  The association of equal heartbeat intervals with cardiac conditions and the
effect of the equality on permutation-based time irreversibility are
investigated in this paper. We measure the distributions of equal heartbeat
intervals under three conditions, namely congestive heart failure (CHF),
healthy young and elderly, whose time irreversibility of heartbeats is detected
by measuring the probabilistic difference between permutations instead of raw
vectors. We demonstrate that heartbeats contain high rates of equal states,
particularly the CHF with around 20% equalities, and the distributions of equal
values discriminate the heartbeats at very short data length. The CHF have more
equal values than the healthy young (p<1.47*10^{-15}) and elderly
(p<2.48*10^{-11}), and the healthy young have less equalities than the elderly
(p<3.16*10^{-4}). Time irreversibility considering equal values is promising to
extract nonlinear behaviors of heartbeats, confirming the decreased nonlinear
complexity of the diseased and aging heart rates, while that involving no
equality leads to erroneous nonlinearity detection. In our contribution, we
highlight the pathological or physiological information contained by the
distribution of equal heartbeat intervals that might contribute to develop
relevant biomarkers in the area of heart analysis, and demonstrate the
effectiveness of equality-based time irreversibility in the nonlinearity
detection of heartbeats.


  Shortcomings of a momentum-space treatment of strong absorption, as discussed
in the previous Comment [nucl-th/9611025], are only of concern at low
projectile energies, $\gamma$< 1.5. At intermediate and high energies, for
which the quantum-mechanical equivalent-photon spectrum is intended, the
quantum-mechanical cross sections are reduced relative to the semi-classical
results whether one treats strong effects via a momentum-space or an
impact-parameter (spatial) cut-off. At these energies the origin of the
discrepancy between the predictions of full quantuym-mechanical and the
semi-classical calulations cannot be traced to differences in the treatment of
strong-interaction effects. Rather, they arise from quantum effects neglected
in the semi-classical calculations.


  A beautiful theorem of Zeckendorf states that every positive integer has a
unique decomposition as a sum of non-adjacent Fibonacci numbers. Such
decompositions exist more generally, and much is known about them. First, for
any positive linear recurrence {Gn} the number of summands in the legal
decompositions for integers in [Gn, Gn+1) converges to a Gaussian distribution.
Second, Bower, Insoft, Li, Miller, and Tosteson proved that the probability of
a gap between summands in a decomposition which is larger than the recurrence
length converges to geometric decay. While most of the literature involves
one-dimensional sequences, some recent work by Chen, Guo, Jiang, Miller,
Siktar, and Yu have extended these decompositions to d-dimensional lattices,
where a legal decomposition is a chain of points such that one moves in all d
dimensions to get from one point to the next. They proved that some but not all
properties from 1-dimensional sequences still hold. We continue this work and
look at the distribution of gaps between terms of legal decompositions, and
prove similar to the 1-dimensional cases that when d = 2 the gap vectors
converge to a bivariate geometric random variable.


  We consider the edge of a two-dimensional electron system that is in the
quantum-Hall-effect regime at filling factor 1-1/m with m being an odd integer,
where microscopic theory explaining the occurrence of the quantum Hall effect
in the bulk predicts the existence of two counterpropagating edge-excitation
modes. These two modes are the classical edge-magnetoplasmon mode and a
slow-moving neutral mode. Assuming the electrons to be confined by a coplanar
neutralizing background of positive charges, and taking careful account of
long-range Coulomb interactions, we determine microscopically the velocity of
the neutral mode and the edge width. Our results are intended to guide
experimental efforts aimed at verifying the existence of the neutral mode,
which would provide a powerful confirmation of the current microscopic
understanding of quantum-Hall physics at the simplest hierarchical filling
Factors 1-1/m.


  Let X be a geometrically integral projective cubic hypersurface defined over
the rationals, with dimension D and singular locus of dimension at most D-4.
For any \epsilon>0, we show that X contains O(B^{D+\epsilon}) rational points
of height at most B. The implied constant in this estimate depends upon the
choice of \epsilon and the coefficients of the cubic form defining X.


  Computational color constancy refers to the problem of computing the
illuminant color so that the images of a scene under varying illumination can
be normalized to an image under the canonical illumination. In this paper, we
adopt a deep learning framework for the illumination estimation problem. The
proposed method works under the assumption of uniform illumination over the
scene and aims for the accurate illuminant color computation. Specifically, we
trained the convolutional neural network to solve the problem by casting the
color constancy problem as an illumination classification problem. We designed
the deep learning architecture so that the output of the network can be
directly used for computing the color of the illumination. Experimental results
show that our deep network is able to extract useful features for the
illumination estimation and our method outperforms all previous color constancy
methods on multiple test datasets.


  Launching large (> 1 g) well-characterized projectiles to velocities beyond
10 km/s is of interest for a number of scientific fields but is beyond the
reach of current hypervelocity launcher technology. This paper reports the
development of an explosively driven light-gas gun that has demonstrated the
ability to launch 8 mm diameter, 0.36 g magnesium projectiles to 10.4 km/s. The
implosion-driven launcher (IDL) uses the linear implosion of a pressurized tube
to shock-compress helium gas to a pressure of \SI{5}{\giga\pascal}, which then
expands to propel a projectile to hypervelocity. The launch cycle of the IDL is
explored with the use of down-bore velocimetry experiments and a
quasi-one-dimensional internal ballistics solver. A detailed overview of the
design of the 8 mm launcher is presented, with an emphasis on the unique
considerations which arise from the explosively driven propellant compression
and the resulting extreme pressures and temperatures. The high average driving
pressure results in a launcher that is compact, with a total length typically
less than a meter. The possibility to scale the design to larger projectile
sizes (25 mm diameter) is demonstrated. Finally, concepts for a modified launch
cycle which may allow the IDL to reach significantly greater projectile
velocities are explored conceptually and with preliminary experiments.


  The broad absorption lines (BALs) of QSOs indicate abundances of heavy
elements, relative to hydrogen, that are 1 to 2 orders of magnitude higher than
the solar values. In at least one QSO, an especially large enhancement of
phosphorus is observed. These abundances resemble those in Galactic novae, and
this suggests that novae may produce the BAL gas. The needed rate of nova
outbursts may come from single white dwarfs that accrete gas as they pass
through a supermassive accretion disk around a central black hole.


  Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing community lacks a similar setup to systematically explore the
paradigm. To bridge this gap, we introduce Speech processing Universal
PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the
performance of a shared model across a wide range of speech processing tasks
with minimal architecture changes and labeled data. Among multiple usages of
the shared model, we especially focus on extracting the representation learned
from SSL due to its preferable re-usability. We present a simple framework to
solve SUPERB tasks by learning task-specialized lightweight prediction heads on
top of the frozen shared model. Our results demonstrate that the framework is
promising as SSL representations show competitive generalizability and
accessibility across SUPERB tasks. We release SUPERB as a challenge with a
leaderboard and a benchmark toolkit to fuel the research in representation
learning and general speech processing.


  This paper studies the feasibility of Wi-Fi and 5G technologies in the
unlicensed 60 GHz band for humanitarian assistance and disaster relief (HA/DR)
operations. Building a wireless communications system in the 60 GHz band can
benefit HA/DR activities for two reasons: (i) no license is needed; (ii) a wide
bandwidth is available. Our simulation results show that both of Wi-Fi and 5G
can achieve data rates exceeding the requirements for most of the HA/DR
missions, which proves the feasibility of the two wireless technologies for
operation of high-data-rate HA/DR activities such as real-time video streaming.


  Making data compliant with the FAIR Data principles (Findable, Accessible,
Interoperable, Reusable) is still a challenge for many researchers, who are not
sure which criteria should be met first and how. Illustrated from experimental
data tables associated with a Design of Experiments, we propose an approach
that can serve as a model for a research data management that allows
researchers to disseminate their data by satisfying the main FAIR criteria
without insurmountable efforts. More importantly, this approach aims to
facilitate the FAIRification process by providing researchers with tools to
improve their data management practices.


  We determined the spin value of supermassive black hole (SMBH) in active
galactic nuclei (AGN) with investigated ultraviolet-to-optical spectral energy
distribution, presented in the sample of Shang et al. (2005). The estimates of
the spin values have been produced at the base of the standard geometrically
thin accretion disk model and with using the results of the polarimetric
observations. The polarimetric observations are very important for determining
the inclination angle of AGN disk. We presented the results of our
determinations of the radiation efficiency of the accretion flow and values of
the spins of SMBHs, that derives the coefficient of radiation efficiency. The
majority of SMBHs of AGNs from Shang et al. (2005) sample are to be the Kerr
black holes with the high spin value.


  The $b$-ghost of the pure spinor formalism in a general curved background is
not holomorphic. For such theories, the construction of the string measure
requires the knowledge of the action of diffeomorphisms on the BV phase space.
We construct such an action for the pure spinor sigma-model in $AdS_5\times
S^5$. From the point of view of the BV formalism, this sigma-model belongs to
the class of theories where the expansion of the Master Action in antifields
terminates at the quadratic order. We show that it can be reduced to a simpler
degenerate sigma-model, preserving the AdS symmetries. We construct the action
of the algebra of worldsheet vector fields on the BV phase space of this
minimalistic sigma-model, and explain how to lift it to the original model.


  A sizeable difference in the differential production cross section of top and
antitop quarks, respectively, is predicted for hadronically produced heavy
quarks. It is of order $\alpha_s$ and arises from the interference between
charge odd and even amplitudes respectively. For the TEVATRON it amounts to
approximately 5-10% in the region where the cross section is large and could
therefore be measured in the next round of experiments. At the LHC the
asymmetry can be studied by selecting appropriately chosen kinematical regions.


  Motivated by possible observations of the black hole candidate in the center
of our galaxy and the galaxy M87, ray-tracing methods are applied to both
standard General Relativity (GR) and a recently proposed extension, the
pseudo-complex General Relativity (pc-GR). The correction terms due to the
investigated pc-GR model lead to slower orbital motions close to massive
objects. Also the concept of an innermost stable circular orbit (ISCO) is
modified for the pc-GR model, allowing particles to get closer to the central
object for most values of the spin parameter $a$ than in GR. Thus, the
accretion disk, surrounding a massive object, is brighter in pc-GR than in GR.
Iron K$\alpha$ emission line profiles are also calculated as those are good
observables for regions of strong gravity. Differences between the two theories
are pointed out.


