton portal as a new dark matter annihilation channel. We study the
impact of such a new portal in the low mass regime and show the parameter space
allowed by direct and indirect searches of dark matter. Remarkably, we show
that the region for $m_{H^0} \lesssim$ 70 GeV is recovered for $\lambda_L
\lesssim 10^{-3}$. We also show that future experiments like LZ and DARWIN
could test a large region of the parameter space of the model.


  Considering nonlinear processes which are subject to unknown but measurable
disturbances, we provide both stability and feasibility proofs for nonlinear
model predictive controllers with abstract updates without the use of
stabilizing terminal constraints or cost. The result utilizes a relaxed
Lyapunov inequality for the nominal system and reasonable affine Lipschitz
conditions on the open loop change of the optimal value function and the stage
costs. For this methodology, we provide proofs that the known MPC updating
techniques based on sensitivities, realtime iterations and hierachically
structured MPC updates satisfy our assumptions revealing a common stability
framework. To illustrate our approach we present a quartercar example and show
performance improvements of the updated solution with respect to comfort and
handling properties.


  The subject of this study is Quantum and Statistical Mechanics of the Early
Universe. In it a new approach to investigation of these two theories - density
matrix deformation - is proposed. The distinguishing feature of the proposed
approach as compared to the previous ones is the fact that here the density
matrix is subjected to deformation, rather than commutators or (that is the
same) Heisenberg's Algebra. The deformation is understood as an extension of a
particular theory by inclusion of one or several additional parameters in such
a way that the initial theory appears in the limiting transition. Some
consequences of this approach for unitarity problem in Early Universe,black
hole entropy, information paradox problem,for symmetry restoration in the
associated deformed field model with scalar field are proposed.


  Using the almost all-sky 2MASS Photometric Redshift catalogue (2MPZ) we
perform for the first time a tomographic analysis of galaxy angular clustering
in the local Universe ($z<0.24$). We estimate the angular auto- and cross-power
spectra of 2MPZ galaxies in three photometric redshift bins, and use dedicated
mock catalogues to assess their errors. We measure a subset of cosmological
parameters, having fixed the others at their Planck values, namely the baryon
fraction $f_{b}=0.14^{+0.09}_{-0.06}$, the total matter density parameter
$\Omega_{\rm m}=0.30\pm 0.06$, and the effective linear bias of 2MPZ galaxies
$b_{\rm eff}$, which grows from $1.1^{+0.3}_{-0.4}$ at $\langle z \rangle=0.05$
up to $2.1^{+0.3}_{-0.5}$ at $\langle z\rangle=0.2$, largely because of the
flux-limited nature of the dataset. The results obtained here for the local
Universe agree with those derived with the same methodology at higher
redshifts, and confirm the importance of the tomographic technique for
next-generation photometric surveys such as Euclid or LSST.


  Recently, continuous diffusion models (CDM) have been introduced into
non-autoregressive (NAR) text-to-text generation. However, the discrete nature
of text increases the difficulty of CDM to generate coherent and fluent texts,
and also causes the incompatibility problem between CDM and advanced NLP
techniques, especially the popular pre-trained language models~(PLMs). To solve
it, we propose Diffusion-NAT, which introduces discrete diffusion models~(DDM)
into NAR text-to-text generation and integrates BART to improve the
performance. By revising the decoding process of BART and the typical settings
of DDM, we unify the inference process of BART and the denoising process of DDM
into the same NAR masked tokens recovering task. In this way, DDM can rely on
BART to perform denoising, which can benefit from both the rich pre-learned
knowledge of BART and the iterative refining paradigm of DDM. Besides, we also
propose the iterative self-prompting strategy to further improve the generation
quality. Experimental results on 7 datasets show that our approach can
outperform competitive NAR methods, and even surpass autoregressive methods.
Our code and data will be publicly released.


  Using the Schwinger-Dyson equations the possible infrared behaviour of the
gluon propagator is studied. Previous work performed in axial gauges is
reviewed, the approximations needed detailed and the difficulties of their
justification discussed. We then turn to the Landau gauge and investigate the
possibility of a gluon propagator less singular than $1/p^{2}$ when $p^{2}
\rightarrow 0$. We find that this infrared softened behaviour of the gluon
propagator is inconsistent; only an infrared enhanced gluon, as singular as
$1/p^{4}$ when $p^{2} \rightarrow 0$ is consistent with the truncated
Schwinger-Dyson equation. The implications for confinement and for the
modelling of the Pomeron are discussed.


  Given an entire transcendental function f with a non-completely invariant
Baker domain, we define a Baker lamination on geodesics to study the divergence
and convergence of a pinching process of curves in U. If the boundary of some
curve in the Baker lamination of f contains infinity, then the pinching
deformation does not converge. On the other hand, if f is semihyperbolic, its
Julia set J(f) is thin at infinity, and the lamination does not contain
infinity, then the pinching deformation converges uniformly to an entire
function F. With this theorem, we prove the existence of an entire function
with a wandering domain W to a positive euclidean distance of its postsingular
set P(F).


  We investigate the structure of the [bmim][Tf2N]/silica interface by
simulating the indentation of a thin (4 nm) [bmim][Tf2N] film by a hard
nanometric tip. The ionic liquid/silica interface is represented in atomistic
detail, while the tip is modelled by a spherical mesoscopic particle
interacting via an effective short-range potential. Plots of the normal force
(Fz) on the tip as a function of its distance from the silica surface highlight
the effect of weak layering in the ionic liquid structure, as well as the
progressive loss of fluidity in approaching the silica surface. The simulation
results for Fz are in near-quantitative agreement with new AFM data measured on
the same [bmim][Tf2N]/silica interface at comparable thermodynamic conditions.


