  Web search engines retrieve a vast amount of information for a given search
query. But the user needs only trustworthy and high-quality information from
this vast retrieved data. The response time of the search engine must be a
minimum value in order to satisfy the user. An optimum level of response time
should be maintained even when the system is overloaded. This paper proposes an
optimal Load Shedding algorithm which is used to handle overload conditions in
real-time data stream applications and is adapted to the Information Retrieval
System of a web search engine. Experiment results show that the proposed
algorithm enables a web search engine to provide trustworthy search results to
the user within an optimum response time, even during overload conditions.


  We study the fall-off behaviour of test electromagnetic fields in higher
dimensions as one approaches infinity along a congruence of "expanding" null
geodesics. The considered backgrounds are Einstein spacetimes including, in
particular, (asymptotically) flat and (anti-)de Sitter spacetimes. Various
possible boundary conditions result in different characteristic fall-offs, in
which the leading component can be of any algebraic type (N, II or G). In
particular, the peeling-off of radiative fields F=Nr^{1-n/2}+Gr^{-n/2}+...
differs from the standard four-dimensional one (instead it qualitatively
resembles the recently determined behaviour of the Weyl tensor in higher
dimensions). General p-form fields are also briefly discussed. In even n
dimensions, the special case p=n/2 displays unique properties and peels off in
the "standard way" as F=Nr^{1-n/2}+IIr^{-n/2}+.... A few explicit examples are
mentioned.


  The system mentioned in the title belongs to the family of the so-called
massively multi-player online social games (MMOSG). It features a scoring
system for the elements of the game that is prone to herding effects. We
analyze in detail its stationary regime in the thermodynamic limit, when the
number of players tends to infinity. In particular, for some classes of input
sequences and selection policies, we provide necessary and sufficient
conditions for the existence of a complete meanfield-like measure, showing off
an interesting condensation phenomenon.


  Compact objects occupy a pivotal role in the exploration of Nature. The
interest spans from the role of compact objects in astrophysics to their
detection through various methods (gravitational waves interferometry,
microlensing, imaging). While the existence of compact objects made of fermions
(neutron stars and white dwarfs) has been assessed, a parallel search for
localized solitons made of bosons is ongoing, stemming from Wheeler's original
proposal of electromagnetic "geons". Boson fields can clump up and form compact
objects such as boson stars (for complex scalar fields), oscillons and
oscillatons (for real scalar fields), or Proca stars (for massive vector
fields), which can show up in searches as black hole mimickers, dark matter
sources, and a variety of other phenomena. I review some crucial properties of
these bosonic systems, including recent progress in the field.


  As neural networks (NNs) become more prevalent in safety-critical
applications such as control of vehicles, there is a growing need to certify
that systems with NN components are safe. This paper presents a set of backward
reachability approaches for safety certification of neural feedback loops
(NFLs), i.e., closed-loop systems with NN control policies. While backward
reachability strategies have been developed for systems without NN components,
the nonlinearities in NN activation functions and general noninvertibility of
NN weight matrices make backward reachability for NFLs a challenging problem.
To avoid the difficulties associated with propagating sets backward through
NNs, we introduce a framework that leverages standard forward NN analysis tools
to efficiently find over-approximations to backprojection (BP) sets, i.e., sets
of states for which an NN policy will lead a system to a given target set. We
present frameworks for calculating BP over approximations for both linear and
nonlinear systems with control policies represented by feedforward NNs and
propose computationally efficient strategies. We use numerical results from a
variety of models to showcase the proposed algorithms, including a
demonstration of safety certification for a 6D system.


  Although it is generally thought that long-duration gamma-ray bursts (LGRBs)
are associated with core-collapse supernovae (SNe), so far only four pairs of
GRBs and SNe with firmly established connection have been found. All the four
GRB-SNe are among a special class of Type Ic--called the broad-lined SNe
indicative of a large explosion energy, suggesting that only a small fraction
of SNe Ibc have GRBs associated with them. This scheme has been refreshed by
the discovery of a bright X-ray transient in NGC 2770 on 9 January 2008, which
was followed by a rather normal Type Ib SN 2008D. In this paper, I argue that
the transient 080109 is an X-ray flash (XRF, the soft version of a GRB) because
of the following evidences: (1) The transient cannot be interpreted as a SN
shock breakout event; (2) The GRB X-ray flare interpretation is not supported
by the high-energy observation. Then I show that XRF 080109 satisfies the
well-known relation between the isotropic-equivalent energy and the peak
spectral energy for LGRBs, which highly strengthens the XRF interpretation.
Finally, I point out that, the peak spectral energy of XRF 080109 and the
maximum bolometric luminosity of SN 2008D agree with the
$E_{\gamma,\p}$--$L_{{\rm SN},\max}$ relationship of Li (2006), strengthening
the validity of the relationship. I speculate that events like XRF 080109 may
occur at a rate comparable to SNe Ibc, and a soft X-ray telescope devoted to
surveying for nearby X-ray flares will be very fruitful in discovering them.


  We present a new method for determining the Galactic gravitational potential
based on forward modeling of tidal stellar streams. We use this method to test
the performance of smooth and static analytic potentials in representing
realistic dark matter halos, which have substructure and are continually
evolving by accretion. Our FAST-FORWARD method uses a Markov Chain Monte Carlo
algorithm to compare, in 6D phase space, an "observed" stream to models created
in trial analytic potentials. We analyze a large sample of streams evolved in
the Via Lactea II (VL2) simulation, which represents a realistic Galactic halo
potential. The recovered potential parameters are in agreement with the best
fit to the global, present-day VL2 potential. However, merely assuming an
analytic potential limits the dark matter halo mass measurement to an accuracy
of 5 to 20%, depending on the choice of analytic parametrization. Collectively,
mass estimates using streams from our sample reach this fundamental limit, but
individually they can be highly biased. Individual streams can both under- and
overestimate the mass, and the bias is progressively worse for those with
smaller perigalacticons, motivating the search for tidal streams at
galactocentric distances larger than 70 kpc. We estimate that the assumption of
a static and smooth dark matter potential in modeling of the GD-1 and Pal5-like
streams introduces an error of up to 50% in the Milky Way mass estimates.


  This article introduces a tensor network subspace algorithm for the
identification of specific polynomial state space models. The polynomial
nonlinearity in the state space model is completely written in terms of a
tensor network, thus avoiding the curse of dimensionality. We also prove how
the block Hankel data matrices in the subspace method can be exactly
represented by low rank tensor networks, reducing the computational and storage
complexity significantly. The performance and accuracy of our subspace
identification algorithm are illustrated by numerical experiments, showing that
our tensor network implementation is around 20 times faster than the standard
matrix implementation before the latter fails due to insufficient memory, is
robust with respect to noise and can model real-world systems.


  We study the problem of augmenting online algorithms with machine learned
(ML) advice. In particular, we consider the \emph{multi-shop ski rental} (MSSR)
problem, which is a generalization of the classical ski rental problem. In
MSSR, each shop has different prices for buying and renting a pair of skis, and
a skier has to make decisions on when and where to buy. We obtain both
deterministic and randomized online algorithms with provably improved
performance when either a single or multiple ML predictions are used to make
decisions. These online algorithms have no knowledge about the quality or the
prediction error type of the ML prediction. The performance of these online
algorithms are robust to the poor performance of the predictors, but improve
with better predictions. Extensive experiments using both synthetic and real
world data traces verify our theoretical observations and show better
performance against algorithms that purely rely on online decision making.


  Nearby, Galactic gamma-ray bursts (GRBs) may affect the terrestrial biota if
their radiation is beamed towards the Earth. Compact stellar binary mergers are
possible central engines of short GRBs and their rate could be boosted in
globular clusters. Globular cluster typically follow well defined orbits around
the galactic center. Therefore their position relative to the solar system can
be calculated back in time. This fact is used to demonstrate that globular
cluster - solar system encounters define possible points in time when a nearby
GRB could have exploded. Additionally, potential terrestrial signatures in the
geological record connected to such an event are discussed. Assuming rates of
GRBs launched in globular cluster found from the redshift distribution of short
burst and adopting the current globular cluster space-density around the solar
system it is found that the expected minimal distance d_min for such a GRB in
the last Gyr is in the range d_min ~ 1 - 3.5 kpc. From the average gamma-ray
luminosity of a short GRB significant depletion of the terrestrial ozone-layer
is expected if such an event explodes at a distance of ~1 kpc. In the last Gyr
a few globular cluster passages are expected within a distance of d_min from
the solar system and a GRB should have exploded during one of these passages.
Globular cluster - solar system encounters and events of mass extinction in the
history of life can be correlated to investigate the impact of a nearby GRB on
the terrestrial biota. To explore such a correlation reliable globular cluster
positions relative to the solar system have to be calculated for the time span
of the fossil record of the last 600 Myr. The upcoming GAIA mission will be
crucial to determine the possible time intervals of the occurrence of nearby
GRBs launched in globular clusters.


  Generalizations and extensions of a first order autoregressive model of
Lawrance and Lewis (1981) are considered and characterized here.


  We show that the Klein bottle entropy [Phys. Rev. Lett. 119, 261603 (2017)]
for conformal field theories (CFTs) perturbed by a relevant operator is a
universal function of the dimensionless coupling constant. The universal
scaling of the Klein bottle entropy near criticality provides an efficient
approach to extract the scaling dimension of lattice operators via data
collapse. As paradigmatic examples, we validate the universal scaling of the
Klein bottle entropy for Ising and Z3 parafermion CFTs with various
perturbations using numerical simulation with continuous matrix product
operator approach.


  We present rapidly rotating neutron stars featuring wormholes in their
centers. They arise in general relativity in the presence of a ghost scalar
field. The nuclear matter is described by a polytropic equation of state,
yielding realistic masses and radii for the neutron stars. The wormholes
possess small circumferential radii of size up to 3 km. With increasing
wormhole size, the masses and radii of the stars decrease, while the domain of
existence of these rotating mixed neutron-star-plus-wormhole systems retains
the characteristic properties of a rotating neutron star domain. The question
of stability of the mixed configurations under consideration is briefly
discussed.


