  This review illustrates how Local Fermi Liquid (LFL) theories describe the
strongly correlated and coherent low-energy dynamics of quantum dot devices.
This approach consists in an effective elastic scattering theory, accounting
exactly for strong correlations. Here, we focus on the mesoscopic capacitor and
recent experiments achieving Coulomb-induced quantum state transfer. Extending
to out-of-equilibrium regimes, aiming at triggered single electron emission, we
illustrate how inelastic effects become crucial, requiring approaches beyond
LFLs, shedding new light on past experimental data, by showing clear
interaction effects in the dynamics of mesoscopic capacitors.


  Hubble Space Telescope Observations of Black Hole X-Ray Transients are
discussed in the context of the disk instability outburst model. We focus on
the multiwavelength campaign following GRO J1655-40 through the summer 1996
outburst.


  We aim at assessing what are the most dominant dust species or types,
including silicate and iron oxide grains present in the ISM, by using recent
observations of dust depletion of galaxies at various evolutionary stages. We
use the observed elemental abundances in dust of several metals (O, S, Si, Mg,
and Fe) in different environments, considering systems with different
metallicities and dust content, namely damped Lyman-{\alpha} absorbers (DLAs)
towards quasars and the Galaxy. We derive a possible dust composition by
computationally finding the statistically expected elemental abundances in dust
assuming a set of key dust species with the iron content as a free parameter.
Carbonaceous dust is not considered in the present study. Metallic iron (likely
in the form of inclusions in silicate grains) and iron oxides is an important
component of the mass composition of carbon-free dust. Iron oxides make up a
significant mass fraction (~1/4 in some cases) of the oxygen-bearing dust and
there are good reasons to believe that metallic iron constitutes a similar mass
fraction of dust. W\"ustite (FeO) could be a simple explanation for the
depletion of iron and oxygen because it is easily formed. There appears to be
no silicate species clearly dominating the silicate mass, but rather a mix of
iron-poor as well as iron-rich olivine and pyroxene. To what extent sulphur
depletion is due to sulfides remains unclear. In general, there seems to be
little evolution of the dust composition (not considering carbonaceous dust)
from low-metallicity systems to the Galaxy.


  We show that, up to plausible uncertainties in BR$(B_c\to \tau \nu)$, the
$R_2$ leptoquark can simultaneously explain the observation of anomalies in
$R_{K^{(*)}}$ and $R_{D^{(*)}}$ without requiring large couplings. The former
is achieved via a small coupling to first generation leptons which boosts the
decay rate $\Gamma(\bar B\to \bar K^{(*)}e^+e^-)$. Finally we motivate a
neutrino mass model that includes the $S_3$ leptoquark which can alleviate a
mild tension with the most conservative limits on BR$(B_c\to \tau \nu)$.


  Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with
widespread applications in iterative algorithms. Over the past decades,
research on SpMV optimization has made remarkable strides, giving rise to
various optimization contributions. However, the comprehensive and systematic
literature survey that introduces, analyzes, discusses, and summarizes the
advancements of SpMV in recent years is currently lacking. Aiming to fill this
gap, this paper compares existing techniques and analyzes their strengths and
weaknesses. We begin by highlighting two representative applications of SpMV,
then conduct an in-depth overview of the important techniques that optimize
SpMV on modern architectures, which we specifically classify as classic,
auto-tuning, machine learning, and mixed-precision-based optimization. We also
elaborate on the hardware-based architectures, including CPU, GPU, FPGA,
processing in Memory, heterogeneous, and distributed platforms. We present a
comprehensive experimental evaluation that compares the performance of
state-of-the-art SpMV implementations. Based on our findings, we identify
several challenges and point out future research directions. This survey is
intended to provide researchers with a comprehensive understanding of SpMV
optimization on modern architectures and provide guidance for future work.


  Turbulent flow evolution and energy cascades are significantly different in
two-dimensional (2D) and three-dimensional (3D) flows. Studies have
investigated these differences in obstacle-free turbulent flows, but solid
boundaries have an important impact on the cross-over between 3D to 2D
turbulence dynamics. In this work, we investigate the span effect on the
turbulence nature of flow past a circular cylinder at Re=10000. It is found
that even for highly anisotropic geometries, 3D small-scale structures detach
from the walls. Additionally, the natural large-scale rotation of the
K\'arm\'an vortices rapidly two-dimensionalises those structures if the span is
50% of the diameter or less. We show this is linked to the span being shorter
than the Mode B instability wavelength. The conflicting 3D small-scale
structures and 2D K\'arm\'an vortices result in 2D and 3D turbulence dynamics
which can coexist at certain locations of the wake depending on the domain
geometric anisotropy.


  The electronic structure of Pb$_{1-x}$Sn$_{x}$Te is studied by using the
relativistic Korringa-Kohn-Rostoker Green function method in the framework of
density functional theory. For all concentrations $x$, Pb$_{1-x}$Sn$_{x}$Te is
a direct semiconductor with a narrow band gap. In contrast to pure lead
telluride, tin telluride shows an inverted band characteristic close to the
Fermi energy. It will be shown that this particular property can be tuned,
first, by alloying PbTe and SnTe and, second, by applying hydrostatic pressure
or uniaxial strain. Furthermore, the magnitude of strain needed to switch
between the regular and inverted band gap can be tuned by the alloy
composition. Thus, there is range of potential usage of Pb$_{1-x}$Sn$_{x}$Te
for spintronic applications.


  Liu and collaborators recently proposed an elliptical accretion disk model
for tidal disruption events (TDEs). They showed that the accretion disks of
optical/UV TDEs are large and highly eccentric and suggested that the broad
optical emission lines with complex and diverse profiles originate in the cool
eccentric accretion disk of random inclination and orientation. In this paper,
we calculate the radiation efficiency of the elliptical accretion disk and
investigate the implications for the observations of TDEs. We compile
observational data for the peak bolometric luminosity and total radiation
energy after peak brightness of 18 TDE sources and compare these data to the
predictions from the elliptical accretion disk model. Our results show that the
observations are consistent with the theoretical predictions and that the
majority of the orbital energy of the stellar debris is advected into the black
hole (BH) without being converted into radiation. Furthermore, we derive the
masses of the disrupted stars and the masses of the BHs of the TDEs. The BH
masses obtained in this paper are also consistent with those calculated with
the $M_{\rm BH} - \sigma_*$ relation. Our results provide an effective method
for measuring the masses of BHs in large numbers of TDEs to be discovered in
ongoing and next-generation sky surveys, regardless of whether the BHs are
located at the centers of galactic nuclei or wander in disks and halos.


  Baryons containing two heavy quarks are treated in the Born-Oppenheimer
approximation. Schr\"odinger equation for two center Coulomb plus harmonic
oscillator potential is solved by the method of ethalon equation at large
intercenter separations. Asymptotical expansions for energy term and wave
function are obtained in the analytical form. Using those formulas, the energy
spectra of doubly heavy baryons with various quark compositions are calculated
analytically.


  Let H^2_m be the Drury-Arveson (DA) module which is the reproducing kernel
Hilbert space with the kernel function (z, w) \in B^m \times B^m \raro (1 -
<z,w>)^{-1}. We investigate for which multipliers \theta : \mathbb{B}^m \raro
\cll(\cle, \cle_*) the quotient module \clh_{\theta} is similar to H^2_m
\otimes \clf for some Hilbert space \clf, where M_{\theta} is the corresponding
multiplication operator in \cll(H^2_m \otimes \cle, H^2_m \otimes \cle_*) for
Hilbert spaces \cle and \cle_* and \clh_{\theta} is the quotient module (H^2_m
\otimes \cle_*)/ {clos} [M_{\theta}(H^2_m \otimes \cle)]. We show that a
necessary condition is the existence of a multiplier $\psi$ in \clm(\cle_*,
\cle) such that \theta \psi \theta = \theta. Moreover, we show that the
converse is equivalent to a structure theorem for complemented submodules of
H^2_m \otimes \cle for a Hilbert space \cle, which is valid for the case of
m=1. The latter result generalizes a known theorem on similarity to the
unilateral shift, but the above statement is new. Further, we show that a
finite resolution of DA-modules of arbitrary multiplicity using partially
isometric module maps must be trivial. Finally, we discuss the analogous
questions when the underlying operator tuple or algebra is not necessarily
commuting. In this case the converse to the similarity result is always valid.


We show the details of certain computations that are described in [BMW19].


  We calculate the corrections to the strange matrix elements of the nucleon
resulting from the breaking of rotational invariance on the light-cone. In the
meson cloud model, the strange magnetic moment mu_S is seen to change sign once
the spurious form factors arising from this violation are subtracted. The
resulting mu_S is small and slightly positive, in agreement with the trend of
the recent data from the SAMPLE experiment. The value of the strange magnetic
form factor is predicted to be largely Q^2 independent over the range
accessible in upcoming experiments.


  We study the offline data-driven sequential decision making problem in the
framework of Markov decision process (MDP). In order to enhance the
generalizability and adaptivity of the learned policy, we propose to evaluate
each policy by a set of the average rewards with respect to distributions
centered at the policy induced stationary distribution. Given a pre-collected
dataset of multiple trajectories generated by some behavior policy, our goal is
to learn a robust policy in a pre-specified policy class that can maximize the
smallest value of this set. Leveraging the theory of semi-parametric
statistics, we develop a statistically efficient policy learning method for
estimating the de ned robust optimal policy. A rate-optimal regret bound up to
a logarithmic factor is established in terms of total decision points in the
dataset.


  In nonrelativistic QCD (NRQCD), we study $e^+ e^- \to J/\psi(\psi(2S))
+\chi_{c0}$ at $B$ factories, where the P-wave state $\chi_{c0}$ is associated
with an S-wave state $J/\psi$ or $\psi(2S)$. In contrast to the failure of
factorization in most cases involving P-wave states, e.g. in B decays, we find
that factorization holds in this process at next to leading order (NLO) in
$\alpha_s$ and leading order (LO) in $v$, where the associated S-wave state
plays a crucial rule in canceling the infrared (IR) divergences. We also give
some general analyses for factorization in various double charmonium
production. The NLO corrections in $e^+ e^- \to J/\psi(\psi(2S)) +\chi_{c0}$ at
$\sqrt{s}=10.6$ GeV are found to substantially enhance the cross sections by a
factor of about 2.8; hence crucially reduce the large discrepancy between
theory and experiment. With $m_c=1.5{\rm GeV}$ and $\mu=2m_c$, the NLO cross
sections are estimated to be $17.9(11.3)$ fb for $e^+ e^- \to J/\psi(\psi(2S))+
\chi_{c0}$, which reach the lower bounds of experiment.


