  In the presence of background noise, arrival times picked from a surface
microseismic data set usually include a number of false picks that can lead to
uncertainty in location estimation. To eliminate false picks and improve the
accuracy of location estimates, we develop an association algorithm termed
RANSAC-based Arrival Time Event Clustering (RATEC) that clusters picked arrival
times into event groups based on random sampling and fitting moveout curves
that approximate hyperbolas. Arrival times far from the fitted hyperbolas are
classified as false picks and removed from the data set prior to location
estimation. Simulations of synthetic data for a 1-D linear array show that
RATEC is robust under different noise conditions and generally applicable to
various types of subsurface structures. By generalizing the underlying moveout
model, RATEC is extended to the case of a 2-D surface monitoring array. The
effectiveness of event location for the 2-D case is demonstrated using a data
set collected by the 5200-element dense Long Beach array. The obtained results
suggest that RATEC is effective in removing false picks and hence can be used
for phase association before location estimates.


  While reinforcement learning (RL) algorithms are achieving state-of-the-art
performance in various challenging tasks, they can easily encounter
catastrophic forgetting or interference when faced with lifelong streaming
information. In the paper, we propose a scalable lifelong RL method that
dynamically expands the network capacity to accommodate new knowledge while
preventing past memories from being perturbed. We use a Dirichlet process
mixture to model the non-stationary task distribution, which captures task
relatedness by estimating the likelihood of task-to-cluster assignments and
clusters the task models in a latent space. We formulate the prior distribution
of the mixture as a Chinese restaurant process (CRP) that instantiates new
mixture components as needed. The update and expansion of the mixture are
governed by the Bayesian non-parametric framework with an expectation
maximization (EM) procedure, which dynamically adapts the model complexity
without explicit task boundaries or heuristics. Moreover, we use the domain
randomization technique to train robust prior parameters for the initialization
of each task model in the mixture, thus the resulting model can better
generalize and adapt to unseen tasks. With extensive experiments conducted on
robot navigation and locomotion domains, we show that our method successfully
facilitates scalable lifelong RL and outperforms relevant existing methods.


  Recent studies report that many machine reading comprehension (MRC) models
can perform closely to or even better than humans on benchmark datasets.
However, existing works indicate that many MRC models may learn shortcuts to
outwit these benchmarks, but the performance is unsatisfactory in real-world
applications. In this work, we attempt to explore, instead of the expected
comprehension skills, why these models learn the shortcuts. Based on the
observation that a large portion of questions in current datasets have shortcut
solutions, we argue that larger proportion of shortcut questions in training
data make models rely on shortcut tricks excessively. To investigate this
hypothesis, we carefully design two synthetic datasets with annotations that
indicate whether a question can be answered using shortcut solutions. We
further propose two new methods to quantitatively analyze the learning
difficulty regarding shortcut and challenging questions, and revealing the
inherent learning mechanism behind the different performance between the two
kinds of questions. A thorough empirical analysis shows that MRC models tend to
learn shortcut questions earlier than challenging questions, and the high
proportions of shortcut questions in training sets hinder models from exploring
the sophisticated reasoning skills in the later stage of training.


  IEEE 802.15.4 takes a center stage in IoT as Low- Rate Wireless Personal Area
Networks(LR-WPANs). The standard specifies Offset Quadrature Phase Shift Keying
Physical Layer (O-QPSK PHY) with half-sine pulse shaping which can be either
analyzed under the class of M-ary PSK signals (QPSK signal with offset) or as
Minimum Shift Keying (MSK) signal. M-ary PSK demodulation is requires perfect
carrier and has minimal error. MSK signals which falls under Continuous Phase
Frequency Shift Keying can be demodulated non-coherently but error performance
is not as good. In our paper, this dual nature of IEEE 802.15.4 PHY is
exploited to propose a dual mode receiver comprising of QPSK demodulator chain
and MSK demodulator chain as a single system on chip. The mode can be
configured manually depending on the type of application or based on the
feedback from a Signal to Noise (SNR) indicator employed in the proposed
receiver. M-ary PSK chain is selected for lower SNRs and MSK for higher SNRs.
Each of these properties are analyzed in detail for both demodulator chains and
we go on to prove that MSK detection can be used for low power, low complex and
low latency while QPSK detection is employed for minimal error.


  We study families of one-dimensional CFTs relevant for describing gapped QFTs
in AdS$_2$. Using the Polyakov bootstrap as our main tool, we explain how
S-matrices emerge from the flat space limit of CFT correlators. In this limit
we prove that the CFT OPE density matches that of a generalized free field, and
that this implies unitarity of the S-matrix. We establish a CFT dispersion
formula for the S-matrix, proving its analyticity except for singularities on
the real axis which we characterize in terms of the CFT data. In particular
positivity of the OPE establishes that any such S-matrix must satisfy extended
unitarity conditions. We also carefully prove that for physical kinematics the
S-matrix may be more directly described by a phase shift formula. Our results
crucially depend on the assumption of a certain gap in the spectrum of
operators. We bootstrap perturbative AdS bubble, triangle and box diagrams and
find that the presence of anomalous thresholds in S-matrices are precisely
signaled by an unbounded OPE arising from violating this assumption. Finally we
clarify the relation between unitarity saturating S-matrices and extremal CFTs,
establish a mapping between the dual S-matrix and CFT bootstraps, and discuss
how our results help understand UV completeness or lack thereof for specific
S-matrices.


  The Hierarchical Task Network (HTN) formalism is used to express a wide
variety of planning problems in terms of decompositions of tasks into subtaks.
Many techniques have been proposed to solve such hierarchical planning
problems. A particular technique is to encode hierarchical planning problems as
classical STRIPS planning problems. One advantage of this technique is to
benefit directly from the constant improvements made by STRIPS planners.
However, there are still few effective and expressive encodings. In this paper,
we present a new HTN to STRIPS encoding allowing to generate concurrent plans.
We show experimentally that this encoding outperforms previous approaches on
hierarchical IPC benchmarks.


  There is a higher dimensional analogue of the perturbative Chern-Simons
theory in the sense that a similar perturbative series as in 3-dimension, which
is computed via configuration space integral, yields an invariant of higher
dimensional knots (Bott-Cattaneo-Rossi invariant), which is constructed by Bott
for degree 2 and by Cattaneo-Rossi for higher degrees. However, its feature is
yet unknown. In this paper we restrict the study to long ribbon n-knots and
characterize the Bott-Cattaneo-Rossi invariant as a finite type invariant of
long ribbon n-knots in [HKS]. As a consequence, we obtain a non-trivial
description of the Bott-Cattaneo-Rossi invariant in terms of the Alexander
polynomial.
  The results for higher codimension knots are also given. In those cases
similar differential forms to define Bott-Cattaneo-Rossi invariant yields
infinitely many cohomology classes of Emb(R^n, R^m) if m,n>= 3 odd and m>n+2.
We observe that half of these classes are non-trivial, along a line similar to
Cattaneo-CottaRamusino-Longoni [CCL].


  A perspective on the contemporary use of Dyson-Schwinger equations, focusing
on some recent phenomenological applications: a description and unification of
light-meson observables using a one-parameter model of the effective
quark-quark interaction, and studies of leptonic and nonleptonic nucleon form
factors.


  We present inverted spin-valves fabricated from CVD-grown bilayer graphene
(BLG) that show more than a doubling in device performance at room temperature
compared to state-of-the art bilayer graphene spin-valves. This is made
possible by a PDMS droplet-assisted full-dry transfer technique that
compensates for previous process drawbacks in device fabrication.
Gate-dependent Hanle measurements show spin lifetimes of up to 5.8 ns and a
spin diffusion length of up to 26 $\mu$m at room temperature combined with a
charge carrier mobility of $\approx$ 24 000 cm$^{2}$(Vs)$^{-1}$ for the best
device. Our results demonstrate that CVD-grown BLG shows equally good room
temperature spin transport properties as both CVD-graphene and even exfoliated
single-layer graphene.


  Career opportunities are often a matter of chance, but also of willingness to
cross interdisciplinary boundaries.


  We derive the strongest individual fitness distribution on a variation for a
species survival model proposed by Guiol, Machado and Schinazi \cite{GMS11}. We
point out to the fact that this distribution relies on the Gauss hypergeometric
function and when $p=\frac{1}{2}$ on the Hypergeometric function type I
distribution


  A point-to-point wireless communication system in which the transmitter is
equipped with an energy harvesting device and a rechargeable battery, is
studied. Both the energy and the data arrivals at the transmitter are modeled
as Markov processes. Delay-limited communication is considered assuming that
the underlying channel is block fading with memory, and the instantaneous
channel state information is available at both the transmitter and the
receiver. The expected total transmitted data during the transmitter's
activation time is maximized under three different sets of assumptions
regarding the information available at the transmitter about the underlying
stochastic processes. A learning theoretic approach is introduced, which does
not assume any a priori information on the Markov processes governing the
communication system. In addition, online and offline optimization problems are
studied for the same setting. Full statistical knowledge and causal information
on the realizations of the underlying stochastic processes are assumed in the
online optimization problem, while the offline optimization problem assumes
non-causal knowledge of the realizations in advance. Comparing the optimal
solutions in all three frameworks, the performance loss due to the lack of the
transmitter's information regarding the behaviors of the underlying Markov
processes is quantified.


  A primary interest in dynamic inverse problems is to identify the underlying
temporal behaviour of the system from outside measurements. In this work we
consider the case, where the target can be represented by a decomposition of
spatial and temporal basis functions and hence can be efficiently represented
by a low-rank decomposition. We then propose a joint reconstruction and
low-rank decomposition method based on the Nonnegative Matrix Factorisation to
obtain the unknown from highly undersampled dynamic measurement data. The
proposed framework allows for flexible incorporation of separate regularisers
for spatial and temporal features. For the special case of a stationary
operator, we can effectively use the decomposition to reduce the computational
complexity and obtain a substantial speed-up. The proposed methods are
evaluated for two simulated phantoms and we compare the obtained results to a
separate low-rank reconstruction and subsequent decomposition approach based on
the widely used principal component analysis.


