  In recent years, artificial intelligence (AI) driven video generation has
garnered significant attention due to advancements in stable diffusion and
large language model techniques. Thus, there is a great demand for accurate
video quality assessment (VQA) models to measure the perceptual quality of
AI-generated content (AIGC) videos as well as optimize video generation
techniques. However, assessing the quality of AIGC videos is quite challenging
due to the highly complex distortions they exhibit (e.g., unnatural action,
irrational objects, etc.). Therefore, in this paper, we try to systemically
investigate the AIGC-VQA problem from both subjective and objective quality
assessment perspectives. For the subjective perspective, we construct a
Large-scale Generated Vdeo Quality assessment (LGVQ) dataset, consisting of
2,808 AIGC videos generated by 6 video generation models using 468 carefully
selected text prompts. Unlike previous subjective VQA experiments, we evaluate
the perceptual quality of AIGC videos from three dimensions: spatial quality,
temporal quality, and text-to-video alignment, which hold utmost importance for
current video generation techniques. For the objective perspective, we
establish a benchmark for evaluating existing quality assessment metrics on the
LGVQ dataset, which reveals that current metrics perform poorly on the LGVQ
dataset. Thus, we propose a Unify Generated Video Quality assessment (UGVQ)
model to comprehensively and accurately evaluate the quality of AIGC videos
across three aspects using a unified model, which uses visual, textual and
motion features of video and corresponding prompt, and integrates key features
to enhance feature expression. We hope that our benchmark can promote the
development of quality evaluation metrics for AIGC videos. The LGVQ dataset and
the UGVQ metric will be publicly released.


  The symmetry SU(2) and its geometric Bloch Sphere rendering are familiar for
a qubit (spin-1/2) but extension of symmetries and geometries have been
investigated far less for multiple qubits, even just a pair of them, that are
central to quantum information. In the last two decades, two different
approaches with independent starting points and motivations have come together
for this purpose. One was to develop the unitary time evolution of two or more
qubits for studying quantum correlations, exploiting the relevant Lie algebras
and especially sub-algebras of the Hamiltonians involved, and arriving at
connections to finite projective geometries and combinatorial designs.
Independently, geometers studying projective ring lines and associated finite
geometries have come to parallel conclusions. This review brings together both
the Lie algebraic and group representation perspective of quantum physics and
the geometric algebraic one, along with connections to complex quaternions.
Together, all this may be seen as further development of Felix Klein's Erlangen
Program for symmetries and geometries. In particular, the fifteen generators of
the continuous SU(4) Lie group for two-qubits can be placed in one-to-one
correspondence with finite projective geometries, combinatorial Steiner
designs, and finite quaternionic groups. The very different perspectives may
provide further insight into problems in quantum information. Extensions are
considered for multiple qubits and higher spin or higher dimensional qudits.


  We propose a scheme of generating and verifying mesoscopic-level entanglement
between two atomic ensembles using non-resonant stimulated Raman scattering.
Entanglement can be generated by direct detection or balanced homodyne
detection of the Stokes fields from the two cells, after they interfere on a
beam splitter. The entanglement of the collective atomic fields can be
transferred to the anti-Stokes fields in a readout process. By measuring the
operator moments of the anti-Stokes fields, we can verify the presence of
entanglement. We model the effects of practical factors such as Stokes field
detector quantum efficiency and additive thermal noise in the entanglement
generating process, and anti-Stokes field losses in the entanglement
verification process, and find achievable regimes in which entanglement can be
verified at the levels of tens to hundreds of atomic excitations in the
ensembles.


  We develop a formalism to study the implications of causality on OPE
coefficients in conformal field theories with large central charge and a sparse
spectrum of higher spin operators. The formalism has the interpretation of a
new conformal collider-type experiment for these class of CFTs and hence it has
the advantage of requiring knowledge only about CFT three-point functions. This
is accomplished by considering the holographic null energy operator which was
introduced in arXiv:1709.03597 as a generalization of the averaged null energy
operator. Analyticity properties of correlators in the Regge limit imply that
the holographic null energy operator is a positive operator in a subspace of
the total CFT Hilbert space. Utilizing this positivity condition, we derive
bounds on three-point functions $\langle TO_1O_2\rangle$ of the stress tensor
with various operators for CFTs with large central charge and a sparse
spectrum. After imposing these constraints, we also find that the operator
product expansions of all primary operators in the Regge limit have certain
universal properties. All of these results are consistent with the expectation
that CFTs in this class, irrespective of their microscopic details, admit
universal gravity-like holographic dual descriptions. Furthermore, this
connection enables us to constrain various inflationary observables such as the
amplitude of chiral gravity waves, non-gaussanity of gravity waves and
tensor-to-scalar ratio.


  Electronic structure and magnetic properties of ZrFe$_2$ with a cubic Laves
phase are investigated by calculations based on the density functional theory.
The total magnetic moment (m) of 3.14 $\mu_B$ per formula unit (\emph{f.u.}) is
obtained at the experimental lattice constant (7.06 \AA), which is larger than
3.06 $\mu_B$/\emph{f.u.} obtained at the theoretical equilibrium lattice
constant (6.85 \AA). The localized $3d$ magnetic moment is in negative
diffusive sp background moment. We predict a two-step magnetic collapse under
pressure: one is from 3.06 $\mu_B$/\emph{f.u.} to 1.26 $\mu_B$/\emph{f.u.} at
about 3.6 GPa, and the other is from 0.5 $\mu_B$/\emph{f.u.} to nonmagnetic
state at about 15 GPa. We understand this process by the changes of density of
states. The magnetic moment decreases under the pressure in the vicinity of the
experimental lattice constant with $d\ln m/dp=-0.038$ GPa$^{-1}$. The
spontaneous volume magnetostriction is 0.015. We suggest that the Invar effect
of this alloy may be understood when considering the magnetic moment variation
according to the Weiss $2\gamma$-model.


  We analyze in detail the spatial distribution and kinematic properties of two
different stellar populations in Andromeda II (And II) dwarf spheroidal galaxy.
We obtained their detailed surface density maps, together with their radial
density profiles. The two populations differ not only in age and metallicity,
but also in their spatial distribution and kinematics. Old stars ($\gtrsim 11$
Gyr) follow a round distribution well fitted by truncated density profiles.
These stars rotate around the projected optical major axis of the galaxy with
line-of-sight velocities $v_{los}(r_h) = 16 \pm 3$ km s$^{-1}$ and a velocity
gradient of $2.06 \pm 0.21$ km s$^{-1}$ arcmin$^{-1}$. Intermediate-age stars
($\lesssim 9$ Gyr) concentrate in the centre of the galaxy and form an
elongated structure extending along the projected optical major axis. This
structure appears to rotate with a velocity gradient of $2.24 \pm 0.22$ km
s$^{-1}$ arcmin$^{-1}$, and around the optical minor axis. The centres of
rotation and kinetic position angles (PA$_{\rm kin}$) of both populations
differ. For intermediate-age stars we obtained PA$_{\rm kin} = 18^\circ \pm
2^\circ$ and for the old ones PA$_{\rm kin} = 63^\circ \pm 3^\circ$ in good
agreement with photometric PA measured from isopleths fitted to the photometry.
We conclude that the two stellar populations may not be fully relaxed and thus
be the result of a merger occurred at redshift $z\sim 1.75$.


  We investigate the phase accumulated by a charged particle in an extended
quantum state as it encircles one or more magnetic fluxons, each carrying half
a flux unit. A simple, essentially topological analysis reveals an interplay
between the Aharonov-Bohm phase and Berry's phase.


  Recently, prompt-based fine-tuning has garnered considerable interest as a
core technique for few-shot text classification task. This approach
reformulates the fine-tuning objective to align with the Masked Language
Modeling (MLM) objective. Leveraging unlabeled data, prompt-based self-training
has shown greater effectiveness in binary and three-class classification.
However, prompt-based self-training for multi-class classification has not been
adequately investigated, despite its significant applicability to real-world
scenarios. Moreover, extending current methods to multi-class classification
suffers from the verbalizer that extracts the predicted value of manually
pre-defined single label word for each class from MLM predictions.
Consequently, we introduce a novel, efficient verbalizer structure, named
Mapping-free Automatic Verbalizer (MAV). Comprising two fully connected layers,
MAV serves as a trainable verbalizer that automatically extracts the requisite
word features for classification by capitalizing on all available information
from MLM predictions. Experimental results on five multi-class classification
datasets indicate MAV's superior self-training efficacy.


  We present an analysis of the Nb3Sn surface layers grown on a bulk niobium
(Nb) coupon prepared at the same time and by the same vapor diffusion process
used to make Nb3Sn coatings on 1.3 GHz cavities. Tunneling spectroscopy reveals
a well-developed, homogeneous superconducting density of states at the surface
with a gap value distribution centered around 2.7 meV and superconducting
critical temperature (Tc) up to 16.3 K. Scanning Electron microscopy (STEM)
performed on cross section of the sample's surface region shows a 2 microns
thick Nb3Sn surface layer. The elemental composition map exhibits a Nb over Sn
ratio of 3 and reveals the presence of buried sub-stoichiometric regions that
have a ratio f 5. Synchrotron x-ray diffraction experiments indicate a
polycrystalline Nb3Sn film and confirm the presence of Nb rich regions that
occupy about a third of the coating volume. These low Tc regions could play an
important role in the dissipation mechanism occurring during RF tests of
Nb3Sn-coated cavities and open the way for further improving a very promising
alternative to pure Nb cavities for particle accelerators.


  We consider the spin boson model with external magnetic field. We prove a
path integral formula for the heat kernel, known as Feynman-Kac-Nelson (FKN)
formula. We use this path integral representation to express the ground state
energy as a stochastic integral. Based on this connection, we determine the
expansion coefficients of the ground state energy with respect to the magnetic
field strength and express them in terms of correlation functions of a
continuous Ising model. From a recently proven correlation inequality, we can
then deduce that the second order derivative is finite. As an application, we
show existence of ground states in infrared-singular situations.


  The Zygmund functions form an intermediate class between Lipschitz and
H\"older functions; their second order divided differences are uniformly
bounded. It is well known that for $d \geq 1$ the graph of any Lipschitz
function $f:\R^d \rightarrow \R$ is thin for doubling measures, and we extend
this result to the Zygmund class.


