  Recently, we have presented the first large-scale radiative transfer
simulations of reionization. Here we present new simulations which extend the
source halo mass range downward to 10^8M_solar, to capture the full range of
halo masses thought to be primarily responsible for reionization by their star
formation following atomic hydrogen radiative cooling and gravitational
collapse. Haloes below about 10^9M_solar, however, are subject to Jeans-mass
filtering in the ionized regions, which suppresses their baryonic content and
their ability to release ionizing radiation. By including these smaller-mass
haloes but accounting for their suppression, too, we find that reionization is
``self-regulating,'' as follows. As the mean ionized fraction rises, so does
the fraction of the volume within which suppression occurs. Hence, the degree
of suppression is related to the mean ionized fraction. Since low-mass haloes
with high emissivity achieve a given mean ionized fraction earlier than do
those with low efficiency, Jeans-mass filtering compensates for the difference
in the emissivity of the suppressible haloes in these two cases. As a result,
in the presence of lower-mass source haloes, reionization begins earlier, but
the later stages of reionization and the time of overlap are dictated by the
efficiency of the higher-mass haloes, independent of the efficiency of the
suppressible, lower-mass haloes. Reionization histories consistent with current
observational constraints are shown to be achievable with standard stellar
sources in haloes above 10^8M_solar. Neither minihalos nor exotic sources are
required, and the phenomenon of ``double reionization'' previously suggested
does not occur. (abridged)


  Speaker anonymization aims to protect the privacy of speakers while
preserving spoken linguistic information from speech. Current mainstream neural
network speaker anonymization systems are complicated, containing an F0
extractor, speaker encoder, automatic speech recognition acoustic model (ASR
AM), speech synthesis acoustic model and speech waveform generation model.
Moreover, as an ASR AM is language-dependent, trained on English data, it is
hard to adapt it into another language. In this paper, we propose a simpler
self-supervised learning (SSL)-based method for language-independent speaker
anonymization without any explicit language-dependent model, which can be
easily used for other languages. Extensive experiments were conducted on the
VoicePrivacy Challenge 2020 datasets in English and AISHELL-3 datasets in
Mandarin to demonstrate the effectiveness of our proposed SSL-based
language-independent speaker anonymization method.


  We consider the problem of finding the $k^{th}$ highest element in a totally
ordered set of $n$ elements (select), and partitioning a totally ordered set
into the top $k$ and bottom $n-k$ elements (partition) using pairwise
comparisons. Motivated by settings like peer grading or crowdsourcing, where
multiple rounds of interaction are costly and queried comparisons may be
inconsistent with the ground truth, we evaluate algorithms based both on their
total runtime and the number of interactive rounds in three comparison models:
noiseless (where the comparisons are correct), erasure (where comparisons are
erased with probability $1-\gamma$), and noisy (where comparisons are correct
with probability $1/2+\gamma/2$ and incorrect otherwise). We provide numerous
matching upper and lower bounds in all three models. Even our results in the
noiseless model, which is quite well-studied in the TCS literature on parallel
algorithms, are novel.


  Action-at-a-distance electrodynamics - alternative approach to field theory -
can be extended to cosmological models using conformal symmetry. An advantage
of this is that the origin of arrow of time in electromagnetism can be
attributed to the cosmological structure. Different cosmological models can be
investigated, based on Wheeler-Feynman absorber theory, and only those models
can be considered viable for our universe which have net full retarded
electromagnetic interactions i.e. forward direction of time. This work
evaluates quasi-steady-state model and demonstrates that it admits full
retarded and not advanced solution. Thus QSSC satisfies this necessary
condition for a correct cosmological model, based on action-at-a-distance
formulation.


  Smoothly varying lattice strain in graphene affects the Dirac carriers
through a synthetic gauge field. When the lattice strain is time dependent, as
in connection with phononic excitations, the gauge field becomes time dependent
and the synthetic vector potential is also associated with an electric field.
We show that this synthetic electric field has observable consequences. Joule
heating associated with the currents driven by the synthetic electric field
dominates the intrinsic damping, caused by the electron-phonon interaction, of
many acoustic phonon modes of graphene and metallic carbon nanotubes when
including the effects of disorder and Coulomb interactions. Several important
consequences follow from the observation that by time-reversal symmetry, the
synthetic electric field associated with the vector potential has opposite
signs for the two valleys. First, this implies that the synthetic electric
field drives charge-neutral valley currents and is therefore unaffected by
screening. This frequently makes the effects of the synthetic vector potential
more relevant than a competing effect of the scalar deformation potential which
has a much larger bare coupling constant. Second, valley currents decay by
electron-electron scattering (valley Coulomb drag) which causes interesting
temperature dependence of the damping rates. While our theory pertains first
and foremost to metallic systems such as doped graphene and metallic carbon
nanotubes, the underlying mechanisms should also be relevant for semiconducting
carbon nanotubes when they are doped.


  We study one-step implementation of the Fredkin gate in a bi-modal cavity
under both resonant and large detuning conditions based on quantum Zeno
dynamics, which reduces the complexity of experiment operations. The influence
of cavity decay and atomic spontaneous emission is discussed by numerical
calculation. The results demonstrate that the fidelity and the success
probability are robust against cavity decay in both models and they are also
insensitive to atomic spontaneous emission in the large detuning model. In
addition, the interaction time is rather short in the resonant model compared
to the large detuning model.


  The Thue-Morse sequence is an aperiodically ordered infinite binary sequence.
It is used as a one-dimensional way to model the structure of a quasicrystal.
For example, taking autocorrelations of these sequences (roughly, measuring how
similar a Thue-Morse sequence is to translates of itself) we can gain
understanding of the diffraction patterns of quasicrystals. We generate
abstract art images from these Thue-Morse autocorrelation functions, that
capture the aperiodic structure of the Thue-Morse sequence in a compelling way.