  The steady-state population of bodies resulting from a collisional cascade
depends on how material strength varies with size. We find a simple expression
for the power-law index of the population, given a power law that describes how
material strength varies with size. This result is extended to the case
relevant for the asteroid belt and Kuiper belt, in which the material strength
is described by 2 separate power laws--one for small bodies and one for larger
bodies. We find that the power-law index of the small body population is
unaffected by the strength law for the large bodies, and vice versa. Simple
analytical expressions describe a wave that is superimposed on the large body
population because of the transition between the two power laws describing the
strength. These analytical results yield excellent agreement with a numerical
simulation of collisional evolution. These results will help to interpret
observations of the asteroids and KBOs, and constrain the strength properties
of those objects.


  Modified neutrino dispersion relations, which still obey the relativity
principle, can have both a superluminal (muon-type) neutrino and a luminal
(electron-type) neutrino, as long as neutrino-mass effects can be neglected.
The idea is to allow for flavor-dependent deformed Lorentz transformations and
an appropriate hierarchy of energy scales. If OPERA's result on a superluminal
velocity of the muon-neutrino is confirmed, the model has a matching
superluminal velocity of the corresponding charged lepton, the muon, at equal
particle energy. Assuming that this model is not already ruled out, new
TeV-scale effects in the muon sector are predicted. Also discussed is a
different model with a superluminal sterile neutrino propagating in the usual 4
spacetime dimensions.


  Triplet excited state generation plays a pivotal role in photosensitizers,
however the reliance on transition metals and heavy atoms can limit the utility
of these systems. In this study, we demonstrate that an interplay of competing
quantum effects control the high triplet quantum yield in a prototypical boron
dipyrromethene-anthracene (BD-An) donor-acceptor dyad photosensitizer, which is
only captured by an accurate treatment of both inner and outer sphere
reorganization energies. Our ab initio-derived model provides excellent
agreement with experimentally measured spectra, triplet yields and excited
state kinetic data, including the triplet lifetime. We find that rapid triplet
state formation occurs primarily via high-energy triplet states through both
spin-orbit coupled charge transfer and El-Sayed's rule breaking intersystem
crossing, rather than direct spin-orbit coupled charge transfer to the lowest
lying triplet state. Our calculations also reveal that competing effects of
nuclear tunneling, electronic state recrossing, and electronic polarizability
dictate the rate of non-productive ground state recombination. This study sheds
light on the quantum effects driving efficient triplet formation in the BD-An
system, and offers a promising simulation methodology for diverse photochemical
systems.


  We adapt the transfer matrix ($\T$-matrix) method originally designed for
one-dimensional quantum mechanical problems to solve the circularly symmetric
two-dimensional problem of graphene quantum dots. In similarity to
one-dimensional problems, we show that the generalized $\T$-matrix contains
rich information about the physical properties of these quantum dots. In
particular, it is shown that the spectral equations for bound states as well as
quasi-bound states of a circular graphene quantum dot and related quantities
such as the local density of states and the scattering coefficients are all
expressed exactly in terms of the $\T$-matrix for the radial confinement
potential. As an example, we use the developed formalism to analyse physical
aspects of a graphene quantum dot induced by a trapezoidal radial potential.
Among the obtained results, it is in particular suggested that the thermal
fluctuations and electrostatic disorders may appear as an obstacle to
controlling the valley polarization of Dirac electrons.


  High energy photon colliders (gamma-gamma, gamma-electron) based on backward
Compton scattering of laser light is a very natural addition to e+e- linear
colliders. In this report we consider this option for the TESLA project. Recent
study has shown that the horizontal emittance in the TESLA damping ring can be
further decreased by a factor of four. In this case the gamma-gamma luminosity
luminosity in the high energy part of spectrum can reach (1/3)L_{e+e-}. Typical
cross sections of interesting processes in gamma-gamma collisions are higher
than those in e+e- collisions by about one order of magnitude, so the number of
events in gamma-gamma collisions will be more than that in e+e- collisions.
Photon colliders can, certainly, give additional information and they are the
best for the study of many phenomena. The main question is now the technical
feasibility. The key new element in photon colliders is a very powerful laser
system. An external optical cavity is a promising approach for the TESLA
project. A free electron laser is another option. However, a more
straightforward solution is ``an optical storage ring (optical trap)'' with
diode pumped solid state laser injector which is today technically feasible.
This paper briefly reviews the status of a photon collider based at TESLA, its
possible parameters and existing problems.


  We apply the covariant spectator quark model to the study of the
electromagnetic structure of the $N^\ast(1520)$ state ($J^{P}= \frac{3}{2}^-$),
an important resonance from the second resonance region in both spacelike and
timelike regimes. The contributions from the valence quark effects are
calculated for the $\gamma^\ast N \to N^\ast(1520)$ helicity amplitudes. The
results are used to parametrize the meson cloud dominant at low $Q^2$.


  The objective of this work is twofold: to expand the depression models
proposed by Tobin and analyse a supply shock, such as the Covid-19 pandemic, in
this Keynesian conceptual environment. The expansion allows us to propose the
evolution of all endogenous macroeconomic variables. The result obtained is
relevant due to its theoretical and practical implications. A quantity or
Keynesian adjustment to the shock produces a depression through the effect on
aggregate demand. This depression worsens in the medium/long-term. It is
accompanied by increases in inflation, inflation expectations and the real
interest rate. A stimulus tax policy is also recommended, as well as an active
monetary policy to reduce real interest rates. On the other hand, the pricing
or Marshallian adjustment foresees a more severe and rapid depression in the
short-term. There would be a reduction in inflation and inflation expectations,
and an increase in the real interest rates. The tax or monetary stimulus
measures would only impact inflation. This result makes it possible to clarify
and assess the resulting depression, as well as propose policies. Finally, it
offers conflicting predictions that allow one of the two models to be
falsified.


  We give a new geometric proof of a conjecture of Fulton on the
Littlewood-Richardson coefficients. This conjecture was firstly proved by
Knutson, Tao and Woodward using the Honeycomb theory. A geometric proof was
given by Belkale. Our proof is based on the geometry of Horn's cones.


  We show that first order semilinear PDEs by stochastic perturbation are
well-posedness for globally Holder continuous and bounded vector field, with an
integrability condition on the divergence. This result extends the liner case
presented in [2]. The proof is based on in the stochastic characteristics
method and a version of the commuting Lemma.


  We present an extension of the Goldstone-boson-exchange constituent quark
model including additional interactions beyond the ones used hitherto. For the
hyperfine interaction between the constituent quarks we assume pseudoscalar,
vector, and scalar meson exchanges and consider all relevant force components
produced by these types of exchanges. The resulting model, which corresponds to
a relativistic Poincare-invariant Hamiltonian (or equivalently mass operator),
provides a unified framework for a covariant description of all light and
strange baryons. The ground states and resonances up to an excitation energy of
about 2 GeV are reproduced in fair agreement with phenomenology, with the
exception of the first excitations above the Lambda and Xi ground states.


  Clustering using deep autoencoders has been thoroughly investigated in recent
years. Current approaches rely on simultaneously learning embedded features and
clustering the data points in the latent space. Although numerous deep
clustering approaches outperform the shallow models in achieving favorable
results on several high-semantic datasets, a critical weakness of such models
has been overlooked. In the absence of concrete supervisory signals, the
embedded clustering objective function may distort the latent space by learning
from unreliable pseudo-labels. Thus, the network can learn non-representative
features, which in turn undermines the discriminative ability, yielding worse
pseudo-labels. In order to alleviate the effect of random discriminative
features, modern autoencoder-based clustering papers propose to use the
reconstruction loss for pretraining and as a regularizer during the clustering
phase. Nevertheless, a clustering-reconstruction trade-off can cause the
\textit{Feature Drift} phenomena. In this paper, we propose ADEC (Adversarial
Deep Embedded Clustering) a novel autoencoder-based clustering model, which
addresses a dual problem, namely, \textit{Feature Randomness} and
\textit{Feature Drift}, using adversarial training. We empirically demonstrate
the suitability of our model on handling these problems using benchmark real
datasets. Experimental results validate that our model outperforms
state-of-the-art autoencoder-based clustering methods.


  The non-Ohmic effect of a high electric field on the out-of-plane
magneto-conductivity of a layered superconductor near the superconducting
transition is studied in the frame of the Langevin approach to the
time-dependent Ginzburg-Landau equation. The transverse fluctuation
conductivity is computed in the self-consistent Hartree approximation for an
arbitrarily strong electric field and a magnetic field perpendicular to the
layers. Our results indicate that high electric fields can be effectively used
to suppress the out-of-plane fluctuation conductivity in high-temperature
superconductors and a significant broadening of the transition induced by a
strong electric field is predicted. Extensions of the results are provided for
the case when the electric field is applied at an arbitrary angle with respect
to the layers, as well as for the three-dimensional anisotropic regime of a
strong interlayer coupling.


  The transformer architecture has prevailed in various deep learning settings
due to its exceptional capabilities to select and compose structural
information. Motivated by these capabilities, Sanford et al. proposed the
sparse token selection task, in which transformers excel while fully-connected
networks (FCNs) fail in the worst case. Building upon that, we strengthen the
FCN lower bound to an average-case setting and establish an algorithmic
separation of transformers over FCNs. Specifically, a one-layer transformer
trained with gradient descent provably learns the sparse token selection task
and, surprisingly, exhibits strong out-of-distribution length generalization.
We provide empirical simulations to justify our theoretical findings.


  To explore the strength, isospin dependence, and density dependence of the
in-medium spin-orbit coupling that is poorly known but relevant for
understanding the structure of rare isotopes, the nucleon spin up-down
differential transverse flow is studied systematically by varying the beam
energy and impact parameter of Au+Au collisions within a spin-isospin dependent
transport model recently developed for investigating spin-related phenomena in
intermediate-energy heavy-ion collisions. The optimal reaction conditions for
delineating and disentangling effects of different terms in the spin-orbit
coupling are discussed.


  The amount of personal information contributed by individuals to digital
repositories such as social network sites has grown substantially. The
existence of this data offers unprecedented opportunities for data analytics
research in various domains of societal importance including medicine and
public policy. The results of these analyses can be considered a public good
which benefits data contributors as well as individuals who are not making
their data available. At the same time, the release of personal information
carries perceived and actual privacy risks to the contributors. Our research
addresses this problem area. In our work, we study a game-theoretic model in
which individuals take control over participation in data analytics projects in
two ways: 1) individuals can contribute data at a self-chosen level of
precision, and 2) individuals can decide whether they want to contribute at all
(or not). From the analyst's perspective, we investigate to which degree the
research analyst has flexibility to set requirements for data precision, so
that individuals are still willing to contribute to the project, and the
quality of the estimation improves. We study this tradeoff scenario for
populations of homogeneous and heterogeneous individuals, and determine Nash
equilibria that reflect the optimal level of participation and precision of
contributions. We further prove that the analyst can substantially increase the
accuracy of the analysis by imposing a lower bound on the precision of the data
that users can reveal.