  In this paper, we propose and analyze a new stochastic homogenization method
for diffusion equations with random and fast oscillatory coefficients. In the
proposed method, the homogenized solutions are sought through a two-stage
procedure. In the first stage, the original oscillatory diffusion equation is
approximated, for each fixed random sample w, by a spatially homogenized
diffusion equation with piecewise constant coefficients, resulting a random
diffusion equation. In the second stage, the resulted random diffusion equation
is approximated and computed by using an efficient multi-modes Monte Carlo
method which only requires to solve a diffusion equation with a constant
diffusion coefficient and a random right-hand side. The main advantage of the
proposed method is that it separates the computational difficulty caused by the
spatial fast oscillation of the solution and that caused by the randomness of
the solution, so they can be overcome separately using different strategies.
The convergence of the solution of the spatially homogenized equation (from the
first stage) to the solution of the original random diffusion equation is
established and the optimal rate of convergence is also obtained for the
proposed multi-modes Monte Carlo method. Numerical experiments on some
benchmark test problems for random composite materials are also presented to
gauge the efficiency and accuracy of the proposed two-stage stochastic
homogenization method.


  The evolution of the spectrum of cosmological fluctuations from one cycle to
the next is studied. It is pointed out that each cycle leads to a reddening of
the spectrum. This opens up new ways to generate a scale-invariant spectrum of
curvature perturbations. The large increase in the amplitude of the
fluctuations quickly leads to a breakdown of the linear theory. More generaly,
we see that, after including linearized cosmological perturbations, a cyclic
universe cannot be truly cyclic.


  Incorporating external knowledge into dialogue generation (KIDG) is crucial
for improving the correctness of response, where evidence fragments serve as
knowledgeable snippets supporting the factual dialogue replies. However,
introducing irrelevant content often adversely impacts reply quality and easily
leads to hallucinated responses. Prior work on evidence retrieval and
integration in dialogue systems falls short of fully leveraging existing
evidence since the model fails to locate useful fragments accurately and
overlooks hidden evidence labels within the KIDG dataset. To fully Unleash the
potential of evidence, we propose a framework to effectively incorporate
Evidence in knowledge-Intensive Dialogue Generation (u-EIDG). Specifically, we
introduce an automatic evidence generation framework that harnesses the power
of Large Language Models (LLMs) to mine reliable evidence veracity labels from
unlabeled data. By utilizing these evidence labels, we train a reliable
evidence indicator to effectively identify relevant evidence from retrieved
passages. Furthermore, we propose an evidence-augmented generator with an
evidence-focused attention mechanism, which allows the model to concentrate on
evidenced segments. Experimental results on MultiDoc2Dial demonstrate the
efficacy of evidential label augmentation and refined attention mechanisms in
improving model performance. Further analysis confirms that the proposed method
outperforms other baselines (+3~+5 points) regarding coherence and factual
consistency.


  Multi-baseline interferometric synthetic aperture radar (InSAR) techniques
are effective approaches for retrieving the 3-D information of urban areas. In
order to obtain a plausible reconstruction, it is necessary to use large-stack
interferograms. Hence, these methods are commonly not appropriate for
large-scale 3-D urban mapping using TanDEM-X data where only a few acquisitions
are available in average for each city. This work proposes a new SAR
tomographic processing framework to work with those extremely small stacks,
which integrates the non-local filtering into SAR tomography inversion. The
applicability of the algorithm is demonstrated using a TanDEM-X multi-baseline
stack with 5 bistatic interferograms over the whole city of Munich, Germany.
Systematic comparison of our result with airborne LiDAR data shows that the
relative height accuracy of two third buildings is within two meters, which
outperforms the TanDEM-X raw DEM. The promising performance of the proposed
algorithm paved the first step towards high quality large-scale 3-D urban
mapping.


  We study the spectrum of a planetary atmosphere to derive detectable features
in low resolution of different global geochemical cycles on exoplanets - using
the sulphur cycle as our example. We derive low resolution detectable features
for first generation space- and ground- based telescopes as a first step in
comparative planetology. We assume that the surfaces and atmospheres of
terrestrial exoplanets (Earth-like and super-Earths) will most often be
dominated by a specific geochemical cycle. Here we concentrate on the sulphur
cycle driven by outgassing of SO2 and H2S followed by their transformation to
other sulphur-bearing species which is clearly distinguishable from the carbon
cycle which is driven by outgassing of CO2. Due to increased volcanism, the
sulphur cycle is potentially the dominant global geochemical cycle on dry
super-Earths with active tectonics. We calculate planetary emission, reflection
and transmission spectrum from 0.4 to 40 micrometer with high and low
resolution to assess detectable features using current and Archean Earth models
with varying SO2 and H2S concentrations to explore reducing and oxidizing
habitable environments on rocky planets. We find specific spectral signatures
that are observable with low resolution in a planetary atmosphere with high SO2
and H2S concentration. Therefore first generation space and ground based
telescopes can test our understanding of geochemical cycles on rocky planets
and potentially distinguish planetary environments dominated by the carbon and
sulphur cycle.


  Recommendation, information retrieval, and other information access systems
pose unique challenges for investigating and applying the fairness and
non-discrimination concepts that have been developed for studying other machine
learning systems. While fair information access shares many commonalities with
fair classification, the multistakeholder nature of information access
applications, the rank-based problem setting, the centrality of personalization
in many cases, and the role of user response complicate the problem of
identifying precisely what types and operationalizations of fairness may be
relevant, let alone measuring or promoting them.
  In this monograph, we present a taxonomy of the various dimensions of fair
information access and survey the literature to date on this new and
rapidly-growing topic. We preface this with brief introductions to information
access and algorithmic fairness, to facilitate use of this work by scholars
with experience in one (or neither) of these fields who wish to learn about
their intersection. We conclude with several open problems in fair information
access, along with some suggestions for how to approach research in this space.


