  A review over recent experimental progress in the physics of the fifth quark
is given.


  We revisit the Hartree-Fock (HF) calculation for the uniform electron gas, or
jellium model, whose predictions -- divergent derivative of the energy
dispersion relation and vanishing density of states (DOS) at the Fermi level --
are in qualitative disagreement with experimental evidence for simple metals.
Currently, this qualitative failure is attributed to the lack of screening in
the HF equations. Employing Slater's hyper-Hartree-Fock (HHF) equations,
derived variationally, to study the ground state and the excited states of
jellium, we find that the divergent derivative of the energy dispersion
relation and the zero in the DOS are still present, but shifted from the Fermi
wavevector and energy of jellium to the boundary between the set of
variationally optimised and unoptimised HHF orbitals. The location of this
boundary is not fixed, but it can be chosen to lie at arbitrarily high values
of wavevector and energy, well clear from the Fermi level of jellium. We
conclude that, rather than the lack of screening in the HF equations, the
well-known qualitative failure of the ground-state HF approximation is an
artifact of its nonlocal exchange operator. Other similar artifacts of the HF
nonlocal exchange operator, not associated with the lack of electronic
correlation, are known in the literature.


  Despite the recent achievements made in the multi-modal emotion recognition
task, two problems still exist and have not been well investigated: 1) the
relationship between different emotion categories are not utilized, which leads
to sub-optimal performance; and 2) current models fail to cope well with
low-resource emotions, especially for unseen emotions. In this paper, we
propose a modality-transferable model with emotion embeddings to tackle the
aforementioned issues. We use pre-trained word embeddings to represent emotion
categories for textual data. Then, two mapping functions are learned to
transfer these embeddings into visual and acoustic spaces. For each modality,
the model calculates the representation distance between the input sequence and
target emotions and makes predictions based on the distances. By doing so, our
model can directly adapt to the unseen emotions in any modality since we have
their pre-trained embeddings and modality mapping functions. Experiments show
that our model achieves state-of-the-art performance on most of the emotion
categories. In addition, our model also outperforms existing baselines in the
zero-shot and few-shot scenarios for unseen emotions.


  Critical Casimir forces can play an important role for applications in
nano-science and nanotechnology, owing to their piconewton strength, nanometric
action range, fine tunability as a function of temperature, and exquisite
dependence on the surface properties of the involved objects. Here, we
investigate the effects of critical Casimir forces on the free dynamics of a
pair of colloidal particles dispersed in the bulk of a near-critical binary
liquid solvent, using blinking optical tweezers. In particular we measure the
time evolution of the distance between the two colloids to determine their
relative diffusion and drift velocity. Furthermore, we show how critical
Casimir forces change the dynamic properties of this two-colloid system by
studying the temperature dependence of the distribution of the so-called
first-passage time, i.e., of the time necessary for the particles to reach for
the first time a certain separation, starting from an initially assigned one.
These data are in good agreement with theoretical results obtained from Monte
Carlo simulations and Langevin dynamics.


  Racism and intolerance on social media contribute to a toxic online
environment which may spill offline to foster hatred, and eventually lead to
physical violence. That is the case with online antisemitism, the specific
category of hatred considered in this study. Tracking antisemitic themes and
their associated terminology over time in online discussions could help monitor
the sentiments of their participants and their evolution, and possibly offer
avenues for intervention that may prevent the escalation of hatred. Due to the
large volume and constant evolution of online traffic, monitoring conversations
manually is impractical. Instead, we propose an automated method that extracts
antisemitic themes and terminology from extremist social media over time and
captures their evolution. Since supervised learning would be too limited for
such a task, we created an unsupervised online machine learning approach that
uses large language models to assess the contextual similarity of posts. The
method clusters similar posts together, dividing, and creating additional
clusters over time when sub-themes emerge from existing ones or new themes
appear. The antisemitic terminology used within each theme is extracted from
the posts in each cluster. Our experiments show that our methodology
outperforms existing baselines and demonstrates the kind of themes and
sub-themes it discovers within antisemitic discourse along with their
associated terminology. We believe that our approach will be useful for
monitoring the evolution of all kinds of hatred beyond antisemitism on social
platforms.


  In machine learning, Feature Selection (FS) is a major part of efficient
algorithm. It fuels the algorithm and is the starting block for our prediction.
In this paper, we present a new method, called Optimal Coordinate Ascent (OCA)
that allows us selecting features among block and individual features. OCA
relies on coordinate ascent to find an optimal solution for gradient boosting
methods score (number of correctly classified samples). OCA takes into account
the notion of dependencies between variables forming blocks in our
optimization. The coordinate ascent optimization solves the issue of the NP
hard original problem where the number of combinations rapidly explode making a
grid search unfeasible. It reduces considerably the number of iterations
changing this NP hard problem into a polynomial search one. OCA brings
substantial differences and improvements compared to previous coordinate ascent
feature selection method: we group variables into block and individual
variables instead of a binary selection. Our initial guess is based on the
k-best group variables making our initial point more robust. We also introduced
new stopping criteria making our optimization faster. We compare these two
methods on our data set. We found that our method outperforms the initial one.
We also compare our method to the Recursive Feature Elimination (RFE) method
and find that OCA leads to the minimum feature set with the highest score. This
is a nice byproduct of our method as it provides empirically the most compact
data set with optimal performance.


  Quantum walks (QWs) exhibit different properties compared with classical
random walks (RWs), most notably by linear spreading and localization. In the
meantime, random walks that replicate quantum walks, which we refer to as
quantum-walk-replicating random walks (QWRWs), have been studied in the
literature where the eventual properties of QWRW coincide with those of QWs.
However, we consider that the unique attributes of QWRWs have not been fully
utilized in the former studies to obtain deeper or new insights into QWs. In
this paper, we highlight the directivity of one-dimensional discrete quantum
walks via QWRWs. By exploiting the fact that QWRW allows trajectories of
individual walkers to be considered, we first discuss the determination of
future directions of QWRWs, through which the effect of linear spreading and
localization is manifested in another way. Furthermore, the transition
probabilities of QWRWs can also be visualized and show a highly complex shape,
representing QWs in a novel way. Moreover, we discuss the first return time to
the origin between RWs and QWs, which is made possible via the notion of QWRWs.
We observe that the first return time statistics of QWs are quite different
from RWs, caused by both the linear spreading and localization properties of
QWs.


  The deepening penetration of renewable energy is challenging how power system
operators cope with the associated variability and uncertainty in the unit
commitment problem. Given its computational complexity, several
optimization-based methods have been proposed to lighten the full unit
commitment formulation by removing redundant line flow constraints. These
approaches often ignore the spatial couplings of multi-side renewable
generation and demand. To address this pitfall, we rule out redundant
constraints over a tightened linear programming relaxation of the original unit
commitment feasibility region by adding a constraint that efficiently models
the correlation of residual demand variations. We set forth a novel, tractable
and robust polyhedral uncertainty envelope induced by a given set of scenarios
to characterize the tightening constraint. We propose a data-driven umbrella
constraint discovery problem formulation that substantially increase the
network constraints filtration in unit commitment. Numerical tests are
performed on standard IEEE test networks to substantiate the effectiveness of
the approach.


  Asteroid binaries found amongst the Near-Earth objects are believed to have
formed from rotational fission. In this paper, we aim to study the dynamical
evolution of asteroid systems the moment after fission. The initial condition
is modelled as a contact binary, similar to that of Boldrin et al. (2016). Both
bodies are modelled as ellipsoids, and the secondary is given an initial
rotation angle about its body-fixed $y$-axis. Moreover, we consider six
different cases, three where the density of the secondary varies, and three
where we vary its shape. The simulations consider 45 different initial tilt
angles of the secondary, each with 37 different mass ratios. We start the
dynamical simulations at the moment the contact binary reaches a spin fission
limit, and our model ensures that the closest distance between the surfaces of
the two bodies is always kept at 1 cm. The forces, torques and gravitational
potential between the two bodies are modelled using a newly developed surface
integration scheme, giving exact results for two ellipsoids. We find that more
than 80% of the simulations end with the two bodies impacting, and collisions
between the bodies are more common when the density of the secondary is lower,
or when it becomes more elongated. When comparing with data on asteroid pairs
from Pravec et al. (2019) we find that variations in density and shape of the
secondary can account for some of the spread seen in the rotation period for
observed pairs. Furthermore, the secondary may also reach a spin limit for
surface disruption, creating a ternary/multiple system. We find that secondary
fission typically occurs within the first five hours after the contact binary
separates, and is more common when the secondary is less dense or more
elongated.


  Non-autoregressive text to speech (NAR-TTS) models have attracted much
attention from both academia and industry due to their fast generation speed.
One limitation of NAR-TTS models is that they ignore the correlation in time
and frequency domains while generating speech mel-spectrograms, and thus cause
blurry and over-smoothed results. In this work, we revisit this over-smoothing
problem from a novel perspective: the degree of over-smoothness is determined
by the gap between the complexity of data distributions and the capability of
modeling methods. Both simplifying data distributions and improving modeling
methods can alleviate the problem. Accordingly, we first study methods reducing
the complexity of data distributions. Then we conduct a comprehensive study on
NAR-TTS models that use some advanced modeling methods. Based on these studies,
we find that 1) methods that provide additional condition inputs reduce the
complexity of data distributions to model, thus alleviating the over-smoothing
problem and achieving better voice quality. 2) Among advanced modeling methods,
Laplacian mixture loss performs well at modeling multimodal distributions and
enjoys its simplicity, while GAN and Glow achieve the best voice quality while
suffering from increased training or model complexity. 3) The two categories of
methods can be combined to further alleviate the over-smoothness and improve
the voice quality. 4) Our experiments on the multi-speaker dataset lead to
similar conclusions as above and providing more variance information can reduce
the difficulty of modeling the target data distribution and alleviate the
requirements for model capacity.


  Context. Since 1996, during periods of low solar activity, the HSTOF
instrument onboard the SOHO satellite has been measuring weak fluxes of He
atoms of 28-58 keV/n (helium energetic neutral atoms - He ENA). The probable
source region is the inner heliosheath.
  Aims. We aim to understand the emission mechanism of He ENA based on
knowledge of heliosheath spatial extent and plasma content resulting from
Voyager 1 & 2 measurements in the period after termination shock crossings.
  Methods. He ENA are generated by charge-exchange neutralization of energetic
helium ions on interstellar neutral H and He. Energy spectra of helium ions in
the heliosheath are calculated by following the evolution of their velocity
distribution functions when carried by and undergoing binary interactions with
plasma constituents of a background flow whose particle populations are modeled
to approximately render post-termination-shock Voyager data.
  Results. The observed HSTOF He ENA form a higher energy part of general
heliospheric He ENA fluxes and can be explained by the proposed mechanism to
within 2{\sigma} error. The main factor determining the level of emission (and
its uncertainty) is the energy spectrum of He^+ pickup ions in post-termination
shock plasmas.


