  Extreme helium stars are very rare low-mass supergiants in a late stage of
evolution. They are probably contracting to become white dwarfs following a
violent phase of evolution which caused them to become hydrogen-deficient
giants, possibly R CrB stars. Using the latest generation of models for
spherically expanding stellar atmospheres, we set out to measure mass-loss
rates for a representative fraction of these stars. We have used
high-resolution ultraviolet and optical spectra, and ultraviolet, optical and
near-infrared photometry from a variety of archives. Overall atmospheric
parameters have mostly been taken from previous analyses and checked for
consistency. Mass-loss rates were measured by fitting the P-Cygni and
asymmetric profiles of C, N and Si ultraviolet resonance lines and lie in the
range 10^{-10} - 10^{-7} M_{\odot} yr^{-1}. These rates follow a Castor-type
(\dot{M} \propto L^{1.5}) relation marking a lower limit for the mass loss from
hot stars of all kinds. The mass-loss rates of the studied stars also show a
strong correlation with their proximity to the Eddington limit. There is no
firm evidence for variability in the stellar wind, although photospheric
pulsations have been reported in many cases.


  In a magnetic field graphene trilayers support a characteristic multiplet of
12 zero(-energy)-mode Landau levels with a threefold degeneracy in Landau
orbitals. It was earlier noted for bilayer graphene that Coulombic vacuum
fluctuations, specific to graphene, lift the orbital degeneracy of such
zero-energy modes and that these Lamb-shfted" orbital modes, with filling, get
mixed via the Coulomb interaction. It is pointed out that analogous orbital
Lamb shift and mixing of zero-mode levels can also take place, with an enriched
symmetry content, in ABC-stacked trilayer graphene; and its consequences are
discussed in the light of experimental results.


  Molecular dynamics simulations produce data with complex nonlinear dynamics.
If the timestep behavior of such a dynamic system can be represented by a
linear operator, future states can be inferred directly without expensive
simulations. The use of an autoencoder in combination with a physical timestep
operator allows both the relevant structural characteristics of the molecular
graphs and the underlying physics of the system to be isolated during the
training process. In this work, we develop a pipeline for establishing
graph-structured representations of time-series volumetric data from molecular
dynamics simulations. We then train an autoencoder to find nonlinear mappings
to a latent space where future timesteps can be predicted through application
of a linear operator trained in tandem with the autoencoder. Increasing the
dimensionality of the autoencoder output is shown to improve the accuracy of
the physical timestep operator.


  We have studied systematically the free-electron laser in the context of high
brightness pulsar radio emission. In this paper, we have numerically examined
the case where a transverse electromagnetic wave is distorting the motion of a
relativistic electron beam while travelling over one stellar radius ($ approx
10 :{km}$). For different sets of parameters, coherent emission is generated by
bunches of beam electrons in the radio domain, with bandwidths of 3 GHz. Pulse
power often reached $10^{13} :{W}$, which corresponds with brightness
temperature of $10^{30} :{K}$. The duration of these pulses is of the order of
nanoseconds. In the context of pulsar radio emission, our results indicate that
the laser can produce elementary bursts of radiation which build up the
observed microstructures of a few tens of microseconds duration. The process is
sensitive mostly to the beam particles energy, number density and the
background magnetic field, but much less so to the transverse wave parameters.
We demonstrate that the operation of a free-electron laser with a transverse
electromagnetic wiggler in the pulsar magnetosphere occurs preferably at moder
ate Lorentz factors $ gamma geq 100$, high beam density $n gtrsim 0.1
n_{textrm{GJ}}(r_ ast)$ where $n_{textrm{GJ}}(r_ ast)$ is the Goldrei ch-Julian
density at a stellar radius $r_ ast$, and finally, at large altitude where the
background magnetic field is low $B_0 leq 10^{-2} textrm{T}$.


  We study linear models under heavy-tailed priors from a probabilistic
viewpoint. Instead of computing a single sparse most probable (MAP) solution as
in standard deterministic approaches, the focus in the Bayesian compressed
sensing framework shifts towards capturing the full posterior distribution on
the latent variables, which allows quantifying the estimation uncertainty and
learning model parameters using maximum likelihood. The exact posterior
distribution under the sparse linear model is intractable and we concentrate on
variational Bayesian techniques to approximate it. Repeatedly computing
Gaussian variances turns out to be a key requisite and constitutes the main
computational bottleneck in applying variational techniques in large-scale
problems. We leverage on the recently proposed Perturb-and-MAP algorithm for
drawing exact samples from Gaussian Markov random fields (GMRF). The main
technical contribution of our paper is to show that estimating Gaussian
variances using a relatively small number of such efficiently drawn random
samples is much more effective than alternative general-purpose variance
estimation techniques. By reducing the problem of variance estimation to
standard optimization primitives, the resulting variational algorithms are
fully scalable and parallelizable, allowing Bayesian computations in extremely
large-scale problems with the same memory and time complexity requirements as
conventional point estimation techniques. We illustrate these ideas with
experiments in image deblurring.


  People rely on news to know what is happening around the world and inform
their daily lives. In today's world, when the proliferation of fake news is
rampant, having a large-scale and high-quality source of authentic news
articles with the published category information is valuable to learning
authentic news' Natural Language syntax and semantics. As part of this work, we
present a News Category Dataset that contains around 210k news headlines from
the year 2012 to 2022 obtained from HuffPost, along with useful metadata to
enable various NLP tasks. In this paper, we also produce some novel insights
from the dataset and describe various existing and potential applications of
our dataset.


  Universality of strongly interacting fermions is a topic of great interest in
diverse fields. Here we investigate theoretically the universal dynamic density
response of resonantly interacting ultracold Fermi atoms in the limit of either
high temperature or large frequency: (1) At high temperature, we use quantum
virial expansion to derive universal, non-perturbative expansion functions of
dynamic structure factor; (2) At large momentum, we identify that the
second-order expansion function gives the Wilson coefficient used in the
operator expansion product method. The dynamic structure factor is therefore
determined by its second-order expansion function with an overall normalization
factor given by Tan's contact parameter. We show that the spin parallel and
antiparallel dynamic structure factors have respectively a tail of the form
$\sim \pm \omega^{-5/2}$ for $\omega\rightarrow\infty$, decaying slower than
the total dynamic structure factor found previously ($\sim \omega^{-7/2}$). Our
predictions for dynamic structure factor at high temperature or large frequency
are testable using Bragg spectroscopy for ultracold atomic Fermi gases.


  We examine the question of scale versus conformal invariance on maximally
symmetric curved backgrounds and study general 2-derivative conformally
invariant free theories of vectors and tensors. For spacetime dimension $D>4$,
these conformal theories can be diagonalized into standard massive fields in
which unbroken conformal symmetry non-trivially mixes components of different
spins. In $D=4$, the tensor case becomes a conformal theory mixing a partially
massless spin-2 field with a massless spin-1 field. For massless linearized
gravity in $D = 4$, we confirm through direct calculation that correlation
functions of gauge-invariant operators take the conformally invariant form,
despite the absence of standard conformal symmetry at the level of the action.


  We examine the analogue gravity model within the context of f(R,T) gravity
applied to graphene. The derivation of the Lagrangian density in two dimensions
(2D) is undertaken, accounting for the altered gravitational effects as
characterized by the function f(R,T). The Lagrangian encompasses the
quasiparticle field $\psi(x)$, its adjoint $\overline{\psi}$, the effective
metric tensor $g^{\mu\nu}$, and the gauge field $A_{\nu}$. The equations of
motion are established through variational principles applied to the
Lagrangian, resulting in modified Dirac equations. We discuss the
interpretation of the additional terms in the equations of motion and their
significance in capturing the modified gravitational dynamics in the graphene
system. Our findings contribute to the understanding of analogue gravity models
and their applications in condensed matter systems.


  Single top production via the flavour changing neutral current reactions e+e-
-> \bar{t}c, \bar{t}u is searched for within the 214 pb-1 of data collected by
ALEPH at centre-of-mass energies between 204 and 209 GeV. No deviation from the
Standard Model expectation is observed and upper limits on the single top
production cross sections are derived. The combination with data collected at
lower centre-of-mass energies yields an upper limit on the branching ratio BR(t
-> Zc)+BR(t -> Zu) 14%, for BR(t -> \gamma c)+BR(t -> \gamma u)= 0 and mt=174
GeV/c2.


  This study presents a proof of concept for a contactless elevator operation
system aimed at minimizing human intervention while enhancing safety,
intelligence, and efficiency. A microcontroller-based edge device executing
tiny Machine Learning (tinyML) inferences is developed for elevator operation.
Using person detection and keyword spotting algorithms, the system offers
cost-effective and robust units requiring minimal infrastructural changes. The
design incorporates preprocessing steps and quantized convolutional neural
networks in a multitenant framework to optimize accuracy and response time.
Results show a person detection accuracy of 83.34% and keyword spotting
efficacy of 80.5%, with an overall latency under 5 seconds, indicating
effectiveness in real-world scenarios. Unlike current high-cost and
inconsistent contactless technologies, this system leverages tinyML to provide
a cost-effective, reliable, and scalable solution, enhancing user safety and
operational efficiency without significant infrastructural changes. The study
highlights promising results, though further exploration is needed for
scalability and integration with existing systems. The demonstrated energy
efficiency, simplicity, and safety benefits suggest that tinyML adoption could
revolutionize elevator systems, serving as a model for future technological
advancements. This technology could significantly impact public health and
convenience in multi-floor buildings by reducing physical contact and improving
operational efficiency, particularly relevant in the context of pandemics or
hygiene concerns.


  We study invariant operators in general tensor models. We show that
representation theory provides an efficient framework to count and classify
invariants in tensor models. In continuation and completion of our earlier
work, we present two natural ways of counting invariants, one for arbitrary
rank of the group and another valid for large rank. We construct basis of
invariant operators based on the counting, and compute correlators of their
elements. The basis associated with finite rank diagonalizes two-point
function. It is analogous to the restricted Schur basis used in matrix models.
We show that the constructions get almost identical as we swap the
Littlewood-Richardson numbers in multi-matrix models with Kronecker
coefficients in general tensor models. We explore this parallelism between
matrix model and tensor model in depth from the perspective of representation
theory and comment on several ideas for future investigation.


