  In this paper, non-orthogonal multiple access (NOMA) networks assisted by
multiple intelligent reflecting surfaces (IRSs) with discrete phase shifts are
investigated, in which each user device (UD) is served by an IRS to improve the
quality of the received signal. Two scenarios are considered according to
whether there is a direct link between the base station (BS) and each UD, and
the outage performance is analyzed for each of them. Specifically, the
asymptotic expressions for the upper and lower bounds of the outage probability
in the high signal-to-noise ratio (SNR) regime are derived. Following that, the
diversity order is obtained. It is shown that the use of discrete phase shifts
does not degrade diversity order. More importantly, simulation results reveal
that a 3-bit resolution for discrete phase shifts is sufficient to achieve
near-optimal outage performance. Simulation results also imply the superiority
of IRSs over full-duplex decode-and-forward relays.


  Unlike general relativity, in bimetric gravity linear gravitational waves do
not evolve as free fields. In this theory there are two types of tensor
perturbations, whose interactions are inherited from non-trivial couplings
between two dynamical metric tensor fields in the Hassan-Rosen action, and are
responsible for the phenomenon of bigravity oscillations. In this work, we
analyze the dynamics of cosmological tensor modes in bimetric gravity on
sub-horizon scales and close to the general relativity limit. In this limit,
the system has a characteristic length scale $L$ that is strictly contained
within the comoving Hubble radius. Thus, depending on the magnitude of the
comoving wavelength $\lambda$ relative to $L$, we identify two regimes of
interest where the system can be studied analytically: (i) deep sub-horizon
modes with $\lambda\ll L$, whose dynamics can be studied using multiple scale
analysis and are characterized by small and slowly evolving super-imposed
perturbations; (ii) sub-horizon modes with $\lambda\gg L$, where the dynamics
is characterized by fast super-imposed oscillations that can be studied using
asymptotic techniques for highly oscillatory problems. Furthermore, our
analysis represents a substantial improvement compared to previous analyses
based on a generalization of the WKB method, which, as we show, is ill-suited
to study the system at hand.


  We study the thermodynamic properties associated with black hole horizon and
cosmological horizon for the Gauss-Bonnet solution in de Sitter space. When the
Gauss-Bonnet coefficient is positive, a locally stable small black hole appears
in the case of spacetime dimension $d=5$, the stable small black hole
disappears and the Gauss-Bonnet black hole is always unstable quantum
mechanically when $d \ge 6$. On the other hand, the cosmological horizon is
found always locally stable independent of the spacetime dimension. But the
solution is not globally preferred, instead the pure de Sitter space is
globally preferred. When the Gauss-Bonnet coefficient is negative, there is a
constraint on the value of the coefficient, beyond which the gravity theory is
not well defined. As a result, there is not only an upper bound on the size of
black hole horizon radius at which the black hole horizon and cosmological
horizon coincide with each other, but also a lower bound depending on the
Gauss-Bonnet coefficient and spacetime dimension. Within the physical phase
space, the black hole horizon is always thermodynamically unstable and the
cosmological horizon is always stable, further, as the case of the positive
coefficient, the pure de Sitter space is still globally preferred. This result
is consistent with the argument that the pure de Sitter space corresponds to an
UV fixed point of dual field theory.


  We first give a brief overview of the charmed baryon spectroscopy and discuss
their possible structure and spin-parity assignments in the quark model. With
the new Belle measurement of the widths of $\Sigma_c(2455)$ and
$\Sigma_c(2520)$ and the recent CDF measurement of the strong decays of
$\Lambda_c(2595)$ and $\Lambda_c(2625)$, we give updated coupling constants in
heavy hadron chiral perturbation theory. We find $g_2=0.565^{+0.011}_{-0.024}$
for $P$-wave transitions between $s$-wave and $s$-wave baryons, and $h_2$, one
of the couplings responsible for $S$-wave transitions between $s$-wave and
$p$-wave baryons, is extracted from $\Lambda_c(2595)^+\to\Lambda_c^+\pi\pi$ to
be $0.63\pm0.07$. It is substantially enhanced compared to the old value of
order 0.437. With the help from the quark model, two of the couplings $h_{10}$
and $h_{11}$ responsible for $D$-wave transitions between $s$-wave and $p$-wave
baryons are determined from $\Sigma_c(2880)$ decays. There is a tension for the
coupling $h_2$ as its value extracted from $\Lambda_c(2595)^+\to
\Lambda_c^+\pi\pi$ will imply $\Xi_c(2790)^0\to\Xi'_c\pi$ and
$\Xi_c(2815)^+\to\Xi_c^*\pi$ rates slightly above the current limits. It is
conceivable that SU(3) flavor symmetry breaking can help account for the
discrepancy.


  In this thesis we investigate the Renormalization Group (RG) approach in
finite-dimensional glassy systems, whose critical features are still not
well-established, or simply unknown. We focus on spin and structural-glass
models built on hierarchical lattices, which are the simplest non-mean-field
systems where the RG framework emerges in a natural way. The resulting critical
properties shed light on the critical behavior of spin and structural glasses
beyond mean field, and suggest future directions for understanding the
criticality of more realistic glassy systems.


  Several platforms are being considered as hardware for quantum technologies.
For quantum computing (QC), superconducting qubits and artificially trapped
ions are among the leading platforms, but many others also show promise, e.g.
photons, cold atoms, defect centers including Rare-Earth (RE) ions. So far,
results are limited to the regime of noisy intermediate scale qubits (NISQ),
with a small number of qubits and a limited connectivity, and it is likely that
future QC hardware will utilize several existing platforms in different ways.
Thus, it currently makes sense to invest resources broadly and explore the full
range of promising routes to quantum technology. Rare-earth ions in solids
constitute one of the most versatile platforms for future quantum technology.
One advantage is good coherence properties even when confined in strong natural
traps inside a solid-state matrix. This confinement allows very high qubit
densities and correspondingly strong ion-ion couplings. In addition, although
their fluorescence is generally weak, cavity integration can enhance the
emission greatly and enable very good connections to photonic circuits,
including at the telecom wavelengths, making them promising systems for
long-term scalability. The primary aim of this roadmap is to provide a complete
picture of what components a RE quantum computer would consist of, to describe
the details of all parts required to achieve a scalable system, and to discuss
the most promising paths to reach it. In brief, we find that clusters of 50-100
single RE ions can act as high fidelity qubits in small processors, occupying
only about (10 nm)^3. Due to the high capacity for integration of the RE
systems, they be optically read out and connected to other such clusters for
larger scalability. We make suggestions for future improvements, which could
allow the REQC platform to be a leading one.


  While password managers are a vital tool for internet security, they can also
create a massive central point of failure, as evidenced by several major recent
data breaches. For over 20 years, deterministic password generators (DPGs) have
been proposed, and largely rejected, as a viable alternative to password
management tools. In this paper, we survey 45 existing DPGs to asses the main
security, privacy, and usability issues hindering their adoption. We then
present a new multi-factor deterministic password generator (MFDPG) design that
aims to address these shortcomings. The result not only achieves strong,
practical password management with zero credential storage, but also
effectively serves as a progressive client-side upgrade of weak password-only
websites to strong multi-factor authentication.


  Scientists in some fields are concerned that many, or even most, published
results are false. A high rate of false positives might arise accidentally,
from shoddy research practices. Or it might be the inevitable result of
institutional incentives that reward publication irrespective of veracity.
Recent models and discussion of scientific culture predict selection for
false-positive publications, as research labs that publish more positive
findings out-compete more diligent labs. There is widespread debate about how
scientific practices should be modified to avoid this degeneration. Some
analyses suggest that "bad science" will persist even when labs are
incentivized to undertake replication studies, and penalized for publications
that later fail to replicate. Here we develop a framework for modelling the
cultural evolution of research practices that allows labs to expend effort on
theory - enabling them, at a cost, to focus on hypotheses that are more likely
to be true on theoretical grounds. Theory restores the evolution of high effort
in laboratory practice, and it suppresses false-positive publications to a
technical minimum, even in the absence of replication. In fact, the mere
ability choose between two sets of hypotheses - one with greater chance of
being correct than the other - promotes better science than can be achieved by
having effortless access to the better set of hypotheses. Combining theory and
replication can have a synergistic effect in promoting good scientific
methodology and reducing the rate of false-positive publications. Based on our
analysis we propose four simple rules to promote good science in the face of
pressure to publish.


  Recent advancements have seen Large Language Models (LLMs) and Large
Multimodal Models (LMMs) surpassing general human capabilities in various
tasks, approaching the proficiency level of human experts across multiple
domains. With traditional benchmarks becoming less challenging for these
models, new rigorous challenges are essential to gauge their advanced
abilities. In this work, we present OlympiadBench, an Olympiad-level bilingual
multimodal scientific benchmark, featuring 8,476 problems from Olympiad-level
mathematics and physics competitions, including the Chinese college entrance
exam. Each problem is detailed with expert-level annotations for step-by-step
reasoning. Evaluating top-tier models on OlympiadBench, we implement a
comprehensive assessment methodology to accurately evaluate model responses.
Notably, the best-performing model, GPT-4V, attains an average score of 17.97%
on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark
rigor and the intricacy of physical reasoning. Our analysis orienting GPT-4V
points out prevalent issues with hallucinations, knowledge omissions, and
logical fallacies. We hope that our challenging benchmark can serve as a
valuable resource for helping future AGI research endeavors. The data and
evaluation code are available at \url{https://github.com/OpenBMB/OlympiadBench}


  We discuss the methods to calculate the roughness exponent alpha and the
dynamic exponent z from the scaling properties of the local roughness, which is
frequently used in the analysis of experimental data. Through numerical
simulations, we studied the Family, the restricted solid-on-solid (RSOS), the
Das Sarma-Tamborenea (DT) and the Wolf-Villain (WV) models in one- and two
dimensional substrates, in order to compare different methods to obtain those
exponents. The scaling at small length scales do not give reliable estimates of
alpha, suggesting that the usual methods to estimate that exponent from
experimental data may provide misleading conclusions concerning the
universality classes of the growth processes. On the other hand, we propose a
more efficient method to calculate the dynamic exponent z, based on the scaling
of characteristic correlation lengths, which gives estimates in good agreement
with the expected universality classes and indicates expected crossover
behavior. Our results also provide evidence of Edwards-Wilkinson asymptotic
behavior for the DT and the WV models in two-dimensional substrates.