  We present a sufficient condition for the presence of spontaneous
magnetization for the Ising model on a general graph, related to its long-range
topology. Applying this condition we are able to prove the existence of a phase
transition at temperature T > 0 on a wide class of general networks. The
possibility of further extensions of our results is discussed.


  Airport service quality evaluation is commonly found on social media,
including Google Maps. This valuable for airport management in order to enhance
the quality of services provided. However; prior studies either provide general
review for topics discussed by travellers or provide sentimental value to tag
the entire review without specifically mentioning the airport service that is
behind such value. Accordingly, this work proposes using aspect based
sentimental analysis in order to provide more detailed analysis for travellers
reviews. This works applied aspect based sentimental analysis on data collected
from Google Map about Dubai and Doha airports. The results provide tangible
reasons to use aspect based sentimental analysis in order to understand more
the travellers and spot airport services that are in need for improvement.


  Deep networks have recently enjoyed enormous success when applied to
recognition and classification problems in computer vision, but their use in
graphics problems has been limited. In this work, we present a novel deep
architecture that performs new view synthesis directly from pixels, trained
from a large number of posed image sets. In contrast to traditional approaches
which consist of multiple complex stages of processing, each of which require
careful tuning and can fail in unexpected ways, our system is trained
end-to-end. The pixels from neighboring views of a scene are presented to the
network which then directly produces the pixels of the unseen view. The
benefits of our approach include generality (we only require posed image sets
and can easily apply our method to different domains), and high quality results
on traditionally difficult scenes. We believe this is due to the end-to-end
nature of our system which is able to plausibly generate pixels according to
color, depth, and texture priors learnt automatically from the training data.
To verify our method we show that it can convincingly reproduce known test
views from nearby imagery. Additionally we show images rendered from novel
viewpoints. To our knowledge, our work is the first to apply deep learning to
the problem of new view synthesis from sets of real-world, natural imagery.


  Recent advances in nonlinear optics have revolutionized the area of
integrated photonics, providing on-chip solutions to a wide range of new
applications. Currently, the state of the art integrated nonlinear photonic
devices are mainly based on dielectric material platforms, such as Si3N4 and
SiO2. While semiconductor materials hold much higher nonlinear coefficients and
convenience in active integration, they suffered in the past from high
waveguide losses that prevented the realization of highly efficient nonlinear
processes on-chip. Here we challenge this status quo and demonstrate an
ultra-low loss AlGaAs-on-insulator (AlGaAsOI) platform with anomalous
dispersion and quality (Q) factors beyond 1.5*10^6. Such a high quality factor,
combined with the high nonlinear coefficient and the small mode volume, enabled
us to demonstrate a record low Kerr frequency comb generation threshold of ~36
uW for a resonator with a 1 THz free spectral range (FSR), ~100 times lower
compared to that in previous semiconductor platform. Combs with >250 nm broad
span have been generated under a pump power lower than the threshold power of
state of the art dielectric micro combs. A soliton-step transition has also
been observed for the first time from an AlGaAs resonator. This work is an
important step towards ultra-efficient semiconductor-based nonlinear photonics
and will lead to fully integrated nonlinear photonic integrated circuits (PICs)
in near future.


  Hierarchical reinforcement learning (HRL) has seen widespread interest as an
approach to tractable learning of complex modular behaviors. However, existing
work either assume access to expert-constructed hierarchies, or use
hierarchy-learning heuristics with no provable guarantees. To address this gap,
we analyze HRL in the meta-RL setting, where a learner learns latent
hierarchical structure during meta-training for use in a downstream task. We
consider a tabular setting where natural hierarchical structure is embedded in
the transition dynamics. Analogous to supervised meta-learning theory, we
provide "diversity conditions" which, together with a tractable optimism-based
algorithm, guarantee sample-efficient recovery of this natural hierarchy.
Furthermore, we provide regret bounds on a learner using the recovered
hierarchy to solve a meta-test task. Our bounds incorporate common notions in
HRL literature such as temporal and state/action abstractions, suggesting that
our setting and analysis capture important features of HRL in practice.


  As the third stage of the project multi-indexed orthogonal polynomials, we
present, in the framework of 'discrete quantum mechanics' with pure imaginary
shifts in one dimension, the multi-indexed Wilson and Askey-Wilson polynomials.
They are obtained from the original Wilson and Askey-Wilson polynomials by
multiple application of the discrete analogue of the Darboux transformations or
the Crum-Krein-Adler deletion of 'virtual state solutions' of type I and II, in
a similar way to the multi-indexed Laguerre, Jacobi and (q-)Racah polynomials
reported earlier.


  The main purpose of this paper is to establish weighted estimates for
singular integrals associated with Zygmund dilations via a discrete
Littlewood--Paley theory, and then apply it to obtain the upper bound of the
norm of commutators of such singular integrals with a function in the little
bmo space associated with Zygmund dilations. Examples of such singular
integrals associated with Zygmund dilations include a class of singular
integrals studied by Ricci--Stein and Fefferman--Pipher, as well as a singular
integral along a particular surface studied by Nagel--Wainger. We show that the
lower bound of the norm of this commutator is not true for any singular
integral in the class considered in Ricci--Stein and Fefferman--Pipher, but
does in fact hold for the specific singular integral studied in Nagel--Wainger.
In particular this implies that the family of singular integrals studied in
these papers is not sufficiently general to contain the operator of
Nagel--Wainger, which we show is of significance in this theory.


  Traditional discriminative approaches in mental health analysis are known for
their strong capacity but lack interpretability and demand large-scale
annotated data. The generative approaches, such as those based on large
language models (LLMs), have the potential to get rid of heavy annotations and
provide explanations but their capabilities still fall short compared to
discriminative approaches, and their explanations may be unreliable due to the
fact that the generation of explanation is a black-box process. Inspired by the
psychological assessment practice of using scales to evaluate mental states,
our method which is called Mental Analysis by Incorporating Mental Scales
(MAIMS), incorporates two procedures via LLMs. First, the patient completes
mental scales, and second, the psychologist interprets the collected
information from the mental scales and makes informed decisions. Experimental
results show that MAIMS outperforms other zero-shot methods. MAIMS can generate
more rigorous explanation based on the outputs of mental scales


  We calculate the $B\to D^{(*)}$ form factors in the heavy-quark and
large-recoil limits in the perturbative QCD framework based on $k_T$
factorization theorem, assuming the hierachy $M_B\gg M_{D^{(*)}}\gg
\bar\Lambda$, with the $B$ meson mass $M_B$, the $D^{(*)}$ meson mass
$M_{D^{(*)}}$, and the heavy meson and heavy quark mass difference
$\bar\Lambda$. The qualitative behavior of the light-cone $D^{(*)}$ meson wave
function and the associated Sudakov resummation are derived. The leading-power
contributions to the $B\to D^{(*)}$ form factors, characterized by the scale
$\bar\Lambda\sqrt{M_B/M_{D^{(*)}}}$, respect the heavy-quark symmetry. The
next-to-leading-power corrections in $1/M_B$ and $1/M_{D^{(*)}}$, characterized
by a scale larger than $\sqrt{\bar\Lambda M_B}$, are estimated to be less than
20%. The $D^{(*)}$ meson wave function is determined from the fit to the
observed $B\to D^{(*)} l\nu$ decay spectrum, which can be employed to make
predictions for nonleptonic decays, such as $B\to D^{(*)}\pi(\rho)$.


  Extreme U-statistics arise when the kernel of a U-statistic has a high degree
but depends only on its arguments through a small number of top order
statistics. As the kernel degree of the U-statistic grows to infinity with the
sample size, estimators built out of such statistics form an intermediate
family in between those constructed in the block maxima and
peaks-over-threshold frameworks in extreme value analysis. The asymptotic
normality of extreme U-statistics based on location-scale invariant kernels is
established. Although the asymptotic variance coincides with the one of the
H\'ajek projection, the proof goes beyond considering the first term in
Hoeffding's variance decomposition. We propose a kernel depending on the three
highest order statistics leading to a location-scale invariant estimator of the
extreme value index resembling the Pickands estimator. This extreme Pickands
U-estimator is asymptotically normal and its finite-sample performance is
competitive with that of the pseudo-maximum likelihood estimator.


  The \lya forest at high redshifts is a powerful probe of reionization.
Modeling and observing this imprint comes with significant technical
challenges: inhomogeneous reionization must be taken into account while
simultaneously being able to resolve the web-like small-scale structure prior
to reionization. In this work we quantify the impact of inhomogeneous
reionization on the \lya forest at lower redshifts ($2 < z < 4$), where
upcoming surveys such as DESI will enable precision measurements of the flux
power spectrum. We use both small box simulations capable of handling the
small-scale structure of the \lya forest and semi-numerical large box
simulations capable of representing the effects of inhomogeneous reionization.
We find that inhomogeneous reionization could produce a measurable effect on
the \lya forest power spectrum. The deviation in the 3D power spectrum at
$z_{\rm obs} = 4$ and $k = 0.14 \ \rm{Mpc}^{-1}$ ranges from $19 - 36\%$, with
a larger effect for later reionization. The corrections decrease to $2.0 -
4.1\%$ by $z_{\rm obs} = 2$. The impact on the 1D power spectrum is smaller,
and ranges from $3.3 - 6.5\%$ at $z_{\rm obs}=4$ to $0.35 - 0.75\%$ at $z_{\rm
obs}=2$, values which are comparable to the statistical uncertainties in
current and upcoming surveys. Furthermore, we study how can this systematic be
constrained with the help of the quadrupole of the 21 cm power spectrum.


  The new Planck constraints on the local bispectrum parameter fnl are about
10^5 times tighter than the current constraints on the trispectrum parameter
gnl, which means that the allowed numerical values of the second and third
order terms in the perturbative expansion of the curvature perturbation are
comparable. We show that a consequence of this is that if gnl is large enough
to be detectable, then it will induce a large variation between the observable
value of fnl and its value in a larger inflated volume. Even if there were only
a few extra efoldings between the beginning of inflation and horizon crossing
of our Hubble horizon, an observably large gnl means that fnl is unlikely to be
as small as its current constraint, regardless of its true background value.
This result is very general, it holds regardless of how many fields contributed
to the curvature perturbation. We also generalise this result to other shapes
of non-Gaussianity, beyond the local model. We show that the variance of the
3-point function in the squeezed limit is bounded from below by the square of
the squeezed limit of the 4-point function.