  We observe that the classical notion of numerical radius gives rise to a
notion of smoothness in the space of bounded linear operators on certain Banach
spaces, whenever the numerical radius is a norm. We demonstrate an important
class of \emph{real} Banach space $ \mathbb{X} $ for which the numerical radius
defines a norm on $ \mathbb{L}(\mathbb{X}), $ the space of all bounded linear
operators on $ \mathbb{X} $. We characterize Birkhoff-James orthogonality in
the space of bounded linear operators on a finite-dimensional Banach space,
endowed with the numerical radius norm. Some examples are also discussed to
illustrate the geometric differences between the numerical radius norm and the
usual operator norm, from the viewpoint of operator smoothness.


  We present the most comprehensive analysis to date of the Upper Centaurus
Lupus eclipsing binary MML 53 (2.097892 d), and for the first time, confirm the
bound-nature of the third star (~9yr orbit). Our analysis uses new and archival
spectra and time-series photometry. We determined the temperature of the
primary star to be 4880+-100 K. The study of the close binary incorporated
treatment of spots and dilution by the tertiary in the light curves, allowing
for the robust measurement of the masses of the eclipsing components within 1%
(M1=1.0400+-0.0067 and M2=0.8907+-0.0058 Msun), their radii within 4.5%
(R1=1.283+-0.043 and R2=1.107+-0.049 Rsun), and the secondary temperature
(4379+-100K). From the analysis of the eclipse timings, and the change in
systemic velocity of the eclipsing binary and the radial velocities of the
third star, we measured the mass of the outer companion to be 0.7 Msun within
20%. The age we derived from the evolution of the temperature ratio between the
eclipsing components is fully consistent with previous estimates of the age of
UCL (16+-2 Myr). At this age, the tightening of the MML 53 eclipsing binary has
already occurred, thus supporting close-binary formation mechanisms that act
early in their evolution. The eclipsing stars roughly follow the same
theoretical isochrone, but appear to be inflated in radius (by 20% for the
primary and 10% for the secondary). However, our primary radius measurement of
is in full agreement with the independent measurement of the secondary of NP
Per which has the same mass and a similar age. The eclipsing stars of MML 53
are found to be larger but not cooler than predicted by non-magnetic models, it
is not clear what is the mechanism that is causing the radius inflation given
that activity, spots and/or magnetic fields slowing their contraction, require
the inflated stars to be cooler to remain in thermal equilibrium.


  For $r \geq 2$, we show that every maximal $K_{r+1}$-free graph $G$ on $n$
vertices with $(1-\frac{1}{r})\frac{n^2}{2}-o(n^{\frac{r+1}{r}})$ edges
contains a complete $r$-partite subgraph on $(1 - o(1))n$ vertices. We also
show that this is best possible. This result answers a question of Tyomkyn and
Uzzell.


  U-duality imposes strong constraints on the structure of divergences in
supergravity. But Gaillard-Zumino Sp$(2n_v, \mathbb{R})$ duality in D=4 has
more symmetries than U-duality. For example, in $N = 8$ the dimension of Sp(56)
is 1596, whereas its U-duality subgroup $E_{7(7)}$ has a dimension of 133. In
comparison, in D $> 4$ maximal dualities are U-dualities. We argue that the
extra dualities in 4D, enhancing U-duality, determine the properties of
perturbative quantum supergravity. The presence/absence of enhanced dualities
suggests a possible explanation of the results of the amplitude loop
computations in D-dimensional supergravities and of the special status of D = 4
in this respect.


  The Wide-field Infrared Survey Explorer (WISE) is an extremely capable and
efficient black hole finder. We present a simple mid-infrared color criterion,
W1-W2 \geq 0.8 (i.e., [3.4]-[4.6] \geq 0.8, Vega), which identifies 61.9 \pm
5.4 AGN candidates per deg2 to a depth of W2 = 15.0. This implies a much larger
census of luminous AGN than found by typical wide-area surveys, attributable to
the fact that mid-infrared selection identifies both unobscured (type 1) and
obscured (type 2) AGN. Optical and soft X-ray surveys alone are highly biased
towards only unobscured AGN, while this simple WISE selection likely identifies
even heavily obscured, Compton-thick AGN. Using deep, public data in the COSMOS
field, we explore the properties of WISE-selected AGN candidates. At the
mid-infrared depth considered, 160 uJy at 4.6 microns, this simple criterion
identifies 78% of Spitzer mid-infrared AGN candidates according to the criteria
of Stern et al. (2005) and the reliability is 95%. We explore the demographics,
multiwavelength properties and redshift distribution of WISE-selected AGN
candidates in the COSMOS field.


  Equilibrium is a rather ideal situation, the exception rather than the rule
in Nature. Whenever the external or internal parameters of a physical system
are varied its subsequent relaxation to equilibrium may be either impossible or
take very long times. From the point of view of fundamental physics no generic
principle such as the ones of thermodynamics allows us to fully understand
their behaviour. The alternative is to treat each case separately. It is
illusionary to attempt to give, at least at this stage, a complete description
of all non-equilibrium situations. Still, one can try to identify and
characterise some concrete but still general features of a class of out of
equilibrium problems - yet to be identified - and search for a unified
description of these. In this report I briefly describe the behaviour and
theory of a set of non-equilibrium systems and I try to highlight common
features and some general laws that have emerged in recent years.


  We present a new NLP task and dataset from the domain of the U.S. civil
procedure. Each instance of the dataset consists of a general introduction to
the case, a particular question, and a possible solution argument, accompanied
by a detailed analysis of why the argument applies in that case. Since the
dataset is based on a book aimed at law students, we believe that it represents
a truly complex task for benchmarking modern legal language models. Our
baseline evaluation shows that fine-tuning a legal transformer provides some
advantage over random baseline models, but our analysis reveals that the actual
ability to infer legal arguments remains a challenging open research question.


  In this paper, we study the large-scale boundary regularity for the Stokes
system in periodically oscillating John domains. Our main contribution is the
construction of boundary layer correctors of arbitrary order. This is a
significant generalization of the known results restricted to the first and
second orders. As an application, we prove the large-scale regularity estimate,
as well as a Liouville theorem, of arbitrary order for the Stokes system. Our
results are also related to higher-order boundary layer tails and wall laws in
viscous fluids over rough boundaries.


  Quantum Random Access Memory (qRAM) is an essential computing element for
running oracle-based quantum algorithms. qRAM exploits the principle of quantum
superposition to access all data stored in the memory cell simultaneously and
guarantees the superior performance of quantum algorithms. A qRAM memory cell
comprises logical qubits encoded through quantum error correction technology
for the successful operation of qRAM against various quantum noises. In
addition to quantum noise, the low-technology nodes based on silicon technology
can increase the qubit density and may introduce defective qubits. As qRAM
comprises many qubits, its yield will be reduced by defective qubits; these
qubits must be handled using QEC scheme. However, the QEC scheme requires
numerous physical qubits, which burdens resource overhead. To resolve this
overhead problem, we propose a quantum memory architecture that compensates for
defective qubits by introducing redundant qubits. We also analyze the yield
improvement offered by our proposed architecture by varying the ideal
fabrication error rate from 0.5% to 1% for different numbers of logical qubits
in the qRAM. In the qRAM comprising 1,024 logical qubits, eight redundant
logical qubits improved the yield by 95.92% from that of qRAM not employing the
redundant repair scheme.


  Query expansion, pivotal in search engines, enhances the representation of
user information needs with additional terms. While existing methods expand
queries using retrieved or generated contextual documents, each approach has
notable limitations. Retrieval-based methods often fail to accurately capture
search intent, particularly with brief or ambiguous queries. Generation-based
methods, utilizing large language models (LLMs), generally lack corpus-specific
knowledge and entail high fine-tuning costs. To address these gaps, we propose
a novel zero-shot query expansion framework utilizing LLMs for mutual
verification. Specifically, we first design a query-query-document generation
method, leveraging LLMs' zero-shot reasoning ability to produce diverse
sub-queries and corresponding documents. Then, a mutual verification process
synergizes generated and retrieved documents for optimal expansion. Our
proposed method is fully zero-shot, and extensive experiments on three public
benchmark datasets are conducted to demonstrate its effectiveness over existing
methods. Our code is available online at
https://github.com/Applied-Machine-Learning-Lab/MILL to ease reproduction.


  We report on resonant optical spectroscopy of self-assembled InGaAs quantum
dots in which the number of electrons can accurately be tuned to N=0,1,2 by an
external gate voltage. Polarization, wave vector and magnetic field dependent
measurements enable us to clearly distinguish between resonant Raman and
resonant photoluminescence processes. The Raman spectra for N=1 and 2 electrons
considerably differ from each other. In particular, for N=2, the quantum-dot
He, the spectra exhibit both singlet and triplet transitions reflecting the
elementary many-particle interaction. Also the resonant photoluminescence
spectra are significantly changing by varying the number of electrons in the
QDs. For N=1 we observe strong polaronic effects which are suppressed for N=2.


  In the field of explainable Artificial Intelligence (XAI), sequential
counterfactual (SCF) examples are often used to alter the decision of a trained
classifier by implementing a sequence of modifications to the input instance.
Although certain test-time algorithms aim to optimize for each new instance
individually, recently Reinforcement Learning (RL) methods have been proposed
that seek to learn policies for discovering SCFs, thereby enhancing
scalability. As is typical in RL, the formulation of the RL problem, including
the specification of state space, actions, and rewards, can often be ambiguous.
In this work, we identify shortcomings in existing methods that can result in
policies with undesired properties, such as a bias towards specific actions. We
propose to use the output probabilities of the classifier to create a more
informative reward, to mitigate this effect.


  The paper is devoted to optimization of resonances in a 1-D open optical
cavity. The cavity's structure is represented by its dielectric permittivity
function e(s). It is assumed that e(s) takes values in the range 1 <= e_1 <=
e(s) <= e_2. The problem is to design, for a given (real) frequency, a cavity
having a resonance with the minimal possible decay rate. Restricting ourselves
to resonances of a given frequency, we define cavities and resonant modes with
locally extremal decay rate, and then study their properties. We show that such
locally extremal cavities are 1-D photonic crystals consisting of alternating
layers of two materials with extreme allowed dielectric permittivities e_1 and
e_2. To find thicknesses of these layers, a nonlinear eigenvalue problem for
locally extremal resonant modes is derived. It occurs that coordinates of
interface planes between the layers can be expressed via arg-function of
corresponding modes. As a result, the question of minimization of the decay
rate is reduced to a four-dimensional problem of finding the zeroes of a
function of two variables.


