  Studies of topological bands and their associated low-dimensional boundary
modes have largely focused on linear systems. This work reports robust
dynamical features of three-dimensional (3D) nonlinear systems in connection
with intriguing topological bands in 3D. Specifically, for a 3D setting of
coupled rock-paper-scissors cycles governed by the antisymmetric Lotka-Volterra
equation (ALVE) that is inherently nonlinear, we unveil distinct
characteristics and robustness of surface polarized masses and analyze them in
connection with the dynamics and topological bands of the linearized
Lotka-Volterra (LV) equation. Our analysis indicated that insights learned from
Weyl semimetal phases with type-I and type-II Weyl singularities based on a
linearized version of the ALVE are still remarkably useful, even though the
system dynamics is far beyond the linear regime. This work indicates the
relevance and importance of the concept of topological boundary modes in
analyzing high-dimensional nonlinear systems and hopes to stimulate another
wave of topological studies in nonlinear systems.


  The complex oxide heterostructures such as LaAlO3/SrTiO3 (LAO/STO) interface
are paradigmatic platforms to explore emerging multi-degrees of freedom
coupling and the associated exotic phenomena. In this study, we reveal the
effects of multiorbital and magnetic ordering on Rashba spin-orbit coupling
(SOC) at the LAO/STO (001) interface. Based on first-principles calculations,
we show that the Rashba spin splitting near the conduction band edge can be
tuned substantially by the interfacial insulator-metal transition due to the
multiorbital effect of the lowest t_2g bands. We further unravel a competition
between Rashba SOC and intrinsic magnetism, in which the Rashba SOC induced
spin polarization is suppressed by the interfacial magnetic ordering. These
results deepen our understanding of intricate electronic and magnetic
reconstruction at the perovskite oxide interfaces and shed light on the
engineering of oxide heterostructures for all-oxides-based spintronic devices.


  This paper is devoted to study the energy problem in general relativity using
approximate Lie symmetry methods for differential equations. We evaluate
second-order approximate symmetries of the geodesic equations for the stringy
charged black hole solutions. It is concluded that energy must be re-scaled by
some factor in the second-order approximation.


  A 1D Dirac tight-binding model is considered and it is shown that its
nonrelativistic limit is the 1D discrete Schr?odinger model. For random
Bernoulli potentials taking two values (without correlations), for typical
realizations and for all values of the mass, it is shown that its spectrum is
pure point, whereas the zero mass case presents dynamical delocalization for
specific values of the energy. The massive case presents dynamical localization
(excluding some particular values of the energy). Finally, for general
potentials the dynamical moments for distinct masses are compared, especially
the massless and massive Bernoulli cases.


  Statistical properties of ensembles of random density matrices are
investigated. We compute traces and von Neumann entropies averaged over
ensembles of random density matrices distributed according to the Bures
measure. The eigenvalues of the random density matrices are analyzed: we derive
the eigenvalue distribution for the Bures ensemble which is shown to be broader
then the quarter--circle distribution characteristic of the Hilbert--Schmidt
ensemble. For measures induced by partial tracing over the environment we
compute exactly the two-point eigenvalue correlation function.


  We numerically simulate a non-Abelian lattice gauge theory in two spatial
dimensions, with tensor networks (TN), up to intermediate sizes (>30 matter
sites) well beyond exact diagonalization. We focus on the SU(2) Yang-Mills
model in Hamiltonian formulation, with dynamical matter and minimally truncated
gauge field (hardcore gluon). Thanks to the TN sign-problem-free approach, we
characterize the phase diagram of the model at zero and finite baryon number as
a function of the quark bare mass and color charge. At intermediate system
sizes, we detect a liquid phase of quark-pair bound-state quasiparticles
(baryons), whose mass is finite towards the continuum limit. Interesting
phenomena arise at the transition boundary where color-electric and
color-magnetic terms are maximally frustrated: For low quark masses, we see
traces of potential deconfinement, while for high masses, signatures of a
possible topological order.


  Many-body correlations are known to be responsible for a broad range of
fascinating physical phenomena, introducing corrections that appear elusive at
the mean-field level. An example of this is the Lifshitz transition that occurs
as the Fermi surface topology changes when {\it e.g.} Coulomb interaction
effects break into the picture. In particular, the Fermi velocity
renormalization can lead a type-II Weyl semimetal at mean-field level to become
a trivial or a type-I Dirac material when correlations are accounted for, which
is far from being obvious. In this work we scrutinize the band structure of
NiTe$_2$, a material that features a type-II Dirac point near the Fermi level
within the mean-field approach. Including GW-level correlations, our findings
showcase anisotropic corrections on the Dirac carrier velocity exceeding $100
\, \%$ enhancements, underscoring the nuanced influence of electronic
interactions in the band structure. We also consider type-II Dirac crossings in
PtSe$_2$ and PtTe$_2$ and observe that including many-body effects via GW the
band topology changes, featuring trivial topology and type-I Dirac crossings,
respectively. Our findings highlights the necessity to evaluate the many-body
effects on non-trivial bands, contributing essential insights to the broader
exploration of many-body correlation effects in type-II Dirac points of
condensed-matter systems.


  We investigate broken rotational symmetry (BRS) states for the fractional
quantum Hall effect (FQHE) at 1/3-filling of the valence Landau level (LL).
Recent Monte Carlo calculations by Musaelian and Joynt [J. Phys.: Condens.\
Matter {\bf 8}, L105 (1996)] suggest that Laughlin's state becomes unstable to
a BRS state for some critical finite thickness value. We study in detail the
properties of such state by performing a hypernetted-chain calculation that
gives results in the thermodynamic limit, complementing other methods which are
limited to a finite number of particles. Our results indicate that while
Laughlin's state is stable in the lowest LL, in higher LLs a BRS instability
occurs, perhaps indicating the absence of FQHE at partial fillings of higher
LLs. Possible connections to the newly discovered liquid crystalline phases in
higher LLs are also discussed.


  While classical or quantum interacting liquids become turbulent under
sufficiently strong driving, it is not obvious what flow pattern an ideal
quantum gas develops under similar conditions. Unlike classical noninteracting
particles which exhibit rather trivial flow, ideal fermions have to satisfy the
exclusion principle, which acts as a form of collective repulsion. We thus
study the flow of an ideal Fermi gas as it is driven out of a narrow orifice of
width comparable to the Fermi wavelength, employing both a microcanonical
approach to transport, and solving a Lindblad equation for Markovian driving
leads. Both methods are in good agreement and predict an outflowing current
density with a complex microscopic pattern of vorticity in the steady state.
Applying a bias of the order of the chemical potential results in a short-range
correlated antiferromagnetic vorticity pattern, corresponding to local moments
of the order of a tenth of a magneton, $e\hbar/2m$, if the fermions are
charged. The latter may be detectable by magnetosensitive spectroscopy in
strongly driven cold gases (atoms) or clean electronic nanostructures
(electrons).


  Contact interactions can be used to describe a system of particles at
unitarity, contribute to the leading part of nuclear interactions and are
numerically non-trivial because they require a proper regularization and
renormalization scheme. We explain how to tune the coefficient of a contact
interaction between non-relativistic particles on a discretized space in 1, 2,
and 3 spatial dimensions such that we can remove all discretization artifacts.
By taking advantage of a latticized L\"uscher zeta function, we can achieve a
momentum-independent scattering amplitude at any finite lattice spacing.


  The aim of this paper is to demonstrate the feasibility of authenticated
throughput-efficient routing in an unreliable and dynamically changing
synchronous network in which the majority of malicious insiders try to destroy
and alter messages or disrupt communication in any way. More specifically, in
this paper we seek to answer the following question: Given a network in which
the majority of nodes are controlled by a malicious adversary and whose
topology is changing every round, is it possible to develop a protocol with
polynomially-bounded memory per processor that guarantees throughput-efficient
and correct end-to-end communication? We answer the question affirmatively for
extremely general corruption patterns: we only request that the topology of the
network and the corruption pattern of the adversary leaves at least one path
each round connecting the sender and receiver through honest nodes (though this
path may change at every round). Out construction works in the public-key
setting and enjoys bounded memory per processor (that does not depend on the
amount of traffic and is polynomial in the network size.) Our protocol achieves
optimal transfer rate with negligible decoding error. We stress that our
protocol assumes no knowledge of which nodes are corrupted nor which path is
reliable at any round, and is also fully distributed with nodes making
decisions locally, so that they need not know the topology of the network at
any time.


  In this paper a notion of functional "distance" in the Mellin transform
setting is introduced and a general representation formula is obtained for it.
Also, a determination of the distance is given in terms of Lipschitz classes
and Mellin-Sobolev spaces. Finally applications to approximate versions of
certain basic relations valid for Mellin band-limited functions are studied in
details.


  The Podium mechanism guarantees ($\epsilon, 0$)-differential privacy by
sampling noise from a \emph{finite} mixture of three uniform distributions. By
carefully constructing such a mixture distribution, we trivially guarantee
privacy properties, while minimizing the variance of the noise added to our
continuous outcome. Our gains in variance control are due to the "truncated"
nature of the Podium mechanism where support for the noise distribution is
maintained as close as possible to the sensitivity of our data collection,
unlike the \emph{infinite} support that characterizes both the Laplace and
Staircase mechanisms. In a high-privacy regime ($\epsilon < 1$), the Podium
mechanism outperforms the other two by 50-70\% in terms of the noise variance
reduction, while in a low privacy regime ($\epsilon \to \infty$), it
asymptotically approaches the Staircase mechanism.


  The complete contribution to the muonium hyperfine splitting of relative
order alpha^3(m_e/m_mu)ln(alpha) is calculated. The result amounts to 0.013
kHZ, much smaller than suggested by a previous estimate, and leads to a 2-sigma
shift of the most precise value for the muon-electron mass ratio, with the
error reduced by approximately 30%. Analogous contributions are calculated for
the positronium hyperfine splitting:
(217/90-17*ln{2}/3)m_e(alpha^7/pi)ln(1/alpha) \approx -0.32 MHz; the remaining
theoretical uncertainty should be well below experimental error, leaving
discrepancies of 2.5-sigma and 3.5-sigma with the two most precise
measurements.