  Moving object detection is critical for automated video analysis in many
vision-related tasks, such as surveillance tracking, video compression coding,
etc. Robust Principal Component Analysis (RPCA), as one of the most popular
moving object modelling methods, aims to separate the temporally varying (i.e.,
moving) foreground objects from the static background in video, assuming the
background frames to be low-rank while the foreground to be spatially sparse.
Classic RPCA imposes sparsity of the foreground component using l1-norm, and
minimizes the modeling error via 2-norm. We show that such assumptions can be
too restrictive in practice, which limits the effectiveness of the classic
RPCA, especially when processing videos with dynamic background, camera jitter,
camouflaged moving object, etc. In this paper, we propose a novel RPCA-based
model, called Hyper RPCA, to detect moving objects on the fly. Different from
classic RPCA, the proposed Hyper RPCA jointly applies the maximum correntropy
criterion (MCC) for the modeling error, and Laplacian scale mixture (LSM) model
for foreground objects. Extensive experiments have been conducted, and the
results demonstrate that the proposed Hyper RPCA has competitive performance
for foreground detection to the state-of-the-art algorithms on several
well-known benchmark datasets.


  In the realm of sound object-oriented program analyses for information-flow
control, very few approaches adopt flow-sensitive abstractions of the heap that
enable a precise modeling of implicit flows. To tackle this challenge, we
advance a new symbolic abstraction approach for modeling the heap in Java-like
programs. We use a store-less representation that is parameterized with a
family of relations among references to offer various levels of precision based
on user preferences. This enables us to automatically infer polymorphic
information-flow guards for methods via a co-reachability analysis of a
symbolic finite-state system. We instantiate the heap abstraction with three
different families of relations. We prove the soundness of our approach and
compare the precision and scalability obtained with each instantiated heap
domain by using the IFSpec benchmarks and real-life applications.


  We present Xspect, a method to obtain estimates of the angular power spectrum
of the Cosmic Microwave Background (CMB) temperature anisotropies including
analytical error bars developed for the Archeops experiment. Cross-power
spectra are computed from a set of maps and each of them is in itself an
unbiased estimate of the power spectrum as long as the detector noises are
uncorrelated. Then, the cross-power spectra are combined into a final
temperature power spectrum with error bars analytically derived from the
cross-correlation matrix.
  This method presents three main useful properties : (1) no estimation of the
noise power spectrum is needed, (2) complex weighting schemes including sky
covering and map noise properties can be easily taken into account, and
corrected for, for each input map, (3) error bars are quickly computed
analytically from the data themselves with no Monte-Carlo simulations involved.
Xspect also permits the study of common fluctuations between maps from
different sky surveys such as CMB, Sunyaev-Zel'dovich effect or mass
fluctuations from weak lensing observations.


  We present the findings of a pilot plan of active learning implemented in
introductory physics in a Chilean public university. The model is research
based as it considered a literature review for adequate selection and design of
activities, consistent with the levels of students' reasoning skills. The level
of scientific reasoning is positively correlated to student success. By
contrast to a control group of students following traditional lectures, we
observed a significant reduction in failure rate for students that do not yet
posses formal scientific reasoning. This profile of student being the majority,
we conclude that implementing active learning is particularly suited to first
year of higher education in the context of a developing country. It fits the
particularities of student profile and typical classroom size, leading to
learning improvement and reduction of academic risk as well as being
financially sound.


  The asymptotic behavior of the effective mass $m_{\rm eff}(\Lambda)$ of the
so-called Nelson model in quantum field theory is considered, where $\Lambda$
is an ultraviolet cutoff parameter of the model. Let $m$ be the bare mass of
the model. It is shown that for sufficiently small coupling constant $|\alpha|$
of the model, $m_{{\rm eff}}(\Lambda)/m$ can be expanded as $m_{{\rm
eff}}(\Lambda)/m= 1+\sum_{n=1}^\infty a_n(\Lambda) \alpha^{2n}$. A physical
folklore is that $a_n(\Lambda)\sim [\log \Lambda]^{(n-1)}$ as $\Lambda\to
\infty$. It is rigorously shown that
$$0<\lim_{\Lambda\to\infty}a_1(\Lambda)<C,\quad C_1\leq
\lim_{\Lambda\to\infty}a_2(\Lambda)/\log\Lambda\leq C_2$$ with some constants
$C$, $C_1$ and $C_2$.


  We study a novel pure exploration problem: the $\epsilon$-Thresholding Bandit
Problem (TBP) with fixed confidence in stochastic linear bandits. We prove a
lower bound for the sample complexity and extend an algorithm designed for Best
Arm Identification in the linear case to TBP that is asymptotically optimal.


  We present a simple phenomenological model of hadronic interaction between
protons accelerated in an old supernova remnant (SNR) and cold protons situated
within the associated molecular clouds (MCs). The accelerated protons from the
old SNR escaped the SNR shock front, and got injected into the MCs at an
earlier time, producing ultra high energy gamma-rays and neutrinos through
inelastic proton-proton interaction. We also take into account the acceleration
and subsequent escape of electrons from the SNR shock front. The escaped
electrons produce gamma-rays through various radiative cooling mechanisms,
after getting injected into the MCs. We use the model discussed in this letter
to explain the multiwavelength (MWL) spectral energy distribution (SED) of
unidentified Galactic ultra high energy gamma-ray source LHAASO J2108+5157. We
also discuss the feasibility of applying this model in other cases as well.
Future observations can test the viability of the model discussed in this
letter, which will in turn confirm that the SNRs can, in fact, accelerate
particles up to PeV energies.


  We study the partition properties enjoyed by the "next best thing to a
P-point'' ultrafilters introduced recently in joint work with Dobrinen and
Raghavan. That work established some finite-exponent partition relations, and
we now analyze the connections between these relations for different exponents
and the notion of conservativity introduced much earlier by Phillips. In
addition, we establish some infinite-exponent partition relations for these
ultrafilters and also for sums of non-isomorphic selective ultrafilters indexed
by selective ultrafilters.


  Within eleven dimensional supergravity we compute the logarithmic correction
to the entropy of magnetically charged asymptotically AdS${}_4$ black holes
with arbitrary horizon topology. We find perfect agreement with the expected
microscopic result arising from the dual field theory computation of the
topologically twisted index. Our result relies crucially on a particular limit
to the extremal black hole case and clarifies some aspects of quantum
corrections in asymptotically AdS spacetimes.


  Bayesian model averaging (BMA) is the state of the art approach for
overcoming model uncertainty. Yet, especially on small data sets, the results
yielded by BMA might be sensitive to the prior over the models. Credal Model
Averaging (CMA) addresses this problem by substituting the single prior over
the models by a set of priors (credal set). Such approach solves the problem of
how to choose the prior over the models and automates sensitivity analysis. We
discuss various CMA algorithms for building an ensemble of logistic regressors
characterized by different sets of covariates. We show how CMA can be
appropriately tuned to the case in which one is prior-ignorant and to the case
in which instead domain knowledge is available. CMA detects prior-dependent
instances, namely instances in which a different class is more probable
depending on the prior over the models. On such instances CMA suspends the
judgment, returning multiple classes. We thoroughly compare different BMA and
CMA variants on a real case study, predicting presence of Alpine marmot burrows
in an Alpine valley. We find that BMA is almost a random guesser on the
instances recognized as prior-dependent by CMA.


  Two physical principles, Macroscopic Locality (ML) and Information Causality
(IC), so far, have been most successful in distinguishing quantum correlations
from post-quantum correlations. However, there are also some post-quantum
probability distributions which cannot be distinguished with the help of these
principles. Thus, it is interesting to see whether consideration of these two
principles, separately, along with some additional physically plausible
constraints, can explain some interesting quantum features which are otherwise
hard to reproduce. In this paper we show that, in a Bell-CHSH scenario, ML
along with constraint of equal-biasness for the concerned observables, almost
reproduces the quantum joint probability distribution corresponding to maximal
quantum Bell violation, which is unique up to relabeling. From this example and
earlier work of Cavalcanti, Salles and Scarani, we conclude that IC and ML are
in-equivalent physical principles; satisfying one does not imply that the other
is satisfied.


  We analyze two representative systems containing a three-phase-contact line:
a liquid lens at a fluid--fluid interface and a liquid drop in contact with a
gas phase residing on a solid substrate. We discuss to which extent the
decomposition of the grand canonical free energy of such systems into volume,
surface, and line contributions is unique in spite of the freedom one has in
positioning the Gibbs dividing interfaces. In the case of a lens it is found
that the line tension is independent of arbitrary choices of the Gibbs dividing
interfaces. In the case of a drop, however, one arrives at two different
possible definitions of the line tension. One of them corresponds seamlessly to
that applicable to the lens. The line tension defined this way turns out to be
independent of choices of the Gibbs dividing interfaces. In the case of the
second definition,however, the line tension does depend on the choice of the
Gibbs dividing interfaces. We provide equations for the equilibrium contact
angles which are form-invariant with respect to notional shifts of dividing
interfaces which only change the description of the system. Conceptual
consistency requires to introduce additional stiffness constants attributed to
the line. We show how these constants transform as a function of the relative
displacements of the dividing interfaces. The dependences of the contact angles
on lens or drop volumes do not render the line tension alone but a combination
of the line tension, the Tolman length, and the stiffness constants of the
line.


  One of the challenges currently problems in the use of cloud services is the
task of designing of specialized data management systems. This is especially
important for hybrid systems in which the data are located in public and
private clouds. Implementation monitoring functions querying, scheduling and
processing software must be properly implemented and is an integral part of the
system. To provide these functions is proposed to use an object-relational
mapping (ORM). The article devoted to presenting the approach of designing
databases for information systems hosted in a hybrid cloud infrastructure. It
also provides an example of the development of ORM library.


  In this article, we study shape fitting problems, $\epsilon$-coresets, and
total sensitivity. We focus on the $(j,k)$-projective clustering problems,
including $k$-median/$k$-means, $k$-line clustering, $j$-subspace
approximation, and the integer $(j,k)$-projective clustering problem. We derive
upper bounds of total sensitivities for these problems, and obtain
$\epsilon$-coresets using these upper bounds. Using a dimension-reduction type
argument, we are able to greatly simplify earlier results on total sensitivity
for the $k$-median/$k$-means clustering problems, and obtain
positively-weighted $\epsilon$-coresets for several variants of the
$(j,k)$-projective clustering problem. We also extend an earlier result on
$\epsilon$-coresets for the integer $(j,k)$-projective clustering problem in
fixed dimension to the case of high dimension.