  Einstein-Podolsky-Rosen (EPR) steering, a fundamental concept of quantum
nonlocality, describes one observer's capability to remotely affect another
distant observer's state by local measurements. Unlike quantum entanglement and
Bell nonlocality, both associated with the symmetric quantum correlation, EPR
steering depicts the unique asymmetric property of quantum nonlocality. With
the local filter operation in which some system components are discarded,
quantum nonlocality can be distilled to enhance the nonlocal correlation, and
even the hidden nonlocality can be activated. However, asymmetric quantum
nonlocality in the filter operation still lacks a well-rounded investigation,
especially considering the discarded parts where quantum nonlocal correlations
may still exist with probabilities. Here, in both theory and experiment, we
investigate the effect of reusing the discarded particles from local filter. We
observe all configurations of EPR steering simultaneously and other intriguing
evolution of asymmetric quantum nonlocality, such as reversing the direction of
one-way EPR steering. This work provides a perspective to answer "What is the
essential role of utilizing quantum steering as a resource?", and demonstrates
a practical toolbox for manipulating asymmetric quantum systems with
significant potential applications in quantum information tasks.


  We study ordinary, zero-form symmetry $G$ and its anomalies in a system with
a one-form symmetry $\Gamma$. In a theory with one-form symmetry, the action of
$G$ on charged line operators is not completely determined, and additional
data, a fractionalization class, needs to be specified. Distinct choices of a
fractionalization class can result in different values for the anomalies of $G$
if the theory has an anomaly involving $\Gamma$. Therefore, the computation of
the 't Hooft anomaly for an ordinary symmetry $G$ generally requires first
discovering the one-form symmetry $\Gamma$ of the physical system. We show that
the multiple values of the anomaly for $G$ can be realized by twisted gauge
transformations, since twisted gauge transformations shift fractionalization
classes. We illustrate these ideas in QCD theories in diverse dimensions. We
successfully match the anomalies of time-reversal symmetries in $2+1d$ gauge
theories, across the different fractionalization classes, with previous
conjectures for the infrared phases of such strongly coupled theories, and also
provide new checks of these proposals. We perform consistency checks of recent
proposals about two-dimensional adjoint QCD and present new results about the
anomaly of the axial $\mathbb{Z}_{2N}$ symmetry in $3+1d$ ${\cal N}=1$
super-Yang-Mills. Finally, we study fractionalization classes that lead to
2-group symmetry, both in QCD-like theories, and in $2+1d$ $\mathbb{Z}_2$ gauge
theory.


  We generalize the classical Satake equivalence as follows. Let k be an
algebraically closed field, set O=k[[t]] and F=k((t)). For an almost simple
algebraic group G we classify central extensions of G(F) by the multiplicative
group. Any such extension E splits canonically over G(O). Consider the category
of G(O)-biinvariant perverse sheaves on E with a given Gm-monodromy . We show
that this is a tensor category, which is tensor equivalent to the category of
representations of a reductive group. We compute the root datum of this group.


  Over the last decade, the long-running endeavour to automate high-level
processes in machine learning (ML) has risen to mainstream prominence,
stimulated by advances in optimisation techniques and their impact on selecting
ML models/algorithms. Central to this drive is the appeal of engineering a
computational system that both discovers and deploys high-performance solutions
to arbitrary ML problems with minimal human interaction. Beyond this, an even
loftier goal is the pursuit of autonomy, which describes the capability of the
system to independently adjust an ML solution over a lifetime of changing
contexts. However, these ambitions are unlikely to be achieved in a robust
manner without the broader synthesis of various mechanisms and theoretical
frameworks, which, at the present time, remain scattered across numerous
research threads. Accordingly, this review seeks to motivate a more expansive
perspective on what constitutes an automated/autonomous ML system, alongside
consideration of how best to consolidate those elements. In doing so, we survey
developments in the following research areas: hyperparameter optimisation,
multi-component models, neural architecture search, automated feature
engineering, meta-learning, multi-level ensembling, dynamic adaptation,
multi-objective evaluation, resource constraints, flexible user involvement,
and the principles of generalisation. We also develop a conceptual framework
throughout the review, augmented by each topic, to illustrate one possible way
of fusing high-level mechanisms into an autonomous ML system. Ultimately, we
conclude that the notion of architectural integration deserves more discussion,
without which the field of automated ML risks stifling both its technical
advantages and general uptake.


  We construct a general Ansatz for the three-particle vertex describing the
interaction of one background and two quantum gluons, by simultaneously solving
the Ward and Slavnov-Taylor identities it satisfies. This vertex is known to be
essential for the gauge-invariant truncation of the Schwinger-Dyson equations
of QCD, based on the pinch technique and the background field method. A key
step in this construction is the formal derivation of a set of crucial
constraints (shown to be valid to all orders), relating the various form
factors of the ghost Green's functions appearing in the aforementioned
Slavnov-Taylor identity. When inserted into the Schwinger-Dyson equation for
the gluon propagator, this vertex gives rise to a number of highly non-trivial
cancellations, which are absolutely indispensable for the self-consistency of
the entire approach.


  We report on lattice QCD results for the thermodynamic equation of state of
quark-gluon matter obtained with Nf=2 degenerate quark flavors. For the fermion
field discretization we are using the Wilson-twisted mass prescription.
Simulations have been carried out at three values of the bare quark masses
corresponding to pion masses of 360, 430 and 640 MeV. We highlight the
importance of a good control of the lattice cutoff dependence of the trace
anomaly which we have studied at several values of the inverse temperature 1/T
= a Nt with a time-like lattice extent up to Nt=12. We contrast our results
with those of other groups obtained for Nf=0 and Nf=2+1. At low temperature we
also confront them with hadron resonance gas model predictions for the trace
anomaly.


  We predict that the speed of a diffusion front is modified by tidal forces.
This effect is small in astrophysical situations, but could be significant -
indeed tunably so - in "gravitational analogues" such as gradient-index lenses.
Confirming this prediction would extend the concept of metamaterials to the
realm of dissipative transport phenomena.


  Yes. Future CMB experiments such as Advanced ACTPol and CMB-S4 should achieve
measurements with S/N of $> 0.1$ for the typical galaxies in redshift surveys.
These measurements will provide complementary measurements of the growth rate
of large scale structure $f$ and the expansion rate of the Universe $H$ to
galaxy clustering measurements. This paper emphasizes that there is significant
information in the anisotropy of the relative pairwise kSZ measurements. We
expand the relative pairwise kSZ power spectrum in Legendre polynomials and
consider up to its octopole. Assuming that the noise in the filtered maps is
uncorrelated between the positions of galaxies in the survey, we derive a
simple analytic form for the power spectrum covariance of the relative pairwise
kSZ temperature in redshift space. While many previous studies have assumed
optimistically that the optical depth of the galaxies $\tau_{\rm T}$ in the
survey is known, we marginalize over $\tau_{\rm T}$, to compute constraints on
the growth rate $f$ and the expansion rate $H$. For realistic sure parameters,
we find that combining kSZ and galaxy redshift survey data reduces the
marginalized $1$-$\sigma$ errors on $H$ and $f$ by $\sim50$-$70\%$ compared to
the galaxy-only analysis.


  Three new heuristic derivations of the Planck scale are described. They are
based on basic principles or phenomena of relativistic gravity and quantum
physics. The Planck scale quantities thus obtained are within one order of
magnitude of the "standard" ones. We contemplate the pair creation of causal
bubbles so small that they can be treated as particles, the scattering of a
matter wave off the background curvature of spacetime that it induces, and the
Hawking evaporation of a black hole in a single burst at the Planck scale.


  We present a first comprehensive study on deterministic spin preparation
employing excited state resonances of droplet etched GaAs quantum dots. This
achievement facilitates future investigations of spin qubit based quantum
memories using the GaAs quantum dot material platform. By observation of
excitation spectra for a range of fundamental excitonic transitions the
properties of different quantum dot energy levels, i.e. shells, are revealed.
The innovative use of polarization resolved excitation and detection in
quasi-resonant excitation spectroscopy facilitates determination of $85$ $\%$
maximum spin preparation fidelity - irrespective of the relative orientations
of lab and quantum dot polarization eigenbases. Additionally, the
characteristic non-radiative decay time is investigated as a function of ground
state, excitation resonance and excitation power level, yielding decay times as
low as $29$ ps for s-p-shell exited state transitions. Finally, by time
resolved correlation spectroscopy it is demonstrated that the employed
excitation scheme has a significant impact on the electronic environment of
quantum dot transitions thereby influencing its charge and coherence.


  With the newly updated version of the ultrarelativistic quantum molecular
dynamics (UrQMD) model, a systematic investigation of the effects of in-medium
nucleon-nucleon ($NN$) elastic cross section on the collective flow and the
stopping observables in $^{197}\text{Au}+^{197}\text{Au}$ collisions at beam
energies from 40 to 150 MeV/nucleon is performed. Simulations with the medium
correction factor
$\mathcal{F}=\sigma^{\text{in-medium}}_{NN}/\sigma^{\text{free}}_{NN}=0.2,~0.3,~0.5$,
and the one obtained with the FU3FP1 parametrization which depends on both the
density and the momentum are compared to the FOPI and INDRA experimental data.
It is found that, to best fit the experimental data of the slope of the
directed flow and the elliptic flow at mid-rapidity as well as the nuclear
stopping, the correction factor $\mathcal{F}$=0.2 and 0.5 are required for
reactions at beam energies of 40 and 150 MeV/nucleon, respectively. While
calculations with the FU3FP1 parametrization can simultaneously reproduce these
experimental data reasonably well. And, the observed increasing nuclear
stopping with increasing beam energy in experimental data can also be
reproduced by using the FU3FP1 parametrization, while the calculated stopping
power in Au+Au collisions with beam energies from 40 to 150 MeV$/$nucleon
almost keeps constant when take $\mathcal{F}$ equal to a fixed value.