  Dust extinction is the most robust tracer of the gas distribution in the
interstellar medium, but measuring extinction is limited by the systematic
uncertainties involved in estimating the intrinsic colors to background stars.
In this paper we present a new technique, PNICER, that estimates intrinsic
colors and extinction for individual stars using unsupervised machine learning
algorithms. This new method aims to be free from any priors with respect to the
column density and intrinsic color distribution. It is applicable to any
combination of parameters and works in arbitrary numbers of dimensions.
Furthermore, it is not restricted to color space. Extinction towards single
sources is determined by fitting Gaussian Mixture Models along the extinction
vector to (extinction-free) control field observations. In this way it becomes
possible to describe the extinction for observed sources with probability
densities. PNICER effectively eliminates known biases found in similar methods
and outperforms them in cases of deep observational data where the number of
background galaxies is significant, or when a large number of parameters is
used to break degeneracies in the intrinsic color distributions. This new
method remains computationally competitive, making it possible to correctly
de-redden millions of sources within a matter of seconds. With the
ever-increasing number of large-scale high-sensitivity imaging surveys, PNICER
offers a fast and reliable way to efficiently calculate extinction for
arbitrary parameter combinations without prior information on source
characteristics. PNICER also offers access to the well-established NICER
technique in a simple unified interface and is capable of building extinction
maps including the NICEST correction for cloud substructure. PNICER is offered
to the community as an open-source software solution and is entirely written in
Python.


  This paper analyzes a two-timescale stochastic algorithm framework for
bilevel optimization. Bilevel optimization is a class of problems which exhibit
a two-level structure, and its goal is to minimize an outer objective function
with variables which are constrained to be the optimal solution to an (inner)
optimization problem. We consider the case when the inner problem is
unconstrained and strongly convex, while the outer problem is constrained and
has a smooth objective function. We propose a two-timescale stochastic
approximation (TTSA) algorithm for tackling such a bilevel problem. In the
algorithm, a stochastic gradient update with a larger step size is used for the
inner problem, while a projected stochastic gradient update with a smaller step
size is used for the outer problem. We analyze the convergence rates for the
TTSA algorithm under various settings: when the outer problem is strongly
convex (resp.~weakly convex), the TTSA algorithm finds an
$\mathcal{O}(K^{-2/3})$-optimal (resp.~$\mathcal{O}(K^{-2/5})$-stationary)
solution, where $K$ is the total iteration number. As an application, we show
that a two-timescale natural actor-critic proximal policy optimization
algorithm can be viewed as a special case of our TTSA framework. Importantly,
the natural actor-critic algorithm is shown to converge at a rate of
$\mathcal{O}(K^{-1/4})$ in terms of the gap in expected discounted reward
compared to a global optimal policy.


  We study rational points on ramified covers of abelian varieties over certain
infinite Galois extensions of $\mathbb{Q}$. In particular, we prove that every
elliptic curve $E$ over $\mathbb{Q}$ has the weak Hilbert property of
Corvaja-Zannier both over the maximal abelian extension $\mathbb{Q}^{\rm ab}$
of $\mathbb{Q}$, and over the field $\mathbb{Q}(A_{\rm tor})$ obtained by
adjoining to $\mathbb{Q}$ all torsion points of some abelian variety $A$ over
$\mathbb{Q}$.


  In this paper, we consider numerical approximation to periodic measure of a
time periodic stochastic differential equations (SDEs) under weakly dissipative
condition. For this we first study the existence of the periodic measure
$\rho_t$ and the large time behaviour of $\mathcal{U}(t+s,s,x) :=
\mathbb{E}\phi(X_{t}^{s,x})-\int\phi d\rho_t,$ where $X_t^{s,x}$ is the
solution of the SDEs and $\phi$ is a test function being smooth and of
polynomial growth at infinity. We prove $\mathcal{U}$ and all its spatial
derivatives decay to 0 with exponential rate on time $t$ in the sense of
average on initial time $s$. We also prove the existence and the geometric
ergodicity of the periodic measure of the discretized semi-flow from the
Euler-Maruyama scheme and moment estimate of any order when the time step is
sufficiently small (uniform for all orders). We thereafter obtain that the weak
error for the numerical scheme of infinite horizon is of the order $1$ in terms
of the time step. We prove that the choice of step size can be uniform for all
test functions $\phi$. Subsequently we are able to estimate the average
periodic measure with ergodic numerical schemes.


  We determine the model-independent component of the couplings of axions to
electroweak gauge bosons, induced by the minimal coupling to QCD inherent to
solving the strong CP problem. The case of the invisible QCD axion is developed
first, and the impact on $W$ and $Z$ axion couplings is discussed. The analysis
is extended next to the generic framework of heavy true axions and low axion
scales, corresponding to scenarios with enlarged confining sector. The mass
dependence of the coupling of heavy axions to photons, $W$ and $Z$ bosons is
determined. Furthermore, we perform a two-coupling-at-a-time phenomenological
study where the gluonic coupling together with individual gauge boson couplings
are considered. In this way, the regions excluded by experimental data for the
axion-$WW$, axion-$ZZ$ and axion-$Z\gamma$ couplings are determined and
analyzed together with the usual photonic ones. The phenomenological results
apply as well to ALPs which have anomalous couplings to both QCD and the
electroweak bosons.


  Credit investigation is critical for financial services. Whereas, traditional
methods are often restricted as the employed data hardly provide sufficient,
timely and reliable information. With the prevalence of smart mobile devices,
peoples' geographic footprints can be automatically and constantly collected
nowadays, which provides an unprecedented opportunity for credit
investigations. Inspired by the observation that locations are somehow related
to peoples' credit level, this research aims to enhance credit investigation
with users' geographic footprints. To this end, a two-stage credit
investigation framework is designed, namely CreditPrint. In the first stage,
CreditPrint explores regions' credit characteristics and learns a credit-aware
embedding for each region by considering both each region's individual
characteristics and cross-region relationships with graph convolutional
networks. In the second stage, a hierarchical attention-based credit assessment
network is proposed to aggregate the credit indications from a user's multiple
trajectories covering diverse regions. The results on real-life user mobility
datasets show that CreditPrint can increase the credit investigation accuracy
by up to 10% compared to baseline methods.