  The storage and subsequent retrieval of coherent pulse trains in the quantum
memory (i.e. cavity-dark state) of three-level $\Lambda$ atoms, are considered
for an optical medium in which adiabatic photon transfer occurs under the
condition of quantum impedance matching. The underlying mechanism is based on
intracavity Electromagnetically-Induced Transparency, by which properties of a
cavity filled with three-level $\Lambda$-type atoms are manipulated by an
external control field. Under the impedance matching condition, we derive
analytic expressions that suggest a complete transfer of an input field into
the cavity-dark state by varying the mixing angle in a specific way, and its
subsequent retrieval at a desired time. We illustrate the scheme by
demonstrating the complete transfer and retrieval of a Gaussian, a single
hyperbolic-secant and a periodic train of time-entangled hyperbolic-secant
input photon pulses in the atom-cavity system. For the time-entangled
hyperbolic-secant input field, a total controllability of the periodic
evolution of the dark state population is made possible by changing the Rabi
frequency of the classical driving field, thus allowing to alternately store
and retrieve high-intensity photons from the optically dense
Electromagnetically-Induced transparent medium. Such multiplexed photon states,
which are expected to allow sharing quantum information among many users, are
currently of very high demand for applications in long-distance and multiplexed
quantum communication.


  Let $P(G,q)$ be the chromatic polynomial for coloring the $n$-vertex graph
$G$ with $q$ colors, and define $W=\lim_{n \to \infty}P(G,q)^{1/n}$. Besides
their mathematical interest, these functions are important in statistical
physics. We give a comparative discussion of exact calculations of $P$ and $W$
for a variety of recursive families of graphs, including strips of regular
lattices with various boundary conditions and homeomorphic expansions thereof.
Generalizing to $q \in {\mathbb C}$, we determine the accumulation sets of the
chromatic zeros constituting the continuous loci of points on which $W$ is
nonanalytic. Various families of graphs with the property that the chromatic
zeros and/or their accumulation sets (i) include support for $Re(q) < 0$; (ii)
bound regions and pass through $q=0$; and (iii) are noncompact are discussed,
and the role of boundary conditions is analyzed. Some corresponding results are
presented for Potts model partition functions for nonzero temperature,
equivalent to the full Tutte polynomials for various families of graphs.


  A set of global optical potential has been derived to describe the
interactions of $^{6}$He at low energies. The elastic scattering angular
distribution data measured so far for many systems, ranging from $^{12}$C to
$^{209}$Bi, have been considered within the framework of the optical model in
order to find a global potential set to describe the experimental data
consistently. We report that very good agreement between theoretical and
experimental results has been obtained with small $\chi^{2}/N$ values by using
the derived potential set. The reaction cross section and volume integrals of
the potentials have been deduced from the theoretical calculations for all
studied systems at relevant energies.


  The holographic entropy cone characterizes the relations between entanglement
entropies for a spatial partitioning of the boundary spacetime of a holographic
CFT in any state describing a classical bulk geometry. We argue that the
holographic entropy cone, for an arbitrary number of parties, can be
reconstructed from more fundamental data determined solely by subadditivity of
quantum entropy. We formulate certain conjectures about graph models of
holographic entanglement, for which we provide strong evidence, and rigorously
prove that they all imply that such a reconstruction is possible. Our
conjectures (except only for the weakest) further imply that the necessary data
is remarkably simple. In essence, all one needs to know to reconstruct the
holographic entropy cone, is a certain subset of the extreme rays of this
simpler "subadditivity cone", namely those which can be realized in holography.
This recasting of the bewildering entanglement structure of geometric states
into primal building blocks paves the way to distilling the essence of
holography for the emergence of a classical bulk spacetime.


  We report that calculating the Gibbs free energy of the alpha-Al_2O_3 (0001)
surfaces in equilibrium with a realistic environment containing both oxygen and
hydrogen species is essential for obtaining theoretical predictions consistent
with experimental observations. Using density-functional theory we find that
even under conditions of high oxygen partial pressure, the metal terminated
surface is surprisingly stable. An oxygen terminated alpha-Al_2O_3 (0001)
surface becomes stable only if hydrogen is present on the surface. In addition,
including hydrogen on the surface resolves discrepancies between previous
theoretical work and experimental results with respect to the magnitude and
direction of surface relaxations.


  The parametric equations of KAM tori for a quasi integrable system, are shown
to be one point Schwinger functions of a suitable euclidean quantum field
theory on the torus. KAM theorem is equivalent to a ultraviolet stability
theorem. A renormalization group treatment of the field theory leads to a
resummation of the formal pertubation series and to an expansion in terms of
new parameters (identified as a family of renormalization constants). The new
parameters are analytic in the coupling (at small coupling): the breakdown of
the tori at large coupling is speculated to be related to the crossing of a
"critical" surface at a value where the renormalization constants are still
finite. A mechanism for the possible universality of the singularities of
parametric equations for the invariant tori, in their parameter dependence as
well as in the coupling dependence, is proposed.}


  Let $(R,{\bf m})$ be a two-dimensional regular local ring with infinite
residue field. We prove an analogue of the Hoskin-Deligne length formula for a
finitely generated, torsion-free, integrally closed $R$-module $M$. As a
consequence, we get a formula for the Buchsbaum-Rim multiplicity of $F/M$,
where $F = M^{**}$.


  Scalability of today's superconducting quantum computers is limited due to
the huge costs of generating/routing microwave control pulses per qubit from
room temperature. One active research area in both industry and academia is to
push the classical controllers to the dilution refrigerator in order to
increase the scalability of quantum computers. Superconducting Single Flux
Quantum (SFQ) is a classical logic technology with low power consumption and
ultra-high speed, and thus is a promising candidate for in-fridge classical
controllers with maximized scalability. Prior work has demonstrated
high-fidelity SFQ-based single-qubit gates. However, little research has been
done on SFQ-based multi-qubit gates, which are necessary to realize SFQ-based
universal quantum computing.
  In this paper, we present the first thorough analysis of SFQ-based two-qubit
gates. Our observations show that SFQ-based two-qubit gates tend to have high
leakage to qubit non-computational subspace, which presents severe design
challenges. We show that despite these challenges, we can realize gates with
high fidelity by carefully designing optimal control methods and qubit
architectures. We develop optimal control methods that suppress leakage, and
also investigate various qubit architectures that reduce the leakage. After
carefully engineering our SFQ-friendly quantum system, we show that it can
achieve similar gate fidelity and gate time to microwave-based quantum systems.
The promising results of this paper show that (1) SFQ-based universal quantum
computation is both feasible and effective; and (2) SFQ is a promising approach
in designing classical controller for quantum machines because it can increase
the scalability while preserving gate fidelity and performance.


  Text summarization is one of the most critical Natural Language Processing
(NLP) tasks. More and more researches are conducted in this field every day.
Pre-trained transformer-based encoder-decoder models have begun to gain
popularity for these tasks. This paper proposes two methods to address this
task and introduces a novel dataset named pn-summary for Persian abstractive
text summarization. The models employed in this paper are mT5 and an
encoder-decoder version of the ParsBERT model (i.e., a monolingual BERT model
for Persian). These models are fine-tuned on the pn-summary dataset. The
current work is the first of its kind and, by achieving promising results, can
serve as a baseline for any future work.


  The volume-filtering of the Navier-Stokes equations allows to consider the
effect that particles have on the fluid without further assumptions, but
closures arise of which the implications are not fully understood. In the
present paper, we carefully study every closure in the volume-filtered fluid
momentum equation and investigate their impact on the momentum and energy
transfer dependent on the filtering characteristics. We provide an analytical
expression for the viscous closure that arises because filter and spatial
derivative in the viscous term do not commute. An analytical expression for the
regularization of the particle momentum source of a single sphere in the Stokes
regime is derived. Furthermore, we propose a model for the subfilter stress
tensor, which originates from filtering the advective term. The model for the
subfilter stress tensor is shown to agree well with the subfilter stress tensor
for small filter widths relative to the size of the particle. We show that the
subfilter stress tensor requires modeling and should not be neglected. For
small filter widths, we find that the commonly applied Gaussian regularization
of the particle momentum source is a poor approximation of the spatial
distribution of the particle momentum source, but for larger filter widths the
spatial distribution approaches a Gaussian. Furthermore, we propose a modified
advective term in the volume-filtered momentum equation that consistently
circumvents the common stability issues observed at locally small fluid volume
fractions and identify inconsistencies in previous studies of the
phase-averaged kinetic energy of the volume-filtered fluid velocity. Finally,
we propose a generally applicable form of the volume-filtered momentum equation
and its closures based on clear and well-founded assumptions and propose
guidelines for point-particle simulations based on the new findings.


  Long-term time series forecasting (LTSF) is a crucial aspect of modern
society, playing a pivotal role in facilitating long-term planning and
developing early warning systems. While many Transformer-based models have
recently been introduced for LTSF, a doubt have been raised regarding the
effectiveness of attention modules in capturing cross-time dependencies. In
this study, we design a mask-series experiment to validate this assumption and
subsequently propose the "Cross-variable Linear Integrated ENhanced Transformer
for Multivariate Long-Term Time Series Forecasting" (Client), an advanced model
that outperforms both traditional Transformer-based models and linear models.
Client employs linear modules to learn trend information and attention modules
to capture cross-variable dependencies. Meanwhile, it simplifies the embedding
and position encoding layers and replaces the decoder module with a projection
layer. Essentially, Client incorporates non-linearity and cross-variable
dependencies, which sets it apart from conventional linear models and
Transformer-based models. Extensive experiments with nine real-world datasets
have confirmed the SOTA performance of Client with the least computation time
and memory consumption compared with the previous Transformer-based models. Our
code is available at https://github.com/daxin007/Client.