  Despite their impressive capabilities, large pre-trained language models
(LMs) struggle with consistent reasoning; recently, prompting LMs to generate
explanations that self-guide the inference has emerged as a promising direction
to amend this. However, these approaches are fundamentally bounded by the
correctness of explanations, which themselves are often noisy and inconsistent.
In this work, we develop Maieutic Prompting, which infers a correct answer to a
question even from the noisy and inconsistent generations of LM. Maieutic
Prompting induces a tree of explanations abductively (e.g. X is true, because
...) and recursively, then frames the inference as a satisfiability problem
over these explanations and their logical relations. We test Maieutic Prompting
for true/false QA on three challenging benchmarks that require complex
commonsense reasoning. Maieutic Prompting achieves up to 20% better accuracy
than state-of-the-art prompting methods, and as a fully unsupervised approach,
performs competitively with supervised models. We also show that Maieutic
Prompting improves robustness in inference while providing interpretable
rationales.


  We present the discovery of 3 transiting planets from the WASP survey, two
hot-Jupiters: WASP-177b (~0.5 M_Jup, ~1.6 R_Jup) in a 3.07-d orbit of a V =
12.6 K2 star, WASP-183b (~0.5 M_Jup, ~1.5 R_Jup) in a 4.11-d orbit of a V =
12.8 G9/K0 star; and one hot-Saturn planet WASP-181b (~0.3 M_Jup, ~1.2 R_Jup)
in a 4.52-d orbit of a V = 12.9 G2 star. Each planet is close to the upper
bound of mass-radius space and has a scaled semi-major axis, a/R_star, between
9.6 and 12.1. These lie in the transition between systems that tend to be in
orbits that are well aligned with their host-star's spin and those that show a
higher dispersion.


  We obtain asymptotic formulas for the second and third moment of quadratic
Dirichlet $L$--functions at the critical point, in the function field setting.
We fix the ground field $\mathbb{F}_q$, and assume for simplicity that $q$ is a
prime with $q \equiv 1 \pmod 4$. We compute the second and third moment of
$L(1/2,\chi_D)$ when $D$ is a monic, square-free polynomial of degree $2g+1$,
as $g \to \infty$. The answer we get for the second moment agrees with Andrade
and Keating's conjectured formula. For the third moment, we check that the
leading term agrees with the conjecture.


  In recent years, the Na-ion SuperIonic CONductor (NASICON) based polyanionics
are considered the pertinent cathode materials in sodium-ion batteries due to
their 3D open framework, which can accommodate a wide range of Na content and
can offer high ionic conductivity with great structural stability. However,
owing to the inferior electronic conductivity, these materials suffer from
unappealing rate capability and cyclic stability for practical applications.
Therefore, in this work we investigate the effect of Co substitution at V site
on the electrochemical performance and diffusion kinetics of
Na$_{3}$V$_{2-x}$Co$_x$(PO$_{4}$)$_{3}$/C ($x=$ 0--0.15) cathodes. All the
samples are characterized through Rietveld refinement of the x-ray diffraction
patterns, Raman spectroscopy, transmission electron microscopy, etc. We
demonstrate improved electrochemical performance for the $x=$ 0.05 electrode
with reversible capacity of 105 mAh g$^{-1}$ at 0.1 C. Interestingly, the
specific capacity of 80 mAh g$^{-1}$ is achieved at 10 C with retention of
about 92\% after 500 cycles and 79.5\% after 1500 cycles and having nearly
100\% Coulombic efficiency. The extracted diffusion coefficient values through
galvanostatic intermittent titration technique and cyclic voltammetry are found
to be in the range of 10$^{-9}$--10$^{-11}$ cm$^{2}$ s$^{-1}$. The postmortem
studies show the excellent structural and morphological stability after testing
for 500 cycles at 10 C. Our study reveals the role of optimal dopant of
Co$^{3+}$ ions at V site to improve the cyclic stability at high current rate.


  Detection of the relationship between two time series is so important in
environmental and hydrological studies. Several parametric and non-parametric
approaches can be applied to detect relationships. These techniques are usually
sensitive to stationarity assumptions. In this research, a new copula-based
method is introduced to detect the relationship between two cylostationary time
series with fractional Brownian motion (fBm) errors. The numerical studies
verify the performance of the introduced approach.


  The determinization of Buchi automata is a celebrated problem, with
applications in synthesis, probabilistic verification, and multi-agent systems.
Since the 1960s, there has been a steady progress of constructions: by
McNaughton, Safra, Piterman, Schewe, and others. Despite the proliferation of
solutions, they are all essentially ad-hoc constructions, with little theory
behind them other than proofs of correctness. Since Safra, all optimal
constructions employ trees as states of the deterministic automaton, and
transitions between states are defined operationally over these trees. The
operational nature of these constructions complicates understanding,
implementing, and reasoning about them, and should be contrasted with
complementation, where a solid theory in terms of automata run DAGs underlies
modern constructions.
  In 2010, we described a profile-based approach to Buchi complementation,
where a profile is simply the history of visits to accepting states. We
developed a structural theory of profiles and used it to describe a
complementation construction that is deterministic in the limit. Here we extend
the theory of profiles to prove that every run DAG contains a profile tree with
at most a finite number of infinite branches. We then show that this property
provides a theoretical grounding for a new determinization construction where
macrostates are doubly preordered sets of states. In contrast to extant
determinization constructions, transitions in the new construction are
described declaratively rather than operationally.


  The properties of galaxy clusters as a function of redshift can be utilized
as an important cosmological tool. We present initial results from a program of
follow-up observations of the Sunyaev-Zeldovich effect (SZE) in high redshift
galaxy clusters detected at infrared wavelengths in the Massive and Distant
Clusters of WISE Survey (MaDCoWS). Using typical on-source integration times of
3-4 hours per cluster, MUSTANG2 on the Green Bank Telescope was able to measure
strong detections of SZE decrements and statistically significant masses on 14
out of 16 targets. On the remaining two, weaker (3.7 sigma) detections of the
SZE signal and strong upper limits on the masses were obtained. In this paper
we present masses and pressure profiles of each target and outline the data
analysis used to recover these quantities. Of the clusters with strong
detections, three show significantly flatter pressure profiles while, from the
MUSTANG2 data, five others show signs of disruption at their cores. However,
outside of the cores of the clusters, we were unable to detect significant
amounts of asymmetry. Finally, there are indications that the relationship
between optical richness used by MaDCoWS and SZE-inferred mass may be
significantly flatter than indicated in previous studies.


  Blau and Michaeli recently introduced a novel concept for inverse problems of
signal processing, that is, the perception-distortion tradeoff. We introduce
their tradeoff into the rate distortion theory of lossy source coding in
information theory, and clarify the tradeoff among information rate, distortion
and perception for general information sources.


  A graph G is said to be Hamiltonian if it contains a spanning cycle. In other
words, G contains a cycle that passes through all the the vertices in the
vertex set V(G) of G. A graph that does not contain a spanning cycle is a
non-Hamiltonian graph. In this work, we investigate the the Hamiltonian
completeness of certain trees. The Hamiltonian complete number of G, a
non-Hamiltonian graph is the optimal number of edges that if added to the edge
set E(G) of G, then G becomes Hamiltonian. Our focus is on certain classes of
caterpillar graphs.


  We present results from 170ksec long RXTE observations of LMC X-1 and LMC
X-3, taken in 1996 December, where their spectra can be described by a disc
black body plus an additional soft (Gamma~2.8) high-energy power-law (detected
up to 50keV in LMC X-3). These observations, as well as archival ASCA
observations, constrain any narrow Fe line present in the spectra to have an
equivalent width <90eV, broad lines (~150eV EW, \sigma ~ 1keV) are permitted.
We also study the variability of LMC X-1. Its X-ray power spectral density
(PSD) is approximately f^{-1} between 10^{-3} and 0.3Hz with a rms variability
of ~7%. Above 5keV the PSD shows evidence of a break at f > 0.2Hz, possibly
indicating an outer disc radius of ~1000GM/c^2 in this likely wind-fed system.
Furthermore, the coherence function between variability in the > 5keV band and
variablity in the lower energy bands is extremely low. We discuss the
implications of these observations for the mechanisms.


  We present the results of a deep (J=19.1 mag) infrared (ZYJHK) survey over
the full Alpha Persei open cluster extracted from the Data Release 9 of the
UKIRT Infrared Deep Sky Survey Galactic Clusters Survey. We have selected ~700
cluster member candidates in ~56 square degrees in \APer{} by combining
photometry in five near-infrared passbands and proper motions derived from the
multiple epochs provided by the UKIDSS GCS DR9. We also provide revised
membership for all previously published APer low-mass stars and brown dwarfs
recovered in GCS based on the new photometry and astrometry provided by DR9. We
find no evidence of $K$-band variability in members of APer with dispersion
less than 0.06-0.09 mag. We employed two independent but complementary methods
to derive the cluster luminosity and mass functions: a probabilistic analysis
and a more standard approach consisting of stricter astrometric and photometric
cuts. We find that the resulting luminosity and mass functions obtained from
both methods are consistent. We find that the shape of the APer mass function
is similar to that of the Pleiades although the characteristic mass may be
higher after including higher mass data from earlier studies (the dispersion is
comparable). We conclude that the mass functions of APer, the Pleiades, and
Praesepe are best reproduced by a log-normal representation similar to the
system field mass function although with some variation in the characteristic
mass and dispersion values.


  Game theory provides a general mathematical background to study the effect of
pair interactions and evolutionary rules on the macroscopic behavior of
multi-player games where players with a finite number of strategies may
represent a wide scale of biological objects, human individuals, or even their
associations. In these systems the interactions are characterized by matrices
that can be decomposed into elementary matrices (games) and classified into
four types. The concept of decomposition helps the identification of potential
games and also the evaluation of the potential that plays a crucial role in the
determination of the preferred Nash equilibrium, and defines the Boltzmann
distribution towards which these systems evolve for suitable types of dynamical
rules. This survey draws parallel between the potential games and the kinetic
Ising type models which are investigated for a wide scale of connectivity
structures. We discuss briefly the applicability of the tools and concepts of
statistical physics and thermodynamics. Additionally the general features of
ordering phenomena, phase transitions and slow relaxations are outlined and
applied to evolutionary games. The discussion extends to games with three or
more strategies. Finally we discuss what happens when the system is weakly
driven out of the "equilibrium state" by switching on non-potential components
representing games of cyclic dominance.