  We prove that both the hyperelliptic mapping class group and the extended
hyperelliptic mapping class group are generated by two torsion elements. We
also compute the index of the subgroup of the hyperelliptic mapping class group
which is generated by involutions and we prove that the extended hyperelliptic
mapping class group is generated by three orientation reversing involutions.


  The increasing popularity of autonomous and remotely-piloted drones have
paved the way for several use-cases, e.g., merchandise delivery and
surveillance. In many scenarios, estimating with zero-touch the weight of the
payload carried by a drone before its physical approach could be attractive,
e.g., to provide an early tampering detection.
  In this paper, we investigate the possibility to remotely detect the weight
of the payload carried by a commercial drone by analyzing its acoustic
fingerprint. We characterize the difference in the thrust needed by the drone
to carry different payloads, resulting in significant variations of the related
acoustic fingerprint. We applied the above findings to different use-cases,
characterized by different computational capabilities of the detection system.
Results are striking: using the Mel-Frequency Cepstral Coefficients (MFCC)
components of the audio signal and different Support Vector Machine (SVM)
classifiers, we achieved a minimum classification accuracy of 98% in the
detection of the specific payload class carried by the drone, using an
acquisition time of 0.25 s---performances improve when using longer time
acquisitions.
  All the data used for our analysis have been released as open-source, to
enable the community to validate our findings and use such data as a
ready-to-use basis for further investigations.


  Knowledge graphs capture structured information and relations between a set
of entities or items. As such knowledge graphs represent an attractive source
of information that could help improve recommender systems. However, existing
approaches in this domain rely on manual feature engineering and do not allow
for an end-to-end training. Here we propose Knowledge-aware Graph Neural
Networks with Label Smoothness regularization (KGNN-LS) to provide better
recommendations. Conceptually, our approach computes user-specific item
embeddings by first applying a trainable function that identifies important
knowledge graph relationships for a given user. This way we transform the
knowledge graph into a user-specific weighted graph and then apply a graph
neural network to compute personalized item embeddings. To provide better
inductive bias, we rely on label smoothness assumption, which posits that
adjacent items in the knowledge graph are likely to have similar user relevance
labels/scores. Label smoothness provides regularization over the edge weights
and we prove that it is equivalent to a label propagation scheme on a graph. We
also develop an efficient implementation that shows strong scalability with
respect to the knowledge graph size. Experiments on four datasets show that our
method outperforms state of the art baselines. KGNN-LS also achieves strong
performance in cold-start scenarios where user-item interactions are sparse.


  In this paper, we derive copula-based and empirical dependency models (DMs)
for simulating non-independent variables, and then propose a new way for
determining the distribution of the model outputs conditional on every subset
of inputs. Our approach relies on equivalent representations of the model
outputs using DMs, and an algorithm is provided for selecting the
representations that are necessary and sufficient for deriving the distribution
of the model outputs given each subset of inputs. In sensitivity analysis, the
selected representations allow for assessing the main, total and interactions
effects of each subset of inputs. We then introduce the first-order and total
indices of every subset of inputs with the former index less than the latter.
Consistent estimators of such indices are provided, including their asymptotic
distributions. Analytical and numerical results are provided using single and
multivariate response models.


  A fundamental problem is to understand why quantum theory only violates some
noncontextuality (NC) inequalities and identify the physical principles that
prevent higher-than-quantum violations. We prove that quantum theory only
violates those NC inequalities whose exclusivity graphs contain, as induced
subgraphs, odd cycles of length five or more, and/or their complements. In
addition, we show that odd cycles are the exclusivity graphs of a well-known
family of NC inequalities and that there is also a family of NC inequalities
whose exclusivity graphs are the complements of odd cycles. We characterize the
maximum noncontextual and quantum values of these inequalities, and provide
evidence supporting the conjecture that the maximum quantum violation of these
inequalities is exactly singled out by the exclusivity principle.


  We consider the system of quantum differential equations for a partial flag
variety and construct a basis of solutions in the form of multidimensional
hypergeometric functions, that is, we construct a Landau-Ginzburg mirror for
that partial flag variety. In our construction, the solutions are labeled by
elements of the $K$-theory algebra of the partial flag variety.
  To establish these facts we consider the equivariant quantum differential
equations for a partial flag variety and introduce a compatible system of
difference equations, which we call the qKZ equations. We construct a basis of
solutions of the joint system of the equivariant quantum differential equations
and qKZ difference equations in the form of multidimensional hypergeometric
functions. Then the facts about the non-equivariant quantum differential
equations are obtained from the facts about the equivariant quantum
differential equations by a suitable limit.
  Analyzing these constructions we obtain a formula for the fundamental Levelt
solution of the quantum differential equations for a partial flag variety.


  We propose a framework for jointly modeling the geometry and functionality in
high dimensional functional surfaces. The proposed mixed effects model
characterizes effects of subject-specific covariates and exogenous stimuli on
functional surfaces while accounting for potential mutual-influence of their
geometry and functionality. This is achieved through a computationally
efficient estimation method that incorporates regularized estimation of the
precision matrix of the random effects. We perform a thorough analysis of
cerebral cortical surface structural MRI and task fMRI data from the Human
Connectome Project and discover relationships between the geometric shapes of
cortical surface and neuronal activation responding to task stimuli. Our
findings highlight new modes of correspondence between cortical surface shape
and functional activation relevant to emotion processing.


  We consider the question of when the $n$-dimensional hypercube can be
decomposed into paths of length $k$. Mollard and Ramras \cite{MR2013} noted
that for odd $n$ it is necessary that $k$ divides $n2^{n-1}$ and that $k\leq
n$. Later, Anick and Ramras \cite{AR2013} showed that these two conditions are
also sufficient for odd $n \leq 2^{32}$ and conjectured that this was true for
all odd $n$. In this note we prove the conjecture.