  Polytropic transonic solutions of spherically symmetric and steady galactic
winds in the gravitational potential of a dark matter halo (DMH) with a
supermassive black hole (SMBH) are studied. The solutions are classified in
terms of their topological features, and the gravitational potential of the
SMBH adds a new branch to the transonic solutions generated by the gravity of
the DMH. The topological types of the transonic solutions depend on the mass
distribution, the amount of supplied energy, the polytropic index $\gamma$, and
the slope $\alpha$ of the DMH mass distribution. When $\alpha$ becomes larger
than a critical value $\alpha_\mathrm{c}$, the transonic solution types change
dramatically. Further, our model predicts that it is possible for a slowly
accelerating outflow to exist, even in quiescent galaxies with small $\gamma$.
This slowly accelerating outflow differs from those considered in many of the
previous studies focusing on supersonic outflows in active star-forming
galaxies. In addition, our model indicates that outflows in active star-forming
galaxies have only one transonic point in the inner region ($\sim$ 0.01 kpc).
The locus of this transonic point does not strongly depend on $\gamma$. We
apply the polytropic model incorporating mass flux supplied by stellar
components to the Sombrero Galaxy, and conclude that it can reproduce the
observed gas density and the temperature distribution well. This result differs
significantly from the isothermal model, which requires an unrealistically
large mass flux (Igarashi et al. 2014). Thus, we conclude that the polytropic
model is more realistic than the isothermal model, and that the Sombrero Galaxy
can have a slowly accelerating outflow.


  We consider vector spin glasses whose energy function is a Gaussian random
field with covariance given in terms of the matrix of scalar products. For
essentially any model in this class, we give an upper bound for the limit free
energy, which is expected to be sharp. The bound is expressed in terms of an
infinite-dimensional Hamilton-Jacobi equation.


  It was shown recently that smooth and continuous "Matsubara" phase-space
loops follow a quantum-Boltzmann-conserving classical dynamics when decoupled
from non-smooth distributions, which was suggested as the reason that many
dynamical observables appear to involve a mixture of classical dynamics and
quantum Boltzmann statistics. Here we derive a mean-field version of this
"Matsubara dynamics" which sufficiently mitigates its serious phase problem to
permit numerical tests on a two-dimensional "champagne-bottle" model of a
rotating OH bond. The Matsubara-dynamics rovibrational spectra are found to
converge toward close agreement with the exact quantum results at all
temperatures tested (200-800 K), the only significant discrepancies being a
temperature-independent 22 cm$^{-1}$ blue-shift in the position of the
vibrational peak and a slight broadening in its line shape. These results are
compared with centroid molecular dynamics (CMD) to assess the importance of
non-centroid fluctuations. Above 250 K, only the lowest-frequency non-centroid
modes are needed to correct small CMD red-shifts in the vibrational peak; below
250 K, more non-centroid modes are needed to correct large CMD red-shifts and
broadening. The transition between these "shallow curvature" and "deep
curvature" regimes happens when imaginary-time Feynman paths become able to
lower their actions by cutting through the curved potential surface, giving
rise to artificial instantons in CMD.


  This paper presents a sampling-based motion planning algorithm for real-time
and propellant-optimized autonomous spacecraft trajectory generation in
near-circular orbits. Specifically, this paper leverages recent algorithmic
advances in the field of robot motion planning to the problem of
impulsively-actuated, propellant-optimized rendezvous and proximity operations
under the Clohessy-Wiltshire-Hill (CWH) dynamics model. The approach calls upon
a modified version of the Fast Marching Tree (FMT*) algorithm to grow a set of
feasible trajectories over a deterministic, low-dispersion set of sample points
covering the free state space. To enforce safety, the tree is only grown over
the subset of actively-safe samples, from which there exists a feasible
one-burn collision avoidance maneuver that can safely circularize the
spacecraft orbit along its coasting arc under a given set of potential thruster
failures. Key features of the proposed algorithm include: (i) theoretical
guarantees in terms of trajectory safety and performance, (ii) amenability to
real-time implementation, and (iii) generality, in the sense that a large class
of constraints can be handled directly. As a result, the proposed algorithm
offers the potential for widespread application, ranging from on-orbit
satellite servicing to orbital debris removal and autonomous inspection
missions.


  We discuss new, theoretically clean strategies to determine the angle
$\gamma$ of the unitarity triangle from $B_d\to DK_{S(L)}$, $B_s\to
D\eta^{(')}, D\phi, ...$ decays, and point out that the $B_s\to DK_{S(L)}$ and
$B_d\to D\pi^0, D\rho^0, ...$ modes allow very interesting determinations of
the $B^0_q$--$\bar{B^0_q}$ mixing phases $\phi_s$ and $\phi_d$, respectively.
Their colour-allowed counterparts $B_s\to D_s^{(*)\pm} K^\mp, ...$ and $B_d\to
D^{(*)\pm} \pi^\mp, ...$ also offer new methods to probe $\gamma$.


  The transportation system is rapidly evolving with new connected and
automated vehicle (CAV) technologies that integrate CAVs with other vehicles
and roadside infrastructure in a cyberphysical system (CPS). Through
connectivity, CAVs affect their environments and vice versa, increasing the
size of the cyberattack surface and the risk of exploitation of security
vulnerabilities by malicious actors. Thus, greater understanding of potential
CAV-CPS cyberattacks and of ways to prevent them is a high priority. In this
article we describe CAV-CPS cyberattack surfaces and security vulnerabilities,
and outline potential cyberattack detection and mitigation strategies. We
examine emerging technologies - artificial intelligence, software-defined
networks, network function virtualization, edge computing, information-centric
and virtual dispersive networking, fifth generation (5G) cellular networks,
blockchain technology, and quantum and postquantum cryptography - as potential
solutions aiding in securing CAVs and transportation infrastructure against
existing and future cyberattacks.


  Current source models for the Earth's main geomagnetic field are calculated
employing conventional discrete inverse theory methods. Source structures are
spherical surfaces placed at the surface of the Earth's core, and at the
surface of the Earth. The data set consists of measurements taken by the MAGSAT
satellite in 1979. The resulting current distributions are discussed in
relation to dipole and current loop models.


  We prove that more than 41% of the zeros of the zeta function are on the
critical line.


  Using new insights into strongly coupled gauge theories arising from analytic
calculations and lattice simulations, we explore a framework for technicolor
model building that relies on a non-trivial infrared fixed point, and an
essential role for QCD. Interestingly, the models lead to a simple relation
between the electroweak scale and the QCD confinement scale, and to the
possible existence of exotic leptoquarks with masses of several hundred GeV.


  Internet usage is an important requirement that supports the performance and
activities on campus. To control internet usage, it is necessary to know the
distribution of internet usage. By utilizing a number of machine learning
algorithms and WEKA software, the research is carried out by observation and
taking data from wifi hotspots on campus. The classification method using
SVM-Based utilizes the classification method owned by Support Vector Machine
(SVM). This study aims to classify data on internet usage so that from this
classification can be known destination network, protocol, and bandwidth that
are widely accessed at certain times. Internet traffic data is retrieved
through Wireshark software. Whereas data processing and data processing of
internet traffic is processed by WEKA. The results showed: 1) UBD internet
usage in the week I 133,196 users, week II 304,042 users,2) Use of Destination
Network 24,150 and Use of Protocol 37,321,3) Destination networks that are
often addressed are 172.21.206.143 (the week I) and 172.21.172.234 (week II),
protocols that are often used by TCP, and4) SVM method is a good data mining
method for classifying network packet patterns so as to produce network traffic
classification according to destination network and protocol.


  We calculate the leading contributions to the connected two-point functions
of protected scalar operators in the defect version of N=4 SYM theory which is
dual to the D5-D3 probe-brane system with k units of background gauge field
flux. This involves several types of two-point functions which are vanishing in
the theory without the defect, such as two-point functions of operators of
unequal conformal dimension. We furthermore exploit the operator product
expansion (OPE) and the boundary operator expansion (BOE), which form the basis
of the boundary conformal bootstrap equations, to extract conformal data both
about the defect CFT and about N=4 SYM theory without the defect. From the
knowledge of the one- and two-point functions of the defect theory, we extract
certain structure constants of N=4 SYM theory using the (bulk) OPE and
constrain certain bulk-bulk-to-boundary couplings using the BOE. The extraction
of the former relies on a non-trivial, polynomial k dependence of the one-point
functions, which we explicitly demonstrate. In addition, it requires the
knowledge of the one-point functions of SU$(2)$ descendant operators, which we
likewise explicitly determine.


  When observations are organized into groups where commonalties exist amongst
them, the dependent random measures can be an ideal choice for modeling. One of
the propositions of the dependent random measures is that the atoms of the
posterior distribution are shared amongst groups, and hence groups can borrow
information from each other. When normalized dependent random measures prior
with independent increments are applied, we can derive appropriate exchangeable
probability partition function (EPPF), and subsequently also deduce its
inference algorithm given any mixture model likelihood. We provide all
necessary derivation and solution to this framework. For demonstration, we used
mixture of Gaussians likelihood in combination with a dependent structure
constructed by linear combinations of CRMs. Our experiments show superior
performance when using this framework, where the inferred values including the
mixing weights and the number of clusters both respond appropriately to the
number of completely random measure used.


  Question-answering for domain-specific applications has recently attracted
much interest due to the latest advancements in large language models (LLMs).
However, accurately assessing the performance of these applications remains a
challenge, mainly due to the lack of suitable benchmarks that effectively
simulate real-world scenarios. To address this challenge, we introduce two
product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop
products to help evaluate the performance of existing models on domain-specific
product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA
framework to enhance the performance of the models in the product QA task. Our
experiments demonstrated that inducing domain knowledge through query
reformulation allowed for increased retrieval and generative performance when
compared to standard RAG-QA methods. This improvement, however, is slight, and
thus illustrates the challenge posed by the datasets introduced.