  A remarkably quantitative understanding of the electrical and mechanical
properties of metal wires with a thickness on the scale of a nanometer has been
obtained within the free-electron model using semiclassical techniques.
Convergent trace formulas for the density of states and cohesive force of a
narrow constriction in an electron gas, whose classical motion is either
chaotic or integrable, are derived. Mode quantization in a metallic point
contact or nanowire leads to universal oscillations in its cohesive force,
whose amplitude depends only on a dimensionless quantum parameter describing
the crossover from chaotic to integrable motion, and is of order 1nN, in
agreement with experiments on gold nanowires. A linear stability analysis shows
that the classical instability of a long wire under surface tension can be
completely suppressed by quantum effects, leading to stable cylindrical
configurations whose electrical conductance is a magic number 1, 3, 5, 6,...
times the conductance quantum, in accord with recent results on alkali metal
nanowires.


  We consider the gauged model of Siegel type chiral boson with a Lorentz
non-covariant mass-like term for the gauge fields which is found to be
equivalent to the chiral Schwinger model with Faddeevian anomaly when it is
described in terms of Floreanini-Jackiw type chiral boson. We carry out the
quantization of gauge non-invariant version this model in both the Lagrangian
and Hamiltonian formulation. The quantization of the gauge-invariant version of
this model in the extended phase space also has been carried out in the
Lagrangian formulation. The gauge-invariant version of this model in the
extended phase space is found to map onto the physical phase space with the
appropriate gauge fixing condition. BRST symmetry associated with this model
has been studied with different gauge fixing terms. It has been shown that the
same model shows off-shell as well as on-shell BRST invariance depending on the
choice of gauge fixing term.


  A new method based on the Monte-Carlo calculation on the lattice is proposed
to study the Casimir effect in the noncompact lattice QED. We have studied the
standard Casimir problem with two parallel plane surfaces (mirrors) and oblique
boundary conditions on those as a test of our method. Physically, this boundary
conditions may appear in the problem of modelling of the thin material films
interaction and are generated by additional Chern-Simons boundary term. This
approach for the boundary condition generation is very suitable for the lattice
formulation of the Casimir problem due to gauge invariance.


  Path-independent (PI) quantum control has recently been proposed to integrate
quantum error correction and quantum control [Phys. Rev. Lett. 125, 110503
(2020)], achieving fault-tolerant quantum gates against ancilla errors. Here we
reveal the underlying algebraic structure of PI quantum control. The PI
Hamiltonians and propagators turn out to lie in an algebra isomorphic to the
ordinary matrix algebra, which we call the PI matrix algebra. The PI matrix
algebra, defined on the Hilbert space of a composite system (including an
ancilla system and a central system), is isomorphic to the matrix algebra
defined on the Hilbert space of the ancilla system. By extending the PI matrix
algebra to the Hilbert-Schmidt space of the composite system, we provide an
exact and unifying condition for PI quantum control against ancilla noise.


  In systems and synthetic biology, much research has focused on the behavior
and design of single pathways, while, more recently, experimental efforts have
focused on how cross-talk (coupling two or more pathways) or inhibiting
molecular function (isolating one part of the pathway) affects systems-level
behavior. However, the theory for tackling these larger systems in general has
lagged behind. Here, we analyze how joining networks (e.g., cross-talk) or
decomposing networks (e.g., inhibition or knock-outs) affects three properties
that reaction networks may possess---identifiability (recoverability of
parameter values from data), steady-state invariants (relationships among
species concentrations at steady state, used in model selection), and
multistationarity (capacity for multiple steady states, which correspond to
multiple cell decisions). Specifically, we prove results that clarify, for a
network obtained by joining two smaller networks, how properties of the smaller
networks can be inferred from or can imply similar properties of the original
network. Our proofs use techniques from computational algebraic geometry,
including elimination theory and differential algebra.


  Teaching object-oriented programming in an introductory programming course
poses considerable challenges to the instructor. An often advocated approach to
meeting this challenge is the use of a simple, object-oriented graphics
library. We have developed a simple, portable graphics library for teaching
object-oriented programming using C++. The library, EzWindows, allows beginning
programmers to design and write programs that use the graphical display found
on all modern desktop computers. In addition to providing simple graphical
objects such as windows, geometric shapes, and bitmaps, EzWindows provides
facilities for introducing event-based programming using the mouse and timers.
EzWindows has proven to be extremely popular; it is currently in use at over
200 universities, colleges, and high schools. This paper describes the
rationale for EzWindows and its high-level design.


  In this research, some of the issues that arise from the scalarization of the
multi-objective optimization problem in the Advantage Actor Critic (A2C)
reinforcement learning algorithm are investigated. The paper shows how a naive
scalarization can lead to gradients overlapping. Furthermore, the possibility
that the entropy regularization term can be a source of uncontrolled noise is
discussed. With respect to the above issues, a technique to avoid gradient
overlapping is proposed, while keeping the same loss formulation. Moreover, a
method to avoid the uncontrolled noise, by sampling the actions from
distributions with a desired minimum entropy, is investigated. Pilot
experiments have been carried out to show how the proposed method speeds up the
training. The proposed approach can be applied to any Advantage-based
Reinforcement Learning algorithm.


  The negative effective magnetic pressure instability discovered recently in
direct numerical simulations (DNS) may play a crucial role in the formation of
sunspots and active regions in the Sun and stars. This instability is caused by
a negative contribution of turbulence to the effective mean Lorentz force (the
sum of turbulent and non-turbulent contributions) and results in formation of
large-scale inhomogeneous magnetic structures from initial uniform magnetic
field. Earlier investigations of this instability in DNS of stably stratified,
externally forced, isothermal hydromagnetic turbulence in the regime of large
plasma beta are now extended into the regime of larger scale separation ratios
where the number of turbulent eddies in the computational domain is about 30.
Strong spontaneous formation of large-scale magnetic structures is seen even
without performing any spatial averaging. These structures encompass many
turbulent eddies. The characteristic time of the instability is comparable to
the turbulent diffusion time, L^2/eta_t, where eta_t is the turbulent
diffusivity and L is the scale of the domain. DNS are used to confirm that the
effective magnetic pressure does indeed become negative for magnetic field
strengths below the equipartition field. The dependence of the effective
magnetic pressure on the field strength is characterized by fit parameters that
seem to show convergence for larger values of the magnetic Reynolds number.


  In this paper we revisit the classical problem of estimating a signal as it
impinges on a multi-sensor array. We focus on the case where the impinging
signal's bandwidth is appreciable and is operating in a broadband regime.
Estimating broadband signals, often termed broadband (or wideband) beamforming,
is traditionally done through filter and summation, true time delay, or a
coupling of the two. Our proposed method deviates substantially from these
paradigms in that it requires no notion of filtering or true time delay. We use
blocks of samples taken directly from the sensor outputs to fit a robust
Slepian subspace model using a least squares approach. We then leverage this
model to estimate uniformly spaced samples of the impinging signal. Alongside a
careful discussion of this model and how to choose its parameters we show how
to fit the model to new blocks of samples as they are received, producing a
streaming output. We then go on to show how this method naturally extends to
adaptive beamforming scenarios, where we leverage signal statistics to
attenuate interfering sources. Finally, we discuss how to use our model to
estimate from dimensionality reducing measurements. Accompanying these
discussions are extensive numerical experiments establishing that our method
outperforms existing filter based approaches while being comparable in terms of
computational complexity.


  We consider $(M,d)$ a connected and compact manifold and we denote by $X$ the
Bernoulli space $M^{\mathbb{N}}$. The shift acting on $X$ is denoted by
$\sigma$.
  We analyze the general XY model, as presented in a recent paper by A. T.
Baraviera, L. M. Cioletti, A. O. Lopes, J. Mohr and R. R. Souza. Denote the
Gibbs measure by $\mu_{c}:=h_{c}\nu_{c}$, where $h_{c}$ is the eigenfunction,
and, $\nu_{c}$ is the eigenmeasure of the Ruelle operator associated to $cf$.
We are going to prove that any measure selected by $\mu_{c}$, as $c\to
+\infty$, is a maximizing measure for $f$. We also show, when the maximizing
probability measure is unique, that it is true a Large Deviation Principle,
with the deviation function $R_{+}^{\infty}=\sum_{j=0}^\infty R_{+}
(\sigma^f)$, where $R_{+}:= \beta(f) + V\circ\sigma - V - f$, and, $V$ is any
calibrated subaction.


  The one-dimensional Schroedinger's equation is analysed with regard to the
existence of exact solutions for decatic polynomial potentials. Under certain
conditions on the potential's parameters, we show that the decatic polynomial
potential $V(x)=ax^{10}+bx^8+cx^6+dx^4+ex^2$, $a>0$ is exactly solvable. By
examining the polynomial solutions of certain linear differential equations
with polynomial coefficients, the necessary and sufficient conditions for
corresponding energy-dependent polynomial solutions are given in detail. It is
also shown that these polynomials satisfy a four-term recurrence relation,
whose real roots are the exact energy eigenvalues. Further, it is shown that
these polynomials generate the eigenfunction solutions of the corresponding
Schroedinger equation. Further analysis for arbitrary values of the potential
parameters using the asymptotic iteration method is also presented.


  We combine electron energy-loss spectroscopy (EELS) and density functional
theory (DFT) calculations to study the dispersion and effective mass of
excitons in the bulk of WSe$_2$. Our EELS data suggest substantial deviations
from the generic quadratic momentum dependence along the $\Gamma K$-direction.
From the DFT-derived Kohn-Sham states we deduce the EELS response without the
inclusion of particle-hole attraction to study the possible role of the
single-particle band structure on the exciton behavior. Based on this analysis
we argue in favor of a strongly momentum dependent particle-hole interaction in
WSe$_2$ and other group VI-transition-metal dichalcogenides.


  The gravitational entropy and no-hair conjectures seems to predict
contradictory future states of our Universe. The growth of the gravitational
entropy is associated with the growth of inhomogeneity, while the no-hair
conjecture argues that a universe dominated by dark energy should
asymptotically approach a homogeneous and isotropic de Sitter state. The aim of
this paper is to study these two conjectures. The investigation is based on the
Simsilun simulation, which simulates the universe using the approximation of
the Silent Universe. The Silent Universe is a solution to the Einstein
equations that assumes irrotational, non-viscous, and insulated dust, with
vanishing magnetic part of the Weyl curvature. The initial conditions for the
Simsilun simulation are sourced from the Millennium simulation, which results
with a realistically appearing but relativistic at origin simulation of a
universe. The Simsilun simulation is evolved from the early universe (t = 25
Myr) till far future (t = 1000 Gyr). The results of this investigation show
that both conjectures are correct. On global scales, a universe with a positive
cosmological constant and non-positive spatial curvature does indeed approach
the de Sitter state. At the same time it keeps generating the gravitational
entropy.