  We study the asymptotic behavior of Masur-Veech volumes as the genus goes to
infinity. We show the existence of a complete asymptotic expansion of these
volumes that depends only on the genus and the number of singularities. The
computation of the first term of this asymptotics expansion was a long standing
problem. This problem was recently solved in by Aggarwal using purely
combinatorial arguments, and then by D. Chen, M. Moeller, D. Zagier and the
author using algebro-geometric insights. Our proof relies on a combination of
both methods.


  In this paper, we study the tunneling of charged fermions from the stationary
axially symmetric black holes using the generalized uncertainty principle (GUP)
via Wentzel, Kramers, and Brillouin (WKB) method. The emission rate of the
charged fermions and corresponding modified Hawking temperature of Ker-Newman
black hole, Einstein-Maxwell-Dilaton-Axion (EMDA) black hole, Kaluza-Klein
dilaton black hole, and then, charged rotating black string are obtained and we
show that the corrected thermal spectrum is not purely thermal because of the
minimal scale length which cause the black hole's remnant.


  We study the impact of the cosmological environment on the solar
gravitational system by the imbedding formalism of Gautreau. It appears that
the cosmic mean-mass density and the cosmological constant give negligibly
small contribution to gravity potentials. On the contrary, the cosmic
acceleration beyond the Robertson-Walker geometry can considerably influence
the curvature of spacetime in the solar system. The resulting anomalous
constant acceleration towards the Sun is an order of magnitude smaller than
that measured by Pioneer 10 and 11. However, it is larger than second order
terms of potentials, thus well within the sensitivity of new gravity probes
such as the LATOR mission.


  We produce a new photometric calibration for the combined six-filter system
formed by $Gaia$ DR2 + EDR3 $G$+$G_{\rm BP}$+$G_{\rm RP}$ using an improved
STIS/HST spectrophotometric library with very red stars. The comparison between
observed and synthetic photometry yields residual dispersions of just 3.4-8.7
mmag, resulting in the most accurate and precise whole-sky large-dynamic-range
optical photometric system ever obtained. We include some tests and
applications.


  This paper is devoted to the asymptotics of the density of surfacic states
near the spectral edges for a discrete surfacic Anderson model. Two types of
spectral edges have to be considered : fluctuating edges and stable edges. Each
type has its own type of asymptotics. In the case of fluctuating edges, one
obtains Lifshitz tails the parameters of which are given by the initial
operator suitably "reduced" to the surface. For stable edges, the surface
density of states behaves like the surface density of states of a constant
(equal to the expectation of the random potential) surface potential. Among the
tools used to establish this are the asymptotics of the surface density of
states for constant surface potentials.


  Under Space Tower the author understands structures having height from 100 km
to the geosynchronous orbit and supported by surface of Earths. The classical
Space Elevator is not included in space towers. That has three main identifiers
which distingue from Space Tower: Space Elevator has part over Geosynchronous
Orbit (GSO) and all installation supported only the centrifugal force of Earth,
immobile cable connected to surface of Earth, no pressure on the surface. A lot
of new concepts, ideas and innovation in space towers were offered, developed
and researched in last years especially after 2000. For example: optimal solid
space towers, inflatable space towers (include optimal space tower), circle and
centrifugal space towers, kinetic space towers, electrostatic space towers,
electromagnetic space towers, and so on. Given review shortly summarizes there
researches and gives a brief description them, note some their main advantages,
shortcomings, defects and limitations.


  Systemic risk is a rapidly developing area of research. Classical financial
models often do not adequately reflect the phenomena of bubbles, crises, and
transitions between them during credit cycles. To study very improbable events,
systemic risk methodologies utilise advanced mathematical and computational
tools, such as complex systems, chaos theory, and Monte Carlo simulations. In
this paper, a relatively simple mathematical formalism is applied to provide a
unified framework for modeling credit cycles and systemic risk assessment. The
proposed model is analyzed in detail to assess whether it can reflect very
different states of the economy. Basing on those results, measures of systemic
risk are constructed to provide information regarding the stability of the
system. The formalism is then applied to describe the full credit cycle with
the explanation of causal relationships between the phases expressed in terms
of parameters derived from real-world quantities. The framework can be
naturally interpreted and understood with respect to different economic
situations and easily incorporated into the analysis and decision-making
process based on classical models, significantly enhancing their quality and
flexibility.


  In this paper we propose an ansatz that applies to the slepton mass matrices.
In our approach these matrices contain a dominant sector that can be
diagonalized exactly. We study the numerical results for the slepton mass
eigenstates, looking for deviations from universality, which is usually assumed
when one evaluates the production of sleptons at future colliders.


  The space-time evolution of the (1+1)-dimensional viscous hydrodynamics with
an initial quark-gluon plasma (QGP) produced in ultrarelativistic heavy ion
collisions is studied numerically. The particle-emitting sources undergo a
crossover transition from the QGP to hadronic gas. We take into account a usual
shear viscosity for the strongly coupled QGP as well as the bulk viscosity
which increases significantly in the crossover region. The two-pion
Hanbury-Brown-Twiss (HBT) interferometry for the viscous hydrodynamic sources
is performed. The HBT analyses indicate that the viscosity effect on the
two-pion HBT results is small if only the shear viscosity is taken into
consideration in the calculations. The bulk viscosity leads to a larger
transverse freeze-out configuration of the pion-emitting sources, and thus
increases the transverse HBT radii. The results of the longitudinal HBT radius
for the source with Bjorken longitudinal scaling are consistent with the
experimental data.


  This article addresses a problem of micromagnetics: the reversal of magnetic
moments in layered spring magnets. A one-dimensional model is used of a film
consisting of several atomic layers of a soft material on top of several atomic
layers of a hard material. Each atomic layer is taken to be uniformly
magnetized, and spatial inhomogeneities within an atomic layer are neglected.
The state of such a system is described by a chain of magnetic spin vectors.
Each spin vector behaves like a spinning top driven locally by the effective
magnetic field and subject to damping (Landau-Lifshitz-Gilbert equation). A
numerical integration scheme for the LLG equation is presented that is
unconditionally stable and preserves the magnitude of the magnetization vector
at all times. The results of numerical investigations for a bilayer in a
rotating in-plane magnetic field show hysteresis with a basic period of $2\pi$
at moderate fields and hysteresis with a basic period of $\pi$ at strong
fields.


  A one-factor asset pricing model with an Ornstein--Uhlenbeck process as its
state variable is studied under partial information: the mean-reverting level
and the mean-reverting speed parameters are modeled as hidden/unobservable
stochastic variables. No-arbitrage pricing formulas for derivative securities
written on a liquid asset and exponential utility indifference pricing formulas
for derivative securities written on an illiquid asset are presented. Moreover,
a conditionally linear filtering result is introduced to compute the
pricing/hedging formulas and the Bayesian estimators of the hidden variables.


  QR bar codes are prototypical images for which part of the image is a priori
known (required patterns). Open source bar code readers, such as ZBar, are
readily available. We exploit both these facts to provide and assess purely
regularization-based methods for blind deblurring of QR bar codes in the
presence of noise.


  In this paper, we analyze how different path aspects affect a user's
experience, mainly VR sickness and overall comfort, while immersed in an
autonomously moving telepresence robot through a virtual reality headset. In
particular, we focus on how the robot turns and the distance it keeps from
objects, with the goal of planning suitable trajectories for an autonomously
moving immersive telepresence robot in mind; rotational acceleration is known
for causing the majority of VR sickness, and distance to objects modulates the
optical flow. We ran a within-subjects user study (n = 36, women = 18) in which
the participants watched three panoramic videos recorded in a virtual museum
while aboard an autonomously moving telepresence robot taking three different
paths varying in aspects such as turns, speeds, or distances to walls and
objects. We found a moderate correlation between the users' sickness as
measured by the SSQ and comfort on a 6-point Likert scale across all paths.
However, we detected no association between sickness and the choice of the most
comfortable path, showing that sickness is not the only factor affecting the
comfort of the user. The subjective experience of turn speed did not correlate
with either the SSQ scores or comfort, even though people often mentioned
turning speed as a source of discomfort in the open-ended questions. Through
exploring the open-ended answers more carefully, a possible reason is that the
length and lack of predictability also play a large role in making people
observe turns as uncomfortable. A larger subjective distance from walls and
objects increased comfort and decreased sickness both in quantitative and
qualitative data. Finally, the SSQ subscales and total weighted scores showed
differences by age group and by gender.


  In this paper we extend the investigation of cooperation in some classical
evolutionary games on populations were the network of interactions among
individuals is of the scale-free type. We show that the update rule, the payoff
computation and, to some extent the timing of the operations, have a marked
influence on the transient dynamics and on the amount of cooperation that can
be established at equilibrium. We also study the dynamical behavior of the
populations and their evolutionary stability.


  Semi-supervised learning (SSL) approaches have been successfully applied in a
wide range of engineering and scientific fields. This paper investigates the
generative model framework with a missingness mechanism for unclassified
observations, as introduced by Ahfock and McLachlan(2020). We show that in a
partially classified sample, a classifier using Bayes rule of allocation with a
missing-data mechanism can surpass a fully supervised classifier in a two-class
normal homoscedastic model, especially with moderate to low overlap and
proportion of missing class labels, or with large overlap but few missing
labels. It also outperforms a classifier with no missing-data mechanism
regardless of the overlap region or the proportion of missing class labels. Our
exploration of two- and three-component normal mixture models with unequal
covariances through simulations further corroborates our findings. Finally, we
illustrate the use of the proposed classifier with a missing-data mechanism on
interneuronal and skin lesion datasets.


  An optimal control approach based on multiple parameter genetic algorithms is
applied to the design of plasmonic nanoconstructs with pre-determined optical
properties and functionalities. We first develop nanoscale metallic lenses that
focus an incident plane wave onto a pre-specified, spatially confined spot. Our
results illustrate the role of symmetry breaking and unravel the principles
that favor dimeric constructs for optimal light localization. Next we design a
periodic array of silver particles to modify the polarization of an incident,
linearly-polarized plane wave in a desired fashion while localizing the light
in space. The results provide insight into the structural features that
determine the birefringence properties of metal nanoparticles and their arrays.
Of the variety of potential applications that may be envisioned, we note the
design of nanoscale light sources with controllable coherence and polarization
properties that could serve for coherent control of molecular or electronic
dynamics in the nanoscale.