  Human decision-making often relies on visual information from multiple
perspectives or views. In contrast, machine learning-based object recognition
utilizes information from a single image of the object. However, the
information conveyed by a single image may not be sufficient for accurate
decision-making, particularly in complex recognition problems. The utilization
of multi-view 3D representations for object recognition has thus far
demonstrated the most promising results for achieving state-of-the-art
performance. This review paper comprehensively covers recent progress in
multi-view 3D object recognition methods for 3D classification and retrieval
tasks. Specifically, we focus on deep learning-based and transformer-based
techniques, as they are widely utilized and have achieved state-of-the-art
performance. We provide detailed information about existing deep learning-based
and transformer-based multi-view 3D object recognition models, including the
most commonly used 3D datasets, camera configurations and number of views, view
selection strategies, pre-trained CNN architectures, fusion strategies, and
recognition performance on 3D classification and 3D retrieval tasks.
Additionally, we examine various computer vision applications that use
multi-view classification. Finally, we highlight key findings and future
directions for developing multi-view 3D object recognition methods to provide
readers with a comprehensive understanding of the field.


  We apply the super duality formalism recently developed by the authors to
obtain new equivalences of various module categories of general linear Lie
superalgebras. We establish the correspondence of standard, tilting, and simple
modules, as well as the identification of the u-homology groups, under these
category equivalences. As an application, we obtain a complete solution of the
irreducible character problem for some new parabolic BGG categories of
gl(m|n)-modules, including the full BGG category of gl(m|2)-modules, in terms
of type A Kazhdan-Lusztig polynomials.


  Although a catalogue of synthetic RGB magnitudes, providing photometric data
for a sample of 1346 bright stars, has been recently published, its usefulness
is still limited due to the small number of reference stars available,
considering that they are distributed throughout the whole celestial sphere,
and the fact that they are restricted to Johnson V < 6.6 mag. This work
presents synthetic RGB magnitudes for ~15 million stars brighter than Gaia G =
18 mag, making use of a calibration between the RGB magnitudes of the reference
bright star sample and the corresponding high quality photometric G, G_BP and
G_RP magnitudes provided by the Gaia EDR3. The calibration has been restricted
to stars exhibiting -0.5 < G_BP - G_RP < 2.0 mag, and aims to predict RGB
magnitudes within an error interval of $\pm 0.1$ mag. Since the reference
bright star sample is dominated by nearby stars with slightly undersolar
metallicity, systematic variations in the predictions are expected, as modelled
with the help of stellar atmosphere models. These deviations are constrained to
the $\pm 0.1$ mag interval when applying the calibration only to stars scarcely
affected by interstellar extinction and with metallicity compatible with the
median value for the bright star sample. The large number of Gaia sources
available in each region of the sky should guarantee high-quality RGB
photometric calibrations.


  In the search for life in the cosmos, NASA's Transiting Exoplanet Survey
Satellite (TESS) mission has already monitored about 74% of the sky for
transiting extrasolar planets, including potentially habitable worlds. However,
TESS only observed a fraction of the stars long enough to be able to find
planets like Earth. We use the primary mission data - the first two years of
observations - and identify 4,239 stars within 210pc that TESS observed long
enough to see 3 transits of an exoplanet that receives similar irradiation to
Earth: 738 of these stars are located within 30pc. We provide reliable stellar
parameters from the TESS Input Catalog that incorporates Gaia DR2 and also
calculate the transit depth and radial velocity semi-amplitude for an
Earth-analog planet. Of the 4,239 stars in the Revised TESS HZ Catalog, 9 are
known exoplanet hosts - GJ 1061, GJ 1132, GJ 3512, GJ 685, Kepler-42, LHS 1815,
L98-59, RR Cae, TOI 700 - around which TESS could identify additional
Earth-like planetary companions. 37 additional stars host yet unconfirmed TESS
Objects of Interest: three of these orbit in the habitable zone - TOI 203, TOI
715, and TOI 2298. For a subset of 614 of the 4,239 stars, TESS has observed
the star long enough to be able to observe planets throughout the full
temperate, habitable zone out to the equivalent of Mars' orbit. Thus, the
Revised TESS Habitable Zone Catalog provides a tool for observers to prioritize
stars for follow-up observation to discover life in the cosmos. These stars are
the best path towards the discovery of habitable planets using the TESS mission
data.


  We report initial results of far-infrared observations of the Lockman hole
with Far-Infrared Surveyor (FIS) onboard the AKARI infrared satellite. On the
basis of slow scan observations of a 0.6 deg x 1.2 deg contiguous area, we
obtained source number counts at 65, 90 and 140 um down to 77, 26 and 194 mJy
(3 sigma), respectively. The counts at 65 and 140 um show good agreement with
the Spitzer results. However, our 90 um counts are clearly lower than the
predicted counts by recent evolutionary models that fit the Spitzer counts in
all the MIPS bands. Our 90 um counts above 26 mJy account for about 7% of the
cosmic background. These results provide strong constraints on the evolutionary
scenario and suggest that the current models may require modifications.


  We examine the photonic spin Hall effect (SHE) in a graphene-substrate system
with the presence of external magnetic field. In the quantum Hall regime, we
demonstrate that the in-plane and transverse spin-dependent splittings in
photonic SHE exhibit different quantized behaviors. The quantized SHE can be
described as a consequence of a quantized geometric phase (Berry phase), which
corresponds to the quantized spin-orbit interaction. Furthermore, an
experimental scheme based on quantum weak value amplification is proposed to
detect the quantized SHE in terahertz frequency regime. By incorporating the
quantum weak measurement techniques, the quantized photonic SHE holds great
promise for detecting quantized Hall conductivity and Berry phase. These
results may bridge the gap between the electronic SHE and photonic SHE in
graphene.


  A new family of high order methods for systems of conservation laws are
introduced: the Compact Approximate Taylor (CAT) methods. These methods are
based on centered (2p + 1)-point stencils where p is an arbitrary integer. We
prove that the order of accuracy is 2p and that CAT methods are an extension of
high-order Lax-Wendroff methods for linear problems. Due to this, they are
linearly L2-stable under a CFL<1 condition. In order to prevent the spurious
oscillations that appear close to discontinuities two shock-capturing
techniques have been considered: a fux-limiter technique (FL-CAT methods) and
WENO reconstruction for the frst time derivative (WENO-CAT methods). We follow
the WENO-Lax Wendroff Approximate Taylor method of Zorio, Baeza and Mullet
(2017) in the second approach. A number of test cases are considered to compare
these methods with other WENO-based schemes: the linear transport equation,
Burgers equation, and the 1D compressible Euler system are considered. Although
CAT methods present an extra computational cost due to the local character,
this extra cost is compensated by the fact that they still give good solutions
with CFL values close to 1.


  We present a method of directly optimizing on deviations in clinical goal
values in radiation therapy treatment planning. Using a new mathematical
framework in which metrics derived from the dose-volume histogram are regarded
as functionals of an auxiliary random variable, we are able to obtain
volume-at-dose and dose-at-volume as infinitely differentiable functions of the
dose distribution with easily evaluable function values and gradients.
Motivated by the connection to risk measures in finance, which is formalized in
this framework, we also derive closed-form formulas for mean-tail-dose and
demonstrate its capability of reducing extreme dose values in tail
distributions. Numerical experiments performed on a prostate and a
head-and-neck patient case show that the direct optimization of dose-volume
histogram metrics produced marginally better results than or outperformed
conventional planning objectives in terms of clinical goal fulfilment, control
of low- and high-dose tails of target distributions and general plan quality
defined by a pre-specified evaluation measure. The proposed framework
eliminates the disconnect between optimization functions and evaluation metrics
and may thus reduce the need for repetitive user interaction associated with
conventional treatment planning. The method also has the potential of enhancing
plan optimization in other settings such as multicriteria optimization and
automated treatment planning.


  In this study we reproduced this compression-induced reduction in muscle
force through the use of a three-dimensional finite element model of
contracting muscle. The model used the principle of minimum total energy and
allowed for the redistribution of energy through different strain
energy-densities; this allowed us to determine the importance of the strain
energy-densities to the transverse forces developed by the muscle. Furthermore,
we were able to study how external work done on the muscle by transverse
compression affects the internal work and strain-energy distribution of the
muscle.


  Calibration of optically trapped particles in-vivo has been complicated given
the frequency dependence and spatial inhomogeneity of the cytoplasmic
viscosity, and the requirement of accurate knowledge of the medium refractive
index. Further, it has been demonstrated that the medium viscosity is dependent
upon the measurement probe leading to reliability issues for measurements with
even micrometer sized particles. Here, we employ a recent extension of
Jeffery's model of viscoelasticity in the microscopic domain to fit the passive
motional power spectra of micrometer-sized optically trapped particles embedded
in a viscoelastic medium. We find excellent agreement between the 0 Hz
viscosity in MCF7 cells and the typical values of viscosity in literature,
between 2 to 16 mPa sec expected for the typical concentration of proteins
inside the cytoplasmic solvent. This bypasses the dependence on probe size by
relying upon small thermal displacements. Our measurements of the relaxation
time also match values reported with magnetic tweezers, at about 0.1 sec.
Finally, we calibrate the optical tweezers and demonstrate the efficacy of the
technique to the study of in-vivo translational motion.


  This paper is a first draft of the introduction to the special issue on
volunteered geographic information published in Computers, Environment and
Urban Systems (2015, 53, 1-122). In this short paper, I put georeferenced big
data (hereafter, big data) such as tweets locations in comparison with small
data such as census data in terms of data characteristics, and further argued
that big data differs fundamentally from small data in terms of data analytics,
both geometrially and statistically. I would like to thank my colleague Dr.
Jean-Claude Thill, who expanded the draft towards a broader scope.


  This paper proposes a two-stream flow-guided convolutional attention networks
for action recognition in videos. The central idea is that optical flows, when
properly compensated for the camera motion, can be used to guide attention to
the human foreground. We thus develop cross-link layers from the temporal
network (trained on flows) to the spatial network (trained on RGB frames).
These cross-link layers guide the spatial-stream to pay more attention to the
human foreground areas and be less affected by background clutter. We obtain
promising performances with our approach on the UCF101, HMDB51 and Hollywood2
datasets.


  In this paper, we investigate the learnability of the function approximator
that approximates Nash equilibrium (NE) for games generated from a
distribution. First, we offer a generalization bound using the Probably
Approximately Correct (PAC) learning model. The bound describes the gap between
the expected loss and empirical loss of the NE approximator. Afterward, we
prove the agnostic PAC learnability of the Nash approximator. In addition to
theoretical analysis, we demonstrate an application of NE approximator in
experiments. The trained NE approximator can be used to warm-start and
accelerate classical NE solvers. Together, our results show the practicability
of approximating NE through function approximation.