  Braces were introduced by Rump to study involutive non-degenerate
set-theoretic solutions of the Yang-Baxter equation. A constructive method for
producing all such finite solutions from a description of all finite left
braces has been recently discovered. It is thus a fundamental problem to
construct and classify all simple left braces, as they can be considered as
building blocks for the general theory. This program recently has been
initiated by Bachiller and the authors. In this paper we study the simple
finite left braces such that the Sylow subgroups of their multiplicative groups
are abelian. We provide several new families of such simple left braces. In
particular, they lead to the main, surprising result, that shows that there is
an abundance of such simple left braces.


  The method of Damage Spreading was used to simulate the influence that a
single persons' change of opionion has on the consensus opinion built up in a
population if one assumes opinions to form according to the Sznajd Model. The
results confirm the intuitive assumption that there is hardly any chance for
one person to change the consensus, that the effect of this change dies out
after a certain time and its range decreases with time. The consensus times
were compared and it turned out that the consensus can be delayed or
accelerated by this slight modification and that the amount of the difference
in the consensus times obeys a certain power law as well as the lifetime of the
effect. Moreover two scaling laws concerning temporal and spatial aspects could
be observed up to a certain size of population.


  We discuss main issues and R&D Required for the Intensity Frontier
Accelerators and therefore provide input for the 2013 APS/DPF Community Summer
Study (Snowmass-2013).


  Magnetic Resonance Imaging (MRI) has become one of the most important tools
to screen humans in medicine, virtually every modern hospital is equipped with
an NMR tomograph. The potential of NMR in 3D imaging tasks is by far greater,
but there is only 'a handful' of MRI studies of particulate matter. The method
is expensive, time-consuming, and requires a deep understanding of pulse
sequences, signal acquisition and processing. We give a short introduction into
the physical principles of this imaging technique, describe its advantages and
limitations for the screening of granular matter and present a number of
examples of different application purposes, from the exploration of granular
packing, via the detection of flow and particle diffusion, to real dynamic
measurements. Probably, X-ray computed tomography is preferable in most
applications, but fast imaging of single slices with modern MRI techniques is
unmatched, and the additional opportunity to retrieve spatially resolved flow
and diffusion profiles without particle tracking is a unique feature.


  We measure the local correlation between radio emission and Compton-$y$
signal across two galaxy clusters, Abell~399 and Abell~401, using maps from the
Low-Frequency Array (LOFAR) and the Atacama Cosmology Telescope (ACT) +
\Planck. These datasets allow us to make the first measurement of this kind at
$\sim$arcminute resolution. We find that the radio brightness scales as
$F_{\mathrm{radio}} \propto y^{1.5}$ for Abell~401 and $F_{\mathrm{radio}}
\propto y^{2.8}$ for Abell~399. Furthermore, using \XMM data, we derive a
sublinear correlation between radio and X-ray brightness for both the clusters
($F_{\mathrm{radio}} \propto F_{\rm X}^{0.7}$). Finally, we correlate the
Compton-$y$ and X-ray data, finding that an isothermal model is consistent with
the cluster profiles, $y \propto F_{\rm X}^{0.5}$. By adopting an
isothermal--$\beta$ model, we are able, for the first time, to jointly use
radio, X-ray, and Compton-$y$ data to estimate the scaling index for the
magnetic field profile, $B(r) \propto n_{\mathrm{e}}(r)^{\eta}$ in the
injection and re-acceleration scenarios. Applying this model, we find that the
combined radio and Compton-$y$ signal exhibits a significantly tighter
correlation with the X-ray across the clusters than when the datasets are
independently correlated. We find $\eta \sim 0.6{-}0.8$. These results are
consistent with the upper limit we derive for the scaling index of the magnetic
field using rotation measure values for two radio galaxies in Abell~401. We
also measure the radio, Compton-$y$, and X-ray correlations in the filament
between the clusters but conclude that deeper data are required for a
convincing determination of the correlations in the filament.


  We calculate the polarization tensor of charged gluons in a Abelian
homogeneous magnetic background field at finite temperature in one loop order
Lorentz background field gauge in full generality. Thereby we first determine
the ten independent tensor structures. For the calculation of the corresponding
form factors we use the Schwinger representation and represent form factors as
double parametric integrals and a sum resulting from the Matsubara formalism
used. The integrands are given explicitly in terms of hyperbolic trigonometric
functions. Like in the case of neutral gluons, the polarization tensor is not
transversal. Out of the tensor structures, seven are transversal and three are
not. The nontransversal part follows explicitly from our calculations.


  We present a novel, self-consistent and robust theoretical model to
investigate elastic interactions between topological defects in liquid crystal
shells. Accounting for the non-concentric nature of the shell in a simple
manner, we are able to successfully and accurately explain and predict the
positions of the defects, most relevant in the context of colloidal
self-assembly. We calibrate and test our model on existing experimental data,
and extend it to all newly observed defects configurations in chiral nematic
shells. We perform new experiments to check further and confirm the validity of
the present model. Moreover, we are able to obtain quantitative estimates of
the energies of $+1$ or $+3/2$ disclination lines in cholesterics, whose
intricate nature was only reported recently.


  We propose group theory interpretation of the integral representation of the
quantum open Toda chain wave function due to Givental. In particular we
construct the representation of $U((\mathfrak{gl}(N))$ in terms of first order
differential operators in Givental variables. The construction of this
representation turns out to be closely connected with the integral
representation based on the factorized Gauss decomposition. We also reveal the
recursive structure of the Givental representation and provide the connection
with the Baxter $Q$-operator formalism. Finally the generalization of the
integral representation to the infinite and periodic quantum Toda wave
functions is discussed.