  Coherent emission of light by free charged particles is ubiquitous in many
areas of physics and engineering, with the light's properties believed to be
successfully captured by classical electromagnetism in all relevant
experimental settings. The advent of interactions between light and free
quantum matter waves brought about fundamental questions regarding the role of
the particle wavefunction. Here we show that even in seemingly classical
experimental regimes, light emission is fundamentally tied to quantum
properties of the emitting particles, such as their quantum coherence and
correlations. By employing quantum electrodynamics, we unveil the role of the
particle's coherent momentum uncertainty, without which decoherence of light
becomes dominant. As an example, we consider Cherenkov radiation, envisioned
for almost a century as a shockwave of light. We find instead that the
shockwave's duration is fundamentally bound from below by the particle's
coherent momentum uncertainty due to the underlying entanglement between the
particle and light. This quantum optical paradigm opens new capabilities in
analytical electron microscopy, enabling the measurement of quantum
correlations of shaped electron wavepackets. Our findings also have
applications for Cherenkov detectors in particle physics. For example, by
measuring spectral photon autocorrelations, one can unveil the particle's
wavefunction size, shape and coherence. Such schemes are especially intriguing
for many high-energy particles, where other techniques are not available.


  Class imbalance poses new challenges when it comes to classifying data
streams. Many algorithms recently proposed in the literature tackle this
problem using a variety of data-level, algorithm-level, and ensemble
approaches. However, there is a lack of standardized and agreed-upon procedures
and benchmarks on how to evaluate these algorithms. This work proposes a
standardized, exhaustive, and comprehensive experimental framework to evaluate
algorithms in a collection of diverse and challenging imbalanced data stream
scenarios. The experimental study evaluates 24 state-of-the-art data streams
algorithms on 515 imbalanced data streams that combine static and dynamic class
imbalance ratios, instance-level difficulties, concept drift, real-world and
semi-synthetic datasets in binary and multi-class scenarios. This leads to a
large-scale experimental study comparing state-of-the-art classifiers in the
data stream mining domain. We discuss the advantages and disadvantages of
state-of-the-art classifiers in each of these scenarios and we provide general
recommendations to end-users for selecting the best algorithms for imbalanced
data streams. Additionally, we formulate open challenges and future directions
for this domain. Our experimental framework is fully reproducible and easy to
extend with new methods. This way, we propose a standardized approach to
conducting experiments in imbalanced data streams that can be used by other
researchers to create complete, trustworthy, and fair evaluation of newly
proposed methods. Our experimental framework can be downloaded from
https://github.com/canoalberto/imbalanced-streams.


  Many developmental processes in biology utilize Notch-Delta signaling to
construct an ordered pattern of cellular differentiation. This signaling
modality is based on nearest-neighbor contact, as opposed to the more familiar
mechanism driven by the release of diffusible ligands. Here, exploiting this
"juxtacrine" property, we present an exact treatment of the pattern formation
problem via a system of nine coupled ordinary differential equations. The
possible patterns that are realized for realistic parameters can be analyzed by
considering a co-dimension 2 pitchfork bifurcation of this system. This
analysis explains the observed prevalence of hexagonal patterns with high Delta
at their center, as opposed to those with central high Notch levels. We show
that outside this range of parameters, in particular for low cis-coupling, a
novel kind of pattern is produced, where high Delta cells have high Notch as
well. It also suggests that the biological system is only weakly first order,
so that an additional mechanism is required to generate the observed
defect-free patterns. We construct a simple strategy for producing such
defect-free patterns.


  Surface charges play a fundamental role in physics and chemistry,
particularly in shaping the catalytic properties of nanomaterials. Tracking
nanoscale surface charge dynamics remains challenging due to the involved
length and time scales. Here, we demonstrate real-time access to the nanoscale
charge dynamics on dielectric nanoparticles employing reaction nanoscopy. We
present a four-dimensional visualization of the non-linear charge dynamics on
strong-field irradiated single SiO$_2$ nanoparticles with femtosecond-nanometer
resolution and reveal how surface charges affect surface molecular bonding with
quantum dynamical simulations. We performed semi-classical simulations to
uncover the roles of diffusion and charge loss in the surface charge
redistribution process. Understanding nanoscale surface charge dynamics and its
influence on chemical bonding on a single nanoparticle level unlocks an
increased ability to address global needs in renewable energy and advanced
healthcare.


  Negative dimensional integration method (NDIM) is a technique to deal with
D-dimensional Feynman loop integrals. Since most of the physical quantities in
perturbative Quantum Field Theory (pQFT) require the ability of solving them,
the quicker and easier the method to evaluate them the better. The NDIM is a
novel and promising technique, ipso facto requiring that we put it to test in
different contexts and situations and compare the results it yields with those
that we already know by other well-established methods. It is in this
perspective that we consider here the calculation of an on-shell two-loop three
point function in a massless theory. Surprisingly this approach provides twelve
non-trivial results in terms of double power series. More astonishing than this
is the fact that we can show these twelve solutions to be different
representations for the same well-known single result obtained via other
methods. It really comes to us as a surprise that the solution for the
particular integral we are dealing with is twelvefold degenerate.


  We construct a metrizable semitopological semilattice $X$ whose partial order
$P=\{(x,y)\in X\times X:xy=x\}$ is a non-closed dense subset of $X\times X$. As
a by-product we find necessary and sufficient conditions for the existence of a
(metrizable) Hausdorff topology on a set, act, semigroup or semilattice, having
a prescribed countable family of convergent sequences.


  We study the critical behavior of the Ising model in annealed scale-free (SF)
networks of finite system size with forced upper cutoff in degree. By mapping
the model onto the weighted fully connected Ising model, we derive analytic
results for the finite-size scaling (FSS) near the phase transition,
characterized by the cutoff-dependent two-parameter scaling with four distinct
scaling regimes, in highly heterogeneous networks. These results are
essentially the same as those found for the nonequilibrium contact process in
annealed SF networks, except for an additional complication due to the trivial
critical point shift in finite systems. The discrepancy of the FSS theories
between annealed and quenched SF networks still remains in the equilibrium
Ising model, like some other nonequilibrium models. All of our analytic results
are confirmed reasonably well by numerical simulations.


  We present the results of experimental investigations of magnetic switching
and magnetotransport in a new generation of magnetic devices containing
artificially patterned domains. Our devices are realised by locally reducing
the coercive field of a perpendicularly magnetised Pt (3.5 nm)/Co (0.5 nm)/Pt
(1.6 nm) trilayer structure using a gallium focused ion beam (FIB). Artificial
domain walls are created at the interfaces between dosed and undosed regions
when an external magnetic field switches the former but not the latter. We have
exploited this property to create stripe-like domains with widths down to
sub-micron lengthscales, separated by undosed regions. Using the extraordinary
Hall effect to monitor the local magnetisation we have investigated the
reversal dynamics of these artificial domains by measuring major and minor
hysteresis loops. The coercive field of regions irradiated with identical doses
systematically increases as their size decreases. In the lower branch of minor
loops, reversal is seen to occur via a few large Barkhausen events. Preliminary
measurements of transport across domain walls reveal a positive domain wall
resistance, that does not change sign from 4.2 K to 300 K.


  In this essay, a general case of information systems contains quantum
information systems is considered. By presenting an algorithmic method a new
kind of information topology is defined and considered. Continuous maps between
two information topological spaces are studied. Moreover, open and compact
information systems are taken into consideration. It is also proved that a
finite product of compact information systems is a compact information system.
Following that, two methods for constructing new open covers for a class of
compact information systems are presented, and information topological entropy
for continuous self maps of an information topological space is considered. We
show that information topological entropy is an invariant object under a
conjugate relation. Finally, as an applied example, a mathematical model for
knowledge spread is introduced.


  The competition between drift wave and interchange physics in general
E-cross-B drift turbulence is studied with computations in three dimensional
tokamak flux tube geometry. For a given set of background scales, the parameter
space can be covered by the plasma beta and drift wave collisionality. At large
enough plasma beta the turbulence breaks out into ideal ballooning modes and
saturates only by depleting the free energy in the background pressure
gradient. At high collisionality it finds a more gradual transition to
resistive ballooning. At moderate beta and collisionality it retains drift wave
character, qualitatively identical to simple two dimensional slab models. The
underlying cause is the nonlinear vorticity advection through which the self
sustained drift wave turbulence supersedes the linear instabilities, scattering
them apart before they can grow, imposing its own physical character on the
dynamics. This vorticity advection catalyses the gradient drive, while
saturation occurs solely through turbulent mixing of pressure disturbances.
This situation persists in the whole of tokamak edge parameter space. Both
simplified isothermal models and complete warm ion models are treated.


  Being able to recognize emotions in human users is considered a highly
desirable trait in Human-Robot Interaction (HRI) scenarios. However, most
contemporary approaches rarely attempt to apply recognized emotional features
in an active manner to modulate robot decision-making and dialogue for the
benefit of the user. In this position paper, we propose a method of
incorporating recognized emotions into a Reinforcement Learning (RL) based
dialogue management module that adapts its dialogue responses in order to
attempt to make cognitive training tasks, like the 2048 Puzzle Game, more
enjoyable for the users.


  The IRAS source 03256+3055 in the NGC 1333 star forming region is associated
with extended sub-millimeter emission of complex morphology, showing multiple
clumps. One of these is found to coincide with the driving source of a bipolar
jet of S-shaped morphology seen in the emission lines of H_alpha and [SII] as
well as in the H2 emission lines in the K-band. Detailed images of the driving
source at the wavelengths of H_alpha and [SII] and in the I, J, H, and K bands
as well as a K-band spectrum and polarimetry are discussed. The near-infrared
morphology is characterized by a combination of line emission from the jet and
scattered light from a source with a steep continuum spectrum. The morphology
and proper motion of the jet are discussed in the context of a binary system
with a precessing disk. We conclude that the molecular core associated with
IRAS 03256+3055 consists of several clumps, only one of which shows evidence of
recent star formation at optical and near-infrared wavelengths.We also briefly
discuss a second, newly found near-infrared source associated with a compact
sub-millimeter continuum source near IRAS 03256+3055, and conclude that this
source may be physically unrelated the cluster of molecular clumps.