  Special relativity was discovered at the eve of the century, but finds its
roots in the 19th century efforts to understand the optics and electromagnetism
of moving bodies. These roots are reviewed in Parts 1 and 2, the latter being
specially devoted to the works of Lorentz and of Poincare up to 1904. Part 3
contains a detailed comparison of the works of Einstein and of Poincare in
1905. It is shown that both authors succeeded in constructing a coherent and
fully relativistic theory, although their ideas about the ether were radically
different. In Part 4, the question of the respective merits of the three
potential fathers of special relativity (i.e. Lorentz, Poincare, Einstein) is
discussed again, at the light of the preceding analysis and of the "post-1905"
developments of the theory.


  I explore many aspects of jet substructure at the Large Hadron Collider,
ranging from theoretical techniques for jet calculations, to phenomenological
tools for better searches with jets, to software for implementing and comparing
such tools. I begin with an application of soft-collinear effective theory, an
effective theory of QCD applied to high-energy quarks and gluons. This material
is taken from Ref. 1, in which we demonstrate factorization and logarithmic
resummation for a certain class of observables in electron-positron collisions.
I then explore various phenomenological aspects of jet substructure in
simulated events. After observing numerous features of jets at hadron
colliders, I describe a method -- jet pruning -- for improving searches for
heavy particles that decay to one or more jets. This material is a greatly
expanded version of Ref. 2. Finally, I give an overview of the software tools
available for these kinds of studies, with a focus on SpartyJet, a package for
implementing and comparing jet-based analyses I have collaborated on. Several
detailed calculations and software examples are given in the appendices.
Sections with no new content are italic in the Table of Contents.


  Whether turbulence induced anomalous resistivity (AR) can facilitate a fast
magnetic reconnection in collisionless plasma is a subject of active debate for
decades. Recent space observations suggest that the reconnection rate can be
higher than the Hall-reconnection rate and turbulent dissipation is required.
In this paper, using particle-in-cell simulations, we present a case study of
how AR produced by Buneman instability accelerates magnetic reconnection. We
first show that the AR/drag produced by Buneman instability in a thin electron
current layer 1) can dissipate magnetic energy stored in the current layer
through dissipation of the kinetic energy of electron beams; 2) The
inhomogeneous drag caused by wave couplings spontaneously breaks the magnetic
field lines and causes impulsive fast non-Hall magnetic reconnection on
electron-scales with a mean rate reaching 0.6 $V_A$. We then show that a
Buneman instability driven by intense electron beams around the x-point in a 3D
magnetic reconnection significantly enhances the dissipation of the magnetic
energy. Electron-scale magnetic reconnections driven by the inhomogeneous drag
around the x-line enhances the reconnection electric field and the in-plane
perpendicular magnetic field. About 40\% of the released magnetic energy is
converted into the electron thermal energy by AR while 50\% is converted into
the kinetic energy of the electron beams through the acceleration by the
reconnection electric field. The enhanced magnetic energy dissipation is
balanced by a net Poynting flux in-flow. About 10\% of the released magnetic
energy is brought out by an enhanced Poynting flux out-flow. These results
suggest that AR with sufficient intensity and electron-scale inhomogeneity can
significantly accelerate magnetic reconnection.


  We have searched for the presence of simple P and S-bearing molecules in
Titan's atmosphere, by looking for the characteristic signatures of phosphine
and hydrogen sulfide in infrared spectra obtained by Cassini CIRS. As a result
we have placed the first upper limits on the stratospheric abundances, which
are 1 ppb (PH3) and 330 ppb (H2S), at the 2-sigma significance level.


  Given non-collinear points a_1, a_2, a_3, there is a unique compact, say
\Delta, that has minimal logarithmic capacity among all continua joining a_1,
a_2, and a_3. For h be a complex-valued non-vanishing Dini-continuous function
on \Delta, we consider f_h(z) := (1/\pi i)\int_\Delta h(t)/(t-z) dt/w^+(t),
where w(z) := \sqrt{\prod_{k=0}^3(z-a_k)} and w^+ the one-sided value according
to some orientation of \Delta. In this work we present strong asymptotics of
diagonal Pad\'e approximants to f_h and describe the behavior of the spurious
pole and the regions of locally uniform convergence from a generic perspective.


  This paper studies novel attack and defense strategies, based on a class of
stealthy attacks, namely the zero-dynamics attack (ZDA), for multi-agent
control systems. ZDA poses a formidable security challenge since its attack
signal is hidden in the null-space of the state-space representation of the
control system and hence it can evade conventional detection methods. An
intuitive defense strategy builds on changing the aforementioned representation
via switching through a set of carefully crafted topologies. In this paper, we
propose realistic ZDA variations where the attacker is aware of this
topology-switching strategy, and hence employs the following policies to avoid
detection: (i) pause, update and resume ZDA according to the knowledge of
switching topologies; (ii) cooperate with a concurrent stealthy topology attack
that alters network topology at switching times, such that the original ZDA is
feasible under the corrupted topology. We first systematically study the
proposed ZDA variations, and then develop defense strategies against them under
the realistic assumption that the defender has no knowledge of attack starting,
pausing, and resuming times and the number of misbehaving agents. Particularly,
we characterize conditions for detectability of the proposed ZDA variations, in
terms of the network topologies to be maintained, the set of agents to be
monitored, and the measurements of the monitored agents that should be
extracted, while simultaneously preserving the privacy of the states of the
non-monitored agents. We then propose an attack detection algorithm based on
the Luenberger observer, using the characterized detectability conditions. We
provide numerical simulation results to demonstrate our theoretical findings.


  We discuss the Heisenberg model and its chiral extension in an extended
truncation with the help of functional methods. Employing computer algebra to
derive the beta functions, and pseudo-spectral methods to solve them, we are
able to go significantly beyond earlier approximations, and provide new
estimates on the critical quantities of both models. The fixed point of the
Heisenberg model is mostly understood, and our results are in agreement with
estimates from various other approaches, including Monte Carlo and conformal
bootstrap studies. By contrast, in the chiral case, the formerly known
disagreement with lattice studies persists, raising the question whether
actually the same universality class is described.


  The growth kinetics is studied of free-growing helium crystals within the
temperature range 0.5-0.83 K and supersaturations to 10 mbar. For the pressure
supersaturations above 0.5 mbar, numerous drastic changes are observed in the
growth kinetics for all crystalline facets from the normal and slow kinetics to
the anomalously high or burst-like one. It is found for the first time that the
anomalous to normal transition in the facet growth kinetics occurs in a jump
like manner within a time 80 {\mu}s interval of the measurement record.


  The electric solar wind sail (E-sail) propellantless propulsion device uses
long, charged metallic tethers to tap momentum from the solar wind to produce
spacecraft propulsion. If flying through planetary or moon eclipse, the long
E-sail tethers can undergo significant thermal contraction and expansion. Rapid
shortening of the tether increases its tension due to inertia of the tether and
a Remote Unit that is located on the tether tip (a Remote Unit is part of
typical E-sail designs). We analyse by numerical simulation the conditions
under which eclipse induced stresses are safe for E-sail tethers. We calculate
the closest safe approach distances for Earth, Moon, Venus, Mars, Jupiter,
Ceres and an exemplary 300 km main belt asteroid Interamnia for circular,
parabolic and hyperbolic orbits. We find that any kind of eclipsing is safe
beyond approximately 2.5 au distance, but for terrestrial planets safety
depends on the parameters of the orbit. For example, for Mars the safe distance
with 20 km E-sail tether lies between Phobos and Deimos orbits.


  Synchronization is the Achilles heel of concurrent programs. Synchronization
requirement is often used to ensure that the execution of the concurrent
program can be serialized. Without synchronization requirement, a program
suffers from consistency violations. Recently, it was shown that if programs
are designed to tolerate such consistency violation faults (\cvf{s}) then one
can obtain substantial performance gain. Previous efforts to analyze the effect
of \cvf-tolerance are limited to run-time analysis of the program to determine
if tolerating \cvf{s} can improve the performance. Such run-time analysis is
very expensive and provides limited insight.
  In this work, we consider the question, `Can static analysis of the program
predict the benefit of \cvf-tolerance?' We find that the answer to this
question is affirmative. Specifically, we use static analysis to evaluate the
cost of a \cvf and demonstrate that it can be used to predict the benefit of
\cvf-tolerance. We also find that when faced with a large state space, partial
analysis of the state space (via sampling) also provides the required
information to predict the benefit of \cvf-tolerance. Furthermore, we observe
that the \cvf-cost distribution is exponential in nature, i.e., the probability
that a \cvf has a cost of $c$ is $A.B^{-c}$, where $A$ and $B$ are constants,
i.e., most \cvf{s} cause no/low perturbation whereas a small number of \cvf{s}
cause a large perturbation. This opens up new aveneus to evaluate the benefit
of \cvf-tolerance.


  We apply principal component analysis to the study of event-by-event
fluctuations in relativistic heavy-ion collisions. This method brings out all
the information contained in two-particle correlations in a physically
transparent way. We present a guide to the method, and apply it to multiplicity
fluctuations and anisotropic flow, using ALICE data and simulated events. In
particular, we study elliptic and triangular flow fluctuations as a function of
transverse momentum and rapidity. This method reveals previously unknown
subleading modes in both rapidity and transverse momentum for the momentum
distribution as well as elliptic and triangular flows.


  We compute threshold effects to gauge couplings in four-dimensional $Z_N$
orientifold models of type I strings with ${\cal N}=2$ and ${\cal N}=1$
supersymmetry, and study their dependence on the geometric moduli. We also
compute the tree-level (disk) couplings of the open sector gauge fields to the
twisted closed string moduli of the orbifold in various models and study their
effects and that of the one-loop threshold corrections on gauge coupling
unification. We interpret the results from the (supergravity) effective theory
point of view and comment on the conjectured heterotic-type I duality


  We present a theoretical study of Coulomb exchange interaction for electrons
confined in a cylindrical quantum wire and subject to a Rashba-type spin-orbit
coupling with radial electric field. The effect of spin splitting on the
single-particle band dispersions, the quasiparticle effective mass, and the
system's total exchange energy per particle are discussed. Exchange interaction
generally suppresses the quasiparticle effective mass in the lowest nanowire
subband, and a finite spin splitting is found to significantly increase the
magnitude of the quasiparticle-mass suppression (by upto 15\% in the
experimentally relevant parameter regime). In contrast, spin-orbit coupling
causes a modest (1\%-level) reduction of the magnitude of the exchange energy
per particle. Our results shed new light on the interplay of spin-orbit
coupling and Coulomb interaction in quantum-confined systems, including those
that are expected to host exotic quasiparticle excitations.