  Reliability is a critical aspect of multi-agent system coordination as it
ensures that the system functions correctly and consistently. If one agent in
the system fails or behaves unexpectedly, it can negatively impact the
performance and effectiveness of the entire system. Therefore, it is important
to design and implement multi-agent systems with a high level of reliability to
ensure that they can operate safely and move smoothly in the presence of
unforeseen agent failure or lack of communication with some agent teams moving
in a shared motion space. This paper presents a novel fluid flow navigation
model that, in an ideal fluid flow, divides agents into cooperative
(non-singular) and noncooperative (singular) agents, with cooperative agents
sliding along streamlines safely enclosing noncooperative agents in a shared
motion space. A series of flight experiments utilizing crazyflie quadcopters
will experimentally validate the suggested model.


  We study combinatorial and order theoretic structures arising from the
fragment of combinatory logic spanned by the basic combinator ${\bf M}$. This
basic combinator, named as the Mockingbird by Smullyan, is defined by the
rewrite rule ${\bf M} x_1 \to x_1 x_1$. We prove that the reflexive and
transitive closure of this rewrite relation is a partial order on terms on
${\bf M}$ and that all connected components of its rewrite graph are Hasse
diagrams of lattices. This last result is based on the introduction of lattices
on some forests. We enumerate the elements, the edges of the Hasse diagrams,
and the intervals of these lattices with the help of formal power series on
terms and on forests.


  In a variety of models the motion of the inflaton may trigger the production
of some non-inflaton particles during inflation, for example via parametric
resonance or a phase transition. Such models have attracted interest recently
for a variety of reasons, including the possibility of slowing the motion of
the inflaton on a steep potential. In this review we show that interactions
between the produced particles and the inflaton condensate can lead to a
qualitatively new mechanism for generating cosmological fluctuations from
inflation. We illustrate this effect using a simple prototype model g^2
(\phi-\phi_0)^2\chi^2 for the interaction between the inflaton, \phi, and
iso-inflaton, \chi. Such interactions are quite natural in a variety of
inflation models from supersymmetry and string theory. Using both lattice field
theory simulations and analytical calculations, we study the quantum production
of \chi particles and their subsequent rescatterings off the condensate
\phi(t), which generates bremsstrahlung radiation of light inflaton
fluctuations \delta\phi. This mechanism leads to observable features in the
primordial power spectrum. We derive observational constraints on such features
and discuss their implications for popular models of inflation, including
brane/axion monodromy. Inflationary particle production also leads to a very
novel kind of nongaussian signature which may be observable in future missions.
We argue that this mechanism provides a simple and well-motivated option to
generate large nongaussianity, without fine-tuning the inflationary trajectory
or appealing to re-summation of an infinite series of high dimension operators.


  We consider a certain sequence of random walks. The state space of the n-th
random walk is the set of all strict partitions of n (that is, partitions
without equal parts).
  We prove that, as n goes to infinity, these random walks converge to a
continuous-time Markov process. The state space of this process is the
infinite-dimensional simplex consisting of all nonincreasing infinite sequences
of nonnegative numbers with sum less than or equal to one. The main result
about the limit process is the expression of its the pre-generator as a formal
second order differential operator in a polynomial algebra.
  Of separate interest is the generalization of Kerov interlacing coordinates
to the case of shifted Young diagrams.


  Understanding the behaviour of topologically ordered lattice systems at
finite temperature is a way of assessing their potential as fault-tolerant
quantum memories. We compute the natural extension of the topological
entanglement entropy for T > 0, namely the subleading correction
$I_{\textrm{topo}}$ to the area law for mutual information. Its dependence on T
can be written, for Abelian Kitaev models, in terms of information-theoretic
functions and readily identifiable scaling behaviour, from which the interplay
between volume, temperature, and topological order, can be read. These
arguments are extended to non-Abelian quantum double models, and numerical
results are given for the $D(S_3)$ model, showing qualitative agreement with
the Abelian case.


  Identifying intrinsic noncollinear magnetic order in monolayer van der Waals
(vdW) crystals is highly desirable for understanding the delicate magnetic
interactions at reduced spatial constraints and miniaturized spintronic
applications, but remains elusive in experiments. Here, we achieved
spin-resolved imaging of helimagnetism at atomic scale in monolayer NiI2
crystals, that were grown on graphene-covered SiC(0001) substrate, using
spin-polarized scanning tunneling microscopy. Our experiments identify the
existence of a spin spiral state with canted plane in monolayer NiI2. The spin
modulation Q vector of the spin spiral is determined as (0.2203, 0, 0), which
is different from its bulk value or its in-plane projection, but agrees well
with our first principles calculations. The spin spiral surprisingly indicates
collective spin switching behavior under magnetic field, whose origin is
ascribed to the incommensurability between the spin spiral and the crystal
lattice. Our work unambiguously identifies the helimagnetic state in monolayer
NiI2, paving the way for illuminating its expected type-II multiferroic order
and developing spintronic devices based on vdW magnets.


  The randomized unbiased estimators of Rhee and Glynn (Operations
Research:63(5), 1026-1043, 2015) can be highly efficient at approximating
expectations of path functionals associated with stochastic differential
equations (SDEs). However, there is a lack of algorithms for calculating the
optimal distributions with an infinite horizon. In this article, based on the
method of Cui et.al. (Operations Research Letters: 477-484, 2021), we prove
that, under mild assumptions, there is a simple representation of the optimal
distributions. Then, we develop an adaptive algorithm to compute the optimal
distributions with an infinite horizon, which requires only a small amount of
computational time in prior estimation. Finally, we provide numerical results
to illustrate the efficiency of our adaptive algorithm.


  A method for creating a vision-and-language (V&L) model is to extend a
language model through structural modifications and V&L pre-training. Such an
extension aims to make a V&L model inherit the capability of natural language
understanding (NLU) from the original language model. To see how well this is
achieved, we propose to evaluate V&L models using an NLU benchmark (GLUE). We
compare five V&L models, including single-stream and dual-stream models,
trained with the same pre-training. Dual-stream models, with their higher
modality independence achieved by approximately doubling the number of
parameters, are expected to preserve the NLU capability better. Our main
finding is that the dual-stream scores are not much different than the
single-stream scores, contrary to expectation. Further analysis shows that
pre-training causes the performance drop in NLU tasks with few exceptions.
These results suggest that adopting a single-stream structure and devising the
pre-training could be an effective method for improving the maintenance of
language knowledge in V&L extensions.


  In this paper we prove two new cases of Langlands functoriality. The first is
a functorial product for cusp forms on $GL_2\times GL_3$ as automorphic forms
on $GL_6$, from which we obtain our second case, the long awaited functorial
symmetric cube map for cusp forms on $GL_2$. We prove these by applying a
recent version of converse theorems of Cogdell and Piatetski-Shapiro to
analytic properties of certain $L$-functions obtained from the method of
Eisenstein series (Langlands-Shahidi method). As a consequence, we prove the
bound 5/34 for Hecke eigenvalues of Maass forms over any number field and at
every place, finite or infinite, breaking the crucial bound 1/6 (see below and
Section 7 and 8) towards Ramanujan-Petersson and Selberg conjectures for
$GL_2$. Many other applications are obtained.


  We review the different results obtained in the last decade in the field of
Raman scattering of graphene based systems, with an applied magnetic field.
Electronic properties of graphene based systems with an applied magnetic field
will first be described. The phonon response in magnetic field, the
magneto-phono resonance, will then be introduced and described in different
systems, including graphene, multilayer graphene and bulk graphite. Electronic
Raman scattering is then be discussed in the context of Landau level
spectroscopy, of electron phonon interaction and of electron-electron
interaction.


  There is a need for information retrieval from large collections of
low-resolution (LR) binary document images, which can be found in digital
libraries across the world, where the high-resolution (HR) counterpart is not
available. This gives rise to the problem of binary document image
super-resolution (BDISR). The objective of this paper is to address the
interesting and challenging problem of super resolution of binary Tamil
document images for improved readability and better optical character
recognition (OCR). We propose multiple deep neural network architectures to
address this problem and analyze their performance. The proposed models are all
single image super-resolution techniques, which learn a generalized spatial
correspondence between the LR and HR binary document images. We employ
convolutional layers for feature extraction followed by transposed convolution
and sub-pixel convolution layers for upscaling the features. Since the outputs
of the neural networks are gray scale, we utilize the advantage of power law
transformation as a post-processing technique to improve the character level
pixel connectivity. The performance of our models is evaluated by comparing the
OCR accuracies and the mean opinion scores given by human evaluators on LR
images and the corresponding model-generated HR images.


  The rigorous coupled wave analysis method (RCWA) is applied to the graded
chiral TiO2 thin films in axial and non-axial excited states using the
Bruggeman homogenization formalism. The dependence of the filtering frequency
and polarization selectivity as a function of different structural parameters,
are investigated. A good agreement with experimental results reported by other
researchers is achieved.


  When Fourier series are employed to solve partial differential equations,
low-pass filters can be used to regularize divergent series that may appear. In
this paper we show that the linear low-pass filters defined in a previous paper
can be interpreted in terms of the correspondence between Fourier Conjugate
(FC) pairs of Definite Parity (DP) Fourier series and inner analytic functions,
which was established in earlier papers. The action of the first-order linear
low-pass filter corresponds to an operation in the complex plane that we refer
to as "singularity splitting", in which any given singularity of an inner
analytic function on the unit circle is replaced by two softer singularities on
that same circle, thus leading to corresponding DP Fourier series with better
convergence characteristics. Higher-order linear low-pass filters can be easily
defined within the unit disk of the complex plane, in terms of the first-order
one. The construction of infinite-order filters, which always result in
$C^{\infty}$ real functions over the unit circle, and in corresponding DP
Fourier series which are absolutely and uniformly convergent to these
functions, is presented and discussed.


  We report muon spin spectroscopy data (muSR) obtained under hydrostatic
pressure on a large single crystal of the itinerant helimagnet MnSi, and
recorded down to 0.235 K and up to 15.1 kbar. Up to the critical pressure p_c =
14.9 (2) kbar, where the magnetic order is suppressed, the muSR data
unambiguously demonstrate that the ground state of the system is magnetic with
no indication of any phase separation.