  Polynomial generalizations of all 130 of the identities in Slater's list of
identities of the Rogers-Ramanujan type are presented. Furthermore, duality
relationships among many of the identities are derived. Some of the these
polynomial identities were previously known but many are new. The author has
implemented much of the finitization process in a Maple package which is
available for free download from the author's website.


  In this work we propose a method based on geometric deep learning to predict
the complete surface of the liver, given a partial point cloud of the organ
obtained during the surgical laparoscopic procedure. We introduce a new data
augmentation technique that randomly perturbs shapes in their frequency domain
to compensate the limited size of our dataset. The core of our method is a
variational autoencoder (VAE) that is trained to learn a latent space for
complete shapes of the liver. At inference time, the generative part of the
model is embedded in an optimisation procedure where the latent representation
is iteratively updated to generate a model that matches the intraoperative
partial point cloud. The effect of this optimisation is a progressive non-rigid
deformation of the initially generated shape. Our method is qualitatively
evaluated on real data and quantitatively evaluated on synthetic data. We
compared with a state-of-the-art rigid registration algorithm, that our method
outperformed in visible areas.


  In this paper, an incremental selective decode-and-forward (ISDF) relay
strategy is proposed for power line communication (PLC) systems to improve the
spectral efficiency. Traditional decode-and-forward (DF) relaying employs two
time slots by using half-duplex relays which significantly reduces the spectral
efficiency. The ISDF strategy utilizes the relay only if the direct link
quality fails to attain a certain information rate, thereby improving the
spectral efficiency. The path gain is assumed to be log-normally distributed
with very high distance dependent signal attenuation. Furthermore, the additive
noise is modeled as a Bernoulli-Gaussian process to incorporate the effects of
impulsive noise contents. Closed-form expressions for the outage probability
and the fraction of times the relay is in use, and an approximate closed-form
expression for the average bit error rate (BER) are derived for the binary
phase-shift keying signaling scheme. We observe that the fraction of times the
relay is in use can be significantly reduced compared to the traditional DF
strategy. It is also observed that at high transmit power, the spectral
efficiency increases while the average BER decreases with increase in the
required rate.


  A distinguishing feature of certain intractable problems in prime number
theory is the sparsity of the underlying sequence. Motivated by the general
problem of finding primes in sparse polynomial sequences, we give an estimate
for the number of primes of the shape $x^3 + 2y^3$ where $y$ is small.


  Preference orderings are orderings of a set of items according to the
preferences (of judges). Such orderings arise in a variety of domains,
including group decision making, consumer marketing, voting and machine
learning. Measuring the mutual information and extracting the common patterns
in a set of preference orderings are key to these areas. In this paper we deal
with the representation of sets of preference orderings, the quantification of
the degree to which judges agree on their ordering of the items (i.e. the
concordance), and the efficient, meaningful description of such sets.
  We propose to represent the orderings in a subsequence-based feature space
and present a new algorithm to calculate the size of the set of all common
subsequences - the basis of a quantification of concordance, not only for pairs
of orderings but also for sets of orderings. The new algorithm is fast and
storage efficient with a time complexity of only $O(Nn^2)$ for the orderings of
$n$ items by $N$ judges and a space complexity of only $O(\min\{Nn,n^2\})$.
  Also, we propose to represent the set of all $N$ orderings through a smallest
set of covering preferences and present an algorithm to construct this smallest
covering set.
  The source code for the algorithms is available at
https://github.com/zhiweiuu/secs


  The Dirac-Born-Infeld (DBI) action of the 3-dimensional brane for its
dynamical embeddings and the gauge fields has been studied. The evolution of
both the D3-brane and the ambient space has been obtained. For the special
constraint put on the transverse coordinates a family of the contracting and
expanding spaces has been found.


  Manipulation of magnetization by electric field is a central goal of
spintronics because it enables energy-efficient operation of spin-based
devices. Spin wave devices are promising candidates for low-power information
processing but a method for energy-efficient excitation of short-wavelength
spin waves has been lacking. Here we show that spin waves in nanoscale magnetic
tunnel junctions can be generated via parametric resonance induced by electric
field. Parametric excitation of magnetization is a versatile method of
short-wavelength spin wave generation, and thus our results pave the way
towards energy-efficient nanomagnonic devices.


  In this paper we consider the trace regression model where $n$ entries or
linear combinations of entries of an unknown $m_1\times m_2$ matrix $A_0$
corrupted by noise are observed. We establish for the nuclear-norm penalized
estimator of $A_0$ introduced in \cite{KLT} a general sharp oracle inequality
with the spectral norm for arbitrary values of $n,m_1,m_2$ under an incoherence
condition on the sampling distribution $\Pi$ of the observed entries. Then, we
apply this method to the matrix completion problem. In this case, we prove that
it satisfies an optimal oracle inequality for the spectral norm, thus improving
upon the only existing result \cite{KLT} concerning the spectral norm, which
assumes that the sampling distribution is uniform. Note that our result is
valid, in particular, in the high-dimensional setting $m_1m_2\gg n$. Finally we
show that the obtained rate is optimal up to logarithmic factors in a minimax
sense.


  We investigate the spin structure of the nucleon in an extended Jaffe-Lipkin
quark model. In addition to the conventional $3q$ structure, different
$(3q)(Q\bar{Q})$ admixtures in the nucleon wavefunction are also taken into
account. The contributions to the nucleon spin from various components of the
nucleon wavefunction are discussed. The effect due to the Melosh-Wigner
rotation is also studied.


  We propose methods that enable efficient hierarchical classification in
parallel. Our methods transform a batch of classification scores and labels,
corresponding to given nodes in a semantic tree, to scores and labels
corresponding to all nodes in the ancestral paths going down the tree to every
given node, relying only on tensor operations that execute efficiently on
hardware accelerators. We implement our methods and test them on current
hardware accelerators with a tree incorporating all English-language synsets in
WordNet 3.0, spanning 117,659 classes in 20 levels of depth. We transform
batches of scores and labels to their respective ancestral paths, incurring
negligible computation and consuming only a fixed 0.04GB of memory over the
footprint of data.


  Angular momentum loss via the emission of gravitational waves must eventually
drive compact binaries containing black holes and/or neutron stars to coalesce.
The resulting events are primary candidate sources for detectors such as VIRGO
and LIGO. We present calculations of gravitational radiation waveforms and
luminosities for the coalescence of a black hole-neutron star binary, performed
in the quadrupole approximation using a Newtonian smooth particle hydrodynamics
code. We discuss the dependence of the waveforms and the total emitted
luminosity as well as the final configuration of the system on the initial mass
ratio and the degree of tidal locking.


  We investigate experimentally the dynamic phase transition of a
two-dimensional active nematic layer interfaced with a passive liquid crystal.
Under a temperature ramp that leads to the transition of the passive liquid
into a highly anisotropic lamellar smectic-A phase, and in the presence of a
magnetic field, the coupled active nematic reorganizes its flow and
orientational patterns from the turbulent into a quasi-laminar regime aligned
perpendicularly to the field. Remarkably, while the phase transition of the
passive fluid is known to be continuous, our observations reveal intermittent
dynamics of the order parameter and the coexistence of aligned and turbulent
regions in the active nematic, a signature of discontinuous, or first order,
phase transitions.


  We define temporal axioms that are sound and complete for the temporal
validities over $(\reals^2, <)$.


  The speed and scale at which machine learning (ML) systems are deployed are
accelerating even as an increasing number of studies highlight their potential
for negative impact. There is a clear need for companies and regulators to
manage the risk from proposed ML systems before they harm people. To achieve
this, private and public sector actors first need to identify the risks posed
by a proposed ML system. A system's overall risk is influenced by its direct
and indirect effects. However, existing frameworks for ML risk/impact
assessment often address an abstract notion of risk or do not concretize this
dependence.
  We propose to address this gap with a context-sensitive framework for
identifying ML system risks comprising two components: a taxonomy of the first-
and second-order risks posed by ML systems, and their contributing factors.
First-order risks stem from aspects of the ML system, while second-order risks
stem from the consequences of first-order risks. These consequences are system
failures that result from design and development choices. We explore how
different risks may manifest in various types of ML systems, the factors that
affect each risk, and how first-order risks may lead to second-order effects
when the system interacts with the real world.
  Throughout the paper, we show how real events and prior research fit into our
Machine Learning System Risk framework (MLSR). MLSR operates on ML systems
rather than technologies or domains, recognizing that a system's design,
implementation, and use case all contribute to its risk. In doing so, it
unifies the risks that are commonly discussed in the ethical AI community
(e.g., ethical/human rights risks) with system-level risks (e.g., application,
design, control risks), paving the way for holistic risk assessments of ML
systems.


  In this article we construct the parameter region where the existence of a
homoclinic orbit to a zero equilibrium state of saddle type in the Lorenz-like
system will be analytically proved in the case of a nonnegative saddle value.
Then, for a qualitative description of the different types of homoclinic
bifurcations, a numerical analysis of the detected parameter region is carried
out to discover several new interesting bifurcation scenarios.


  We present a theory of magnetooscillations around the $\nu =1/2$ Landau level
filling factor based on a model with a fluctuating Chern--Simons field. The
quasiclassical treatment of the problem is appropriate and leads to an
unconventional $\exp\left[-(\pi/\omega_c\tau^*_{1/2})^4\right]$ behavior of the
amplitude of oscillations. This result is in good qualitative agreement with
available experimental data.