  A set of Grand Unified Theories based upon the gauge groups $SU(5)_\L \times
SU(5)_\R$, $SO(10)_\L \times SO(10)_\R$ and $SU(4)_\C \times SU(4)_\L \times
SU(4)_\R$ is explored. Several novel features distinguish these theories from
the well-known $SU(5)$, $SO(10)$ and $SU(4)_\C \times SU(2)_\L \times SU(2)_\R$
models which they generalize. Firstly, Standard Model quarks and leptons are
accompanied by and mix with heavy $SU(2)_\L \times SU(2)_\R$ singlet partners.
The resulting fermion mass matrices are seesaw in form. Discrete parity
symmetries render the determinants of these mass matrices real and eliminate CP
violating gauge terms. The unified seesaw models consequently provide a
possible resolution to the strong CP problem. Secondly, $\sinsq$ at the
unification scale is numerically smaller than the experimentally measured $Z$
scale value. The weak angle must therefore increase as it evolves down in
energy. Finally, proton decay is suppressed by small seesaw mixing factors in
all these theories.


  The density matrix of a graph is the combinatorial laplacian matrix of a
graph normalized to have unit trace. In this paper we generalize the
entanglement properties of mixed density matrices from combinatorial laplacian
matrices of graphs discussed in Braunstein {\it et al.} Annals of
Combinatorics, {\bf 10}(2006)291 to tripartite states. Then we proved that the
degree condition defined in Braunstein {\it et al.} Phys. Rev. A {\bf 73},
(2006)012320 is sufficient and necessary for the tripartite separability of the
density matrix of a nearest point graph.


  Applying the anholonomic frame deformation method, we construct various
classes of cosmological solutions for effective Einstein -- Yang-Mills --
Higgs, and two measure theories. The types of models considered are
Freedman-Lema\^{i}tre-Robertson-Walker, Bianchi, Kasner and models with
attractor configurations. The various regimes pertaining to plateau--type
inflation, quadratic inflation, Starobinsky type and Higgs type inflation are
presented.


  We propose to demonstrate nonreciprocal conversion between microwave and
optical photons in an electro-optomechanical system where a microwave mode and
an optical mode are coupled indirectly via two non-degenerate mechanical modes.
The nonreciprocal conversion is obtained in the broken time-reversal symmetry
regime, where the conversion of photons from one frequency to the other is
enhanced for constructive quantum interference while the conversion in the
reversal direction is suppressed due to destructive quantum interference. It is
interesting that the nonreciprocal response between the microwave and optical
modes in the electro-optomechanical system appears at two different frequencies
with opposite directions. The proposal can be used to realize nonreciprocal
conversion between photons of any two distinctive modes with different
frequencies. Moreover, the electro-optomechanical system can also be used to
construct a three-port circulator for three optical modes with distinctively
different frequencies by adding an auxiliary optical mode coupled to one of the
mechanical modes.


  Let $X_1, \ldots, X_n$ be independent non-negative random variables with
cumulative distribution functions $F_1,F_2,\ldots,F_n$, each satisfying certain
(rather mild) conditions. We show that the median of $k$-th smallest order
statistic of the vector $(X_1, \ldots, X_n)$ is equivalent to the quantile of
order $(k-1/2)/n$ with respect to the averaged distribution
$F=\frac{1}{n}\sum_{i=1}^n F_i$.


  Cavitation is a complex multiphase phenomenon characterised by vapour bubbles
forming due to a sudden pressure drop and is often accompanied by increased
hull vibrations, increased radiated noise and decrease in propeller and
impeller performance. Although the Reynolds-Averaged Navier-Stokes (RANS)
method coupled with a cavitation model is still considered a practical tool to
predict cavitating flows owing to its computational efficiency, it is unable to
predict the unsteadiness of vapor shedding and over-predicts the eddy
viscosity. To improve the prediction, an empirical eddy viscosity
correction,[Reboud et al. 1998] was proposed to consider the compressibility
effects produced by cavitation. Additionally, a new type of models termed as
hybrid RANS-Large Eddy Simulation (LES) models have also been recently
introduced in the community, having the ability to behave as a RANS or a LES
model in different regions of the flow in order to combine the computational
cost efficiency of RANS with the accuracy of LES modelling. However, there
exists a lack of a comprehensive review of various such turbulence models like
the k-{\omega} Shear Stress Transport Model (SST), k-{\omega} SST
Scale-adaptive Simulation (SAS), k-{\omega} SST Detached Eddy Simulation (DES),
k-{\omega} SST Delayed DES (DDES), Filter-Based Method (FBM) and
Partially-Averaged Navier Stokes Method (PANS) to predict cavitating flows. In
this work, we conduct such a review to compare their ability to predict cloud
cavitating flows by comparing them with x-ray experimental data in a venturi.
It is shown that with mesh refinement, standard models do show the vapor
unsteadiness as seen in the experiment similar to that seen when using the
Reboud correction. However, on local comparison of turbulence quantities, it is
observed both forms of models have huge discrepancies with experimental data
that does not improve downstream.


  It has been conjectured that the optimal canonical degree of a minimal
surface of general type is 36, from a work in the 70's of Beauville who proved
that 36 was an upper bound. The highest canonical degree known for the problem
was 16 by Persson in 1978. The purpose of this article is to confirm the
conjecture by providing an explicit surface.


  In this work, we investigate the spectra of the prospective tetracharm and
tetrabottom baryonium, viz. the baryon-antibaryon states, in the framework of
QCD sum rules. The non-perturbative contributions up to dimension 10 are taken
into account. Numerical results indicate that there might exist 4 possible
tetracharm baryonium states with masses $(7.33\pm0.12)$, $(7.42\pm0.13)$,
$(7.68\pm0.17)$, and $(7.76\pm0.12)$ GeV for the quantum numbers of $0^{-+}$,
$1^{--}$, $0^{++}$, and $1^{++}$, respectively. The corresponding tetrabottom
partners are found lying respectively at $(19.41\pm 0.15)$, $(19.48\pm 0.15)$,
$(19.77\pm 0.19)$ and $(19.84\pm 0.19)$ GeV. Additionally, we also analyze the
spectra of the $\bar{\Xi}_{bc}\Xi_{bc}$ and $\bar{\Xi}_{cc}\Xi_{bb}$ baryonium
states, which lie in the range of $13.77-14.40$ GeV and $13.39-14.14$ GeV,
respectively. The possible baryonium decay modes are analyzed, which are
hopefully measurable in LHC experiments.