  A priori subcell limiting approach is developed for high-order flux
reconstruction/correction procedure via reconstruction (FR/CPR) on
two-dimensional unstructured quadrilateral meshes. Firstly, a modified
indicator based on modal energy coefficients is proposed to detect troubled
cells. Then, troubled cells are decomposed into nonuniform subcells and each
subcell has one solution point. A second-order finite difference
shock-capturing scheme based on nonuniform nonlinear weighted (NNW)
interpolation is constructed to calculate troubled cells while smooth cells are
calculated by the CPR method. Numerical investigations show that the subcell
limiting strategy on unstructured quadrilateral meshes is robust in
shock-capturing.


  This paper builds on prior work investigating the adaptation of session types
to provide behavioural information about Elixir modules. A type system called
ElixirST has been constructed to statically determine whether functions in an
Elixir module observe their endpoint specifications, expressed as session
types; a corresponding tool automating this typechecking has also been
constructed. In this paper we formally validate this type system. An LTS-based
operational semantics for the language fragment supported by the type system is
developed, modelling its runtime behaviour when invoked by the module client.
This operational semantics is then used to prove session fidelity for ElixirST.


  The present paper focuses on the order-disorder transition of an Ising model
on a self-similar lattice. We present a detailed numerical study, based on the
Monte Carlo method in conjunction with the finite size scaling method, of the
critical properties of the Ising model on some two dimensional deterministic
fractal lattices with different Hausdorff dimensions. Those with finite
ramification order do not display ordered phases at any finite temperature,
whereas the lattices with infinite connectivity show genuine critical behavior.
In particular we considered two Sierpinski carpets constructed using different
generators and characterized by Hausdorff dimensions d_H=log 8/log 3 = 1.8927..
and d_H=log 12/log 4 = 1.7924.., respectively.
  The data show in a clear way the existence of an order-disorder transition at
finite temperature in both Sierpinski carpets.
  By performing several Monte Carlo simulations at different temperatures and
on lattices of increasing size in conjunction with a finite size scaling
analysis, we were able to determine numerically the critical exponents in each
case and to provide an estimate of their errors.
  Finally we considered the hyperscaling relation and found indications that it
holds, if one assumes that the relevant dimension in this case is the Hausdorff
dimension of the lattice.


  Bootstrap is a popular methodology for simulating input uncertainty. However,
it can be computationally expensive when the number of samples is large. We
propose a new approach called \textbf{Orthogonal Bootstrap} that reduces the
number of required Monte Carlo replications. We decomposes the target being
simulated into two parts: the \textit{non-orthogonal part} which has a
closed-form result known as Infinitesimal Jackknife and the \textit{orthogonal
part} which is easier to be simulated. We theoretically and numerically show
that Orthogonal Bootstrap significantly reduces the computational cost of
Bootstrap while improving empirical accuracy and maintaining the same width of
the constructed interval.


  To schedule a large number of EVs with the presence of practical nonconvex
charging constraints, a distributed and randomized algorithm is proposed in
this paper. The algorithm assumes the availability of a coordinator which can
communicate with all EVs. In each iteration of the algorithm, the coordinator
receives tentative charging profiles from the EVs and computes a broadcast
control signal. After receiving this broadcast control signal, each EV
generates a probability distribution over its admissible charging profiles, and
samples from the distribution to update its tentative charging profile.
  We prove that the algorithm converges almost surely to a charging profile in
finite iterations. The final charging profile (that the algorithm converges to)
is random, i.e., it depends on the realization. We characterize the final
charging profile---a charging profile can be a realization of the final
charging profile if and only if it is a Nash equilibrium of the game in which
each EV seeks to minimize the inner product of its own charging profile and the
aggregate electricity demand. Furthermore, we provide a uniform suboptimality
upper bound, that scales O(1/n) in the number n of EVs, for all realizations of
the final charging profile.


  We comparatively study three of the most useful quantum information tools to
detect quantum critical points (QCPs) when only finite temperature data are
available. We investigate quantitatively how the quantum discord, the quantum
teleportation based QCP detectors, and the quantum coherence spectrum pinpoint
the QCPs of several spin-$1/2$ chains. We work in the thermodynamic limit
(infinite number of spins) and with the spin chains in equilibrium with a
thermal reservoir at temperature $T$. The models here studied are the $XXZ$
model with and without an external longitudinal magnetic field, the Ising
transverse model, and the $XY$ model subjected to an external transverse
magnetic field.


  The construction of attractors of a dissipative difference equation is
usually based on compactness assumptions. In this paper, we replace them with
contractivity assumptions under which the pullback and forward attractors are
identical. As a consequence, attractors degenerate to unique bounded entire
solutions. As an application, we investigate attractors of integrodifference
equations which are popular models in theoretical ecology.


  Very recently, PAMELA Collaboration has formally reported two sets of data on
positron and antiproton flux measurements done at very high energies and with
unprecedented accuracy. The reports reveal a puzzle of great topical interest
and importance : it is decisively found that there is a sharp smooth rise of
the positron fraction, whereas for antiproton production no such rises occur;
rather the fraction either flattens, or shows signs of falling off gradually
with increasing energies. The present work is just an attempt to decipher the
riddle with the help of a host of radically new ideas about the
particle-structure, the multiparticle-production mechanisms and the concept of
nucleon break-downs into the constituent partons. The application of these
ideas found remarkable successes in the past; exactly similar or more striking
are the findings by the present study.


  X-ray observations of galaxy clusters reveal a large range of morphologies
with various degrees of disturbance, showing that the assumptions of
hydrostatic equilibrium and spherical shape which are used to determine the
cluster mass from X-ray data are not always satisfied. It is therefore
important for the understanding of cluster properties as well as for
cosmological applications to detect and quantify substructure in X-ray images
of galaxy clusters. Two promising methods to do so are power ratios and center
shifts. Since these estimators can be heavily affected by Poisson noise and
X-ray background, we performed an extensive analysis of their statistical
properties using a large sample of simulated X-ray observations of clusters
from hydrodynamical simulations. We quantify the measurement bias and error in
detail and give ranges where morphological analysis is feasible. A new,
computationally fast method to correct for the Poisson bias and the X-ray
background contribution in power ratio and center shift measurements is
presented and tested for typical XMM-Newton observational data sets. We studied
the morphology of 121 simulated cluster images and establish structure
boundaries to divide samples into relaxed, mildly disturbed and disturbed
clusters. In addition, we present a new morphology estimator - the peak of the
0.3-1 r500 P3/P0 profile to better identify merging clusters. The analysis
methods were applied to a sample of 80 galaxy clusters observed with
XMM-Newton. We give structure parameters (P3/P0 in r500, w and P3/P0_max) for
all 80 observed clusters. Using our definition of the P3/P0 (w) substructure
boundary, we find 41% (47%) of our observed clusters to be disturbed.


  The F-index of a graph $G$ is the sum of the cubes of the degrees of the
vertices of $G$. In this paper, explicit expressions for the F-index of
different transformation graphs of type ${{G}^{xyz}}$ with $x, y, z\in \{-, +
\}$ are obtained. F-index for semitotal point graph and semitotal line graph
are also obtained here.


  Learning 3D geometry directly from raw data, such as point clouds, triangle
soups, or unoriented meshes is still a challenging task that feeds many
downstream computer vision and graphics applications.
  In this paper, we introduce SALD: a method for learning implicit neural
representations of shapes directly from raw data. We generalize sign agnostic
learning (SAL) to include derivatives: given an unsigned distance function to
the input raw data, we advocate a novel sign agnostic regression loss,
incorporating both pointwise values and gradients of the unsigned distance
function. Optimizing this loss leads to a signed implicit function solution,
the zero level set of which is a high quality and valid manifold approximation
to the input 3D data. The motivation behind SALD is that incorporating
derivatives in a regression loss leads to a lower sample complexity, and
consequently better fitting. In addition, we prove that SAL enjoys a minimal
length property in 2D, favoring minimal length solutions. More importantly, we
are able to show that this property still holds for SALD, i.e., with
derivatives included.
  We demonstrate the efficacy of SALD for shape space learning on two
challenging datasets: ShapeNet that contains inconsistent orientation and
non-manifold meshes, and D-Faust that contains raw 3D scans (triangle soups).
On both these datasets, we present state-of-the-art results.


  For urban governments, introducing policies has long been adopted as a main
approach to instigate regeneration processes, and to promote social mixing and
vitality within the city. However, due to the absence of large fine-grained
datasets, the effects of these policies have been historically hard to
evaluate. In this research, we illustrate how a combination of large-scale
datasets, the Index of Deprivation and Foursquare data (an online geo-social
network service) could be used to investigate the impact of the 2012 Olympic
Games on the regeneration of East London neighbourhoods. We study and quantify
both the physical and socio-economic aspects of this, where our empirical
findings suggest that the target areas did indeed undergo regeneration after
the Olympic project in some ways. In general, the growth rate of Foursquare
venue density in Olympic host boroughs is higher than the city's average level
since the preparation period of the Games and up to two years after the event.
Furthermore, the deprivation levels in East London boroughs also saw
improvements in various aspects after the Olympic Games. One negative outcome
we notice is that the housing affordability becomes even more of an issue in
East London areas with the regeneration gradually unfolding.


  In nonlinear electrodynamics, QED included, we find a static solution to the
field equations with an electric charge as its source, which is comprised of
homogeneous parallel magnetic and electric fields, and a radial
spherically-nonsymmetric long-range magnetic field, whose magnetic charge is
proportional to the electric charge and also depends on the homogeneous
component of the solution.


  In 2010, the Human Space Technology Initiative (HSTI) was launched by the
United Nations Office for Outer Space Affairs (UNOOSA) within the United
Nations Programme on Space Applications. The Initiative aims at promoting
international cooperation in human spaceflight and space exploration-related
activities, creating awareness among countries on the benefits of utilizing
human space technology and its applications, and building capacity in
microgravity education and research. HSTI has conducted a series of outreach
activities and expert meetings bringing together participants from around the
world. HSTI will also be implementing science and educational activities in
relevant areas to raise the capacities, particularly in developing countries,
in pursuit of the development goals of the United Nations, thus contributing to
promoting the peaceful uses of outer space.