  This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor
network, in which one UAV flies in the sky to collect the data transmitted from
a set of ground nodes (GNs) via distributed beamforming. We consider two
scenarios with delay-tolerant and delay-sensitive applications, in which the
GNs send the common/shared messages to the UAV via adaptive- and fixed-rate
transmissions, respectively. For the two scenarios, we aim to maximize the
average data-rate throughput and minimize the transmission outage probability,
respectively, by jointly optimizing the UAV's trajectory design and the GNs'
transmit power allocation over time, subject to the UAV's flight speed
constraints and the GNs' individual average power constraints. However, the two
formulated problems are both non-convex and thus generally difficult to be
optimally solved. To tackle this issue, we first consider the relaxed problems
in the ideal case with the UAV's flight speed constraints ignored, for which
the well-structured optimal solutions are obtained to reveal the fundamental
performance upper bounds. It is shown that for the two approximate problems,
the optimal trajectory solutions have the same multi-location-hovering
structure, but with different optimal power allocation strategies. Next, for
the general problems with the UAV's flight speed constraints considered, we
propose efficient algorithms to obtain high-quality solutions by using the
techniques from convex optimization and approximation. Finally, numerical
results show that our proposed designs significantly outperform other benchmark
schemes, in terms of the achieved data-rate throughput and outage probability
under the two scenarios. It is also observed that when the mission period
becomes sufficiently long, our proposed designs approach the performance upper
bounds when the UAV's flight speed constraints are ignored.


  In this work, we introduce the notion of Ball dentable property in Banach
spaces. We study certain stability results for the $w^*$-Ball dentable property
leading to a discussion on Ball dentability in the context of ideals of Banach
spaces. We prove that the $w^*$-Ball-dentable property can be lifted from an
$M$-ideal to the whole Banach Space. We also prove similar results for strict
ideals of a Banach space. We note that the space $C(K,X)^*$ has $w*$-Ball
dentable property when $K$ is dispersed and $X^\ast$ has the $w^*$-Ball
dentable property.


  We present the results of an experimental and numerical investigation into
temporally non-local coherent interactions between ultrashort pulses, mediated
by Raman coherence, in gas-filled kagom\'{e}-style hollow-core photonic crystal
fiber. A pump pulse first set up the Raman coherence, creating a refractive
index grating in the gas that travels at the group velocity of the pump pulse.
Varying the arrival time of a second probe pulse allows high degree of control
over its evolution as it propagates along the fiber, in particular soliton
self-compression and dispersive wave (DW) emission. In the experiments
reported, a DW is emitted at ~300 nm, with a central frequency that oscillates
with the pump-probe delay. The results demonstrate that strong Raman coherence
created in broadband guiding gas-filled kagom\'e-PCF can be used to control the
dynamics of ultrashort probe pulses, even in difficult-to-access spectral
regions such as the deep and vacuum ultraviolet.


  Learning the distribution of data on Riemannian manifolds is crucial for
modeling data from non-Euclidean space, which is required by many applications
in diverse scientific fields. Yet, existing generative models on manifolds
suffer from expensive divergence computation or rely on approximations of heat
kernel. These limitations restrict their applicability to simple geometries and
hinder scalability to high dimensions. In this work, we introduce the
Riemannian Diffusion Mixture, a principled framework for building a generative
diffusion process on manifolds. Instead of following the denoising approach of
previous diffusion models, we construct a diffusion process using a mixture of
bridge processes derived on general manifolds without requiring heat kernel
estimations. We develop a geometric understanding of the mixture process,
deriving the drift as a weighted mean of tangent directions to the data points
that guides the process toward the data distribution. We further propose a
scalable training objective for learning the mixture process that readily
applies to general manifolds. Our method achieves superior performance on
diverse manifolds with dramatically reduced number of in-training simulation
steps for general manifolds.


  In this article, we construct the multivariate fractal interpolation
functions for a given data points and explore the existence of $\alpha$-fractal
function corresponding to the multivariate continuous function defined on
$[0,1]\times \cdots \times [0,1](q\text{-times})$. The parameters are selected
such that the corresponding fractal version preserves some of the original
function's properties, for instance, if the given function is H\"older
continuous, then the corresponding $\alpha$-fractal function is also H\"older
continuous. Moreover, we explore the restriction of the $\alpha$-fractal
function on the co-ordinate axis. Furthermore, the box dimension and Hausdorff
dimension of the graph of the multivariate $\alpha$-fractal function and its
restriction are investigated. In the last section, we prove that the mixed
Riemann-Liouville fractional integral of fractal function satisfies a
self-referential equation.


  Finite element method is one of powerful numerical methods to solve PDE.
Usually, if a finite element solution to a Poisson equation based on a
triangulation of the underlying domain is not accurate enough, one will discard
the solution and then refine the triangulation uniformly and compute a new
finite element solution over the refined triangulation. It is wasteful to
discard the original finite element solution. We propose a prewavelet method to
save the original solution by adding a prewavelet subsolution to obtain the
refined level finite element solution. To increase the accuracy of numerical
solution to Poisson equations, we can keep adding prewavelet subsolutions.
  Our prewavelets are orthogonal in the $H^1$ norm and they are compactly
supported except for one globally supported basis function in a rectangular
domain. We have implemented these prewavelet basis functions in MATLAB and used
them for numerical solution of Poisson equation with Dirichlet boundary
conditions. Numerical simulation demonstrates that our prewavelet solution is
much more efficient than the standard finite element method.


  We propose a dissipative implementation of a variety of Kondo models by means
of strong two-body losses localized on a few impurity sites of a fermionic
lattice--a setup which is suited to experiments with ultracold atomic gases. We
study in detail the simplest scenario of just one dissipated site, showing that
it is effectively described by the Anderson impurity model with infinite
repulsion, perturbed by a small residual dissipation. We compute a number of
signatures of the Kondo effect in transport across the impurity, finding a
competition between the Kondo resonance and the residual dissipation. Our
dissipative setup can be generalized to two or more sites subject to
losses--realizing an impurity with spin 1 or higher--and more reservoirs,
opening up the possibility of simulating several kinds of Kondo-like models
with ultracold atoms.


  Vectorized high-definition map (HD-map) construction, which focuses on the
perception of centimeter-level environmental information, has attracted
significant research interest in the autonomous driving community. Most
existing approaches first obtain rasterized map with the segmentation-based
pipeline and then conduct heavy post-processing for downstream-friendly
vectorization. In this paper, by delving into parameterization-based methods,
we pioneer a concise and elegant scheme that adopts unified piecewise Bezier
curve. In order to vectorize changeful map elements end-to-end, we elaborate a
simple yet effective architecture, named Piecewise Bezier HD-map Network
(BeMapNet), which is formulated as a direct set prediction paradigm and
postprocessing-free. Concretely, we first introduce a novel IPM-PE Align module
to inject 3D geometry prior into BEV features through common position encoding
in Transformer. Then a well-designed Piecewise Bezier Head is proposed to
output the details of each map element, including the coordinate of control
points and the segment number of curves. In addition, based on the
progressively restoration of Bezier curve, we also present an efficient
Point-Curve-Region Loss for supervising more robust and precise HD-map
modeling. Extensive comparisons show that our method is remarkably superior to
other existing SOTAs by 18.0 mAP at least.


  Paths $P_1,\ldots, P_k$ in a graph $G=(V,E)$ are mutually induced if any two
distinct $P_i$ and $P_j$ have neither common vertices nor adjacent vertices.
The Induced Disjoint Paths problem is to decide if a graph $G$ with $k$ pairs
of specified vertices $(s_i,t_i)$ contains $k$ mutually induced paths $P_i$
such that each $P_i$ starts from $s_i$ and ends at $t_i$. This is a classical
graph problem that is NP-complete even for $k=2$. We introduce a natural
generalization, Induced Disjoint Connected Subgraphs: instead of connecting
pairs of terminals, we must connect sets of terminals. We give almost-complete
dichotomies of the computational complexity of both problems for H-free graphs,
that is, graphs that do not contain some fixed graph H as an induced subgraph.
Finally, we give a complete classification of the complexity of the second
problem if the number k of terminal sets is fixed, that is, not part of the
input.


  Transformers are widely used for solving tasks in natural language
processing, computer vision, speech, and music domains. In this paper, we talk
about the efficiency of transformers in terms of memory (the number of
parameters), computation cost (number of floating points operations), and
performance of models, including accuracy, the robustness of the model, and
fair \& bias-free features. We mainly discuss the vision transformer for the
image classification task. Our contribution is to introduce an efficient 360
framework, which includes various aspects of the vision transformer, to make it
more efficient for industrial applications. By considering those applications,
we categorize them into multiple dimensions such as privacy, robustness,
transparency, fairness, inclusiveness, continual learning, probabilistic
models, approximation, computational complexity, and spectral complexity. We
compare various vision transformer models based on their performance, the
number of parameters, and the number of floating point operations (FLOPs) on
multiple datasets.


  The classes Delay-FPT and Total-FPT recently have been introduced into
parameterized complexity in order to capture the notion of efficiently solvable
parameterized enumeration problems. In this paper we focus on ordered
enumeration and will show how to obtain Delay-FPT and Total-FPT enumeration
algorithms for several important problems. We propose a generic algorithmic
strategy, combining well-known principles stemming from both parameterized
algorithmics and enumeration, which shows that, under certain preconditions,
the existence of a so-called neighbourhood function among the solutions implies
the existence of a Delay-FPT algorithm which outputs all ordered solutions. In
many cases, the cornerstone to obtain such a neighbourhood function is a
Total-FPT algorithm that outputs all minimal solutions. This strategy is
formalized in the context of graph modification problems, and shown to be
applicable to numerous other kinds of problems.


  We study the problem of learning representations of entities and relations in
knowledge graphs for predicting missing links. The success of such a task
heavily relies on the ability of modeling and inferring the patterns of (or
between) the relations. In this paper, we present a new approach for knowledge
graph embedding called RotatE, which is able to model and infer various
relation patterns including: symmetry/antisymmetry, inversion, and composition.
Specifically, the RotatE model defines each relation as a rotation from the
source entity to the target entity in the complex vector space. In addition, we
propose a novel self-adversarial negative sampling technique for efficiently
and effectively training the RotatE model. Experimental results on multiple
benchmark knowledge graphs show that the proposed RotatE model is not only
scalable, but also able to infer and model various relation patterns and
significantly outperform existing state-of-the-art models for link prediction.


  This letter proposes a new wake word detection system based on Res2Net. As a
variant of ResNet, Res2Net was first applied to objection detection. Res2Net
realizes multiple feature scales by increasing possible receptive fields. This
multiple scaling mechanism significantly improves the detection ability of wake
words with different durations. Compared with the ResNet-based model, Res2Net
also significantly reduces the model size and is more suitable for detecting
wake words. The proposed system can determine the positions of wake words from
the audio stream without any additional assistance. The proposed method is
verified on the Mobvoi dataset containing two wake words. At a false alarm rate
of 0.5 per hour, the system reduced the false rejection of the two wake words
by more than 12% over prior works.