  An equivalent formulation of the Gribov-Zwanziger theory accounting for the
gauge fixing ambiguity in the Landau gauge is presented. The resulting action
is constrained by a Slavnov-Taylor identity stemming from a nilpotent exact
BRST invariance which is spontaneously broken due to the presence of the Gribov
horizon. This spontaneous symmetry breaking can be described in a purely
algebraic way through the introduction of a pair of auxiliary fields which give
rise to a set of linearly broken Ward identities. The Goldstone sector turns
out to be decoupled. The underlying exact nilpotent BRST invariance allows to
employ BRST cohomology tools within the Gribov horizon to identify
renormalizable extensions of gauge invariant operators. Using a simple toy
model and appropriate Dirac bracket quantization, we discuss the time-evolution
invariance of the operator cohomology. We further comment on the unitarity
issue in a confining theory, and stress that BRST cohomology alone is not
sufficient to ensure unitarity, a fact, although well known, frequently
ignored.


  The FLAIR Shapley-Hydra (FLASH) redshift survey catalogue consists of 4613
galaxies brighter than $\bJ = 16.7$ (corrected for Galactic extinction) over a
605 sq. degree region of sky in the general direction of the Local Group
motion. The survey region is an approximately $60\degr \times 10\degr$ strip
spanning the sky from the Shapley Supercluster to the Hydra cluster, and
contains 3141 galaxies with measured redshifts. Designed to explore the effect
of the galaxy concentrations in this direction (in particular the Supergalactic
plane and the Shapley Supercluster) upon the Local Group motion, the 68%
completeness allows us to sample the large-scale structure better than similar
sparsely-sampled surveys. The survey region does not overlap with the areas
covered by ongoing wide-angle (Sloan or 2dF) complete redshift surveys. In this
paper, the first in a series, we describe the observation and data reduction
procedures, the analysis for the redshift errors and survey completeness, and
present the survey data.


  Let $X$ be a smooth complex projective variety with nef $\bigwedge^2 T_X$ and
$\dim X \geq 3$. We prove that, up to a finite \'etale cover $\tilde{X} \to X$,
the Albanese map $\tilde{X} \to {\rm Alb}(\tilde{X})$ is a locally trivial
fibration whose fibers are isomorphic to a smooth Fano variety $F$ with nef
$\bigwedge^2 T_F$. As a bi-product, we see that either $T_X$ is nef or $X$ is a
Fano variety. Moreover we study a contraction of a $K_X$-negative extremal ray
$\varphi: X \to Y$. In particular, we prove that $X$ is isomorphic to the
blow-up of a projective space at a point if $\varphi$ is of birational type. We
also prove that $\varphi$ is a smooth morphism if $\varphi$ is of fiber type.
As a consequence, we give a structure theorem of varieties with nef
$\bigwedge^2 T_X$.


  CP-violation in the loop-mediated photon-photon interactions would modify the
birefringence of the quantum vacuum. We discuss the implications of this effect
on the PVLAS-Phase II experiment, in which a time-dependent magnetic field is
modulated. We show that the violation of CP would imply the existence of
characteristic Fourier components, in the intensity spectrum of the outgoing
wave. In order to estimate the magnitude of this effect within the Standard
Model, we use chiral perturbation theory to compute the theta-term contribution
to the coefficient of the leading CP-odd vertex, in the low-energy effective
field theory for photon dynamics.


  Based on the stabilizer formalism underlying Quantum Error Correction (QEC),
the design of an original Lindblad master equation for the density operator of
a quantum harmonic oscillator is proposed. This Lindblad dynamics stabilizes
exactly the finite-energy grid states introduced in 2001 by Gottesman, Kitaev
and Preskill for quantum computation. Stabilization results from an exponential
Lyapunov function with an explicit lower-bound on the convergence rate.
Numerical simulations indicate the potential interest of such autonomous QEC in
presence of non-negligible photon-losses.


  We show how to use quantum mechanics on the group manifold U(N) as a tool for
problems in U(N) representation theory. The quantum mechanics reduces to free
fermions on the circle, which in the large N limit become relativistic. The
theory can be bosonized giving the Das-Jevicki-Sakita collective field theory.
The formalism is particularly suited to problems involving tensor product
multiplicity (Littlewood-Richardson) coefficients. As examples, we discuss the
partition function of two-dimensional Yang-Mills theory on the sphere, and the
zero magnetic field limit of D-dimensional Eguchi-Kawai Yang-Mills theory. We
give the leading O(N^0) solution of the latter theory, using a method which
allows computing corrections. Largely (but not completely) superseded by
hep-th/9311130.


  We study the conductivity of two-dimensional interacting electrons on the
half-filled $N$th Landau level with $N \gg 1$ in the presence of the quenched
disorder. The existence of the unidirectional charge-density wave state at
temperature $T<T_c$, where $T_c$ is the transition temperature, leads to the
anisotropic conductivity tensor. We find that the leading anisotropic
corrections are proportional to $(T_c-T)/T_c$ just below the transition in
accordance with the experimental findings. Above $T_c$ the correlations
corresponding to the unidirectional charge-density wave state below $T_c$
result in the corrections to the conductivity proportional to
$\sqrt{T_c/(T-T_c)}$.


  We prove that any genus-2 Lefschetz fibration without reducible fibers and
with ``transitive monodromy'' is holomorphic. The latter condition comprises
all cases where the number of singular fibers is not congruent to 0 modulo 40.
An auxiliary statement of independent interest is the holomorphicity of
symplectic surfaces in S^2-bundles over S^2, of relative degree up to 7 over
the base, and of symplectic surfaces in CP^2 of degree up to 17.


  We present the STAR preliminary results on mid-rapidity and low transverse
momentum mean, standard deviation, skewness, and kurtosis of net-charge and
net-proton distributions in Au+Au and Cu+Cu collisions at $\sqrt{s_{NN}}$ = 200
GeV for various collision centralities. All the measured high moments of these
distributions can be scaled by the number of participating nucleons, consistent
with the soft process emissions. The ratios of fourth to second order cumulants
of both the net-charge and net-proton distributions are consistent with models
without QCD critical point.


  In the landmark article \cite{Muller}, M\"uller et. al. introduced the notion
of fractional stochastic dominance (SD) to interpolate between first and second
SD relations. In this article, we introduce a novel family of
\textit{multi-fractional} stochastic orders that generalizes fractional SD in a
natural manner. The family of multi-fractional SD is parametrized by an
arbitrary non-decreasing function $\gamma$ ranging between $0$ and $1$ which
provides the feature of local interpolation rather than a global one. We show
that the multi-fractional $(1+\gamma)$-SD is generated by a class of increasing
utility functions allowing local non-concavity where the steepness of the
non-concavity depends on its location and it is controlled by function
$\gamma$. We also introduced the notion of \text{local greediness} that allows
us, among other things, to systematically study multi-fractional utility class.
The multi-fractional utility class is well-suited for representing a decision
maker's preferences in terms of risk aversion and greediness at a local level.
Several basic properties as well as illustrating examples are presented.


  We consider the homogenization limit of the compressible barotropic
Navier-Stokes equations in a three-dimensional domain perforated by
periodically distributed identical particles. We study the regime of particle
sizes and distances such that the volume fraction of particles tends to zero
but their resistance density tends to infinity. Assuming that the Mach number
is increasing with a certain rate, the rescaled velocity and pressure of the
microscopic system converges to the solution of an effective equation which is
given by Darcy's law. The range of sizes of particles we consider are exactly
the same which lead to Darcy's law in the homogenization limit of
incompressible fluids. Unlike previous results for the Darcy regime we estimate
the deficit related to the pressure approximation via the Bogovski\u{{\i}}
operator This allows for more flexible estimates of the pressure in Lebesgue
and Sobolev spaces and allows to proof convergence results for all barotropic
exponents $\gamma> \frac32$.


  We introduce a framework for model learning and planning in stochastic
domains with continuous state and action spaces and non-Gaussian transition
models. It is efficient because (1) local models are estimated only when the
planner requires them; (2) the planner focuses on the most relevant states to
the current planning problem; and (3) the planner focuses on the most
informative and/or high-value actions. Our theoretical analysis shows the
validity and asymptotic optimality of the proposed approach. Empirically, we
demonstrate the effectiveness of our algorithm on a simulated multi-modal
pushing problem.


  We consider random polynomials of the form $H_n(z)=\sum_{j=0}^n\xi_jq_j(z)$
where the $\{\xi_j\}$ are i.i.d non-degenerate complex random variables, and
the $\{q_j(z)\}$ are orthonormal polynomials with respect to a compactly
supported measure $\tau$ satisfying the Bernstein-Markov property on a regular
compact set $K \subset \mathbb{C}$. We show that if
$\mathbb{P}(|\xi_0|>e^{|z|})=o(|z|^{-1})$, then the normalized counting measure
of the zeros of $H_n$ converges weakly in probability to the equilibrium
measure of $K.$ This is the best possible result, in the sense that the roots
of $G_n(z)=\sum_{j=0}^n\xi_jz^j$ fail to converge in probability to the
appropriate equilibrium measure when the above condition on the $\xi_j$ is not
satisfied. In addition, we give a multivariable version of this result.
  We also consider random polynomials of the form
$\sum_{k=0}^n\xi_kf_{n,k}z^k$, where the coefficients $f_{n,k}$ are complex
constants satisfying certain conditions, and the random variables $\{\xi_k\}$
satisfy $\mathbb{E} \log(1 + |\xi_0|) < \infty$. In this case, we establish
almost sure convergence of the normalized counting measure of the zeros to an
appropriate limiting measure. Again, this is the best possible result in the
same sense as above.


  Furstenberg and Glasner proved that for an arbitrary k in N, any piecewise
syndetic set contains k term arithmetic progression and such collection is also
piecewise syndetic in Z. They used algebraic structure of beta N. The above
result was extended for arbitrary semigroups by Bergelson and Hindman, again
using the structure of Stone-Cech compactification of general semigroup.
Beiglbock provided an elementary proof of the above result and asked whether
the combinatorial argument in his proof can be enhanced in a way which makes it
applicable to a more abstract setting. In this work we provide a positive
answer to Beiglbock question for commutative semigroup.