  All the iron-based superconductors identified to date share a square lattice
composed of Fe atoms as a common feature, despite having different crystal
structures. In copper-based materials, the superconducting phase emerges not
only in square lattice structures but also in ladder structures. Yet iron-based
superconductors without a square lattice motif have not been found despite
being actively sought out. Here, we report the discovery of pressure-induced
superconductivity in the iron-based spin-ladder material BaFe2S3, a Mott
insulator with striped-type magnetic ordering below ~120 K. On the application
of pressure this compound exhibits a metal-insulator transition at about 11
GPa, followed by the appearance of superconductivity below Tc = 14 K, right
after the onset of the metallic phase. Our findings indicate that iron-based
ladder compounds represent promising material platforms, in particular for
studying the fundamentals of iron-based superconductivity.


  Fixed-point optimization of deep neural networks plays an important role in
hardware based design and low-power implementations. Many deep neural networks
show fairly good performance even with 2- or 3-bit precision when quantized
weights are fine-tuned by retraining. We propose an improved fixedpoint
optimization algorithm that estimates the quantization step size dynamically
during the retraining. In addition, a gradual quantization scheme is also
tested, which sequentially applies fixed-point optimizations from high- to
low-precision. The experiments are conducted for feed-forward deep neural
networks (FFDNNs), convolutional neural networks (CNNs), and recurrent neural
networks (RNNs).


  Building upon the bulk-boundary correspondence in topological phases of
matter, disclinations have recently been harnessed to trap fractionally
quantized density of states (DoS) in classical wave systems. While these
fractional DoS have associated states localized to the disclination's core,
such states are not protected from deconfinement due to the breaking of chiral
symmetry, generally leading to resonances which, even in principle, have finite
lifetimes and suboptimal confinement. Here, we devise and experimentally
validate in acoustic lattices a paradigm by which topological states bind to
disclinations without a fractional DoS but which preserve chiral symmetry. The
preservation of chiral symmetry pins the states at the mid-gap, resulting in
their protected maximal confinement. The integer DoS at the defect results in
two-fold degenerate states that, due to symmetry constraints, do not gap out.
Our study provides a fresh perspective on the interplay between
symmetry-protection in topological phases and topological defects, with
possible applications in classical and quantum systems alike.


  The paper purposes to contribute to the classification of pointed Hopf
algebras by the method of Andruskiewitsch and Schneider. The structure of
arithmetic root systems is enlightened such that their relation to ordinary
root systems associated to semi-simple Lie algebras becomes more astounding. As
an application all arithmetic root systems of rank 3 are determined. The result
gives in particular all finite dimensional rank 3 Nichols algebras of diagonal
type over a field of characteristic zero.
  Key Words: Weyl groupoid, Hopf algebra, Nichols algebra


  Context: X-ray surveys of Active Galactic Nuclei (AGN) indicate `cosmic
downsizing', with the comoving number density of high-luminosity objects
peaking at higher redshifts (z about 2) than low-luminosity AGN (z<1). Aims: We
test whether downsizing is caused by activity shifting towards low-mass black
holes accreting at near-Eddington rates, or by a change in the average rate of
accretion onto supermassive black holes. We estimate the black hole masses and
Eddington ratios of an X-ray selected sample of AGN in the Chandra Deep Field
South at z<1, probing the epoch where AGN cosmic downsizing has been reported.
Methods: Black hole masses are estimated both from host galaxy stellar masses,
which are estimated from fitting to published optical and near-infrared
photometry, and from near-infrared luminosities, applying established
correlations between black hole mass and host galaxy properties. Both methods
give consistent results. Comparison and calibration of possible
redshift-dependent effects is also made using published faint host galaxy
velocity dispersion measurements. Results: The Eddington ratios in our sample
span the range 10^{-5} to 1, with median log(L_bol/L_Edd)=-2.87, and with
typical black hole masses about 10^{8} solar masses. The broad distribution of
Eddington ratios is consistent with that expected for AGN samples at low and
moderate luminosity. We find no evidence that the CDF-S AGN population is
dominated by low-mass black holes accreting at near-Eddington ratios and the
results suggest that diminishing accretion rates onto average-sized black holes
are responsible for the reported AGN downsizing at redshifts below unity.


  A famous result of D. Walkup is that an $m\times n$ rectangle may be tiled by
T-tetrominos if and only if both $m$ and $n$ are multiples of 4. The "if"
portion may be proved by tiling a $4\times 4$ block, and then copying that
block to fill the rectangle; but, this leads to regular, periodic tilings. In
this paper we investigate how much "order" must be present in every tiling of a
rectangle by T-tetrominos, where we measure order by length of arithmetic
progressions of tiles.


  For a subset $D$ of boxes in an $n\times n$ square grid, let $\chi_{D}(x)$
denote the dual character of the flagged Weyl module associated to $D$. It is
known that $\chi_{D}(x)$ specifies to a Schubert polynomial (resp., a key
polynomial) in the case when $D$ is the Rothe diagram of a permutation (resp.,
the skyline diagram of a composition). One can naturally define a lower and an
upper bound of $\chi_{D}(x)$. M{\'e}sz{\'a}ros, St. Dizier and Tanjaya
conjectured that $\chi_{D}(x)$ attains the upper bound if and only if $D$
avoids a certain subdiagram. We provide a proof of this conjecture.


  We numerically explore the possibility that the large orbital inclination of
the martian satellite Deimos originated in an orbital resonance with an ancient
inner satellite of Mars more massive than Phobos. We find that Deimos's
inclination can be reliably generated by outward evolution of a martian
satellite that is about 20 times more massive than Phobos through the 3:1
mean-motion resonance with Deimos at 3.3 Mars radii. This outward migration, in
the opposite direction from tidal evolution within the synchronous radius,
requires interaction with a past massive ring of Mars. Our results therefore
strongly support the cyclic martian ring-satellite hypothesis of Hesselbrock
and Minton (2017). Our findings, combined with the model of Hesselbrock and
Minton (2017), suggest that the age of the surface of Deimos is about 3.5-4
Gyr, and require Phobos to be significantly younger.


  In this paper we study a new class of shell models, defined in terms of two
complex dynamical variables per shell, transporting positive and negative
helicity respectively. The dynamical equations are derived from a decomposition
into helical modes of the velocity Fourier components of Navier-Stokes
equations (F. Waleffe, Phys. Fluids A {\bf 4}, 350 (1992)). This decomposition
leads to four different types of shell models, according to the possible
non-equivalent combinations of helicities of the three interacting modes in
each triad. Free parameters are fixed by imposing the conservation of energy
and of a ``generalized helicity'' $H_\alpha$ in the inviscid and unforced
limit. For $\alpha=1$ this non-positive invariant looks exactly like helicity
in the Fourier-helical decomposition of the Navier-Stokes equations. Long
numerical integrations are performed, allowing the computation of the scaling
exponents of the velocity increments and energy flux moments. The dependence of
the models on the generalized helicity parameter $\alpha$ and on the scale
parameter $\lambda$ is also studied. PDEs are finally derived in the limit when
the ratio between shells goes to one.


  This paper investigates one of the most fundamental computer vision problems:
image segmentation. We propose a supervised hierarchical approach to
object-independent image segmentation. Starting with over-segmenting
superpixels, we use a tree structure to represent the hierarchy of region
merging, by which we reduce the problem of segmenting image regions to finding
a set of label assignment to tree nodes. We formulate the tree structure as a
constrained conditional model to associate region merging with likelihoods
predicted using an ensemble boundary classifier. Final segmentations can then
be inferred by finding globally optimal solutions to the model efficiently. We
also present an iterative training and testing algorithm that generates various
tree structures and combines them to emphasize accurate boundaries by
segmentation accumulation. Experiment results and comparisons with other very
recent methods on six public data sets demonstrate that our approach achieves
the state-of-the-art region accuracy and is very competitive in image
segmentation without semantic priors.


  Standard enthalpies of formation are used for assessing the efficiency and
safety of chemical processes in the chemical industry. However, the number of
compounds for which the enthalpies of formation are available is many orders of
magnitude smaller than the number of known compounds. Thermochemical data
prediction methods are therefore clearly needed. Several commercial and free
chemical databases are currently available, the NIST WebBook being the most
used free source. To overcome this problem a cheminformatics system was
designed and built with two main objectives in mind: collecting and retrieving
critically evaluated thermochemical values, and estimating new data. In its
present version, by using cheminformatics techniques, ThermInfo allows the
retrieval of the value of a thermochemical property, such as a gas-phase
standard enthalpy of formation, by inputting, for example, the molecular
structure or the name of a compound. The same inputs can also be used to
estimate data (presently restricted to non-polycyclic hydrocarbons) by using
the Extended Laidler Bond Additivity (ELBA) method. The information system is
publicly available at http://www.therminfo.com or
http://therminfo.lasige.di.fc.ul.pt. ThermInfo's strength lies in the data
quality, availability (free access), search capabilities, and, in particular,
prediction ability, based on a user-friendly interface that accepts inputs in
several formats.


  This volume contains the papers presented at the 1st workshop on Quantitative
Formal Methods: Theory and Applications, which was held in Eindhoven on 3
November 2009 as part of the International Symposium on Formal Methods 2009.
This volume contains the final versions of all contributions accepted for
presentation at the workshop.


  A comprehensive and precise analysis of shale gas production performance is
crucial for evaluating resource potential, designing field development plan,
and making investment decisions. However, quantitative analysis can be
challenging because production performance is dominated by a complex
interaction among a series of geological and engineering factors. In this
study, we propose a hybrid data-driven procedure for analyzing shale gas
production performance, which consists of a complete workflow for dominant
factor analysis, production forecast, and development optimization. More
specifically, game theory and machine learning models are coupled to determine
the dominating geological and engineering factors. The Shapley value with
definite physical meanings is employed to quantitatively measure the effects of
individual factors. A multi-model-fused stacked model is trained for production
forecast, on the basis of which derivative-free optimization algorithms are
introduced to optimize the development plan. The complete workflow is validated
with actual production data collected from the Fuling shale gas field, Sichuan
Basin, China. The validation results show that the proposed procedure can draw
rigorous conclusions with quantified evidence and thereby provide specific and
reliable suggestions for development plan optimization. Comparing with
traditional and experience-based approaches, the hybrid data-driven procedure
is advanced in terms of both efficiency and accuracy.