  Liquid crystals allow for the real-time control of the polarization of light.
We describe and provide some experimental examples of the types of general
polarization transformations, including universal polarization transformations,
that can be accomplished with liquid crystals in tandem with fixed waveplates.
Implementing these transformations with an array of liquid crystals, e.g., a
spatial light modulator, allows for the manipulation of the polarization across
a beam's transverse plane. We outline applications of such general spatial
polarization transformations in the generation of exotic types of vector
polarized beams, a polarization magnifier, and the correction of polarization
aberrations in light fields.


  We address the problem of achieving a random laser with a cloud of cold
atoms, in which gain and scattering are provided by the same atoms. In this
system, the elastic scattering cross-section is related to the complex atomic
polarizability. As a consequence, the random laser threshold is expressed as a
function of this polarizability, which can be fully determined by spectroscopic
measurements. We apply this idea to experimentally evaluate the threshold of a
random laser based on Raman gain between non-degenerate Zeeman states and find
a critical optical thickness on the order of 200, which is within reach of
state-of-the-art cold-atom experiments.


  Let $S\subset\mathbb{R}^3$ be a $C^4$-smooth relatively compact orientable
surface with a sufficiently regular boundary. For $\beta\in\mathbb{R}_+$, let
$E_j(\beta)$ denote the $j$th negative eigenvalue of the operator associated
with the quadratic form \[ H^1(\mathbb{R}^3)\ni u\mapsto \iiint_{\mathbb{R}^3}
|\nabla u|^2dx -\beta \iint_S |u|^2d\sigma, \] where $\sigma$ is the
two-dimensional Hausdorff measure on $S$. We show that for each fixed $j$ one
has the asymptotic expansion \[ E_j(\beta)=-\dfrac{\beta^2}{4}+\mu^D_j+ o(1)
\;\text{ as }\; \beta\to+\infty\,, \] where $\mu_j^D$ is the $j$th eigenvalue
of the operator $-\Delta_S+K-M^2$ on $L^2(S)$, in which $K$ and $M$ are the
Gauss and mean curvatures, respectively, and $-\Delta_S$ is the
Laplace-Beltrami operator with the Dirichlet condition at the boundary of $S$.
If, in addition, the boundary of $S$ is $C^2$-smooth, then the remainder
estimate can be improved to ${\mathcal O}(\beta^{-1}\log\beta)$.


  The paper proposes a method for measuring available bandwidth, based on
testing network packets of various sizes (Variable Packet Size method, VPS).
The boundaries of applicability of the model have been found, which are based
on the accuracy of measurements of packet delays, also we have derived a
formula of measuring the upper limit of bandwidth. The computer simulation has
been performed and relationship between the measurement error of available
bandwidth and the number of measurements has been found. Experimental
verification with the use of RIPE Test Box measuring system has shown that the
suggested method has advantages over existing measurement techniques. Pathload
utility has been chosen as an alternative technique of measurement, and to
ensure reliable results statistics by SNMP agent has been withdrawn directly
from the router.


  We re-examine the correlation between the frequencies of upper and lower kHz
quasi-periodic oscillations (QPO) in bright neutron-star low-mass X-ray
binaries. By including the kHz QPO frequencies of the X-ray binary Cir X-1 and
two accreting millisecond pulsars in our sample, we show that the full sample
does not support the class of theoretical models based on a single resonance,
while models based on relativistic precession or Alfven waves describe the data
better. Moreover, we show that the fact that all sources follow roughly the
same correlation over a finite frequency range creates a correlation between
the linear parameters of the fits to any sub-sample.


  We propose a linear seesaw model to realise light Dirac neutrinos within the
framework of $A_4$ discrete flavour symmetry. The additional fields and their
transformations under the flavour symmetries are chosen in such a way that
naturally predicts the hierarchies of different elements of the seesaw mass
matrix and also keeps the unwanted terms away. For generic choices of flavon
alignments, the model predicts normal hierarchical light neutrino masses with
the atmospheric mixing angle in the lower octant. Apart from predicting
interesting correlations between different neutrino parameters as well as
between neutrino and model parameters, the model also predicts the leptonic
Dirac CP phase to lie in a specific range $-\pi/2\lesssim \delta \lesssim
-\pi/5$ and $\pi/5\lesssim \delta \lesssim \pi/2$ that includes the currently
preferred maximal value. The predictions for the absolute neutrino masses in
one specific version of the model can also saturate the cosmological upper
bound on sum of absolute neutrino masses.


  The lottery ticket hypothesis questions the role of overparameterization in
supervised deep learning. But how is the performance of winning lottery tickets
affected by the distributional shift inherent to reinforcement learning
problems? In this work, we address this question by comparing sparse agents who
have to address the non-stationarity of the exploration-exploitation problem
with supervised agents trained to imitate an expert. We show that feed-forward
networks trained with behavioural cloning compared to reinforcement learning
can be pruned to higher levels of sparsity without performance degradation.
This suggests that in order to solve the RL-specific distributional shift
agents require more degrees of freedom. Using a set of carefully designed
baseline conditions, we find that the majority of the lottery ticket effect in
both learning paradigms can be attributed to the identified mask rather than
the weight initialization. The input layer mask selectively prunes entire input
dimensions that turn out to be irrelevant for the task at hand. At a moderate
level of sparsity the mask identified by iterative magnitude pruning yields
minimal task-relevant representations, i.e., an interpretable inductive bias.
Finally, we propose a simple initialization rescaling which promotes the robust
identification of sparse task representations in low-dimensional control tasks.


  A real number $x$ is absolutely normal if, for every base $b\ge 2$, every two
equally long strings of digits appear with equal asymptotic frequency in the
base-$b$ expansion of $x$. This paper presents an explicit algorithm that
generates the binary expansion of an absolutely normal number $x$, with the
$n$th bit of $x$ appearing after $n$polylog$(n)$ computation steps. This speed
is achieved by simultaneously computing and diagonalizing against a martingale
that incorporates Lempel-Ziv parsing algorithms in all bases.


  We provide a development that unifies, simplifies and extends considerably a
number of minimax results in the restricted parameter space literature. Various
applications follow, such as that of estimating location or scale parameters
under a lower (or upper) bound restriction, location parameter vectors
restricted to a polyhedral cone, scale parameters subject to restricted ratios
or products, linear combinations of restricted location parameters, location
parameters bounded to an interval with unknown scale, quantiles for
location-scale families with parametric restrictions and restricted covariance
matrices.


  Motivated by a proposed olfactory mechanism based on a
vibrationally-activated molecular switch, we study electron transport within a
donor-acceptor pair that is coupled to a vibrational mode and embedded in a
surrounding environment. We derive a polaron master equation with which we
study the dynamics of both the electronic and vibrational degrees of freedom
beyond previously employed semiclassical (Marcus-Jortner) rate analyses. We
show: (i) that in the absence of explicit dissipation of the vibrational mode,
the semiclassical approach is generally unable to capture the dynamics
predicted by our master equation due to both its assumption of one-way
(exponential) electron transfer from donor to acceptor and its neglect of the
spectral details of the environment; (ii) that by additionally allowing strong
dissipation to act on the odorant vibrational mode we can recover exponential
electron transfer, though typically at a rate that differs from that given by
the Marcus-Jortner expression; (iii) that the ability of the molecular switch
to discriminate between the presence and absence of the odorant, and its
sensitivity to the odorant vibrational frequency, are enhanced significantly in
this strong dissipation regime, when compared to the case without mode
dissipation; and (iv) that details of the environment absent from previous
Marcus-Jortner analyses can also dramatically alter the sensitivity of the
molecular switch, in particular allowing its frequency resolution to be
improved. Our results thus demonstrate the constructive role dissipation can
play in facilitating sensitive and selective operation in molecular switch
devices, as well as the inadequacy of semiclassical rate equations in analysing
such behaviour over a wide range of parameters.


  Strict privacy is of paramount importance in distributed machine learning.
Federated learning, with the main idea of communicating only what is needed for
learning, has been recently introduced as a general approach for distributed
learning to enhance learning and improve security. However, federated learning
by itself does not guarantee any privacy for data subjects. To quantify and
control how much privacy is compromised in the worst-case, we can use
differential privacy.
  In this paper we combine additively homomorphic secure summation protocols
with differential privacy in the so-called cross-silo federated learning
setting. The goal is to learn complex models like neural networks while
guaranteeing strict privacy for the individual data subjects. We demonstrate
that our proposed solutions give prediction accuracy that is comparable to the
non-distributed setting, and are fast enough to enable learning models with
millions of parameters in a reasonable time.
  To enable learning under strict privacy guarantees that need privacy
amplification by subsampling, we present a general algorithm for oblivious
distributed subsampling. However, we also argue that when malicious parties are
present, a simple approach using distributed Poisson subsampling gives better
privacy.
  Finally, we show that by leveraging random projections we can further
scale-up our approach to larger models while suffering only a modest
performance loss.


  We give examples of cohomological automorphic forms for unitary groups which
are $p$-adically rigid.


  It is argued that the breakdown of factorization observed recently in the
diffractive dijet production in deep inelastic lepton induced and hadron
induced processes is naturally explained in the Good-Walker picture of
diffraction dissociation. The explicit formula for the hadronic cross-section
is given and successfully compared with the existing data.


  The truncated two-point function of the nearest-neighbor ferromagnetic Ising
model on $\mathbb Z^d$ ($d\ge3$) in its pure phases is proven to decays
exponentially fast throughout the ordered regime ($T<T_c$). Together with known
results, this implies that the exponential clustering property holds throughout
the model's phase diagram except for the critical point: $(T,h) = (T_c,0)$.


  In this paper we analyse a problem of a hydraulic fracture driven by a
non-Newtonian shear-thinning fluid. For the PKN fracture geometry we consider
three different rheological models of fluid: i) the Carreau fluid, ii) the
truncated power-law fluid, iii) the power-law fluid. For each of these models a
number of simulations are performed. The results are post-processed and
compared with each other in order to find decisive factors for
similarities/dissimilarities. It is shown that under certain conditions even
the basic power-law rheology can be a good substitute for the Carreau
characteristics. Although for a particular fluid such a conclusion cannot be
made a priori, post-processing based on average values of the fluid shear rates
is a very good tool to verify credibility of the results obtained for
simplified rheological models. The truncated power-law rheology is a good
alternative for the Carreau model. It always produces results that are very
similar to those obtained with the equivalent Carreau fluid and simultaneously
provides a relative ease of numerical implementation.