  We study the system of up to four negatively charged magneto-excitons (X-'s)
in the spherical geometry, using the exact-diagonalization techniques. At low
energies, X-'s are bound and behave like charged particles without internal
dynamics. The pseudopotential describing X-:X- scattering is almost identical
to that of electrons and the low-lying few X- states correspond to the
few-electron states. The total angular momentum of the ground state depends on
the effective filling factor nu and vanishes at its special values. The analogs
to the Laughlin nu=1/3 state and the Jain nu=2/5 state of electrons are found.
The X- system is predicted to exhibit the fractional quantum Hall effect.


  A stability criterion is worked out for the superconducting phase. The
validity of a prerequisite, established previously for persistent currents, is
thereby confirmed. Temperature dependence is given for the specific heat and
concentration of superconducting electrons in the vicinity of the critical
temperature $T_c$. The isotope effect, mediated by electron-phonon interaction
and hyperfine coupling, is analyzed. Several experiments, intended at
validating this analysis, are presented, including one giving access to the
specific heat of high-$T_c$ compounds.


  Out-of-Distribution (OOD) detection, i.e., identifying whether an input is
sampled from a novel distribution other than the training distribution, is a
critical task for safely deploying machine learning systems in the open world.
Recently, post hoc detection utilizing pre-trained models has shown promising
performance and can be scaled to large-scale problems. This advance raises a
natural question: Can we leverage the diversity of multiple pre-trained models
to improve the performance of post hoc detection methods? In this work, we
propose a detection enhancement method by ensembling multiple detection
decisions derived from a zoo of pre-trained models. Our approach uses the
p-value instead of the commonly used hard threshold and leverages a fundamental
framework of multiple hypothesis testing to control the true positive rate of
In-Distribution (ID) data. We focus on the usage of model zoos and provide
systematic empirical comparisons with current state-of-the-art methods on
various OOD detection benchmarks. The proposed ensemble scheme shows consistent
improvement compared to single-model detectors and significantly outperforms
the current competitive methods. Our method substantially improves the relative
performance by 65.40% and 26.96% on the CIFAR10 and ImageNet benchmarks.


  Autonomous vehicles and mobile robotic systems are typically equipped with
multiple sensors to provide redundancy. By integrating the observations from
different sensors, these mobile agents are able to perceive the environment and
estimate system states, e.g. locations and orientations. Although deep learning
approaches for multimodal odometry estimation and localization have gained
traction, they rarely focus on the issue of robust sensor fusion - a necessary
consideration to deal with noisy or incomplete sensor observations in the real
world. Moreover, current deep odometry models suffer from a lack of
interpretability. To this extent, we propose SelectFusion, an end-to-end
selective sensor fusion module which can be applied to useful pairs of sensor
modalities such as monocular images and inertial measurements, depth images and
LIDAR point clouds. Our model is a uniform framework that is not restricted to
specific modality or task. During prediction, the network is able to assess the
reliability of the latent features from different sensor modalities and
estimate trajectory both at scale and global pose. In particular, we propose
two fusion modules - a deterministic soft fusion and a stochastic hard fusion,
and offer a comprehensive study of the new strategies compared to trivial
direct fusion. We extensively evaluate all fusion strategies in both public
datasets and on progressively degraded datasets that present synthetic
occlusions, noisy and missing data and time misalignment between sensors, and
we investigate the effectiveness of the different fusion strategies in
attending the most reliable features, which in itself, provides insights into
the operation of the various models.


  We investigate numerically the transverse and longitudinal correlation
lengths of the three-dimensional O(4) model as a function of the external field
H. In the low-temperature phase we verify explicitly the H^{-1/2}-dependence of
the transverse correlation length, which is expected due to the Goldstone modes
of the model. On the critical line we find the universal amplitude ratio xi^c_T
/ xi^c_L = 1.99(1). From our data we derive the universal scaling function for
the transverse correlation length. The H-dependencies of the correlation
lengths in the high temperature phase are discussed and shown to be in accord
with the scaling functions.


  We propose a simplified chain inflation model and calculate the primordial
power spectra of the scalar and tensor fluctuations. The spectral index and the
tensor-scalar ratio are respectively 0.972 and 0.089 which are consistent with
present cosmological observations.


  Autonomous agents, such as driverless cars, require large amounts of labeled
visual data for their training. A viable approach for acquiring such data is
training a generative model with collected real data, and then augmenting the
collected real dataset with synthetic images from the model, generated with
control of the scene layout and ground truth labeling. In this paper we propose
Full-Glow, a fully conditional Glow-based architecture for generating plausible
and realistic images of novel street scenes given a semantic segmentation map
indicating the scene layout. Benchmark comparisons show our model to outperform
recent works in terms of the semantic segmentation performance of a pretrained
PSPNet. This indicates that images from our model are, to a higher degree than
from other models, similar to real images of the same kinds of scenes and
objects, making them suitable as training data for a visual semantic
segmentation or object recognition system.


  The Large Language models (LLMs) have demonstrated supreme capabilities in
text understanding and generation, but cannot be directly applied to
cross-modal tasks without fine-tuning. This paper proposes a cross-modal
in-context learning approach, empowering the frozen LLMs to achieve multiple
audio tasks in a few-shot style without any parameter update. Specifically, we
propose a novel and LLMs-driven audio codec model, LLM-Codec, to transfer the
audio modality into the textual space, \textit{i.e.} representing audio tokens
with words or sub-words in the vocabulary of LLMs, while keeping high audio
reconstruction quality. The key idea is to reduce the modality heterogeneity
between text and audio by compressing the audio modality into a well-trained
LLMs token space. Thus, the audio representation can be viewed as a new
\textit{foreign language}, and LLMs can learn the new \textit{foreign language}
with several demonstrations. In experiments, we investigate the performance of
the proposed approach across multiple audio understanding and generation tasks,
\textit{e.g.} speech emotion classification, audio classification,
text-to-speech generation, speech enhancement, etc. The experimental results
demonstrate that the LLMs equipped with the proposed LLM-Codec, named as
UniAudio 1.5, prompted by only a few examples, can achieve the expected
functions in simple scenarios. It validates the feasibility and effectiveness
of the proposed cross-modal in-context learning approach. To facilitate
research on few-shot audio task learning and multi-modal LLMs, we have
open-sourced the LLM-Codec model.


  We re-examine the time-dependent harmonic oscillator $\ddot q=-\omega^2 q$
under various regularity assumptions. Where $\omega(t)$ is continuously
differentiable we reduce its integration to that of a single first order
equation, i.e. the Hamilton equation for the angle variable $\psi$ alone (the
action variable ${\cal I}$ does not appear). Reformulating the generic Cauchy
problem for $\psi(t)$ as a Volterra-type integral equation and applying the
fixed point theorem we obtain a sequence $\{\psi^{(h)}\}_{h\in\mathbb{N}_0}$
converging uniformly to $\psi$ in every compact time interval; if $\omega$
varies slowly or little, already $\psi^{(0)}$ approximates $\psi$ well for
rather long time lapses. The discontinuities of $\omega$, if any, determine
those of $\psi,{\cal I}$. The zeroes of $q,\dot q$ are investigated with the
help of two Riccati equations. As a demo of the potential of our approach, we
briefly argue that it yields good approximations of the solutions (as well as
exact bounds on them) and thereby may simplify the study of: the stability of
the trivial one; the occurrence of parametric resonance when $\omega(t)$ is
periodic; the adiabatic invariance of ${\cal I}$; the asymptotic expansions in
a slow time parameter $\varepsilon$; etc.


  In 1988 Kalai construct a large class of simplicial spheres, called squeezed
spheres, and in 1991 presented a conjectured about generic initial ideals of
Stanley--Reisner ideals of squeezed spheres. In the present paper this
conjecture will be proved. In order to prove Kalai's conjecture, based on the
fact that every squeezed $(d-1)$-sphere is the boundary of a certain $d$-ball,
called a squeezed $d$-ball, generic initial ideals of Stanley--Reisner ideals
of squeezed balls will be determined. In addition, generic initial ideals of
exterior face ideals of squeezed balls are determined. On the other hand, we
study the squeezing operation, which assigns to each Gorenstein* complex
$\Gamma$ having the weak Lefschetz property a squeezed sphere
$\mathrm{Sq}(\Gamma)$, and show that this operation increases graded Betti
numbers.


  Let $G$ be a graph and let $l$ be an integer-valued function on subsets of
$V(G)$. The graph $G$ is said to be $l$-partition-connected, if for every
partition $P$ of $V(G)$, $e_G(P)\ge \sum_{A\in P} l(A)-l(V(G))$, where $e_G(P)$
denotes the number of edges of $G$ joining different parts of $P$. We say that
$G$ is $l$-rigid, if it contains a spanning $l$-partition-connected subgraph
$H$ with $|E(H)|=\sum_{v\in V(H)} l(v)-l(V(H))$. In this paper, we investigate
decomposition of graphs into spanning partition-connected and spanning rigid
subgraphs. As a consequence, we improve a recent result due to Gu (2017) by
proving that every $(4kp-2p+2m)$-connected graph $G$ with $k\ge 2$ has a
spanning subgraph $H$ containing a packing of $m$ spanning trees and $p$
spanning $(2k-1)$-edge-connected subgraphs $H_1,\ldots, H_p$ such that for each
vertex $v$, every $H_i-v$ remains $(k-1)$-edge-connected and also $d_H(v)\le
\lceil \frac{d_G(v)}{2}\rceil +2kp-p+m$. From this result, we refine a result
on arc-connected orientations of graphs.


  In plasmonic chirality, the phenomenon of circular dichroism for achiral
nanoparitcles caused by Coulomb interaction between metal nanoparticles (NPs)
and chiral molecules have been studied. At the same time, under the resonance
condition, the dye molecules and metal NPs will produce huge Rabi splitting due
to strong coupling. If the chiral molecules are at the resonance of the
plasmon, what will happen for the strong interaction between the plasmon and
molecules with chirality introduced? In this paper, we investigate a spherical
core-shell model and analyze its spectral phenomena under the excitation of
circularly polarized light (CPL). Based on Coulomb interaction between NPs and
chiral molecules, we will show how the various factors affect the strong
coupling. We have obtained three mechanisms for the interaction between
plasmons and chiral molecules: strong coupling (Rabi splitting up to 243mev),
enhanced absorption and induced transparency. The interaction between CPL and
chiral molecules with the opposite chirality to CPL is stronger than that of
the same chirality, and the line width of the two peaks is closer than that of
the same chirality, which shows that for the Rabi splitting with chirality,
there are deeper mechanisms for the interaction. This result will be helpful
for further research on the interaction between plasmon and molecules with
chirality.