  Neutrino emission caused by singlet Cooper pairing of baryons in neutron
stars is recalculated by accurately taking into account for conservation of the
vector weak currents. The neutrino emissivity via the vector weak currents is
found to be several orders of magnitude smaller than that obtained before by
different authors. This makes unimportant the neutrino radiation from singlet
pairing of protons or hyperons.


  Over a dozen millisecond pulsars are ablating low-mass companions in close
binary systems. In the original "black widow", the 8-hour orbital period
eclipsing pulsar PSR J1959+2048 (PSR B1957+20), high energy emission
originating from the pulsar is irradiating and may eventually destroy a
low-mass companion. These systems are not only physical laboratories that
reveal the dramatic result of exposing a close companion star to the
relativistic energy output of a pulsar, but are also believed to harbour some
of the most massive neutron stars, allowing for robust tests of the neutron
star equation of state. Here, we report observations of ZTF J1406+1222, a wide
hierarchical triple hosting a 62-minute orbital period black widow candidate
whose optical flux varies by a factor of more than 10. ZTF J1406+1222 pushes
the boundaries of evolutionary models, falling below the 80 minute minimum
orbital period of hydrogen-rich systems. The wide tertiary companion is a rare
low metallicity cool subdwarf star, and the system has a Galactic halo orbit
consistent with passing near the Galactic center, making it a probe of
formation channels, neutron star kick physics, and binary evolution.


  We study the gauge dependence of the neutrino mass renormalization in a two
Higgs doublet model, that is extended with one singlet seesaw neutrino. This
model gives only one light neutrino a mass at tree level, while the second
light mass is generated at loop level via the interaction with the second Higgs
doublet. At one loop level, one neutrino stays massless. We use multiplicative
renormalization constants to define counterterms. The renormalized mass
parameters are defined as the complex poles of the propagators, using the
complex mass scheme for mass renormalization. With this setup, we analytically
get the expressions for the neutrino mass counterterms and isolate the gauge
dependent part. We show, how relating this gauge dependent part with the
tadpole renormalization leads to gauge independent counterterm definitions,
hence gauge independent bare masses for neutrinos.


  Small non-spherical particles settling in a quiescent fluid tend to orient so
that their broad side faces down, because this is a stable fixed point of their
angular dynamics at small particle Reynolds number. Turbulence randomises the
orientations to some extent, and this affects the reflection patterns of
polarised light from turbulent clouds containing ice crystals. An overdamped
theory predicts that turbulence-induced fluctuations of the orientation are
very small when the settling number Sv (a dimensionless measure of the settling
speed) is large. At small Sv, by contrast, the overdamped theory predicts that
turbulence randomises the orientations. This overdamped theory neglects the
effect of particle inertia. Therefore we consider here how particle inertia
affects the orientation of small crystals settling in turbulent air. We find
that it can significantly increase the orientation variance, even when the
Stokes number St (a dimensionless measure of particle inertia) is quite small.
We identify different asymptotic parameter regimes where the tilt-angle
variance is proportional to different inverse powers of Sv. We estimate
parameter values for ice crystals in turbulent clouds and show that they cover
several of the identified regimes. The theory predicts how the degree of
alignment depends on particle size, shape and turbulence intensity, and that
the strong horizontal alignment of small crystals is only possible when the
turbulent energy dissipation is weak, of the order of $1\,$cm$^2$/s$^3$ or
less.


  Since a long time the application of small electric potentials on biological
membranes (iontophoresis) proved enabling control and improvement of
transdermal delivery of substances across this barrier. In spite of a large
experimental data, the detailed molecular mechanism of iontophoresis is absent.
In the present work the interaction among the external continuous electric
field with the outermost layer of the skin (\textit{stratum corneum}) was
studied by coarse-grained molecular dynamics. Our results pointed out the
occurrence of water-rich vesicles formation depending on the field strength.
The corresponding phase diagram indicated that the large set of phenomena
(vesicle formation, reversibility, phase transition, disruption) could be
completely controlled by tuning external continuous electric fields.
Interestingly, electric field shielding effects are in the origin of observed
effects and followed a general Arrhenius-like time dependence. Direct current
(DC) electric field usage would also have booster diffusion effects due to
vesicles creation and reincorporation which would have direct beneficial
absorption effects on water-soluble topical agents (vitamins) or dermal
jet-injection of drugs by fine needles.


  We prove local background independence of the complete quantum closed string
field theory using the recursion relations for string vertices and the
existence of connections in CFT theory space. Indeed, with this data we
construct an antibracket preserving map between the state spaces of two nearby
conformal theories taking the corresponding string field measures $d\mu
e^{2S/\hbar}$ into each other. A geometrical construction of the map is
achieved by introducing a Batalin-Vilkovisky (BV) algebra on spaces of Riemann
surfaces, together with a map to the BV algebra of string functionals. The
conditions of background independence show that the field independent terms of
the master action arise from vacuum vertices $\V_{g,0}$, and that the overall
$\hbar$-independent normalization of the string field measure involves the
theory space connection. Our result puts on firm ground the widely believed
statement that string theories built from nearby conformal theories are
different states of the same theory.


  With the advent of deep learning methods replacing the ISP in transforming
sensor RAW readings into RGB images, numerous methodologies solidified into
real-life applications. Equally potent is the task of inverting this process
which will have applications in enhancing computational photography tasks that
are conducted in the RAW domain, addressing lack of available RAW data while
reaping from the benefits of performing tasks directly on sensor readings. This
paper's proposed methodology is a state-of-the-art solution to the task of RAW
reconstruction, and the multi-step refinement process integrating an
overexposure mask is novel in three ways: instead of from RGB to bayer, the
pipeline trains from RGB to demosaiced RAW allowing use of perceptual loss
functions; the multi-step processes has greatly enhanced the performance of the
baseline U-Net from start to end; the pipeline is a generalizable process of
refinement that can enhance other high performance methodologies that support
end-to-end learning.


  We investigate the structure of the molecular cloud complexes (MCCs) as a
group of several giant molecular clouds (GMCs) in the Galaxy. Then, we find
that the mass-size relation which has been reported for the GMCs establishes
well even for the very large MCCs whose size is about 1 kpc. Since the
horizontal size of the MCCs is more than the thickness of the Galactic disk, we
can no longer consider the MCCs to be a sphere. Thus, we construct a structural
model of the MCCs, adopting a rectangular-solid geometry. As a result, our
model explains the observed mass-size relation of the MCCs very well. {}From
the estimated external pressure around the MCCs, we find that they are in a
rough pressure balance with the interstellar medium. Moreover, we find there is
observational deficiency of the MCCs with a large size and surface density.
Then, we suggest that the external pressure has a significant effect on the
structure and evolution of the MCCs. We also discuss the effect of H {\sc ii}
regions in the MCCs.


  We present a technique to improve the transferability of deep representations
learned on small labeled datasets by introducing self-supervised tasks as
auxiliary loss functions. While recent approaches for self-supervised learning
have shown the benefits of training on large unlabeled datasets, we find
improvements in generalization even on small datasets and when combined with
strong supervision. Learning representations with self-supervised losses
reduces the relative error rate of a state-of-the-art meta-learner by 5-25% on
several few-shot learning benchmarks, as well as off-the-shelf deep networks on
standard classification tasks when training from scratch. We find the benefits
of self-supervision increase with the difficulty of the task. Our approach
utilizes the images within the dataset to construct self-supervised losses and
hence is an effective way of learning transferable representations without
relying on any external training data.


  Parabolic integro-differential model Cauchy problem is considered in the
scale of Lp -spaces of functions whose regularity is defined by a scalable Levy
measure. Existence and uniqueness of a solution is proved by deriving apriori
estimates. Some rough probability density function estimates of the associated
Levy process are used as well.


  The ability to synthesize high-quality samples over large areas and at low
cost is one of the biggest challenges during the developmental stage of any
novel material. While chemical vapor deposition (CVD) methods provide a
promising low-cost route for CMOS compatible, large-scale growth of materials,
it often falls short of the high-quality demands in nanoelectronics and
optoelectronics. We present large-scale CVD synthesis of single- and few-
layered MoS2 using direct vapor-phase sulfurization of MoO2, which enables us
to obtain extremely high-quality single-crystal monolayer MoS2 samples with
field-effect mobility exceeding 30 cm2/Vs in monolayers. These samples can be
readily synthesized on a variety of substrates, and demonstrate a high-degree
of optoelectronic uniformity in Raman and photoluminescence mapping over entire
crystals with areas exceeding hundreds of square micrometers. Owing to their
high crystalline quality, Raman spectroscopy on these samples reveal a range of
multi-phonon processes through peaks with equal or better clarity compared to
past reports on mechanically exfoliated samples. This enables us to investigate
the layer thickness- and substrate-dependence of the extremely weak phonon
processes at 285 cm-1 and 487 cm-1 in 2D MoS2. The ultra-high,
optoelectronic-grade crystalline quality of these samples could be further
established through photocurrent spectroscopy, which clearly reveal excitonic
states at room temperature, a feat that has been previously demonstrated only
on device fabricated with mechanically exfoliated that were artificially
suspended across trenches. Our method reflects a big step in the development of
atomically thin, 2D MoS2 for scalable, high-quality optoelectronics.


  We study the distance-redshift relation in a universe filled with 'walls' of
pressure-less dust separated by under dense regions. We show that as long as
the density contrast of the walls is small, or the diameter of the under dense
regions is much smaller than the Hubble scale, the distance-redshift relation
remains close to what is obtained in a Friedmann universe. However, when
arbitrary density contrasts are allowed, every prescribed distance-redshift
relation can be reproduced with such models.