  Robustness of deep neural networks against adversarial perturbations is a
pressing concern motivated by recent findings showing the pervasive nature of
such vulnerabilities. One method of characterizing the robustness of a neural
network model is through its Lipschitz constant, which forms a robustness
certificate. A natural question to ask is, for a fixed model class (such as
neural networks) and a dataset of size $n$, what is the smallest achievable
Lipschitz constant among all models that fit the dataset? Recently, (Bubeck et
al., 2020) conjectured that when using two-layer networks with $k$ neurons to
fit a generic dataset, the smallest Lipschitz constant is
$\Omega(\sqrt{\frac{n}{k}})$. This implies that one would require one neuron
per data point to robustly fit the data. In this work we derive a lower bound
on the Lipschitz constant for any arbitrary model class with bounded Rademacher
complexity. Our result coincides with that conjectured in (Bubeck et al., 2020)
for two-layer networks under the assumption of bounded weights. However, due to
our result's generality, we also derive bounds for multi-layer neural networks,
discovering that one requires $\log n$ constant-sized layers to robustly fit
the data. Thus, our work establishes a law of robustness for weight bounded
neural networks and provides formal evidence on the necessity of
over-parametrization in deep learning.


  We propose the application of nonlinear optics for studies of spatially
indirect excitons in coupled quantum wells. We demonstrate, that despite their
vanishing oscillator strength, indirect excitons can strongly contribute to the
photoinduced reflectivity and Kerr rotation. This phenomenon is governed by the
interaction between direct and indirect excitons. Both dark and bright states
of indirect excitons can be probed by these nonlinear optical techniques.


  We have conducted radio timing observations of the eclipsing millisecond
binary pulsar J2051-0827 with the European Pulsar Timing Array network of
telescopes and the Parkes radio telescope, spanning over 13 years. The
increased data span allows significant measurements of the orbital
eccentricity, e = (6.2 {\pm} 1.3) {\times} 10^{-5} and composite proper motion,
{\mu}_t = 7.3 {\pm} 0.4 mas/yr. Our timing observations have revealed secular
variations of the projected semi-major axis of the pulsar orbit which are much
more extreme than those previously published; and of the orbital period of the
system. Investigation of the physical mechanisms producing such variations
confirm that the variations of the semi-major axis are most probably caused by
classical spin-orbit coupling in the binary system, while the variations in
orbital period are most likely caused by tidal dissipation leading to changes
in the gravitational quadrupole moment of the companion.


  In continual learning, the learner learns multiple tasks in sequence, with
data being acquired only once for each task. Catastrophic forgetting is a major
challenge to continual learning. To reduce forgetting, some existing
rehearsal-based methods use episodic memory to replay samples of previous
tasks. However, in the process of knowledge integration when learning a new
task, this strategy also suffers from catastrophic forgetting due to an
imbalance between old and new knowledge. To address this problem, we propose a
novel replay strategy called Manifold Expansion Replay (MaER). We argue that
expanding the implicit manifold of the knowledge representation in the episodic
memory helps to improve the robustness and expressiveness of the model. To this
end, we propose a greedy strategy to keep increasing the diameter of the
implicit manifold represented by the knowledge in the buffer during memory
management. In addition, we introduce Wasserstein distance instead of cross
entropy as distillation loss to preserve previous knowledge. With extensive
experimental validation on MNIST, CIFAR10, CIFAR100, and TinyImageNet, we show
that the proposed method significantly improves the accuracy in continual
learning setup, outperforming the state of the arts.


  The instability toward a magnetic skyrmion crystal in centrosymmetric
trilayer magnets is investigated based on a spin model with layer-dependent
Dzayloshinskii-Moriya interaction. We find various types of skyrmion crystal
phases with different skyrmion numbers in a low-temperature phase diagram by
performing the simulated annealing. In addition to the N\'eel skyrmion crystal
phase that is expected to emerge in the presence of the polar-type
Dzayloshinskii-Moriya interaction, we obtain the skyrmion crystal phases
characteristics of the layered system: the twisted surface skyrmion crystal,
anti-skyrmion crystal, and high-topological-number skyrmion crystal phases. The
rich magnetic phases are brought about by the synergy among the layer-dependent
Dzayloshinskii-Moriya interaction, interlayer exchange interaction, and an
external magnetic field. Our results indicate that the layer degree of freedom
at the surface and heterostructures provides a good platform to engineer and
design the topological spin textures.


  The Newman-Watts model is given by taking a cycle graph of n vertices and
then adding each possible edge $(i,j), |i-j|\neq 1 \mod n$ with probability
$\rho/n$ for some $\rho>0$ constant. In this paper we add i.i.d. exponential
edge weights to this graph, and investigate typical distances in the
corresponding random metric space given by the least weight paths between
vertices. We show that typical distances grow as $\frac1\lambda \log n$ for a
$\lambda>0$ and determine the distribution of smaller order terms in terms of
limits of branching process random variables. We prove that the number of edges
along the shortest weight path follows a Central Limit Theorem, and show that
in a corresponding epidemic spread model the fraction of infected vertices
follows a deterministic curve with a random shift.


  This is the stars chapter of the Kavli workshop report, which resulted from
the community-based study of needed US OIR capabilities in the LSST era. The
full report, which will include this chapter, is anticipated to be available in
Fall 2016. See NOAO website (http://www.noao.edu/meetings/lsst-oir-study/) for
more details.


  Three-dimensional topological semimetals can support band crossings along
one-dimensional curves in the momentum space (nodal lines or Dirac lines)
protected by structural symmetries and topology. We consider rhombohedrally
(ABC) stacked honeycomb lattices supporting Dirac lines protected by
time-reversal, inversion and spin rotation symmetries. For typical band
structure parameters there exists a pair of nodal lines in the momentum space
extending through the whole Brillouin zone in the stacking direction. We show
that these Dirac lines are topologically distinct from the usual Dirac lines
which form closed loops inside the Brillouin zone. In particular, an energy gap
can be opened only by first merging the Dirac lines going through the Brillouin
zone in a pairwise manner so that they turn into closed loops inside the
Brillouin zone, and then by shrinking these loops into points. We show that
this kind of topological phase transition can occur in rhombohedrally stacked
honeycomb lattices by tuning the ratio of the tunneling amplitudes in the
directions perpendicular and parallel to the layers. We also discuss the
properties of the surface states in the different phases of the model.


  A generalization of Born-Infeld non-linear vacuum electrodynamics involving
axion and dilaton fields is constructed with couplings dictated by
electromagnetic duality and SL(2,R) symmetries in the weak field limit. Besides
the Newtonian gravitational constant the model contains a single fundamental
coupling parameter b0. In the absence of axion and dilaton interactions it
reduces, in the limit as b0 tends to infinity, to Maxwell's linear vacuum
theory while for finite b0 it reduces to the original Born-Infeld model. The
spherically symmetric static sector of the theory is explored in a background
Minkowski spacetime where numerical evidence suggests the existence of
axion-dilaton bound states possessing confined electric flux.


  This experimental study analyzes the relationship between the dimensionality
of turbulence and the upscale or downscale nature of its energy transfers. We
do so by forcing low-$Rm$ magnetohydrodynamic (MHD) turbulence in a confined
channel, while precisely controlling its dimensionality by means of an
externally applied magnetic field. We first identify a specific lengthscale
$\hat{l}_\perp^c$ that separates smaller three-dimensional structures from
larger quasi-two-dimensional ones. We then show that an inverse energy cascade
of horizontal kinetic energy along horizontal scales is always observable at
large scales, but that it extends well into the region of 3D structures. At the
same time, a direct energy cascade confined to the smallest and strongly 3D
scales is observed. These dynamics therefore appear not to be simply determined
by the dimensionality of individual scales, nor by the forcing scale, unlike in
other studies. In fact, our findings suggest that the relationship between
kinematics and dynamics is not universal and may strongly depend on the forcing
and dissipating mechanisms at play.


  An eigenvalue based framework is developed for the stability analysis and
stabilization of coupled systems with time-delays, which are naturally
described by delay differential algebraic equations. The spectral properties of
these equations are analyzed and their stability properties are studied, taking
into account the effect of small delay perturbations. Subsequently, numerical
methods for stability assessment and for designing stabilizing controllers with
a prescribed structure or order, based on a direct optimization approach, are
briefly addressed. The effectiveness of the approach is illustrated with a
software demo. The paper concludes by pointing out the similarities with the
computation and optimization of H-infinity norms.


  Based on the assumption that the warp factor of four-dimensional spacetime
and the one of fifth dimension are tied through a parameter $\alpha$, we
consider five-dimensional gravity with a 3-brane coupled to a bulk scalar
field. For arbitrary value of $\alpha$, the form of the warp factor is
implicitly determined by hypergeometric function. Concretely, we show that the
warp factor becomes explicit form for appropriate value of $\alpha$, and study
the relation between four-dimensional effective Planck scale and the brane
tension. This setup allows the possibility of extending the diversity of brane
world.


  Kristiansen and Murwanashyaka recently proved that Robinson arithmetic Q is
interpretable in an elementary theory of full binary trees, T. We prove that,
conversely, T is interpretable in Q by producing a formal interpretation of T
in an elementary concatenation theory, thereby also establishing mutual
interpretability of T with several well-known weak essentially undecidable
theories of numbers, strings and sets. We also in introduce a "hybrid"
elementary theory of strings and trees and establish its mutual
interpretability with Robinson's weak arithmetic R, the weak theory of binary
trees WT of Kristiansen and Murwanashyaka and a weak concatenation theory of
Higuchi and Hirohata.


  To understand the origin of the dunes on Titan, it is important to
investigate the material properties of Titan's organic sand particles on Titan.
The organic sand may behave distinctively compared to the quartz/basaltic sand
on terrestrial planets (Earth, Venus, Mars) due to differences in interparticle
forces. We measured the surface energy (through contact angle measurements) and
elastic modulus (through Atomic Force Microscopy, AFM) of the Titan aerosol
analog (tholin). We find the surface energy of a tholin thin film is about 70.9
mN/m and its elastic modulus is about 3.0 GPa (similar to hard polymers like
PMMA and polystyrene). For two 20 $\mu$m diameter particles, the theoretical
cohesion force is therefore 3.3 $\mu$N. We directly measured interparticle
forces for relevant materials: tholin particles are 0.8$\pm$0.6 $\mu$N, while
the interparticle cohesion between walnut shell particles (a typical model
materials for the Titan Wind Tunnel, TWT) is only 0.4$\pm$0.1 $\mu$N. The
interparticle cohesion forces are much larger for tholins and presumably Titan
sand particles than materials used in the TWT. This suggests we should increase
the interparticle force in both analog experiments (TWT) and threshold models
to correctly translate the results to real Titan conditions. The strong
cohesion of tholins may also inform us how the small aerosol particles ($\sim$1
$\mu$m) in Titan's atmosphere are transformed into large sand particles
($\sim$200 $\mu$m). It may also support the cohesive sand formation mechanism
suggested by Rubin and Hesp (2009), where only unidirectional wind is needed to
form linear dunes on Titan.