  I will show, via effective field theory (EFT) techniques, that obtaining an
observationally consistent relic density while evading stringent direct
detection limits and maintaining $h_{125}$ phenomenology in an extended Higgs
sector can be easily achieved. I will then map such an EFT to the low energy
limit of the NMSSM with the Higgsinos integrated out. Both the singlino and the
singlet-like CP-odd and even scalars in the NMSSM may play a relevant role in
such a scenario, while being difficult to probe via conventional searches. The
singlet sector of the general NMSSM can be mapped on to a 2HDM+S, and I will
discuss prospects of probing this at the LHC using signatures such as
mono-Higgs and mono-Z. This proceeding is mostly based on Refs.
arXiv:1712.09873 and arXiv:1808.02667.


  The semiclassical long-time limit of free evolution of quantum wave packets
on the torus is under consideration. Despite of simplicity of this system,
there are still open questions concerning the detailed description of the
evolution on time scales beyond the Ehrenfest time. One of the approaches is
based on the limiting Wigner or Husimi distributions of time-evolved wave
packets as the Planck constant tends to zero and time tends to infinity. We
derive explicit expressions for semiclassical measures corresponding to all
time scales and the corresponding stages of evolution: classical-like motion,
spreading of the wave packet, and its revivals. Also we discuss limitations of
the approach based on semiclassical measures and suggest its generalization.


  Transformer-based methods have demonstrated impressive performance in 4D
light field (LF) super-resolution by effectively modeling long-range
spatial-angular correlations, but their quadratic complexity hinders the
efficient processing of high resolution 4D inputs, resulting in slow inference
speed and high memory cost. As a compromise, most prior work adopts a
patch-based strategy, which fails to leverage the full information from the
entire input LFs. The recently proposed selective state-space model, Mamba, has
gained popularity for its efficient long-range sequence modeling. In this
paper, we propose a Mamba-based Light Field Super-Resolution method, named
MLFSR, by designing an efficient subspace scanning strategy. Specifically, we
tokenize 4D LFs into subspace sequences and conduct bi-directional scanning on
each subspace. Based on our scanning strategy, we then design the Mamba-based
Global Interaction (MGI) module to capture global information and the local
Spatial- Angular Modulator (SAM) to complement local details. Additionally, we
introduce a Transformer-to-Mamba (T2M) loss to further enhance overall
performance. Extensive experiments on public benchmarks demonstrate that MLFSR
surpasses CNN-based models and rivals Transformer-based methods in performance
while maintaining higher efficiency. With quicker inference speed and reduced
memory demand, MLFSR facilitates full-image processing of high-resolution 4D
LFs with enhanced performance.


  We propose 4 insights that help to significantly improve the performance of
deep learning models that predict surface normals and semantic labels from a
single RGB image. These insights are: (1) denoise the "ground truth" surface
normals in the training set to ensure consistency with the semantic labels; (2)
concurrently train on a mix of real and synthetic data, instead of pretraining
on synthetic and finetuning on real; (3) jointly predict normals and semantics
using a shared model, but only backpropagate errors on pixels that have valid
training labels; (4) slim down the model and use grayscale instead of color
inputs. Despite the simplicity of these steps, we demonstrate consistently
improved results on several datasets, using a model that runs at 12 fps on a
standard mobile phone.


  The analysis of the covariant brackets on the space of functions on the
solutions to a variational problem in the framework of contact geometry
initiated in the companion letter Ref.19 is extended to the case of the
multisymplectic formulation of the free Klein-Gordon theory and of the free
Schr\"{o}dinger equation.


  A large number of experimental facts and theoretical arguments favor a
two-gap model for superconductivity in MgB$_{2}$. However, this model predicts
strong suppression of the critical temperature by interband impurity scattering
and, presumably, a strong correlation between the critical temperature and the
residual resistivity. No such correlation has been observed. We argue that this
fact can be understood if the band disparity of the electronic structure is
taken into account, not only in the superconducting state, but also in normal
transport.


  Artificial Intelligence (AI) is having a tremendous impact across most areas
of science. Applications of AI in healthcare have the potential to improve our
ability to detect, diagnose, prognose, and intervene on human disease. For AI
models to be used clinically, they need to be made safe, reproducible and
robust, and the underlying software framework must be aware of the
particularities (e.g. geometry, physiology, physics) of medical data being
processed. This work introduces MONAI, a freely available, community-supported,
and consortium-led PyTorch-based framework for deep learning in healthcare.
MONAI extends PyTorch to support medical data, with a particular focus on
imaging, and provide purpose-specific AI model architectures, transformations
and utilities that streamline the development and deployment of medical AI
models. MONAI follows best practices for software-development, providing an
easy-to-use, robust, well-documented, and well-tested software framework. MONAI
preserves the simple, additive, and compositional approach of its underlying
PyTorch libraries. MONAI is being used by and receiving contributions from
research, clinical and industrial teams from around the world, who are pursuing
applications spanning nearly every aspect of healthcare.


  As a substantial amount of multivariate time series data is being produced by
the complex systems in Smart Manufacturing, improved anomaly detection
frameworks are needed to reduce the operational risks and the monitoring burden
placed on the system operators. However, building such frameworks is
challenging, as a sufficiently large amount of defective training data is often
not available and frameworks are required to capture both the temporal and
contextual dependencies across different time steps while being robust to
noise. In this paper, we propose an unsupervised Attention-based Convolutional
Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding
(ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time
series. The framework starts by pre-processing and enriching the data, before
constructing feature images to characterize the system statuses across
different time steps by capturing the inter-correlations between pairs of time
series. Afterwards, the constructed feature images are fed into an
attention-based ConvLSTM autoencoder, which aims to encode the constructed
feature images and capture the temporal behavior, followed by decoding the
compressed knowledge representation to reconstruct the feature images input.
The reconstruction errors are then computed and subjected to a
statistical-based, dynamic thresholding mechanism to detect and diagnose the
anomalies. Evaluation results conducted on real-life manufacturing data
demonstrate the performance strengths of the proposed approach over
state-of-the-art methods under different experimental settings.


  We show that the coefficients of a power series occurring in $p$-adic Fourier
theory for $\mathbf{Q}_{p^2}$ have valuations that are given by an intriguing
formula.


  We show that the scattering of a general spin-0 sector of dark radiation off
the pre-recombination thermal plasma results in undetectably small spectral
distortions of the Cosmic Microwave Background.


  Post-collision space-times of the Cartesian product form M'xM'', where M' and
M'' are two-dimensional manifolds, are known with M' and M'' having constant
curvatures of equal and opposite sign (for the collision of electromagnetic
shock waves) or of the same sign (for the collision of gravitational shock
waves). We construct here a new explicit post-collision solution of the
Einstein-Maxwell vacuum field equations with a cosmological constant for which
M' has constant (nonzero) curvature and M'' has zero curvature.


  Computing the {\em matching statistics} of a string $P[1..m]$ with respect to
a text $T[1..n]$ is a fundamental problem which has application to genome
sequence comparison. In this paper, we study the problem of computing the
matching statistics upon highly repetitive texts. We design three different
data structures that are similar to LZ-compressed indexes. The space costs of
all of them can be measured by $\gamma$, the size of the smallest string
attractor [STOC'2018] and $\delta$, a better measure of repetitiveness
[LATIN'2020].


  We propose a novel loss function that dynamically rescales the cross entropy
based on prediction difficulty regarding a sample. Deep neural network
architectures in image classification tasks struggle to disambiguate visually
similar objects. Likewise, in human pose estimation symmetric body parts often
confuse the network with assigning indiscriminative scores to them. This is due
to the output prediction, in which only the highest confidence label is
selected without taking into consideration a measure of uncertainty. In this
work, we define the prediction difficulty as a relative property coming from
the confidence score gap between positive and negative labels. More precisely,
the proposed loss function penalizes the network to avoid the score of a false
prediction being significant. To demonstrate the efficacy of our loss function,
we evaluate it on two different domains: image classification and human pose
estimation. We find improvements in both applications by achieving higher
accuracy compared to the baseline methods.


  Gentrification is associated with rapid demographic changes within inner-city
neighborhoods. While many fear that gentrification drives low-income people
from their homes and communities, there is limited evidence of the consequences
of these changes. I use Canadian administrative tax files to track the
movements of incumbent workers and their income trajectory as their
neighborhood gentrifies. I exploit the timing at which neighborhoods gentrify
in a matched staggered event-study framework. I find no evidence of
displacement effects, even for low socioeconomic status households. In fact,
families living in gentrifying neighborhoods are more likely to stay longer. I
suggest that this might be related to tenant rights protection laws. When they
endogenously decide to leave, low-income families do not relocate to worse
neighborhoods. Finally, I find no adverse effects on their income trajectory,
suggesting no repercussions on their labor market outcomes.


  A survey is given of approaches to the problem of distributed consensus,
focusing particularly on methods based on cellular automata and related
systems. A variety of new results are given, as well as a history of the field
and an extensive bibliography. Distributed consensus is of current relevance in
a new generation of blockchain-related systems.


  We present a new technique based on multi-band near ultraviolet and optical
photometry to measure both the stellar intrinsic properties, ie luminosity and
effective temperature, and the interstellar dust extinction along the line of
sight to hundreds of stars per square arcminute. The yield is twofold. On the
one hand, the resulting reddening map has a very high spatial resolution, of
the order of a few arcseconds, and can be quite effectively used in regions
where the interstellar material is patchy, thus producing considerable
differential extinction on small angular scales. On the other hand, combining
the photometric information over a wide baseline in wavelength provides an
accurate determination of temperature and luminosity for thousands of stars. As
a test case, we present the results for the region around Supernova 1987A in
the Large Magellanic Cloud imaged with the WFPC2 on board the Hubble Space
Telescope.