  Recently, many geometric aspects of $\mathcal{N}$-extended AdS supergravity
in chiral variables have been encountered and clarified. In particular, if the
theory is supposed to be invariant under SUSY transformations also on
boundaries, the boundary term has to be the action of a
$\mathrm{OSp}(\mathcal{N}|2)_{\mathbb{C}}$ super Chern-Simons theory, and
particular boundary conditions must be met. Based on this, we propose a way to
calculate an entropy $S$ for surfaces, presumably including black hole
horizons, in the supersymmetric version of loop quantum gravity for the minimal
case $\mathcal{N}=1$. It proceeds in analogy to the non-supersymmetric theory,
by calculating dimensions of quantum state spaces of the super Chern-Simons
theory with punctures, for fixed quantum (super) area of the surface. We find
$S = a_H/4$ for large areas and determine the subleading correction. Due to the
non-compactness of $\mathrm{OSp}(1|2)_{\mathbb{C}}$ and the corresponding
difficulties with the Chern-Simons quantum theory, we use analytic continuation
from the Verlinde formula for a compact real form, $\mathrm{UOSp}(1|2)$, in
analogy to work by Noui et al. This also entails studying some properties of
$\mathrm{OSp}(1|2)_{\mathbb{C}}$ representations that we have not found
elsewhere in the literature.


  In this paper, we consider the problem of maximizing the throughput of
Byzantine agreement, given that the sum capacity of all links in between nodes
in the system is finite. We have proposed a highly efficient Byzantine
agreement algorithm on values of length l>1 bits. This algorithm uses error
detecting network codes to ensure that fault-free nodes will never disagree,
and routing scheme that is adaptive to the result of error detection. Our
algorithm has a bit complexity of n(n-1)l/(n-t), which leads to a linear cost
(O(n)) per bit agreed upon, and overcomes the quadratic lower bound
(Omega(n^2)) in the literature. Such linear per bit complexity has only been
achieved in the literature by allowing a positive probability of error. Our
algorithm achieves the linear per bit complexity while guaranteeing agreement
is achieved correctly even in the worst case. We also conjecture that our
algorithm can be used to achieve agreement throughput arbitrarily close to the
agreement capacity of a network, when the sum capacity is given.


  In this short survey, we show how two (classes of) known examples of
inhomogeneous, curvature homogeneous Riemannian manifolds with nontrivial
$\kappa$-nullity can be seen as deformations of homogeneous metrics along the
vertical distribution of an integrable Riemannian submersion. We also pose two
open questions.


  We present a detailed analysis of the Landau-Zener problem for an interacting
Bose-Einstein condensate in a time-varying double-well trap, especially
focussing on the relation between the full many-particle problem and the
mean-field approximation. Due to the nonlinear self-interaction a dynamical
instability occurs, which leads to a breakdown of adiabaticity condition and
thus fundamentally alters the dynamics. It is shown that essentially all
features of the Landau-Zener problem including the depletion of the condensate
mode can be already understood within a semiclassical phase space picture. In
particular, this treatment resolves the formerly imputed incommutability of the
adiabatic and semiclassical limits. The possibility to exploit Landau-Zener
sweeps to generate squeezed states for spectroscopic tasks is analysed in
detail. Moreover, we study the influence of phase noise and propose a
Landau-Zener sweep as a sensitive, yet readily implementable probe for
decoherence, since this has a significant effect on the transition rate for
slow parameter variations.


  Supernova remnants (SNRs) are thought to be the primary sources of Galactic
Cosmic Rays (CRs). In the last few years, the wealth of gamma-ray data
collected by GeV and TeV instruments has provided important information about
particle energisation in these astrophysical sources, allowing us to make
progress in assessing their role as CR accelerators. In particular, the
spectrum of the gamma-ray emission detected by AGILE and Fermi-LAT from the two
middle aged Supernova Remnants (SNRs) W44 and IC443, has been proposed as a
proof of CR acceleration in SNRs. Here we discuss the possibility that the
radio and gamma-ray spectra from W44 may be explained in terms of
re-acceleration and compression of Galactic CRs. The recent measurement of the
interstellar CR flux by Voyager I has been instrumental for our work, in that
the result of the reprocessing of CRs by the shock in W44 depends on the CR
spectrum at energies that are precluded to terrestrial measurement due to solar
modulation. We introduce both CR protons and helium nuclei in our calculations,
and secondary electrons produced in situ are compared with the flux of Galactic
CR electrons reprocessed by the slow shock of this SNR.We find that the
multi-wavelength spectrum of W44 can be explained by reaccelerated particles
with no need of imposing any break on their distribution, but just a high
energy cut-off at the maximum energy the accelerator can provide.We also find
that a model including both re-acceleration and a very small fraction of
freshly accelerated particles may be more satisfactory on physical grounds


  Strong resonant dipole-dipole interactions in flexible Rydberg aggregates
enable the formation of exciton pulses, the interplay of atomic motion and
electronic excitation transfer which feature high fidelity entanglement
transport.
  Here, we demonstrate the coherent splitting of such pulses into two modes,
which induce strongly different atomic motion, leading to clear signatures of
nonadiabatic effects in atomic density profiles. The mechanism exploits local
nonadiabatic effects at a conical intersection, turning them from a decoherence
source into an asset. The conical intersection is a consequence of the exciton
pulses moving along a linear Rydberg chain and approaching an additional
linear, perpendicularly aligned Rydberg chain. The intersection provides a
sensitive knob controlling the propagation direction and coherence properties
of exciton pulses.
  We demonstrate that this scenario can be exploited as an exciton switch,
controlling direction and coherence properties of the joint pulse on the second
of the chains. Initially, we demonstrate the pulse splitting on planar
aggregates with atomic motion one-dimensionally constrained and employing
isotropic interactions. Subsequently, we confirm the splitting mechanism for a
fully realistic scenario in which all spatial restrictions are removed and the
full anisotropy of the dipole-dipole interactions is taken into account. Our
results enable the experimental observation of non-adiabatic electronic
dynamics and entanglement transport with Rydberg atoms. The conical
intersection crossings are clearly evident, both in atomic mean position
information and excited state spectra of the Rydberg system. This suggests
flexible Rydberg aggregates as a test-bench for quantum chemical effects in
experiments on much-inflated length scales. The fundamental ideas discussed
here have general implications for excitons on a dynamic network.


  A sunflower with a core $Y$ is a family ${\cal B}$ of sets such that $U \cap
U' = Y$ for each two different elements $U$ and $U'$ in ${\cal B}$. The
well-known sunflower lemma states that a given family ${\cal F}$ of sets, each
of cardinality at most $s$, includes a sunflower of cardinality $k$ if $|{\cal
F}|> (k-1)^s s!$. Since Erd\"os and Rado proved it in 1960, it has not been
known for more than half a century whether the sunflower bound $(k-1)^s s!$ can
be improved asymptotically for any $k$ and $s$. It is conjectured that it can
be reduced to $c_k^s$ for some real number $c_k>0$ depending only on $k$, which
is called the sunflower conjecture. This paper shows that the general sunflower
bound can be indeed reduced by an exponential factor: We prove that ${\cal F}$
includes a sunflower of cardinality $k$ if \[
  |{\cal F|} \ge \left( \sqrt{10} -2 \right)^2 \left[ k \cdot \min \left(
\frac{1}{\sqrt{10}-2}, \frac{c}{\log \min(k, s)} \right) \right]^s s!, \] for a
constant $c>0$, and any $k \ge 2$ and $s \ge 2$. For instance, whenever $k \ge
s^\epsilon$ for a given constant $\epsilon \in (0,1)$, the sunflower bound is
reduced from $(k-1)^s s!$ to $(k-1)^s s! \cdot \left[ O \left( \frac{1}{\log s}
\right) \right]^s$, achieving the reduction ratio of $\left[ O \left(
\frac{1}{\log s} \right) \right]^s$. Also any ${\cal F}$ of cardinality at
least $\left(\sqrt{10}-2 \right)^2 \left( \frac{k}{\sqrt{10}-2} \right)^s s!$
includes a sunflower of cardinality $k$, where
$\frac{1}{\sqrt{10}-2}=0.8603796\ldots$. Our result demonstrates that the
sunflower bound can be improved by a factor of less than a small constant to
the power $s$, giving hope for further update.


  XTE J1710-281 is a transient eclipsing binary system with a period close to
3.28 hours, hosting a neutron star. The average eclipse duration is 420
seconds, and eclipse arrival times reported in the literature span from 1999 to
2017. A previous analysis of the eclipse arrival times using the eclipse timing
technique revealed a complex pattern of delays, indicating the presence of
three orbital glitches. These glitches correspond to sudden variations in the
orbital period, allowing for the identification of four distinct epochs. We
have re-analyzed the 78 eclipse arrival times spanning 18 years utilizing the
eclipse timing technique to derive the corresponding delays as a function of
time. We find that the observed delays align well with a fitting model
employing an eccentric sine function characterized by an amplitude of $6.1 \pm
0.5$ s, eccentricity of $0.38 \pm 0.17$, and a period of $17.1 \pm 1.5$ years.
Additionally, we identified the orbital period as 3.28106345(13) hours, with a
reference epoch of $T_0=54112.83200(2)$ Modified Julian Date (MJD). We obtained
an upper limit of the orbital period derivative of $3.6 \times 10^{-13}$
s~s$^{-1}$. From the average value of the eclipse duration, we estimate that
the companion star has a mass of 0.22~\Msun for a neutron star mass of
1.4~\Msun, and the inclination of the source is $78.1^{+1.5}_{-1.2}$ degrees.
The companion star is in thermal equilibrium. The orbital period derivative is
consistent with a conservative mass transfer scenario, where the angular
momentum loss due to magnetic braking dominates over gravitational radiation
angular momentum loss if the former is present. The eccentric modulation can be
explained by a third body with a mass of 2.7 Jovian masses, orbiting with a
revolution period close to 17 years and an eccentricity of 0.38. (abridged
abstract)


  We reconsider the elliptic estimates for magnetic operators in two and three
dimensions used in connection with Ginzburg-Landau theory. Furthermore we
discuss the so-called blow-up technique in order to obtain optimal estimates in
the limiting cases.