  We propose an estimator for the trispectrum of a scalar random field on a
sphere, discuss its geometrical and statistical properties, and outline its
implementation. By estimating the trispectrum of the 4 year COBE-DMR data (in
HEALPix pixelization) we find new evidence of a non-Gaussian signal associated
with a known systematic effect. We find that by removing data from the sky maps
for those periods of time perturbed by this effect, the amplitudes of the
trispectrum coefficients become completely consistent with predictions for a
Gaussian sky. These results reinforce the importance of statistical methods
based in harmonic space for quantifying non-Gaussianity.


  The presence of two globular cluster subpopulations in early-type galaxies is
now the norm rather than the exception. Here we present two more examples for
which the host galaxy appears to have undergone a recent merger. Using
multi-colour Keck imaging of NGC 1052 and NGC 7332 we find evidence for a
bimodal globular cluster colour distribution in both galaxies, with roughly
equal numbers of blue and red globular clusters. The blue ones have similar
colours to those in the Milky Way halo and are thus probably very old and
metal-poor. If the red GC subpopulations are at least solar metallicity, then
stellar population models indicate young ages. We discuss the origin of
globular clusters within the framework of formation models. We conclude that
recent merger events in these two galaxies have had little effect on their
overall GC systems. We also derive globular cluster density profiles, global
specific frequencies and in the case of NGC 1052, radial colour gradients and
azimuthal distribution. In general these globular cluster properties are normal
for early-type galaxies.


  This paper is a sequel to \cite{Xu}. In this paper, an estimation of the
Bergman Kernel of K\"ahler hyperbolic manifold is given by the $L^2$ estimate
and the Bochner formula. As an application, an effective criterion of the very
ampleness of the canonical line bundle of K\"ahler hyperbolic manifold is
given, which is a generalization of Yeung's result.


  Path planning for multiple robots is well studied in the AI and robotics
communities. For a given discretized environment, robots need to find
collision-free paths to a set of specified goal locations. Robots can be fully
anonymous, non-anonymous, or organized in groups. Although powerful solvers for
this abstract problem exist, they make simplifying assumptions by ignoring
kinematic constraints, making it difficult to use the resulting plans on actual
robots. In this paper, we present a solution which takes kinematic constraints,
such as maximum velocities, into account, while guaranteeing a user-specified
minimum safety distance between robots. We demonstrate our approach in
simulation and on real robots in 2D and 3D environments.


  Recent terrorist attacks in major cities around the world have brought many
casualties among innocent citizens. One potential threat is represented by
abandoned luggage items (that could contain bombs or biological warfare) in
public areas. In this paper, we describe an approach for real-time automatic
detection of abandoned luggage in video captured by surveillance cameras. The
approach is comprised of two stages: (i) static object detection based on
background subtraction and motion estimation and (ii) abandoned luggage
recognition based on a cascade of convolutional neural networks (CNN). To train
our neural networks we provide two types of examples: images collected from the
Internet and realistic examples generated by imposing various suitcases and
bags over the scene's background. We present empirical results demonstrating
that our approach yields better performance than a strong CNN baseline method.


  Effects of the relativistic correction of the Coulomb interaction on
doubly-magic nuclei are discussed with Skyrme Hartree--Fock calculations. The
relativistic correction is treated by using the local density approximation. It
is found that the correction to the total energy is about $ 2.4 \, \mathrm{MeV}
$ for $ {}^{208} \mathrm{Pb} $, while proton and neutron radii do not change
significantly. This difference is larger than the difference of the Coulomb
exchange (Fock) energy calculated with the local density
(Hartree--Fock--Slater) approximation and that with the exact treatment and the
neutron finite-size effect. Effects of the correction are also compared to the
correction due to the vacuum polarization. It is shown that the two
contributions to the total energy are comparable in light nuclei, but the
latter dominates in heavy nuclei, while the contribution of the relativistic
correction to the total energy is non-negligible compared to the target
accuracy of the DFT calculation.


  Efficient trapping of light in nanostructures is essential for the
development of optical devices that are based on the interaction between light
and matter. In this work, we show theoretically and experimentally that
one-dimensional arrays of subwavelength dielectric Mie-resonant particles can
support collective resonances with increased $Q$-factors. We demonstrate that
the increase of the $Q$-factor can be explained by interaction between the
collective electric and magnetic dipole modes of the chain resulting in
appearance of the inflection point at the band edge. The considered effect is
studied experimentally in the chain of high-index ceramic cylinders in the
microwave spectral range.


  Mixing colloid shapes with distinctly different anisotropy generates
composite nematics in which the order of the individual components can be
fundamentally different. In colloidal rod-disk mixtures or hybrid nematics
composed of anisotropic colloids immersed in a thermotropic liquid crystal, one
of the components may adopt so-called anti-nematic order while the other
exhibits conventional nematic alignment. Focussing on simple models for hard
rods and disks, we employ Onsager-Straley's second-virial theory to derive
scaling expressions for the elastic moduli of rods and disks in both nematic
and anti-nematic configurations and identify their explicit dependence on
particle concentration and shape. We demonstrate that the splay, bend and twist
elasticity of anti-nematically ordered particles scale logarithmically with the
degree of anti-nematic order, with the bend-splay ratio for anti-nematic
discotic nematics being far greater than for conventional nematic systems. The
impact of surface anchoring on the elastic properties of hybrid nematics will
also be discussed in detail. We further demonstrate that the elasticity of
mixed uniaxial rod-disk nematics depends exquisitely on the shape of the
components and we provide simple scaling expressions that could help engineer
the elastic properties of composite nematic liquid crystals.


  Under the assumption that the infinite product of evolution process converges
almost surely, the set of strong solutions are characterized by a compact
space, which may be regarded as the set of possible initial states.


  We introduce a novel methodology for identifying adversarial attacks on
deepfake detectors using eXplainable Artificial Intelligence (XAI). In an era
characterized by digital advancement, deepfakes have emerged as a potent tool,
creating a demand for efficient detection systems. However, these systems are
frequently targeted by adversarial attacks that inhibit their performance. We
address this gap, developing a defensible deepfake detector by leveraging the
power of XAI. The proposed methodology uses XAI to generate interpretability
maps for a given method, providing explicit visualizations of decision-making
factors within the AI models. We subsequently employ a pretrained feature
extractor that processes both the input image and its corresponding XAI image.
The feature embeddings extracted from this process are then used for training a
simple yet effective classifier. Our approach contributes not only to the
detection of deepfakes but also enhances the understanding of possible
adversarial attacks, pinpointing potential vulnerabilities. Furthermore, this
approach does not change the performance of the deepfake detector. The paper
demonstrates promising results suggesting a potential pathway for future
deepfake detection mechanisms. We believe this study will serve as a valuable
contribution to the community, sparking much-needed discourse on safeguarding
deepfake detectors.


  The Korkine--Zolotareff (KZ) reduction, and its generalisations, are widely
used lattice reduction strategies in communications and cryptography. The KZ
constant and Schnorr's constant were defined by Schnorr in 1987. The KZ
constant can be used to quantify some useful properties of KZ reduced matrices.
Schnorr's constant can be used to characterize the output quality of his block
$2k$-reduction and is used to define his semi block $2k$-reduction, which was
also developed in 1987. Hermite's constant, which is a fundamental constant
lattices, has many applications, such as bounding the length of the shortest
nonzero lattice vector and the orthogonality defect of lattices. Rankin's
constant was introduced by Rankin in 1953 as a generalization of Hermite's
constant. It plays an important role in characterizing the output quality of
block-Rankin reduction, proposed by Gama et al. in 2006. In this paper, we
first develop a linear upper bound on Hermite's constant and then use it to
develop an upper bound on the KZ constant. These upper bounds are sharper than
those obtained recently by the authors, and the ratio of the new linear upper
bound to the nonlinear upper bound, developed by Blichfeldt in 1929, on
Hermite's constant is asymptotically 1.0047. Furthermore, we develop lower and
upper bounds on Schnorr's constant. The improvement to the lower bound over the
sharpest existing one developed by Gama et al. is around 1.7 times
asymptotically, and the improvement to the upper bound over the sharpest
existing one which was also developed by Gama et al. is around 4 times
asymptotically. Finally, we develop lower and upper bounds on Rankin's
constant. The improvements of the bounds over the sharpest existing ones, also
developed by Gama et al., are exponential in the parameter defining the
constant.


  We analyze the distribution of the molecular gas and the dust in the
molecular clump linked to IRAS 10361-5830, located in the environs of the
bubble-shaped HII region Gum 31 in the Carina region, with the aim of
determining the main parameters of the associated material and investigating
the evolutionary state of the young stellar objects identified there.
  Using the APEX telescope, we mapped the molecular emission in the J=3-2
transition of three CO isotopologues, 12CO, 13CO and C18O, over a 1.5' x 1.5'
region around the IRAS position. We also observed the high density tracers CS
and HCO+ toward the source. The cold dust distribution was analyzed using
submillimeter continuum data at 870 \mu\ obtained with the APEX telescope.
Complementary IR and radio data at different wavelengths were used to complete
the study of the ISM.
  The molecular gas distribution reveals a cavity and a shell-like structure of
~ 0.32 pc in radius centered at the position of the IRAS source, with some
young stellar objects (YSOs) projected onto the cavity. The total molecular
mass in the shell and the mean H$_2$ volume density are ~ 40 solar masses and
~(1-2) x 10$^3$ cm$^{-3}$, respectively. The cold dust counterpart of the
molecular shell has been detected in the far-IR at 870 \mu\ and in Herschel
data at 350 \mu. Weak extended emission at 24 \mu\ from warm dust is projected
onto the cavity, as well as weak radio continuum emission.
  A comparison of the distribution of cold and warm dust, and molecular and
ionized gas allows us to conclude that a compact HII region has developed in
the molecular clump, indicating that this is an area of recent massive star
formation. Probable exciting sources capable of creating the compact HII region
are investigated. The 2MASS source 10380461-5846233 (MSX G286.3773-00.2563)
seems to be responsible for the formation of the HII region.