  The robustness of Bell's inequality (in CHSH form) violation by entangled
state in the simultaneous presence of colored and white noise in the system is
considered. A twophoton polarization state is modeled by twoparameter density
matrix. Setting parameter values one can vary the relative fraction of pure
entangled Bell's state as well as white and colored noise fractions. Bell's
operator-parameter dependence analysis is made. Computational results are
compared with experimental data [quant-ph/0511265] and with results computed
using a oneparameter density matrix [doi: 10.1103/PhysRevA.72.052112], which
one can get as a special case of the model considered in this work.


  The emergence of oxygenic photosynthesis was a major event in Earth's
evolutionary history and was facilitated by chlorophylls (a major category of
photopigments). The accurate modelling of photopigments is important to
understand the characteristics of putative extraterrestrial life and its
spectral signatures (detectable by future telescopes). In this paper, we
perform a detailed assessment of various time-dependent density-functional
theory (TD-DFT) methods for predicting the absorption spectra of chlorophyll a,
with particular emphasis on modern low-cost approximations. We also investigate
a potential extraterrestrial photopigment called phot0 and demonstrate that the
electronegativity of the metal ion may exert a direct influence on the
locations of the absorption peaks, with higher electronegativity inducing
blue-shifting and vice-versa. Based on these calculations, we established that
global-hybrid approximations with a moderate percentage of exact exchange --
such as M06 and PW6B95 -- are the most appropriate compromise between cost and
accuracy for the computational characterization of photopigments of
astrobiological interest. We conclude with a brief assessment of the
implications and avenues for future research.


  Let R denote the smallest class of compact spaces containing all metric
compacta and closed under limits of continuous inverse sequences of
retractions. Class R is striclty larger than the class of Valdivia compact
spaces. We show that every compact connected Abelian group which is a
topological retract of a space from class R is necessarily isomorphic to a
product of metric groups. This completes the result of V. Uspenskij and the
author, where a compact connected Abelian group outside class R has been
described.


  1. We first show a lower bound of 2N/3-1 for the connected minimum queen
domination (or cover) problem on the NXN chessboard - the upper bound is only 2
higher at most and is easy to show. 2. We then define the k-colored connected
minimum queen domination, and extend the above proof to show a lower bound of
(2 N - k - 2)/3, where the parameter k can be increased to get decreasing lower
bounds LB(N, k) until one reaches the simple domination lower bound of floor
N/2. 3. We also discuss extensions of the connected domination problem and
additional directions.


  Acceleration of deep neural networks to meet a specific latency constraint is
essential for their deployment on mobile devices. In this paper, we design an
architecture aware latency constrained sparse (ALCS) framework to prune and
accelerate CNN models. Taking modern mobile computation architectures into
consideration, we propose Single Instruction Multiple Data (SIMD)-structured
pruning, along with a novel sparse convolution algorithm for efficient
computation. Besides, we propose to estimate the run time of sparse models with
piece-wise linear interpolation. The whole latency constrained pruning task is
formulated as a constrained optimization problem that can be efficiently solved
with Alternating Direction Method of Multipliers (ADMM). Extensive experiments
show that our system-algorithm co-design framework can achieve much better
Pareto frontier among network accuracy and latency on resource-constrained
mobile devices.


  We have studied three-dimensional hydrodynamic interactions of relativistic
extragalactic jets with two-phase ambient media. These jets propagate through a
denser homogeneous gas and then impact clouds with densities 100 to 1000 times
higher than the initial beam density. The deflection angle of the jet is
influenced more by the density contrast of the cloud than by the beam Mach
number of the jet. A relativistic jet with low relativistic beam Mach number
can eventually be slightly bent after it crosses the dense cloud; however, we
have not seen permanently bent structures in the interaction of a high
relativistic beam Mach number jet with a cloud. The relativistic jet impacts on
dense clouds do not necessarily destroy the clouds completely, and much of the
cloud body can survive as a coherent blob. This enhancement of cloud durability
is partly due to the geometric influence of the off-axis collisions we consider
and also arises from the lower rate of cloud fragmentation through the
Kelvin-Helmholtz instability for relativistic jets. To compare our simulations
with observed extragalactic radio jets, we have computed the approximate
surface distributions of synchrotron emission at different viewing angles.
These surface intensity maps show relativistic jets interacting with clouds can
produce synchrotron emission knots similar to structures observed in many
VLBI-scale radio sources. We find that the synchrotron emission increases
steeply at the moment of impact and the emission peaks right before the jet
passes through the cloud.


  We investigate the relation between fragility and phase space properties -
such as the distribution of states - in the mean field p-spin model, a solvable
model that has been frequently used in studies of the glass transition. By
direct computation of all the relevant quantities, we find that: i) the
recently observed correlation between fragility and vibrational properties at
low temperature is present in this model; ii) the total number of states is a
decreasing function of fragility, at variance of what is currently believed. We
explain these findings by taking into account the contribution to fragility
coming from the transition paths between different states. Finally, we propose
a geometric picture of the phase space that explains the correlation between
properties of the transition paths, distribution of states and their
vibrational properties. However, our analysis may not apply to strong systems
where inflection points in the configurational entropy as a function of the
temperature are found.