  The Lie group Sol(p,q) is the semidirect product induced by the action of the
real numbers R on the plane R^2 which is given by (x,y) --> (exp{p z} x, exp{-q
z} y), where z is in R. Viewing Sol(p,q) as a 3-dimensional manifold, it
carries a natural Riemannian metric and Laplace-Beltrami operator. We add a
linear drift term in the z-variable to the latter, and study the associated
Brownian motion with drift. We derive a central limit theorem and compute the
rate of escape. Also, we introduce the natural geometric compactification of
Sol(p,q) and explain how Brownian motion converges almost surely to the
boundary in the resulting topology. We also study all positive harmonic
functions for the Laplacian with drift, and determine explicitly all minimal
harmonic functions. All this is carried out with a strong emphasis on
understanding and using the geometric features of Sol(p,q), and in particular
the fact that it can be described as the horocyclic product of two hyperbolic
planes with curvatures -p^2 and -q^2, respectively.


  Wishart random matrices with a sparse or diluted structure are ubiquitous in
the processing of large datasets, with applications in physics, biology and
economy. In this work we develop a theory for the eigenvalue fluctuations of
diluted Wishart random matrices, based on the replica approach of disordered
systems. We derive an analytical expression for the cumulant generating
function of the number of eigenvalues $\mathcal{I}_N(x)$ smaller than
$x\in\mathbb{R}^{+}$, from which all cumulants of $\mathcal{I}_N(x)$ and the
rate function $\Psi_{x}(k)$ controlling its large deviation probability
$\text{Prob}[\mathcal{I}_N(x)=kN] \asymp e^{-N\Psi_{x}(k)}$ follow. Explicit
results for the mean value and the variance of $\mathcal{I}_N(x)$, its rate
function, and its third cumulant are discussed and thoroughly compared to
numerical diagonalization, showing a very good agreement. The present work
establishes the theoretical framework put forward in a recent letter [Phys.
Rev. Lett. 117, 104101] as an exact and compelling approach to deal with
eigenvalue fluctuations of sparse random matrices.


  We establish some identities relating two sequences that are, as explained,
related to the Tribonacci sequence. One of these sequences bears the same
resemblance to the Tribonacci sequence as the Lucas sequence does to the
Fibonacci sequence. Defining a matrix that we call Tribomatrix, which extends
the Fibonacci matrix, we see that the other sequence is related to the sum of
the determinants of the 2nd order principal minors of this matrix.


  We show, for mean curvature flows in Euclidean space, that if one of the
tangent flows at a given space-time point consists of a closed,
multiplicity-one, smoothly embedded self-similar shrinker, then it is the
unique tangent flow at that point. That is the limit of the parabolic
rescalings does not depend on the chosen sequence of rescalings. Furthermore,
given such a closed, multiplicity-one, smoothly embedded self-similarly
shrinker $\Sigma$, we show that any solution of the rescaled flow, which is
sufficiently close to $\Sigma$, with Gaussian density ratios greater or equal
to that of $\Sigma$, stays for all time close to $\Sigma$ and converges to a
possibly different self-similarly shrinking solution $\Sigma'$. The central
point in the argument is a direct application of the Simon-{\L}ojasiewicz
inequality to Huisken's monotone Gaussian integral for Mean Curvature Flow.


  Watt balances are used to measure the Planck constant and will be used in the
future to realize mass at the kilogram level. They increasingly rely on
permanent magnet systems to generate the magnetic flux. It has been known that
the weighing current might effect the magnetization state of the permanent
magnetic system used in these systems causing a systematic bias that can lead
to an error in the result if not accounted for. In this article a simple model
explaining the effect of the weighing current on the yoke of the magnet is
developed. This model leads to a nonlinear dependence of the magnetic flux
density in the gap that is proportional to the squared value of the coil
current. The effect arises from changing the reluctance of the yoke by the
additional field produced by the coil. Our analysis shows that the effect
depends on the width of the air gap, the magnetic flux density in the air gap,
and the $BH$ curve of the yoke material. Suggestions to reduce the nonlinear
effect are discussed.


  The feasibility of using ultra-low-field magnetic resonance (ULF MR) for
direct neuronal current imaging (NCI) is investigated by phantom measurements.
The aim of NCI is to improve the current localization accuracy for neuronal
activity of established methods like electroencephalography (EEG) or
magnetoencephalography (MEG) (~1 cm). A measurement setup was developed
addressing the main challenge of reaching the necessary sensitivity in order to
possibly resolve the faint influence of neuronal magnetic fields on 1H nuclear
spin precession. Phantom measurements close to physiology conditions are
performed simulating a specific long-lasting neuronal activity evoked in the
secondary somatosensory cortex showing that the signal-to-noise ratio (SNR) of
the setup needs to be further increased by at least a factor of 2.


  Motivated by the widespread increase in the phenomenon of code-switching
between Egyptian Arabic and English in recent times, this paper explores the
intricacies of machine translation (MT) and automatic speech recognition (ASR)
systems, focusing on translating code-switched Egyptian Arabic-English to
either English or Egyptian Arabic. Our goal is to present the methodologies
employed in developing these systems, utilizing large language models such as
LLama and Gemma. In the field of ASR, we explore the utilization of the Whisper
model for code-switched Egyptian Arabic recognition, detailing our experimental
procedures including data preprocessing and training techniques. Through the
implementation of a consecutive speech-to-text translation system that
integrates ASR with MT, we aim to overcome challenges posed by limited
resources and the unique characteristics of the Egyptian Arabic dialect.
Evaluation against established metrics showcases promising results, with our
methodologies yielding a significant improvement of $56\%$ in English
translation over the state-of-the-art and $9.3\%$ in Arabic translation. Since
code-switching is deeply inherent in spoken languages, it is crucial that ASR
systems can effectively handle this phenomenon. This capability is crucial for
enabling seamless interaction in various domains, including business
negotiations, cultural exchanges, and academic discourse. Our models and code
are available as open-source resources. Code:
\url{http://github.com/ahmedheakl/arazn-llm}}, Models:
\url{http://huggingface.co/collections/ahmedheakl/arazn-llm-662ceaf12777656607b9524e}.


  According to 't Hooft the combination of quantum mechanics and gravity
requires the three dimensional world to be an image of data that can be stored
on a two dimensional projection much like a holographic image. The two
dimensional description only requires one discrete degree of freedom per Planck
area and yet it is rich enough to describe all three dimensional phenomena.
After outlining 't Hooft's proposal I give a preliminary informal description
of how it may be implemented. One finds a basic requirement that particles must
grow in size as their momenta are increased far above the Planck scale. The
consequences for high energy particle collisions are described. The phenomena
of particle growth with momentum was previously discussed in the context of
string theory and was related to information spreading near black hole
horizons. The considerations of this paper indicate that the effect is much
more rapid at all but the earliest times. In fact the rate of spreading is
found to saturate the bound from causality. Finally we consider string theory
as a possible realization of 't Hooft's idea. The light front lattice string
model of Klebanov and Susskind is reviewed and its similarities with the
holographic theory are demonstrated. The agreement between the two requires
unproven but plausible assumptions about the nonperturbative behavior of string
theory. Very similar ideas to those in this paper have been long held by
Charles Thorn.


  Programs that calculate observables in quantum chromodynamics at
next-to-leading order typically generate events that consist of partons rather
than hadrons -- and just a few partons at that. These programs would be much
more useful if the few partons were turned into parton showers, which could be
given to one of the Monte Carlo event generators to produce hadron showers. In
a previous paper, we have seen how to generate parton showers related to the
final state collinear singularities of the perturbative calculation for the
example of e+ + e- --> 3 jets. This paper discusses the treatment of the soft
singularities.


  We compute the symbol of the two-loop five-point scattering amplitude in
$\mathcal{N}$ = 4 super-Yang-Mills theory, including its full color dependence.
This requires constructing the symbol of all two-loop five-point nonplanar
massless master integrals, for which we give explicit results.


  We explore the cosmological constraints from cosmic shear using a new way of
modelling the non-linear matter correlation functions. The new formalism
extends the method of Angulo & White (2010), which manipulates outputs of
$N$-body simulations to represent the three-dimensional nonlinear mass
distribution in different cosmological scenarios. We show that predictions from
our approach for shear two-point correlations at $1$ to $300$ arcmin
separations are accurate at the $\sim10$\% level, even for extreme changes in
cosmology. For moderate changes, with target cosmologies similar to that
preferred by analyses of recent Planck data, the accuracy is close to
$\sim5$\%. We combine this approach with a MonteCarlo Markov Chain sampler to
explore constraints on a $\Lambda$CDM model from the shear correlation
functions measured in the Canada-France Hawaii Telescope Lensing Survey
(CFHTLenS). We obtain constraints on the parameter combination $\sigma_8
(\Omega_m/0.27)^{0.6} = 0.801 \pm 0.028$. Combined with results from CMB data,
we obtain marginalised constraints on $\sigma_8 = 0.81 \pm 0.01$ and $\Omega_m
= 0.29 \pm 0.01$. These results are fully compatible with previous analyses,
which supports the validity of our approach. We discuss the advantages of our
method and the potential it offers, including a path to incorporate in detail
the effects of baryons, among others effects, in future high-precision
cosmological analyses.


  The problem of estimating the baseline signal from multisample noisy curves
is investigated. We consider the functional mixed effects model, and we suppose
that the functional fixed effect belongs to the Besov class. This framework
allows us to model curves that can exhibit strong irregularities, such as peaks
or jumps for instance. The lower bound for the $L_2$ minimax risk is provided,
as well as the upper bound of the minimax rate, that is derived by constructing
a wavelet estimator for the functional fixed effect. Our work constitutes the
first theoretical functional results in multisample non parametric regression.
Our approach is illustrated on realistic simulated datasets as well as on
experimental data.


  In this paper, we study the problem of physical layer security in a
large-scale multiple-input multiple-output (LS-MIMO) relaying system. The
advantage of LS-MIMO relaying systems is exploited to enhance both wireless
security and spectral efficiency. In particular, the challenging issue incurred
by short interception distance is well addressed. Under very practical
assumptions, i.e., no eavesdropper's channel state information (CSI) and
imperfect legitimate channel CSI, this paper gives a thorough investigation of
the impact of imperfect CSI in two classic relaying systems, i.e.,
amplify-and-forward (AF) and decode-and-forward (DF) systems, and obtain
explicit expressions of secrecy outage capacities for both cases. Finally, our
theoretical claims are validated by the numerical results.