  Explicit representations of densities for linear parabolic partial
differential equations are useful in order to design computation schemes of
high accuracy for a considerable class of diffusion models. Approximations of
lower order based on the WKB-expansion have been used in order to compute
Greeks in standard models of the interest rate market (cf. [2]). However, it
turns out that for higher order approximations another related expansion leads
to more accurate schemes. We compute a local explicit formula for a class of
parabolic problems and determine a lower bound of the time horizon where it
holds (given a certain bounded domain). Although the local analytic expansions
hold only for strictly elliptic equations we show that the expansions can be
used in order to design higher order schemes for various types of
(micro)-hypoelliptic and semi-elliptic equations, e.g. the reduced market
models considered in [7] or front fixing schemes for multivariate American
derivatives [3].


  We revisit a recent claim that the Earth's climate system is characterized by
sensitive dependence to parameters; in particular, that the system exhibits an
asymmetric, large-amplitude response to normally distributed feedback forcing.
Such a response would imply irreducible uncertainty in climate change
predictions and thus have notable implications for climate science and
climate-related policy making. We show that equilibrium climate sensitivity in
all generality does not support such an intrinsic indeterminacy; the latter
appears only in essentially linear systems. The main flaw in the analysis that
led to this claim is inappropriate linearization of an intrinsically nonlinear
model; there is no room for physical interpretations or policy conclusions
based on this mathematical error. Sensitive dependence nonetheless does exist
in the climate system, as well as in climate models -- albeit in a very
different sense from the one claimed in the linear work under scrutiny -- and
we illustrate it using a classical energy balance model (EBM) with nonlinear
feedbacks. EBMs exhibit two saddle-node bifurcations, more recently called
"tipping points", which give rise to three distinct steady-state climates, two
of which are stable. Such bistable behavior is, furthermore, supported by
results from more realistic, nonequilibrium climate models. In a truly
nonlinear setting, indeterminacy in the size of the response is observed only
in the vicinity of tipping points. We show, in fact, that small disturbances
cannot result in a large-amplitude response, unless the system is at or near
such a point. We discuss briefly how the distance to the bifurcation may be
related to the strength of Earth's ice-albedo feedback.


  Integrated Sachs-Wolfe (ISW) effect can be estimated by cross-correlating
Cosmic Microwave Background (CMB) sky with tracers of the local matter
distribution. At late cosmic time, the dark energy induced decay of gravitation
potential generates a cross-correlation signal on large angular scales. The
dominant noise are the intrinsic CMB anisotropies from the inflationary epoch.
In this Letter we use CMB polarization to reduce this intrinsic noise. We
cross-correlate the microwave sky observed by Wilkinson Microwave Anisotropy
Probe (WMAP) with the radio source catalog compiled by NRAO VLA Sky Survey
(NVSS) to study the efficiency of the noise suppression . We find that the
error bars are reduced about 5-12 %, improving the statistical power.


  Linear bandits have a wide variety of applications including recommendation
systems yet they make one strong assumption: the algorithms must know an upper
bound $S$ on the norm of the unknown parameter $\theta^*$ that governs the
reward generation. Such an assumption forces the practitioner to guess $S$
involved in the confidence bound, leaving no choice but to wish that
$\|\theta^*\|\le S$ is true to guarantee that the regret will be low. In this
paper, we propose novel algorithms that do not require such knowledge for the
first time. Specifically, we propose two algorithms and analyze their regret
bounds: one for the changing arm set setting and the other for the fixed arm
set setting. Our regret bound for the former shows that the price of not
knowing $S$ does not affect the leading term in the regret bound and inflates
only the lower order term. For the latter, we do not pay any price in the
regret for now knowing $S$. Our numerical experiments show standard algorithms
assuming knowledge of $S$ can fail catastrophically when $\|\theta^*\|\le S$ is
not true whereas our algorithms enjoy low regret.


  We find evidence that low-OSNR operation causes substantial penalty on system
maximum reach due to non-linearity generated by ASE noise and due to
signal-power conversion into non-linear noise. Neglecting these effects may
lead to a quite substantial performance prediction error.


  Randomized search algorithms for hard combinatorial problems exhibit a large
variability of performances. We study the different types of rare events which
occur in such out-of-equilibrium stochastic processes and we show how they
cooperate in determining the final distribution of running times. As a
byproduct of our analysis we show how search algorithms are optimized by random
restarts.


  Off-Policy reinforcement learning (RL) is an important class of methods for
many problem domains, such as robotics, where the cost of collecting data is
high and on-policy methods are consequently intractable. Standard methods for
applying Q-learning to continuous-valued action domains involve iteratively
sampling the Q-function to find a good action (e.g. via hill-climbing), or by
learning a policy network at the same time as the Q-function (e.g. DDPG). Both
approaches make tradeoffs between stability, speed, and accuracy. We propose a
novel approach, called Cross-Entropy Guided Policies, or CGP, that draws
inspiration from both classes of techniques. CGP aims to combine the stability
and performance of iterative sampling policies with the low computational cost
of a policy network. Our approach trains the Q-function using iterative
sampling with the Cross-Entropy Method (CEM), while training a policy network
to imitate CEM's sampling behavior. We demonstrate that our method is more
stable to train than state of the art policy network methods, while preserving
equivalent inference time compute costs, and achieving competitive total reward
on standard benchmarks.


  Nowadays, metro systems play an important role in meeting the urban
transportation demand in large cities. The understanding of passenger route
choice is critical for public transit management. The wide deployment of
Automated Fare Collection(AFC) systems opens up a new opportunity. However,
only each trip's tap-in and tap-out timestamp and stations can be directly
obtained from AFC system records; the train and route chosen by a passenger are
unknown, which are necessary to solve our problem. While existing methods work
well in some specific situations, they don't work for complicated situations.
In this paper, we propose a solution that needs no additional equipment or
human involvement than the AFC systems. We develop a probabilistic model that
can estimate from empirical analysis how the passenger flows are dispatched to
different routes and trains. We validate our approach using a large scale data
set collected from the Shenzhen metro system. The measured results provide us
with useful inputs when building the passenger path choice model.


  Applications like environmental sensing, and health and activity sensing, are
supported by networks of devices (nodes) that send periodic packet
transmissions over the wireless channel to a sink node. We look at simple
abstractions that capture the following commonalities of such networks (a) the
nodes send periodically sensed information that is temporal and must be
delivered in a timely manner, (b) they share a multiple access channel and (c)
channels between the nodes and the sink are unreliable (packets may be received
in error) and differ in quality.
  We consider scheduled access and slotted ALOHA-like random access. Under
scheduled access, nodes take turns and get feedback on whether a transmitted
packet was received successfully by the sink. During its turn, a node may
transmit more than once to counter channel uncertainty. For slotted ALOHA-like
access, each node attempts transmission in every slot with a certain
probability. For these access mechanisms we derive the age of information
(AoI), which is a timeliness metric, and arrive at conditions that optimize AoI
at the sink. We also analyze the case of symmetric updating, in which updates
from different nodes must have the same AoI. We show that ALOHA-like access,
while simple, leads to AoI that is worse by a factor of about 2e, in comparison
to scheduled access.


  Transport properties of a quantum dot coupled to a photon cavity are
investigated using a quantum master equation in the steady-state regime. In the
off-resonance regime, when the photon energy is smaller than the energy spacing
between the lowest electron states of the quantum dot, we calculate the current
that is generated by photon replica states as the electronic system is pumped
with photons.Tuning the electron-photon coupling strength, the photocurrent can
be enhanced by the influences of the photon polarization, and the cavity-photon
coupling strength of the environment. We show that the current generated
through the photon replicas is very sensitive on the photon polarization, but
it is not strongly dependent on the average number of photons in the
environment.


  We propose a new class of ground states for doped Mott insulators in the
electron second-quantization representation. They are obtained from a bosonic
resonating valence bond (RVB) theory of the t-J model. At half filling, the
ground state describes spin correlations of the S=1/2 Heisenberg model very
accurately. Its spin degrees of freedom are characterized by RVB pairing of
spins, the size of which decreases continuously as holes are doped into the
system. Charge degrees of freedom emerge upon doping and are described by
twisted holes in the RVB background. We show that the twisted holes exhibit an
off diagonal long range order (ODLRO) in the pseudogap ground state, which has
a finite pairing amplitude, but is short of phase coherence. Unpaired spins in
such a pseudogap ground state behave as free vortices, preventing
superconducting phase coherence. The existence of nodal quasiparticles is also
ensured by such a hidden ODLRO in the ground state, which is
non-Fermi-liquid-like in the absence of superconducting phase coherence. Two
distinct types of spin excitations can also be constructed. The superconducting
instability of the pseudogap ground state is discussed and a d-wave
superconducting ground state is obtained. This class of pseudogap and
superconducting ground states unifies antiferromagnetism, pseudogap,
superconductivity, and Mott physics into a new state of matter.


  In this paper, we present a semantic mapping approach with multiple
hypothesis tracking for data association. As semantic information has the
potential to overcome ambiguity in measurements and place recognition, it forms
an eminent modality for autonomous systems. This is particularly evident in
urban scenarios with several similar looking surroundings. Nevertheless, it
requires the handling of a non-Gaussian and discrete random variable coming
from object detectors. Previous methods facilitate semantic information for
global localization and data association to reduce the instance ambiguity
between the landmarks. However, many of these approaches do not deal with the
creation of complete globally consistent representations of the environment and
typically do not scale well. We utilize multiple hypothesis trees to derive a
probabilistic data association for semantic measurements by means of position,
instance and class to create a semantic representation. We propose an optimized
mapping method and make use of a pose graph to derive a novel semantic SLAM
solution. Furthermore, we show that semantic covisibility graphs allow for a
precise place recognition in urban environments. We verify our approach using
real-world outdoor dataset and demonstrate an average drift reduction of 33 %
w.r.t. the raw odometry source. Moreover, our approach produces 55 % less
hypotheses on average than a regular multiple hypotheses approach.