  [Context] The broadband spectral energy distribution of microquasars has
proven to be a valuable tool for assessing the roles of jets and accretion
flows in microquasars, as well as the coupling between the two. The coupling
might manifest itself observationally in the correlated radio and X-ray
variabilities of a source. Such a radio/X-ray correlation has indeed been seen
in several microquasars during the low-hard state and subsequently been claimed
to be universal for all. If proven, the universal correlation would have
profound implications on theoretical models. However, there is already
observational evidence that suggests otherwise. [Aims] In this paper, we
critically examine the radio/X-ray correlation in a sample of microquasars, in
the low-hard state as well as during state transitions, with a goal of testing
the claimed universality of the correlation on observational grounds. [Methods]
We have assembled a comprehensive data set from the
simultaneous/contemporaneous radio and X-ray observations of representative
microquasars. The data have allowed us to quantify the radio/X-ray correlation
on a source by source basis. [Results] We find that the radio/X-ray correlation
of microquasars exhibits diverse behaviors, both in the low-hard and
transitional states, ranging from being very week (or none at all) to very
strong. There is even a hint of spectral dependence of the correlation in some
cases. [Conclusions] Our results rule out the claimed universality of the
radio/X-ray correlation of microquasars, even for the low-hard state. On the
other hand, we do find that the radio and X-ray variabilities are, to varying
degrees, correlated in most cases.


  - In this paper we introduce a new method to solve fixed-delay optimal
control problems which exploits numerical homotopy procedures. It is known that
solving this kind of problems via indirect methods is complex and
computationally demanding because their implementation is faced with two
difficulties: the extremal equations are of mixed type, and besides, the
shooting method has to be carefully initialized. Here, starting from the
solution of the non-delayed version of the optimal control problem, the delay
is introduced by numerical homotopy methods. Convergence results, which ensure
the effectiveness of the whole procedure, are provided. The numerical
efficiency is illustrated on an example.


  Although there is not a complete "proof" of the second law of thermo-
dynamics based on microscopic dynamics, two properties of Hamiltonian systems
have been used to prove the impossibility of work extraction from a single
thermal reservoir: Liouville's theorem and the adiabatic invariance of the
volume enclosed by an energy shell. In this paper we analyze these two
properties in the Szilard engine and other systems related with the Maxwell
demon. In particular, we recall that the enclosed volume is no longer an
adiabatic invariant in non ergodic systems and explore the consequences of this
on the second law.


  Context. The metal mass fraction of the Sun Z is a key constraint in solar
modelling, but its value is still under debate. The standard solar chemical
composition of the late 2000s have the ratio of metals to hydrogen Z/X =
0.0181, with a small increase to 0.0187 in 2021, as inferred from 3D non-LTE
spectroscopy. However, more recent work on a horizontally and temporally
averaged <3D> model claim Z/X = 0.0225, consistent with the high values of
twenty-five years ago based on 1D LTE spectroscopy. Aims. We aim to determine a
precise and robust value of the solar metal mass fraction from helioseismic
inversions, thus providing independent constraints from spectroscopic methods.
Methods. We devise a detailed seismic reconstruction technique of the solar
envelope, combining multiple inversions and equations of state to accurately
and precisely determine the metal mass fraction value. Results. We show that a
low value of the solar metal mass fraction corresponding to Z/X = 0.0187 is
favoured by helioseismic constraints and that a higher metal mass fraction
corresponding to Z/X = 0.0225 are strongly rejected by helioseismic data.
Conclusions. We conclude that direct measurement of the metal mass fraction in
the solar envelope favours a low metallicity, in line with the 3D non-LTE
spectroscopic determination of 2021. A high metal mass fraction as measured
using a <3D> model in 2022 is disfavoured by helioseismology for all modern
equations of state used to model the solar convective envelope.


  Generative Adversarial Networks (GANs) have significantly advanced image
synthesis, however, the synthesis quality drops significantly given a limited
amount of training data. To improve the data efficiency of GAN training, prior
work typically employs data augmentation to mitigate the overfitting of the
discriminator yet still learn the discriminator with a bi-classification (i.e.,
real vs. fake) task. In this work, we propose a data-efficient Instance
Generation (InsGen) method based on instance discrimination. Concretely,
besides differentiating the real domain from the fake domain, the discriminator
is required to distinguish every individual image, no matter it comes from the
training set or from the generator. In this way, the discriminator can benefit
from the infinite synthesized samples for training, alleviating the overfitting
problem caused by insufficient training data. A noise perturbation strategy is
further introduced to improve its discriminative power. Meanwhile, the learned
instance discrimination capability from the discriminator is in turn exploited
to encourage the generator for diverse generation. Extensive experiments
demonstrate the effectiveness of our method on a variety of datasets and
training settings. Noticeably, on the setting of 2K training images from the
FFHQ dataset, we outperform the state-of-the-art approach with 23.5% FID
improvement.


  Bayesian approaches for learning deep neural networks (BNN) have been
received much attention and successfully applied to various applications.
Particularly, BNNs have the merit of having better generalization ability as
well as better uncertainty quantification. For the success of BNN, search an
appropriate architecture of the neural networks is an important task, and
various algorithms to find good sparse neural networks have been proposed. In
this paper, we propose a new node-sparse BNN model which has good theoretical
properties and is computationally feasible. We prove that the posterior
concentration rate to the true model is near minimax optimal and adaptive to
the smoothness of the true model. In particular the adaptiveness is the first
of its kind for node-sparse BNNs. In addition, we develop a novel MCMC
algorithm which makes the Bayesian inference of the node-sparse BNN model
feasible in practice.


  We study the response of a magnetic-field-driven superconducting qubit
strongly coupled to a superconducting coplanar waveguide resonator. We observed
a strong amplification/damping of a probing signal at different resonance
points corresponding to a one and two-photon emission/absorption. The sign of
the detuning between the qubit frequency and the probe determines whether
amplification or damping is observed. The larger blue detuned driving leads to
two-photon lasing while the larger red detuning cools the resonator. Our
experimental results are in good agreement with the theoretical model of qubit
lasing and cooling at the Rabi frequency.


  Duality refers to two equivalent descriptions of the same theory from
different points of view. Recently there has been tremendous progress in
formulating and understanding possible dualities of quantum many body theories
in $2+1$-spacetime dimensions. Of particular interest are dualities that
describe conformally invariant quantum field theories in $(2+1)d$. These arise
as descriptions of quantum critical points in condensed matter physics. The
appreciation of the possible dual descriptions of such theories has greatly
enhanced our understanding of some challenging questions about such quantum
critical points. Perhaps surprisingly the same dualities also underlie recent
progress in our understanding of other problems such as the half-filled Landau
level and correlated surface states of topological insulators. Here we provide
a pedagogical review of these recent developments from a point of view geared
toward condensed matter physics.


  Discovering cause-effect relationships between variables from observational
data is a fundamental challenge in many scientific disciplines. However, in
many situations it is desirable to directly estimate the change in causal
relationships across two different conditions, e.g., estimating the change in
genetic expression across healthy and diseased subjects can help isolate
genetic factors behind the disease. This paper focuses on the problem of
directly estimating the structural difference between two structural equation
models (SEMs), having the same topological ordering, given two sets of samples
drawn from the individual SEMs. We present an principled algorithm that can
recover the difference SEM in $\mathcal{O}(d^2 \log p)$ samples, where $d$ is
related to the number of edges in the difference SEM of $p$ nodes. We also
study the fundamental limits and show that any method requires at least
$\Omega(d' \log \frac{p}{d'})$ samples to learn difference SEMs with at most
$d'$ parents per node. Finally, we validate our theoretical results with
synthetic experiments and show that our method outperforms the
state-of-the-art. Moreover, we show the usefulness of our method by using data
from the medical domain.


  We review and compare the reconstruction methods of the inclusive deep
inelastic scattering variables used at HERA. We introduce a new prescription,
the $\Sigma$ method, which allows to measure the structure function of the
proton $F_2(x,Q^2)$ in a large kinematic domain, and in particular in the low
x-low$Q^2$ region, with small systematic errors and small radiative
corrections. A detailed comparison between the $\Sigma$ method and the other
methods is shown. Extensions of the $\Sigma$ method are presented. The effect
of QED radiation on the kinematic reconstruction and on the structure function
measurement is discussed.


  We start by studying the subgroup structures underlying stabilizer circuits.
Then we apply our results to provide two normal forms for stabilizer circuits.
These forms are computed by induction using simple conjugation rules in the
Clifford group and our algorithms do not rely on a special decomposition in the
symplectic group. The first normal form has shape CX-CZ-P-Z-X-H-CZ-P-H, where
CX (resp. CZ) denotes a layer of CNOT (resp. controlled-Z) gates, P a layer of
phase gates, X (resp. Z) a layer of Pauli-X (resp. Pauli-Z) gates. Then we
replace most of the controlled-Z gates by CNOT gates to obtain a second normal
form of type P-CX-CZ-CX-Z-X-H-CZ-CX-P-H. In this second form, both controlled-Z
layers have depth 1 and together contain therefore at most n controlled-Z
gates. We also consider normal forms for stabilizer states and graph states.
Finally we carry out a few tests on classical and quantum computers in order to
show experimentally the utility of these normal forms to reduce the gate count
of a stabilizer circuit.


  Nodal line semimetals have lately aroused much experimental and theoretical
interest, with their gap closing along unconventional trajectories in the 3D
Brillouin zone. These trajectories or nodal lines can close into loops and
trace out intricate knotted or linked configurations with complicated
topologies. In this work, we investigate the semi-classical optical response of
two nodal loops in linked, unlinked and touching configurations, focusing
particularly on the interplay of response anisotropy and non-linearity. We
provide a geometric picture that unifies these aspects and sheds light on the
effects of nodal topology and geometry. Based on a model abstracted from
generic multi nodal loop scenarios, both with or without linkages, our findings
will be applicable for a large class of nodal semimetal materials with multiple
nodal lines or loops.


  The question of whether the Kaluza-Klein (KK) graviphoton $h_{5\mu}$ and
graviscalar $h_{55}$ are localized or not on the brane is one of the important
issues. In this letter, we address this problem in five dimensions. Here we
consider the massless (zero-mode) propagations without requiring the
Z$_2$-symmetry on $h_{5\mu}$. We obtain the graviton $h_{\mu\nu}$, graviphoton,
and graviscalar exchange amplitudes on shell. We find that the graviscalar has
a tachyonic mass. It turns out that $h_{5\mu}$ admits the localized zero-modes
on the brane while $h_{55}$ does not have a localized zero-mode. This is
contrasted to the fact that the bulk spin-0 field has a localized zero-mode on
the brane but the bulk spin-1 field does not have a localized solution in five
dimensions.