  Here we propose a method of the excitation of perpendicular standing spin
waves (PSSWs) of different orders in an optomagnonic microcavity by ultrashort
laser pulses. The microcavity is formed by a magnetic dielectric film
surrounded by dielectric non-magnetic Bragg mirrors. Optical cavity modes in
the magnetic layer provide concentration and strongly non-uniform distribution
of the optical power over the layer thickness and therefore induce the
effective field of the inverse Faraday effect also spatially non-uniform. It
results in excitation of PSSWs. PSSWs whose wavevector is closest to the
wavevector characterizing distribution of the inverse Faraday effect field are
excited most efficiently. Consequently, a key advantage of this approach is a
selectivity of the PSSW excitation which allows to launch PSSWs of required
orders only. All-optical operation of the optomagnonic cavities opens new
possibilities for their applications for quantum technologies.


  Symmetry conserving density functional theory (DFT) based
no-core-configuration-interaction framework (DFT-NCCI) is an excellent tool for
precision calculation of diverse (pseudo-)observables related to isospin
symmetry breaking from elusive isospin impurities trough isospin corrections to
superallowed beta decays to mirror- and triplet-displacement energies and
mirror energy differences (MED) along rotational bands. In our recent work
[Phys. Rev. C {\bf 106}, 024327 (2022)] we performed axial DFT-NCCI
calculations and failed to reproduce a sign of MED in positive-parity bands of
45Sc/45Cr T=3/2 mirror pair what casts a shadow on credibility of the model. In
this work we aim to perform a thorough analysis of this case with the focus on
sensitivity of our predictions with respect to: (i) low-energy constants (LECs)
of our effective contact charge symmetry breaking (CSB) force and (ii) nuclear
shape. We demonstrate, among the other, that inclusion of triaxial
positive-parity ground-state - which is actually the global positive-parity
minimum in our unconstrained mean-field calculation - in the DFT-NCCI
calculations instead of the axial one used before leads to MED which are
consistent with experimental data concerning both their sign as well as
magnitude without any need for fine-tuning of the model's LECs.


  We (1) determine the number of Latin rectangles with 11 columns and each
possible number of rows, including the Latin squares of order~11, (2) answer
some questions of Alter by showing that the number of reduced Latin squares of
order $n$ is divisible by $f!$ where $f$ is a particular integer close to
$\frac12n$, (3) provide a formula for the number of Latin squares in terms of
permanents of $(+1,-1)$-matrices, (4) find the extremal values for the number
of 1-factorisations of $k$-regular bipartite graphs on $2n$ vertices whenever
$1\leq k\leq n\leq11$, (5) show that the proportion of Latin squares with a
non-trivial symmetry group tends quickly to zero as the order increases.


  We investigate the problem of autonomous agents processing pieces of
information that may be corrupted (tainted). Agents have the option of
contacting a central database for a reliable check of the status of the
message, but this procedure is costly and therefore should be used with
parsimony. Agents have to evaluate the risk of being infected, and decide if
and when communicating partners are affordable. Trustability is implemented as
a personal (one-to-one) record of past contacts among agents, and as a
mean-field monitoring of the level of message corruption. Moreover, this
information is slowly forgotten in time, so that at the end everybody is
checked against the database. We explore the behavior of a homogeneous system
in the case of a fixed pool of spreaders of corrupted messages, and in the case
of spontaneous appearance of corrupted messages.


  Cross-modal retrieval methods have been significantly improved in last years
with the use of deep neural networks and large-scale annotated datasets such as
ImageNet and Places. However, collecting and annotating such datasets requires
a tremendous amount of human effort and, besides, their annotations are usually
limited to discrete sets of popular visual classes that may not be
representative of the richer semantics found on large-scale cross-modal
retrieval datasets. In this paper, we present a self-supervised cross-modal
retrieval framework that leverages as training data the correlations between
images and text on the entire set of Wikipedia articles. Our method consists in
training a CNN to predict: (1) the semantic context of the article in which an
image is more probable to appear as an illustration (global context), and (2)
the semantic context of its caption (local context). Our experiments
demonstrate that the proposed method is not only capable of learning
discriminative visual representations for solving vision tasks like image
classification and object detection, but that the learned representations are
better for cross-modal retrieval when compared to supervised pre-training of
the network on the ImageNet dataset.


  The crystallographic and magnetic phase diagram of the n=2 layered manganite
La2-2xSr1+2xMn2O7 in the region x=>0.5 has been studied using temperature
dependent neutron powder diffraction. The magnetic phase diagram reveals a
progression of ordered magnetic structures generally paralleling that of 3-D
perovskites with similar electronic doping: A (0.5<=x<=0.66) --> C
(0.75<=x<=0.90) --> G (0.90<=x<=1.0). However, the quasi-2-D structure
amplifies this progression to expose features of manganite physics uniquely
accessible in the layered systems: (a) a "frustrated" region between the A and
C regimes where no long-range magnetic order is observed; (b) magnetic
polytypism arising from weak inter-bilayer magnetic exchange in the Type-C
regime; and (c) a tetragonal to orthorhombic phase transition whose temperature
evolution directly measures ordering of d3y2-r2 orbitals in the a-b plane. This
orbital-ordering transition is precursory to Type-C magnetic ordering, where
ferromagnetic rods lie parallel to the b-axis. These observations support the
notion that eg orbital polarisation is the driving force behind magnetic spin
ordering. Finally, in the crossover region between Type-C and Type-G states, we
see some evidence for the development of local Type-C clusters embedded in a
Type-G framework, directly addressing proposals of similar short-range magnetic
ordering in highly-doped La1-xCaxMnO3 perovskites.


  This paper presents a novel Dialect Identification (DID) system developed for
the Fifth Edition of the Multi-Genre Broadcast challenge, the task of
Fine-grained Arabic Dialect Identification (MGB-5 ADI Challenge). The system
improves upon traditional DNN x-vector performance by employing a Convolutional
and Long Short Term Memory-Recurrent (CLSTM) architecture to combine the
benefits of a convolutional neural network front-end for feature extraction and
a back-end recurrent neural to capture longer temporal dependencies.
Furthermore we investigate intensive augmentation of one low resource dialect
in the highly unbalanced training set using time-scale modification (TSM). This
converts an utterance to several time-stretched or time-compressed versions,
subsequently used to train the CLSTM system without using any other corpus. In
this paper, we also investigate speech augmentation using MUSAN and the RIR
datasets to increase the quantity and diversity of the existing training data
in the normal way. Results show firstly that the CLSTM architecture outperforms
a traditional DNN x-vector implementation. Secondly, adopting TSM-based speed
perturbation yields a small performance improvement for the unbalanced data,
finally that traditional data augmentation techniques yield further benefit, in
line with evidence from related speaker and language recognition tasks. Our
system achieved 2nd place ranking out of 15 entries in the MGB-5 ADI challenge,
presented at ASRU 2019.


  We study the geometric phase (GP) in neutrino oscillation for both Dirac and
Majorana neutrinos. We apply the kinematic generalization of the GP to quantum
open systems that take into account the coupling to a dissipative environment.
In the dissipationless case, the GP does not depend on the Majorana angle. It
is not the case in the presence of dissipation and hence the GP can serve as a
tool determining the type of the Dirac vs the Majorana neutrino.


  In this study, we examine the perceived influence of others, across both
strong and weak social ties, on COVID-19 vaccination decisions in the United
States. We add context to social influence by measuring related concepts, such
as perceived agreement of others and perceived danger of COVID-19 to others. We
find that vaccinated populations perceived more influence from their social
circles than unvaccinated populations. This finding holds true across various
social groups, including family, close friends, and neighbors. Vaccinated
participants perceived that others agreed with their decision to get vaccinated
more than unvaccinated participants perceived others to agree with their
decision to not get vaccinated. Despite the clear differences in perceived
social influence and agreement across the groups, the majority of participants
across both vaccinated and unvaccinated populations perceived no social
influence from all social group in their decisions. Aligning with this result,
we find through open-ended responses that both vaccinated and unvaccinated
participants frequently cited fear as a motivating factor in their decision,
rather than social influence: vaccinated participants feared COVID-19, while
unvaccinated participants feared the vaccine itself.


  A ubiquitous challenge in machine learning is the problem of domain
generalisation. This can exacerbate bias against groups or labels that are
underrepresented in the datasets used for model development. Model bias can
lead to unintended harms, especially in safety-critical applications like
healthcare. Furthermore, the challenge is compounded by the difficulty of
obtaining labelled data due to high cost or lack of readily available domain
expertise. In our work, we show that learning realistic augmentations
automatically from data is possible in a label-efficient manner using
generative models. In particular, we leverage the higher abundance of
unlabelled data to capture the underlying data distribution of different
conditions and subgroups for an imaging modality. By conditioning generative
models on appropriate labels, we can steer the distribution of synthetic
examples according to specific requirements. We demonstrate that these learned
augmentations can surpass heuristic ones by making models more robust and
statistically fair in- and out-of-distribution. To evaluate the generality of
our approach, we study 3 distinct medical imaging contexts of varying
difficulty: (i) histopathology images from a publicly available generalisation
benchmark, (ii) chest X-rays from publicly available clinical datasets, and
(iii) dermatology images characterised by complex shifts and imaging
conditions. Complementing real training samples with synthetic ones improves
the robustness of models in all three medical tasks and increases fairness by
improving the accuracy of diagnosis within underrepresented groups. This
approach leads to stark improvements OOD across modalities: 7.7% prediction
accuracy improvement in histopathology, 5.2% in chest radiology with 44.6%
lower fairness gap and a striking 63.5% improvement in high-risk sensitivity
for dermatology with a 7.5x reduction in fairness gap.


  Here, we delineate a comprehensive abundance analysis of four $r$-process
enhanced metal-poor stars observed with HORuS spectrograph on a 10-m class
telescope, GTC. The high signal-to-noise ratio at $R \approx 25000$ spectral
resolution allowed us to detect 16 light and 20 neutron-capture elements along
with Th in two stars. Four of our program stars show signatures of mixing in
their atmosphere. Through detailed abundance analysis of four $r$-process
enhanced stars together with already identified $r$-process-rich stars in
literature, we probe the production sites of neutron-capture elements. The
[Zr/Eu] ratio as a function of metallicity shows the evidence of multiple
channels for the production of $r$-process. Thorium to first and second
$r$-process peak elements ratios also support the similar non-universality of
neutron-capture elements. An increased sample of $r$-process enhanced stars
will enable us understand different formation channels of neutron capture
elements. Using the kinematic analysis, we found the clues of accretion for two
of our program stars.