  We use the recently developed CFT techniques of Rychkov and Tan to compute
anomalous dimensions in the $O(N)$ Gross-Neveu model in $d=2+\epsilon$
dimensions. To do this, we extend the "cowpie contraction" algorithm of
arXiv:1506.06616 to theories with fermions. Our results match perfectly with
Feynman diagram computations.


  The physics of superconductivity in magic-angle twisted bilayer graphene
(MATBG) is a topic of keen interest in moir\'e systems research, and it may
provide insight into the pairing mechanism of other strongly correlated
materials such as high-$T_{\mathrm{c}}$ superconductors. Here, we use
DC-transport and microwave circuit quantum electrodynamics (cQED) to measure
directly the superfluid stiffness of superconducting MATBG via its kinetic
inductance. We find the superfluid stiffness to be much larger than expected
from conventional single-band Fermi liquid theory; rather, it aligns well with
theory involving quantum geometric effects that are dominant at the magic
angle. The temperature dependence of the superfluid stiffness exhibits a
power-law behavior, which contraindicates an isotropic BCS model; instead, the
extracted power-law exponents indicate an anisotropic superconducting gap,
whether interpreted using the conventional anisotropic BCS model or a quantum
geometric theory of flat-band superconductivity. Moreover, the quadratic
dependence of the stiffness on both DC and microwave current is consistent with
Ginzburg-Landau theory. Taken together, these findings strongly suggest a
connection between quantum geometry, superfluid stiffness, and unconventional
superconductivity in MATBG. Finally, the combined DC-microwave measurement
platform used here is applicable to the investigation of other atomically thin
superconductors.


  We revisit the polarisation induced by Faraday rotation when Cosmic Microwave
Background photons traverse magnetised plasma. We compute the secondary B-mode
angular power spectrum from Faraday rotation due to magnetic fields in galaxies
and galaxy clusters with masses ranging from $10^{11}$ to $10^{16.5} M_\odot$.
We investigate its dependence on the electron and the magnetic field profiles.
Namely, we consider both the beta-profile of electron density as well as an
electron density distribution based on the Navarro-Frenk-White dark matter
profile. We model the magnetic field structure in galaxies and clusters
motivated by recent observations. We further account for its redshift evolution
and we examine the importance of its coherence length. We find that the B-mode
polarisation from Faraday rotation depends on the normalisation parameter
$C_l\propto \sigma_8^{5-6}$. At 30 GHz for $\sigma_8=0.8$, the B-modes from
Faraday rotation range between $0.01 {\mu \rm K}^2$ and $4 \times 10^{-3} {\mu
\rm K}^2$ at $l=10^4$ in the case of a maximally coherent fields. For smaller
coherence lengths, those amplitudes are smaller and they peak at higher
multipoles.


  The linear quadratic regulator problem is central in optimal control and was
investigated since the very beginning of control theory. Nevertheless, when it
includes affine state constraints, it remains very challenging from the
classical ``maximum principle`` perspective. In this study we present how
matrix-valued reproducing kernels allow for an alternative viewpoint. We show
that the quadratic objective paired with the linear dynamics encode the
relevant kernel, defining a Hilbert space of controlled trajectories. Drawing
upon kernel formalism, we introduce a strengthened continuous-time convex
optimization problem which can be tackled exactly with finite dimensional
solvers, and which solution is interior to the constraints. When refining a
time-discretization grid, this solution can be made arbitrarily close to the
solution of the state-constrained Linear Quadratic Regulator. We illustrate the
implementation of this method on a path-planning problem.


  An acceleration scale of order $10^{-10}\mathrm{m/s^2}$ is implicit in the
baryonic Tully-Fisher and baryonic Faber-Jackson relations, independently of
any theoretical preference or bias. We show that the existence of this scale in
the baryonic Faber-Jackson relation is most apparent when data from pressure
supported systems of vastly different scales including globular clusters,
elliptical galaxies, and galaxy clusters are analyzed together. This suggests
the relevance of the acceleration scale $10^{-10}\mathrm{m/s^2}$ to structure
formation processes at many different length scales and could be pointing to a
heretofore unknown property of dark matter.


  We have studied the phi(1020)a0(980) S-wave scattering at threshold energies
employing chiral Lagrangians coupled to vector mesons by minimal coupling. The
interaction is described without new free parameters by considering the scalar
isovector a0(980) resonance as dynamically generated in coupled channels, and
demanding that the recently measured e+ e- -> phi(1020) f0(980) cross section
is reproduced. For some realistic choices of the parameters, the presence of a
dynamically generated isovector companion of the Y(2175) is revealed. We have
also investigated the corrections to the e+ e- -> phi(1020) pi0 eta reaction
cross section that arise from phi(1020)a0(980) re-scattering in the final
state. They are typically large and modify substantially the cross section. For
a suitable choice of parameters, the presence of the resonance would manifest
itself as a clear peak at sqrt{s}~2.03 GeV in e+ e- -> phi(1020) pi0 eta.


  Solid-state systems whose properties at high pressure (exceeding 1 GPa) mimic
those of MgSiO3 are of large importance in the study of the interior of
planets. By means of Density Functional Theory (DFT) calculations we studied
the high pressure properties of a MgSiO3 analogue, NaZnF3. We reproduce the
phase-transition sequence previously reported for this compound (GdFeO3 ->
CaIrO3 -> La2S3), and predict that it should undergo a two-step dissociation:
decomposition into a equimolar mixture of Na2ZnF4 and NaZn2F5 at 25.4 GPa,
followed by a breakdown into ZnF2 and NaF at 66.8 GPa. These processes are
analogous to those predicted for compressed MgSiO3. Moreover, both Na2ZnF4 and
NaZn2F5 are isostructural with analogous phases from the Mg-Si-O system. We
also find that both these novel compounds are thermodynamically stable at
ambient conditions (Na2ZnF4) or at low pressure of 19 GPa (NaZn2F5). Our study
indicates that NaZnF3 could serve as a good low-pressure analogue of MgSiO3
exhibiting the same sequence of phase transitions, and pressure induced
decomposition, but at pressures an order of magnitude lower.


  It has been shown that Linear Indexed Grammars can be processed in polynomial
time by exploiting constraints which make possible the extensive use of
structure-sharing. This paper describes a formalism that is more powerful than
Linear Indexed Grammar, but which can also be processed in polynomial time
using similar techniques. The formalism, which we refer to as Partially Linear
PATR manipulates feature structures rather than stacks.


  Looking for and studying very distant galaxy clusters, clusters at z>1, are
one of the prime subjects of the modern observational cosmology. If the
metallicity of the hot intra-cluster medium in very distant galaxy clusters is
measured for example, it provides fruitful informations for us to understand
the formation and evolution of galaxies. However, difficulty of the study is
that there is few confirmed very distant galaxy clusters yet. We first have to
search for very distant clusters but it requires very deep observations. A
random selection of sky is not practical. We have to select the sky. In this
article, it is demonstrated that missing lens problem has close connection with
very distant cluster of galaxies and dark lens searches could open a new window
for studying very distant cluster of galaxies. Firstly, the missing lens
problem is summarized and dark lens search is defined. Next, our results for
two dark lenses are summarized especially focused on the topics related to the
nature of the very distant clusters.


  This paper exploits the weak approximation method to study a zero-sum linear
differential game under mixed strategies. The stochastic nature of mixed
strategies poses challenges in evaluating the game value and deriving the
optimal strategies. To overcome these challenges, we first define the mixed
strategy based on time discretization given the control period $\delta$. Then,
we design a stochastic differential equation (SDE) to approximate the
discretized game dynamic with a small approximation error of scale
$\mathcal{O}(\delta^2)$ in the weak sense. Moreover, we prove that the game
payoff is also approximated in the same order of accuracy. Next, we solve the
optimal mixed strategies and game values for the linear quadratic differential
games. The effect of the control period is explicitly analyzed when the payoff
is a terminal cost. Our results provide the first implementable form of the
optimal mixed strategies for a zero-sum linear differential game. Finally, we
provide numerical examples to illustrate and elaborate on our results.


  Using expansion of the three-body wave function of the $pep$ system in the
initial state on hyperharmonic functions, the rate of the $p+p+e^{-}
\rightarrow d+\nu_e$ reaction in the Sun is calculated. The results of
calculation of the flux at 1 AU are compared with the results of a measurement
made by the Borexino collaboration and Bahcall \textit{et al.} theoretical
predictions.


  Chemistry plays an important role in the interstellar medium (ISM),
regulating heating and cooling of the gas, and determining abundances of
molecular species that trace gas properties in observations. Although solving
the time-dependent equations is necessary for accurate abundances and
temperature in the dynamic ISM, a full chemical network is too computationally
expensive to incorporate in numerical simulations. In this paper, we propose a
new simplified chemical network for hydrogen and carbon chemistry in the atomic
and molecular ISM. We compare results from our chemical network in detail with
results from a full photo-dissociation region (PDR) code, and also with the
Nelson & Langer (1999) (NL99) network previously adopted in the simulation
literature. We show that our chemical network gives similar results to the PDR
code in the equilibrium abundances of all species over a wide range of
densities, temperature, and metallicities, whereas the NL99 network shows
significant disagreement. Applying our network in 1D models, we find that the
$\mathrm{CO}$-dominated regime delimits the coldest gas and that the
corresponding temperature tracks the cosmic ray ionization rate in molecular
clouds. We provide a simple fit for the locus of $\mathrm{CO}$ dominated
regions as a function of gas density and column. We also compare with
observations of diffuse and translucent clouds. We find that the $\mathrm{CO}$,
$\mathrm{CHx}$ and $\mathrm{OHx}$ abundances are consistent with equilibrium
predictions for densities $n=100-1000~\mathrm{cm^{-3}}$, but the predicted
equilibrium $\mathrm{C}$ abundance is higher than observations, signaling the
potential importance of non-equilibrium/dynamical effects.


  We show that in the two-dimensional case, every objective, isotropic and
isochoric energy function which is rank-one convex on $\mathrm{GL}^+(2)$ is
already polyconvex on $\mathrm{GL}^+(2)$. Thus we negatively answer Morrey's
conjecture in the subclass of isochoric nonlinear energies, since polyconvexity
implies quasiconvexity. Our methods are based on different representation
formulae for objective and isotropic functions in general as well as for
isochoric functions in particular. We also state criteria for these convexity
conditions in terms of the deviatoric part of the logarithmic strain tensor.