  We formulate a new conceptual approach for one-shot complete polarization
state measurement with nanostructured metasurfaces applicable to classical
light and multi-photon quantum states, by drawing on the principles of
generalized quantum measurements based on positive operator-valued measures
(POVMs). Accurate polarization reconstruction from a combination of photon
counts or correlations from several diffraction orders is robust with respect
to even strong fabrication inaccuracies, requiring only a single classical
calibration of metasurface transmission.
  Furthermore, this approach operates with a single metagrating without
interleaving, allowing for the metasurface size reduction while preserving the
high transmission efficiency and output beam quality. We theoretically obtained
original metasurface designs, fabricated the metasurface from amorphous silicon
nanostructures deposited on glass, and experimentally confirmed accurate
polarization reconstruction for laser beams.
  We also anticipate robust operation under changes in environmental
conditions, opening new possibilities for space-based imaging and satellite
optics.


  To solve the problem of poor performance of deep neural network models due to
insufficient data, a simple yet effective interpolation-based data augmentation
method is proposed: MSMix (Manifold Swap Mixup). This method feeds two
different samples to the same deep neural network model, and then randomly
select a specific layer and partially replace hidden features at that layer of
one of the samples by the counterpart of the other. The mixed hidden features
are fed to the model and go through the rest of the network. Two different
selection strategies are also proposed to obtain richer hidden representation.
Experiments are conducted on three Chinese intention recognition datasets, and
the results show that the MSMix method achieves better results than other
methods in both full-sample and small-sample configurations.


  Millions of mosques around the world are suffering some problems such as
ventilation and difficulty getting rid of bacteria, especially in rush hours
where congestion in mosques leads to air pollution and spread of bacteria, in
addition to unpleasant odors and to a state of discomfort during the pray
times, where in most mosques there are no enough windows to ventilate the
mosque well. This paper aims to solve these problems by building a model of
smart mosques domes using weather features and outside temperatures. Machine
learning algorithms such as k Nearest Neighbors and Decision Tree were applied
to predict the state of the domes open or close. The experiments of this paper
were applied on Prophet mosque in Saudi Arabia, which basically contains twenty
seven manually moving domes. Both machine learning algorithms were tested and
evaluated using different evaluation methods. After comparing the results for
both algorithms, DT algorithm was achieved higher accuracy 98% comparing with
95% accuracy for kNN algorithm. Finally, the results of this study were
promising and will be helpful for all mosques to use our proposed model for
controlling domes automatically.


  In this note we investigate the normality of closures of orthogonal and
symplectic nilpotent orbits in positive characteristic. We prove that the
closure of such a nilpotent orbit is normal provided that neither type d nor
type e minimal irreducible degeneration occurs in the closure, and conversely
if the closure is normal, then any type e minimal irreducible degeneration does
not occur in it. Here, the minimal irreducible degenerations of a nilpotent
orbit are introduced by W. Hesselink in [6] (or see [11] from which we take
Table 1 for the complete list of all minimal irreducible degenerations). Our
result is a weak version in positive characteristic of [11, Theorem 16.2(ii)],
one of the main results of [11] over complex numbers.


  Recent results on the nature of the quantum critical point between Neel and
valence bond solid(VBS) ordered phases of two dimensional quantum magnets are
examined by an attack from the VBS side. This approach leads to an appealingly
simple physical description, and further insight into the properties of the
transition.


  Given a solution to a recursive distributional equation, a natural (and
non-trivial) question is whether the corresponding recursive tree process is
endogenous. That is, whether the random environment almost surely defines the
tree process. We propose a new method of proving endogeny, which applies to
various processes. As explicit examples, we establish endogeny of the random
metrics on non-pivotal hierarchical graphs defined by multiplicative cascades
and of mean-field optimization problems as the mean-field matching and
travelling salesman problems in pseudo-dimension q>1.


  We consider the quantum focusing conjecture (QFC) for two-dimensional
evaporating black holes. The QFC is closely related to the behavior of the
generalized entropy -- the sum of the area entropy for a given co-dimension two
surface and the entanglement entropy for quantum fields outside the area. In
the context of the black hole evaporation, the entanglement entropy of the
Hawking radiation is decreasing after the Page time, and therefore it is not
obvious whether the QFC holds in the black hole evaporation process especially
after the Page time. One of the present authors previously addressed this
problem in a four-dimensional spherically symmetric dynamical black hole model
and showed that the QFC is satisfied. However the background spacetime
considered was approximated by the Vaidya metric, and quantum effects of
matters in the semiclassical regime is not fully taken into consideration. It
remains to be seen if the QFC in fact holds for exact solutions of the
semiclassical Einstein equations. In this paper, we address this problem in a
two-dimensional dynamical black hole of the Russo-Susskind-Thorlacius (RST)
model, which allows us to solve the semiclassical equations of motion exactly.
We first give a suitable definition of the quantum expansion in two-dimensions
and then prove that the QFC is satisfied for evaporating black holes in the RST
model with the island formation taken into account.


  Supernova neutrinos can exhibit a rich variety of flavor conversion
mechanisms. In particular, they can experience "fast" self-induced flavor
conversions almost immediately above the core. Very recently, a novel method
has been proposed to investigate these phenomena, in terms of the dispersion
relation for the complex frequency and wave number ($\omega$,$k$) of
disturbances in the mean field of the $\nu_e\nu_x$ flavor coherence. We discuss
a systematic approach to such instabilities, originally developed in the
context of plasma physics, and based of the time-asymptotic behavior of the
Green's function of the system. Instabilities are typically seen to emerge for
complex $\omega$, and can be further characterized as convective (moving away
faster than they spread) and absolute (growing locally), depending on
$k$-dependent features. Stable cases emerge when $k$ (but not $\omega$) is
complex, leading to disturbances damped in space, or when both $\omega$ and $k$
are real, corresponding to complete stability. The analytical classification of
both unstable and stable modes leads not only to qualitative insights about
their features but also to quantitative predictions about the growth rates of
instabilities. Representative numerical solutions are discussed in a simple
two-beam model of interacting neutrinos. As an application, we argue that
supernova and binary neutron star mergers exhibiting a "crossing" in the
electron lepton number would lead to an absolute instability in the flavor
content of the neutrino gas.


  Establishing relations between fundamental effects in far-flung areas of
physics is a subject of great interest in the current research. We here report
realization of a novel photonic system akin to the radio-frequency
superconducting quantum interference device (RF-SQUID), in a fiber laser cavity
with epsilon-near-zero (ENZ) nanolayers as intra-cavity components. Emulating
the RF-SQUID scheme, the photonic counterpart of the supercurrent, represented
by the optical wave, circulates in the cavity, passing through effective
optical potential barriers. Different ENZ wavelengths translate into distinct
spectral outputs through the variation of cavity resonances, emulating the
situation with a frequency-varying tank circuit in the RF-SQUID. Due to the
presence of the ENZ element, the optical potential barrier is far lower for
selected frequency components, granting them advantage in the gain-resource
competition. The findings reported in this work provide a deeper insight into
the ultrafast ENZ photonics, revealing a new path towards the design of
nanophotonic on-chip devices with various operational functions, and offer a
new approach to study superconducting and quantum-mechanical systems.


  In this work we propose a multi-scale recurrent encoder-decoder architecture
to predict the breathing induced organ deformation in future frames. The model
was trained end-to-end from input images to predict a sequence of motion
labels. Targets were created by quantizing the displacement fields obtained
from deformable image registration. We report results using MRI free-breathing
acquisitions from 12 volunteers. Experiments were aimed at investigating the
proposed multi-scale design and the effect of increasing the number of
predicted frames on the overall accuracy of the model. The proposed model was
able to predict vessel positions in the next temporal image with a mean
accuracy of 2.07 (2.95) mm showing increased performance in comparison with
state-of-the-art approaches.


  A simulated annealing (SA) relaxation method is used for calculation of
high-beta reduced magnetohydrodynamics (MHD) equilibria in toroidal geometry.
The SA method, based on artificial dynamics derived from the MHD Hamiltonian
structure, is used to calculate equilibria of large-aspect-ratio and
circular-cross-section tokamaks as well as toroidally averaged stellarators.
Tokamak equilibria including incompressible poloidal rotations are obtained and
the Shafranov shift is seen to increase nearly quadratically in the rotation
speed. A mapping procedure between non-rotating and poloidally rotating
equilibria is shown to explain a quadratic dependence of equilibria shift on
rotation. Calculated stellarator equilibria are seen to agree reasonably with
previous results. The numerical results demonstrate the first successful
application of the SA method to obtain toroidal equilibria.


  The three electromagnetic form factors of the $\Delta(1232)$ resonance,
$G^*_M(q^2)$, $G^*_E(q^2)$, and $G^*_C(q^2)$ are obtained in the space-like
region using a Dual Resonance Model realization of QCD in the large $N_c$ limit
(Dual-${QCD}_{\infty}$). Each form factor involves a single free parameter
which is fixed by fitting data on $G^*_M(q^2)$, and on the ratios $R_{EM}(q^2)$
and $R_{SM}(q^2)$. Good agreement with experiment is obtained for all three
quantities. Results are then used to predict the $q^2$-dependence of the chiral
effective-field theory form factors $g_M(q^2)$, $g_E(q^2)$, and $g_C(q^2)$.


  Discontinuous Galerkin (DG) methods offer an enormous flexibility regarding
local grid refinement and variation of polynomial degrees for a variety of
different problem classes. With a focus on diffusion problems, we consider DG
discretizations for elliptic boundary value problems, in particular the
efficient solution of the linear systems of equations that arise from the
Symmetric Interior Penalty DG method. We announce a multi-stage preconditioner
which produces uniformly bounded condition numbers and aims at supporting the
full flexibility of DG methods under mild grading conditions. The constructions
and proofs are detailed in an upcoming series of papers by the authors. Our
preconditioner is based on the concept of the auxiliary space method and
techniques from spectral element methods such as Legendre-Gau{\ss}-Lobatto
grids. The presentation for the case of geometrically conforming meshes is
complemented by numerical studies that shed some light on constants arising in
four basic estimates used in the second stage.