  We investigate the statistical methods applied throughout safety analysis of
complex systems. The tolerance interval method implemented in the widely
utilized 0.95|0.95 methodology is analyzed. We point out a remarkable weakness
of the tolerance interval method concerning the principle of repeatability. It
is proved that repeating twice the procedure, the probability that the second
maximum will be larger/smaller than the first one is 50\%. This statement is
not surprising, it holds for any random variable with continuous distribution
function. In order to demonstrate the undesirable consequences of the tolerance
interval method in the decision making, the results of the analysis of an
elementary example are discussed. Instead of the tolerance interval method, we
suggest another method based on the sign test which has more encouraging
features, especially in the case of several output variables. The problematic
aspects of the method are also discussed. Finally, we suggest a simple test
case which is able to reveal if the tolerance interval method would not be
capable of determining the risky states of the system, when there are only a
few of them. If there are many, then the method may not explore each one in the
analysis.


  The scaling limit of large simple outerplanar maps was established by
Caraceni using a bijection due to Bonichon, Gavoille and Hanusse. The present
paper introduces a new bijection between outerplanar maps and trees decorated
with ordered sequences of edge-rooted dissections of polygons. We apply this
decomposition in order to provide a new, short proof of the scaling limit that
also applies to the general setting of first-passage percolation. We obtain
sharp tail-bounds for the diameter and recover the asymptotic enumeration
formula for outerplanar maps. Our methods also enable us treat subclasses such
as bipartite outerplanar maps.


  A comparison between theory and experiment, concerning the
K(4s)+K(7s)\rightarrow K(4s)+K(5f) reaction of energy transfer in thermal
collisions, is presented. Relevant cross sections are calculated for the
potassium vapour temperatures in the range of 310-1000 K. They are based on the
theoretical adiabatic K_2 potential energy curves and on the use of the
multicrossing Landau-Zener model. In the temperature range of the present
experiment, 428-451 K, the computed cross sections vary little, from
2.09x10^-14 cm^2 to 2.04x10^-14 cm^2, and agree well with the value
1.8(8)x10^-14 cm^2, which is the average of the corresponding experimental
results.


  In this paper we find explicit formulas for the Poisson and heat semigroups
associated to the modified Bessel operator and on the hyperbolic spaces
$\mathbb{H}^n$.


  The need for improved diagnostic methods in ophthalmology is acute,
especially in the less developed regions with limited access to specialists and
advanced equipment. Therefore, we introduce VisionUnite, a novel
vision-language foundation model for ophthalmology enhanced with clinical
knowledge. VisionUnite has been pretrained on an extensive dataset comprising
1.24 million image-text pairs, and further refined using our proposed MMFundus
dataset, which includes 296,379 high-quality fundus image-text pairs and
889,137 simulated doctor-patient dialogue instances. Our experiments indicate
that VisionUnite outperforms existing generative foundation models such as
GPT-4V and Gemini Pro. It also demonstrates diagnostic capabilities comparable
to junior ophthalmologists. VisionUnite performs well in various clinical
scenarios including open-ended multi-disease diagnosis, clinical explanation,
and patient interaction, making it a highly versatile tool for initial
ophthalmic disease screening. VisionUnite can also serve as an educational aid
for junior ophthalmologists, accelerating their acquisition of knowledge
regarding both common and rare ophthalmic conditions. VisionUnite represents a
significant advancement in ophthalmology, with broad implications for
diagnostics, medical education, and understanding of disease mechanisms.


  J. Hopcroft and D. Sheldon originally introduced network reputation games to
investigate the self-interested behavior of web authors who want to maximize
their PageRank on a directed web graph by choosing their outlinks in a game
theoretic manner. They give best response strategies for each player and
characterize properties of web graphs which are Nash equilibria. In this paper
we consider three different models for PageRank games on undirected graphs such
as certain social networks. In undirected graphs players may delete links at
will, but typically cannot add links without the other player's permission. In
the deletion-model players are free to delete any of their bidirectional links
but may not add links. We study the problem of determining whether the given
graph represents a Nash equilibrium or not in this model. We give an $O(n^{2})$
time algorithm for a tree, and a parametric $O(2^{k}n^{4})$ time algorithm for
general graphs, where $k$ is the maximum vertex degree in any biconnected
component of the graph. In the request- delete-model players are free to delete
any bidirectional links and add any directed links, since these additions can
be done unilaterally and can be viewed as requests for bidirected links. For
this model we give an $O(n^3)$ time algorithm for verifying Nash equilibria in
trees. Finally, in the add-delete- model we allow a node to make arbitrary
deletions and the addition of a single bidirectional link if it would increase
the page rank of the other player also. In this model we give a parametric
algorithm for verifying Nash equilibria in general graphs and characterize so
called $\alpha$-insensitive Nash Equilibria. We also give a result showing a
large class of graphs where there is an edge addition that causes the PageRank
of both of its endpoints to increase, suggesting convergence towards complete
subgraphs.


  The production of $\alpha$-particles in reactions using both the strongly and
weakly bound projectiles at energies around the Coulomb barrier show several
interesting features. To understand these, the role of various reaction
mechanisms responsible for $\alpha$-production, such as non-capture breakup,
capture of only one of the fragments subsequent to projectile breakup and their
contribution to reaction cross sections have been investigated. A systematic
study of the $\alpha$-particle production based on available data for various
projectile target systems have been performed and a classification based on
projectile type is obtained.