  Graph Convolutional Networks (GCNs) have shown excellent performance in
dealing with various graph structures such as node classification, graph
classification and other tasks. However,recent studies have shown that GCNs are
vulnerable to a novel threat known as backdoor attacks. However, all existing
backdoor attacks in the graph domain require modifying the training samples to
accomplish the backdoor injection, which may not be practical in many realistic
scenarios where adversaries have no access to modify the training samples and
may leads to the backdoor attack being detected easily. In order to explore the
backdoor vulnerability of GCNs and create a more practical and stealthy
backdoor attack method, this paper proposes a clean-graph backdoor attack
against GCNs (CBAG) in the node classification task,which only poisons the
training labels without any modification to the training samples, revealing
that GCNs have this security vulnerability. Specifically, CBAG designs a new
trigger exploration method to find important feature dimensions as the trigger
patterns to improve the attack performance. By poisoning the training labels, a
hidden backdoor is injected into the GCNs model. Experimental results show that
our clean graph backdoor can achieve 99% attack success rate while maintaining
the functionality of the GCNs model on benign samples.


  Using $(1.0087\pm0.0044)\times10^{10}$ $J/\psi$ events collected with the
BESIII detector at the BEPCII storage ring, the process
$\Xi^{0}n\rightarrow\Xi^{-}p$ is studied, where the $\Xi^0$ baryon is produced
in the process $J/\psi\rightarrow\Xi^0\bar{\Xi}^0$ and the neutron is a
component of the $^9\rm{Be}$, $^{12}\rm{C}$ and $^{197}\rm{Au}$ nuclei in the
beam pipe. A clear signal is observed with a statistical significance of
$7.1\sigma$. The cross section of the reaction
$\Xi^0+{^9\rm{Be}}\rightarrow\Xi^-+p+{^8\rm{Be}}$ is determined to be
$\sigma(\Xi^0+{^9\rm{Be}}\rightarrow\Xi^-+p+{^8\rm{Be}})=(22.1\pm5.3_{\rm{stat}}\pm4.5_{\rm{sys}})$
mb at the $\Xi^0$ momentum of $0.818$ GeV/$c$, where the first uncertainty is
statistical and the second is systematic. No significant $H$-dibaryon signal is
observed in the $\Xi^-p$ final state. This is the first study of
hyperon-nucleon interactions in electron-positron collisions and opens up a new
direction for such research.


  Up to hundreds of black hole binaries individually resolvable by eLISA will
coalesce in the Advanced LIGO/Virgo band within ten years, allowing for
multi-band gravitational wave observations. Binaries formed via dynamical
interactions in dense star clusters are expected to have eccentricities
$e_0\sim 10^{-3}$-$10^{-1}$ at the frequencies $f_0=10^{-2}$ Hz where eLISA is
most sensitive, while binaries formed in the field should have negligible
eccentricity in both frequency bands. We estimate that eLISA should always be
able to detect a nonzero $e_0$ whenever $e_0\gtrsim 10^{-2}$; if $e_0\sim
10^{-3}$, eLISA should detect nonzero eccentricity for a fraction $\sim 90\%$
($\sim 25\%$) of binaries when the observation time is $T_{\rm obs}=5$ ($2$)
years, respectively. Therefore eLISA observations of BH binaries have the
potential to distinguish between field and cluster formation scenarios.


  We study the Fano effect and the visibility of the Aharonov-Bohm oscillations
for a mesoscopic interferometer with an embedded quantum dot in the presence of
a nearby second dot. When the electron-electron interaction between the two
dots is considered the nearby dot acts as a charge detector. We compute the
currents through the interferometer and detector within the Keldysh formalism
and the self-energy of the non-equilibrium Green functions is found up to the
second order in the interaction strength. The current formula contains a
correction to the Landauer-B\"{uttiker} formula. Its contribution to transport
and dephasing is discussed. As the bias applied on the detector is increased,
the amplitude of both the Fano resonance and Aharonov-Bohm oscillations are
considerably reduced due to controlled dephasing. This result is explained by
analyzing the behavior of the imaginary part of the self-energy as a function
of energy and bias. We investigate as well the role of the ring-dot coupling.
Our theoretical results are consistent to the experimental observation of Buks
{\it et al.} [Nature {\bf 391}, 871 (1998)].


  We consider linear partial differential equations on resistance spaces that
are uniformly elliptic and parabolic in the sense of quadratic forms and
involve abstract gradient and divergence terms. Our main interest is to provide
graph and metric graph approximations for their unique solutions. For families
of equations with different coefficients on a single compact resistance space
we prove that solutions have accumulation points with respect to the uniform
convergence in space, provided that the coefficients remain bounded. If in a
sequence of equations the coefficients converge suitably, the solutions
converge uniformly along a subsequence. For the special case of local
resistance forms on finitely ramified sets we also consider sequences of
resistance spaces approximating the finitely ramified set from within. Under
suitable assumptions on the coefficients (extensions of) linearizations of the
solutions of equations on the approximating spaces accumulate or even converge
uniformly along a subsequence to the solution of the target equation on the
finitely ramified set. The results cover discrete and metric graph
approximations, and both are discussed.


  In this work we study the problem of State Estimation(SE) in large-scale,
3-phase coupled, unbalanced distribution systems. More specifically, we address
the problem of including mixed real-time measurements, synchronized and
unsynchronized, from phasor measurement units and smart meters, into existing
solutions. We propose a computationally efficient two-step method to update a
prior solution using the measurements, while taking into account physical
constraint caused by buses with no loads. We test the method on a benchmark
test feeder to illustrate the effectiveness of the approach.


  Accurately and efficiently characterizing the decision boundary of
classifiers is important for problems related to model selection and
meta-learning. Inspired by topological data analysis, the characterization of
decision boundaries using their homology has recently emerged as a general and
powerful tool. In this paper, we propose an active learning algorithm to
recover the homology of decision boundaries. Our algorithm sequentially and
adaptively selects which samples it requires the labels of. We theoretically
analyze the proposed framework and show that the query complexity of our active
learning algorithm depends naturally on the intrinsic complexity of the
underlying manifold. We demonstrate the effectiveness of our framework in
selecting best-performing machine learning models for datasets just using their
respective homological summaries. Experiments on several standard datasets show
the sample complexity improvement in recovering the homology and demonstrate
the practical utility of the framework for model selection. Source code for our
algorithms and experimental results is available at
https://github.com/wayne0908/Active-Learning-Homology.


  Continuous physical interaction between robots and their environment is a
requirement in many industrial and household tasks, such as sanding and
cleaning. Due to the complex tactile information, these tasks are notoriously
difficult to model and to sense. In this article, we introduce a closed-loop
control method that is constrained to surfaces. The applications that we target
have in common that they can be represented by probability distributions on the
surface that correlate to the time the robot should spend in a region. These
surfaces can easily be captured jointly with the target distributions using
coloured point clouds. We present the extension of an ergodic control approach
that can be used with point clouds, based on heat equation-driven area coverage
(HEDAC). Our method enables closed-loop exploration by measuring the actual
coverage using vision. Unlike existing approaches, we approximate the potential
field from non-stationary diffusion using spectral acceleration, which does not
require complex preprocessing steps and achieves real-time closed-loop control
frequencies. We exploit geometric algebra to stay in contact with the target
surface by tracking a line while simultaneously exerting a desired force along
that line. Our approach is suitable for fully autonomous and human-robot
interaction settings where the robot can either directly measure the coverage
of the target with its sensors or by being guided online by markings or
annotations of a human expert. We tested the performance of the approach in
kinematic simulation using point clouds, ranging from the Stanford bunny to a
variety of kitchen utensils. Our real-world experiments demonstrate that the
proposed approach can successfully be used to wash kitchenware with curved
surfaces, by cleaning the dirt detected by vision in an online manner. Website:
https://geometric-algebra.tobiloew.ch/tactile_ergodic_control


  This paper introduces an adversarial method to stress-test trained metrics to
evaluate conversational dialogue systems. The method leverages Reinforcement
Learning to find response strategies that elicit optimal scores from the
trained metrics. We apply our method to test recently proposed trained metrics.
We find that they all are susceptible to giving high scores to responses
generated by relatively simple and obviously flawed strategies that our method
converges on. For instance, simply copying parts of the conversation context to
form a response yields competitive scores or even outperforms responses written
by humans.


  We study learning dynamics induced by strategic agents who repeatedly play a
game with an unknown payoff-relevant parameter. In each step, an information
system estimates a belief distribution of the parameter based on the players'
strategies and realized payoffs using Bayes' rule. Players adjust their
strategies by accounting for an equilibrium strategy or a best response
strategy based on the updated belief. We prove that beliefs and strategies
converge to a fixed point with probability 1. We also provide conditions that
guarantee local and global stability of fixed points. Any fixed point belief
consistently estimates the payoff distribution given the fixed point strategy
profile. However, convergence to a complete information Nash equilibrium is not
always guaranteed. We provide a sufficient and necessary condition under which
fixed point belief recovers the unknown parameter. We also provide a sufficient
condition for convergence to complete information equilibrium even when
parameter learning is incomplete.


  Coherence vortices are screw-type topological defects in the phase of
Glauber's two-point degree of quantum coherence, associated with pairs of
spatial points at which an ensemble-averaged stochastic quantum field is
uncorrelated. Coherence vortices may be present in systems whose dimensionality
is too low to support spatial vortices. We exhibit lattices of such
quantum-coherence phase defects for a one-dimensional model quantum system. We
discuss the physical meaning of coherence vortices and propose how they may be
realized experimentally.


  In this article, we give a precise mathematical meaning to `linear? time'
that matches experimental behaviour of the algorithm. The sorting algorithm is
not our own, it is a variant of radix sort with counting sort as a subroutine.
The true result of this article is an efficient universality result for
lexicographic order, or more generally for some linear extensions of the
partial order `Next': `if current items are equal, compare next items'. We
define new classes of orders: (Finite width) Tree Structured Orders. We show
that an instance of a finite width tree structured order can be converted in
linear time and space to an instance of lexicographic order. The constants
implied by the `nextification' algorithm are small (around 3 for real world
orders). The class of finite width tree structured orders contains finite
orders ({0, 1}, int32, int64, ..., float, double, ...), and orders constructed
from them on a tree structure. In particular, unbounded integers, strings with
arbitrary collation, and all orders used for sorting SQL queries are finite
width tree structured orders.