  We study vertex colourings of digraphs so that no out-neighbourhood is
monochromatic and call such a colouring an {\bf out-colouring}. The problem of
deciding whether a given digraph has an out-colouring with only two colours
(called a 2-out-colouring) is ${\cal
  NP}$-complete. We show that for every choice of positive integers $r,k$ there
exists a $k$-strong bipartite tournament which needs at least $r$ colours in
every out-colouring. Our main results are on tournaments and semicomplete
digraphs. We prove that, except for the Paley tournament $P_7$, every strong
semicomplete digraph of minimum out-degree at least 3 has a 2-out-colouring.
Furthermore, we show that every semicomplete digraph on at least 7 vertices has
a 2-out-colouring if and only if it has a {\bf balanced} such colouring, that
is, the difference between the number of vertices that receive colour 1 and
colour 2 is at most one. In the second half of the paper we consider the
generalization of 2-out-colourings to vertex partitions $(V_1,V_2)$ of a
digraph $D$ so that each of the three digraphs induced by respectively, the
vertices of $V_1$, the vertices of $V_2$ and all arcs between $V_1$ and $V_2$
have minimum out-degree $k$ for a prescribed integer $k\geq 1$. Using
probabilistic arguments we prove that there exists an absolute positive
constant $c$ so that every semicomplete digraph of minimum out-degree at least
$2k+c\sqrt{k}$ has such a partition. This is tight up to the value of $c$.


  In this paper, it is proved that, no special timelike Frenet curve is a
Bertrand curve in $\mathbb{E}_2^4$ and also, in $\mathbb{E}_\nu^{n+1}$ $ ({n
\ge 3})$, such that the notion of Bertrand curve is definite only in
$\mathbb{E}_1^2$ and $\mathbb{E}_1^3$. Therefore, a generalization of timelike
Bertrand curve is defined and called as timelike (1,3)-Bertrand curve in
$\mathbb{E}_2^4$. Moreover, the characterization of timelike (1,3)-Bertrand
curve is given in $\mathbb{E}_2^4$.


  The Klein-Gordon equation in D-dimensions for a recently proposed Kratzer
potential plus ring-shaped potential is solved analytically by means of the
conventional Nikiforov-Uvarov method. The exact energy bound-states and the
corresponding wave functions of the Klein-Gordon are obtained in the presence
of the noncentral equal scalar and vector potentials. The results obtained in
this work are more general and can be reduced to the standard forms in
three-dimensions given by other works.


  We briefly review the current status of theoretical calculations for Higgs
production at hadron colliders within the Standard Model. We focus on the main
production mechanisms and decay modes at the Tevatron and the LHC.


  In this work, we studied the properties of the spherical indicatrices of a
Bertrand curve and its mate curve and presented some characteristic properties
in the cases that Bertrand curve and its mate curve are slant helices,
spherical indicatrices are slant helices and we also researched that whether
the spherical indicatrices made new curve pairs in the means of Mannheim,
involte-evolute and Bertrand pairs. Further more, we investigated the relations
between the spherical images and introduced new representations of spherical
indicatrices.


  We deduce general expressions for the line element of universe models with
positive spatial curvature described by conformally flat spacetime coordinates.
Models with dust, radiation and vacuum energy are exhibited. Discussing the
existence of particle horizons we show that there is continual annihilation of
space, matter and energy in a dust and radiation dominated universe, and
continual creation in a LIVE domined universe when conformal time is used in
Friedmann-Robertson-Walker models with positive spatial curvature. A general
procedure is given for finding coordinates to be used in Penrose diagrams. We
also calculate the age and the redshift of some universe models using conformal
time.


  Many real world network problems often concern multivariate nodal attributes
such as image, textual, and multi-view feature vectors on nodes, rather than
simple univariate nodal attributes. The existing graph estimation methods built
on Gaussian graphical models and covariance selection algorithms can not handle
such data, neither can the theories developed around such methods be directly
applied. In this paper, we propose a new principled framework for estimating
graphs from multi-attribute data. Instead of estimating the partial correlation
as in current literature, our method estimates the partial canonical
correlations that naturally accommodate complex nodal features.
Computationally, we provide an efficient algorithm which utilizes the
multi-attribute structure. Theoretically, we provide sufficient conditions
which guarantee consistent graph recovery. Extensive simulation studies
demonstrate performance of our method under various conditions. Furthermore, we
provide illustrative applications to uncovering gene regulatory networks from
gene and protein profiles, and uncovering brain connectivity graph from
functional magnetic resonance imaging data.


  Flavour physics is a priceless window on physics beyond the Standard Model.
In particular, flavour violation in the lepton sector looks very promising, as
high precision measurements are prospected in future experiments investigating
on $\mu\rightarrow e$ conversion in atomic nuclei: the predictions for this
observable are analysed in the context of the type I Seesaw mechanism.
Furthermore, new ideas to explain the Flavour Puzzle recently appeared, mainly
based on a possible dynamical origin of the Yukawa couplings and on flavour
symmetries. The focus of this proceeding will be set on the Minimal Flavour
Violation ansatz and on the role of the neutrino Majorana character: when an
$O(2)_{N}$ flavour symmetry acts on the right-handed neutrino sector, the
minimum of the scalar potential allows for large mixing angles -in contrast to
the simplest quark case- and predicts a maximal Majorana phase. This leads to a
strong correlation between neutrino mass hierarchy and mixing pattern.


  Let K be a complete discrete valuation field of mixed characteristic (0,p)
with perfect residue field. Let (\pi_n)_{n\ge 0} be a system of p-power roots
of a uniformizer \pi=\pi_0 of K with \pi^p_{n+1}=\pi_n, and define G_s (resp.\
G_{\infty}) the absolute Galois group of K(\pi_s) (resp.\
K_{\infty}:=\bigcup_{n\ge 0} K(\pi_n)). In this paper, we study
G_s-equivatiantness properties of G_{\infty}-equivariant homomorphisms between
torsion (potentially) crystalline representations


  In this note, we fill in a gap in the literature by proving that the
Teichmueller modular groups (mapping class groups) are not Poincare duality
groups and the complexes of curves of surfaces have infinite homotopy type
(i.e. are not homotopy equivalent to a finite CW-complex).