  Tools constitute an essential contribution to natural language processing for
requirements engineering (NLP4RE) research. They are executable instruments
that make research usable and applicable in practice. In this chapter, we first
introduce a systematic classification of NLP4RE tools to improve the
understanding of their types and properties. Then, we extend an existing
overview with a systematic summary of 126 NLP4RE tools published between April
2019 and June 2023 to ease reuse and evolution of existing tools. Finally, we
provide instructions on how to create, maintain, and disseminate NLP4RE tools
to support a more rigorous management and dissemination.


  A Cu-Cr composite was processed by severe plastic deformation to investigate
the role of interphase boundaries on the grain size reduction mechanisms. The
as-deformed material exhibits a grain size of only 20nm. This gives rise to a
dramatic increase of the hardness. Some deformation induced Cu super saturated
solid solutions were clearly exhibited and it is shown that they decrease the
hardness. The formation of such supersaturated solid solution and their
influence on the mechanical properties are discussed.


  Using density functional theory we show that the interaction between two Mn
atoms can be tuned from anti-ferromagnetic (AFM) to ferromagnetic (FM) state by
creating charge disproportion between the two on a 2D surface. The non-metallic
planar heterostructures, the 2D surface, in our work is designed by doping
carbon hexagon rings in a hexagonal boron nitride (h-BN) sheet. In addition, we
show that an external electric field can be used to control the charge
disproportion and hence the magnetism. In fact, our calculations demonstrate
that the magnetic states of the dimer can be switched from AFM to FM or vice
versa in an external electric field. The origin of this magnetic switching is
explained using the charge transfer from (or to) the Mn dimer to (or from) the
2D material. The switching between anti-ferromagnetic to ferromagnetic states
can be useful for future spintronic applications.


  Mobile devices have evolved from just communication devices into an
indispensable part of people's lives in form of smartphones, tablets and smart
watches. Devices are now more personal than ever and carry more information
about a person than any other. Extracting user behaviour is rather difficult
and time-consuming as most of the work previously has been manual or requires
feature extraction. In this paper, a novel approach of user behavior detection
is proposed with Deep Learning Network (DNN). Initial approach was to use
recurrent neural network (RNN) along with LSTM for completely unsupervised
analysis of mobile devices. Next approach is to extract features by using Long
Short Term Memory (LSTM) to understand the user behaviour, which are then fed
into the Convolution Neural Network (CNN). This work mainly concentrates on
detection of user behaviour and anomaly detection for usage analysis of mobile
devices. Both the approaches are compared against some baseline methods.
Experiments are conducted on the publicly available dataset to show that these
methods can successfully capture the user behaviors.


  A fault injection framework for the decryption algorithm of the Niederreiter
public-key cryptosystem using binary irreducible Goppa codes and classical
decoding techniques is described. In particular, we obtain low-degree
polynomial equations in parts of the secret key. For the resulting system of
polynomial equations, we present an efficient solving strategy and show how to
extend certain solutions to alternative secret keys. We also provide estimates
for the expected number of required fault injections, apply the framework to
state-of-the-art security levels, and propose countermeasures against this type
of fault attack.


  We classify the Boolean degree $1$ functions of $k$-spaces in a vector space
of dimension $n$ (also known as Cameron-Liebler classes) over the field with
$q$ elements for $n \geq n_0(k, q)$. This also implies that two-intersecting
sets with respect to $k$-spaces do not exist for $n \geq n_0(k, q)$. Our main
ingredient is the Ramsey theory for geometric lattices.


  We develop a new approach for solving stochastic quantum master equations
with mixed initial states. First, we obtain that the solution of the
jump-diffusion stochastic master equation is represented by a mixture of pure
states satisfying a system of stochastic differential equations of
Schr\"odinger type. Then, we design three exponential schemes for these coupled
stochastic Schr\"odinger equations, which are driven by Brownian motions and
jump processes. Hence, we have constructed efficient numerical methods for the
stochastic master equations based on quantum trajectories. The good performance
of the new numerical integrators is illustrated by simulations of two quantum
measurement processes.


  In this paper, we introduce the Gompertz power series class of distributions
which is obtained by compounding Gompertz and power series distributions. This
distribution contains several lifetime models such as Gompertz-geometric,
Gompertz-Poisson, Gompertz-binomial, and Gompertz-logarithmic distributions as
special cases. Sub-models of the GPS distribution are studied in details. The
hazard rate function of the GPS distribution can be increasing, decreasing, and
bathtub-shaped. We obtain several properties of the GPS distribution such as
its probability density function, and failure rate function, Shannon entropy,
mean residual life function, quantiles and moments. The maximum likelihood
estimation procedure via a EM-algorithm is presented, and simulation studies
are performed for evaluation of this estimation for complete data, and the MLE
of parameters for censored data. At the end, a real example is given.


  In Part I we discuss accumulating experimental evidence related to the
structure and origin of the bosonic spectral function \alpha ^{2}F in
high-temperature superconducting (HTSC) cuprates near optimal doping. Some
global properties of \alpha ^{2}F such as number and positions of peaks, are
extracted by combining optics, neutron scattering, ARPES and tunnelling
measurements. These methods give convincing evidence for strong electron-phonon
interaction (EPI) with 1<\lambda \lesssim 3 in cuprates near optimal doping.
Here we clarify how these results are in favor of the Eliashberg-like theory
for HTSC cuprates near optimal doping.We argue that the neglect of EPI in some
previous studies of HTSC was based on a number of deceptive prejudices related
to the strength of EPI, on some physical misconceptions and misleading
interpretation of experimental results.


  An adaptive-gain super-twisting sliding mode observer is proposed for fault
reconstruction in electro-hydraulic servo systems (EHSS) receiving bounded
perturbations with unknown bounds. The objective is to address challenging
problems in classic sliding mode observers: chattering effect, conservatism of
observer gains, strong condition on the distribution of faults and
uncertainties. In this paper, the proposed super-twisting sliding mode observer
relaxes the condition on the distribution of uncertainties and faults, and the
gain adaptation law leads to eliminate observer gain overestimation and
attenuate chattering effects. After using the equivalent output-error-injection
feature of sliding mode techniques, a fault reconstruction strategy is
proposed. The experimental results are presented, confirming the effectiveness
of the proposed adaptive super-twisting observer for precise fault
reconstruction in electro-hydraulic servo systems.


  After an introduction to the ESA Herschel Space Observatory including a
mission overview, science objectives, results and productivity we examine the
process and outcomes of the announcements of observing opportunities (AOs). For
Herschel, in common with other ESA observatories, there were no rules, quotas,
or guidelines for the allocation of observing time based on the geographical
location of the lead proposer's institute, gender, or seniority (academic age);
scientific excellence was the most important single factor. We investigate
whether and how success rates vary with these (other) parameters. Due to the
relatively short operational duration of Herschel -- compared to XMM-Newton and
INTEGRAL -- in addition to the pre-launch AO in 2007 there was just two further
AOs, in 2010 and 2011. In order to extend the time-frame we compare results
with those from the ESA Infrared Space Observatory (ISO) whose time allocation
took place approximately 15 years earlier.


  The LHC Run III will be a crucial run for the two LHC forward experiments:
LHCf and FASER. In particular, Run III will be the last run where the LHCf
detector can operate and, at the same time, the first run of the new FASER
project. The LHCf experiment is dedicated to precise measurements of forward
production, necessary to tune hadronic interaction models employed in
cosmic-ray physics. In Run III, the experiment will accomplish two fundamental
goals: operating in p-p collisions at $\sqrt{s} = $ 14 TeV, it will acquire a
ten times larger statistics respect to Run II, in order to have precise
measurements of $\pi^{0}$ production; operating in high energy p-O and O-O
collisions, it will measure forward production in a configuration that is very
similar to the first interaction of an Ultra High Energy Cosmic Ray with an
atmospheric nucleus. The FASER experiment is dedicated to the search of new
weakly-interacting light particles, thanks to a forward detector with proper
shielding from Standard Model background. In Run III, the experiment will
search for new particles with a good sensitivity, which can be strongly
improved by a following possible upgrade before HL-LHC. In addition, thanks to
the dedicated FASER$\nu$ detector, the experiment will measure neutrino
production at a collider for the first time. In this contribution, we discuss
the main results expected from the LHCf and FASER experiments in Run III,
highlighting their fundamental contribution in research fields that are not
accessible to the four large LHC experiments.


  We present results at one-loop order of perturbation theory for various
improvement coefficients in on-shell O($a$) improved lattice QCD. In particular
we determine the additive counterterm required for on-shell improvement of the
isovector vector current. Employing a general mass-independent renormalization
scheme we also obtain the coefficients of the O($a$) counterterms which are
proportional to the quark mass in the improved isovector pseudo-scalar, axial
vector and vector operators. In the latter case a comparison with a recent
non-perturbative study is made.


  A window function is a generalization of the aggregation operation. Unlike
aggregation, the cardinality of its output is always the same as the
cardinality of input. That is, the semantics of this operator imply computing
values for extra attributes for each row, depending on its context, either
expressed by a sliding window or a previously evaluated row. Window functions
are a very powerful tool, which is also popular among data analysts and
supported by the majority of industrial DBMSes. It allows to gracefully express
quite complex use-cases, such as running sums and averages, local maximum and
minimum, and different types of ranking. Since they can be expressed without
self-joins and correlated subqueries, their evaluation can be performed much
more efficiently.
  In this paper we discuss an implementation of window functions inside a
disk-based column-store with late materialization. Late materialization is a
technique that aims to keep tuple reconstruction back from individual columns
as long as possible. Initially popular in the late 00's, it is rarely
considered nowadays. However, in case of window functions it allows to
substantially lower memory footprint. Another contribution of this paper is the
application of a segment tree to computing RANGE-based window functions.