  Modified gravity theories on cosmic scales have three key deviations from
general relativity. They can cause cosmic acceleration without a physical,
highly negative pressure fluid, can cause a gravitational slip between the two
metric potentials, and can cause gravitational waves to propagate differently,
e.g. with a speed different from the speed of light. We examine whether the
deviations in the metric potentials as observable through modified Poisson
equations for scalar density perturbations are related to or independent from
deviations in the tensor gravitational waves. We show analytically they are
independent instantaneously in covariant Galileon gravity -- e.g. at some time
one of them can have the general relativity value while the other deviates --
though related globally -- if one deviates over a finite period, the other at
some point shows a deviation. We present expressions for the early time and
late time de Sitter limits, and numerically illustrate their full evolution.
This in(ter)dependence of the scalar and tensor deviations highlights
complementarity between cosmic structure surveys and future gravitational wave
measurements.


  Text-based talking-head video editing aims to efficiently insert, delete, and
substitute segments of talking videos through a user-friendly text editing
approach. It is challenging because of \textbf{1)} generalizable talking-face
representation, \textbf{2)} seamless audio-visual transitions, and \textbf{3)}
identity-preserved talking faces. Previous works either require minutes of
talking-face video training data and expensive test-time optimization for
customized talking video editing or directly generate a video sequence without
considering in-context information, leading to a poor generalizable
representation, or incoherent transitions, or even inconsistent identity. In
this paper, we propose an efficient cascaded conditional diffusion-based
framework, which consists of two stages: audio to dense-landmark motion and
motion to video. \textit{\textbf{In the first stage}}, we first propose a
dynamic weighted in-context diffusion module to synthesize dense-landmark
motions given an edited audio. \textit{\textbf{In the second stage}}, we
introduce a warping-guided conditional diffusion module. The module first
interpolates between the start and end frames of the editing interval to
generate smooth intermediate frames. Then, with the help of the audio-to-dense
motion images, these intermediate frames are warped to obtain coarse
intermediate frames. Conditioned on the warped intermedia frames, a diffusion
model is adopted to generate detailed and high-resolution target frames, which
guarantees coherent and identity-preserved transitions. The cascaded
conditional diffusion model decomposes the complex talking editing task into
two flexible generation tasks, which provides a generalizable talking-face
representation, seamless audio-visual transitions, and identity-preserved faces
on a small dataset. Experiments show the effectiveness and superiority of the
proposed method.


  Using data collected by the fixed target Fermilab experiment FOCUS, we
present several first measurements for the semileptonic decay $D^0 \to
\bar{K}^0\pi^-\mu^+\nu$. Using a model that includes a $\bar{K}^0 \pi^-$ S-wave
component, we measure the form factor ratios to be r_v= 1.706+-0.677+-0.342 and
r_2= 0.912+-0.370+-0.104 and the S-wave amplitude to be A=0.347+-0.222+-0.053
GeV^-1. Finally, we measure the vector semileptonic branching ratio
$\frac{\Gamma(D^0 \to K^{*}(892){-}\mu^+\nu)}{\Gamma(D^0 \to
\bar{K}^0\pi^-\pi^+)}= 0.337+-0.034+-0.013.


  A graph is cubical if it is a subgraph of a hypercube. For a cubical graph
$H$ and a hypercube $Q_n$, $ex(Q_n, H)$ is the largest number of edges in an
$H$-free subgraph of $Q_n$. If $ex(Q_n, H)$ is at least a positive proportion
of the number of edges in $Q_n$, $H$ is said to have a positive Tur\'an density
in a hypercube or simply a positive Tur\'an density; otherwise it has a zero
Tur\'an density. Determining $ex(Q_n, H)$ and even identifying whether $H$ has
a positive or a zero Tur\'an density remains a widely open question for general
$H$. By relating extremal numbers in a hypercube and certain corresponding
hypergraphs, Conlon found a large class of cubical graphs, ones having
so-called partite representation, that have a zero Tur\'an density. He raised a
question whether this gives a characterisation, i.e., whether a cubical graph
has zero Tur\'an density if and only if it has partite representation. Here, we
show that, as suspected by Conlon, this is not the case. We give an example of
a class of cubical graphs which have no partite representation, but on the
other hand, have a zero Tur\'an density. In addition, we show that any graph
whose every block has partite representation has a zero Tur\'an density in a
hypercube.


  We study shedding in the setting of data linkage dynamics, a simple model of
computation that bears on the use of dynamic data structures in programming.
Shedding is complementary to garbage collection. With shedding, each time a
link to a data object is updated by a program, it is determined whether or not
the link will possibly be used once again by the program, and if not the link
is automatically removed. Thus, everything is made garbage as soon as it can be
viewed as garbage. By that, the effectiveness of garbage collection becomes
maximal.


  In this paper, using the Bondi coordinates, we discuss the angular momentum
at null infinity in five dimensions and address the Poincare covariance of the
Bondi mass and angular momentum. We also show the angular momentum loss/gain
law due to gravitational waves. In four dimensions, the angular momentum at
null infinity has the supertranslational ambiguity and then it is known that we
cannot construct well-defined angular momentum there. On the other hand, we
would stress that we can define angular momentum at null infinity without any
ambiguity in higher dimensions. This is because of the non-existence of
supertranslations in higher dimensions.


  We construct a natural generalized complex structure on the total space of
any bundle endowed with a Chern connection and whose typical fibre is a
homogeneous symplectic manifold. This extends known constructions of
generalized complex structures on Lie groups and leads to natural examples of
holomorphic maps between generalized complex manifolds.


  We report results from a fast, efficient, and first-principles full-potential
N$^{th}$-order muffin-tin orbital (FP-NMTO) method combined with van
Leeuwen-Baerends correction to local density exchange-correlation potential. We
show that more complete and compact basis set is critical in improving the
electronic and structural properties. We exemplify the self-consistent FP-NMTO
calculations on group IV and III-V semiconductors. Notably, predicted bandgaps,
lattice constants, and bulk moduli are in good agreement with experiments
(e.g., we find for Ge $0.86~e$V, $5.57$~\AA, $75$~GPa vs. measured $0.74~e$V,
$5.66$~\AA, $77.2$~GPa). We also showcase its application to the electronic
properties of 2-dimensional $h-$BN and $h-$SiC, again finding good agreement
with experiments.


  Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive
investigation of whole-brain connectivity, which can potentially help to reveal
the brain's global network architecture and abnormalities involved in
neurological and mental disorders. However, the reliability of connection
inferences from dMRI-based fiber tracking is still debated, due to low
sensitivity, dominance of false positives, and inaccurate and incomplete
reconstruction of long-range connections. Furthermore, parameters of tracking
algorithms are typically tuned in a heuristic way, which leaves room for
manipulation of an intended result. Here we propose a data-driven framework to
optimize and validate parameters of dMRI-based fiber-tracking algorithms using
neural tracer data as a reference. Japan's Brain/MINDS Project provides
invaluable datasets containing both dMRI and neural tracer data from the same
primates. We considered four criteria for goodness of fiber tracking:
distance-weighted coverage, true/false positive ratio, projection coincidence,
and commissural passage, applied using a multi-objective optimization
algorithm. We implemented a variant of non-dominated sorting genetic algorithm
II (NSGA-II) to optimize five major parameters of a global fiber-tracking
algorithm over multiple brain samples in parallel. Using optimized parameters
compared to the default parameters, dMRI-based fiber tracking performance was
significantly improved, while minimizing false positives and impossible
cross-hemisphere connections. Parameters optimized for 10 tracer injection
sites showed good generalization capability for other brain samples. These
results demonstrate the importance of data-driven adjustment of fiber-tracking
algorithms and support the validity of dMRI-based tractography, if appropriate
adjustments are employed.


  In graph theory, knowing the number of complete subgraphs with r vertices
that a graph g has, limits the number of its complete subgraphs with s
vertices, for s > r. A useful upper bound is provided by the Kruskal-Katona
theorem, but this bound is often not tight. In this note, we add to the known
cases where this bound is tight and begin an investigation of tight bounded
graph sequences.


  Based on dynamical mean-field theory with a continuous-time quantum
Monte-Carlo impurity solver, static as well as dynamic spin and charge
susceptibilites for the phase diagram of the sodium cobaltate system
Na$_x$CoO$_2$ are discussed. The approach includes important vertex
contributions to the q-dependent two-particle response functions by means of a
local approximation to the irreducible vertex function in the particle-hole
channel. A single-band Hubbard model suffices to reveal several charge- and
spin-instability tendencies in accordance with experiment, including the
stabilization of an effective kagome sublattice close to x=0.67, without
invoking the doping-dependent Na-potential landscape. The in-plane
antiferromagnetic-to-ferromagnetic crossover is additionally verified by means
of the computed Korringa ratio. Moreover an intricate high-energy mode in the
transverse spin susceptiblity is revealed, pointing towards a strong energy
dependence of the effective intersite exchange.


  We study the effect of fractal initial conditions in closed reactive systems
in the cases of both mobile and immobile reactants. For the reaction $A+A\to
A$, in the absence of diffusion, the mean number of particles $A$ is shown to
decay exponentially to a steady state which depends on the details of the
initial conditions. The nature of this dependence is demonstrated both
analytically and numerically. In contrast, when diffusion is incorporated, it
is shown that the mean number of particles $<N(t)>$ decays asymptotically as
$t^{-d_f/2}$, the memory of the initial conditions being now carried by the
dynamical power law exponent. The latter is fully determined by the fractal
dimension $d_f$ of the initial conditions.


  In this paper, we aim to learn a semantic radiance field from multiple scenes
that is accurate, efficient and generalizable. While most existing NeRFs target
at the tasks of neural scene rendering, image synthesis and multi-view
reconstruction, there are a few attempts such as Semantic-NeRF that explore to
learn high-level semantic understanding with the NeRF structure. However,
Semantic-NeRF simultaneously learns color and semantic label from a single ray
with multiple heads, where the single ray fails to provide rich semantic
information. As a result, Semantic NeRF relies on positional encoding and needs
to train one specific model for each scene. To address this, we propose
Semantic Ray (S-Ray) to fully exploit semantic information along the ray
direction from its multi-view reprojections. As directly performing dense
attention over multi-view reprojected rays would suffer from heavy
computational cost, we design a Cross-Reprojection Attention module with
consecutive intra-view radial and cross-view sparse attentions, which
decomposes contextual information along reprojected rays and cross multiple
views and then collects dense connections by stacking the modules. Experiments
show that our S-Ray is able to learn from multiple scenes, and it presents
strong generalization ability to adapt to unseen scenes.