  Compressed sensing has reinvigorated the field of non-Cartesian sampling in
magnetic resonance imaging (MRI). Until now there has been no 3D radial
view-order which meets all the desired characteristics for simultaneous
dynamic/high-resolution imaging, such as for self-navigated motion-corrected
high resolution neuroimaging. In this work, we examine the use of Hierarchical
Equal Area iso-Latitude Pixelization (HEALPix) for generation of
three-dimensional (3D) radial view-orders for MRI, and compare to a selection
of commonly used 3D view-orders. The resulting trajectories were evaluated
through simulation of the point spread function and slanted surface object
suitable for modulation transfer function, contrast ratio, and SNR measurement.
Results from the HEALPix view-order were compared to Generalized Spiral, 2D
Golden Means, and Random view-orders. Finally, we show the first use of the
HEALPix view-order to acquire in-vivo brain images.


  The interaction of the partonic fluctuation of the virtual photon in deep
inelastic scattering with soft color fields describing the hadron is treated in
an eikonal approximation. It is known that, in this approach, the small-x limit
of the leading-order gluon distribution xg(x,Q^2) is a constant characterizing
the averaged local field strength in the target. Matching the next-to-leading
order calculation in this semiclassical framework with the one-loop parton
model result, we obtain the next-to-leading order contribution to xg(x,Q^2). It
shows a ln(1/x) enhancement at small x and is sensitive to the large distance
structure of the target. The final expression is a simple integral over
non-Abelian eikonal factors measuring the target color field. We derive a
quantitative relation between the short-distance cutoff of this integral and
the scale of the gluon distribution function in the MS-bar scheme. Our
calculation demonstrates that higher order contributions can be systematically
included in the semiclassical approach.


  The spin degree of freedom in quantum phases of the second Landau level is
probed by resonant light scattering. The long wavelength spin wave, which
monitors the degree of spin polarization, is at the Zeeman energy in the fully
spin-polarized state at $\nu$=3. At lower filling factors the intensity of the
Zeeman mode collapses indicating loss of polarization. A novel continuum of
low-lying excitations emerges that dominates near $\nu$=8/3 and $\nu$=5/2.
Resonant Rayleigh scattering reveals that quantum fluids for $\nu<3$ break up
into robust domain structures. While the state at $\nu$=5/2 is considered to be
fully polarized, these results reveal unprecedented roles for spin degrees of
freedom.


  The inter-connection between the elasticity of a dielectric film and the
wetting of a sessile drop on the same, under an applied electrical voltage,
remains unaddressed. Here, we report the electrowetting-on-dielectric (EWOD)
behaviour of sessile drops on dielectric elastomer films of varying
elasticities- from an apparently rigid dielectric film to a soft, deformable
dielectric film. Our results reveal that the elasticity of the underlying
dielectric film provides an additional control over the droplet electrowetting
behaviour, which may be best addressed from free energy based consideration,
leading to a modification of the classical Lippman-Young paradigm. We also
provide an explanation on the displacement profiles for the deformation of the
soft dielectric surface, due to the interfacial electro-elastocapillary
interaction triggered by the electrowetted sessile droplet. These results can
be of profound importance in various emerging applications, ranging from the
development of soft liquid lenses to drug delivery.


  Using effective field theory (EFT) techniques we calculate the
next-to-leading order (NLO) spin-orbit contributions to the gravitational
potential of inspiralling compact binaries. We use the covariant spin
supplementarity condition (SSC), and explicitly prove the equivalence with
previous results by Faye et al. in arXiv:gr-qc/0605139. We also show that the
direct application of the Newton-Wigner SSC at the level of the action leads to
the correct dynamics using a canonical (Dirac) algebra. This paper then
completes the calculation of the necessary spin dynamics within the EFT
formalism that will be used in a separate paper to compute the spin
contributions to the energy flux and phase evolution to NLO.


  Suppose that the vertex set of a connected graph $G$ is
$V(G)=\{v_1,\cdots,v_n\}$. Then we denote by $Tr_{G}(v_i)$ the sum of distances
between $v_i$ and all other vertices of $G$. Let $Tr(G)$ be the $n\times n$
diagonal matrix with its $(i,i)$-entry equal to $Tr_{G}(v_{i})$ and $D(G)$ be
the distance matrix of $G$. Then $Q_{D}(G)=Tr(G)+D(G)$ and
$L_{D}(G)=Tr(G)-D(G)$ are respectively the distance signless Laplacian matrix
and distance Laplacian matrix of $G$. The largest eigenvalues $\rho_{Q}(G)$ and
$\rho_{L}(G)$ of $Q_D(G)$ and $L_D(G)$ are respectively called distance
signless Laplacian spectral radius and distance Laplacian spectral radius of
$G$. In this paper we give some graft transformations and use them to
characterize the tree $T$ such that $\rho_{Q}(T)$ and $\rho_{L}(T)$ attain the
maximum among all trees of order $n$ with given number of pendant vertices.


  Chudnovsky and Seymour's structure theorem for claw-free graphs has led to a
multitude of recent results that exploit two structural operations: {\em
compositions of strips} and {\em thickenings}. In this paper we consider the
latter, proving that every claw-free graph has a unique optimal {\em
antithickening}, where our definition of {\em optimal} is chosen carefully to
respect the structural foundation of the graph. Furthermore, we give an
algorithm to find the optimal antithickening in $O(m^2)$ time. For the sake of
both completeness and ease of proof, we prove stronger results in the more
general setting of trigraphs.


  TiSe2 is a member of transition metal dichalcogenide family of layered
van-der-Waals materials that exhibits some distinctive electronic and optical
properties. Here, we perform Raman spectroscopy studies on single crystal TiSe2
to investigate photon-induced defects associated with formation of selenium
vacancies. Two additional Eg phonon peaks are observed in the laser-irradiated
regions, consistent with Raman spectra of selenium deficient TiSe2. Temperature
dependent studies of the threshold laser intensity necessary to form selenium
vacancies show that there is a linear dependence. We extract the relevant
activation energy for selenium vacancy nucleation. The impact of these results
on the properties of strongly correlated electron states in TiSe2 are
discussed.


  We provide a general framework to study convergence properties of families of
maps. For manifolds $M$ and $N$ where $M$ is equipped with a volume form
$\mathcal{V}$ we consider families of maps in the collection $\{(\phi, B) : B
\subset M, \phi:B \rightarrow N\text{ with both measurable}\}$ and we define a
distance function $\mathcal{D}$ similar to the $L^1$ distance on such a
collection. The definition of $\mathcal{D}$ depends on several parameters, but
we show that the properties and topology of the metric space do not depend on
these choices. In particular we show that the metric space is always complete.
After exploring the properties of $\mathcal{D}$ we shift our focus to exploring
the convergence properties of families of such maps.


  Observations of HDO are an important complement for studies of water, because
they give strong constraints on the formation processes -- grain surfaces
versus energetic process in the gas phase, e.g. in shocks. The HIFI
observations of multiple transitions of HDO in Sgr~B2(M) presented here allow
the determination of the HDO abundance throughout the envelope, which has not
been possible before with ground-based observations only. The abundance
structure has been modeled with the spherical Monte Carlo radiative transfer
code RATRAN, which also takes radiative pumping by continuum emission from dust
into account. The modeling reveals that the abundance of HDO rises steeply with
temperature from a low abundance ($2.5\times 10^{-11}$) in the outer envelope
at temperatures below 100~K through a medium abundance ($1.5\times 10^{-9}$) in
the inner envelope/outer core, at temperatures between 100 and 200~K, and
finally a high abundance ($3.5\times 10^{-9}$) at temperatures above 200~K in
the hot core.


  In the paper "Finite-rank Bratteli-Vershik diagrams are expansive" [DM],
Downarowicz and Maass proved that the Cantor minimal system associated to a
properly ordered Bratteli diagram of finite rank is either an odometer system
or an expansive system. We give a new proof of this truly remarkable result
which we think is more transparent and easier to understand. We also address
the question (QUESTION 1) raised in [DM] and we find a better (i.e. lower)
bound than the one given in [DM]. In fact, we conjecture that the bound we have
found is optimal.


  We examine how intense optical beams can penetrate deeply into highly
absorbing media by a non-linear, photo-bleaching process. The role of
stimulated recovery to the dye ground state can be important and is delineated.
This analysis of non-linear absorption processes is applicable in general to
situations where chromophores are irradiated, for instance in biology. We
examine the implications for the bending of cantilevers made of heavily
dye-loaded nematic photo-solids, that is nematic glasses and elastomers that
have large mechanical reactions to light. In particular we describe the bending
of cantilevers sufficiently absorbing that they would not bend if Beer's Law
were applicable. We quantify the role of optically-generated heat in
determining the mechanical response and conclude that in general it is minor in
importance compared with optical effects.


  The speed of writing of state-of-the-art ferromagnetic memories is physically
limited by an intrinsic GHz threshold. Recently, an alternative research
direction has been initiated by realizing memory devices based on
antiferromagnets in which spin directions periodically alternate from one
atomic lattice site to the next. In our work we experimentally demonstrate at
room temperature that the speed of reversible electrical writing in a memory
device can be scaled up to THz using an antiferromagnet. Efficient
current-induced spin-torque mechanism is responsible for the switching in our
memory devices throughout the twelve orders of magnitude range of writing
speeds from Hz to THz. Our work opens the path towards the development of
memory-logic technology reaching the elusive THz band.


  We study the non-abelian Hopf cohomology theory of Radford products with
coefficients in a comodule algebra. We show that these sets can be expressed in
terms of the non-abelian Hopf cohomology theory of each factor of the Radford
product. We write down an exact sequence relating these objects. This allows to
compute explicitly the non-abelian Hopf cohomology sets in large classes of
examples.


  This paper explores the intrinsic connections between the Bayesian false
discovery rate (FDR) control procedures and their counterpart of frequentist
procedures. We attempt to offer a unified view of FDR control within and beyond
the setting of testing exchangeable hypotheses. Under the standard two-groups
model and the Oracle condition, we show that the Bayesian and the frequentist
methods can achieve asymptotically equivalent FDR control at arbitrary levels.
Built on this result, we further illustrate that rigorous post-fitting model
diagnosis is necessary and effective to ensure robust FDR controls for
parametric Bayesian approaches. Additionally, we show that the Bayesian FDR
control approaches are coherent and naturally extended to the setting beyond
testing exchangeable hypotheses. Particularly, we illustrate that $p$-values
are no longer the natural statistical instruments for optimal frequentist FDR
control in testing non-exchangeable hypotheses. Finally, we illustrate that
simple numerical recipes motivated by our theoretical results can be effective
in examining some key model assumptions commonly assumed in both Bayesian and
frequentist procedures (e.g., zero assumption).