  The production of $\eta$ and $\eta^{\prime}$ mesons in photon- and
hadron-induced reactions on free and quasi-free nucleons and on nuclei is
reviewed. The extensive database on $\gamma N \to \eta N$, for both proton and
neutron targets, is described in detail and its implications for the search for
$N^{\star}$ resonances much heavier than the dominant $S_{11}(1535)$ discussed.
Though less is currently known about the production of the $\eta^{\prime}$ or
of $\eta\pi$ pairs, these also offer tantalizing prospects in the search for
the missing isobars. The more limited data available on pion-induced production
are still necessary ingredients in the partial wave analysis discussed.
  The production of the $\eta$-meson in $pp$ and $pn$ collisions shows once
again the strong influence of the $S_{11}(1535)$ isobar, which is in contrast
to the relatively weak behaviour seen near threshold for $\eta^{\prime}$
production. This difference is reflected in the important final state
interaction effects of the $\eta$ in nuclei that may even lead to this meson
being "bound" in some systems. The evidence for this is reviewed for both
$\gamma A$ and $p A$ collisions. The inclusive photoproduction of $\eta$,
$\eta^{\prime}$, and $\eta\pi$ pairs from nuclei provides further information
regarding the production mechanism and the interaction of the $\eta$ and
$\eta^{\prime}$ with nuclei and the $\eta\pi$ pairs may even allow access to
low mass $\eta A$ systems that are forbidden in direct single-meson
photoproduction.


  Deep convolutional neural network has made great achievements in sea-land
clutter classification for over-the-horizon-radar (OTHR). The premise is that a
large number of labeled training samples must be provided for a sea-land
clutter classifier. In practical engineering applications, it is relatively
easy to obtain label-free sea-land clutter samples. However, the labeling
process is extremely cumbersome and requires expertise in the field of OTHR. To
solve this problem, we propose an improved generative adversarial network,
namely weighted loss semi-supervised generative adversarial network (WL-SSGAN).
Specifically, we propose a joint feature matching loss by weighting the middle
layer features of the discriminator of semi-supervised generative adversarial
network. Furthermore, we propose the weighted loss of WL-SSGAN by linearly
weighting standard adversarial loss and joint feature matching loss. The
semi-supervised classification performance of WL-SSGAN is evaluated on a
sea-land clutter dataset. The experimental results show that WL-SSGAN can
improve the performance of the fully supervised classifier with only a small
number of labeled samples by utilizing a large number of unlabeled sea-land
clutter samples. Further, the proposed weighted loss is superior to both the
adversarial loss and the feature matching loss. Additionally, we compare
WL-SSGAN with conventional semi-supervised classification methods and
demonstrate that WL-SSGAN achieves the highest classification accuracy.


  A snapshot object simulates the behavior of an array of
single-writer/multi-reader shared registers that can be read atomically.
Delporte-Gallet et al. proposed two fault-tolerant algorithms for snapshot
objects in asynchronous crash-prone message-passing systems. Their first
algorithm is \emph{non-blocking}; it allows snapshot operations to terminate
once all write operations have ceased. It uses $O(n)$ messages of $O(n \nu)$
bits, where $n$ is the number of nodes and $\nu$ is the number of bits it takes
to represent the object. Their second algorithm allows snapshot operations to
always terminate independently of write operations. It incurs $O(n^2)$
messages.
  The fault model of Delporte-Gallet et al. considers node crashes. We aim at
the design of even more robust snapshot objects via the lenses of
self-stabilization---a very strong notion of fault-tolerance. In addition to
Delporte-Gallet et al.'s fault model, our self-stabilizing algorithm can
recover after the occurrence of transient faults; these faults represent
arbitrary violations of the assumptions according to which the system was
designed to operate.
  We propose self-stabilizing variations of Delporte-Gallet et al.'s
non-blocking algorithm and always-terminating algorithm. Our algorithms have
similar communication costs to the ones by Delporte-Gallet et al. and $O(1)$
recovery time from transient faults. The main differences are that our proposal
considers repeated gossiping of $O(\nu)$ bit messages and deals with bounded
space. We also consider an input parameter, $\delta$, for which we claim an
ability to balance the costs of snapshot operations. We validate our
correctness proof, evaluate the performance of Delporte-Gallet et al.'s
algorithms and our proposed variations and investigate the properties of
$\delta$ via PlanetLab experiments, where significant latency and communication
costs reduction are observed.


  Machine learning (ML) models can fail in unexpected ways in the real world,
but not all model failures are equal. With finite time and resources, ML
practitioners are forced to prioritize their model debugging and improvement
efforts. Through interviews with 13 ML practitioners at Apple, we found that
practitioners construct small targeted test sets to estimate an error's nature,
scope, and impact on users. We built on this insight in a case study with
machine translation models, and developed Angler, an interactive visual
analytics tool to help practitioners prioritize model improvements. In a user
study with 7 machine translation experts, we used Angler to understand
prioritization practices when the input space is infinite, and obtaining
reliable signals of model quality is expensive. Our study revealed that
participants could form more interesting and user-focused hypotheses for
prioritization by analyzing quantitative summary statistics and qualitatively
assessing data by reading sentences.


  We express the classic ARMA time-series model as a directed graphical model.
In doing so, we find that the deterministic relationships in the model make it
effectively impossible to use the EM algorithm for learning model parameters.
To remedy this problem, we replace the deterministic relationships with
Gaussian distributions having a small variance, yielding the stochastic ARMA
(ARMA) model. This modification allows us to use the EM algorithm to learn
parmeters and to forecast,even in situations where some data is missing. This
modification, in conjunction with the graphicalmodel approach, also allows us
to include cross predictors in situations where there are multiple times series
and/or additional nontemporal covariates. More surprising,experiments suggest
that the move to stochastic ARMA yields improved accuracy through better
smoothing. We demonstrate improvements afforded by cross prediction and better
smoothing on real data.


  Many modern big data applications feature large scale in both numbers of
responses and predictors. Better statistical efficiency and scientific insights
can be enabled by understanding the large-scale response-predictor association
network structures via layers of sparse latent factors ranked by importance.
Yet sparsity and orthogonality have been two largely incompatible goals. To
accommodate both features, in this paper we suggest the method of sparse
orthogonal factor regression (SOFAR) via the sparse singular value
decomposition with orthogonality constrained optimization to learn the
underlying association networks, with broad applications to both unsupervised
and supervised learning tasks such as biclustering with sparse singular value
decomposition, sparse principal component analysis, sparse factor analysis, and
spare vector autoregression analysis. Exploiting the framework of
convexity-assisted nonconvex optimization, we derive nonasymptotic error bounds
for the suggested procedure characterizing the theoretical advantages. The
statistical guarantees are powered by an efficient SOFAR algorithm with
convergence property. Both computational and theoretical advantages of our
procedure are demonstrated with several simulation and real data examples.


  We introduce an overlapping-domain approach to large-area metasurface design,
in which each simulated domain consists of a unit cell and overlapping regions
from the neighboring cells plus PML absorbers. We show that our approach
generates greatly improved metalens quality compared to designs produced using
a locally periodic approximation, thanks to $\sim 10\times$ better accuracy
with similar computational cost. We use the new approach with topology
optimization to design large-area ($200\lambda$) high-NA (0.71) multichrome and
broadband achromatic lenses with high focusing efficiency ($\sim 50\%$),
greatly improving upon previously reported works.


  We extend our recent work on the quasilocal formulation of conserved charges
to a theory of gravity containing a gravitational Chern-Simons term. As an
application of our formulation, we compute the off-shell potential and
quasilocal conserved charges of some black holes in three-dimensional
topologically massive gravity. Our formulation for conserved charges reproduces
very effectively the well-known expressions on conserved charges and the
entropy expression of black holes in the topologically massive gravity.


  We realize the two dimensional anti-de Sitter ($AdS_2$) space as a
Kaluza-Klein reduction of the $AdS_3$ space in the framework of the discrete
light cone quantization (DLCQ). Introducing DLCQ coordinates which interpolate
the original (unboosted) coordinates and the light cone coordinates, we discuss
that $AdS_2/CFT$ correspondence can be deduced from the $AdS_3/CFT$. In
particular, we elaborate on the deformation of WZW model to obtain the boundary
theory for the $AdS_2$ black hole. This enables us to derive the entropy of the
$AdS_2$ black hole from that of the $AdS_3$ black hole.


  We present the status of a lattice calculation for the K-->pipi matrix
elements of the (delta S=1) effective weak Hamiltonian, directly with two pion
in the final state. We study the energy shift of two pion in a finite volume
both in the I=0 and I=2 channels. We explain a method to avoid the Goldstone
pole contamination in the computation of renormalization constants for (delta
I=3/2) operators. Finally we show some preliminary results for the matrix
elements of (delta I=1/2) operators. Our quenched simulation is done at
beta=6.0, with Wilson fermions, on a (24^3 X 64) lattice.


  Cold temperatures during fall and spring have the potential to cause frost
damage to grapevines and other fruit plants, which can significantly decrease
harvest yields. To help prevent these losses, farmers deploy expensive frost
mitigation measures such as sprinklers, heaters, and wind machines when they
judge that damage may occur. This judgment, however, is challenging because the
cold hardiness of plants changes throughout the dormancy period and it is
difficult to directly measure. This has led scientists to develop cold
hardiness prediction models that can be tuned to different grape cultivars
based on laborious field measurement data. In this paper, we study whether deep
learning models can improve cold hardiness prediction for grapes based on data
that has been collected over a 30-year time period. A key challenge is that the
amount of data per cultivar is highly variable, with some cultivars having only
a small amount. For this purpose, we investigate the use of multi-task learning
to leverage data across cultivars in order to improve prediction performance
for individual cultivars. We evaluate a number of multi-task learning
approaches and show that the highest performing approach is able to
significantly improve over learning for single cultivars and outperforms the
current state-of-the-art scientific model for most cultivars.