  Computational Law has begun taking the role in society which has been
predicted for some time. Automated decision-making and systems which assist
users are now used in various jurisdictions, but with this maturity come
certain caveats. Computational Law exists on the platforms which enable it, in
this case digital systems, which means that it inherits the same flaws.
Cybersecurity addresses these potential weaknesses. In this paper we go through
known issues and discuss them in the various levels, from design to the
physical realm. We also look at machine-learning specific adversarial problems.
Additionally, we make certain considerations regarding computational law and
existing and future legislation. Finally, we present three recommendations
which are necessary for computational law to function globally, and which
follow ideas in safety and security engineering. As indicated, we find that
computational law must seriously consider that not only does it face the same
risks as other types of software and computer systems, but that failures within
it may cause financial or physical damage, as well as injustice. Consequences
of Computational Legal systems failing are greater than if they were merely
software and hardware. If the system employs machine-learning, it must take
note of the very specific dangers which this brings, of which data poisoning is
the classic example. Computational law must also be explicitly legislated for,
which we show is not the case currently in the EU, and this is also true for
the cybersecurity aspects that will be relevant to it. But there is great hope
in EU's proposed AI Act, which makes an important attempt at taking the
specific problems which Computational Law bring into the legal sphere. Our
recommendations for Computational Law and Cybersecurity are: Accommodation of
threats, adequate use, and that humans remain in the centre of their
deployment.


  We present a powerful method to generate various equations which possess the
Lax representations on noncommutative (1+1) and (1+2)-dimensional spaces. The
generated equations contain noncommutative integrable equations obtained by
using the bicomplex method and by reductions of the noncommutative
(anti-)self-dual Yang-Mills equation. This suggests that the noncommutative Lax
equations would be integrable and be derived from reductions of the
noncommutative (anti-)self-dual Yang-Mills equation, which implies the
noncommutative version of Richard Ward conjecture. The integrability and the
relation to string theories are also discussed.


  We discuss some of the problems that may occur in the calculation of
complicated Feynman diagrams. These include the group independent evaluation of
color factors, and the summation techniques that are needed for the expansion
of diagrams into their Mellin moments.


  The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented.


  In this paper, we introduce a new method for testing the stationarity of time
series, where the test statistic is obtained from measuring and maximising the
difference in the second-order structure over pairs of randomly drawn
intervals. The asymptotic normality of the test statistic is established for
both Gaussian and a range of non-Gaussian time series, and a bootstrap
procedure is proposed for estimating the variance of the main statistics.
Further, we show the consistency of our test under local alternatives. Due to
the flexibility inherent in the random, unsystematic sub-samples used for test
statistic construction, the proposed method is able to identify the intervals
of significant departure from the stationarity without any dyadic constraints,
which is an advantage over other tests employing systematic designs. We
demonstrate its good finite sample performance on both simulated and real data,
particularly in detecting localised departure from the stationarity.


  We show that the blazar PKS 1424+240, which has been recently associated by
IceCube with a neutrino excess at the $3.3\,\sigma$ level together with three
other sources, is similar to the first plausible non-stellar neutrino source,
TXS 0506+056, in being also a masquerading BL Lac object, i.e., intrinsically a
flat-spectrum radio quasar with hidden broad lines and a standard accretion
disk. We point out that these two sources share other properties, including
spectral energy distribution, high powers, parsec scale properties, and
possibly radio morphology. We speculate that the relatively rare combination of
proton-loaded jets, possibly typical of high-excitation sources, and efficient
particle acceleration processes, related to their relatively high synchrotron
peak frequencies, might favour neutrino production in these two sources. GB6
J1542+6129, which has also recently appeared twice in a list of IceCube
associations, seems also to belong to this rare blazar sub-class, which
includes at most ~ 20 Fermi-4LAC blazars.


  Collective dynamics of many particle systems is tightly linked to their
underlying symmetry and phase transitions. Higgs and Nambu-Goldstone modes are,
respectively, collective amplitude and phase modes of the order parameter that
are widely observed in various physical systems at different energy scales,
ranging from magnets, superfluids, superconductors to our universe. The Higgs
mode is a massive excitation, which is a condensed-matter analog of Higgs
particle in high-energy physics, while the Nambu-Goldstone mode is a massless
excitation that appears when a continuous symmetry is spontaneously broken.
They provide important information on the fundamental aspects of many particle
systems, such as symmetry, phases, dynamics, response to external fields, and
so on. In this article, we review the physics of Higgs and Nambu-Goldstone
modes in condensed matter physics. Especially, we focus on the development on
the study of collective modes in superconductors and cold-atom systems.


  We study non-interacting fermionic systems dissipatively driven at their
boundaries, focusing in particular on the case of a non-number-conserving
Hamiltonian, which for example describes an $XY$ spin chain. We show that
despite the lack of number conservation, it is possible to convert the problem
of calculating the normal modes of the master equations and their corresponding
rapidities, into diagonalizing simply an $L\times L$ tridiagonal bordered
$2-$Toeplitz matrix, where $L$ is the size of the system. Such structure of
matrix allows us to further reduce the problem into solving a scalar
trigonometric non-linear equation for which we also show, in the case of an
Ising chain, exact analytical explicit, and system size independent, solutions.


  The single-nucleon potential in hot nuclear matter is investigated in the
framework of the Brueckner theory by adopting the realistic Argonne V18 or
Nijmegen 93 two-body nucleon-nucleon interaction supplemented by a microscopic
three-body force. The rearrangement contribution to the single-particle
potential induced by the ground state correlations is calculated in terms of
the hole-line expansion of the mass operator and provides a significant
repulsive contribution in the low-momentum region around and below the Fermi
surface. Increasing temperature leads to a reduction of the effect, while
increasing density makes it become stronger. The three-body force suppresses
somewhat the ground state correlations due to its strong short-range repulsion,
increasing with density. Inclusion of the three-body force contribution results
in a quite different temperature dependence of the single-particle potential at
high enough densities as compared to that adopting the pure two-body force. The
effects of three-body force and ground state correlations on the nucleon
effective mass are also discussed.


  The adiabatic approximation in time-dependent density functional theory
(TDDFT) yields reliable excitation spectra with great efficiency in many cases,
but fundamentally fails for states of double excitation character. We discuss
how double-excitations are at the root of some of the most challenging problems
for TDDFT today. We then present new results for (i) the calculation of
autoionizing resonances in the helium atom, (ii) understanding the nature of
the double excitations appearing in the quadratic response function, and (iii)
retrieving double-excitations through a real-time semiclassical approach to
correlation in a model quantum dot.


  Some personal reminiscences are followed by a brief illustration of how
effective field theories are used in condensed matter physics. Examples include
Landau's Fermi liquid, sigma models with topological terms, Dirac fermions and
the Gross Neveu model.


  We discuss the physics of the Rabi-Hubbard model describing large arrays of
coupled cavities interacting with two level atoms via a Rabi non-linearity. We
show that the inclusion of counter-rotating terms in the light-matter
interaction, often neglected in theoretical descriptions based on
Jaynes-Cumming models, is crucial to stabilize finite-density quantum phases of
correlated photons with no need for an artificially engineered chemical
potential. We show that the physical properties of these phases and the quantum
phase transition occurring between them is remarkably different from those of
interacting bosonic massive quantum particles. The competition between photon
delocalization and Rabi non-linearity drives the system across a novel $Z_2$
parity symmetry-breaking quantum phase transition between two gapped phases, a
Rabi insulator and a delocalized super-radiant phase.


  Using a statistical model-based data generation, we develop an experimental
setup for the evaluation of neural networks (NNs). The setup helps to benchmark
a set of NNs vis-a-vis minimum-mean-square-error (MMSE) performance bounds.
This allows us to test the effects of training data size, data dimension, data
geometry, noise, and mismatch between training and testing conditions. In the
proposed setup, we use a Gaussian mixture distribution to generate data for
training and testing a set of competing NNs. Our experiments show the
importance of understanding the type and statistical conditions of data for
appropriate application and design of NNs


  In this paper, we prove that every graph with average degree at least $s+t+2$
has a vertex partition into two parts, such that one part has average degree at
least $s$, and the other part has average degree at least $t$. This solves a
conjecture of Cs\'{o}ka, Lo, Norin, Wu and Yepremyan.


  Let be $(X_t, t\geq 0)$ be a L\'evy process which is the sum of a Brownian
motion with drift and a compound Poisson process. We consider the first passage
time $\tau_x$ at a fixed level $x>0$ by $(X_t, t\geq 0)$ and $K_x:=
X_{\tau_x}-x$ the overshoot and $L_x:= x-X_{\tau_x^-}$ the undershoot. We first
study the regularity of the density of the first passage time. Secondly, we
calculate the joint law of $(\tau_x, K_x, L_x).$


  Human dialogues are scenario-based and appropriate responses generally relate
to the latent context knowledge entailed by the specific scenario. To enable
responses that are more meaningful and context-specific, we propose to improve
generative dialogue systems from the scenario perspective, where both dialogue
history and future conversation are taken into account to implicitly
reconstruct the scenario knowledge. More importantly, the conversation
scenarios are further internalized using imitation learning framework, where
the conventional dialogue model that has no access to future conversations is
effectively regularized by transferring the scenario knowledge contained in
hierarchical supervising signals from the scenario-based dialogue model, so
that the future conversation is not required in actual inference. Extensive
evaluations show that our approach significantly outperforms state-of-the-art
baselines on diversity and relevance, and expresses scenario-specific
knowledge.


  Networks with a given degree distribution may be very resilient to one type
of failure or attack but not to another. The goal of this work is to determine
network design guidelines which maximize the robustness of networks to both
random failure and intentional attack while keeping the cost of the network
(which we take to be the average number of links per node) constant. We find
optimal parameters for: (i) scale free networks having degree distributions
with a single power-law regime, (ii) networks having degree distributions with
two power-law regimes, and (iii) networks described by degree distributions
containing two peaks. Of these various kinds of distributions we find that the
optimal network design is one in which all but one of the nodes have the same
degree, $k_1$ (close to the average number of links per node), and one node is
of very large degree, $k_2 \sim N^{2/3}$, where $N$ is the number of nodes in
the network.