  We report the results of an experiment that searches for causal non-linear
state-dependent terms in quantum field theory. Our approach correlates a binary
macroscopic classical voltage with the outcome of a projective measurement of a
quantum bit, prepared in a coherent superposition state. Measurement results
are recorded in a bit string, which is used to control a voltage switch.
Presence of a non-zero voltage reading in cases of no applied voltage is the
experimental signature of a non-linear state-dependent shift of the
electromagnetic field operator. We implement blinded measurement and data
analysis with three control bit strings. Control of systematic effects is
realized by producing one of the control bit strings with a classical
random-bit generator. The other two bit strings are generated by measurements
performed on a superconduting qubit in an IBM Quantum processor, and on a
$^{15}$N nuclear spin in an NV center in diamond. Our measurements find no
evidence for electromagnetic quantum state-dependent non-linearity. We set a
bound on the parameter that quantifies this non-linearity
$|\epsilon_{\gamma}|<4.7\times 10^{-11}$, at 90% confidence level. Within the
Everett many-worlds interpretation of quantum theory, our measurements place
limits on the electromagnetic interaction between different branches of the
universe, created by preparing the qubit in a superposition state.


  Two discrete N-level alternatives to the popular imaginary cubic oscillator
are proposed and studied. In a certain domain ${\cal D}$ of parameters $a$ and
$z$ of the model, the spectrum of energies is shown real (i.e., potentially,
observable) and the unitarity of the evolution is shown mediated by the
construction of a (non-unique) physical, ad hoc Hilbert space endowed with a
nontrivial, Hamiltonian-dependent inner-product metric $\Theta$. Beyond ${\cal
D}$ the complex-energy curves are shown to form a "Fibonacci-numbered"
geometric pattern and/or a "topologically complete" set of spectral loci. The
dynamics-determining construction of the set of the eligible metrics is shown
tractable by a combination of the computer-assisted algebra with the
perturbation and extrapolation techniques. Confirming the expectation that for
the local potentials the effect of the metric cannot be short-ranged.


  Inspired by the work of Bank on the hypertranscendence of $\Gamma e^h$ where
$\Gamma$ is the Euler gamma function and $h$ is an entire function, we
investigate when a meromorphic function $fe^g$ cannot satisfy any algebraic
differential equation over certain field of meromorphic functions, where $f$
and $g$ are meromorphic and entire on the complex plane, respectively. Our
results (Theorem 1 and 2) give partial solutions to Bank's Conjecture (1977) on
the hypertranscendence of $\Gamma e^h$. We also give some sufficient conditions
for hypertranscendence of meromorphic function of the form $f+g$, $f\cdot g$
and $f\circ g$ in Theorem 3 and 4.


  We explore the possibility to use advanced germanium (Ge) detectors as a
low-energy solar neutrino observatory by means of neutrino-nucleus elastic
scattering. A Ge detector utilizing internal charge amplification for the
charge carriers created by the ionization of impurities is a novel technology
with experimental sensitivity for detecting low-energy solar neutrinos. Ge
internal charge amplification (GeICA) will amplify the charge carriers induced
by neutrino interacting with Ge atoms through emission of phonons. It is those
phonons that will create charge carriers through the ionization of impurities
to achieve an extremely low energy threshold of $\sim$0.01 eV. We demonstrate
the phonon absorption, excitation, and ionization probability of impurities in
a Ge detector with impurity levels of 3$\times$10$^{10}$ cm$^{-3}$,
9$\times$10$^{10}$ cm$^{-3}$, and 2$\times$10$^{11}$ cm$^{-3}$. We present the
sensitivity of such a Ge experiment for detecting solar neutrinos in the
low-energy region. We show that, if GeICA technology becomes available, then a
new opportunity arises to observe $pp$ and 7Be solar neutrinos. Such a novel
detector with only 1 kg of high-purity Ge will give $\sim$ 10 events per year
for $pp$ neutrinos and $\sim$ 5 events per year for 7Be neutrinos with a
detection energy threshold of 0.01 eV.


  We demonstrate that polarization-sensitive optical coherence tomography
(PS-OCT) is suitable to map the stress distribution within materials in a
contactless and non-destructive way. In contrast to transmission
photoelasticity measurements the samples do not have to be transparent but can
be of scattering nature. Denoising and analysis of fringe patterns in single
PS-OCT retardation images are demonstrated to deliver the basis for a
quantitative whole-field evaluation of the internal stress state of samples
under investigation.


  We present an approximation to topological spaces by {\it noncommutative}
lattices. This approximation has a deep physical flavour based on the
impossibility to fully localize particles in any position measurement. The
original space being approximated is recovered out of a projective limit.


  Image-to-video generation, which aims to generate a video starting from a
given reference image, has drawn great attention. Existing methods try to
extend pre-trained text-guided image diffusion models to image-guided video
generation models. Nevertheless, these methods often result in either low
fidelity or flickering over time due to their limitation to shallow image
guidance and poor temporal consistency. To tackle these problems, we propose a
high-fidelity image-to-video generation method by devising a frame retention
branch based on a pre-trained video diffusion model, named DreamVideo. Instead
of integrating the reference image into the diffusion process at a semantic
level, our DreamVideo perceives the reference image via convolution layers and
concatenates the features with the noisy latents as model input. By this means,
the details of the reference image can be preserved to the greatest extent. In
addition, by incorporating double-condition classifier-free guidance, a single
image can be directed to videos of different actions by providing varying
prompt texts. This has significant implications for controllable video
generation and holds broad application prospects. We conduct comprehensive
experiments on the public dataset, and both quantitative and qualitative
results indicate that our method outperforms the state-of-the-art method.
Especially for fidelity, our model has a powerful image retention ability and
delivers the best results in UCF101 compared to other image-to-video models to
our best knowledge. Also, precise control can be achieved by giving different
text prompts. Further details and comprehensive results of our model will be
presented in https://anonymous0769.github.io/DreamVideo/.


  We discuss the status of a subset of penetrating probes in relativistic
nuclear collisions. Thermal photons and dileptons are considered, as well as
the electromagnetic signature of jets.