  We previously reported a rare class of variable star light curves isolated
from a sample of 4.7 million candidate variables from the ATLAS survey. Dubbed
`UCBH' light curves, they have broad minima and narrow, symmetrical maxima,
with typical periods of 1-10 days and amplitudes of 0.05--0.20 mag. They
maintain constant amplitude, shape, and phase coherence over multiple years,
but do not match any known class of pulsating variables. A localized bright
spot near the equator of a rotating star will produce a UCBH-type light curve
for most viewing geometries. Most stars that exhibit rotational variability
caused primarily by a single bright feature should therefore appear as UCBH
stars, although a rotating bright spot is not the only thing that could produce
a UCBH-type lightcurve. We have spectroscopically investigated fourteen UCBH
stars and found ten of them to be Ap/Bp stars: A-type or B-type stars with
greatly enhanced photospheric abundances of specific heavy elements.
Rotationally variable Ap/Bp stars are referred to as $\alpha^2$ CVn variables.
Most ATLAS UCBH stars are therefore $\alpha^2$ CVn stars, although only a
minority of $\alpha^2$ CVn stars in the literature have UCBH light curves. The
fact that $\alpha^2$ CVn stars dominate the UCBH class suggests that lone
bright spots with sufficient size and contrast develop more readily on Ap/Bp
stars than on any other type. The $\alpha^2$ CVn UCBH stars may be
characterized by a specific magnetic field topology, making them intriguing
targets for future Zeeman-Doppler imaging.


  A shape change of the self-consistent mean-field induced by a configuration
change is discussed within the conventional constrained Hartree-Fock (CHF)
theory. It is stressed that a single-particle level crossing dynamics should be
treated carefully, because the shape of the mean-field in such a finite
many-body system as the nucleus strongly changes depending on its
configuration. This situation is clearly shown by applying an adiabatic
assumption, where the most energetically favorable single-particle states are
assumed to be occupied. The excited HF states and the continuously-connected
potential energy curves are given by applying the configuration dictated CHF
method. The effect of pairing correlation is discussed in the level crossing
region. Triaxial deformed results in our Hartree-Fock-Bogoliubov (HFB)
calculation with Gogny force nicely reproduce the available experimental data
of Ge isotopes. From our numerical calculation, it is concluded that the CHFB
state is more fragile than the CHF state in the level crossing region.


  Recently, it was pointed out that the solar and atmospheric neutrino deficits
can be explained by oscillations between electroweak doublet and singlet
neutrinos in the model of Majorana neutrinos. However, since the model includes
no flavor mixing, it cannot explain the recent LSND result. We extend the model
to include the flavor mixing, and obtain the explanation of the LSND result
together with the solar and atmospheric neutrino deficits. The requirement for
the neutrinos to be the hot dark matter selects out only the vacuum oscillation
solution to the solar neutrino deficit.


  Within the conjectured duality between N=4 super Yang-Mills and Anti-deSitter
string theory, the BFKL Pomeron of the gauge theory corresponds to the graviton
mode of the dual string. As a first step towards analyzing multigraviton
exchange, we investigate R-current six-point functions within the supergravity
approximation. We compute the analogue of diffractive scattering, and we
analyze the triple Regge limit. In the supergravity approximation the triple
graviton vertex is found to vanish.


  This paper studies the asymmetries of pulsar distributions relative to the
Galactic plane in various Galactic longitudes. It is shown that the observed
asymmetric distribution may be explained by the warped and flaring (i.e.
increase of scale-height at the peripheries of the Galaxy) structure of the
Galaxy. At the peripheries of the Galaxy, the amplitude of the warp, derived
from these data, may be as high as nearly 1 kpc. The scale-height of the pulsar
distribution increases exponentially from nearly 0.5 kpc to nearly 1 kpc while
increasing the galactocentric distance from 5 kpc to 15 kpc. The warp and flare
parameters derived from the pulsar data are compared with the stellar and
gaseous warp and flare parameters of the Galaxy.


  We present a formalism for simulating quantum dynamics of lattice spin-one
systems by first introducing local hidden variables and then doing
semiclassical (truncated Wigner) approximation in the extended phase space. In
this way we exactly take into account the local on-site Hamiltonian and
approximately treat spin-spin interactions. In particular, we represent each
spin with eight classical SU(3) variables. Three of them represent the usual
spin components and five others are hidden variables representing local
spin-spin correlations. We compare our formalism with exact quantum dynamics of
fully connected spin systems and find very good agreement. As an application we
discuss quench dynamics of a Bose-Hubbard model near the superfluid-insulator
transition for a 3D lattice system consisting of 1000 sites.


  We report a new calculation of the soft-gluon threshold resummation for the
Higgs boson pair production in the dominant production mode -- gluon-gluon
fusion -- up to the next-to-next-to-next-to-leading logarithmic (N$^3$LL)
accuracy. After matching N$^3$LL to the next-to-next-to-next-to-leading order
(N$^3$LO) QCD calculation in the infinite top quark mass approximation, we show
that the central values of the inclusive cross sections are quite stable with
respect to N$^3$LO, while the conventional renormalisation and factorisation
scale uncertainties are reduced by a factor of two, reaching to the subpercent
level. Our study further consolidates the good asymptotic perturbative
convergence. After combining with the full top-quark mass dependent
next-to-leading order QCD results, our most advanced predictions are presented
for both the inclusive total cross sections and the differential invariant mass
distributions of the Higgs pair.


  We analyze the last 125 years optical light curve of the symbiotic star AX
Per through some remarkable correlations that we discovered in its power
spectrum. The data were assembled from the literature and from the AAVSO
database. A series of 6 major outbursts dominate the light curve. They are
presented in the power spectrum as 13 harmonics of the fundamental frequency
fa=1/Pa=1/23172 day-1. We refer to them as the "red" frequencies. Oscillations
with the binary periodicity of the system Pb=1/fb=681.48 d are also seen in the
light curve, with particularly large amplitudes during outbursts. The fb peak
in the power spectrum is accompanied by 13 other peaks on each side, to which
we refer as the "blue" frequencies. A distinct structure in the frequency
distribution of the blue peaks, as well as in their peak power are best
interpreted as reflecting beating of the 13 "red" frequencies with the binary
one. We suggest, following others, that the major outbursts of the system
result from events of intense mass loss from the giant star. Mass accretion
onto the hot component, partially through the L1 point of the system, took
place in the last 125 years at a rate that oscillated with the 13 first
harmonics of the fa frequency. The binary orbit is slightly eccentric and
periastron passages induced modulation of the L1 accretion at the binary
frequency. Hence the fb oscillations in the brightness of the star of amplitude
that is modulated by the "red" frequencies of the system.


  A measurement of the elliptic flow ($v_2$) of prompt J/$\psi$ mesons in
high-multiplicity pPb collisions is reported using data collected by the CMS
experiment at a nucleon-nucleon center-of-mass energy $\sqrt{s_\mathrm{NN}} =$
8.16 TeV. Prompt J/$\psi$ mesons decaying into two muons are reconstructed in
the rapidity region in the nucleon-nucleon center-of-mass frame
($y_\mathrm{cm}$), corresponding to either $-$2.86 $\lt y_{\mathrm{cm}} <$
$-$1.86 or 0.94 $\lt y_{\mathrm{cm}} <$ 1.94. The average $v_2$ result from the
two rapidity ranges is reported over the transverse momentum (p$_\mathrm{T}$)
range from 0.2 to 10 GeV. Positive $v_2$ values are observed for the prompt
J/$\psi$ meson, as extracted from long-range two-particle correlations with
charged hadrons, for 2 $\lt p_{\mathrm{T}}<$ 8 GeV. The prompt J/$\psi$ results
are compared with previous CMS measurements of elliptic flow for open charm
mesons (D$^0$) and strange hadrons. From these measurements, constraints can be
obtained on the collective dynamics of charm quarks produced in
high-multiplicity events arising from small systems.


  We show that tautological integrals on Hilbert schemes of points can be
written in terms of universal polynomials in Chern numbers. The results hold in
all dimensions, though they strengthen known results even for surfaces by
allowing integrals over arbitrary "geometric" subsets (and their
Chern-Schwartz-MacPherson classes).
  We apply this to enumerative questions, proving a generalised G\"ottsche
Conjecture for all singularity types and in all dimensions. So if L is a
sufficiently ample line bundle on a smooth variety X, in a general subsystem
P^d of |L| of appropriate dimension the number of hypersurfaces with given
singularity types is a polynomial in the Chern numbers of (X,L). When X is a
surface, we get similar results for the locus of curves with fixed "BPS
spectrum" in the sense of stable pairs theory.


  Applications such as employees sharing office spaces over a workweek can be
modeled as problems where agents are matched to resources over multiple rounds.
Agents' requirements limit the set of compatible resources and the rounds in
which they want to be matched. Viewing such an application as a multi-round
matching problem on a bipartite compatibility graph between agents and
resources, we show that a solution (i.e., a set of matchings, with one matching
per round) can be found efficiently if one exists. To cope with situations
where a solution does not exist, we consider two extensions. In the first
extension, a benefit function is defined for each agent and the objective is to
find a multi-round matching to maximize the total benefit. For a general class
of benefit functions satisfying certain properties (including diminishing
returns), we show that this multi-round matching problem is efficiently
solvable. This class includes utilitarian and Rawlsian welfare functions. For
another benefit function, we show that the maximization problem is NP-hard. In
the second extension, the objective is to generate advice to each agent (i.e.,
a subset of requirements to be relaxed) subject to a budget constraint so that
the agent can be matched. We show that this budget-constrained advice
generation problem is NP-hard. For this problem, we develop an integer linear
programming formulation as well as a heuristic based on local search. We
experimentally evaluate our algorithms on synthetic networks and apply them to
two real-world situations: shared office spaces and matching courses to
classrooms.


  In the framework of superanalysis we get a functions theory close to complex
analysis, under a suitable condition (A) on the real superalgebras in
consideration. Under the condition (A), we get an integral representation
formula for the superdifferentiable functions.We give a result of Hartogs type
of separated superdifferentiability and a continuation theorem of
Hartogs-Bochner type for the superdifferentiable functions.