  We apply the Generalized Uncertainty Principle (GUP) to the problem of
maximum entropy and evaporation/absorption of energy of black holes near the
Planck scale. We find within this general approach corrections to the maximum
entropy, and indications for quenching of the evaporation because not only the
evaporation term goes to a finite limit, but also because absorption of quanta
seems to help the balance for black holes in a thermal bath. Then, residual
masses around the Planck scale may be the final outcome of primordial black
hole evaporation.


  When employing Direct Volume Rendering (DVR) for visualizing volumetric
scalar fields, classification is generally performed on a piecewise constant or
piecewise linear approximation of the field on a viewing ray. Smoothed Particle
Hydrodynamics (SPH) data sets define volumetric scalar fields as the sum of
individual particle contributions, at highly varying spatial resolution. We
present an approach for approximating SPH scalar fields along viewing rays with
piece-wise polynomial functions of higher order. This is done by approximating
each particle contribution individually and then efficiently summing the
results, thus generating a higher-order representation of the field with a
resolution adapting to the data resolution in the volume.


  We consider the sampling problem for functional PCA (fPCA), where the
simplest example is the case of taking time samples of the underlying
functional components. More generally, we model the sampling operation as a
continuous linear map from $\mathcal{H}$ to $\mathbb{R}^m$, where the
functional components to lie in some Hilbert subspace $\mathcal{H}$ of $L^2$,
such as a reproducing kernel Hilbert space of smooth functions. This model
includes time and frequency sampling as special cases. In contrast to classical
approach in fPCA in which access to entire functions is assumed, having a
limited number m of functional samples places limitations on the performance of
statistical procedures. We study these effects by analyzing the rate of
convergence of an M-estimator for the subspace spanned by the leading
components in a multi-spiked covariance model. The estimator takes the form of
regularized PCA, and hence is computationally attractive. We analyze the
behavior of this estimator within a nonasymptotic framework, and provide bounds
that hold with high probability as a function of the number of statistical
samples n and the number of functional samples m. We also derive lower bounds
showing that the rates obtained are minimax optimal.


  We study the anisotropic magnetic structures exhibited by electron-doped
manganites using a model which incorporates the double-exchange between orbital
ly degenerate $e_{g}$ electrons and the super-exchange between $t_{2g}$
electrons with realistic values of the Hund's coupling($J_H$), the
super-exchange coupling($J_{AF}$), and the bandwidth($W$). We look at the
relative stabilities of the G, C and A type antiferromagnetic ph ases. In
particular we find that the G-phase is stable for low electron doping as seen
in experiments. We find good agreement with the experimentally observed
magnetic phase diagrams of electron-doped manganites
  ($x > 0.5$) such as Nd$_{1-x}$Sr$_{x}$MnO$_{3}$, Pr$_{1-x}$Sr$_{x}$MnO$_{3}$,
and Sm$_{1-x}$Ca$_{x}$MnO$_{3}$. We can also explain the experimentally
observed orbital structures of the C a nd A phases.
  We also extend our calculation for electron-doped bilayer manganites of the
form R$_{2-2x}$A$_{1+2x}$Mn$_2$O$_7$ and predict that the C-phase will be
absent in t hese systems due to their reduced dimensionality.


  Increasing levels of far-right extremist violence have generated public
concern about the spread of radicalization in the United States. Previous
research suggests that radicalized individuals are destabilized by various
environmental (or endemic) factors, exposed to extremist ideology, and
subsequently reinforced by members of their community. As such, the spread of
radicalization may proceed through a social contagion process, in which
extremist ideologies behave like complex contagions that require multiple
exposures for adoption. In this study, I applied an epidemiological method
called two-component spatio-temporal intensity modeling to data from 416
far-right extremists exposed in the United States between 2005 and 2017. The
results indicate that patterns of far-right radicalization in the United States
are consistent with a complex contagion process, in which reinforcement is
required for transmission. Both social media usage and group membership enhance
the spread of extremist ideology, suggesting that online and physical
organizing remain primary recruitment tools of the far-right movement.
Additionally, I identified several endemic factors, such as poverty, that
increase the probability of radicalization in particular regions. Future
research should investigate how specific interventions, such as online
counter-narratives to battle propaganda, may be effectively implemented to
mitigate the spread of far-right extremism in the United States.


  The purpose of this paper is to obtain exact solutions of the Einstein field
equations describing traversable wormholes supported by phantom energy. Their
relationship to exact solutions in the literature is also discussed, as well as
the conditions required to determine such solutions.


  The purpose of this note is to prove the existence of a randomized mechanism,
a social decision scheme (SDS), with desirable fairness, efficiency, and
strategyproofness properties unmatched by all known SDSs. In particular, we
disprove a conjecture by Aziz et al. (2013). Additionally, we obtain a strong
existence result for the domain of random assignment. Both, the notion of
efficiency and strategyproofness are based on stochastic dominance and have
been studied extensively for random assignment. The proof makes crucial use of
Brouwer's fixed-point theorem and is hence non-constructive. To the best of our
knowledge, this is the first application of a fixed-point theorem to show the
existence of a social choice function or mechanism.


  Spatial confinement of matter in functional nanostructures has propelled
these systems to the forefront of nanoscience, both as a playground for exotic
physics and quantum phenomena and in multiple applications including
plasmonics, optoelectronics, and sensing. In parallel, the emergence of
monochromated electron energy loss spectroscopy (EELS) has enabled exploration
of local nanoplasmonic functionalities within single nanoparticles and the
collective response of nanoparticle assemblies, providing deep insight into the
associated mechanisms. However, modern synthesis processes for plasmonic
nanostructures are often limited in the types of accessible geometry and
materials, and even then, limited to spatial precisions on the order of tens of
nm, precluding the direct exploration of critical aspects of the
structure-property relationships. Here, we use the atomic-sized probe of the
scanning transmission electron microscope (STEM) to perform precise sculpting
and design of nanoparticle configurations. Furthermore, using low-loss (EELS),
we provide dynamic analyses of evolution of the plasmonic response during the
sculpting process. We show that within self-assembled systems of nanoparticles,
individual nanoparticles can be selectively removed, reshaped, or arbitrarily
patterned with nanometer-level resolution, effectively modifying the plasmonic
response in both space and energy domains. This process significantly increases
the scope for design possibilities and presents opportunities for arbitrary
structure development, which are ultimately key for nanophotonic design.
Nanosculpting introduces yet another capability to the electron microscope.


  In this paper, we provide a construction of the so-called backbone
decomposition for multitype supercritical superprocesses. While backbone
decompositions are fairly well-known for both continuous-state branching
processes and superprocesses in the one-type case, so far no such
decompositions or even description of prolific genealogies have been given for
the multitype cases. Here we focus on superprocesses, but by turning the
movement off, we get the prolific backbone decomposition for multitype
continuous-state branching processes as an easy consequence of our results.


  We sketch a procedure to capture general non-invertible symmetries of a
d-dimensional quantum field theory in the data of a higher-category, which
captures the local properties of topological defects associated to the
symmetries. We also discuss fusions of topological defects, which involve
condensations/gaugings of higher-categorical symmetries localized on the
worldvolumes of topological defects. Recently some fusions of topological
defects were discussed in the literature where the dimension of topological
defects seems to jump under fusion. This is not possible in the standard
description of higher-categories. We explain that the dimension-changing
fusions are understood as higher-morphisms of the higher-category describing
the symmetry. We also discuss how a 0-form sub-symmetry of a higher-categorical
symmetry can be gauged and describe the higher-categorical symmetry of the
theory obtained after gauging. This provides a procedure for constructing
non-invertible higher-categorical symmetries starting from invertible
higher-form or higher-group symmetries and gauging a 0-form symmetry. We
illustrate this procedure by constructing non-invertible 2-categorical
symmetries in 4d gauge theories and non-invertible 3-categorical symmetries in
5d and 6d theories. We check some of the results obtained using our approach
against the results obtained using a recently proposed approach based on 't
Hooft anomalies.


  In this paper we propose a framework for identifying patterns and
regularities in the pseudo-anonymized Call Data Records (CDR) pertaining a
generic subscriber of a mobile operator. We face the challenging task of
automatically deriving meaningful information from the available data, by using
an unsupervised procedure of cluster analysis and without including in the
model any \textit{a-priori} knowledge on the applicative context. Clusters
mining results are employed for understanding users' habits and to draw their
characterizing profiles. We propose two implementations of the data mining
procedure; the first is based on a novel system for clusters and knowledge
discovery called LD-ABCD, capable of retrieving clusters and, at the same time,
to automatically discover for each returned cluster the most appropriate
dissimilarity measure (local metric). The second approach instead is based on
PROCLUS, the well-know subclustering algorithm. The dataset under analysis
contains records characterized only by few features and, consequently, we show
how to generate additional fields which describe implicit information hidden in
data. Finally, we propose an effective graphical representation of the results
of the data-mining procedure, which can be easily understood and employed by
analysts for practical applications.


  As deep neural networks are increasingly used in solving high-stake problems,
there is a pressing need to understand their internal decision mechanisms.
Visualization has helped address this problem by assisting with interpreting
complex deep neural networks. However, current tools often support only single
data instances, or visualize layers in isolation. We present NeuralDivergence,
an interactive visualization system that uses activation distributions as a
high-level summary of what a model has learned. NeuralDivergence enables users
to interactively summarize and compare activation distributions across layers,
classes, and instances (e.g., pairs of adversarial attacked and benign images),
helping them gain better understanding of neural network models.


  There is still no proof that the new particle $X$ recently discovered by the
ATLAS and CMS Collaborations indeed has spin zero and positive parity, as
confidently expected. We show here that the energy dependence of associated
$W/Z + X$ production would be much less for a $J^P = 0^+$ boson with minimal
couplings, such as the Higgs boson of the Standard Model, than for a spin-two
particle with graviton-like couplings or a spin-zero boson with non-minimal
couplings. The $W/Z + (X \to {\bar b}b)$ signal apparently observed by the CDF
and D0 Collaborations can be used to predict the cross section for the same
signal at the LHC that should be measured under the spin-two and different
spin-zero hypotheses. The spin-two prediction exceeds by an order of magnitude
the upper limits established by the ATLAS and CMS Collaborations, which are
consistent with the minimal $0^+$ prediction, thereby providing {\it secunda
facie} evidence against spin-two Higgs impostors. Similar analyses of energy
dependences provide evidence against $0^-$ impostors, non-minimal scalar boson
couplings, including the best LHC limits on dimension-six operators. Comparing
the LHC vector boson fusion cross sections at 7 and 8 TeV in the centre of mass
provides additional but weaker evidence in favour of the identification of the
$X$ particle as a $J^P = 0^+$ boson with minimal couplings.