  This paper concerns the numerical solution of the finite-horizon Optimal
Investment problem with transaction costs under Potential Utility. The problem
is initially posed in terms of an evolutive HJB equation with gradient
constraints. In Finite-Horizon Optimal Investment with Transaction Costs: A
Parabolic Double Obstacle Problem, Day-Yi, the problem is reformulated as a
non-linear parabolic double obstacle problem posed in one spatial variable and
defined in an unbounded domain where several explicit properties and formulas
are obtained. The restatement of the problem in polar coordinates allows to
pose the problem in one spatial variable in a finite domain, avoiding some of
the technical difficulties of the numerical solution of the previous statement
of the problem. If high precision is required, the spectral numerical method
proposed becomes more efficient than simpler methods as finite differences for
example.


  Both self-supervised depth estimation and Structure-from-Motion (SfM) recover
scene depth from RGB videos. Despite sharing a similar objective, the two
approaches are disconnected. Prior works of self-supervision backpropagate
losses defined within immediate neighboring frames. Instead of
learning-through-loss, this work proposes an alternative scheme by performing
local SfM. First, with calibrated RGB or RGB-D images, we employ a depth and
correspondence estimator to infer depthmaps and pair-wise correspondence maps.
Then, a novel bundle-RANSAC-adjustment algorithm jointly optimizes camera poses
and one depth adjustment for each depthmap. Finally, we fix camera poses and
employ a NeRF, however, without a neural network, for dense triangulation and
geometric verification. Poses, depth adjustments, and triangulated sparse
depths are our outputs. For the first time, we show self-supervision within $5$
frames already benefits SoTA supervised depth and correspondence models. The
project page is held in the link (https://shngjz.github.io/SSfM.github.io/).


  The extended nebulae formed as pulsar winds expand into their surroundings
provide information about the composition of the winds, the injection history
from the host pulsar, and the material into which the nebulae are expanding.
Observations from across the electromagnetic spectrum provide constraints on
the evolution of the nebulae, the density and composition of the surrounding
ejecta, the geometry of the central engines, and the long-term fate of the
energetic particles produced in these systems. Such observations reveal the
presence of jets and wind termination shocks, time-varying compact emission
structures, shocked supernova ejecta, and newly formed dust. Here I provide a
broad overview of the structure of pulsar wind nebulae, with specific examples
from observations extending from the radio band to very-high-energy gamma-rays
that demonstrate our ability to constrain the history and ultimate fate of the
energy released in the spin-down of young pulsars.


  A kernel based method is proposed for the construction of signature
(defining) functions of subsets of $\mathbb{R}^d$. The subsets can range from
full dimensional manifolds (open subsets) to point clouds (a finite number of
points) and include bounded smooth manifolds of any codimension. The
interpolation and analysis of point clouds are the main application. Two
extreme cases in terms of regularity are considered, where the data set is
interpolated by an analytic surface, at the one extreme, and by a H\"older
continuous surface, at the other. The signature function can be computed as a
linear combination of translated kernels, the coefficients of which are the
solution of a finite dimensional linear problem. Once it is obtained, it can be
used to estimate the dimension as well as the normal and the curvatures of the
interpolated surface. The method is global and does not require explicit
knowledge of local neighborhoods or any other structure present in the data
set. It admits a variational formulation with a natural ``regularized''
counterpart, that proves to be useful in dealing with data sets corrupted by
numerical error or noise. The underlying analytical structure of the approach
is presented in general before it is applied to the case of point clouds.


  In multicast network design games, a set of agents choose paths from their
source locations to a common sink with the goal of minimizing their individual
costs, where the cost of an edge is divided equally among the agents using it.
Since the work of Anshelevich et al. (FOCS 2004) that introduced network design
games, the main open problem in this field has been the price of stability
(PoS) of multicast games. For the special case of broadcast games (every vertex
is a terminal, i.e., has an agent), a series of works has culminated in a
constant upper bound on the PoS (Bilo` et al., FOCS 2013). However, no
significantly sub-logarithmic bound is known for multicast games. In this
paper, we make progress toward resolving this question by showing a constant
upper bound on the PoS of multicast games for quasi-bipartite graphs. These are
graphs where all edges are between two terminals (as in broadcast games) or
between a terminal and a nonterminal, but there is no edge between
nonterminals. This represents a natural class of intermediate generality
between broadcast and multicast games. In addition to the result itself, our
techniques overcome some of the fundamental difficulties of analyzing the PoS
of general multicast games, and are a promising step toward resolving this
major open problem.


  Starting from integrable cellular automata we present a novel form of
Painlev\'e equations. These equations are discrete in both the independent
variable and the dependent one. We show that they capture the essence of the
behavior of the Painlev\'e equations organizing themselves into a coalescence
cascade and possessing special solutions. A necessary condition for the
integrability of cellular automata is also presented.


  We consider a Brownian particle in harmonic confinement of stiffness $k$, in
one dimension in the underdamped regime. The whole setup is immersed in a heat
bath at temperature $T$. The center of harmonic trap is dragged under any
arbitrary protocol. The thermodynamic uncertainty relations for both position
of the particle and current at time $t$ are obtained using the second law of
thermodynamics as well as the positive semi-definite property of the
correlation matrix of work and degrees of freedom of the system for both
underdamped and overdamped cases.


  We consider the most general correlations that can be obtained by a group of
parties whose causal relations are well-defined, although possibly
probabilistic and dependent on past parties' operations. We show that, for any
fixed number of parties and inputs and outputs for each party, the set of such
correlations forms a convex polytope, whose vertices correspond to
deterministic strategies, and whose (nontrivial) facets define so-called causal
inequalities. We completely characterize the simplest tripartite polytope in
terms of its facet inequalities, propose generalizations of some inequalities
to scenarios with more parties, and show that our tripartite inequalities can
be violated within the process matrix formalism, where quantum mechanics is
locally valid but no global causal structure is assumed.


  Clunie and Hayman proved that if the spherical derivative of an entire
function has order of growth sigma then the function itself has order at most
sigma+1. We extend this result to holomorphic curves in projective space of
dimension n omitting n hyperplanes in general position.