  Entanglement criteria for an $n$-partite quantum system with continuous
variables are formulated in terms of R\'{e}nyi entropies. R\'{e}nyi entropies
are widely used as a good information measure due to many nice properties.
Derived entanglement criteria are based on several mathematical results such as
the Hausdorff-Young inequality, Young's inequality for convolution and its
converse. From the historical viewpoint, the formulations of these results with
sharp constants were obtained comparatively recently. Using the position and
momentum observables of subsystems, one can build two total-system measurements
with the following property. For product states, the final density in each
global measurement appears as a convolution of $n$ local densities. Hence,
restrictions in terms of two R\'{e}nyi entropies with constrained entropic
indices are formulated for $n$-separable states of an $n$-partite quantum
system with continuous variables. Experimental results are typically sampled
into bins between prescribed discrete points. For these aims, we give
appropriate reformulations of the derived entanglement criteria.


  The so-called 'LSND anomaly', a 3.8 sigma excess of anti-nu_e events
interpreted as originating from anti-nu_mu -> anti-nu_e oscillation, gave rise
to many theoretical speculations. The MiniBooNE Collaboration reported
inconsistency of this interpretation with the findings from their search for
nu_mu -> nu_e oscillations. Yet the origin of the LSND anomaly was never
clarified. A critical issue is the prediction of the background anti-nu_e flux
that was used in the analysis of the LSND experiment. For this, decisive input
comes from pion spectra measured with the HARP large-angle spectrometer under
conditions that closely resemble the LSND situation: a proton beam with 800 MeV
kinetic energy hitting a water target.


  We investigated the emission of active galactic nuclei dusty tori in the
infrared domain, with a focus on the 10 micron silicate feature. We modeled the
dusty torus as a clumpy two-phase medium with high-density clumps and a
low-density medium filling the space between the clumps. We employed a
three-dimensional radiative transfer code to obtain spectral energy
distributions and images of tori at different wavelengths. We calculated a grid
of models for different parameters and analyzed the influence of these
parameters on the shape of the mid-infrared emission. A corresponding set of
clumps-only models and models with a smooth dust distribution is calculated for
comparison. We found that the dust distribution, the optical depth and a random
arrangement of clumps in the innermost region, all have an impact on the shape
and strength of the silicate feature. The 10 micron silicate feature can be
suppressed for some parameters, but models with smooth dust distribution are
also able to produce a wide range of the silicate feature strength.


  We consider the possibility of a gravitational wave signal in an asymmetric
dark matter model. In this model a generative sector produces both the baryon
asymmetry and a dark matter asymmetry in a strong first-order phase transtion.
Bubble collisions during the phase transition lead to sound waves in the plasma
which are a source of a stochastic gravitational wave background. We consider
the prospects of future graviational wave observatories such as LISA and BBO
detecting such a signal. Constraints on the model from Halo ellipticity,
$\Delta N_{\rm eff}$ and direct detection experiments are also discussed.


  The ability of stacks of superconducting tapes to trap large magnetic fields
makes them ideal candidates for creating powerful permanent magnets of compact
size and mass. Experimentally, several techniques are used to trap the maximum
possible field in a given practical application. However, regardless of the
magnetization method used, there is a physical limit to the maximum magnetic
field that a given superconducting magnet can trap. This limit is given by the
geometric design, the particular superconducting material used and the
temperature of operation. Knowing the maximum possible trapped field is
important for device design as it provides an upper limit for applications such
as magnetic bearings or rotating machinery. In this work we present a
collection of finite element method (FEM) models in 2D and 3D capable of
estimating the maximum trapped field of stacked tape superconducting magnets.
The models are computationally fast and can be used to perform parametric
studies with ease. For the case of square stacks tape magnets, various sizes
are considered and their estimated maximum trapped field is compared with
experimental results.


  We propose a new data-driven approach for learning the fundamental solutions
(Green's functions) of various linear partial differential equations (PDEs)
given sample pairs of input-output functions. Building off the theory of
functional linear regression (FLR), we estimate the best-fit Green's function
and bias term of the fundamental solution in a reproducing kernel Hilbert space
(RKHS) which allows us to regularize their smoothness and impose various
structural constraints. We derive a general representer theorem for operator
RKHSs to approximate the original infinite-dimensional regression problem by a
finite-dimensional one, reducing the search space to a parametric class of
Green's functions. In order to study the prediction error of our Green's
function estimator, we extend prior results on FLR with scalar outputs to the
case with functional outputs. Finally, we demonstrate our method on several
linear PDEs including the Poisson, Helmholtz, Schr\"{o}dinger, Fokker-Planck,
and heat equation. We highlight its robustness to noise as well as its ability
to generalize to new data with varying degrees of smoothness and mesh
discretization without any additional training.


  Since the first online demonstration of Neural Machine Translation (NMT) by
LISA, NMT development has recently moved from laboratory to production systems
as demonstrated by several entities announcing roll-out of NMT engines to
replace their existing technologies. NMT systems have a large number of
training configurations and the training process of such systems is usually
very long, often a few weeks, so role of experimentation is critical and
important to share. In this work, we present our approach to production-ready
systems simultaneously with release of online demonstrators covering a large
variety of languages (12 languages, for 32 language pairs). We explore
different practical choices: an efficient and evolutive open-source framework;
data preparation; network architecture; additional implemented features; tuning
for production; etc. We discuss about evaluation methodology, present our first
findings and we finally outline further work.
  Our ultimate goal is to share our expertise to build competitive production
systems for "generic" translation. We aim at contributing to set up a
collaborative framework to speed-up adoption of the technology, foster further
research efforts and enable the delivery and adoption to/by industry of
use-case specific engines integrated in real production workflows. Mastering of
the technology would allow us to build translation engines suited for
particular needs, outperforming current simplest/uniform systems.


  We reveal elegant relations between the shape dependence of the Casimir
effects and Weyl anomaly in boundary conformal field theories (BCFT). We show
that for any BCFT which has a description in terms of an effective action, the
near boundary divergent behavior of the renormalized stress tensor is
completely determined by the central charges of the theory. These relations are
verified by free BCFTs. We test them with holographic models of BCFT and find
exact agreement. We propose that these relations between Casimir coefficients
and central charges hold for any BCFT. With the holographic models, we
reproduce not only the precise form of the near boundary divergent behavior of
the stress tensor, but also the surface counter term that is needed to make the
total energy finite. As they are proportional to the central charges, the near
boundary divergence of the stress tensor must be physical and cannot be dropped
by further artificial renormalization.Our results thus provide affirmative
support on the physical nature of the divergent energy density near the
boundary, whose reality has been a long-standing controversy in the literature.


  The Auxiliary Field Diffusion Monte Carlo method has been applied to simulate
droplets of 7 and 8 neutrons. Results for realistic nucleon-nucleon
interactions, which include tensor, spin--orbit and three--body forces, plus a
standard one--body confining potential, have been compared with analogous
calculations obtained with Green's Function Monte Carlo methods. We have
studied the dependence of the binding energy, the one--body density and the
spin--orbit splittings of $^7n$ on the depth of the confining potential. The
results obtained show an overall agreement between the two quantum Monte Carlo
methods, although there persist differences in the evaluation of spin--orbit
forces, as previously indicated by bulk neutron matter calculations. Energy
density functional models, largely used in astrophysical applications, seem to
provide results significantly different from those of quantum simulations.
Given its scaling behavior in the number of nucleons, the Auxiliary Field
Diffusion Monte Carlo method seems to be one of the best candidate to perform
{\sl ab initio} calculations on neutron rich nuclei.


  In this thesis we showed that Non-equilibrium Green's Function Perturbation
Theory (NEGF) is really the overarching perturbative transport theory. This is
shown in great detail by using NEGF as a starting point and developing in 3
directions to obtain the usual transport-related expressions. The 3 directions
are: Landauer-like theory, kinetic theory and Green-Kubo linear response
theory. This thesis is concerned with using NEGF to generalize the 2 directions
of Landauer-like theory and the kinetic theory.
  Firstly, NEGF is used to derive phonon-phonon Hedin-like functional
derivative equations which generates conserving self energy approximations for
phonon-phonon interaction. Secondly, for the Landauer-like theory, using the
perturbation expansion, we easily obtain anharmonic (or phonon-phonon
interaction) corrections to the ballistic energy current and to the noise
associated to the energy current. The lowest 3-phonon interaction, the lowest
and the second lowest 4-phonon interaction corrections to the ballistic energy
current are obtained. The lowest 3-phonon interaction correction to the noise
is obtained. Along a seperate line, we found that we can incooperate high mass
disorder into the ballistic energy current formula. The coherent potential
approximation (CPA) for treating high mass disorder is found to be compatible
with the ballistic energy current expression.
  Lastly, for the kinetic theory, Wigner coordinates + gradient expansion
easily allow the reproduction of the usual phonon Boltzmann kinetic equation.
It is also straightforward to derive phonon-phonon correlation corrections to
kinetic equations. Kinetic equations lead to hydrodynamic (balance) equations
and we derived phonon-phonon correlation corrections to the entropy, energy and
momentum balance equations.


  A formulation of nucleon-nucleon scattering is developed in which the
S-matrix, rather than an effective-field theory (EFT) action, is the
fundamental object. Spacetime plays no role in this description: the S-matrix
is a trajectory that moves between RG fixed points in a compact theory space
defined by unitarity. This theory space has a natural operator definition, and
a geometric embedding of the unitarity constraints in four-dimensional
Euclidean space yields a flat torus, which serves as the stage on which the
S-matrix propagates. Trajectories with vanishing entanglement are special
geodesics between RG fixed points on the flat torus, while entanglement is
driven by an external potential. The system of equations describing S-matrix
trajectories is in general complicated, however the very-low-energy S-matrix --
that appears at leading-order in the EFT description -- possesses a UV/IR
conformal invariance which renders the system of equations integrable, and
completely determines the potential. In this geometric viewpoint, inelasticity
is in correspondence with the radius of a three-dimensional hyperbolic space
whose two-dimensional boundary is the flat torus. This space has a singularity
at vanishing radius, corresponding to maximal violation of unitarity. The
trajectory on the flat torus boundary can be explicitly constructed from a bulk
trajectory with a quantifiable error, providing a simple example of a
holographic quantum error correcting code.