  The use of adaptive spatial resolution to simulate flows of practical
interest using Smoothed Particle Hydrodynamics (SPH) is of considerable
importance. Recently, Muta and Ramachandran [1] have proposed an efficient
adaptive SPH method which is capable of handling large changes in particle
resolution. This allows the authors to simulate problems with much fewer
particles than was possible earlier. The method was not demonstrated or tested
with moving bodies or multiple bodies. In addition, the original method
employed a large number of background particles to determine the spatial
resolution of the fluid particles. In the present work we establish the
formulation's effectiveness for simulating flow around stationary and moving
geometries. We eliminate the need for the background particles in order to
specify the geometry-based or solution-based adaptivity and we discuss the
algorithms employed in detail. We consider a variety of benchmark problems,
including the flow past two stationary cylinders, flow past different NACA
airfoils at a range of Reynolds numbers, a moving square at various Reynolds
numbers, and the flow past an oscillating cylinder. We also demonstrate
different types of motions using single and multiple bodies. The source code is
made available under an open source license, and our results are reproducible.


  In this letter we show that supersymmetry like geometry can be approximated
using finite dimensional matrix models and fuzzy manifolds. In particular we
propose a non-perturbative regularization of {\cal N}=2 supersymmetric U(n)
gauge action in 4D. In some planar large N limits we recover exact SUSY
together with the smooth geometry of R^4_{theta}.


  Entity information network is used to describe structural relationships
between entities. Taking advantage of its extension and heterogeneity, entity
information network is more and more widely applied to relationship modeling.
Recent years, lots of researches about entity information network modeling have
been proposed, while seldom of them concentrate on equipment-standard system
with properties of multi-layer, multi-dimension and multi-scale. In order to
efficiently deal with some complex issues in equipment-standard system such as
standard revising, standard controlling, and production designing, a
heterogeneous information network model for equipment-standard system is
proposed in this paper. Three types of entities and six types of relationships
are considered in the proposed model. Correspondingly, several different
similarity-measuring methods are used in the modeling process. The experiments
show that the heterogeneous information network model established in this paper
can reflect relationships between entities accurately. Meanwhile, the modeling
process has a good performance on time consumption.


  We present non-extremal magnetic black string solutions in five-dimensional
gauged supergravity. The conformal infinity is the product of time and S^1xS_h,
where S_h denotes a compact Riemann surface of genus h. The construction is
based on both analytical and numerical techniques. We compute the holographic
stress tensor, the Euclidean action and the conserved charges of the solutions
and show that the latter satisfy a Smarr-type formula. The phase structure is
determined in the canonical ensemble, and it is shown that there is a first
order phase transition from small to large black strings, which disappears
above a certain critical magnetic charge that is obtained numerically. For
another particular value of the magnetic charge, that corresponds to a twisting
of the dual super Yang-Mills theory, the conformal anomalies coming from the
background curvature and those arising from the coupling to external gauge
fields exactly cancel. We also obtain supersymmetric solutions describing waves
propagating on extremal BPS magnetic black strings, and show that they possess
a Siklos-Virasoro reparametrization invariance.


  Using parsec-resolution simulations of a typical galaxy merger, we study the
triggering of starbursts by connecting the (inter-)galactic dynamics to the
structure of the interstellar medium. The gravitational encounter between two
galaxies enhances tidal compression over large volumes, which increases and
modifies the turbulence, in particular its compressive mode with respect to the
solenoidal one. This generates an excess of dense gas leading to intense star
formation activity. Along the interaction, the compressive turbulence modifies
the efficiency of gas-to-star conversion which, in the Schmidt-Kennicutt
diagram, drives the galaxies from the sequence of discs to that of starbursts.


  Vision-language (VL) understanding tasks evaluate models' comprehension of
complex visual scenes through multiple-choice questions. However, we have
identified two dataset biases that models can exploit as shortcuts to resolve
various VL tasks correctly without proper understanding. The first type of
dataset bias is \emph{Unbalanced Matching} bias, where the correct answer
overlaps the question and image more than the incorrect answers. The second
type of dataset bias is \emph{Distractor Similarity} bias, where incorrect
answers are overly dissimilar to the correct answer but significantly similar
to other incorrect answers within the same sample. To address these dataset
biases, we first propose Adversarial Data Synthesis (ADS) to generate synthetic
training and debiased evaluation data. We then introduce Intra-sample
Counterfactual Training (ICT) to assist models in utilizing the synthesized
training data, particularly the counterfactual data, via focusing on
intra-sample differentiation. Extensive experiments demonstrate the
effectiveness of ADS and ICT in consistently improving model performance across
different benchmarks, even in domain-shifted scenarios.


  Rao and Mitra in 1972 introduced two different types of constraints to extend
the concept of Bott-Duffin inverse and defined a new constrained inverse. Mary
in 2011 defined the inverse along an element that generalizes the Moore-Penrose
and Drazin inverses in a semigroup. Drazin in 2012 introduced the
$(b,c)$-inverse generalizing the Mary inverse. In 2017, Raki\'c noted that the
Rao-Mitra inverse is a direct precursor of the $(b,c)$-inverse. In this paper,
we introduce the notion of $EF$-inverse as a unified approach to the
aforementioned generalized inverses. Moreover, we show that the recently
introduced generalized bilateral inverses that in turn contain the OMP, MPO,
and MPOMP inverses can also be considered as special cases of the $EF$-inverse.


  The thesis deals with applications of fractional calculus to fractals. It
introduces the notion of local fractional derivative (LFD). Fractal and
multifractal functions have been studied in the thesis using LFD. New kind of
equations are introduced which involve LFD and one example, local fractional
Fokker-Planck equation, is studied in detail.


  Novel effects emerge from an interplay between multiple Andreev reflections
and Coulomb interaction in quantum dot coupled to superconducting leads and
subject to a finite potential bias $V$. Combining an intuitive physical picture
with rigorous path integral formalism we evaluate the current $I$ through the
dot and find that the interaction shifts the subharmonic pattern of the $I-V$
curve is shifted toward higher $V$. For sufficiently ^M strong interaction the
subgap current (at $eV < 2 \Delta$) is virtually suppressed.


  The fundamental diagram for tunnel traffic is constructed based on the
empirical data collected during the last two years in the deep long branch of
the Lefortovo tunnel located on the 3$^\text{rd}$ circular highway of Moscow.
This tunnel of length 3 km is equipped with a dense system of stationary
radiodetetors distributed uniformly along it chequerwise at spacing of 60 m.
The data were averaged over 30 s. Each detector measures three characteristics
of the vehicle ensemble; the flow rate, the car velocity, and the occupancy for
three lanes individually. The conducted analysis reveals an original complex
structure of the fundamental diagram.


  In existing mobile network systems, the data plane (DP) is mainly considered
a pipeline consisting of network elements end-to-end forwarding user data
traffics. With the rapid maturity of programmable network devices, however,
mobile network infrastructure mutates towards a programmable computing
platform. Therefore, such a programmable DP can provide in-network computing
capability for many application services. In this paper, we target to enhance
the data plane with in-network deep learning (DL) capability. However,
in-network intelligence can be a significant load for network devices. Then,
the paradigm of the functional split is applied so that the deep neural network
(DNN) is decomposed into sub-elements of the data plane for making machine
learning inference jobs more efficient. As a proof-of-concept, we take a Blind
Source Separation (BSS) problem as an example to exhibit the benefits of such
an approach. We implement the proposed enhancement in a full-stack emulator and
we provide a quantitative evaluation with professional datasets. As an initial
trial, our study provides insightful guidelines for the design of the future
mobile network system, employing in-network intelligence (e.g., 6G).


  Bound Magnetic Polarons (BMP) have been proposed to play an important role in
doped wide band gap semiconductors. We report the experimental evidence
supporting polaronic magnetism and the role of defects on it. Temperature
variation of electron paramagnetic resonance study reveals that the valence
state of cobalt is 2+ and its nearest environment in the system is distorted
tetrahedral in nature. Room temperature ferromagnetism have been found in all
the doped [Co (1, 3, 5) % doped 3C-SiC] samples. The Curie temperature, TC of
the samples have been found to be above 800 K. The isothermal magnetization
plots, to determine the strength of domain wall pinning were analyzed using the
Jiles-Atherton Model. The temperature variation of the anisotropic constant was
determined using Law of Approach to saturation (LAS), which decreases with
increase in temperature. Quantitative analysis of magnetic interaction has been
carried out and the result is explained by using BMP Model, which probably
arises out of the exchange interaction of Co2+ ions with related (Si, C)
defects.


  We explain how a new type of fields called shadows and the use of twisted
variables allow for a better description of Yang-Mills supersymmetric theories.
  (Based on lectures given in Cargese, June 2006.)


  Consider a flat vector bundle F over compact Riemannian manifold M and let f
be a self-indexing Morse function on M. Let g be a smooth Euclidean metric on
F. Set g_t=exp(-2tf)g and let \rho(t) be the Ray-Singer analytic torsion of F
associated to the metric g_t. Assuming that the vector field $grad f$ satisfies
the Morse-Smale transversality conditions, we provide an asymptotic expansion
for log(\rho(t)) for t\to\infty of the form a_0+a_1t+b log(t)+o(1). We present
explicit formulae for coefficients a_0,a_1 and b. In particular, we show that b
is a half integer.