  This paper is part of a project that aims at modelling wave propagation in
random media by means of Fourier integral operators. A partial aspect is
addressed here, namely explicit models of stochastic, highly irregular
transport speeds in one-dimensional transport, which will form the basis for
more complex models. Starting from the concept of a Goupillaud medium (a
layered medium in which the layer thickness is proportional to the propagation
speed), a class of stochastic assumptions and limiting procedures leads to
characteristic curves that are L\'evy processes. Solutions corresponding to
discretely layered media are shown to converge to limits as the time step goes
to zero (almost surely pointwise almost everywhere). This translates into
limits in the Fourier integral operator representations.


  In the present paper, experimental observations of the multifragmentation
processes of light relativistic nuclei carried out by means of emulsions are
reviewed. Events of the type of "white" stars in which the dissociation of
relativistic nuclei is not accompanied by the production of mesons and the
target-nucleus fragments are considered.
  A distinctive feature of the charge topology in the dissociation of the Ne,
Mg, Si, and S nuclei is an almost total suppression of the binary splitting of
nuclei to fragments with charges higher than 2. The growth of the nuclear
fragmentation degree is revealed in an increase in the multiplicity of singly
and doubly charged fragments with decreasing charge of the non-excited part of
the fragmenting nucleus.
  The processes of dissociation of stable Li, Be, B, C, N, and O isotopes to
charged fragments were used to study special features of the formation of
systems consisting of the lightest $\alpha$, d, and t nuclei. Clustering in
form of the $^3$He nucleus can be detected in "white" stars via the
dissociation of neutron-deficient Be, B, C, and N isotopes.


  The monthly pattern of suicides has remained a puzzle ever since it was
discovered in the second half of the 19th century. In this paper we intend to
"explain" not the pattern itself but rather its changes across countries and in
the course of time. First, we show that the fairly common idea according to
which this pattern is decaying in "modern" societies is not altogether true.
For instance, around 2000, in well urbanized countries like South Korea or
Spain this pattern was still as strong as it was in France (and other European
countries) in the late 19th century. The method that we use in order to make
some progress in our understanding is the time-honoured Cartesian approach of
breaking up the problem under consideration "into as many parts as might be
necessary to solve it". More specifically, we try two decompositions of monthly
suicides: (i) according to suicide methods (ii) according to age-groups.
  The first decomposition points out the key-role of hanging and drowning. The
second shows the crucial role of the $ 15-20 $ and $ 65+ $ age-groups. Then, we
present a number of cases in which age-group decomposition provides adequate
predictions. It turns out that the cases in which the predictions do not work
are newly urbanized countries. The discrepancies may be due to a memory effect
which induces a time-lag extending over one or two generations. Finally, in the
light of the new results presented in the present paper, we re-examine the
theory proposed by Emile Durkheim.


  We report the discovery of 15 quasars and bright galaxies at 5.7 < z < 6.9.
This is the initial result from the Subaru High-z Exploration of Low-Luminosity
Quasars (SHELLQs) project, which exploits the exquisite multiband imaging data
produced by the Subaru Hyper Suprime-Cam (HSC) Strategic Program survey. The
candidate selection is performed by combining several photometric approaches
including a Bayesian probabilistic algorithm to reject stars and dwarfs. The
spectroscopic identification was carried out with the Gran Telescopio Canarias
and the Subaru Telescope for the first 80 deg2 of the survey footprint. The
success rate of our photometric selection is quite high, approaching 100 % at
the brighter magnitudes (zAB < 23.5 mag). Our selection also recovered all the
known high-z quasars on the HSC images. Among the 15 discovered objects, six
are likely quasars, while the other six with interstellar absorption lines and
in some cases narrow emission lines are likely bright Lyman-break galaxies. The
remaining three objects have weak continua and very strong and narrow Ly alpha
lines, which may be excited by ultraviolet light from both young stars and
quasars. These results indicate that we are starting to see the steep rise of
the luminosity function of z > 6 galaxies, compared with that of quasars, at
magnitudes fainter than M1450 ~ -22 mag or zAB ~24 mag. Follow-up studies of
the discovered objects as well as further survey observations are ongoing.


  A theory of nucleus-nucleus collisions has been developed for kinetic
energies substantially in excess of the binding energy. The very high pressure
produced in the compound system as a result of the fusion of the two colliding
nuclei is the reason for the subsequent hydrodynamic expansion of the nuclear
medium. The energy and angular distributions of the reaction products are
investigated. The charge distribution is also determined in the case where the
nucleon and ion components of the reaction products are predominant. A solution
is found for the expansion into vacuum of a sphere in which the initially
uniformly distributed material is initially at rest and at an ultrarelativistic
temperature.


  Reliability of sequential hypothesis testing can be greatly improved when
decision maker is given the freedom to adaptively take an action that
determines the distribution of the current collected sample. Such advantage of
sampling adaptivity has been realized since Chernoff's seminal paper in 1959.
While a large body of works have explored and investigated the gain of
adaptivity, in the general multiple-hypothesis setting, the fundamental limits
of individual error probabilities have not been fully understood. In
particular, in the asymptotic regime as the expected stopping time tends to
infinity, the error exponents are only characterized in specific cases, such as
that of the total error probability. In this paper, we consider a general setup
of active sequential multiple-hypothesis testing where at each time slot, a
temporally varying subset of data sources (out of a known set) emerges from
which the decision maker can select to collect samples, subject to a family of
expected selection budget constraints. The selection of sources, understood as
the "action" at each time slot, is constrained in a predefined action space. At
the end of each time slot, the decision maker either decides to make the
inference on the $M$ hypotheses, or continues to observe the data sources for
the next time slot. The optimal tradeoffs among $M(M-1)$ types of error
exponents are characterized, and the achievable region is shown to be a convex
polytope. A companion asymptotically optimal test that strikes the balance
between exploration and exploitation is proposed to achieve any target error
exponents within the region. To the best of our knowledge, this is the first
time in the literature to identify such tradeoffs among error exponents, and it
uncovers the tension among different action taking policies even in the basic
setting of Chernoff.


  Distributional regression is extended to Gaussian response vectors of
dimension greater than two by parameterizing the covariance matrix $\Sigma$ of
the response distribution using the entries of its Cholesky decomposition. The
more common variance-correlation parameterization limits such regressions to
bivariate responses -- higher dimensions require complicated constraints among
the correlations to ensure positive definite $\Sigma$ and a well-defined
probability density function. In contrast, Cholesky-based parameterizations
ensure positive definiteness for all distributional dimensions no matter what
values the parameters take, enabling estimation and regularization as for other
distributional regression models. In cases where components of the response
vector are assumed to be conditionally independent beyond a certain lag $r$,
model complexity can be further reduced by setting Cholesky parameters beyond
this lag to zero a priori. Cholesky-based multivariate Gaussian regression is
first illustrated and assessed on artificial data and subsequently applied to a
real-world 10-dimensional weather forecasting problem. There the regression is
used to obtain reliable joint probabilities of temperature across ten future
times, leveraging temporal correlations over the prediction period to obtain
more precise and meteorologically consistent probabilistic forecasts.


  Operating experience from fusion research shows how Spitzer resistivity may
render ohmic heating in the chromosphere self limiting and thus serve to define
the lower margin of the transition region. Its upper margin is at about 6000 K,
where radiative cooling of He:H plasma decelerates sharply. The third and last
stage in the proposed scheme is expansion into the tenuous plasma of space,
which leads to the acceleration of ions to high energies, long recorded by
spacecraft instruments. There is thus dynamic continuity all the way from the
solar interior, the energy source for spinning columns in the Rayleigh Benard
setting of the convection zone, to the coronal exhalation of the solar wind, a
finding which should benefit the analysis of space weather, witness the
association between helium in the solar wind and the incidence of coronal mass
ejections.


  We describe a model independent lattice QCD method for determining the
deviation from unity for h_{A1}(1), the B -> D* l nu form factor at zero
recoil. We extend the double ratio method previously used to determine the B ->
D l nu form factor. The bulk of statistical and systematic errors cancel in the
double ratios we consider, yielding form factors which promise to reduce
present theoretical uncertainties in the determination of V_{cb}. We present
results from a prototype calculation at a single lattice spacing corresponding
to beta=5.7.


  During 2014 the second experimental area EAR2 was completed at the n-TOF
neutron beam facility at CERN. As the neutrons are produced via spallation, by
means of a high-intensity 20 GeV pulsed proton beam impinging on a thick
target, the resulting neutron beam covers an enormous energy range, from
thermal to several GeV. In this paper we describe two beam diagnostic devices,
designed and built at INFN-LNS, both exploiting silicon detectors coupled with
neutron converter foils containing 6Li. The first one is based on four silicon
pads and allows to monitor the neutron beam flux as a function of the neutron
energy. The second one, based on position sensitive silicon detectors, is
intended for the reconstruction of the beam profile, again as a function of the
neutron energy. Several electronic setups have been explored in order to
overcome the issues related to the gamma flash, namely a huge pulse present at
the start of each neutron bunch which may blind the detectors for some time.
The two devices have been characterized with radioactive sources at INFN-LNS
and then tested at n-TOF. The wide energy and intensity range they proved
capable of sustaining, made them quite attractive and suitable to be used in
both EAR1 and EAR2 n-TOF experimental areas, where they became immediately
operational.


  The AdS/CFT correspondence is established for the case of AdS$_3$ space
compactified on a filled rectangular torus with the CFT field on the boundary.


  Although the P\'olya enumeration theorem has been used extensively for
decades, an optimized, purely numerical algorithm for calculating its
coefficients is not readily available. We present such an algorithm for finding
the number of unique colorings of a finite set under the action of a finite
group.