  The propagation of intense acoustic waves in a one-dimensional phononic
crystal is studied. The medium consists in a structured fluid, formed by a
periodic array of fluid layers with alternating linear acoustic properties and
quadratic nonlinearity coefficient. The spacing between layers is of the order
of the wavelength, therefore Bragg effects such as band-gaps appear. We show
that the interplay between strong dispersion and nonlinearity leads to new
scenarios of wave propagation. The classical waveform distortion process
typical of intense acoustic waves in homogeneous media can be strongly altered
when nonlinearly generated harmonics lie inside or close to band gaps. This
allows the possibility of engineer a medium in order to get a particular
waveform. Examples of this include the design of media with effective (e.g.
cubic) nonlinearities, or extremely linear media (where distortion can be
cancelled). The presented ideas open a way towards the control of acoustic wave
propagation in nonlinear regime.


  A brief review of the associated strangeness electromagnetic production is
presented. Very recent K+ Lambda photoproduction data on the proton from
threshold up to E=2.6 GeV are interpreted within a chiral constituent quark
formalism, which embodies all known nucleonic and hyperonic resonances. The
preliminary results of this work are reported here.


  This paper investigates the potential to detect tau neutrinos in the energy
range of 1-1000 PeV searching for very inclined showers with imaging Cherenkov
telescopes. A neutrino induced tau lepton escaping from the Earth may decay and
initiate an air shower which can be detected by a fluorescence or Cherenkov
telescope. We present here a study of the detection potential of Earth-skimming
neutrinos taking into account neutrino interactions in the Earth crust, local
matter distributions at various detector sites, the development of tau-induced
showers in air and the detection of Cherenkov photons with IACTs. We analyzed
simulated shower images on the camera focal plane and implemented generic
reconstruction chains based on Hillas parameters. We find that present IACTs
can distinguish air showers induced by tau neutrinos from the background of
hadronic showers in the PeV-EeV energy range. We present the neutrino trigger
efficiency obtained for a few configurations being considered for the
next-generation Cherenkov telescopes, i.e. the Cherenkov Telescope Array.
Finally, for a few representative neutrino spectra expected from astrophysical
sources, we compare the expected event rates at running IACTs to what is
expected for the dedicated IceCube neutrino telescope.


  Generation of even-order optical harmonics requires noncentrosymmetric
structures being conventionally observed in crystals lacking the center of
inversion. In centrosymmetric systems, even-order harmonics may arise, e.g., at
surfaces but such effects are usually very weak. Here we observe optical
harmonics up to 4-th order generated under the normal incidence from
centrosymmetric dielectric metasurfaces empowered by resonances. We design
silicon metasurfaces supporting optical quasibound states in the continuum and
guided-mode resonances, and demonstrate the enhancement of second-harmonic
signals by over three orders of magnitude compared to nonresonant thin films.
Under the optimal conditions, the brightness of the second harmonic approaches
that of the third harmonic, and the 4th-order harmonic becomes detectable.


  Millions of people use online e-commerce platforms to search and buy
products. Identifying attributes in a query is a critical component in
connecting users to relevant items. However, in many cases, the queries have
multiple attributes, and some of them will be in conflict with each other. For
example, the query "maroon 5 dvds" has two candidate attributes, the color
"maroon" or the band "maroon 5", where only one of the attributes can be
present. In this paper, we address the problem of resolving conflicting
attributes in e-commerce queries. A challenge in this problem is that knowledge
bases like Wikipedia that are used to understand web queries are not focused on
the e-commerce domain. E-commerce search engines, however, have access to the
catalog which contains detailed information about the items and its attributes.
We propose a framework that constructs knowledge graphs from catalog to resolve
conflicting attributes in e-commerce queries. Our experiments on real-world
queries on e-commerce platforms demonstrate that resolving conflicting
attributes by leveraging catalog information significantly improves attribute
identification, and also gives out more relevant search results.


  Over the past few years, ionic conductors have gained a lot of attention
given the possibility to implement them in various applications such as
supercapacitors, batteries or fuel cells as well as for resistive memories.
Especially, layered two-dimensional (2D) crystals such as h-BN, graphene oxide
and MoSe2 have shown to provide unique properties originating from the specific
2D confinement of moving ions. Two important parameters are the ion
conductivity and the chemical stability over a wide range of operating
conditions. In this vein, Rb2Ti2O5 has been recently found displaying
remarkable properties such as superionic conduction and colossal equivalent
dielectric constant. Here, a first approach to the study of the electrical
properties of layered Rb2Ti2O5 at the 100-nanometer scale is presented.
Characterizations by means of micro-Raman spectroscopy and atomic force
microscope (AFM) measurements of mechanically exfoliated RTO nanocrystals via
the so-called adhesive-tape technique are reported. Finally, the results of
electrical measurements performed on an exfoliated RTO nanocrystals are
presented, and are found to be consistent with the results obtained on
macroscopic crystals. 4


  As part of a stellar population sampling program, a series of photometric
probes at various field sizes and depths have been obtained in a low extinction
window in the galactic anticentre direction. Very deep CCD frames probe the
most external parts of the disc, providing strong evidence that the galactic
density scale length for the old disc population is rather short (2.5 kpc) and
drops abruptly beyond 5.5-6 kpc. Deeper frames in the I band allow to estimate
photometric distances and confirm the position of the disc edge. A few stars
are found at larger distances. Their number is exactly what we expect if the
thick disc does not have any cutoff. We discuss the implications for the
formation and evolution of the disc, for the star formation threshold, and for
the origin of the thick disc population.


  In this work we introduce the concept of generalized exponential
$\mathfrak{D}$-pullback attractor for evolution processes, where $\mathfrak{D}$
is a universe of families in $X$, which is a compact and positively invariant
family that pullback attracts all elements of $\mathfrak{D}$ with an
exponential rate. Such concept was introduced in arXiv:2311.15630 for the
general case of decaying functions (which include the exponential decay), but
for fixed bounded sets rather than to universe of families. We prove a result
that ensures the existence of a generalized exponential
$\mathfrak{D}_{\mathcal{C}^\ast}$-pullback attractor for an evolution process,
where $\mathfrak{D}_{\mathcal{C}^\ast}$ is a specific universe. This required
an adaptation of the results of arXiv:2311.15630, which only covered the case
of a polynomial rate of attraction, for fixed bounded sets. Later, we prove
that a nonautonomous wave equation has a generalized exponential
$\mathfrak{D}_{\mathcal{C}^\ast}$-pullback attractor. This, in turn, also
implies the existence of the $\mathfrak{D}_{\mathcal{C}^\ast}$-pullback
attractor for such problem.


  Fractional Ornstein-Uhlenbeck process of the second kind $(\text{fOU}_{2})$
is solution of the Langevin equation $\mathrm{d}X_t = -\theta
X_t\,\mathrm{d}t+\mathrm{d}Y_t^{(1)}, \ \theta >0$ with driving noise $
Y_t^{(1)} := \int^t_0 e^{-s} \,\mathrm{d}B_{a_s}; \ a_t= H e^{\frac{t}{H}}$
where $B$ is a fractional Brownian motion with Hurst parameter $H \in (0,1)$.
In this article, in the case $H>1/2$, we prove that the least squares estimator
$\hat{\theta}_T$ introduced in [\cite{h-n}, Statist. Probab. Lett. 80, no.
11-12, 1030-1038], provides a consistent estimator. Moreover, using central
limit theorem for multiple Wiener integrals, we prove asymptotic normality of
the estimator valid for the whole range $H \in(1/2,1)$.


  A beacon is a point-like object which can be enabled to exert a magnetic pull
on other point-like objects in space. Those objects then move towards the
beacon in a greedy fashion until they are either stuck at an obstacle or reach
the beacon's location. Beacons placed inside polyhedra can be used to route
point-like objects from one location to another. A second use case is to cover
a polyhedron such that every point-like object at an arbitrary location in the
polyhedron can reach at least one of the beacons once the latter is activated.
  The notion of beacon-based routing and guarding was introduced by Biro et al.
[FWCG'11] in 2011 and covered in detail by Biro in his PhD thesis [SUNY-SB'13],
which focuses on the two-dimensional case.
  We extend Biro's result to three dimensions by considering beacon routing in
polyhedra. We show that $\lfloor\frac{m+1}{3}\rfloor$ beacons are always
sufficient and sometimes necessary to route between any pair of points in a
given polyhedron $P$, where $m$ is the number of tetrahedra in a tetrahedral
decomposition of $P$. This is one of the first results that show that beacon
routing is also possible in three dimensions.


  In the sigma model approach, the $\beta$-function equations for non critical
strings contain a term which acts like a tree level cosmological constant,
$\Lambda$. We analyse the static, spherically symmetric solutions to these
equations in $d = 4$ space time and show that the curvature scalar seen by the
strings is singular if $\Lambda \ne 0$. This singularity is naked. Requiring
its absence in our universe imposes the constraint $| \Lambda | < 10^{- 120}$
in natural units. {}From another point of view, our analysis implies that low
energy $d = 4$ non critical strings lead to naked singularities.


  We prove that the proper amalgam of matroids $M_1$ and $M_2$ along their
common restriction $N$ exists if and only if the tropical fibre product of
Bergman fans ${B(M_1) \times_{B(N)} B(M_2)}$ is positive. We introduce tropical
correspondences between Bergman fans as tropical subcycles in their product,
similar to correspondences in algebraic geometry, and define a "graph
correspondence" of the map of lattices. We prove that graph construction is a
functor for the "covering" maps of lattices, exploiting a generalization of
Bergman fan which we call a "Flag fan".


  We consider the following problem: Given a nested sum expression, find a sum
representation such that the nested depth is minimal. We obtain a symbolic
summation framework that solves this problem for sums defined, e.g., over
hypergeometric, $q$-hypergeometric or mixed hypergeometric expressions.
Recently, our methods have found applications in quantum field theory.


  We illustrate the use of effective theory methods to describe resonant
unstable particles. We outline the necessary ingredients to describe $W$-pair
production close to threshold in $e^-e^+$ collisions.