  This paper develops a deterministic model of quantum mechanics as an
accumulation-and-threshold process. The model arises from an analogy with
signal processing in wireless communications. Complex wavefunctions are
interpreted as expressing the amplitude and phase information of a modulated
carrier wave. Particle transmission events are modeled as the outcome of a
process of signal accumulation that occurs in an extra (non-spacetime)
dimension.
  Besides giving a natural interpretation of the wavefunction and the Born
rule, the model accommodates the collapse of the wave packet and other quantum
paradoxes such as EPR and the Ahanorov-Bohm effect. The model also gives a new
perspective on the 'relational' nature of quantum mechanics: that is, whether
the wave function of a physical system is "real" or simply reflects the
observer's partial knowledge of the system. We simulate the model for a 2-slit
experiment, and indicate possible deviations of the model's predictions from
conventional quantum mechanics. We also indicate how the theory may be extended
to a field theory.


  The paper develops the stiffness relationship between the movements and
forces among a system of discrete interacting grains. The approach is similar
to that used in structural analysis, but the stiffness matrix of granular
material is inherently non-symmetric because of the geometrics of particle
interactions and of the frictional behavior of the contacts. Internal geometric
constraints are imposed by the particles' shapes, in particular, by the surface
curvatures of the particles at their points of contact. Moreover, the stiffness
relationship is incrementally non-linear, and even small assemblies require the
analysis of multiple stiffness branches, with each branch region being a
pointed convex cone in displacement-space. These aspects of the particle-level
stiffness relationship gives rise to three types of micro-scale failure:
neutral equilibrium, bifurcation and path instability, and instability of
equilibrium. These three pathologies are defined in the context of four types
of displacement constraints, which can be readily analyzed with certain
generalized inverses. That is, instability and non-uniqueness are investigated
in the presence of kinematic constraints. Bifurcation paths can be either
stable or unstable, as determined with the Hill-Bazant-Petryk criterion.
Examples of simple granular systems of three, sixteen, and sixty four disks are
analyzed. With each system, multiple contacts were assumed to be at the
friction limit. Even with these small systems, micro-scale failure is expressed
in many different forms, with some systems having hundreds of micro-scale
failure modes. The examples suggest that micro-scale failure is pervasive
within granular materials, with particle arrangements being in a nearly
continual state of instability.


  We study large-deviation principles for a model of wireless networks
consisting of Poisson point processes of transmitters and receivers,
respectively. To each transmitter we associate a family of connectable
receivers whose signal-to-interference-and-noise ratio is larger than a certain
connectivity threshold. First, we show a large-deviation principle for the
empirical measure of connectable receivers associated with transmitters in
large boxes. Second, making use of the observation that the receivers
connectable to the origin form a Cox point process, we derive a large-deviation
principle for the rescaled process of these receivers as the connection
threshold tends to zero. Finally, we show how these results can be used to
develop importance-sampling algorithms that substantially reduce the variance
for the estimation of probabilities of certain rare events such as users being
unable to connect


  The temperature dependence of Coulomb blockade peak height correlation is
used to investigate how adding electrons to a quantum dot alters or "scrambles"
its electronic spectrum. Deviations from finite-temperature random matrix
theory with an unchanging spectrum indicate spectral scrambling after a small
number of electrons are added. Enhanced peak-to-peak correlations at low
temperature are observed. Peak height statistics show similar behavior in
several dot configurations despite significant differences in correlations.


  SYNTAGMA is a rule-based parsing system, structured on two levels: a general
parsing engine and a language specific grammar. The parsing engine is a
language independent program, while grammar and language specific rules and
resources are given as text files, consisting in a list of constituent
structuresand a lexical database with word sense related features and
constraints. Since its theoretical background is principally Tesniere's
Elements de syntaxe, SYNTAGMA's grammar emphasizes the role of argument
structure (valency) in constraint satisfaction, and allows also horizontal
bounds, for instance treating coordination. Notions such as Pro, traces, empty
categories are derived from Generative Grammar and some solutions are close to
Government&Binding Theory, although they are the result of an autonomous
research. These properties allow SYNTAGMA to manage complex syntactic
configurations and well known weak points in parsing engineering. An important
resource is the semantic network, which is used in disambiguation tasks.
Parsing process follows a bottom-up, rule driven strategy. Its behavior can be
controlled and fine-tuned.


  We present the first nontrivial procedure for configuring heuristic
algorithms to maximize the utility provided to their end users while also
offering theoretical guarantees about performance. Existing procedures seek
configurations that minimize expected runtime. However, very recent theoretical
work argues that expected runtime minimization fails to capture algorithm
designers' preferences. Here we show that the utilitarian objective also
confers significant algorithmic benefits. Intuitively, this is because mean
runtime is dominated by extremely long runs even when they are incredibly rare;
indeed, even when an algorithm never gives rise to such long runs,
configuration procedures that provably minimize mean runtime must perform a
huge number of experiments to demonstrate this fact. In contrast, utility is
bounded and monotonically decreasing in runtime, allowing for meaningful
empirical bounds on a configuration's performance. This paper builds on this
idea to describe effective and theoretically sound configuration procedures. We
prove upper bounds on the runtime of these procedures that are similar to
theoretical lower bounds, while also demonstrating their performance
empirically.


  Attention mechanisms have been boosting the performance of deep learning
models on a wide range of applications, ranging from speech understanding to
program induction. However, despite experiments from psychology which suggest
that attention plays an essential role in visual reasoning, the full potential
of attention mechanisms has so far not been explored to solve abstract
cognitive tasks on image data. In this work, we propose a hybrid network
architecture, grounded on self-attention and relational reasoning. We call this
new model Attention Relation Network (ARNe). ARNe combines features from the
recently introduced Transformer and the Wild Relation Network (WReN). We test
ARNe on the Procedurally Generated Matrices (PGMs) datasets for abstract visual
reasoning. ARNe excels the WReN model on this task by 11.28 ppt. Relational
concepts between objects are efficiently learned demanding only 35% of the
training samples to surpass reported accuracy of the base line model. Our
proposed hybrid model, represents an alternative on learning abstract relations
using self-attention and demonstrates that the Transformer network is also well
suited for abstract visual reasoning.


  For a quantum many-body system, the direct population of states of
double-excitation character is a clear indication that correlations importantly
contribute to its nonequilibrium properties. We analyze such
correlation-induced transitions by propagating the nonequilibrium Green's
functions in real-time within the second Born approximation. As crucial
benchmarks, we compute the absorption spectrum of few electrons confined in
quantum wells of different width. Our results include the full two-time
solution of the Kadanoff-Baym equations as well as of their time-diagonal limit
and are compared to Hartree-Fock and exact diagonalization data.


  The bound state is treated as a long-lived quasiparticle with slow momenta
and current relaxation in fermi liquid phase. In this paper, we discuss the
realization of ensemble behavior in a 3D fermi gas system in non-fermi liquid
phase. We reveal the relation between UV cutoff of relative momentum
$\Lambda_{q}$ and its ensemble behavior. The behavior of a bound state system
has rarely been investigated before, and the relation between the scattering
momentum and the physics has not yet been explored before (to best of our
knowledge). We found that the cutoff $\Lambda_{q}$ directly related to the
distribution and statistical variance of coupling term, which becomes Gaussian
variable (or Chi-square variable) in limit (i.e., with a large step number of a
fractional distance $\Lambda_{q}^{-1}\rightarrow\infty$). Also, we show that
the different $\Lambda_{q}^{-1}$ lending support to different phases, including
non-fermi liquid phase and (disordered) fermi liquid phase, which correspond to
ill-defined and well-defined bound states, respectively. The pair condensation
induced by local coupling, which happen at critical temperature, would
suppresses the non-fermi liquid for constant on-site coupling (it is not the
case when the coupling be variant). % We deduce the free energy density and the
divergent behaviors of anomalous propagator in this case. % Finally, we also
present a discussion of ensembles of level statistic that depending on which
symmetry class the system belongs to, and that can be controlled by the type of
disorder. Further, when the range of condensation is larger, the homogeneous
character will emerge and the thermalization can be observed where eigenstates
with higher energy has lower overlap with the low-entangled states.


  We study effects of kinetic helicity fluctuations in a turbulence with
large-scale shear using two different approaches: the spectral
tau-approximation and the second order correlation approximation (or
first-order smoothing approximation). These two approaches demonstrate that
homogeneous kinetic helicity fluctuations alone with zero mean value in a
sheared homogeneous turbulence cannot cause large-scale dynamo. Mean-field
dynamo can be possible when kinetic helicity fluctuations are inhomogeneous
which cause a nonzero mean alpha effect in a sheared turbulence. On the other
hand, shear-current effect can generate large-scale magnetic field even in a
homogeneous nonhelical turbulence with large-scale shear. This effect was
investigated previously for large hydrodynamic and magnetic Reynolds numbers.
In this study we examine the threshold required for the shear-current dynamo
versus Reynolds number. We demonstrate that there is no need for a developed
inertial range in order to maintain the shear-current dynamo (e.g., the
threshold in the Reynolds number is of the order of 1).


  Deep quantization methods have shown high efficiency on large-scale image
retrieval. However, current models heavily rely on ground-truth information,
hindering the application of quantization in label-hungry scenarios. A more
realistic demand is to learn from inexhaustible uploaded images that are
associated with informal tags provided by amateur users. Though such sketchy
tags do not obviously reveal the labels, they actually contain useful semantic
information for supervising deep quantization. To this end, we propose
Weakly-Supervised Deep Hyperspherical Quantization (WSDHQ), which is the first
work to learn deep quantization from weakly tagged images. Specifically, 1) we
use word embeddings to represent the tags and enhance their semantic
information based on a tag correlation graph. 2) To better preserve semantic
information in quantization codes and reduce quantization error, we jointly
learn semantics-preserving embeddings and supervised quantizer on hypersphere
by employing a well-designed fusion layer and tailor-made loss functions.
Extensive experiments show that WSDHQ can achieve state-of-art performance on
weakly-supervised compact coding. Code is available at
https://github.com/gimpong/AAAI21-WSDHQ.