  A general model for the cosmological evolution of the fine structure constant
\alpha driven by a typical Quintessence scenario is presented. We consider a
coupling between the Quintessence scalar \phi and the electromagnetic kinetic
term F^2, given by a general function B_F(\phi). We study the dependence of the
cosmological \Delta\alpha(t) upon the functional form of B_F and discuss the
constraints imposed by the data. We find that different cosmological histories
for \Delta\alpha(t) are possible within the avaliable constraints. We also find
that Quasar absorption spectra evidence for a time variation of alpha, if
confirmed, is not incompatible with Oklo and meteorites limits.


  When waves propagate through a complex or heterogeneous medium the wave field
is corrupted by the heterogeneities. Such corruption limits the performance of
imaging or communication schemes. One may then ask the question: is there an
optimal way of encoding a signal so as to counteract the corruption by the
medium? In the ideal situation the answer is given by time reversal: for a
given target or focusing point, in a first step let the target emit a signal
and then record the signal transmitted to the source antenna, time reverse this
and use it as the source trace at the source antenna in a second step. This
source will give a sharply focused wave at the target location if the source
aperture is large enough. Here we address this scheme in the more practical
situation with a limited aperture, time-harmonic signal, and finite-sized
elements in the source array. Central questions are then the focusing
resolution and signal-to-noise ratio at the target, their dependence on the
physical parameters, and the capacity to focus selectively in the neighborhood
of the target point and therefore to transmit images. Sharp results are
presented for these questions.


  Dempster-Shafer Theory (DST) generalizes Bayesian probability theory,
offering useful additional information, but suffers from a high computational
burden. A lot of work has been done to reduce the complexity of computations
used in information fusion with Dempster's rule. Yet, few research had been
conducted to reduce the complexity of computations for the conjunctive and
disjunctive decompositions of evidence, which are at the core of other
important methods of information fusion. In this paper, we propose a method
designed to exploit the actual evidence (information) contained in these
decompositions in order to compute them. It is based on a new notion that we
call focal point, derived from the notion of focal set. With it, we are able to
reduce these computations up to a linear complexity in the number of focal sets
in some cases. In a broader perspective, our formulas have the potential to be
tractable when the size of the frame of discernment exceeds a few dozen
possible states, contrary to the existing litterature. This article extends
(and translates) our work published at the french conference GRETSI in 2019.


  Analogies between evolutionary dynamics and statistical mechanics, such as
Fisher's second-law-like "fundamental theorem of natural selection" and
Wright's "fitness landscapes", have had a deep and fruitful influence on the
development of evolutionary theory. Here I discuss a new conceptual link
between evolution and statistical physics. I argue that natural selection can
be viewed as a coarsening phenomenon, similar to the growth of domain size in
quenched magnets or to Ostwald ripening in alloys and emulsions. In particular,
I show that the most remarkable features of coarsening---scaling and
self-similarity---have strict equivalents in evolutionary dynamics. This
analogy has three main virtues: it brings a set of well-developed mathematical
tools to bear on evolutionary dynamics; it suggests new problems in theoretical
evolution; and it provides coarsening physics with a new exactly soluble model.


  We performed angle dependent magnetoresistance study of a metallic single
crystal sample of Bi2Te3. We find that the magnetoresistance is highly
asymmetric in positive and negative magnetic fields for small angles between
the magnetic field and the direction perpendicular to the plane of the sample.
The magnetoresistance becomes symmetric as the angle approaches 90 degree. The
quantum Shubnikov de-Haas oscillations are symmetric and show signatures of
topological surface states with Dirac dispersion in the form of non-zero Berry
phase. However, the angular dependence of these oscillations suggests a complex
three dimensional Fermi surface as the source of these oscillations, which does
not exactly conform with the six ellipsoidal model of the Fermi surface of
Bi2Te3. We attribute the asymmetry in the magnetoresistance to a mixing of the
Hall voltage in the longitudinal resistance due to the comparable magnitude of
the Hall and longitudinal resistance in our samples. This provides a clue to
understanding the asymmetric magnetoresistance often seen in this and similar
materials. Moreover, the asymmetric nature evolves with exposure to atmosphere
and thermal cycling, which we believe is either due to exposure to atmosphere
or thermal cycling, or both affecting the carrier concentration and hence the
Hall signal in these samples. However, the quantum oscillations seem to be
robust against these factors which suggests that the two have different
origins.


  We present absolute astrometry of four radio sources in the
Becklin-Neugebauer/Kleinman-Low (BN/KL) region, derived from archival data
(taken in 1991, 1995, and 2000) as well as from new observations (taken in
2006). All data consist of 3.6 cm continuum emission and were taken with the
Very Large Array in its highest angular resolution A configuration. We confirm
the large proper motions of the BN object, the radio source I (GMR I) and the
radio counterpart of the infrared source n (Orion-n), with values from 15 to 26
km/s. The three sources are receding from a point between them from where they
seem to have been ejected about 500 years ago, probably via the disintegration
of a multiple stellar system. We present simulations of very compact stellar
groups that provide a plausible dynamical scenario for the observations. The
radio source Orion-n appeared as a double in the first three epochs, but as
single in 2006. We discuss this morphological change. The fourth source in the
region, GMR D, shows no statistically significant proper motions. We also
present new, accurate relative astrometry between BN and radio source I that
restrict possible dynamical scenarios for the region. During the 2006
observations, the radio source GMR A, located about 1' to the NW of the BN/KL
region, exhibited an increase in its flux density of a factor of ~3.5 over a
timescale of one hour. This rapid variability at cm wavelengths is similar to
that previously found during a flare at millimeter wavelengths that took place
in 2003.


  Marcus, Spielman, and Srivastava in their seminal work \cite{MSS13} resolved
the Kadison-Singer conjecture by proving that for any set of finitely supported
independently distributed random vectors $v_1,\dots, v_n$ which have "small"
expected squared norm and are in isotropic position (in expectation), there is
a positive probability that the sum $\sum v_i v_i^\intercal$ has small spectral
norm. Their proof crucially employs real stability of polynomials which is the
natural generalization of real-rootedness to multivariate polynomials.
  Strongly Rayleigh distributions are families of probability distributions
whose generating polynomials are real stable \cite{BBL09}. As independent
distributions are just special cases of strongly Rayleigh measures, it is a
natural question to see if the main theorem of \cite{MSS13} can be extended to
families of vectors assigned to the elements of a strongly Rayleigh
distribution.
  In this paper we answer this question affirmatively; we show that for any
homogeneous strongly Rayleigh distribution where the marginal probabilities are
upper bounded by $\epsilon_1$ and any isotropic set of vectors assigned to the
underlying elements whose norms are at most $\sqrt{\epsilon_2}$, there is a set
in the support of the distribution such that the spectral norm of the sum of
the natural quadratic forms of the vectors assigned to the elements of the set
is at most $O(\epsilon_1+\epsilon_2)$. We employ our theorem to provide a
sufficient condition for the existence of spectrally thin trees. This, together
with a recent work of the authors \cite{AO14}, provides an improved upper bound
on the integrality gap of the natural LP relaxation of the Asymmetric Traveling
Salesman Problem.


  We have performed 3D radiation MHD simulations extending from the convection
zone to the corona covering a box 16 Mm$^3$ at 32 km spatial resolution. The
simulations show very fine structure in the chromosphere with acoustic shocks
interacting with the magnetic field. Magnetic flux concentrations have a
temperature lower than the surroundings in the photosphere but higher in the
low chromosphere. The heating is there mostly through ohmic dissipation
preferentially at the edges of the flux concentrations. The magnetic field is
often wound up around the flux concentrations. When acoustic waves travel up
along the field this topology leads to swirling motions seen in chromospheric
diagnostic lines such as the calcium infrared triplet.


  Seismic waves bring information from the physical properties of the earth to
the surface. Full waveform inversion (FWI) is a local optimization technique
which tries to invert the recorded wave fields to the physical properties. An
efficient forward-modelling engine along with local differential algorithm, to
compute the gradient and Hessian operators, are two key ingredients of FWI
approach. FWI can be done in time or frequency domain. Each method has its own
pros and cons. Here, we only discuss frequency domain method with Krylov
subspace solvers for time-harmonic wave equation. Nonlinearity of the problem
requires good initial macro model of the physical properties and low frequency
data. Macro models are built based on the kinematic information of the recorded
wave fields. Another difficulty is the data modelling algorithm which is hard
to solve especially for high wavenumbers (high frequencies). Without
incorporation of high frequencies in the FWI algorithm we are not going to be
able to update the macro models to high resolution ones. In the companion paper
we showed that efficient forward modelling algorithms can be reached via proper
preconditioners. Here, we will use the preconditioned data modelling engine in
the context of local optimization method to solve for model parameters. The
results show that we yield better convergence of the method and better quality
of the inverted models after using the preconditioned FWI.


  We study the universal critical properties of the QED$_3$-Gross-Neveu-Yukawa
model with $N$ flavors of four-component Dirac fermions coupled to a real
scalar order parameter at four-loop order in the $\epsilon$ expansion below
four dimensions. For $N=1$, the model is conjectured to be infrared dual to the
$SU(2)$-symmetric noncompact $\mathbb{C}$P$^1$ model, which describes the
deconfined quantum critical point of the N\'eel-valence-bond-solid transition
of spin-1/2 quantum antiferromagnets on the two-dimensional square lattice. For
$N=2$, the model describes a quantum phase transition between an algebraic spin
liquid and a chiral spin liquid in the spin-1/2 kagom\'e antiferromagnet. For
general $N$ we determine the order parameter anomalous dimension, the
correlation length exponent, the stability critical exponent, as well as the
scaling dimensions of $SU(N)$ singlet and adjoint fermion mass bilinears at the
critical point. We use Pad\'e approximants to obtain estimates of critical
properties in 2+1 dimensions.