  We study the Wiener-Hopf factorization for L\'evy processes $X_t$ with
completely monotone jumps. Extending previous results of L.C.G. Rogers, we
prove that the space-time Wiener-Hopf factors are complete Bernstein functions
of both the spatial and the temporal variable. As a corollary, we prove
complete monotonicity of: (a) the tail of the distribution function of the
supremum of $X_t$ up to an independent exponential time; (b) the Laplace
transform of the supremum of $X_t$ up to a fixed time $T$, as a function of
$T$. The proof involves a detailed analysis of the holomorphic extension of the
characteristic exponent $f(\xi)$ of $X_t$, including a peculiar structure of
the curve along which $f(\xi)$ takes real values.


  Machine Science, or Data-driven Research, is a new and interesting scientific
methodology that uses advanced computational techniques to identify, retrieve,
classify and analyse data in order to generate hypotheses and develop models.
In this paper we describe three recent biomedical Machine Science studies, and
use these to assess the current state of the art with specific emphasis on data
mining, data assessment, costs, limitations, skills and tool support.


  Gravitational waves from the coalescence of two black holes carry the
signature of the strong field dynamics of binary black holes. In this work we
have used numerical relativity simulations and post-Newtonian theory to
investigate this dynamics. Post-Newtonian theory is a low-velocity expansion
that assumes the companion bodies to be point-particles, while numerical
relativity treats black holes as extended objects with horizons and fully
captures their dynamics. There is a priori no reason for the waveforms computed
using these disparate methods to agree with each other, especially at late
times when the black holes move close to the speed of light. We find,
remarkably, that the leading order amplitudes in post-Newtonian theory agree
well with the full general relativity solution for a large set of spherical
harmonic modes, even in the most dynamical part of the binary evolution, with
only some modes showing distinctly different behavior than that found by
numerical relativity simulations. In particular, modes with spherical harmonic
indices l = m as well as l = 2, m = 1 are least modified from their dominant
post-Newtonian behavior. Understanding the nature of these modes in terms of
the post-Newtonian description will aid in formulating better models of the
emitted waveforms in the strong field regime of the dynamics.


  In this paper, we present our proposal to Constraint Functional Logic
Programming over Finite Domains (CFLP(FD)) with a lazy functional logic
programming language which seamlessly embodies finite domain (FD) constraints.
This proposal increases the expressiveness and power of constraint logic
programming over finite domains (CLP(FD)) by combining functional and
relational notation, curried expressions, higher-order functions, patterns,
partial applications, non-determinism, lazy evaluation, logical variables,
types, domain variables, constraint composition, and finite domain constraints.
  We describe the syntax of the language, its type discipline, and its
declarative and operational semantics. We also describe TOY(FD), an
implementation for CFLPFD(FD), and a comparison of our approach with respect to
CLP(FD) from a programming point of view, showing the new features we
introduce. And, finally, we show a performance analysis which demonstrates that
our implementation is competitive with respect to existing CLP(FD) systems and
that clearly outperforms the closer approach to CFLP(FD).


  We investigate the structure of conformal $C$-spaces,a class of Riemmanian
manifolds which naturally arises as aconformal generalisation of the Einstein
condition. A basic question is when such a structure is closed, or equivalently
locally conformally Cotton. In dimension 4 we obtain a full answer to this
question and also investigate the incidence of the Bach condition on this class
of metrics. This is related to earlier results obtained in the Einstein-Weyl
context.


  The physical properties of the faint blue galaxy population are reviewed in
the context of observational progress made via deep spectroscopic surveys and
Hubble Space Telescope imaging of field galaxies at various limits, and
theoretical models for the integrated star formation history of the Universe.
Notwithstanding uncertainties in the properties of the local population of
galaxies, convincing evidence has emerged from several independent studies for
a rapid decline in the volume-averaged star formation rate of field galaxies
since a redshift z~1. Together with the small angular sizes and modest mean
redshift of the faintest detectable sources, these results can be understood in
hierarchical models where the bulk of the star formation occurred at redshifts
between z~1-2. The physical processes responsible for the subsequent demise of
the faint blue galaxy population remains unclear. Considerable progress will be
possible when the evolutionary trends can be monitored in the context of
independent physical parameters such as the underlying galactic mass.


  The applications of large language models (LLMs) are promising for biomedical
and healthcare research. Despite the availability of open-source LLMs trained
using a wide range of biomedical data, current research on the applications of
LLMs to genomics and proteomics is still limited. To fill this gap, we propose
a collection of finetuned LLMs and multimodal LLMs (MLLMs), known as Geneverse,
for three novel tasks in genomic and proteomic research. The models in
Geneverse are trained and evaluated based on domain-specific datasets, and we
use advanced parameter-efficient finetuning techniques to achieve the model
adaptation for tasks including the generation of descriptions for gene
functions, protein function inference from its structure, and marker gene
selection from spatial transcriptomic data. We demonstrate that adapted LLMs
and MLLMs perform well for these tasks and may outperform closed-source
large-scale models based on our evaluations focusing on both truthfulness and
structural correctness. All of the training strategies and base models we used
are freely accessible.


  This paper is concerned with the following fractional $p$-Kirchhoff equation
\begin{eqnarray*} \varepsilon ^{sp}M\left( {\varepsilon ^{sp -
N}}\iint_{\mathbb{R}^{2N}}\frac{{{{\left| {u(x) - u(y)} \right|}^p}}}{{{{\left|
{x - y} \right|}^{N + sp}}}}dxdy\right)(-\Delta)_p^su + V(x){u^{p - 1}} =
{u^{p_s^* - 1}}+f(u),\ \ u>0, \ \mbox{in}\ {\mathbb{R}^N}, %u \in
{W^{s,p}}(\mathbb{R}^N), \end{eqnarray*} where $\varepsilon>0$ is a parameter,
$M(t)=a+bt^{\theta-1}$ with $a>0$, $b>0$, $\theta>1$, $(-\Delta)_p^s$ denotes
the fractional $p$-Laplacian operator with $0<s<1$ and $1<p<\infty$, $N>sp$,
$\theta p<p_s^*$ with $p_s^*=\frac{Np}{N-sp}$ is the fractional critical
Sobolev exponent, $f$ is a superlinear continuous function with subcritical
growth and $V$ is a positive continuous potential. Using penalization method
and Ljusternik-Schnirelmann theory, we study the existence, multiplicity and
concentration of nontrivial solutions for $\varepsilon>0$ small enough.


  The burgeoning development of generative artificial intelligence (GenAI) and
the widespread adoption of large language models (LLMs) in educational settings
have sparked considerable debate regarding their efficacy and
acceptability.Despite the potential benefits, the assimilation of these
cutting-edge technologies among educators exhibits a broad spectrum of
attitudes, from enthusiastic advocacy to profound skepticism.This study aims to
dissect the underlying factors influencing educators' perceptions and
acceptance of GenAI and LLMs.We conducted a survey among educators and analyzed
the data through the frameworks of the Technology Acceptance Model (TAM) and
Innovation Diffusion Theory (IDT). Our investigation reveals a strong positive
correlation between the perceived usefulness of GenAI tools and their
acceptance, underscoring the importance of demonstrating tangible benefits to
educators. Additionally, the perceived ease of use emerged as a significant
factor, though to a lesser extent, influencing acceptance. Our findings also
show that the knowledge and acceptance of these tools is not uniform,
suggesting that targeted strategies are required to address the specific needs
and concerns of each adopter category to facilitate broader integration of AI
tools.in education.


  Gremlin is a graph traversal machine and language designed, developed, and
distributed by the Apache TinkerPop project. Gremlin, as a graph traversal
machine, is composed of three interacting components: a graph $G$, a traversal
$\Psi$, and a set of traversers $T$. The traversers move about the graph
according to the instructions specified in the traversal, where the result of
the computation is the ultimate locations of all halted traversers. A Gremlin
machine can be executed over any supporting graph computing system such as an
OLTP graph database and/or an OLAP graph processor. Gremlin, as a graph
traversal language, is a functional language implemented in the user's native
programming language and is used to define the $\Psi$ of a Gremlin machine.
This article provides a mathematical description of Gremlin and details its
automaton and functional properties. These properties enable Gremlin to
naturally support imperative and declarative querying, host language
agnosticism, user-defined domain specific languages, an extensible
compiler/optimizer, single- and multi-machine execution models, hybrid depth-
and breadth-first evaluation, as well as the existence of a Universal Gremlin
Machine and its respective entailments.


  Categorical features are present in about 40% of real world problems,
highlighting the crucial role of encoding as a preprocessing component. Some
recent studies have reported benefits of the various target-based encoders over
classical target-agnostic approaches. However, these claims are not supported
by any statistical analysis, and are based on a single dataset or a very small
and heterogeneous sample of datasets. The present study explores the encoding
effects in an exhaustive sample of classification problems from OpenML
repository. We fitted linear mixed-effects models to the experimental data,
treating task ID as a random effect, and the encoding scheme and the various
characteristics of categorical features as fixed effects. We found that in
multiclass tasks, one-hot encoding and Helmert contrast coding outperform
target-based encoders. In binary tasks, there were no significant differences
across the encoding schemes; however, one-hot encoding demonstrated a
marginally positive effect on the outcome. Importantly, we found no significant
interactions between the encoding schemes and the characteristics of
categorical features. This suggests that our findings are generalizable to a
wide variety of problems across domains.


  In the early 1990s the works of Larson, Wogen and Argyros, Lambrou, Longstaff
disclosed an example of a strong $M$-basis that did not admit a linear
summation method. We study a class of $M$-bases
$\mathfrak{F}=\{f_n\}_{n=1}^\infty$ in the Hilbert space that generalizes the
Larson--Wogen system. We determine the conditions under which $\mathfrak{F}$
admits a linear summation method. In order to do that we employ some of the
graph theory techniques.