  The relationship between the design and functionality of molecular networks
is now a key issue in biology. Comparison of regulatory networks performing
similar tasks can give insights into how network architecture is constrained by
the functions it directs. We here discuss methods of network comparison based
on network architecture and signaling logic. Introducing local and global
signaling scores for the difference between two networks we quantify
similarities between evolutionary closely and distantly related bacteriophages.
Despite the large evolutionary separation between phage $\lambda$ and 186 their
networks are found to be similar when difference is measured in terms of global
signaling. We finally discuss how network alignment can be used to to pinpoint
protein similarities viewed from the network perspective.


  A novel and practical copper-catalyzed approach is developed for the
preparation of 5-arylindazolo[3,2-b]quinazolin-7(5H)-ones. The
2-amino-N'-arylbenzohydrazide, which easily prepared by reaction of isatoic
anhydride with arylhydrazine, through a condensation/intramolecular cyclization
reacted by 2-nitrobenzaldehydes in the present of CuI to afford corresponding
5-arylindazolo[3,2-b]quinazolin-7(5H)-ones in good yields.


  For a type-I $(K,M,Z,N)$-ZCCS, it follows $K \leq M \left\lfloor
\frac{N}{Z}\right\rfloor$. In this paper, we propose a construction of type-II
$(p^{k+n},p^k,p^{n+r}-p^r+1,p^{n+r})$-$Z$ complementary code set (ZCCS) using
an extended Boolean function, its properties of Hamiltonian paths and the
concept of isolated vertices, where $p\ge 2$. However, the proposed type-II
ZCCS provides $K = M(N-Z+1)$ codes, where as for type-I $(K,M,N,Z)$-ZCCS, it is
$K \leq M \left\lfloor \frac{N}{Z}\right\rfloor$. Therefore, the proposed
type-II ZCCS provides a larger number of codes compared to type-I ZCCS.
Further, as a special case of the proposed construction, $(p^k,p^k,p^n)$-CCC
can be generated, for any integral value of $p\ge2$ and $k\le n$.


  Observations of quasar absorption systems relevant for studies of star
formation at redshift 2 </= z </= 4 are briefly reviewed. Emphasis is given on
the role played by dust in our understanding of the star formation history of
galaxies detected as absorption systems. Local interstellar studies are used as
a reference for understanding the properties of high redshift interstellar
media. An example is shown of the potential effects of dust extinction on the
metallicity-N(HI) distribution obtained from magnitude-limited surveys of
damped lyman alpha absorbers.


  In this work we use density functional theory (DFT) calculations to benchmark
empirical potentials for the interaction between nickel and sp$^2$ bonded
carbon nanoparticles. These potentials are then used in order to investigate
how Ni decorated or coated carbon nanotubes (CNT) affect the mechanical
properties of Al/CNT composites. In particular we look at the pull-out
behaviour of pristine as well as Ni-decorated and Ni-coated CNT from an Al
matrix. Our result shows that Ni coating may produce an extended interface
(interphase) where a significant amount of energy is dissipated during CNT
pull-out, leading to a high pull-out force. We also demonstrate that surface
decorated CNT may act as efficient nano-crystallization agents and thus provide
a novel strengthening mechanism not previously discussed in the literature. We
discuss our results in view of promising approaches for engineering CNT-metal
interfaces such as to achieve high strength metal-CNT composite.


  A Model Predictive Controller for Tracking is introduced for rendezvous with
non-cooperative tumbling targets in active debris removal applications. The
target's three-dimensional non-periodic rotational dynamics as well as other
state and control constraints are considered. The approach is based on applying
an intermediate coordinate transformation that eliminates the time-dependency
due to rotations in the constraints. The control law is then found as the
solution to a QP problem with linear constraints and dynamics, as derived from
the HCW equations, that provides feasibility and stability guarantees by means
of a terminal LQR and dead-beat region. The proposed control algorithm performs
well in a realistic simulation scenario, namely a near rendezvous with the
Envisat spacecraft.


  We have performed temperature dependent tunneling experiments through a
single impurity in an asymmetric vertical double barrier tunneling structure.
In particular in the charging direction we observe at zero magnetic field a
clear shift in the onset voltage of the resonant tunneling current through the
impurity. With a magnetic field applied the shift starts to disappear. The
experimental observations are explained in terms of resonant tunneling through
a spin degenerate impurity level.


  In this paper, we have studied tunneling effect of the spin-2 Bose condensate
driven by external magnetic field. We find that the population transfers among
spin-0 and spin-$\pm1$, spin-0 and spin-$\pm2$ exhibit the step structure under
the external cosinusoidal magnetic field respectively, but there do not exist
step structure among spin-$\pm1$ and spin-$\pm2$. The tunneling current among
spin-$\pm1$ and spin-$\pm2$ may exhibit periodically oscillation behavior, but
among spin-0 and spin-$\pm1$, spin-0 and spin-$\pm2$, the tunneling currents
exhibit irregular oscillation behavior.


  Localization based on received signal strength (RSS) has drawn great interest
in the wireless sensor network (WSN). In this paper, we investigate the
RSS-based multi-sources localization problem with unknown transmitted power
under shadow fading. The log-normal shadowing effect is approximated through
Fenton-Wilkinson (F-W) method and maximum likelihood estimation is adopted to
optimize the RSS-based multiple sources localization problem. Moreover, we
exploit a sparse recovery and weighted average of candidates (SR-WAC) based
method to set up an initiation, which can efficiently approach a superior local
optimal solution. It is shown from the simulation results that the proposed
method has a much higher localization accuracy and outperforms the other


  Multimodal Large Language Models (MLLMs) excel in generating responses based
on visual inputs. However, they often suffer from a bias towards generating
responses similar to their pretraining corpus, overshadowing the importance of
visual information. We treat this bias as a "preference" for pretraining
statistics, which hinders the model's grounding in visual input. To mitigate
this issue, we propose Bootstrapped Preference Optimization (BPO), which
conducts preference learning with datasets containing negative responses
bootstrapped from the model itself. Specifically, we propose the following two
strategies: 1) using distorted image inputs to the MLLM for eliciting responses
that contain signified pretraining bias; 2) leveraging text-based LLM to
explicitly inject erroneous but common elements into the original response.
Those undesirable responses are paired with original annotated responses from
the datasets to construct the preference dataset, which is subsequently
utilized to perform preference learning. Our approach effectively suppresses
pretrained LLM bias, enabling enhanced grounding in visual inputs. Extensive
experimentation demonstrates significant performance improvements across
multiple benchmarks, advancing the state-of-the-art in multimodal
conversational systems.