  Automated program repair (APR) techniques have achieved conspicuous progress,
and are now capable of producing genuinely correct fixes in scenarios that were
well beyond their capabilities only a few years ago. Nevertheless, even when an
APR technique can find a correct fix for a bug, it still runs the risk of
ranking the fix lower than other patches that are plausible (they pass all
available tests) but incorrect. This can seriously hurt the technique's
practical effectiveness, as the user will have to peruse a larger number of
patches before finding the correct one.
  This paper presents PrevaRank, a technique that ranks plausible patches
produced by any APR technique according to their feature similarity with
historic programmer-written fixes for similar bugs. PrevaRank implements simple
heuristics, which help make it scalable and applicable to any APR tool that
produces plausible patches. In our experimental evaluation, after training
PrevaRank on the fix history of 81 open-source Java projects, we used it to
rank patches produced by 8 Java APR tools on 168 Defects4J bugs. PrevaRank
consistently improved the ranking of correct fixes: for example, it ranked a
correct fix within the top-3 positions in 27% more cases than the original
tools did. Other experimental results indicate that PrevaRank works robustly
with a variety of APR tools and bugs, with negligible overhead.


  A Bethe-Salpeter-Faddeev (BSF) calculation is performed for the pentaquark
$\Theta^+$ in the diquark picture of Jaffe and Wilczek in which $\Theta^+$ is a
diquark-diquark-${\bar s}$ three-body system. Nambu-Jona-Lasinio (NJL) model is
used to calculate the lowest order diagrams in the two-body scatterings of
${\bar s}D$ and $D D$. With the use of coupling constants determined from the
meson sector, we find that ${\bar s}D$ interaction is attractive in s-wave
while $DD$ interaction is repulsive in p-wave. With only the lowest three-body
channel considered, we do not find a bound $ \frac 12^+$ pentaquark state.
Instead, a bound pentaquark $\Theta^+$ with $ \frac 12^-$ is obtained with a
unphysically strong vector mesonic coupling constants.


  We detail a calculation of W gamma production in hadronic collision, at
Next-to-Leading Order (NLO) QCD interfaced to a shower generator according to
the POWHEG prescription supplemented with the MiNLO procedure. The fixed order
result is matched to an interleaved QCD+QED parton shower, in such a way that
the contribution arising from hadron fragmentation into photons is fully
modeled. In general, our calculation illustrates a new approach to the fully
exclusive simulation of prompt photon production processes accurate at the NLO
level in QCD. We compare our predictions to those of the NLO program MCFM,
which treats the fragmentation contribution in terms of photon fragmentation
functions. We also perform comparisons to available LHC data at 7 TeV, for
which we observe good agreement, and provide phenomenological results for
physics studies of the W gamma production process at the Run II of the LHC. The
new tool, which includes W leptonic decays and the contribution of anomalous
gauge couplings, allows a fully exclusive, hadron-level description of the W
gamma process, and is publicly available at the repository of the POWHEG BOX.
Our approach can be easily adapted to deal with other relevant isolated photon
production processes in hadronic collisions.


  Software Bills of Materials (SBOMs) have emerged as tools to facilitate the
management of software dependencies, vulnerabilities, licenses, and the supply
chain. While significant effort has been devoted to increasing SBOM awareness
and developing SBOM formats and tools, recent studies have shown that SBOMs are
still an early technology not yet adequately adopted in practice. Expanding on
previous research, this paper reports a comprehensive study that investigates
the current challenges stakeholders encounter when creating and using SBOMs.
The study surveyed 138 practitioners belonging to five stakeholder groups
(practitioners familiar with SBOMs, members of critical open source projects,
AI/ML, cyber-physical systems, and legal practitioners) using differentiated
questionnaires, and interviewed 8 survey respondents to gather further insights
about their experience. We identified 12 major challenges facing the creation
and use of SBOMs, including those related to the SBOM content, deficiencies in
SBOM tools, SBOM maintenance and verification, and domain-specific challenges.
We propose and discuss 4 actionable solutions to the identified challenges and
present the major avenues for future research and development.


  Let $\mathcal M_X$ denote the ideal of meager subsets of a topological space
$X$. We prove that if $X$ is a completely metrizable space without isolated
points, then the smallest cardinality of a non-meager subset of $X$, denoted
$\mathrm{non}(\mathcal M_X)$, is exactly $\mathrm{non}(\mathcal M_X) =
\mathrm{cf}[\kappa]^\omega \cdot \mathrm{non}(\mathcal M_{\mathbb R})$, where
$\kappa$ is the minimum weight of a nonempty open subset of $X$. We also
characterize the additivity and covering numbers for $\mathcal M_X$ in terms of
simple topological properties of $X$. Some bounds are proved and some questions
raised concerning the cofinality of $\mathcal M_X$ and the cofinality of the
related ideal of nowhere dense subsets of $X$.
  We also show that if $X$ is a compact Hausdorff space with $\pi$-weight
$\kappa$, then $\mathrm{non}(\mathcal M_X) \leq \mathrm{cf}[\kappa]^\omega
\cdot \mathrm{non}(\mathcal M_{\mathbb R})$. This bound for compact Hausdorff
spaces is not sharp, in the sense that it is consistent for such a space to
have non-meager subsets of even smaller cardinality.


  Achieving impurity and helium ash control is a crucial issue in the path
towards fusion-grade magnetic confinement devices, and this is particularly the
case of helical reactors, whose low-collisionality ion-root operation scenarios
usually display a negative radial electric field which is expected to cause
inwards impurity pinch. In these work we discuss, based on experimental
measurements and standard predictions of neoclassical theory, how plasmas of
very low ion collisionality, similar to those observed in the impurity hole of
the Large Helical Device, can be an exception to this general rule, and how a
negative radial electric field can coexist with an outward impurity flux. This
interpretation is supported by comparison with documented discharges available
in the International Stellarator-Heliotron Profile Database, and it can be
extrapolated to show that achievement of high ion temperature in the core of
helical devices is not fundamentally incompatible with low core impurity
content.


  We investigated the radial colour gradient and two-dimensional (2D) colour
properties of 22 E+A galaxies with 5.5 < Hdelta equivalent width(EW) < 8.5\AA
and 49 normal early-type galaxies as a control sample at a redshift of <0.2 in
the Second Data Release of the Sloan Digital Sky Survey. We found that a
substantial number of E+A galaxies exhibit positive slopes of radial colour
gradient (bluer gradients toward the centre) which are seldom seen in normal
early-type galaxies. We found irregular 'colour morphologies'-- asymmetrical
and clumpy patterns -- at the centre of g-r and r-i 2D colour maps of E+A
galaxies with positive slopes of colour gradient. Kolomogorov-Smirnov
two-sample tests show that g-r and r-i colour gradient distributions of E+A
galaxies differ from those of early-type galaxies with a more than 99.99 per
cent significance level. We also found a tight correlation between radial
colour gradients and colours, and between radial colour gradients and the
4000-\AA break in the E+A sample; E+A galaxies which exhibit bluer colour or
weaker D4000 tend to have positive slopes of radial colour gradient. We
compared the GISSEL model and E+A observational quantities, Hdelta EW, D4000
and u-g colour, and found that almost all our E+A galaxies are situated along a
single evolutionary track. Therefore, these results are interpreted as E+A
galaxies evolving from Hdelta EW ~ 8 \AA to Hdelta EW ~ 5 \AA, with colour
gradients changing from positive to negative, and with the irregular 2D colour
map becoming smoother, during a time-scale of ~300 Myr. Our results favour the
hypothesis that E+A galaxies are post-starburst galaxies caused by
merger/interaction, having undergone a centralized violent starburst.


  We investigate a two-dimensional problem of an isolated self-interacting
end-grafted polymer, pulled by one end. In the thermodynamic limit, we find
that the model has only two different phases, namely a collapsed phase and a
stretched phase. We show that the phase diagram obtained by Kumar {\it at al.\}
[Phys. Rev. Lett. {\bf 98}, 128101 (2007)] for small systems, where differences
between various statistical ensembles play an important role, differ from the
phase diagram obtained here in the thermodynamic limit.


  In this paper we introduce a notion of unilateral slope for the Mumford-Shah
functional, and provide an explicit formula in the case of smooth cracks. We
show that the slope is not lower semicontinuous and study the corresponding
relaxed functional.


  Hubbard-corrected density functional theory, denoted by DFT+U method, was
developed to enable correct prediction of insulating properties for
strongly-correlated electron systems. UO$_2$ is an example having O-$2p$,
U-$6d$, and U-$5f$ incomplete electronic shells. Usually, researchers apply the
Hubbard correction only to the localized incomplete $5f$ electrons of U atoms
and succeed to predict insulating property and good geometric properties by
tweaking the Hubbard-U parameter. However, it turned out that in such a way it
was impossible to obtain reasonable values for both geometry and electronic
band-gap at the same time. In this work, we show that it is possible to produce
good values for those properties just by applying and tuning the Hubbard
corrections to all incomplete shells of O-$2p$, U-$6d$, and U-$5f$.


  We explore the idea that the dynamics of the inflationary multiverse is
encoded in its future boundary, where it is described by a lower dimensional
theory which is conformally invariant in the UV. We propose that a measure for
the multiverse, which is needed in order to extract quantitative probabilistic
predictions, can be derived in terms of the boundary theory by imposing a UV
cutoff. In the inflationary bulk, this is closely related (though not
identical) to the so-called scale factor cutoff measure.


  We study nonconvex distributed optimization in multiagent networks where the
communications between nodes is modeled as a time-varying sequence of arbitrary
digraphs. We introduce a novel broadcast-based distributed algorithmic
framework for the (constrained) minimization of the sum of a smooth (possibly
nonconvex and nonseparable) function, i.e., the agents' sum-utility, plus a
convex (possibly nonsmooth and nonseparable) regularizer. The latter is usually
employed to enforce some structure in the solution, typically sparsity. The
proposed method hinges on Successive Convex Approximation (SCA) techniques
coupled with i) a tracking mechanism instrumental to locally estimate the
gradients of agents' cost functions; and ii) a novel broadcast protocol to
disseminate information and distribute the computation among the agents.
Asymptotic convergence to stationary solutions is established. A key feature of
the proposed algorithm is that it neither requires the double-stochasticity of
the consensus matrices (but only column stochasticity) nor the knowledge of the
graph sequence to implement. To the best of our knowledge, the proposed
framework is the first broadcast-based distributed algorithm for convex and
nonconvex constrained optimization over arbitrary, time-varying digraphs.
Numerical results show that our algorithm outperforms current schemes on both
convex and nonconvex problems.


  We recall the theory of linear discrete Riemann surfaces and show how to use
it in order to interpret a surface embedded in R^3 as a discrete Riemann
surface and compute its basis of holomorphic forms on it. We present numerical
examples, recovering known results to test the numerics and giving the yet
unknown period matrix of the Lawson genus-2 surface.