  An analytical method to compute thermodynamic properties of a given
Hamiltonian system is proposed. This method combines ideas of both dynamical
systems and ensemble approaches to thermodynamics, providing de facto a
possible alternative to traditional Ensemble methods. Thermodynamic properties
are extracted from effective motion equations. These equations are obtained by
introducing a general variational principle applied to an action averaged over
a statistical ensemble of paths defined on the constant energy surface. The
method is applied first to the one dimensional (\beta)-FPU chain and to the two
dimensional lattice (\phi ^{4}) model. In both cases the method gives a good
insight of some of their statistical and dynamical properties.


  We address the problem of Mobility Robustness Optimization (MRO) and describe
centralized Self Organizing Network (SON) solutions that can optimize
connected-mode mobility Key Performance Indicators (KPIs). Our solution extends
the earlier work of eICIC parameter optimization [7], to heterogeneous networks
with mobility, and outline methods of progressive complexity that optimize the
Retaining/Offloading Bias which are macro/pico views of Cell Individual Offset
parameters. Simulation results under real LTE network deployment assumptions of
a US metropolitan area demonstrate the effects of such solutions on the
mobility KPIs. To our knowledge, this solution is the first that demonstrates
the joint optimization of eICIC and MRO.


  We searched for X-ray bursts in XMM-Newton archival data of X-ray sources in
M 31 globular clusters (GCs) and GC candidates. We detected two bursts
simultaneously in EPIC pn and MOS detectors and some more candidates in EPIC
pn. The energy distribution of the burst photons and the intrinsic luminosity
during the peak of the bursts indicate that at least the strongest burst was a
type I radius expansion burst. The bursts identify the sources as neutron star
low mass X-ray binaries in M 31. The type I X-ray bursts in M 31 are the first
detected outside the Milky Way and show that with the help of XMM-Newton X-ray
bursts can be used to classify neutron star low mass X-ray binaries in Local
Group galaxies.


  Concentrated suspensions of Brownian hard-spheres in water are an epitome for
understanding the glassy dynamics of both soft materials and supercooled
molecular liquids. From an experimental point of view, such systems are
especially suited to perform particle tracking easily, and, therefore, are a
benchmark for novel optical techniques, applicable when primary particles
cannot be resolved. Differential Variance Analysis (DVA) is one such novel
technique that simplifies significantly the characterization of structural
relaxation processes of soft glassy materials, since it is directly applicable
to digital image sequences of the sample. DVA succeeds in monitoring not only
the average dynamics, but also its spatio-temporal fluctuations, known as
dynamic heterogeneities. In this work, we study the dynamics of dense
suspensions of Brownian beads in water, imaged through digital
video-microscopy, by using both DVA and single-particle tracking. We focus on
two commonly used signatures of dynamic heterogeneities: the dynamic
susceptibility, $\chi_4$, and the non-Gaussian parameter, $\alpha_2$. By direct
comparison of these two quantities, we are able to highlight similarities and
differences. We do confirm that $\chi_4$ and $\alpha_2$ provide qualitatively
similar information, but we find quantitative discrepancies in the scalings of
characteristic time and length scale on approaching the glass transition.


  We classify the space-like biharmonic surfaces in 3-dimension
pseudo-Riemannian space form, and construct explicit examples of proper
biharmonic hypersurfaces in general ADS space.


  Large cascades are a common occurrence in many natural and engineered complex
systems. In this paper we explore the propagation of cascades across networks
using realistic network topologies, such as heterogeneous degree distributions,
as well as intra- and interlayer degree correlations. We find that three
properties, scale-free degree distribution, internal network assortativity, and
cross-network hub-to-hub connections, are all necessary components to
significantly reduce the size of large cascades in the Bak-Tang-Wiesenfeld
sandpile model. We demonstrate that correlations present in the structure of
the multilayer network influence the dynamical cascading process and can
prevent failures from spreading across connected layers. These findings
highlight the importance of internal and cross-network topology in optimizing
stability and robustness of interconnected systems.


  Recent theoretical work has explored dark matter accumulation in the Earth
and its drift towards the center of the Earth that, for the current age of the
Earth, does not necessarily result in a concentration of dark matter ($\chi$)
in the Earth's core. We consider a scenario of long-lived ($\tau_\chi\sim
10^{28}$ s), super heavy ($m_\chi=10^7-10^{10}$ GeV) dark matter that decays
via $\chi\to \nu_\tau \bar{\nu}_\tau$ or $\chi\to \nu_\mu \bar{\nu}_\mu$. We
show that an IceCube-like detector over 10 years can constrain a dark matter
density that mirrors the Earth's density or has a uniform density with density
fraction $\epsilon_\rho$ combined with the partial decay width $B_{\chi\to
\nu_\tau \bar{\nu}_\tau}\Gamma_\chi$ in the range of $(\epsilon_\rho/10^{-10})
B_{\chi\to \nu_\tau}\Gamma_\chi \lesssim 1.5\times 10^{-29}-1.5\times 10^{-28}$
s$^{-1}$. For $\chi\to \nu_\mu \bar{\nu}_\mu$, $m_\chi = 10^8-10^{10}$ GeV and
$E_\mu>10^7$ GeV, the range of constraints is $(\epsilon_\rho/10^{-10})
B_{\chi\to \nu_\mu}\Gamma_\chi \lesssim 3\times 10^{-29}-7\times 10^{-28}$
s$^{-1}$.


  We report a lattice QCD calculation of the strange quark contribution to the
nucleon's magnetic moment and charge radius. This analysis presents the first
direct determination of strange electromagnetic form factors including at the
physical pion mass. We perform a model-independent extraction of the strange
magnetic moment and the strange charge radius from the electromagnetic form
factors in the momentum transfer range of $0.051 \,\text{GeV}^2 \lesssim Q^2
\lesssim 1.31 \,\text{GeV}^2 $. The finite lattice spacing and finite volume
corrections are included in a global fit with $24$ valence quark masses on four
lattices with different lattice spacings, different volumes, and four sea quark
masses including one at the physical pion mass. We obtain the strange magnetic
moment $G^s_M(0) = - 0.064(14)(09)\, \mu_N$. The four-sigma precision in
statistics is achieved partly due to low-mode averaging of the quark loop and
low-mode substitution to improve the statistics of the nucleon propagator. We
also obtain the strange charge radius $\langle r_s^2\rangle_E = -0.0043
(16)(14)\,$ $\text{fm}^2$.


  The status of the sociophysics is discussed as placed between the physics and
the sociology. In particular we ask if the second law of thermodynamics can be
useful in social sciences.


  Video Watermarking serves as a new technology mainly used to provide security
to the illegal distribution of digital video over the web. The purpose of any
video watermarking scheme is to embed extra information into video in such a
way that must be perceptually undetectable while still holding enough
information in order to extract the watermark beginning with the resultant
video. Information which is embedded within the original image is a Digital
Watermark, which could be visible or invisible. To improved more security,
embedding and extraction Watermark process should be complex against attackers.
Recent research indicates SVD (Singular Value Decomposition) algorithms are
employed owing to their simple scheme with mathematical function. In this
proposed work an advanced SVD transformation algorithm is used for embedding
and extraction process. Experimental results show proposed watermarking process
is more secured than existing SVD approach.


  We discuss the evaluation of power corrections to hard scattering and decay
processes for which an operator product expansion is applicable. The Wilson
coefficient of the leading-twist operator is the difference of two perturbative
series, each of which has a renormalon ambiguity of the same order as the power
corrections themselves, but which cancel in the difference. We stress the
necessity of calculating this coefficient function to sufficiently high orders
in perturbation theory so as to make the uncertainty of the same order or
smaller than the relevant power corrections. We investigate in some simple
examples whether this can be achieved. Our conclusion is that in most of the
theoretical calculations which include power corrections, the uncertainties are
at least comparable to the power corrections themselves, and that it will be a
very difficult task to improve the situation.


  This paper describes a scheme, through which the quantum information as well
as the structural information of a time-reversal invariant system can be
teleported over a distance. I show that my teleportation scheme can be viewed
as a form of reversible purification process by repeated interactions with an
auxiliary quantum system.


  This paper introduces a resource allocation framework specifically tailored
for addressing the problem of dynamic placement (or pinning) of parallelized
applications to processing units. Under the proposed setup each thread of the
parallelized application constitutes an independent decision maker (or agent),
which (based on its own prior performance measurements and its own prior
CPU-affinities) decides on which processing unit to run next. Decisions are
updated recursively for each thread by a resource manager/scheduler which runs
in parallel to the application's threads and periodically records their
performances and assigns to them new CPU affinities. For updating the
CPU-affinities, the scheduler uses a distributed reinforcement-learning
algorithm, each branch of which is responsible for assigning a new placement
strategy to each thread. According to this algorithm, prior allocations are
going to be reinforced in the future proportionally to their prior performance.
The proposed resource allocation framework is flexible enough to address
alternative optimization criteria, such as maximum average processing speed and
minimum speed variance among threads. We demonstrate analytically that
convergence to locally-optimal placements is achieved asymptotically. Finally,
we validate these results through experiments in Linux platforms.


  In this paper we have recalled the semiclassical metric obtained from a
classical analysis of the loop quantum black hole (LQBH). We show that the
regular Reissner-Nordstrom-like metric is self-dual in the sense of T-duality:
the form of the metric obtained in Loop quantum Gravity (LQG) is invariant
under the exchange "r <-> a0/r" where "a0" is proportional to the minimum area
in LQG and "r" is the standard Schwarzschild radial coordinate at asymptotic
infinity. Of particular interest, the symmetry imposes that if an observer at
"r" close to infinity sees a black hole of mass "m" an observer in the other
asymptotic infinity beyond the horizon (at "r" close to "0") sees a dual mass
"mp/m" ("mp" is the Planck mass). We then show that small LQBH are stable and
could be a component of dark matter. Ultra-light LQBHs created shortly after
the Big Bang would now have a mass of approximately "10^(-5) mp" and emit
radiation with a typical energy of about 10^(13) - 10^(14) eV but they would
also emit cosmic rays of much higher energies, albeit few of them. If these
small LQBHs form a majority of the dark matter of the Milky Way's Halo, the
production rate of ultra-high-energy-cosmic-rays (UHECR) by these ultra light
black holes would be compatible with the observed rate of the Auger detector.