  An alternative path is taken for deriving an action for the supersymmetric
5-brane in 11 dimensions. Selfduality does not follow from the action, but is
consistent with the equations of motion for arbitrary supergravity backgrounds.
The action involves a 2-form as well as a 5-form world-volume potential;
inclusion of the latter makes the action, as well as the non-linear selfduality
relation for the 3-form field strength, polynomial. The requirement of
invariance under kappa-transformations determines the form of the selfduality
relation, as well as the action. The formulation is shown to be equivalent to
earlier formulations of 5-brane dynamics.


  We have constructed numerically non-Abelian vortices in an SU(2)
Chern-Simons-Higgs theory with a quartic Higgs potential. We have analyzed
these solutions in detail by means of improved numerical codes and found some
unexpected features we did not find when a sixth-order Higgs potential was
used. The generic non-Abelian solutions have been generated by using their
corresponding Abelian counterparts as initial guess. Typically, the energy of
the non-Abelian solutions is lower than that of the corresponding Abelian one
(except in certain regions of the parameter space). Regarding the angular
momentum, the Abelian solutions possess the maximal value, although there exist
non-Abelian solutions which reach that maximal value too. In order to classify
the solutions it is useful to consider the non-Abelian solutions with
asymptotically vanishing $A_t$ component of the gauge potential, which may be
labelled by an integer number $m$. For vortex number $n=3$ and above, we have
found uniqueness violation: two different non-Abelian solutions with all the
global charges equal. Finally, we have investigated the limit of infinity Higgs
self-coupling parameter and found a piecewise Regge-like relation between the
energy and the angular momentum.


  This paper explores the physics reach of the proton-proton Future Circular
Collider (FCC-hh) and of the High-Energy LHC (HE-LHC) for searches of new
particles produced in the $s$-channel and decaying to two high-energy leptons,
jets (non-tops), tops or W/Z bosons. We discuss the expected discovery
potential and exclusion limits for benchmark models predicting new massive
particles that result in resonant structures in the invariant mass spectrum. We
also present a detailed study of the HE-LHC potential to discriminate among
different models, for a $Z'$ that could be discovered by the end of
High-Luminosity LHC (HL-LHC).


  We calculate using the Boltzmann transport theory the density dependent
mobility of two-dimensional (2D) electrons in GaAs, SiGe and AlAs quantum wells
as well as of 2D holes in GaAs quantum wells. The goal is to precisely
understand the recently reported breakthrough in achieving a record 2D mobility
for electrons confined in a GaAs quantum well. Comparing our theory with the
experimentally reported electron mobility in GaAs quantum wells, we conclude
that the mobility is limited by unintentional background random charged
impurities at an unprecedented low concentration of $\sim10^{13}
\mathrm{cm}^{-3}$. We find that this same low level of background disorder
should lead to 2D GaAs hole and 2D AlAs electron mobilities of $\sim10^7
\mathrm{cm}^2/Vs$ and $\sim4\times10^7 \mathrm{cm}^2/Vs$, respectively, which
are much higher theoretical limits than the currently achieved experimental
values in these systems. We therefore conclude that the current GaAs hole and
AlAs electron systems are much dirtier than the state of the arts 2D GaAs
electron systems. We present theoretical results for 2D mobility as a function
of density, effective mass, quantum well width, and valley degeneracy,
comparing with experimental data.


  Using a one-dimensional $\alpha\omega$-dynamo model appropriate to galaxies,
we study the possibility of dynamo action driven by a stochastic alpha effect
and shear. To determine the field evolution, one needs to examine a large
number of different realizations of the stochastic component of $\alpha$. The
net growth or decay of the field depends not only on the dynamo parameters but
also on the particular realization, the correlation time of the stochastic
$\alpha$ compared to turbulent diffusion timescale and the time over which the
system is evolved. For dynamos where both a coherent and fluctuating $\alpha$
are present, the stochasticity of $\alpha$ can help alleviate catastrophic
dynamo quenching, even in the absence of helicity fluxes. One can obtain final
field strengths up to a fraction $\sim 0.01$ of the equipartition field $
B_{eq}$ for dynamo numbers $| D| \sim 40$, while fields comparable to $ B_{eq}$
require much larger degree of $\alpha$ fluctuations or shear. This type of
dynamo may be particularly useful for amplifying fields in the central regions
of disk galaxies.


  Permutation polynomials over finite fields are an interesting and constantly
active research subject of study for many years. They have important
applications in areas of mathematics and engineering. In recent years,
permutation binomials and permutation trinomials attract people's interests due
to their simple algebraic forms. In this paper, by reversely using Tu's method
for the characterization of permutation polynomials with exponents of Niho
type, we propose a new method to construct permutation trinomials with
coefficients 1. Moreover, we give the explicit compositional inverses of a
class of permutation trinomials for a special case.


  Most machine learning-based coronary artery segmentation methods represent
the vascular lumen surface in an implicit way by the centerline and the
associated lumen radii, which makes the subsequent modeling process to generate
a whole piece of watertight coronary artery tree model difficult. To solve this
problem, in this paper, we propose a modeling method with the learning-based
segmentation results by (1) considering mesh vertices as physical particles and
using interaction force model and particle expansion model to generate
uniformly distributed point cloud on the implicit lumen surface and; (2) doing
incremental Delaunay-based triangulation. Our method has the advantage of being
able to consider the complex shape of the coronary artery tree as a whole
piece; hence no extra stitching or intersection removal algorithm is needed to
generate a watertight model. Experiment results demonstrate that our method is
capable of generating high quality mesh model which is highly consistent with
the given implicit vascular lumen surface, with an average error of 0.08 mm.


  This paper describes the development of Microsoft XiaoIce, the most popular
social chatbot in the world. XiaoIce is uniquely designed as an AI companion
with an emotional connection to satisfy the human need for communication,
affection, and social belonging. We take into account both intelligent quotient
(IQ) and emotional quotient (EQ) in system design, cast human-machine social
chat as decision-making over Markov Decision Processes (MDPs), and optimize
XiaoIce for long-term user engagement, measured in expected Conversation-turns
Per Session (CPS). We detail the system architecture and key components
including dialogue manager, core chat, skills, and an empathetic computing
module. We show how XiaoIce dynamically recognizes human feelings and states,
understands user intent, and responds to user needs throughout long
conversations. Since her launch in 2014, XiaoIce has communicated with over 660
million active users and succeeded in establishing long-term relationships with
many of them. Analysis of large scale online logs shows that XiaoIce has
achieved an average CPS of 23, which is significantly higher than that of other
chatbots and even human conversations.


  Online polarization has attracted the attention of researchers for many
years. Its effects on society are a cause for concern, and the design of
personalized depolarization strategies appears to be a key solution. Such
strategies should rely on a fine and accurate measurement, and a clear
understanding of polarization behaviors. However, the literature still lacks
ways to characterize them finely. We propose GRAIL, the first individual
polarization metric, relying on multiple factors. GRAIL assesses these factors
through entropy and is based on an adaptable Generalized Additive Model. We
evaluate the proposed metric on a Twitter dataset related to the highly
controversial debate about the COVID-19 vaccine. Experiments confirm the
ability of GRAIL to discriminate between polarization behaviors. To go further,
we provide a finer characterization and explanation of the identified behaviors
through an innovative evaluation framework.


  We investigate the periodically driven dynamics of many-body systems, either
classical or quantum, finite-dimensional or mean-field, displaying an unbounded
phase-space. We find that the inclusion of a smooth periodic drive atop an
otherwise ergodic dynamics leads to a long-lived prethermalization, even at
moderate driving frequencies. In specific asymptotic limits, we compute the
corresponding prethermal Hamiltonian from an analytical perturbation scheme.


  In this paper, we investigate a boundary case of the classical prescribed
curvature problem. We focus on prescribing the scalar curvature function K and
the boundary mean curvature H on the standard ball. Our analysis extendes
previous studies by considering the scenario where the curvatures K and H are
close to constants. Using a perturbative approach and leveraging the ansatz
introduced by Han and Li, we establish new existence results for the conformal
metric when the prescribed curvatures are near constants


  We present local velocity measurements in emulsions under shear using
heterodyne Dynamic Light Scattering. Two emulsions are studied: a dilute system
of volume fraction $\phi=20$ % and a concentrated system with $\phi=75$ %.
Velocity profiles in both systems clearly show the presence of wall slip. We
investigate the evolution of slip velocities as a function of shear stress and
discuss the validity of the corrections for wall slip classically used in
rheology. Focussing on the bulk flow, we show that the dilute system is
Newtonian and that the concentrated emulsion is shear-thinning. In the latter
case, the curvature of the velocity profiles is compatible with a
shear-thinning exponent of 0.4 consistent with global rheological data.
However, even if individual profiles can be accounted for by a power-law fluid
(with or without a yield stress), we could not find a fixed set of parameters
that would fit the whole range of applied shear rates. Our data thus raise the
question of the definition of a global flow curve for such a concentrated
system. These results show that local measurements are a crucial complement to
standard rheological tools. They are discussed in light of recent works on soft
glassy materials.


  We have characterized a new Magnetic Force Microscopy (MFM) probe based on an
iron filled carbon nanotube (FeCNT) using MFM imaging on permalloy (Py) disks
saturated in a high magnetic field perpendicular to the disk plane. The
experimental data are accurately modeled by describing the FeCNT probe as
having a single magnetic monopole at its tip whose effective magnetic charge is
determined by the diameter of the iron wire enclosed in the carbon nanotube and
its saturation magnetization 4 \pi M_s ~ 2.2 x 10^4 G. A magnetic monopole
probe enables quantitative measurements of the magnetic field gradient close to
the sample surface. The lateral resolution is defined by the diameter of the
iron wire ~15 nm and the probe-sample separation. As a demonstration, the
magnetic field gradients close to the surface of a Py dot in domain and vortex
states were imaged.