  With the significant successes of large language models (LLMs) in many
natural language processing tasks, there is growing interest among researchers
in exploring LLMs for novel recommender systems. However, we have observed that
directly using LLMs as a recommender system is usually unstable due to its
inherent position bias. To this end, we introduce exploratory research and find
consistent patterns of positional bias in LLMs that influence the performance
of recommendation across a range of scenarios. Then, we propose a Bayesian
probabilistic framework, STELLA (Stable LLM for Recommendation), which involves
a two-stage pipeline. During the first probing stage, we identify patterns in a
transition matrix using a probing detection dataset. And in the second
recommendation stage, a Bayesian strategy is employed to adjust the biased
output of LLMs with an entropy indicator. Therefore, our framework can
capitalize on existing pattern information to calibrate instability of LLMs,
and enhance recommendation performance. Finally, extensive experiments clearly
validate the effectiveness of our framework.


  Object detection with Transformers (DETR) has achieved a competitive
performance over traditional detectors, such as Faster R-CNN. However, the
potential of DETR remains largely unexplored for the more challenging task of
arbitrary-oriented object detection problem. We provide the first attempt and
implement Oriented Object DEtection with TRansformer ($\bf O^2DETR$) based on
an end-to-end network. The contributions of $\rm O^2DETR$ include: 1) we
provide a new insight into oriented object detection, by applying Transformer
to directly and efficiently localize objects without a tedious process of
rotated anchors as in conventional detectors; 2) we design a simple but highly
efficient encoder for Transformer by replacing the attention mechanism with
depthwise separable convolution, which can significantly reduce the memory and
computational cost of using multi-scale features in the original Transformer;
3) our $\rm O^2DETR$ can be another new benchmark in the field of oriented
object detection, which achieves up to 3.85 mAP improvement over Faster R-CNN
and RetinaNet. We simply fine-tune the head mounted on $\rm O^2DETR$ in a
cascaded architecture and achieve a competitive performance over SOTA in the
DOTA dataset.


  Humanoid robots have apparently similar body structure like human beings. Due
to their technical design, they are sharing the same workspace with humans.
They are placed to clean things, to assist old age people, to entertain us and
most importantly to serve us. To be acceptable in the household, they must have
higher level of intelligence than industrial robots and they must be social and
capable of interacting people around it, who are not supposed to be robot
specialist. All these come under the field of human robot interaction (HRI).
There are various modes like speech, gesture, behavior etc. through which human
can interact with robots. To solve all these challenges, a multimodel technique
has been introduced where gesture as well as speech is used as a mode of
interaction.


  Linearizability and progress properties are key correctness notions for
concurrent objects. However, model checking linearizability has suffered from
the PSPACE-hardness of the trace inclusion problem. This paper proposes to
exploit branching bisimulation, a fundamental semantic equivalence relation
developed for process algebras which can be computed efficiently, in checking
these properties. A quotient construction is provided which results in huge
state space reductions. We confirm the advantages of the proposed approach on
more than a dozen benchmark problems.


  A long-term goal of reinforcement learning agents is to be able to perform
tasks in complex real-world scenarios. The use of external information is one
way of scaling agents to more complex problems. However, there is a general
lack of collaboration or interoperability between different approaches using
external information. In this work, while reviewing externally-influenced
methods, we propose a conceptual framework and taxonomy for assisted
reinforcement learning, aimed at fostering collaboration by classifying and
comparing various methods that use external information in the learning
process. The proposed taxonomy details the relationship between the external
information source and the learner agent, highlighting the process of
information decomposition, structure, retention, and how it can be used to
influence agent learning. As well as reviewing state-of-the-art methods, we
identify current streams of reinforcement learning that use external
information in order to improve the agent's performance and its decision-making
process. These include heuristic reinforcement learning, interactive
reinforcement learning, learning from demonstration, transfer learning, and
learning from multiple sources, among others. These streams of reinforcement
learning operate with the shared objective of scaffolding the learner agent.
Lastly, we discuss further possibilities for future work in the field of
assisted reinforcement learning systems.


  Current state-of-the-art deep learning systems for visual object recognition
and detection use purely supervised training with regularization such as
dropout to avoid overfitting. The performance depends critically on the amount
of labeled examples, and in current practice the labels are assumed to be
unambiguous and accurate. However, this assumption often does not hold; e.g. in
recognition, class labels may be missing; in detection, objects in the image
may not be localized; and in general, the labeling may be subjective. In this
work we propose a generic way to handle noisy and incomplete labeling by
augmenting the prediction objective with a notion of consistency. We consider a
prediction consistent if the same prediction is made given similar percepts,
where the notion of similarity is between deep network features computed from
the input data. In experiments we demonstrate that our approach yields
substantial robustness to label noise on several datasets. On MNIST handwritten
digits, we show that our model is robust to label corruption. On the Toronto
Face Database, we show that our model handles well the case of subjective
labels in emotion recognition, achieving state-of-the- art results, and can
also benefit from unlabeled face images with no modification to our method. On
the ILSVRC2014 detection challenge data, we show that our approach extends to
very deep networks, high resolution images and structured outputs, and results
in improved scalable detection.


  The semantic web is an open and distributed environment in which it is hard
to guarantee consistency of knowledge and information. Under the standard
two-valued semantics everything is entailed if knowledge and information is
inconsistent. The semantics of the paraconsistent logic LP offers a solution.
However, if the available knowledge and information is consistent, the set of
conclusions entailed under the three-valued semantics of the paraconsistent
logic LP is smaller than the set of conclusions entailed under the two-valued
semantics. Preferring conflict-minimal three-valued interpretations eliminates
this difference.
  Preferring conflict-minimal interpretations introduces non-monotonicity. To
handle the non-monotonicity, this paper proposes an assumption-based
argumentation system. Assumptions needed to close branches of a semantic
tableaux form the arguments. Stable extensions of the set of derived arguments
correspond to conflict minimal interpretations and conclusions entailed by all
conflict-minimal interpretations are supported by arguments in all stable
extensions.


  In 1927 Philomena Mader derived elegant inversion formulas for the hyperplane
Radon transform on $\bbr^n$. These formulas differ from the original ones by
Radon and seem to be forgotten. We generalize Mader's formulas to totally
geodesic Radon transforms in any dimension on arbitrary constant curvature
space. Another new interesting inversion formula for the $k$-plane transform
was presented in the recent book "Integral geometry and Radon transform" by S.
Helgason. We extend this formula to arbitrary constant curvature space. The
paper combines tools of integral geometry and complex analysis.


  Indirect dark matter (DM) detection typically involves the observation of
standard model (SM) particles emerging from DM annihilation/decay inside
regions of high dark matter concentration. We consider an annihilation scenario
in which this reaction has to be initiated by one of the DMs involved being
boosted while the other is an ambient non-relativistic particle. This "trigger"
DM must be created, for example, in a previous annihilation or decay of a
heavier component of DM. Remarkably, boosted DM annihilating into gamma-rays at
a specific point in a galaxy could actually have traveled from its source at
another point in the same galaxy or even from another galaxy. Such a
"non-local" behavior leads to a non-trivial dependence of the resulting photon
signal on the galactic halo parameters, such as DM density and core size,
encoded in the so-called "astrophysical" $J$-factor. These non-local
$J$-factors are strikingly different than the usual scenario. A distinctive
aspect of this model is that the signal from dwarf galaxies relative to the
Milky Way tends to be suppressed from the typical value to various degrees
depending on their characteristics. This feature can thus potentially alleviate
the mild tension between the DM annihilation explanation of the observed excess
of $\sim$ GeV photons from the Milky Way's galactic center vs. the apparent
non-observation of the corresponding signal from dwarf galaxies.


  The antiproton storage ring HESR to be constructed at GSI will open up a new
range of perturbative and nonperturbative tests of QCD in exclusive and
inclusive reactions. I discuss 21 tests of QCD using antiproton beams which can
illuminate novel features of QCD. The proposed experiments include the
formation of exotic hadrons, measurements of timelike generalized parton
distributions, the production of charm at threshold, transversity measurements
in Drell-Yan reactions, and searches for single-spin asymmetries. The
interactions of antiprotons in nuclear targets will allow tests of exotic
nuclear phenomena such as color transparency, hidden color, reduced nuclear
amplitudes, and the non-universality of nuclear antishadowing. The AdS/CFT
correspondence of large $N_C$ supergravity theory in higher-dimensional anti-de
Sitter space with supersymmetric QCD in 4-dimensional space-time has important
implications for hadron phenomenology in the conformal limit, including the
nonperturbative derivation of counting rules for exclusive processes and the
behavior of structure functions at large $x_{bj}.$ String/gauge duality also
predicts the QCD power-law fall-off of light-front Fock-state hadronic
wavefunctions with arbitrary orbital angular momentum at high momentum
transfer. I also review recent work which shows that the diffractive component
of deep inelastic scattering, single spin asymmetries, as well as nuclear
shadowing and antishadowing, cannot be computed from the LFWFs of hadrons in
isolation.


  We construct the gauge invariant part of the propagator for the massless
gravitino in AdS(d+1) by coupling it to a conserved current. We also derive the
propagator for the massive gravitino.


  We propose an elaborate version of the hadron resonance gas model with the
combined treatment of separate chemical freeze-outs for strange and non-strange
hadrons and with an additional \gamma_{s} factor which accounts for the
remaining strange particle non-equilibration. Two sets of chemical freeze-outs
parameters are connected by the conservation laws of entropy, baryonic charge,
isospin projection and strangeness. The developed approach enables us to
perform a high-quality fit of the hadron multiplicity ratios for AGS, SPS and
RHIC energies with total \chi^2/dof \simeq 1.05. A special attention is paid to
a complete description of the Strangeness Horn. A well-known \bar p, \bar
\Lambda and \bar \Xi selective suppression problem is also discussed.


  In this paper we propose two numerical algorithms to derive the extremal
principal eigenvalue of the bi-Laplacian operator under Navier boundary
conditions or Dirichlet boundary conditions. Consider a non-homogeneous hinged
or clamped plate $\Omega$, the algorithms converge to the density functions on
$\Omega$ which they yield the maximum or minimum basic frequency of the plate.