  We consider some consequences of describing the gauge and matter degrees of
freedom in our universe by open strings, as suggested by the braneworld
scenario. We focus on the geometric effects described by the open string metric
and investigate their observational implications. The causal structure of
spacetime on the brane is altered; it is described not by the usual metric
$g_{\mu\nu}$, but instead by the open string metric, that incorporates the
electromagnetic background, $G_{\mu\nu} = g_{\mu\nu} - (2\pi \alpha^\prime)^2
(F^2)_{\mu\nu}$. The speed of light is now slower when propagating along
directions transverse to electromagnetic fields or an NS-NS two form, so that
Lorentz invariance is explicitly broken. A generalized equivalence principle
guarantees that the propagation of {\it all} particles, not just photons, (with
the exception of gravitons) is slower in these transverse directions. We
describe a variety of astrophysical and laboratory-based experiments designed
to detect the predicted variations in the causal structure. We show that
current technology cannot probe beyond open string lengths of $10^{-13}$ cm,
corresponding to MeV string scales. We also point out that in a braneworld
scenario, constraints on large scale electromagnetic fields together with a
modest phenomenological bound on the NS-NS two-form naturally lead to a bound
on the scale of canonical noncommutativity that is two orders of magnitude
below the string length. By invoking theoretical constraints on the NS-NS
two-form this bound can be improved to give an extremely strong bound on the
noncommutative scale well below the Planck length, $\sqrt{|\theta|_{max}} <
10^{-35} {\rm cm} \times ({{\rm TeV} \over {\rm string} {\rm scale}}


  Let $A$ be a positive (semidefinite) bounded linear operator acting on a
complex Hilbert space $\big(\mathcal{H}, \langle \cdot\mid \cdot\rangle \big)$.
The semi-inner product ${\langle x\mid y\rangle}_A := \langle Ax\mid y\rangle$,
$x, y\in\mathcal{H}$ induces a seminorm ${\|\cdot\|}_A$ on $\mathcal{H}$. Let
$T$ be an $A$-bounded operator on $\mathcal{H}$, the $A$-numerical radius of
$T$ is given by \begin{align*} \omega_A(T) = \sup\Big\{\big|{\langle Tx\mid
x\rangle}_A\big|: \,\,x\in \mathcal{H}, \,{\|x\|}_A = 1\Big\}. \end{align*} In
this paper, we establish several inequalities for
$\omega_\mathbb{A}(\mathbb{T})$, where $\mathbb{T}=(T_{ij})$ is a $d\times d$
operator matrix with $T_{ij}$ are $A$-bounded operators and $\mathbb{A}$ is the
diagonal operator matrix whose each diagonal entry is $A$.


  We present a novel discrete optimization-based approach to generate
downsampled versions of binary images that are guaranteed to have the same
topology as the original, measured by the zeroth and first Betti numbers of the
black regions, while having good similarity to the original image as measured
by IoU and Dice scores. To our best knowledge, all existing binary image
downsampling methods do not have such topology-preserving guarantees. We also
implemented a baseline morphological operation (dilation)-based approach that
always generates topologically correct results. However, we found the
similarity scores to be much worse. We demonstrate several applications of our
approach. First, generating smaller versions of medical image segmentation
masks for easier human inspection. Second, improving the efficiency of binary
image operations, including persistent homology computation and shortest path
computation, by substituting the original images with smaller ones. In
particular, the latter is a novel application that is made feasible only by the
full topology-preservation guarantee of our method.


  New physics searches at the LHC or Tevatron typically look for a specific
number of hard jets, leptons and photons. To obtain an exclusive N-jet sample,
one can measure the event shape "N-jettiness" \tau_N and veto additional
undesired jets by requiring \tau_N <<1. However, this leads to large logarithms
of \tau_N in the cross section, that need to be summed for reliable theory
predictions. For N=0 in hadronic collisions, \tau_N reduces to the "beam
thrust" eventshape \tau_B. We derive a factorization theorem for pp -> XL,
where a central jet veto \tau_B<<1 is imposed on the hadronic final state X and
L is non-hadronic. This factorization theorem enables us to sum large
logarithms of \tau_B in the cross section. The initial state is described by
"beam functions", which depend on the momentum fraction x and the (transverse)
virtuality t of the colliding partons. The dependence on t can be calculated in
perturbation theory by matching beam functions onto the parton distribution
functions (PDFs) at the intermediate scale \mu_B^2 ~ t, and we have calculated
all one-loop matching coefficients. Unlike the PDFs, the evolution of the beam
function is in t and does not affect x or the parton type. We show results for
the beam thrust cross section for Drell-Yan and Higgs production through gluon
fusion at next-to-next-to-leading-logarithmic order. For N>0, we obtain a
factorization formula with inclusive jet and beam functions that allows us to
sum the large logarithms of \tau_N.


  Let $\theta >2$ be real and non-integral with integer part $n = \lfloor
\theta \rfloor$ and let $ \phi (x)$ be a generalised polynomial with leading
term $x^\theta.$ We establish a mean value estimate for the exponential sum
\begin{equation*} \sum_{1 \leq x \leq P} e \left(\alpha_1 x + \cdots + \alpha_n
x^n + \alpha_\phi \phi (x) \right). \end{equation*}


  In this article we study some classical aspects of Podolsky Electrodynamics
in the static regime. We develop the multipole expansion for the theory in both
the electrostatic and the magnetostatic cases. We also address the problem of
consistently truncating the infinite series associated with the several kinds
of multipoles, yielding approximations for the static Podolskian
electromagnetic field to any degree of precision required. Moreover, we apply
the general theory of multipole expansion to some specific physical problems.
In those problems we identify the first terms of the series with the monopole,
dipole and quadrupole terms in the generalized theory. We also propose a
situation in which Podolsky theory can be experimentally tested.


  Video Object Segmentation, and video processing in general, has been
historically dominated by methods that rely on the temporal consistency and
redundancy in consecutive video frames. When the temporal smoothness is
suddenly broken, such as when an object is occluded, or some frames are missing
in a sequence, the result of these methods can deteriorate significantly or
they may not even produce any result at all. This paper explores the orthogonal
approach of processing each frame independently, i.e disregarding the temporal
information. In particular, it tackles the task of semi-supervised video object
segmentation: the separation of an object from the background in a video, given
its mask in the first frame. We present Semantic One-Shot Video Object
Segmentation (OSVOS-S), based on a fully-convolutional neural network
architecture that is able to successively transfer generic semantic
information, learned on ImageNet, to the task of foreground segmentation, and
finally to learning the appearance of a single annotated object of the test
sequence (hence one shot). We show that instance level semantic information,
when combined effectively, can dramatically improve the results of our previous
method, OSVOS. We perform experiments on two recent video segmentation
databases, which show that OSVOS-S is both the fastest and most accurate method
in the state of the art.


  Let $\Gamma$ be a discrete group. A $C^*$-algebra $A$ is an exotic
$C^*$-algebra (associated to $\Gamma$) if there exist proper surjective
$C^*$-quotients $C^*(\Gamma)\to A\to C^*_r(\Gamma)$. In this paper, we show
that a large class of exotic $C^*$-algebras have poor local properties. More
precisely, we demonstrate the failure of local reflexitity, exactness, and
local lifting property. Additionally, $A$ does not admit an amenable trace and,
hence, is not quasidiagonal and does not have the WEP when $A$ is from the
class of exotic $C^*$-algebras defined by Brown and Guentner. In order to
achieve the main results of this paper, we prove a result which implies the
factorization property for the class of discrete groups which are algebraic
subgroups of locally compact amenable groups.


  Constructing an accurate yield curve is essential for evaluating financial
instruments and analyzing market trends in the bond market. However, in the
case of the Burundian sovereign bond market, the presence of missing data poses
a significant challenge to accurately constructing the yield curve. In this
paper, we explore the limitations and data availability constraints specific to
the Burundian sovereign market and propose robust methodologies to effectively
handle missing data. The results indicate that the Linear Regression method,
and the Previous value method perform consistently well across variables,
approximating a normal distribution for the error values. The non parametric
Missing Value Imputation using Random Forest (miss-Forest) method performs well
for coupon rates but poorly for bond prices, and the Next value method shows
mixed results. Ultimately, the Linear Regression (LR) method is recommended for
imputing missing data due to its ability to approximate normality and
predictive capabilities. However, filling missing values with previous values
has high accuracy, thus, it will be the best choice when we have less
information to be able to increase accuracy for LR. This research contributes
to the development of financial products, trading strategies, and overall
market development in Burundi by improving our understanding of the yield curve
dynamics.


  The paper is devoted to a comprehensive study of composite models in
variational analysis and optimization the importance of which for numerous
theoretical, algorithmic, and applied issues of operations research is
difficult to overstate. The underlying theme of our study is a systematical
replacement of conventional metric regularity and related requirements by much
weaker metric subregulatity ones that lead us to significantly stronger and
completely new results of first-order and second-order variational analysis and
optimization. In this way we develop extended calculus rules for first-order
and second-order generalized differential constructions with paying the main
attention in second-order variational theory to the new and rather large class
of fully subamenable compositions. Applications to optimization include
deriving enhanced no-gap second order optimality conditions in constrained
composite models, complete characterizations of the uniqueness of Lagrange
multipliers and strong metric subregularity of KKT systems in parametric
optimization, etc.


  Crystal basis theory for the queer Lie superalgebra was developed by
Grantcharov et al. and it was shown that semistandard decomposition tableaux
admit the structure of crystals for the queer Lie superalgebra or simply
$\mathfrak{q}$-crystal structure. In this paper, we explore the
$\mathfrak{q}$-crystal structure of primed tableaux (semistandard marked
shifted tableaux) and that of signed unimodal factorizations of reduced words
of type $B$. We give the explicit odd Kashiwara operators on primed tableaux
and the forms of the highest and lowest weight vectors. We also give the
explicit algorithms for odd Kashiwara operators on signed unimodal
factorizations of reduced words of type $B$.


  A perfect matching in the complete graph on $2k$ vertices is a set of edges
such that no two edges have a vertex in common and every vertex is covered
exactly once. Two perfect matchings are said to be $t$-intersecting if they
have at least $t$ edges in common. The main result in this paper is an
extension of the famous Erd\H{o}s-Ko-Rado (EKR) theorem \cite{EKR} to
2-intersecting families of perfect matchings for all values of $k$.
Specifically, for $k\geq 3$ a set of 2-intersecting perfect matchings in
$K_{2k}$ of maximum size has $(2k-5)(2k-7)\cdots (1)$ perfect matchings.


  The conformal mapping $f(z)=(z+1)^2 $ from $\mathbb{D}$ onto the standard
cardioid has a homeomorphic extension of finite distortion to entire
$\mathbb{R}^2 .$ We study the optimal regularity of such extensions, in terms
of the integrability degree of the distortion and of the derivatives, and these
for the inverse. We generalize all outcomes to the case of conformal mappings
from $\mathbb{D}$ onto cardioid-type domains.