  Gene expression datasets consist of thousand of genes with relatively small
samplesizes (i.e. are large-$p$-small-$n$). Moreover, dependencies of various
orders co-exist in the datasets. In the Undirected probabilistic Graphical
Model (UGM) framework the Glasso algorithm has been proposed to deal with high
dimensional micro-array datasets forcing sparsity. Also, modifications of the
default Glasso algorithm are developed to overcome the problem of complex
interaction structure. In this work we advocate the use of a simple score-based
Hill Climbing algorithm (HC) that learns Gaussian Bayesian Networks (BNs)
leaning on Directed Acyclic Graphs (DAGs). We compare HC with Glasso and its
modifications in the UGM framework on their capability to reconstruct GRNs from
micro-array data belonging to the Escherichia Coli genome. We benefit from the
analytical properties of the Joint Probability Density (JPD) function on which
both directed and undirected PGMs build to convert DAGs to UGMs.
  We conclude that dependencies in complex data are learned best by the HC
algorithm, presenting them most accurately and efficiently, simultaneously
modelling strong local and weaker but significant global connections coexisting
in the gene expression dataset. The HC algorithm adapts intrinsically to the
complex dependency structure of the dataset, without forcing a specific
structure in advance. On the contrary, Glasso and modifications model
unnecessary dependencies at the expense of the probabilistic information in the
network and of a structural bias in the JPD function that can only be relieved
including many parameters.


  We present observations and modeling results of the propagation and impact at
Earth of a high-latitude, extended filament channel eruption that commenced on
2015 July 9. The coronal mass ejection (CME) that resulted from the filament
eruption was associated with a moderate disturbance at Earth. This event could
be classified as a so-called "problem storm" because it lacked the usual solar
signatures that are characteristic of large, energetic, Earth-directed CMEs
that often result in significant geoeffective impacts. We use solar
observations to constrain the initial parameters and therefore to model the
propagation of the 2015 July 9 eruption from the solar corona up to Earth using
3D magnetohydrodynamic heliospheric simulations with three different
configurations of the modeled CME. We find the best match between observed and
modeled arrival at Earth for the simulation run that features a toroidal flux
rope structure of the CME ejecta, but caution that different approaches may be
more or less useful depending on the CME-observer geometry when evaluating the
space weather impact of eruptions that are extreme in terms of their large size
and high degree of asymmetry. We discuss our results in the context of both
advancing our understanding of the physics of CME evolution and future
improvements to space weather forecasting.


  An $L(2, 1)$-labeling of a graph $G$ is an assignment of a nonnegative
integer to each vertex of $G$ such that adjacent vertices receive integers that
differ by at least two and vertices at distance two receive distinct integers.
The span of such a labeling is the difference between the largest and smallest
integers used. The $\lambda$-number of $G$, denoted by $\lambda(G)$, is the
minimum span over all $L(2, 1)$-labelings of $G$. Bodlaender {\it et al.}
conjectured that if $G$ is an outerplanar graph of maximum degree $\Delta$,
then $\lambda(G)\leq \Delta+2$. Calamoneri and Petreschi proved that this
conjecture is true when $\Delta \geq 8$ but false when $\Delta=3$. Meanwhile,
they proved that $\lambda(G)\leq \Delta+5$ for any outerplanar graph $G$ with
$\Delta=3$ and asked whether or not this bound is sharp. In this paper we
answer this question by proving that $\lambda(G)\leq \Delta + 3$ for every
outerplanar graph with maximum degree $\Delta=3$. We also show that this bound
$\Delta + 3$ can be achieved by infinitely many outerplanar graphs with
$\Delta=3$.


  This work presents the first convolutional neural network that learns an
image-to-graph translation task without needing external supervision. Obtaining
graph representations of image content, where objects are represented as nodes
and their relationships as edges, is an important task in scene understanding.
Current approaches follow a fully-supervised approach thereby requiring
meticulous annotations. To overcome this, we are the first to present a
self-supervised approach based on a fully-differentiable auto-encoder in which
the bottleneck encodes the graph's nodes and edges. This self-supervised
approach can currently encode simple line drawings into graphs and obtains
comparable results to a fully-supervised baseline in terms of F1 score on
triplet matching. Besides these promising results, we provide several
directions for future research on how our approach can be extended to cover
more complex imagery.


  In this paper cross-relaxation between nitrogen-vacancy (NV) centers and
substitutional nitrogen in a diamond crystal was studied. It was demonstrated
that optically detected magnetic resonance signals (ODMR) can be used to
measure these signals successfully. The ODMR were detected at axial magnetic
field values around 51.2~mT in a diamond sample with a relatively high
(200~ppm) nitrogen concentration. We observed transitions that involve magnetic
sublevels that are split by the hyperfine interaction. Microwaves in the
frequency ranges from 1.3 GHz to 1.6 GHz ($m_S=0\longrightarrow m_S=-1$ NV
transitions) and from 4.1 to 4.6 GHz ($m_S=0\longrightarrow m_S=+1$ NV
transitions) were used.
  To understand the cross-relaxation process in more detail and, as a result,
reproduce measured signals more accurately, a model was developed that
describes the microwave-initiated transitions between hyperfine levels of the
NV center that are undergoing anti-crossing and are strongly mixed in the
applied magnetic field. Additionally, we simulated the extent to which the
microwave radiation used to induce ODMR in the NV center also induced
transitions in the substitutional nitrogen via cross-relaxation.
  The improved understanding of the NV processes in the presence of a magnetic
field will be useful for designing NV-diamond-based devices for a wide range of
applications from implementation of q-bits to hyperpolarization of large
molecules to various quantum technological applications such as field sensors.


  Multi-Camera arrays are increasingly employed in both consumer and industrial
applications, and various passive techniques are documented to estimate depth
from such camera arrays. Current depth estimation methods provide useful
estimations of depth in an imaged scene but are often impractical due to
significant computational requirements. This paper presents a novel framework
that generates a high-quality continuous depth map from multi-camera
array/light field cameras. The proposed framework utilizes analysis of the
local Epipolar Plane Image (EPI) to initiate the depth estimation process. The
estimated depth map is then processed using Total Variation (TV) minimization
based on the Fenchel-Rockafellar duality. Evaluation of this method based on a
well-known benchmark indicates that the proposed framework performs well in
terms of accuracy when compared to the top-ranked depth estimation methods and
a baseline algorithm. The test dataset includes both photorealistic and
non-photorealistic scenes. Notably, the computational requirements required to
achieve an equivalent accuracy are significantly reduced when compared to the
top algorithms. As a consequence, the proposed framework is suitable for
deployment in consumer and industrial applications.


  High-resolution ultraviolet observations of nearby bright and faint stars are
required to evaluate changes in the past and future galactic environments of
the Sun, and the possibly impact of these changes on the interplanetary
environment at 1 AU (around the Earth). The boundary conditions of the
heliosphere and interplanetary environments are constrained by the
characteristics of the surrounding interstellar material (ISM), which changes
on timescales of 10^3-10^5 years. An increase in the density of the
interstellar cloud surrounding the solar system to 10 cm^-3 decreases the
heliosphere radius by about an order of magnitude. UV observations of nearby
stars at high spectral resolution (>300,000) and high signal-to-noise are
required to evaluate future modifications to heliosphere properties by the ISM.


  The atmospheric reaction of H$_2$S with Cl has been reinvestigated to check
if, as previously suggested, only explicit dynamical computations can lead to
an accurate evaluation of the reaction rate because of strong recrossing
effects and the breakdown of the variational extension of transition state
theory. For this reason, the corresponding potential energy surface has been
thoroughly investigated, thus leading to an accurate characterization of all
stationary points, whose energetics has been computed at the state of the art.
To this end, coupled-cluster theory including up to quadruple excitations has
been employed, together with the extrapolation to the complete basis set limit
and also incorporating core-valence correlation, spin-orbit, and scalar
relativistic effects as well as diagonal Born-Oppenheimer corrections. This
highly accurate composite scheme has also been paralleled by less expensive yet
promising computational approaches. Moving to kinetics, variational transition
state theory and its variable reaction coordinate extension for barrierless
steps have been exploited, thus obtaining a reaction rate constant (8.16 x
10$^{-11}$ cm$^3$ molecule$^{-1}$ s$^{-1}$ at 300 K and 1 atm) in remarkable
agreement with the experimental counterpart. Therefore, contrary to previous
claims, there is no need to invoke any failure of the transition state theory,
provided that sufficiently accurate quantum-chemical computations are
performed. The investigation of the puzzling case of the H$_2$S + Cl system
allowed us to present a robust approach for disclosing the thermochemistry and
kinetics of reactions of atmospheric and astrophysical interest.


  A number of recent works have proposed attention models for Visual Question
Answering (VQA) that generate spatial maps highlighting image regions relevant
to answering the question. In this paper, we argue that in addition to modeling
"where to look" or visual attention, it is equally important to model "what
words to listen to" or question attention. We present a novel co-attention
model for VQA that jointly reasons about image and question attention. In
addition, our model reasons about the question (and consequently the image via
the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional
convolution neural networks (CNN). Our model improves the state-of-the-art on
the VQA dataset from 60.3% to 60.5%, and from 61.6% to 63.3% on the COCO-QA
dataset. By using ResNet, the performance is further improved to 62.1% for VQA
and 65.4% for COCO-QA.


  Visual servoing involves choosing actions that move a robot in response to
observations from a camera, in order to reach a goal configuration in the
world. Standard visual servoing approaches typically rely on manually designed
features and analytical dynamics models, which limits their generalization
capability and often requires extensive application-specific feature and model
engineering. In this work, we study how learned visual features, learned
predictive dynamics models, and reinforcement learning can be combined to learn
visual servoing mechanisms. We focus on target following, with the goal of
designing algorithms that can learn a visual servo using low amounts of data of
the target in question, to enable quick adaptation to new targets. Our approach
is based on servoing the camera in the space of learned visual features, rather
than image pixels or manually-designed keypoints. We demonstrate that standard
deep features, in our case taken from a model trained for object
classification, can be used together with a bilinear predictive model to learn
an effective visual servo that is robust to visual variation, changes in
viewing angle and appearance, and occlusions. A key component of our approach
is to use a sample-efficient fitted Q-iteration algorithm to learn which
features are best suited for the task at hand. We show that we can learn an
effective visual servo on a complex synthetic car following benchmark using
just 20 training trajectory samples for reinforcement learning. We demonstrate
substantial improvement over a conventional approach based on image pixels or
hand-designed keypoints, and we show an improvement in sample-efficiency of
more than two orders of magnitude over standard model-free deep reinforcement
learning algorithms. Videos are available at
http://rll.berkeley.edu/visual_servoing .