  Multiple initiatives propose utilizing Bluetooth Low Energy (BLE)
advertisements for contact tracing and SARS-CoV-2 exposure notifications. This
demo shows a research tool to analyze BLE advertisements; if universally
enabled by the vendors, the uncovered features could improve exposure
notifications for everyone. We reverse-engineer the firmware-internal
implementation of BLE advertisements on Broadcom and Cypress chips and show how
to extract further physical-layer information at the receiver. The analyzed
firmware works on hundreds of millions of devices, such as all iPhones, the
European Samsung Galaxy S series, and Raspberry Pis.


  Identity document (ID) image analysis has become essential for many online
services, like bank account opening or insurance subscription. In recent years,
much research has been conducted on subjects like document localization, text
recognition and fraud detection, to achieve a level of accuracy reliable enough
to automatize identity verification. However, there are only a few available
datasets to benchmark ID analysis methods, mainly because of privacy
restrictions, security requirements and legal reasons.
  In this paper, we present the DocXPand-25k dataset, which consists of 24,994
richly labeled IDs images, generated using custom-made vectorial templates
representing nine fictitious ID designs, including four identity cards, two
residence permits and three passports designs. These synthetic IDs feature
artificially generated personal information (names, dates, identifiers, faces,
barcodes, ...), and present a rich diversity in the visual layouts and textual
contents.
  We collected about 5.8k diverse backgrounds coming from real-world photos,
scans and screenshots of IDs to guarantee the variety of the backgrounds. The
software we wrote to generate these images has been published
(https://github.com/QuickSign/docxpand/) under the terms of the MIT license,
and our dataset has been published
(https://github.com/QuickSign/docxpand/releases/tag/v1.0.0) under the terms of
the CC-BY-NC-SA 4.0 License.


  The results of an amplitude analysis of the charmless three-body decay $B^+
\rightarrow \pi^+\pi^+\pi^-$, in which $C\!P$-violation effects are taken into
account, are reported. The analysis is based on a data sample corresponding to
an integrated luminosity of $3 \text{fb}^{-1}$ of $pp$ collisions recorded with
the LHCb detector. The most challenging aspect of the analysis is the
description of the behaviour of the $\pi^+ \pi^-$ S-wave contribution, which is
achieved by using three complementary approaches based on the isobar model, the
K-matrix formalism, and a quasi-model-independent procedure. Additional
resonant contributions for all three methods are described using a common
isobar model, and include the $\rho(770)^0$, $\omega(782)$ and $\rho(1450)^0$
resonances in the $\pi^+\pi^-$ P-wave, the $f_2(1270)$ resonance in the
$\pi^+\pi^-$ D-wave, and the $\rho_3(1690)^0$ resonance in the $\pi^+\pi^-$
F-wave. Significant $C\!P$-violation effects are observed in both S- and
D-waves, as well as in the interference between the S- and P-waves. The results
from all three approaches agree and provide new insight into the dynamics and
the origin of $C\!P$-violation effects in $B^+ \rightarrow \pi^+\pi^+\pi^-$
decays.


  Compositional reasoning tasks like multi-hop question answering, require
making latent decisions to get the final answer, given a question. However,
crowdsourced datasets often capture only a slice of the underlying task
distribution, which can induce unanticipated biases in models performing
compositional reasoning. Furthermore, discriminatively trained models exploit
such biases to get a better held-out performance, without learning the right
way to reason, as they do not necessitate paying attention to the question
representation (conditioning variable) in its entirety, to estimate the answer
likelihood. In this work, we propose a generative context selection model for
multi-hop question answering that reasons about how the given question could
have been generated given a context pair. While being comparable to the
state-of-the-art answering performance, our proposed generative passage
selection model has a better performance (4.9% higher than baseline) on
adversarial held-out set which tests robustness of model's multi-hop reasoning
capabilities.


  We study the effectiveness of several techniques to personalize end-to-end
speech models and improve the recognition of proper names relevant to the user.
These techniques differ in the amounts of user effort required to provide
supervision, and are evaluated on how they impact speech recognition
performance. We propose using keyword-dependent precision and recall metrics to
measure vocabulary acquisition performance. We evaluate the algorithms on a
dataset that we designed to contain names of persons that are difficult to
recognize. Therefore, the baseline recall rate for proper names in this dataset
is very low: 2.4%. A data synthesis approach we developed brings it to 48.6%,
with no need for speech input from the user. With speech input, if the user
corrects only the names, the name recall rate improves to 64.4%. If the user
corrects all the recognition errors, we achieve the best recall of 73.5%. To
eliminate the need to upload user data and store personalized models on a
server, we focus on performing the entire personalization workflow on a mobile
device.


  We present general, computable, improvable, and rigorous bounds for the total
energy of a finite heterogeneous volume element or a periodically distributed
unit cell of an elastic composite of any known distribution of inhomogeneities
of any geometry and elasticity, undergoing a harmonic motion at a fixed
frequency or supporting a single-frequency Bloch-form elastic wave of a given
wave-vector. These bounds are rigorously valid for \emph{any consistent
boundary conditions} that produce in the finite sample or in the unit cell,
either a common average strain or a common average momentum. No other
restrictions are imposed. We do not assume statistical homogeneity or isotropy.
Our approach is based on the Hashin-Shtrikman (1962) bounds in elastostatics,
which have been shown to provide strict bounds for the overall elastic moduli
commonly defined (or actually measured) using uniform boundary tractions and/or
linear boundary displacements; i.e., boundary data corresponding to the overall
uniform stress and/or uniform strain conditions. Here we present strict bounds
for the dynamic frequency-dependent constitutive parameters of the composite
and give explicit expressions for a direct calculation of these bounds.


  In the present paper, by using the colored version of the Koszul duality, the
concept of a differential module with $\infty$-simplicial faces is introduced.
The homotopy invariance of the structure of a differential module with
$\infty$-simplicial faces is proved. The relationships between differential
modules with $\infty$-simplicial faces and $A_\infty$-algebras are established.
The notion of a chain realization of a differential module with
$\infty$-simplicial faces and the concept of a tensor product of differential
modules with $\infty$-simplicial faces are introduced. It is proved that for an
arbitrary $A_\infty$-algebra the chain realization of the tensor differential
module with $\infty$-simplicial faces, which corresponds to this
$A_\infty$-algebra, and the $B$-construction of this $A_\infty$-algebra are
isomorphic differential coalgebras.


  Lame curves are a particular class of elliptic curves (with a torsion point
attached to them) which naturally arise when studying Lame operators with
finite monodromy. They can be realized as covers of the projective line
unramified outside three points and can be defined over number fields. This
paper investigates their p-adic properties. The main ingredient is formal/rigid
geometry and in particular the use of p-adic theta functions. As a consequence,
we can completely enumerate Lam\'e curves with bad reduction (by giving, among
the others, the p-adic valuation of their j-invariant) and describe the (local)
Galois action.


  Self-supervised methods have showed promising results on depth estimation
task. However, previous methods estimate the target depth map and camera
ego-motion simultaneously, underusing multi-frame correlation information and
ignoring the motion of dynamic objects. In this paper, we propose a novel
Dyna-Depthformer framework, which predicts scene depth and 3D motion field
jointly and aggregates multi-frame information with transformer. Our
contributions are two-fold. First, we leverage multi-view correlation through a
series of self- and cross-attention layers in order to obtain enhanced depth
feature representation. Specifically, we use the perspective transformation to
acquire the initial reference point, and use deformable attention to reduce the
computational cost. Second, we propose a warping-based Motion Network to
estimate the motion field of dynamic objects without using semantic prior. To
improve the motion field predictions, we propose an iterative optimization
strategy, together with a sparsity-regularized loss. The entire pipeline
achieves end-to-end self-supervised training by constructing a minimum
reprojection loss. Extensive experiments on the KITTI and Cityscapes benchmarks
demonstrate the effectiveness of our method and show that our method
outperforms state-of-the-art algorithms.


  The crosscap number of a knot in the 3-sphere is defined as the minimal first
Betti number of non-orientable subsurfaces bounded by the knot. In this paper,
we determine the crosscap numbers of pretzel knots. The key ingredient to
obtain the result is the algorithm of enumerating all essential surfaces for
Montesinos knots developed by Hatcher and Oertel.


  A method of computing a basis for the second Yang-Baxter cohomology of a
finite biquandle with coefficients in Q and Z_p from a matrix presentation of
the finite biquandle is described. We also describe a method for computing the
Yang-Baxter cocycle invariants of an oriented knot or link represented as a
signed Gauss code. We provide a URL for our Maple implementations of these
algorithms.


  We construct certain Rajchman measures by using integrability properties of
the Fourier and Fourier-Stieltjes transforms. In particular, we state a problem
and prove that it is equivalent to the known and still unsolved question posed
by R. Salem (Trans. Amer. Math. Soc. 53 (3) (1943), p. 439) whether
Fourier-Stieltjes coefficients of the Minkowski's question mark function vanish
at infinity.


  Correlations between the elliptic or triangular flow coefficients $v_m$
($m$=2 or 3) and other flow harmonics $v_n$ ($n$=2 to 5) are measured using
$\sqrt{s_{NN}}=2.76$ TeV Pb+Pb collision data collected in 2010 by the ATLAS
experiment at the LHC, corresponding to an integrated lumonisity of 7
$\mu$b$^{-1}$. The $v_m$-$v_n$ correlations are measured in midrapidity as a
function of centrality, and, for events within the same centrality interval, as
a function of event ellipticity or triangularity defined in a forward rapidity
region. For events within the same centrality interval, $v_3$ is found to be
anticorrelated with $v_2$ and this anticorrelation is consistent with similar
anticorrelations between the corresponding eccentricities $\epsilon_2$ and
$\epsilon_3$. On the other hand, it is observed that $v_4$ increases strongly
with $v_2$, and $v_5$ increases strongly with both $v_2$ and $v_3$. The trend
and strength of the $v_m$-$v_n$ correlations for $n$=4 and 5 are found to
disagree with $\epsilon_m$-$\epsilon_n$ correlations predicted by
initial-geometry models. Instead, these correlations are found to be consistent
with the combined effects of a linear contribution to $v_n$ and a nonlinear
term that is a function of $v_2^2$ or of $v_2v_3$, as predicted by hydrodynamic
models. A simple two-component fit is used to separate these two contributions.
The extracted linear and nonlinear contributions to $v_4$ and $v_5$ are found
to be consistent with previously measured event-plane correlations.