  Is the dynamical evolution of physical systems objectively a manifestation of
information processing by the universe? We find that an affirmative answer has
important consequences for the measurement problem. In particular, we calculate
the amount of quantum information processing involved in the evolution of
physical systems, assuming a finite degree of fine-graining of Hilbert space.
This assumption is shown to imply that there is a finite capacity to sustain
the immense entanglement that measurement entails. When this capacity is
overwhelmed, the system's unitary evolution becomes computationally unstable
and the system suffers an information transition (`collapse'). Classical
behaviour arises from the rapid cycles of unitary evolution and information
transitions.
  Thus, the fine-graining of Hilbert space determines the location of the
`Heisenberg cut', the mesoscopic threshold separating the microscopic, quantum
system from the macroscopic, classical environment. The model can be viewed as
a probablistic complement to decoherence, that completes the measurement
process by turning decohered improper mixtures of states into proper mixtures.
It is shown to provide a natural resolution to the measurement problem and the
basis problem.


  We have measured dramatic flux and spectral variations through the 0.193d
orbit of the optical counterpart of the unidentified gamma-ray source 0FGL
J2339.8-0530. This compact object companion is strongly heated, with T_eff
varying from ~6900K (superior conjunction) to <3000K at minimum. A combined fit
to the light curve and radial velocity amplitudes implies M_1 ~ 0.075M_sun, M_2
~ 1.4 M_sun and inclination i ~ 57deg. Thus this is a likely `black widow'
system with Edot~10^{34-34.5} erg/s pulsar driving companion mass loss. This
wind, also suggested by the X-ray light curve, may prevent radio pulse
detection. Our measurements constrain the pulsar's reflex motion, increasing
the possibility of a pulse detection in the gamma-ray signal.


  We present 3, 15, and 33 GHz imaging towards galaxy nuclei and extranuclear
star-forming regions using the Karl G. Jansky Very Large Array as part of the
Star Formation in Radio Survey. With $3-33$ GHz radio spectra, we measured the
spectral indices and corresponding thermal (free-free) emission fractions for a
sample of 335 discrete regions having significant detections in at least two
radio bands. After removing 14 likely background galaxies, we find that the
median thermal fraction at 33 GHz is $92 \pm 0.8\%$ with a median absolute
deviation of $11\%$, when a two-component power-law model is adopted to fit the
radio spectrum. Limiting the sample to 238 sources that are confidently
identified as star-forming regions, and not affected by potential AGN
contamination (i.e., having galactocentric radii $r_{\rm G} \geq 250$ pc),
results in a median thermal fraction of $93 \pm 0.8 \%$ with a median absolute
deviation of $10\%$. We further measure the thermal fraction at 33 GHz for 163
regions identified at 7" resolution to be $94 \pm 0.8 \%$ with a median
absolute deviation of $8\%$. Together, these results confirm that free-free
emission dominates the radio spectra of star-forming regions on scales up to
$\sim$500 pc in normal star-forming galaxies. We additionally find a factor of
$\sim$1.6 increase in the scatter of the measured spectral index and thermal
fraction distributions as a function of decreasing galactocentric radius. This
trend is likely reflective of the continuous star-formation activity occurring
in the galaxy centers, resulting a larger contribution of diffuse nonthermal
emission relative to star-forming regions in the disk.


  In this paper, we consider a status updating system where updates are
generated at a constant rate at $K$ sources and sent to the corresponding
recipients through a noise-free broadcast channel. We assume that perfect
channel state information (CSI) is available at the transmitter before each
transmission, and the transmitter is able to utilize the CSI information to
precode the updates. Our object is to design optimal precoding schemes to
minimize the summed average \emph{age of information} (AoI) at the recipients.
Under various assumptions on the size of each update $B$, the number of
transmit antennas $M$, and the number of receive antennas $N$ at each user,
this paper identifies the corresponding age-optimal precoding and transmission
scheduling strategies. Specifically, for the case when $N=1$, a round-robin
based updating scheme is shown to be optimal. For the two-user systems with
$N>B$ or $M\notin[N:2N]$, framed updating schemes are proven to be optimal. For
other cases in the two-user systems, a framed alternating updating scheme is
proven to be $2$-optimal.


  Forecast of the atmospheric parameters and optical turbulence applied to the
ground-based astronomy is very crucial mainly for the queue scheduling. So far,
most efforts have been addressed by our group in developing algorithms for the
optical turbulence (CN2) and annexed integrated astroclimatic parameters and
quantifying the performances of the Astro-Meso-Nh package in reconstructing
such parameters. Besides, intensive analyses on the Meso-Nh performances= in
reconstructing atmospheric parameters relevant for the ground-based astronomy
has been carried out. Our studies referred always to the night time regime. To
extend the applications of our studies to the day time regime, we present, in
this contribution, preliminary results obtained by comparing model outputs and
measurements of classical atmospheric parameter relevant for the ground-based
astronomy in night and day time. We chose as a test case, the Roque de los
Muchachos Observatory (Canary Islands), that offers a very extended set of
measurements provided by different sensors belonging to different telescopes on
the same summit/Observatory. The convective regime close to the ground typical
of the day time is pretty different from the stable regime characterising the
night time. This study aims therefore to enlarge the domain of validity of the
Astro-Meso-Nh code to new turbulence regimes and it permits to cover the total
24 hours of a day. Such an approach will permit not only an application to
solar telescopes (e.g. EST) but also applications to a much extended set of
scientific fields, not only in astronomical context such as satellite
communications.


  We construct a class of random potentials for N >> 1 scalar fields using
non-equilibrium random matrix theory, and then characterize multifield
inflation in this setting. By stipulating that the Hessian matrices in adjacent
coordinate patches are related by Dyson Brownian motion, we define the
potential in the vicinity of a trajectory. This method remains computationally
efficient at large N, permitting us to study much larger systems than has been
possible with other constructions. We illustrate the utility of our approach
with a numerical study of inflation in systems with up to 100 coupled scalar
fields. A significant finding is that eigenvalue repulsion sharply reduces the
duration of inflation near a critical point of the potential: even if the
curvature of the potential is fine-tuned to be small at the critical point,
small cross-couplings in the Hessian cause the curvature to grow in the
neighborhood of the critical point.


  It is shown that the dual $\hat{A}$ of a separable $C^{\ast}$-algebra $A$ is
discrete if and only if its Banach space dual has the weak$^{\ast}$-fixed point
property. We prove further that these properties are equivalent to the uniform
weak$^{\ast}$ Kadec-Klee property of $A^{\ast}$ and to the coincidence of the
weak$^{\ast}$ topology with the norm topology on the pure states of $A$.


  We examine scalar quantum electrodynamics in power-law inflation space-time,
and compute the one-loop correction to electric and magnetic field correlators
at superhorizon separations. The effect at one-loop descends from the coupling
of the vector to the charged scalar current which is greatly enhanced due to
gravitational particle production. We conclude that non-perturbative effects
must exist due to (i) secular growth, (ii) spatial running, and (iii) infrared
sensitivity of the one-loop correction to the correlators. Electric and
magnetic correlators exhibit a hierarchy that is due to Faraday's law and
accelerated expansion, and must hold non-perturbatively.


  This paper deals with the construction of a metamodel (i.e. a simplified
mathematical model) for a stochastic computer code (also called stochastic
numerical model or stochastic simulator), where stochastic means that the code
maps the realization of a random variable. The goal is to get, for a given
model input, the main information about the output probability distribution by
using this metamodel and without running the computer code. In practical
applications, such a metamodel enables one to have estimations of every
possible random variable properties, such as the expectation, the probability
of exceeding a threshold or any quantile. The present work is concentrated on
the emulation of the quantile function of the stochastic simulator by
interpolating well chosen basis function and metamodeling their coefficients
(using the Gaussian process metamodel). This quantile function metamodel is
then used to treat a simple optimization strategy maintenance problem using a
stochastic code, in order to optimize the quantile of an economic indicator.
Using the Gaussian process framework, an adaptive design method (called QFEI)
is defined by extending in our case the well known EGO algorithm. This allows
to obtain an "optimal" solution using a small number of simulator runs.


  Collaborative filtering (CF) is a core technique for recommender systems.
Traditional CF approaches exploit user-item relations (e.g., clicks, likes, and
views) only and hence they suffer from the data sparsity issue. Items are
usually associated with unstructured text such as article abstracts and product
reviews. We develop a Personalized Neural Embedding (PNE) framework to exploit
both interactions and words seamlessly. We learn such embeddings of users,
items, and words jointly, and predict user preferences on items based on these
learned representations. PNE estimates the probability that a user will like an
item by two terms---behavior factors and semantic factors. On two real-world
datasets, PNE shows better performance than four state-of-the-art baselines in
terms of three metrics. We also show that PNE learns meaningful word embeddings
by visualization.


  In this article we study constrained variational problems in one independent
variable defined on the space of integral curves of a Frenet system in a
homogeneous space G/H. We prove that if the Lagrangian is G-invariant and
coisotropic then the extremal curves can be found by quadratures. Our proof is
constructive and relies on the reduction theory for coisotropic optimal control
problems. This gives a unified explanation of the integrability of several
classical variational problems such as the total squared curvature functional,
the projective, conformal and pseudo-conformal arc-length functionals, the
Delaunay and the Poincar{\'e} variational problems.


  We study, theoretically and experimentally, a 1-parameter family of
transformations and their limiting vector field on the space of plane polygons.
These transformations are discrete analogs of completely integrable
transformation on closed plane curves, known as the bicycle correspondence,
that is a geometric realization of the B\"acklund transformation of the planar
filament equation. For odd-gons, we construct a symplectic form on the quotient
space by parallel translations and show that the transformations are
symplectic, and the vector field is Hamiltonian. In the case of triangles, we
prove complete integrability of the respective vector field and provide
evidence for the conjecture that the transformations are integrable as well.


  In this paper, we demonstrate that interleaved sampling techniques can be
used to characterize the Hamiltonian of a qubit and its environmental
decoherence rate. The technique offers a significant advantage in terms of the
number of measurements that are required to characterize a qubit. When compared
to the standard Nyquist-Shannon sampling rate, the saving in the total
measurement time for the interleaved method is approximately proportional to
the ratio of the sample rates.