  Formation flight is when multiple objects fly together in a coordination.
Various automatic control methods have been used for the autonomous execution
of formation flight of aerial vehicles. In this paper, the capacity of the
model predictive control (MPC) approach in the autonomous execution of
formation flight is examined. The MPC is a controller that capable of
performing formation flight, maintaining tracking desired trajectory while
avoiding collisions between aerial vehicles, and obstacles faced. Through this
approach, aerial vehicle models with six degrees of freedom in a
three-dimensional environment are performed formation flight autonomously,
mostly in a triangle order. Not only the trajectory for the formation flight
can be tracked through the MPC architecture, also the collision avoidance
strategies of the aerial vehicles can be performed by this architecture.
Simulation studies show that MPC has sufficient capability in both cases.
Therefore, it is concluded that this method can deal with constraints, avoid
obstacles as well as collisions between aerial vehicles. However,
implementation of MPC to aerial vehicles in real time holds challenges.


  We propose a model based on quantum molecular dynamics (QMD) incorporated
with statistical decay model (SDM) to describe various nuclear reactions in an
unified way. In this first part of the work, the basic ingredients of the model
are defined and the model is applied systematically to the nucleon($N$)-induced
reactions. It has been found that our model can give a remarkable agreement in
the energy-angle double differential cross sections of $(N,xN')$ type reactions
for incident energies from 100 MeV to 3 GeV with a fixed parameter set. An
unified description of the major three reaction mechanisms of $(N,xN')$
reactions, i.e. compound, pre-equilibrium and spallation processes, is given
with our model.


  We report on a study of long-term flux variations in a sample of more than
1000 AGN observed with ROSAT and in the XMM-Newton slew survey. Over a period
of 3-19 years, NLS1 galaxies as a class are found to be only slightly more
variable than broad line Seyfert galaxies, despite the strong short term
variability seen in some bright nearby NLS1s. Contrary to expectations, it is
Seyfert II galaxies that exhibit the greatest flux volatility. One particular
Sy II, which has brightened by a factor > 300 over 15 years, has been monitored
in detail with Swift and XMM. The spectrum is extremely soft (kT \sim 60eV)
consistent with pure thermal emission from an accretion disk and reminiscent of
ROSAT observations of the NLS1,WPVS 007. We show that this is likely to be a
"true" Sy II, without a BLR, and speculate that in its new high luminosity
state we may be able to witness a BLR in formation.


  In this paper, we extend Gibbs's approach of quasi-equilibrium thermodynamic
processes, and calculate the microscopic expression of entropy for general
non-equilibrium thermodynamic processes. Also, we analyze the formal structure
of thermodynamic relation in non-equilibrium thermodynamic processes.


  Lyubarov et al. (Research Articles, 22 July 2022, p. 425) claim that the
spontaneous emission rate of an atom vanishes at the momentum gap edges of
photonic Floquet media. We show that their theoretical prediction is based on
assumptions that result in misleading interpretations on the spontaneous
emission rate in photonic Floquet media.


  Construction spaces are constantly evolving, dynamic environments in need of
continuous surveying, inspection, and assessment. Traditional manual inspection
of such spaces proves to be an arduous and time-consuming activity. Automation
using robotic agents can be an effective solution. Robots, with perception
capabilities can autonomously classify and survey indoor construction spaces.
In this paper, we present a novel identification-on-the-fly approach for coarse
classification of indoor spaces using the unique signature of clutter. Using
the context granted by clutter, we recognize common indoor spaces such as
corridors, staircases, shared spaces, and restrooms. The proposed clutter
slices pipeline achieves a maximum accuracy of 93.6% on the presented clutter
slices dataset. This sensor independent approach can be generalized to various
domains to equip intelligent autonomous agents in better perceiving their
environment.


  Music is a string of some of the notes out of 12 notes (Sa, Komal_re, Re,
Komal_ga, Ga, Ma, Kari_ma, Pa, Komal_dha, Dha, Komal_ni, Ni) and their
harmonics. Each note corresponds to a particular frequency. When such strings
are encoded to form discrete sequences, different frequencies present in the
music corresponds to different amplitude levels (value) of the discrete
sequence. Initially, a class of discrete sequences has been generated using
logistic map. All these discrete sequences have at most n-different amplitude
levels (value) (depending on the particular raga). Without loss of generality,
we have chosen two discrete sequences of two types of Indian raga viz. Bhairabi
and Bhupali having same number of amplitude levels to obtain/search close
relatives from the class. The relative / closeness can be assured through
correlation coefficient.The search is unbiased, random and non-adaptive. The
obtained string is that which maximally resembles the given two sequences. The
same can be thought of as a music composition of the given two strings. It is
to be noted that all these string are fractal string which can be persuaded by
fractal dimension.


  The viscosity over entropy density ratio, or KSS number, can help isolate the
critical point in the hadron phase-diagram in Relativistic Heavy Ion
Collisions. We argue that this quantity does have a minimum at a phase
transition or crossover. Although indications from conventional
non-relativistic gases point out to even a divergence in eta/s when the
phase-transition is first-order, since the critical exponent is rather low,
this will be more difficult to ascertain in RHIC or FAIR. The experimental data
are more likely to reveal a discontinuity for a first order phase transition or
a smooth minimum at a crossover.


  A number of black hole X-ray transients show quasi-periodic oscillations
(QPOs) in the optical (ultraviolet) and X-ray bands at the same frequency,
which challenge models for production of radiation at these wavelengths. We
propose a model where the optical radiation is modulated by the oscillating
X-ray flux resulting in varying irradiation of the outer parts of the accretion
disc. The proposed QPO mechanism inevitably takes place in the systems with
sufficiently small ratio of the outer disc radius to the QPO period. We show
that, unlike in the case of the aperiodic variability, it is not possible to
obtain the optical QPO profiles from those observed in the X-rays through the
transfer function, because of different X-ray signals seen by the disc and by
the observer. We demonstrate that with the increasing QPO frequency, occurring
at the rising phase of the X-ray outburst, the rms should be constant for
sufficiently low frequencies, then to increase reaching the peak and finally to
drop substantially when the QPO period becomes comparable to the light-crossing
time to the outer disc. We predict that the QPO rms in this model should
increase towards shorter wavelengths and this fact can be used to distinguish
it from other QPO mechanisms.


  We consider pulse propagation in a linear anomalously dispersive medium where
the group velocity exceeds the speed of light in vacuum (c) or even becomes
negative. A signal velocity is defined operationally based on the optical
signal-to-noise ratio, and is computed for cases appropriate to the recent
experiment where such a negative group velocity was observed. It is found that
quantum fluctuations limit the signal velocity to values less than c.


  We present recent progress in theory of local conformal nets which is an
operator algebraic approach to study chiral conformal field theory. We
emphasize representation theoretic aspects and relations to theory of vertex
operator algebras which gives a different and algebraic formulation of chiral
conformal field theory.


  Tipping points are abrupt, drastic, and often irreversible changes in the
evolution of non-stationary and chaotic dynamical systems. For instance,
increased greenhouse gas concentrations are predicted to lead to drastic
decreases in low cloud cover, referred to as a climatological tipping point. In
this paper, we learn the evolution of such non-stationary dynamical systems
using a novel recurrent neural operator (RNO), which learns mappings between
function spaces. After training RNO on only the pre-tipping dynamics, we employ
it to detect future tipping points using an uncertainty-based approach. In
particular, we propose a conformal prediction framework to forecast tipping
points by monitoring deviations from physics constraints (such as conserved
quantities and partial differential equations), enabling forecasting of these
abrupt changes along with a rigorous measure of uncertainty. We illustrate our
proposed methodology on non-stationary ordinary and partial differential
equations, such as the Lorenz-63 and Kuramoto-Sivashinsky equations. We also
apply our methods to forecast a climate tipping point in stratocumulus cloud
cover. In our experiments, we demonstrate that even partial or approximate
physics constraints can be used to accurately forecast future tipping points.


  We present the results of numerical experiments designed to evaluate the
usefulness of near-infrared luminosity functions for constraining the Initial
Mass Function (IMF) of young stellar populations. From this numerical modeling,
we find that the luminosity function of a young stellar population is
considerably more sensitive to variations in the underlying initial mass
function than to either variations in the star forming history or assumed
pre-main-sequence (PMS) mass-to-luminosity relation. To illustrate the
potential effectiveness of using the KLF of a young cluster to constrain its
IMF, we model the observed K band luminosity function of the nearby Trapezium
cluster. Our derived mass function for the Trapezium spans two orders of
magnitude in stellar mass (5 Msun to 0.02 Msun), has a peak near the hydrogen
burning limit, and has an IMF for Brown Dwarfs which steadily decreases with
decreasing mass.


  We propose a variational formulation for the nonequilibrium thermodynamics of
discrete open systems, i.e., discrete systems which can exchange mass and heat
with the exterior. Our approach is based on a general variational formulation
for systems with time-dependent nonlinear nonholonomic constraints and
time-dependent Lagrangian. For discrete open systems, the~time-dependent
nonlinear constraint is associated with the rate of internal entropy production
of the system. We show that this constraint on the solution curve
systematically yields a constraint on the variations to be used in the action
functional. The proposed variational formulation is intrinsic and provides the
same structure for a wide class of discrete open systems. We illustrate our
theory by presenting examples of open systems experiencing mechanical
interactions, as well as internal diffusion, internal heat transfer, and their
cross-effects. Our approach yields a systematic way to derive the complete
evolution equations for the open systems, including the expression of the
internal entropy production of the system, independently on its complexity. It
might be especially useful for the study of the nonequilibrium thermodynamics
of biophysical systems.


  Studying mechanical resonators via radiation pressure offers a rich avenue
for the exploration of quantum mechanical behavior in a macroscopic regime.
However, quantum state preparation and especially quantum state reconstruction
of mechanical oscillators remains a significant challenge. Here we propose a
scheme to realize quantum state tomography, squeezing and state purification of
a mechanical resonator using short optical pulses. The scheme presented allows
observation of mechanical quantum features despite preparation from a thermal
state and is shown to be experimentally feasible using optical microcavities.
Our framework thus provides a promising means to explore the quantum nature of
massive mechanical oscillators and can be applied to other systems such as
trapped ions.