  Time-distance helioseismology is a set of powerful tools to study features
below the Sun's surface. Inverse methods are needed to interpret time-distance
measurements, with many examples in the literature. However, techniques that
utilize a more statistical approach to inferences, and broadly used in the
astronomical community, are less-commonly found in helioseismology. This
article aims to introduce a potentially powerful inversion scheme based on
Bayesian probability theory and Monte Carlo sampling that is suitable for local
helioseismology. We describe the probabilistic method and how it is
conceptually different from standard inversions used in local helioseismology.
Several example calculations are carried out to compare and contrast the setup
of the problems and the results that are obtained. The examples focus on two
important phenomena studied with helioseismology: meridional circulation and
supergranulation. Numerical models are used to compute synthetic observations,
providing the added benefit of knowing the solution against which the results
can be tested. For demonstration purposes, the problems are formulated in two
and three dimensions, using both ray- and Born-theoretical approaches. The
results seem to indicate that the probabilistic inversions not only find a
better solution with much more realistic estimation of the uncertainties, but
they also provide a broader view of the range of solutions possible for any
given model, making the interpretation of the inversion more quantitative in
nature. Unlike the progress being made in fundamental measurement schemes in
local helioseismology that image the far side of the Sun, or have detected
signatures of global Rossby waves, among many others, inversions of those
measurements have had significantly less success. Such statistical methods may
help overcome some of these barriers to move the field forward.


  We propose a decentralized auction-based algorithm for the solution of
dynamic task allocation problems for spatially distributed multi-agent systems.
In our approach, each member of the multi-agent team is assigned to at most one
task from a set of spatially distributed tasks, while several agents can be
allocated to the same task. The task assignment is dynamic since it is updated
at discrete time stages (iterations) to account for the current states of the
agents as the latter move towards the tasks assigned to them at the previous
stage. Our proposed methods can find applications in problems of resource
allocation by intelligent machines such as the delivery of packages by a fleet
of unmanned or semi-autonomous aerial vehicles. In our approach, the task
allocation accounts for both the cost incurred by the agents for the completion
of their assigned tasks (e.g., energy or fuel consumption) and the rewards
earned for their completion (which may reflect, for instance, the agents'
satisfaction). We propose a Greedy Coalition Auction Algorithm (GCAA) in which
the agents possess bid vectors representing their best evaluations of the task
utilities. The agents propose bids, deduce an allocation based on their bid
vectors and update them after each iteration. The solution estimate of the
proposed task allocation algorithm converges after a finite number of
iterations which cannot exceed the number of agents. Finally, we use numerical
simulations to illustrate the effectiveness of the proposed task allocation
algorithm (in terms of performance and computation time) in several scenarios
involving multiple agents and tasks distributed over a spatial 2D domain.


  The detailed characterization of non-trivial coherence properties of
composite quantum systems of increasing size is an indispensable prerequisite
for scalable quantum computation, as well as for understanding of
nonequilibrium many-body physics. Here we show how autocorrelation functions in
an interacting system of phonons as well as the quantum discord between
distinct degrees of freedoms can be extracted from a small controllable part of
the system. As a benchmark, we show this in chains of up to 42 trapped ions, by
tracing a single phonon excitation through interferometric measurements of only
a single ion in the chain. We observe the spreading and partial refocusing of
the excitation in the chain, even on a background of thermal excitations. We
further show how this local observable reflects the dynamical evolution of
quantum discord between the electronic state and the vibrational degrees of
freedom of the probe ion.


  We use generalized kernel functions to construct explicit solutions by
integrals of the non-stationary Schr\"odinger equation for the Hamiltonian of
the elliptic Calogero-Sutherland model (also known as elliptic
Knizhnik-Zamolodchikov-Bernard equation). Our solutions provide integral
represenations of elliptic generalizations of the Jack polyomials.


  We describe Chandra/ACIS-I observations of the massive ~ 13--14 Myr-old
cluster, h Persei, part of the famous Double Cluster (h and chi Persei) in
Perseus. Combining the list of Chandra-detected sources with new optical/IR
photometry and optical spectroscopy reveals ~ 165 X-ray bright stars with V <
23. Roughly 142 have optical magnitudes and colors consistent with cluster
membership. The observed distribution of Lx peaks at Lx ~ 10^30.3 ergs/s and
likely traces the bright edge of a far larger population of ~ 0.4--2 Msun X-ray
active stars. From a short list of X-ray active stars with IRAC 8 micron excess
from warm, terrestrial-zone dust, we derive a maximum X-ray flux incident on
forming terrestrial planets. Although there is no correlation between X-ray
activity and IRAC excess, the fractional X-ray luminosity correlates with
optical colors and spectral type. By comparing the distribution of Lx/L* vs.
spectral type and V-I in h Per with results for other 1--100 Myr-old clusters,
we show that stars slightly more massive than the Sun (> 1.5 Msun) fall out of
X-ray saturation by ~ 10--15 Myr. Changes in stellar structure for > 1.5 Msun
stars likely play an important role in this decline of X-ray emission.


  Code-switching is a prevalent linguistic phenomenon in which multilingual
individuals seamlessly alternate between languages. Despite its widespread use
online and recent research trends in this area, research in code-switching
presents unique challenges, primarily stemming from the scarcity of labelled
data and available resources. In this study we investigate how pre-trained
Language Models handle code-switched text in three dimensions: a) the ability
of PLMs to detect code-switched text, b) variations in the structural
information that PLMs utilise to capture code-switched text, and c) the
consistency of semantic information representation in code-switched text. To
conduct a systematic and controlled evaluation of the language models in
question, we create a novel dataset of well-formed naturalistic code-switched
text along with parallel translations into the source languages. Our findings
reveal that pre-trained language models are effective in generalising to
code-switched text, shedding light on the abilities of these models to
generalise representations to CS corpora. We release all our code and data
including the novel corpus at https://github.com/francesita/code-mixed-probes.