  We find sufficient conditions for a probability measure $\mu$ to satisfy an
inequality of the type $$ \int_{\R^d} f^2 F\Bigl(\frac{f^2}{\int_{\R^d} f^2 d
\mu} \Bigr) d \mu \le C \int_{\R^d} f^2 c^{*}\Bigl(\frac{|\nabla f|}{|f|}
\Bigr) d \mu + B \int_{\R^d} f^2 d \mu, $$ where $F$ is concave and $c$ (a cost
function) is convex. We show that under broad assumptions on $c$ and $F$ the
above inequality holds if for some $\delta>0$ and $\epsilon>0$ one has $$
\int_{0}^{\epsilon} \Phi\Bigl(\delta c\Bigl[\frac{t F(\frac{1}{t})}{{\mathcal
I}_{\mu}(t)} \Bigr] \Bigr) dt < \infty, $$ where ${\mathcal I}_{\mu}$ is the
isoperimetric function of $\mu$ and $\Phi = (y F(y) -y)^{*}$. In a partial case
$${\mathcal I}_{\mu}(t) \ge k t \phi ^{1-\frac{1}{\alpha}} (1/t), $$ where
$\phi$ is a concave function growing not faster than $\log$, $k>0$, $1 < \alpha
\le 2$ and $t \le 1/2$, we establish a family of tight inequalities
interpolating between the $F$-Sobolev and modified inequalities of log-Sobolev
type. A basic example is given by convex measures satisfying certain
integrability assumptions.


  Digital computing currently uses irreversible logic gates whose energy
dissipation is fundamentally limited. Reversible logic gates can provide an
energy-efficient alternative since they can operate with reversible processes
that have no dissipation, such as with scattering processes involving elastic
particles. The presented logic uses fluxons, topological solitons in Long
Josephson Junctions (LJJs), as inputs into and outputs from logic gates. An
advantage of using LJJs for connections is that they restrict scattering to 1D
paths, in contrast to previous ballistic logic which is based on 2D scattering.
Furthermore, we find through simulation that there is almost no energy loss in
the scattering of fluxons between LJJs of designed unpowered circuit gates. To
switch bit states, the fluxons are made to change polarity during operations --
fluxons in an input LJJ freely propagate into the gate circuit, excite a
nonlinear oscillatory interface mode, and quickly scatter deterministically to
an output LJJ as a fluxon or antifluxon. The numerically simulated soliton
dynamics shows that over $97\%$ of the total energy is preserved as fluxon
energy after gate operations. These phenomena are further analyzed with a
collective coordinate ansatz, reducing the dynamics of many degrees of freedom
to two coordinates which characterize fluxon and antifluxon type excitations in
the input and the output LJJs. The solutions of the reduced model accurately
describe the four possible energy-conserving scattering processes found in
simulations of one-bit gate circuits (with two scattering directions and two
output polarities). Calculated parameter tolerances indicate that the gates can
be manufactured and tested. Results are shown for 1-bit gates as well as a
fundamental 2-bit gate.


  The human gut microbiome is a complex ecosystem, in which hundreds of
microbial species and metabolites coexist, in part due to an extensive network
of cross-feeding interactions. However, both the large-scale trophic
organization of this ecosystem, and its effects on the underlying metabolic
flow, remain unexplored. Here, using a simplified model, we provide
quantitative support for a multi-level trophic organization of the human gut
microbiome, where microbes consume and secrete metabolites in multiple
iterative steps. Using a manually-curated set of metabolic interactions between
microbes, our model suggests about four trophic levels, each characterized by a
high level-to-level metabolic transfer of byproducts. It also quantitatively
predicts the typical metabolic environment of the gut (fecal metabolome) in
approximate agreement with the real data. To understand the consequences of
this trophic organization, we quantify the metabolic flow and biomass
distribution, and explore patterns of microbial and metabolic diversity in
different levels. The hierarchical trophic organization suggested by our model
can help mechanistically establish causal links between the abundances of
microbes and metabolites in the human gut.


  We classify the Morita equivalence classes of blocks with elementary abelian
defect groups of order $16$ with respect to a complete discrete valuation ring
with algebraically closed residue field of characteristic two. As a
consequence, blocks with this defect group are derived equivalent to their
Brauer correspondent in the normalizer of a defect group and so satisfy
Brou\'e's Conjecture.


  Recent work has proposed a promising approach to improving scalability of
program synthesis by allowing the user to supply a syntactic template that
constrains the space of potential programs. Unfortunately, creating templates
often requires nontrivial effort from the user, which impedes the usability of
the synthesizer. We present a solution to this problem in the context of
recursive transformations on algebraic data-types. Our approach relies on
polymorphic synthesis constructs: a small but powerful extension to the
language of syntactic templates, which makes it possible to define a program
space in a concise and highly reusable manner, while at the same time retains
the scalability benefits of conventional templates. This approach enables
end-users to reuse predefined templates from a library for a wide variety of
problems with little effort. The paper also describes a novel optimization that
further improves the performance and scalability of the system. We evaluated
the approach on a set of benchmarks that most notably includes desugaring
functions for lambda calculus, which force the synthesizer to discover Church
encodings for pairs and boolean operations.


  In this work we study the theory of linearized gravity via the
Hamilton-Jacobi formalism. We make a brief review of this theory and its
Lagrangian description, as well as a review of the Hamilton-Jacobi approach for
singular systems. Then we apply this formalism to analyze the constraint
structure of the linearized gravity in instant and front-form dynamics.


  Traditional approaches in speech emotion recognition, such as LSTM, CNN, RNN,
SVM, and MLP, have limitations such as difficulty capturing long-term
dependencies in sequential data, capturing the temporal dynamics, and
struggling to capture complex patterns and relationships in multimodal data.
This research addresses these shortcomings by proposing an ensemble model that
combines Graph Convolutional Networks (GCN) for processing textual data and the
HuBERT transformer for analyzing audio signals. We found that GCNs excel at
capturing Long-term contextual dependencies and relationships within textual
data by leveraging graph-based representations of text and thus detecting the
contextual meaning and semantic relationships between words. On the other hand,
HuBERT utilizes self-attention mechanisms to capture long-range dependencies,
enabling the modeling of temporal dynamics present in speech and capturing
subtle nuances and variations that contribute to emotion recognition. By
combining GCN and HuBERT, our ensemble model can leverage the strengths of both
approaches. This allows for the simultaneous analysis of multimodal data, and
the fusion of these modalities enables the extraction of complementary
information, enhancing the discriminative power of the emotion recognition
system. The results indicate that the combined model can overcome the
limitations of traditional methods, leading to enhanced accuracy in recognizing
emotions from speech.


  Iterative geostatistical seismic inversion integrates seismic and well data
to infer the spatial distribution of subsurface elastic properties. These
methods provide limited assessment to the spatial uncertainty of the inverted
elastic properties, overlooking alternative sources of uncertainty such as
those associated with poor well-log data, upscaling and noise within the
seismic data. We express uncertain well-log samples, due to bad logging reads
and upscaling, in terms of local probability distribution functions (PDFs).
Local PDFs are used as conditioning data to a stochastic sequential simulation
algorithm, included as the model perturbation within the inversion. The problem
of noisy seismic and narrow exploration of the model parameter space,
particularly at early steps of the inversion, is tackled by the introduction of
a cap on local correlation coefficients responsible for the generation of the
new set of models during the next iteration. A single geostatistical framework
is proposed and illustrated with its application to a real case study. When
compared against a conventional iterative geostatistical seismic inversion, the
integration of additional sources of uncertainty increases the match between
real and inverted seismic traces and the variability within the ensemble of
models inverted at the last iteration. The selection of the local PDFs plays a
central role in the reliability of the inverted elastic models. Avoiding high
local correlation coefficients at early stages of the inversion increases
convergence in terms of global correlation between synthetic and real seismic
reflection data at the end of the inversion.


  We present the discovery of a light echo from SN 2007af, a normal Type Ia
supernova (SN Ia) in NGC 5584. Hubble Space Telescope (HST) images taken three
years post explosion reveal two separate echoes; an outer echo and extended
central region, which we propose as an unresolved inner echo. Multiple images
were obtained in the F160W, F350LP, F555W, and F814W using the Wide Field
Camera 3. If the outer echo is produced by an interstellar dust sheet
perpendicular to the line of sight, it is located ~800 pc in front of the SN.
The dust for the inner echo is 0.45 pc < d < 90 pc away from the SN. The inner
echo color is consistent with typical interstellar dust wavelength-dependent
scattering cross-sections, while the outer echo color does not match the
predictions. Both dust sheets, if in the foreground, are optically thin for
scattering, with the outer echo sheet thickness consistent with the inferred
extinction from peak brightness. Whether the inner echo is from interstellar or
circumstellar dust is ambiguous. Overall, the echo characteristics are quite
similar to previously observed SN Ia echoes.


  We present a class of warped extra dimension (composite Higgs) models which
conjointly accommodates the t\bar t forward-backward asymmetry observed at the
Tevatron and the direct CP asymmetry in singly Cabibbo suppressed D decays
first reported by the LHCb collaboration. We argue that both asymmetries, if
arising dominantly from new physics beyond the Standard Model, hint for a
flavor paradigm within partial compositeness models in which the right-handed
quarks of the first two generations are not elementary fields but rather
composite objects. We show that this class of models is consistent with current
data on flavor and CP violating physics, electroweak precision observables,
dijet and top pair resonance searches at hadron colliders. These models have
several predictions which will be tested in forthcoming experiments. The CP
asymmetry in D decays is induced through an effective operator of the form
(\bar u c)_{V+A}(\bar s s)_{V+A} at the charm scale, which implies a larger CP
asymmetry in the D^0\to K^+K^- rate relative the D^0\to \pi^+\pi^- channel.
This prediction is distinctive from other Standard Model or dipole-based new
physics interpretation of the LHCb result. CP violation in D-\bar D mixing as
well as an an excess of dijet production of the LHC are also predicted to be
observed in a near future. A large top asymmetry originates from the exchange
of an axial resonance which dominantly produces left-handed top pairs. As a
result a negative contribution to the lepton-based forward-backward asymmetry
in t\bar t production, as well as O(10%) forward-backward asymmetry in b\bar b
production above m_{b\bar b}\simeq 600GeV at the Tevatron is expected.