  In this paper we present a mathematical way of defining musical modes and we
define the musicality of a mode as a product of three different factors. We
conclude by classifying the modes which are most musical according to our
definition.


  During an intensive Hubble Space Telescope (HST) Cosmic Origins Spectrograph
(COS) UV monitoring campaign of the Seyfert~1 galaxy NGC 5548 performed from
2014 February to July, the normally highly correlated far-UV continuum and
broad emission-line variations decorrelated for ~60 to 70 days, starting ~75
days after the first HST/COS observation. Following this anomalous state, the
flux and variability of the broad emission lines returned to a more normal
state. This transient behavior, characterised by significant deficits in flux
and equivalent width of the strong broad UV emission lines, is the first of its
kind to be unambiguously identified in an active galactic nucleus reverberation
mapping campaign. The largest corresponding emission-line flux deficits
occurred for the high-ionization collisionally excited lines, C IV and Si IV(+O
IV]), and also He II(+O III]), while the anomaly in Ly-alpha was substantially
smaller. This pattern of behavior indicates a depletion in the flux of photons
with E_{\rm ph} > 54 eV, relative to those near 13.6 eV. We suggest two
plausible mechanisms for the observed behavior: (i) temporary obscuration of
the ionizing continuum incident upon BLR clouds by a moving veil of material
lying between the inner accretion disk and inner BLR, perhaps resulting from an
episodic ejection of material from the disk, or (ii) a temporary change in the
intrinsic ionizing continuum spectral energy distribution resulting in a
deficit of ionizing photons with energies > 54 eV, possibly due to a transient
restructuring of the Comptonizing atmosphere above the disk. Current evidence
appears to favor the latter explanation.


  Scholarly document creation continues to face various obstacles. Scholarly
text production requires more complex word processors than other forms of texts
because of the complex structures of citations, formulas and figures. The need
for peer review, often single-blind or double-blind, creates needs for document
management that other texts do not require. Additionally, the need for
collaborative editing, security and strict document access rules means that
many existing word processors are imperfect solutions for academics.
Nevertheless, most papers continue to be written using Microsoft Word (Sadeghi
et al. 2017). We here analyze some of the problems with existing academic
solutions and then present an argument why we believe that running an open
source academic writing solution for academic purposes, such as Fidus Writer,
on a Network Attached Storage (NAS) server could be a viable alternative.


  Asteroids formed in a dynamically quiescent disk but their orbits became
gravitationally stirred enough by Jupiter to lead to high-speed collisions. As
a result, many dozen large asteroids have been disrupted by impacts over the
age of the Solar System, producing groups of fragments known as asteroid
families. Here we explain how the asteroid families are identified, review
their current inventory, and discuss how they can be used to get insights into
long-term dynamics of main belt asteroids. Electronic tables of the membership
for 122 notable families are reported on the Planetary Data System node.


  We report on a peculiar effect regarding the use of the prime's last digit
sequence which is equivalent to a quaternary symbolic sequence. This was used
as a driving sequence for the recently introduced Schramm-Loewner Evolution
after using two different classes of possible binary encodings. We report on a
clear deviation from the standard space-filling curves normally expected from
such a process. We also contrast this behavior with others produced via a
symbolic dynamics applied on standard noise sources as well as deterministic
sequences produced by simple automata. Our findings include the well known,
Morse-Thue sequence as the simplest model exhibiting such behavior apart from
some strongly biased Levy walks.


  Siphons in a chemical reaction system are subsets of the species that have
the potential of being absent in a steady state. We present a characterization
of minimal siphons in terms of primary decomposition of binomial ideals, we
explore the underlying geometry, and we demonstrate the effective computation
of siphons using computer algebra software. This leads to a new method for
determining whether given initial concentrations allow for various boundary
steady states.


  Hardware architectures and machine learning (ML) libraries evolve rapidly.
Traditional compilers often fail to generate high-performance code across the
spectrum of new hardware offerings. To mitigate, engineers develop hand-tuned
kernels for each ML library update and hardware upgrade. Unfortunately, this
approach requires excessive engineering effort to scale or maintain with any
degree of state-of-the-art performance. Here we present a Nested Polyhedral
Model for representing highly parallelizable computations with limited
dependencies between iterations. This model provides an underlying framework
for an intermediate representation (IR) called Stripe, amenable to standard
compiler techniques while naturally modeling key aspects of modern ML
computing. Stripe represents parallelism, efficient memory layout, and multiple
compute units at a level of abstraction amenable to automatic optimization. We
describe how Stripe enables a compiler for ML in the style of LLVM that allows
independent development of algorithms, optimizations, and hardware
accelerators. We also discuss the design exploration advantages of Stripe over
kernel libraries and schedule-based or schedule-space-based code generation.


  We study moduli stacks of principal $\Bbb C^*$-bundles over nodal complex
algebraic curves and determine their rational cohomology algebras in terms of
Chern classes.


  We propose a method of measuring diffusion of the relative phase between two
Bose-Einstein condensates occupying different nuclear or spin hyperfine states
coupled by a two-photon transition via an intermediate level. Due to the
macroscopic quantum coherence the condensates can be decoupled from the
electromagnetic fields. The rate of decoherence and the phase collapse may be
determined from the occupation of the intermediate level or the absorption of
radiation.


  Despite major advancements in nonlinear programming (NLP) and convex
relaxations, most system operators around the world still predominantly use
some form of linear programming (LP) approximation of the AC power flow
equations. This is largely due to LP technology's superior reliability and
computational efficiency, especially in real-time market applications,
security-constrained applications, and extensions involving integer variables,
in addition to its ability to readily generate locational marginal prices (LMP)
for market applications. In the aim of leveraging the advantages of LP while
retaining the accuracy of NLP interior-point methods (IPMs), this paper
proposes a sequential linear programming (SLP) approach consisting of a
sequence of carefully constructed supporting hyperplanes and halfspaces. The
algorithm is numerically demonstrated to converge on 138 test cases with up the
3375 buses to feasible high-quality solutions (i) without AC feasibility
restoration (i.e., using LP solvers exclusively), (ii) in computation times
generally within the same order of magnitude as those from a state-of-the-art
NLP solver, and (iii) with robustness against the choice of starting point. In
particular, the (relative) optimality gaps and the mean constraint violations
are on average around 1e-3% and 1e-7, respectively, under a single parameter
setting for all the 138 test cases. To the best of our knowledge, the proposed
SLP approach is the first to use LP exclusively to reach feasible and
high-quality solutions to the nonconvex AC OPF in a reliable way, which paves
the way for system and market operators to keep using their LP solvers but now
with the ability to accurately capture transmission losses, price reactive
power (Q-LMP), and obtain more accurate LMP.


  RXTE observations of the X-ray transient pulsar 2S 1417-62 between 1999
November and 2000 August with a total exposure of $\sim 394$ ksec were
analyzed. Observations include a main outburst followed by a series of mini
outbursts. Changes in pulse morphology and pulse fraction were found to be
related to the changes in X-ray flux. Particularly low X-ray flux regions were
found to have significantly lower pulse fractions with different pulse
morphologies. The 3-60 keV PCA-HEXTE main outburst spectrum was modeled with an
absorbed power law model with high energy cut-off and a Gaussian Iron line
complex feature. Using the same spectral model, individual 3-20 keV PCA spectra
were found to be softer and less absorbed in low X-ray flux regions between
outbursts. Spectral studies showed that hydrogen column density was correlated,
and the power law index was anti-correlated with the 3-20 keV X-ray flux. X-ray
flux related spectral and timing features in 2S 1417-62 except for low X-ray
flux regions were interpreted as a sign of disc accretion with a similar
accretion geometry with a varying mass accretion rate ($\dot{M}$), whereas
spectral and timing features of the low X-ray flux regions were interpreted as
a sign of possible temporary accretion geometry change prior to the next
periastron where $\dot{M}$ increases again to restore the original accretion
geometry.


  We introduce a class of new one-dimensional linear Fokker--Planck type
equations describing the evolution in time of the wealth in a multi-agent
society. The equations are obtained, via a standard limiting procedure, by
introducing an economically relevant variant to the kinetic model introduced in
2005 by Cordier, Pareschi and Toscani according to previous studies by Bouchaud
and M\'ezard. The steady state of wealth predicted by these new Fokker--Planck
equations remains unchanged with respect to the steady state of the original
Fokker--Planck equation. However, unlike the original equation, it is proven by
a new logarithmic Sobolev inequality with weight and classical entropy methods
that the solution converges exponentially fast to equilibrium.


  We have previously discussed the diffusion limited problem of the bounded
one-dimensional multitrap system where no external fiel is included and pay
special attention to the transmission of the diffusing particles through the
system of imperfect traps. We discuss here the case in which an external field
is included to each trap and find not only the transmission but also the energy
associated with the diffusing particles in the presence and absence of such
fields. From the energy we find the specific heat $C_h$ and show that for
certain values of the parameters associated with the multitrap system it
behaves in a manner which is suggestive of phase transition. Moreover, this
phase transition is demonstrated not only through the conventional single peak
at which the specific heat function is undifferentiable but also through the
less frequent phenomenon of double peaks.


  It has been a primary concern in recent studies of vision and language tasks
to design an effective attention mechanism dealing with interactions between
the two modalities. The Transformer has recently been extended and applied to
several bi-modal tasks, yielding promising results. For visual dialog, it
becomes necessary to consider interactions between three or more inputs, i.e.,
an image, a question, and a dialog history, or even its individual dialog
components. In this paper, we present a neural architecture named Light-weight
Transformer for Many Inputs (LTMI) that can efficiently deal with all the
interactions between multiple such inputs in visual dialog. It has a block
structure similar to the Transformer and employs the same design of attention
computation, whereas it has only a small number of parameters, yet has
sufficient representational power for the purpose. Assuming a standard setting
of visual dialog, a layer built upon the proposed attention block has less than
one-tenth of parameters as compared with its counterpart, a natural Transformer
extension. The experimental results on the VisDial datasets validate the
effectiveness of the proposed approach, showing improvements of the best NDCG
score on the VisDial v1.0 dataset from 57.59 to 60.92 with a single model, from
64.47 to 66.53 with ensemble models, and even to 74.88 with additional
finetuning. Our implementation code is available at
https://github.com/davidnvq/visdial.