  Interaction of an ultrastrong short laser pulse with non-prepolarized
near-critical density plasma is investigated in an ultrarelativistic regime,
with an emphasis on the radiative spin polarization of ejected electrons. Our
particle-in-cell simulations show explicit correlations between the angle
resolved electron polarization and the structure and properties of the
transient quasistatic plasma magnetic field. While the magnitude of the spin
signal is the indicator of the magnetic field strength created by the
longitudinal electron current, the asymmetry of electron polarization is found
to gauge the island-like magnetic distribution which emerges due to the
transverse current induced by the laser wave front. Our studies demonstrate
that the spin degree of freedom of ejected electrons could potentially serve as
an efficient tool to retrieve the features of strong plasma fields.


  For any smooth connected linear algebraic group G over an algebraically
closed field k, we describe the Picard group of the universal moduli stack of
principal G-bundles over pointed smooth k-projective curves.


  Antiperovskite nitrides are potential candidates for applications harvesting
solar light. With a comprehensive state-of-the-art approach combining hybrid
density-functional theory, many-body perturbation theory, the Wannier-Mott
model, density-functional perturbation theory, and the Feynman polaron model,
we explore excitonic and polaronic effects in X$_3$NA (X: Mg, Ca, Sr, Ba, A =
Sb, As). For all of them, we uncover a significant influence of the ionic
dielectric screening on the static dielectric constant. Small exciton binding
energies, weak electron-phonon coupling, and high charge-carrier mobilities
facilitate enhanced charge transport in Mg$_3$NSb, Sr$_3$NSb, and Ba$_3$NSb.
Our results highlight the potential of these nitrides as optimal candidates for
efficient photovoltaic absorbers.


  The idea of unification attempts to explain the structure of the Standard
Model (SM) in terms of fewer fundamental forces and/or matter fields. However,
traditional grand unified theories based on $SU(5)$ and $\mathrm{Spin}(10)$
shed no light on the existence of three generations of fermions, nor the
distinctive pattern of their Yukawa couplings to the Higgs. We discuss two
routes for unifying the SM gauge symmetry with its flavour symmetries: firstly,
unifying flavour with electroweak symmetries via the group $SU(4) \times
Sp(6)_L \times Sp(6)_R$; secondly, unifying flavour and colour via $SU(12)
\times SU(2)_L \times SU(2)_R$. In either case, all three generations of SM
fermions are unified into just two fundamental fields. In the larger part of
this proceeding, we describe how the former model of `electroweak flavour
unification' offers a new explanation of hierarchical fermion masses and CKM
angles. As a postscript, we show that gauge flavour unification can have
unexpected spin-offs not obviously related to flavour. In particular, the
$SU(12) \times SU(2)_L \times SU(2)_R$ symmetry, when broken, can leave behind
remnant discrete gauge symmetries that exactly stabilize protons to all orders.


  The Ericksen-Leslie system is a fundamental hydrodynamic model that describes
the evolution of incompressible liquid crystal flows of nematic type. In this
paper, we prove the uniqueness of global weak solutions to the general
Ericksen-Leslie system with a Ginzburg-Landau type approximation in a two
dimensional periodic domain. The proof is based on some delicate energy
estimates for the difference of two weak solutions within a suitable functional
framework that is less regular than the usual one at the natural energy level,
combined with the Osgood lemma involving a specific double-logarithmic type
modulus of continuity. We overcome the essential mathematical difficulties
arising from those highly nonlinear terms in the Leslie stress tensor and in
particular, the lack of maximum principle for the director equation due to the
stretching effect of the fluid on the director field. Our argument makes full
use of the coupling structure as well as the dissipative nature of the system,
and relies on some techniques from harmonic analysis and paradifferential
calculus in the periodic setting.


  Catalyst particles or complexes suspended in liquid films can trigger
chemical reactions leading to inhomogeneous concentrations of reactants and
products in the film. We demonstrate that the sensitivity of the liquid film's
gas-liquid surface tension to these inhomogeneous concentrations strongly
impacts the film stability. Using linear stability analysis, we identify novel
scenarios in which the film can be either stabilized or destabilized by the
reactions. Furthermore, we find so far unrevealed rupture mechanisms which are
absent in the chemically inactive case. The linear stability predictions are
confirmed by numerical simulations, which also demonstrate that the shape of
chemically active droplets can depart from the spherical cap and that unsteady
states such as traveling and standing waves might appear. Finally, we
critically discuss the relevance of our predictions by showing that the range
of our selected parameters is well accessible by typical experiments.


  How correlated are two quantum systems from the perspective of a third? We
answer this by providing an optimal quantum state redistribution protocol for
multipartite product sources. Specifically, given an arbitrary quantum state of
three systems, where Alice holds two and Bob holds one, we identify the cost,
in terms of quantum communication and entanglement, for Alice to give one of
her parts to Bob. The communication cost gives the first known operational
interpretation to quantum conditional mutual information. The optimal procedure
is self-dual under time reversal and is perfectly composable. This generalizes
known protocols such as the state merging and fully quantum Slepian-Wolf
protocols, from which almost every known protocol in quantum Shannon theory can
be derived.


  Transformer-based large language models (LLMs) provide a powerful foundation
for natural language tasks in large-scale customer-facing applications.
However, studies that explore their vulnerabilities emerging from malicious
user interaction are scarce. By proposing PromptInject, a prosaic alignment
framework for mask-based iterative adversarial prompt composition, we examine
how GPT-3, the most widely deployed language model in production, can be easily
misaligned by simple handcrafted inputs. In particular, we investigate two
types of attacks -- goal hijacking and prompt leaking -- and demonstrate that
even low-aptitude, but sufficiently ill-intentioned agents, can easily exploit
GPT-3's stochastic nature, creating long-tail risks. The code for PromptInject
is available at https://github.com/agencyenterprise/PromptInject.


  A novel approach for solving equations of motion of finite-sized satellite
supposed to be moving in a proximity and around the planet in the elliptic
restricted three-body problem, ER3BP is presented in this semi-analytical
investigation. We consider two primaries, M_Sun and m_planet (the last is
secondary in that binary system), both are orbiting around their barycenter on
elliptic orbits. Satellite is considered to be the solid ellipsoid having
nearly spherical form, with its gravitational potential to be given by a
formula of MacCullagh type. Our aim is to revisit previously presented in work
[Ashenberg, 1996] approach and to investigate the updated type of the satellite
dynamics correlated implicitly to a kind of trapped motion (in the synodic
co-rotating Cartesian coordinate system) in so way that satellite will always
to be located near the secondary planet, m_planet, moving on quasi-stable
elliptic orbit.


  Scalar perturbation represented by the Klein-Gordon field equation of the
system of black holes in 2+1 dimensional Static charged dilaton gravity
proposed by Chan and Mann is investigated analytically. The radial equation is
analyzed near horizon and at the asymptotic region to show the absence of
superradiance instability.


  The maximum amplitude (Rm) of a solar cycle, in the term of mean sunspot
numbers, is well-known to be positively correlated with the preceding minimum
(Rmin). So far as the long term trend is concerned, a low level of Rmin tends
to be followed by a weak Rm, and vice versa. In this paper, we found that the
evidence is insufficient to infer a very weak Cycle 24 from the very low Rmin
in the preceding cycle. This is concluded by analyzing the correlation in the
temporal variations of parameters for two successive cycles.


  For every given $\beta<0$, we study the problem of maximizing the first Robin
eigenvalue of the Laplacian $\lambda_\beta(\Omega)$ among convex (not
necessarily smooth) sets $\Omega\subset\mathbb{S}^{n}$ with fixed perimeter. In
particular, denoting by $\sigma_n$ the perimeter of the $n$-dimensional
hemisphere, we show that for fixed perimeters $P<\sigma_n$, geodesic balls
maximize the eigenvalue. Moreover, we prove a quantitative stability result for
this isoperimetric inequality in terms of volume difference between $\Omega$
and the ball $D$ of the same perimeter.


  We consider a system with a Base Station (BS) and multiple mobile/stationary
users. BS uses millimeter waves (mmWaves) for data transmission and hence needs
to align beams in the directions of the end-users. The idea is to avail regular
user-position estimates, which help in accurate beam alignment towards multiple
users, paving way for opportunistic mmWave schedulers. We propose an online
algorithm that uses a dual opportunistic and fair scheduler to allocate data as
well as position-update channels, in each slot. Towards this, well-known
alpha-fair objective functions of utilities of various users, which further
depend upon the age of position-information, are optimized. We illustrate the
advantages of the opportunistic scheduler, by comparing it with the previously
proposed mmWave schemes; these schedulers choose one user in each slot and
start data transmission only after accurate beam alignment. We also discuss two
ways of introducing fairness in such schemes, both of which perform inferior to
the proposed age-based opportunistic scheduler.


  Sensitivity indices when the inputs of a model are not independent are
estimated by local polynomial techniques. Two original estimators based on
local polynomial smoothers are proposed. Both have good theoretical properties
which are exhibited and also illustrated through analytical examples. They are
used to carry out a sensitivity analysis on a real case of a kinetic model with
correlated parameters.


  We propose a new simple model of acoustic black hole in a thin tube, where
the difference in the gravitational potential is used to create a transonic
flow. The main merit of our transonic flow model is that the Euler equations
can be solved analytically. In fact, we can obtain an exact solution to the
equation in terms of a height function in the monatomic case $\gamma=5/3$. For
arbitrary $\gamma$, we find that it takes a simple form by the near-sonic
approximation. Moreover, we obtain two analytic solutions describing a backward
wave and a forward wave, from which we can confirm the existence of sonic
horizons.


  We give a criterion to detect whether the derivatives of the HOMFLY
polynomial at a point is a Vassiliev invariant or not. In particular, for a
complex number b we show that the derivative P_K^{(m,n)}(b,0)=d^m/da^m d^n/dx^n
P_K(a,x)|(a, x) = (b, 0) of the HOMFLY polynomial of a knot K at (b,0) is a
Vassiliev invariant if and only if b= -+1. Also we analyze the space V_n of
Vassiliev invariants of degree <=n for n = 1,2,3,4,5 by using the bar-operation
and the star-operation in [M-J Jeong, C-Y Park, Vassiliev invariants and knot
polynomials, to appear in Topology and Its Applications]. These two operations
are unified to the hat-operation. For each Vassiliev invariant v of degree <=n,
hat(v) is a Vassiliev invariant of degree <=n and the value hat(v)K) of a knot
K is a polynomial with multi-variables of degree <=n and we give some questions
on polynomial invariants and the Vassiliev invariants.