  We study (p)reheating in modular (closed string) inflationary scenarios, with
a special emphasis on Kahler moduli/Roulette models. It is usually assumed that
reheating in such models occurs through perturbative decays. However, we find
that there are very strong non-perturbative preheating decay channels related
to the particular shape of the inflaton potential (which is highly nonlinear
and has a very steep minimum). Preheating after modular inflation, proceeding
through a combination of tachyonic instability and broad-band parametric
resonance, is perhaps the most violent example of preheating after inflation
known in the literature. Further, we consider the subsequent transfer of energy
to the standard model sector in scenarios where the standard model particles
are confined to a D7-brane wrapping the inflationary blow-up cycle of the
compactification manifold or, more interestingly, a non-inflationary blow up
cycle. We explicitly identify the decay channels of the inflaton in these two
scenarios. We also consider the case where the inflationary cycle shrinks to
the string scale at the end of inflation; here a field theoretical treatment of
reheating is insufficient and one must turn instead to a stringy description.
We estimate the decay rate of the inflaton and the reheat temperature for
various scenarios.


  We quantify the effective radiative forcing (ERF) of anthropogenic aerosols
modelled by the aerosol-climate model CAM5.3-MARC-ARG. CAM5.3-MARC-ARG is a new
configuration of the Community Atmosphere Model version 5.3 (CAM5.3) in which
the default aerosol module has been replaced by the two-Moment, Multi-Modal,
Mixing-state-resolving Aerosol model for Research of Climate (MARC).
CAM5.3-MARC-ARG uses the default ARG aerosol activation scheme, consistent with
the default configuration of CAM5.3. We compute differences between simulations
using year-1850 aerosol emissions and simulations using year-2000 aerosol
emissions in order to assess the radiative effects of anthropogenic aerosols.
We compare the aerosol column burdens, cloud properties, and radiative effects
produced by CAM5.3-MARC-ARG with those produced by the default configuration of
CAM5.3, which uses the modal aerosol module with three log-normal modes (MAM3).
Compared with MAM3, we find that MARC produces stronger cooling via the direct
radiative effect, stronger cooling via the surface albedo radiative effect, and
stronger warming via the cloud longwave radiative effect. The global mean cloud
shortwave radiative effect is similar between MARC and MAM3, although the
regional distributions differ. Overall, MARC produces a global mean net ERF of
-1.75$\pm$0.04 W m$^{-2}$, which is stronger than the global mean net ERF of
-1.57$\pm$0.04 W m$^{-2}$ produced by MAM3. The regional distribution of ERF
also differs between MARC and MAM3, largely due to differences in the regional
distribution of the cloud shortwave radiative effect. We conclude that the
specific representation of aerosols in global climate models, including aerosol
mixing state, has important implications for climate modelling.


  This is the second of a pair of articles on scattering of glue by glue, in
which we give the light-cone gauge calculation of the one-loop on-shell
helicity conserving scattering amplitudes for gluon-gluon scattering
(neglecting quark loops). The 1/p^+ factors in the gluon propagator are
regulated by replacing p^+ integrals with discretized sums omitting the p^+=0
terms in each sum. We also employ a novel ultraviolet regulator that is
convenient for the light-cone worldsheet description of planar Feynman
diagrams. The helicity conserving scattering amplitudes are divergent in the
infra-red. The infrared divergences in the elastic one-loop amplitude are shown
to cancel, in their contribution to cross sections, against ones in the cross
section for unseen bremsstrahlung gluons. We include here the explicit
calculation of the latter, because it assumes an unfamiliar form due to the
peculiar way discretization of p^+ regulates infrared divergences. In resolving
the infrared divergences we employ a covariant definition of jets, which allows
a transparent demonstration of the Lorentz invariance of our final results.
Because we use an explicit cutoff of the ultraviolet divergences in exactly 4
space-time dimensions, we must introduce explicit counterterms to achieve this
final covariant result. These counter-terms are polynomials in the external
momenta of the precise order dictated by power-counting. We discuss the
modifications they entail for the light-cone worldsheet action that reproduces
the ``bare'' planar diagrams of the gluonic sector of QCD. The simplest way to
do this is to interpret the QCD string as moving in six space-time dimensions.


  Multi-agent collaborative perception (MCP) has recently attracted much
attention. It includes three key processes: communication for sharing,
collaboration for integration, and reconstruction for different downstream
tasks. Existing methods pursue designing the collaboration process alone,
ignoring their intrinsic interactions and resulting in suboptimal performance.
In contrast, we aim to propose a Unified Collaborative perception framework
named UMC, optimizing the communication, collaboration, and reconstruction
processes with the Multi-resolution technique. The communication introduces a
novel trainable multi-resolution and selective-region (MRSR) mechanism,
achieving higher quality and lower bandwidth. Then, a graph-based collaboration
is proposed, conducting on each resolution to adapt the MRSR. Finally, the
reconstruction integrates the multi-resolution collaborative features for
downstream tasks. Since the general metric can not reflect the performance
enhancement brought by MCP systematically, we introduce a brand-new evaluation
metric that evaluates the MCP from different perspectives. To verify our
algorithm, we conducted experiments on the V2X-Sim and OPV2V datasets. Our
quantitative and qualitative experiments prove that the proposed UMC greatly
outperforms the state-of-the-art collaborative perception approaches.


  We analyze the spectrum of the massive states for the color flavor locked
phase (CFL) of QCD. We show that the vector mesons have a mass of the order of
the color superconductive gap $\Delta$. We also see that the excitations
associated with the solitonic sector of the CFL low energy theory have a mass
proportional to $F^2_{\pi}/\Delta$ and hence are expected to play no role for
the physics of the CFL phase for large chemical potential. Another interesting
point is that the product of the soliton mass and the vector meson mass is
independent of the gap. We interpret this behavior as a form of electromagnetic
duality in the sense of Montonen and Olive. Our approach for determining the
properties of the massive states is non-perturbative in nature and can be
applied to any theory with multiple scales.