  Directed and elliptic flow measurements for charged pions and protons are
reported as a function of transverse momentum, rapidity, and centrality for 40
and 158A GeV Pb + Pb collisions as recorded by the NA49 detector. Both the
standard method of correlating particles with an event plane, and the cumulant
method of studying multiparticle correlations are used. In the standard method
the directed flow is corrected for conservation of momentum. In the cumulant
method elliptic flow is reconstructed from genuine 4, 6, and 8-particle
correlations, showing the first unequivocal evidence for collective motion in
A+A collisions at SPS energies.


  We describe the $C_2$-equivariant homotopy type of the space of commuting
n-tuples in the stable unitary group in terms of Real K-theory. The result is
used to give a complete calculation of the homotopy groups of the space of
commuting n-tuples in the stable orthogonal group, as well as of the
coefficient ring for commutative orthogonal K-theory.


  Studies of non-interacting lattice fermions give an estimate of the size of
discretization errors and finite size effects for more interesting problems
like finite temperature QCD. We present a calculation of the thermodynamic
equation of state for free domain wall and overlap fermions.


  Data augmentation is one of the regularization strategies for the training of
deep learning models, which enhances generalizability and prevents overfitting,
leading to performance improvement. Although researchers have proposed various
data augmentation techniques, they often lack consideration for the difficulty
of augmented data. Recently, another line of research suggests incorporating
the concept of curriculum learning with data augmentation in the field of
natural language processing. In this study, we adopt curriculum data
augmentation for image data augmentation and propose colorful cutout, which
gradually increases the noise and difficulty introduced in the augmented image.
Our experimental results highlight the possibility of curriculum data
augmentation for image data. We publicly released our source code to improve
the reproducibility of our study.


  This paper addresses navigation in crowded environments by integrating
goal-conditioned generative models with Sampling-based Model Predictive Control
(SMPC). We introduce goal-conditioned autoregressive models to generate crowd
behaviors, capturing intricate interactions among individuals. The model
processes potential robot trajectory samples and predicts the reactions of
surrounding individuals, enabling proactive robotic navigation in complex
scenarios. Extensive experiments show that this algorithm enables real-time
navigation, significantly reducing collision rates and path lengths, and
outperforming selected baseline methods. The practical effectiveness of this
algorithm is validated on an actual robotic platform, demonstrating its
capability in dynamic settings.


  Since the depletion of fossil fuels, the world has started to rely heavily on
renewable sources of energy. With every passing year, our dependency on the
renewable sources of energy is increasing exponentially. As a result, complex
and hybrid generation systems are being designed and developed to meet the
energy demands and ensure energy security in a country. The continual
improvement in the technology and an effort towards the provision of
uninterrupted power to the end-users is strongly dependent on an effective and
fault resilient Operation and Maintenance (O&M) system. Ingenious algorithms
and techniques are hence been introduced aiming to minimize equipment and plant
downtime. Efforts are being made to develop robust Prognostic Maintenance
systems that can identify the faults before they occur. To this aim, complex
Data Analytics and Machine Learning (ML) techniques are being used to increase
the overall efficiency of these prognostic maintenance systems.
  This paper provides an overview of the predictive/prognostic maintenance
frameworks reported in the literature. We pay a particular focus to the
approaches, challenges including data-related issues, such as the availability
and quality of the data and data auditing, feature engineering,
interpretability, and security issues. Being a key aspect of ML-based
solutions, we also discuss some of the commonly used publicly available
datasets in the domain. The paper also identifies key future research
directions. We believe such detailed analysis will provide a baseline for
future research in the domain.


  Time-Sensitive Networking (TSN) extends IEEE 802.1 Ethernet for
safety-critical and real-time applications in several areas, e.g., automotive,
aerospace or industrial automation. However, many of these systems also have
stringent security requirements, and security attacks may impair safety. Given
a TSN-based distributed architecture, a set of applications with tasks and
messages, as well as a set of security and redundancy requirements, we are
interested to synthesize a system configuration such that the real-time, safety
and security requirements are upheld. We use the Timed Efficient Stream
Loss-Tolerant Authentication (TESLA) low-resource multicast authentication
protocol to guarantee the security requirements, and redundant disjunct message
routes to tolerate link failures. We consider that tasks are dispatched using a
static cyclic schedule table and that the messages use the time-sensitive
traffic class in TSN, which relies on schedule tables (called Gate Control
Lists, GCLs) in the network switches. A configuration consists of the schedule
tables for tasks as well as the disjoint routes and GCLs for messages. We
propose a Constraint Programming-based formulation which can be used to find an
optimal solution with respect to our cost function. Additionally, we propose a
Simulated Annealing based metaheuristic, which can find good solution for large
test cases. We evaluate both approaches on several test cases.


  The Friedmann-Lemaitre-Robertson-Walker (FLRW) metric assumes comoving
spatial rigidity of metrical properties. The curvature term in comoving
coordinates is environment-independent and cannot evolve. In the standard
model, structure formation is interpreted accordingly: structures average out
on the chosen metrical background, which remains rigid in comoving coordinates
despite nonlinear structure growth. The latter claim needs to be tested, since
it is a hypothesis that is not derived using general relativity. We introduce a
test of the comoving rigidity assumption by measuring the two-point
auto-correlation function on comoving scales---assuming FLRW comoving spatial
rigidity---in order to detect shifts in the baryon acoustic oscillation (BAO)
peak location for Large Red Galaxy (LRG) pairs of the Sloan Digital Sky Survey
Data Release 7. In tangential directions, subsets of pairs overlapping with
superclusters or voids show the BAO peak. The tangential BAO peak location for
overlap with Nadathur & Hotchkiss superclusters is $4.3\pm1.6$ Mpc/h less than
that for LRG pairs unselected for supercluster overlap, and $6.6\pm2.8$ Mpc/h
less than that of the complementary pairs. Liivamagi et al. superclusters give
corresponding differences of $3.7\pm2.9$ Mpc/h and $6.3\pm2.6$ Mpc/h,
respectively. We have found moderately significant evidence
(Kolmogorov--Smirnov tests suggest very significant evidence) that the BAO peak
location for supercluster-overlapping pairs is compressed by about 6% compared
to that of the complementary sample, providing a potential challenge to FLRW
models and a benchmark for predictions from models based on an averaging
approach that leaves the spatial metric a priori unspecified.


  The geometrical model of an electrical charge is proposed. This model has the
''nake'' charge shunted with ``fur - coat'' consisting of virtual wormholes.
The 5D wormhole solution in the Kaluza - Klein's theory is the ''nake'' charge.
The splitting off the supplementary coordinates happens on the two spheres
(null surfaces) bounding this 5D wormhole. This allows to sew two Reissner -
Nordstr\"om's black holes to it on both sides. Virtual wormholes entrap a part
of the electrical force lines outcoming from ''nake'' charge. This effect can
essentially decrease the charge visible at infinity up to real relation $m^2 <
e^2$. The analogical construction for colour SU(2) gauge charge is made.


  We study the H2 molecular content in high redshift damped Lyman-alpha systems
(DLAs) as a function of the HI column density. We find a significant increase
of the H2 molecular content around log N(HI) (cm^-2)~21.5-22, a regime unprobed
until now in intervening DLAs, beyond which the majority of systems have log
N(H2) > 17. This is in contrast with lines of sight towards nearby stars, where
such H2 column densities are always detected as soon as log N(HI)>20.7. This
can qualitatively be explained by the lower average metallicity and possibly
higher surrounding UV radiation in DLAs. However, unlike in the Milky Way, the
overall molecular fractions remain modest, showing that even at a large N(HI)
only a small fraction of overall HI is actually associated with the
self-shielded H2 gas. Damped Lyman-alpha systems with very high-N(HI) probably
arise along quasar lines of sight passing closer to the centre of the host
galaxy where the gas pressure is higher. We show that the colour changes
induced on the background quasar by continuum (dust) and line absorption (HI
Lyman and H2 Lyman & Werner bands) in DLAs with log N(HI)~22 and metallicity
~1/10 solar is significant, but not responsible for the long-discussed lack of
such systems in optically selected samples. Instead, these systems are likely
to be found towards intrinsically fainter quasars that dominate the quasar
luminosity function. Colour biasing should in turn be severe at higher
metallicities.


  Non-autonomous differential equations exhibit a highly intricate dynamics,
and various concepts have been introduced to describe their qualitative
behavior. In general, it is rare to obtain time dependent invariant compact
attracting sets when time goes to plus infinity. Moreover, there are only a few
papers in the literature that explore the geometric structure of such sets. In
this paper we investigate the long time behaviour of cooperative
$n$-dimensional non-autonomous Lotka-Volterra systems is population dynamics.
We provide sufficient conditions for the existence of a globally stable
(forward in time) entire solution in which one species becomes extinct, or
where all species except one become extinct. Furthermore, we obtain the precise
geometrical structure of the non-autonomous forward attractor in one, two, and
three dimensions by establishing heteroclinic connections between the globally
stable solution and the semi-stable solutions in cases of species permanence
and extinction. We believe that understanding time-dependent forward attractors
paves the way for a comprehensive analysis of both transient and long-term
behavior in non-autonomous phenomena.


  With the fast growing quantity of data generated by smart devices and the
exponential surge of processing demand in the Internet of Things (IoT) era, the
resource-rich cloud centres have been utilised to tackle these challenges. To
relieve the burden on cloud centres, edge-cloud computation offloading becomes
a promising solution since shortening the proximity between the data source and
the computation by offloading computation tasks from the cloud to edge devices
can improve performance and Quality of Service (QoS). Several optimisation
models of edge-cloud computation offloading have been proposed that take
computation costs and heterogeneous communication costs into account. However,
several important factors are not jointly considered, such as heterogeneities
of tasks, load balancing among nodes and the profit yielded by computation
tasks, which lead to the profit and cost-oriented computation offloading
optimisation model PECCO proposed in this paper. Considering that the model is
hard in nature and the optimisation objective is not differentiable, we propose
an improved Moth-flame optimiser PECCO-MFI which addresses some deficiencies of
the original Moth-flame Optimiser and integrate it under the edge-cloud
environment. Comprehensive experiments are conducted to verify the superior
performance of the proposed method when optimising the proposed task offloading
model under the edge-cloud environment.


  In the paper we study a special parameter containing algebraic inequality
involving sum of reciprocals and product of positive real numbers whose sum is
1. We determine the best values of the parameter using a new optimization
argument. In the case of three numbers the algebraic inequality have some
interesting geometric applications involving a generalization of Euler's
inequality about the ratio of radii of circumscribed and inscribed circles of a
triangle.