  We study the entropy of static dyonic BPS black holes in AdS$_4$ in 4d
$\mathcal{N}=2$ gauged supergravities with vector and hyper multiplets, and how
the entropy can be reproduced with a microscopic counting of states in the
AdS/CFT dual field theory. We focus on the particular example of BPS black
holes in AdS$_4 \times S^6$ in massive Type IIA, whose dual three-dimensional
boundary description is known and simple. To count the states in field theory
we employ a supersymmetric topologically twisted index, which can be computed
exactly with localization techniques. We find perfect match at leading order.


  The generalized second law is proven for rapidly-evolving semiclassical
Rindler horizons at each instant of time, for arbitrary interacting quantum
fields minimally coupled to general relativity. The proof requires the
background spacetime to have boost and null translation symmetry. Possible
extensions to more general horizons and matter-gravity couplings are discussed.


  Aims: We aim at constraining the conditions of the wind and high-energy
emission of the host star reproducing the non-detection of Ly$\alpha$ planetary
absorption. Methods: We model the escaping planetary atmosphere, the stellar
wind, and their interaction employing a multi-fluid, three-dimensional
hydrodynamic code. We assume a planetary atmosphere composed of hydrogen and
helium. We run models varying the stellar high-energy emission and stellar
mass-loss rate, further computing for each case the Ly$\alpha$ synthetic
planetary atmospheric absorption and comparing it with the observations.
Results: We find that a non-detection of Ly$\alpha$ in absorption employing the
stellar high-energy emission estimated from far-ultraviolet and X-ray data
requires a stellar wind with a stellar mass-loss rate about six times lower
than solar. This result is a consequence of the fact that, for $\pi$ Men c,
detectable Ly$\alpha$ absorption can be caused exclusively by energetic neutral
atoms, which become more abundant with increasing the velocity and/or the
density of the stellar wind. By considering, instead, that the star has a
solar-like wind, the non-detection requires a stellar ionising radiation about
four times higher than estimated. This is because, despite the fact that a
stronger stellar high-energy emission ionises hydrogen more rapidly, it also
increases the upper atmosphere heating and expansion, pushing the interaction
region with the stellar wind farther away from the planet, where the planet
atmospheric density that remains neutral becomes smaller and the production of
energetic neutral atoms less efficient. Conclusions: Comparing the results of
our grid of models with what is expected and estimated for the stellar wind and
high-energy emission, respectively, we support the idea that the atmosphere of
$\pi$ Men c is likely not hydrogen-dominated.


  We consider the classic problem of scheduling jobs with precedence
constraints on a set of identical machines to minimize the makespan objective
function. Understanding the exact approximability of the problem when the
number of machines is a constant is a well-known question in scheduling theory.
Indeed, an outstanding open problem from the classic book of Garey and Johnson
asks whether this problem is NP-hard even in the case of 3 machines and
unit-length jobs. In a recent breakthrough, Levey and Rothvoss gave a
$(1+\epsilon)$-approximation algorithm, which runs in nearly quasi-polynomial
time, for the case when job have unit lengths. However, a substantially more
difficult case where jobs have arbitrary processing lengths has remained open.
  We make progress on this more general problem. We show that there exists a
$(1+\epsilon)$-approximation algorithm (with similar running time as that of
Levey and Rothvoss) for the non-migratory setting: when every job has to be
scheduled entirely on a single machine, but within a machine the job need not
be scheduled during consecutive time steps. Further, we also show that our
algorithmic framework generalizes to another classic scenario where, along with
the precedence constraints, the jobs also have communication delay constraints.
Both of these fundamental problems are highly relevant to the practice of
datacenter scheduling.


  Previous studies of open clusters have shown that lithium depletion is not
only strongly age dependent but also shows a complex pattern with other
parameters that is not yet understood. For pre- and main-sequence late-type
stars, these parameters include metallicity, mixing mechanisms, convection
structure, rotation, and magnetic activity. We perform a thorough membership
analysis for a large number of stars observed within the Gaia-ESO survey (GES)
in the field of 20 open clusters, ranging in age from young clusters and
associations, to intermediate-age and old open clusters. Based on the
parameters derived from the GES spectroscopic observations, we obtained lists
of candidate members for each of the clusters in the sample by deriving RV
distributions and studying the position of the kinematic selections in the
EW(Li) versus Teff plane to obtain lithium members. We used gravity indicators
to discard field contaminants and studied [Fe/H] metallicity to further confirm
the membership of the candidates. We also made use of studies using recent data
from the Gaia DR1 and DR2 releases to assess our member selections. We
identified likely member candidates for the sample of 20 clusters observed in
GES (iDR4) with UVES and GIRAFFE, and conducted a comparative study that
allowed us to characterize the properties of these members, as well as identify
field contaminant stars, both lithium-rich giants and non-giant outliers. This
work is the first step towards the calibration of the lithium-age relation and
its dependence on other GES parameters. During this project we aim to use this
relation to infer the ages of GES field stars, and identify their potential
membership to young associations and stellar kinematic groups of different
ages.


  We study the massive multiple-input multiple-output (MIMO) multicast
transmission in cellular networks where each base station (BS) is equipped with
a large-scale antenna array and transmits a common message using a single
beamformer to multiple mobile users. We first show that when each BS knows the
perfect channel state information (CSI) of its own served users, the
asymptotically optimal beamformer at each BS is a linear combination of the
channel vectors of its multicast users. Moreover, the optimal combining
coefficients are obtained in closed form. Then we consider the imperfect CSI
scenario where the CSI is obtained through uplink channel estimation in
timedivision duplex systems. We propose a new pilot scheme that estimates the
composite channel which is a linear combination of the individual channels of
multicast users in each cell. This scheme is able to completely eliminate pilot
contamination. The pilot power control for optimizing the multicast beamformer
at each BS is also derived. Numerical results show that the asymptotic
performance of the proposed scheme is close to the ideal case with perfect CSI.
Simulation also verifies the effectiveness of the proposed scheme with finite
number of antennas at each BS.


  The recent progress on image recognition and language modeling is making
automatic description of image content a reality. However, stylized,
non-factual aspects of the written description are missing from the current
systems. One such style is descriptions with emotions, which is commonplace in
everyday communication, and influences decision-making and interpersonal
relationships. We design a system to describe an image with emotions, and
present a model that automatically generates captions with positive or negative
sentiments. We propose a novel switching recurrent neural network with
word-level regularization, which is able to produce emotional image captions
using only 2000+ training sentences containing sentiments. We evaluate the
captions with different automatic and crowd-sourcing metrics. Our model
compares favourably in common quality metrics for image captioning. In 84.6% of
cases the generated positive captions were judged as being at least as
descriptive as the factual captions. Of these positive captions 88% were
confirmed by the crowd-sourced workers as having the appropriate sentiment.


  After brief personal recollections of the author's long-time friendship with
Misha Marinov the problem of particle production by classical time- varying
scalar field is discussed. In the quasiclassical limit the calculations are
done by imaginary time method developed, in particular, in Marinov's works. The
method permits to obtain simple analytical expressions which well agree with
the later found numerical solutions. The results are compared with perturbative
calculations and it is argued that perturbation theory gives an upper limit for
the rate of production.


  By using ideas and strong results borrowed from the classical moment problem,
we show how -under very general conditions- a discrete number of values of the
spectral zeta function (associated generically with a non-decreasing sequence
of numbers, and not necessarily with an operator) yields all the moments
corresponding to the density of states, as well as those of the partition
function of the sequence (the two basic quantities that are always considered
in a quantum mechanical context). This goes beyond the well known expression of
the small-t asymptotic expansion of the heat kernel of an operator in terms of
zeta function values. The precise result for a given situation depends
dramatically on the singularity structure of the zeta function. The different
specific situations that can appear are discussed in detail, using seminal
results from the zeta function literature. Attention is paid to formulations
involving zeta functions with a non-standard pole structure (as those arising
in noncommutative theories and others). Finally, some misuses of the classical
moment problem are pointed out.


  In this paper we find surprisingly small Oka domains in Euclidean spaces
$\mathbb C^n$ of dimension $n>1$ at the very limit of what is possible. Under a
mild geometric assumption on a closed unbounded convex set $E$ in $\mathbb C^n$
we show that $\mathbb C^n\setminus E$ is an Oka domain. In particular, there
are Oka domains which are only slightly bigger than a halfspace, the latter
being neither Oka nor hyperbolic. This gives families of smooth real
hypersurfaces $\Sigma_t\subset \mathbb C^n$ $(t\in\mathbb R)$ dividing $\mathbb
C^n$ in an unbounded hyperbolic domain and an Oka domain such that at the
threshold value $t=0$ the hypersurface $\Sigma_0$ is a hyperplane and the
character of the two sides gets reversed. More generally, we show that if $E$
is a closed set in $\mathbb C^n$ for $n>1$ whose projective closure $\overline
E\subset\mathbb C\mathbb P^n$ avoids a hyperplane $\Lambda\subset\mathbb
C\mathbb P^n$ and is polynomially convex in $\mathbb C\mathbb P^n\setminus
\Lambda\cong\mathbb C^n$, then $\mathbb C^n\setminus E$ is an Oka domain.


  An intense research on financial market microstructure is presently in
progress. Continuous time random walks (CTRWs) are general models capable to
capture the small-scale properties that high frequency data series show. The
use of CTRW models in the analysis of financial problems is quite recent and
their potentials have not been fully developed. Here we present two (closely
related) applications of great interest in risk control. In the first place, we
will review the problem of modelling the behaviour of the mean exit time (MET)
of a process out of a given region of fixed size. The surveyed stochastic
processes are the cumulative returns of asset prices. The link between the
value of the MET and the timescale of the market fluctuations of a certain
degree is crystal clear. In this sense, MET value may help, for instance, in
deciding the optimal time horizon for the investment. The MET is, however, one
among the statistics of a distribution of bigger interest: the survival
probability (SP), the likelihood that after some lapse of time a process
remains inside the given region without having crossed its boundaries. The
final part of the article is devoted to the study of this quantity. Note that
the use of SPs may outperform the standard "Value at Risk" (VaR) method for two
reasons: we can consider other market dynamics than the limited Wiener process
and, even in this case, a risk level derived from the SP will ensure (within
the desired quintile) that the quoted value of the portfolio will not leave the
safety zone. We present some preliminary theoretical and applied results
concerning this topic.