  This paper investigates Voiculescu's theorem on approximate equivalence in
separable properly infinite factors. We establish the norm-denseness of the set
of all reducible operators and prove Voiculescu's bicommutant theorem.
Additionally, we extend these results to the multiplier algebras within
separable type $\mathrm{III}$ factors.


  We study the Erdos distance problem over finite Euclidean and non-Euclidean
spaces. Our main tools are graphs associated to finite Euclidean and
non-Euclidean spaces that are considered in Bannai-Shimabukuro-Tanaka (2004,
2007). These graphs are shown to be asymptotically Ramanujan graphs. The
advantage of using these graphs is twofold. First, we can derive new lower
bounds on the Erdos distance problems with explicit constants. Second, we can
construct many explicit tough Ramsey graphs R(3,k).


  Efficient classical optimizers are crucial in practical implementations of
Variational Quantum Algorithms (VQAs). In particular, to make Stochastic
Gradient Descent (SGD) resource efficient, adaptive strategies have been
proposed to determine the number of measurement shots used to estimate the
gradient. However, existing strategies overlook the overhead that occurs in
each iteration. In terms of wall-clock runtime, significant circuit-switching
and communication latency can slow the optimization process when using a large
number of iterations. In terms of economic cost when using cloud services,
per-task prices can become significant. To address these issues, we present an
adaptive strategy that balances the number of shots in each iteration to
maximize expected gain per unit time or cost by explicitly taking into account
the overhead. Our approach can be applied to not only to the simple SGD but
also its variants, including Adam. Numerical simulations show that our adaptive
shot strategy is actually efficient for Adam, outperforming many existing
state-of-the-art adaptive shot optimizers. However, this is not the case for
the simple SGD. When focusing on the number of shots as the resource, our
adaptive-shots Adam with zero-overhead also outperforms existing optimizers.


  In this dissertation, we analyze equilibrium and out-of-equilibrium quantum
phase transitions present in quantum spin-$\frac{1}{2}$ models, using a quantum
information approach through quantum correlations and phase space formalism,
and a quantum thermodynamic approach through the work statistics formalism.


  In this paper, we give a refinement of a generalized Dedekind's theorem. In
addition, we show that all possible values of integer group determinants of any
group are also possible values of integer group determinants of its any abelian
subgroup. By applying the refinement of a generalized Dedekind's theorem, we
determine all possible values of integer group determinants of the direct
product group of the cyclic group of order $8$ and the cyclic group of order
$2$.


  The legacy of Jordan's canonical form on Poincar\'e's algebraic practices.
This paper proposes a transversal overview on Henri Poincar\'e's early works
(1878-1885). Our investigations start with a case study of a short note
published by Poincar\'e on 1884 : "Sur les nombres complexes". In the
perspective of today's mathematical disciplines - especially linear algebra -,
this note seems completely isolated in Poincar\'e's works. This short paper
actually exemplifies that the categories used today for describing some
collective organizations of knowledge fail to grasp both the collective
dimensions and individual specificity of Poincar\'es work. It also highlights
the crucial and transversal role played in Poincar\'e's works by a specific
algebraic practice of classification of linear groups by reducing the
analytical representation of linear substitution to their Jordan's canonical
forms. We then analyze in detail this algebraic practice as well as the roles
it plays in Poincar\'e's works. We first provide a micro-historical analysis of
Poincar\'e's appropriation of Jordan's approach to linear groups through the
prism of the legacy of Hermite's works on algebraic forms between 1879 and
1881. This mixed legacy illuminates the interrelations between all the papers
published by Poincar\'e between 1878 and 1885 ; especially between some
researches on algebraic forms and the development of the theory of Fuchsian
functions. Moreover, our investigation sheds new light on how the notion of
group came to play a key role in Poincar\'e's approach. The present paper also
offers a historical account of the statement by Jordan of his canonical form
theorem. Further, we analyze how Poincar\'e transformed this theorem by
appealing to Hermite's


  A prompt injection attack aims to inject malicious instruction/data into the
input of an LLM-Integrated Application such that it produces results as an
attacker desires. Existing works are limited to case studies. As a result, the
literature lacks a systematic understanding of prompt injection attacks and
their defenses. We aim to bridge the gap in this work. In particular, we
propose a framework to formalize prompt injection attacks. Existing attacks are
special cases in our framework. Moreover, based on our framework, we design a
new attack by combining existing ones. Using our framework, we conduct a
systematic evaluation on 5 prompt injection attacks and 10 defenses with 10
LLMs and 7 tasks. Our work provides a common benchmark for quantitatively
evaluating future prompt injection attacks and defenses. To facilitate research
on this topic, we make our platform public at
https://github.com/liu00222/Open-Prompt-Injection.


  A reinforcement learning (RL) policy trained in a nominal environment could
fail in a new/perturbed environment due to the existence of dynamic variations.
Existing robust methods try to obtain a fixed policy for all envisioned dynamic
variation scenarios through robust or adversarial training. These methods could
lead to conservative performance due to emphasis on the worst case, and often
involve tedious modifications to the training environment. We propose an
approach to robustifying a pre-trained non-robust RL policy with
$\mathcal{L}_1$ adaptive control. Leveraging the capability of an
$\mathcal{L}_1$ control law in the fast estimation of and active compensation
for dynamic variations, our approach can significantly improve the robustness
of an RL policy trained in a standard (i.e., non-robust) way, either in a
simulator or in the real world. Numerical experiments are provided to validate
the efficacy of the proposed approach.


  This paper presents an end-to-end trainable fast scene text detector, named
TextBoxes, which detects scene text with both high accuracy and efficiency in a
single network forward pass, involving no post-process except for a standard
non-maximum suppression. TextBoxes outperforms competing methods in terms of
text localization accuracy and is much faster, taking only 0.09s per image in a
fast implementation. Furthermore, combined with a text recognizer, TextBoxes
significantly outperforms state-of-the-art approaches on word spotting and
end-to-end text recognition tasks.


  Globally conserved interface-controlled coarsening of fractal clusters
exhibits dynamic scale invariance and normal scaling. This is demonstrated by a
numerical solution of the Ginzburg-Landau equation with a global conservation
law. The sharp-interface limit of this equation is volume preserving motion by
mean curvature. The scaled form of the correlation function has a power-law
tail accommodating the fractal initial condition. The coarsening length
exhibits normal scaling with time. Finally, shrinking of the fractal clusters
with time is observed. The difference between global and local conservation is
discussed.


  Much recent machine learning research has been directed towards leveraging
shared statistics among labels, instances and data views, commonly referred to
as multi-label, multi-instance and multi-view learning. The underlying premises
are that there exist correlations among input parts and among output targets,
and the predictive performance would increase when the correlations are
incorporated. In this paper, we propose Column Bundle (CLB), a novel deep
neural network for capturing the shared statistics in data. CLB is generic that
the same architecture can be applied for various types of shared statistics by
changing only input and output handling. CLB is capable of scaling to thousands
of input parts and output labels by avoiding explicit modeling of pairwise
relations. We evaluate CLB on different types of data: (a) multi-label, (b)
multi-view, (c) multi-view/multi-label and (d) multi-instance. CLB demonstrates
a comparable and competitive performance in all datasets against
state-of-the-art methods designed specifically for each type.


  A hyperbolic transcendental entire function with connected Fatou set is said
to be of disjoint type. It is known that the Julia set of a disjoint-type
function of finite order is a Cantor bouquet; in particular, it is a collection
of arcs (''hairs''), each connecting a finite endpoint to infinity. We show
that the latter property is equivalent to the function being criniferous (a
necessary condition for having a Cantor bouquet Julia set). On the other hand,
we show that there is a criniferous disjoint-type entire function whose Julia
set is not a Cantor bouquet. We also provide a new characterisation of Cantor
bouquet Julia sets in terms of the existence of certain absorbing sets for the
set of escaping points, and use this to give a new intrinsic description of a
class of entire functions previously introduced by the first author. Finally,
the main known sufficient condition for Cantor bouquet Julia sets is the
so-called head-start condition of Rottenfusser et al. Under a mild geometric
assumption, we prove that this condition is also necessary.


  No-insulation (NI) REBCO magnets have many advantages. They are
self-protecting, therefore do not need quench detection and protection which
can be very challenging in a high Tc superconducting magnet. Moreover, by
removing insulation and allowing thinner copper stabilizer, NI REBCO magnets
have significantly higher engineering current density and higher mechanical
strength. On the other hand, NI REBCO magnets have drawbacks of long magnet
charging time and high field-ramp-loss. In principle, these drawbacks can be
mitigated by managing the turn-to-turn contact resistivity (Rc). Evidently the
first step toward managing Rc is to establish a reliable method of accurate Rc
measurement. In this paper, we present experimental Rc measurements of REBCO
tapes as a function of mechanical load up to 144 MPa and load cycles up to 14
times. We found that Rc is in the range of 26-100 uOhm-cm2; it decreases with
increasing pressure, and gradually increases with number of load cycles. The
results are discussed in the framework of Holm's electric contact theory.


  We have obtained measurements of the total cross section for e+e-
annihilation into hadronic final states for 6 energy points (2.6, 3.2, 2.4,
3.55, 4.6, and 5.0 GeV) with the upgraded Beijing Spectrometer (BESII). We
report preliminary values from this data and outline future plans for a finer
scan in the 2-5 GeV energy range.


  We discuss an embedding of a vector field for the nonholonomic Routh sphere
into a subgroup of commuting Hamiltonian vector fields on six dimensional phase
space. The corresponding Poisson brackets are reduced to the canonical Poisson
brackets on the Lie algebra e(3). It allows us to relate nonholonomic Routh
system with the Hamiltonian system on cotangent bundle to the sphere with
canonical Poisson structure.


  The stationary point of Problem 2 is NOT the stationary point of Problem 1.
We are sorry and we are working on fixing this error.


  Computer models being developed to understand the interaction between
demand-response technology, power system deregulation and market transformation
depend in part on understanding the relationship between system frequency and
load-control. Frequency, load, and plant outage events data collected over the
last several years have permitted analysis to determine the Western Electricity
Coordination Council (WECC) system's inertia during each event. This data was
used to evaluate the relationship of system inertia to total load, which is
used to model system response to load curtailment programs in next generation
power system simulations.