  The $\bar{\Lambda} / \Lambda$ and $\bar{\Lambda} / K^0_\mathrm{S}$ production
ratios are measured by the LHCb detector from $0.3\,\mathrm{nb}^{-1}$ of $pp$
collisions delivered by the LHC at $\sqrt{s} = 0.9$\,TeV and
$1.8\,\mathrm{nb}^{-1}$ at $\sqrt{s} = 7$\,TeV. Both ratios are presented as a
function of transverse momentum, $p_\mathrm{T}$, and rapidity, $y$, in the
ranges {$0.15 < p_\mathrm{T} < 2.50\,\mathrm{GeV}/c$} and {$2.0<y<4.5$}.
Results at the two energies are in good agreement as a function of rapidity
loss, $\Delta y = y_\mathrm{beam} - y$, and are consistent with previous
measurements. The ratio $\bar{\Lambda} / \Lambda$, measuring the transport of
baryon number from the collision into the detector, is smaller in data than
predicted in simulation, particularly at high rapidity. The ratio
$\bar{\Lambda} / K^0_\mathrm{S}$, measuring the baryon-to-meson suppression in
strange quark hadronisation, is significantly larger than expected.


  Finite group actions on free resolutions and modules arise naturally in many
interesting examples. Understanding these actions amounts to describing the
terms of a free resolution or the graded components of a module as group
representations which, in the non modular case, are completely determined by
their characters. With this goal in mind, we introduce a Macaulay2 package for
computing characters of finite groups on free resolutions and graded components
of finitely generated graded modules over polynomial rings.


  It has been recently discovered by Bell, Heinle and Levandovskyy that a large
class of algebras, including the ubiquitous $G$-algebras, are finite
factorization domains (FFD for short).
  Utilizing this result, we contribute an algorithm to find all distinct
factorizations of a given element $f \in \mathcal{G}$, where $\mathcal{G}$ is
any $G$-algebra, with minor assumptions on the underlying field.
  Moreover, the property of being an FFD, in combination with the factorization
algorithm, enables us to propose an analogous description of the factorized
Gr\"obner basis algorithm for $G$-algebras. This algorithm is useful for
various applications, e.g. in analysis of solution spaces of systems of linear
partial functional equations with polynomial coefficients, coming from
$\mathcal{G}$. Additionally, it is possible to include inequality constraints
for ideals in the input.


  The dominance of $h\to \eta \eta$ decay mode for the intermediate mass Higgs
boson is highly motivated to solve the little hierarchy problem and to ease the
tension with the precision data. However, the discovery modes for $m_h \alt
150$ GeV, $h \to \gamma\gamma$ and $W/Z h \to (\ell\nu/\ell \bar \ell) (b\bar
b)$, will be substantially affected. We show that $h \to \eta \eta \to 4b$ is
complementary and we can use this decay mode to detect the intermediate Higgs
boson at the LHC, via $Wh$ and $Zh$ production. Requiring at least one charged
lepton and 4 $B$-tags in the final state, we can identify a clean Higgs boson
signal for $m_h \alt 150$ GeV with a high significance and with a full Higgs
mass reconstruction. We use the next-to-minimal supersymmetric standard model
and the simplest little Higgs model for illustration.


  Significant experimental advances in single-electron silicon spin qubits have
opened the possibility of realizing long-range entangling gates mediated by
microwave photons. Recently proposed iSWAP gates, however, require tuning qubit
energies into resonance and have limited fidelity due to charge noise. We
present a novel photon-mediated cross-resonance gate that is consistent with
realistic experimental capabilities and requires no resonant tuning.
Furthermore, we propose gate sequences capable of suppressing errors due to
quasistatic noise for both the cross-resonance and iSWAP gates.


  The ongoing quest for understanding nonequilibrium dynamics of complex
quantum systems underpins the foundation of statistical physics as well as the
development of quantum technology. Quantum many-body scarring has recently
opened a window into novel mechanisms for delaying the onset of thermalization
by preparing the system in special initial states, such as the $\mathbb{Z}_2$
state in a Rydberg atom system. Here we realize many-body scarring in a
Bose-Hubbard quantum simulator from previously unknown initial conditions such
as the unit-filling state. We develop a quantum-interference protocol for
measuring the entanglement entropy and demonstrate that scarring traps the
many-body system in a low-entropy subspace. Our work makes the resource of
scarring accessible to a broad class of ultracold-atom experiments, and it
allows one to explore the relation of scarring to constrained dynamics in
lattice gauge theories, Hilbert space fragmentation, and disorder-free
localization.


  It is well known and readily seen that the maximum of $n$ independent and
uniformly on $[0,1]$ distributed random variables, suitably standardised,
converges in total variation distance, as $n$ increases, to the standard
negative exponential distribution. We extend this result to higher dimensions
by considering copulas. We show that the strong convergence result holds for
copulas that are in a differential neighbourhood of a multivariate generalized
Pareto copula. Sklar's theorem then implies convergence in variational distance
of the maximum of $n$ independent and identically distributed random vectors
with arbitrary common distribution function and (under conditions on the
marginals) of its appropriately normalised version. We illustrate how these
convergence results can be exploited to establish the almost-sure consistency
of some estimation procedures for max-stable models, using sample maxima.


  In the third part of our photometric study of the star-forming region NGC
346/N~66 and its surrounding field in the Small Magellanic Cloud (SMC), we
focus on the large number of low-mass pre-main sequence (PMS) stars revealed by
the Hubble Space Telescope Observations with the Advanced Camera for Surveys.
We investigate the origin of the observed broadening of the pre-main sequence
population in the $V-I$, $V$ CMD. The most likely explanations are either the
presence of differential reddening or an age spread among the young stars.
Assuming the latter, simulations indicate that we cannot exclude the
possibility that stars in NGC 346 might have formed in two distinct events
occurring about 10 and 5 Myr ago, respectively. We find that the PMS stars are
not homogeneously distributed across NGC 346, but instead are grouped in at
least five different clusters. On spatial scales from 0.8$''$ to 8$''$ (0.24 to
2.4 pc at the distance of the SMC) the clustering of the PMS stars as computed
by a two-point angular correlation function is self-similar with a power law
slope $\gamma \approx -0.3$. The clustering properties are quite similar to
Milky Way star forming regions like Orion OB or $\rho$ Oph. Thus molecular
cloud fragmentation in the SMC seems to proceed on the same spatial scales as
in the Milky Way. This is remarkable given the differences in metallicity and
hence dust content between SMC and Milky Way star forming regions.


  The ratio of the proton's elastic electromagnetic form factors was obtained
by measuring the transverse and longitudinal polarizations of recoiling protons
from the elastic scattering of polarized electrons with unpolarized protons.
The ratio of the electric to magnetic form factor is proportional to the ratio
of the transverse to longitudinal recoil polarizations. The ratio was measured
over a range of four-momentum transfer squared between 0.5 and 3.5 GeV-squared.
Simultaneous measurement of transverse and longitudinal polarizations in a
polarimeter provides good control of the systematic uncertainty. The results
for the ratio of the proton's electric to magnetic form factors show a
systematic decrease with increasing four momentum squared, indicating for the
first time a marked difference in the spatial distribution of charge and
magnetization currents in the proton.


  This paper has more formal series with Bernoulli numbers and the Riemann zeta
function. He gives the generating function for the Bernoulli numbers. The paper
is translated from the Latin original into German.


  The expanding ejecta of supernova remnants (SNRs) are believed to form dust
in dense clumps of gas. Before the dust can be expelled into the interstellar
medium and contribute to the interstellar dust budget, it has to survive the
reverse shock that is generated through the interaction of the preceding
supernova blast wave with the surrounding medium. The conditions under which
the reverse shock hits the clumps change with remnant age and define the dust
survival rate. To study the dust destruction in the SNR Cassiopeia A, we
conduct magnetohydrodynamical simulations of the evolution of a supernova blast
wave and of the reverse shock. In a second step we use these evolving
conditions to model clumps that are disrupted by the reverse shock at different
remnant ages. Finally, we compute the amount of dust that is destroyed by the
impact of the reverse shock. We find that most of the dust in the SNR is hit by
the reverse shock within the first 350 yr after the SN explosion. While the
dust destruction in the first 200 yr is almost complete, we expect greater dust
survival rates at later times and almost total survival for clumps that are
first impacted at ages beyond 1000 yr. Integrated over the entire evolution of
the SNR, the dust mass shows the lowest survival fraction (17 per cent) for the
smallest grains (1 nm) and the highest survival fraction (28 per cent) for the
largest grains (1000 nm).


  Covering a network with the minimum possible number of boxes can reveal
interesting features for the network structure, especially in terms of
self-similar or fractal characteristics. Considerable attention has been
recently devoted to this problem, with the finding that many real networks are
self-similar fractals. Here we present, compare and study in detail a number of
algorithms that we have used in previous papers towards this goal. We show that
this problem can be mapped to the well-known graph coloring problem and then we
simply can apply well-established algorithms. This seems to be the most
efficient method, but we also present two other algorithms based on burning
which provide a number of other benefits. We argue that the presented
algorithms provide a solution close to optimal and that another algorithm that
can significantly improve this result in an efficient way does not exist. We
offer to anyone that finds such a method to cover his/her expenses for a 1-week
trip to our lab in New York (details in http://jamlab.org).


  One of the most intriguing features discovered by Swift is a plateau phase in
the X-ray flux decay of about 70% of the afterglows of gamma-ray bursts (GRBs).
The physical origin of this feature is still being debated. We constrain the
proposed interpretations, based on the intrinsic temporal properties of the
plateau phase. We selected and analyzed all the Swift/XRT GRB afterglows at
known redshift observed between March 2005 and June 2008 featuring a shallow
decay phase in their X-ray lightcurves. For our sample of 21 GRBs we find an
anticorrelation of the logarithm of the duration of the shallow phase with re
dshift, with a Spearman rank-order correlation coefficient of r=-0.4 and a null
hypothesis probability of 5%. When we correct the durations for cosmological
dilation, the anticorrelation strenghtens, with r=-0.6 and a null hypothesis
probability of 0.4%. Considering only those GRBs in our sample that have a
well-measured burst peak energy (8 out of 21), we find an anticorrelation
between the energy of the burst and the shallow phase duration, with r=-0.80
and a null hypothesis probability of 1.8%. If the burst energy anticorrelation
with the shallow phase duration is real, then the dependence of the shallow
phase on redshift could be the result of a selection effect, since on average
high-redshift bursts with lower energies and longer plateaus would be missed. A
burst energy anticorrelation with the shallow phase duration would be expected
if the end of the plateau arises from a collimated outflow. Alternative
scenarios are briefly discussed involving a possible cosmological evolution of
the mechanism responsible for the X-ray shallow decay.