  We have used laser guide star adaptive optics and a near-infrared
dual-channel imaging polarimeter to observe light scattered in the
circumstellar environment of Herbig Ae/Be stars on scales of 100-300 AU. We
discover a strongly polarized, biconical nebula 10 arcseconds in diameter (6000
AU) around the star LkHa 198, and also observe a polarized jet-like feature
associated with the deeply embedded source LkHa 198-IR. The star LkHa 233
presents a narrow, unpolarized dark lane consistent with an optically thick
circumstellar disk blocking our direct view of the star. These data show that
the lower-mass T Tauri and intermediate mass Herbig Ae/Be stars share a common
evolutionary sequence.


  We report self flux growth and characterization of FeTe1-xSx (x= 0.00 to
0.25) single crystal series. Surface X-ray diffraction (XRD) exhibited
crystalline nature with growth in (00l) plane. Micro-structural (electron
microscopy) images of representative crystals showed the slab-like morphology
and near stoichiometric composition. Powder XRD analysis (Rietveld) of single
crystals exhibited tetragonal structure with P4/nmm space group and decreasing
a and c lattice parameters with increase in x. Electrical resistivity
measurements (R-T) showed superconductivity with Tconset at 9.5K and 8.5K for x
=0.10 and x =0.25 respectively. The un-doped crystal exhibited known step like
anomaly at around 70K. Upper critical field Hc2(0), as calculated from magneto
transport for x =0.25 crystal is around 60Tesla and 45Tesla in H//ab and H//c
directions. Thermal activation energy [U0(H)] calculated for x =0.10 and 0.25
crystals followed weak power law, indicating single vortex pinning at low
fields. Mossbauer spectra for FeTe1-xSx crystals at 300K and 5K are compared
with non superconducting FeTe. Both quadrupole splitting (QS) and isomer shift
(IS) for S doped crystals were found to decrease. Also at 5K the hyperfine
field for x =0.10 superconducting crystal is decreased substantially from
10.6Tesla (FeTe) to 7.2Tesla. For x =0.25 crystal, though small quantity of
un-reacted Fe is visible at room temperature, but unlike x =0.10, the low
temperature (5K) ordered FeTe hyperfine field is nearly zero.


  Semantic segmentation, a crucial task in computer vision, often relies on
labor-intensive and costly annotated datasets for training. In response to this
challenge, we introduce FuseNet, a dual-stream framework for self-supervised
semantic segmentation that eliminates the need for manual annotation. FuseNet
leverages the shared semantic dependencies between the original and augmented
images to create a clustering space, effectively assigning pixels to
semantically related clusters, and ultimately generating the segmentation map.
Additionally, FuseNet incorporates a cross-modal fusion technique that extends
the principles of CLIP by replacing textual data with augmented images. This
approach enables the model to learn complex visual representations, enhancing
robustness against variations similar to CLIP's text invariance. To further
improve edge alignment and spatial consistency between neighboring pixels, we
introduce an edge refinement loss. This loss function considers edge
information to enhance spatial coherence, facilitating the grouping of nearby
pixels with similar visual features. Extensive experiments on skin lesion and
lung segmentation datasets demonstrate the effectiveness of our method.
\href{https://github.com/xmindflow/FuseNet}{Codebase.}


  Fruit flies are one of the most harmful insect species to fruit yields. In
AlertTrap, implementation of SSD architecture with different state-of-the-art
backbone feature extractors such as MobileNetV1 and MobileNetV2 appear to be
potential solutions for the real-time detection problem. SSD-MobileNetV1 and
SSD-MobileNetV2 perform well and result in<EMAIL_ADDRESS>of 0.957 and 1.0
respectively. YOLOv4-tiny outperforms the SSD family with 1.0 in<EMAIL_ADDRESS>however, its throughput velocity is slightly slower.


  Semi-supervised learning (SSL) has made significant strides in the field of
remote sensing. Finding a large number of labeled datasets for SSL methods is
uncommon, and manually labeling datasets is expensive and time-consuming.
Furthermore, accurately identifying remote sensing satellite images is more
complicated than it is for conventional images. Class-imbalanced datasets are
another prevalent phenomenon, and models trained on these become biased towards
the majority classes. This becomes a critical issue with an SSL model's subpar
performance. We aim to address the issue of labeling unlabeled data and also
solve the model bias problem due to imbalanced datasets while achieving better
accuracy. To accomplish this, we create "artificial" labels and train a model
to have reasonable accuracy. We iteratively redistribute the classes through
resampling using a distribution alignment technique. We use a variety of class
imbalanced satellite image datasets: EuroSAT, UCM, and WHU-RS19. On UCM
balanced dataset, our method outperforms previous methods MSMatch and FixMatch
by 1.21% and 0.6%, respectively. For imbalanced EuroSAT, our method outperforms
MSMatch and FixMatch by 1.08% and 1%, respectively. Our approach significantly
lessens the requirement for labeled data, consistently outperforms alternative
approaches, and resolves the issue of model bias caused by class imbalance in
datasets.


  When neutrinos travel through a normal matter medium, the electron neutrinos
couple differently to gravity compared to the other neutrinos, due to the
presence of electrons in the medium and the absence of the other charged
leptons. The matter-induced gravitational couplings of the neutrinos under such
conditions are calculated and their contribution to the neutrino index of
refraction in the presence of a gravitational potential is determined.


  We consider an hierarchy of integrable 1+2-dimensional equations related to
Lie algebra of the vector fields on the line. The solutions in quadratures are
constructed depending on $n$ arbitrary functions of one argument. The most
interesting result is the simple equation for the generating function of the
hierarchy which defines the dynamics for the negative times and also has
applications to the second order spectral problems. A rather general theory of
integrable 1+1-dimensional equations can be developed by study of polynomial
solutions of this equation under condition of regularity of the corresponding
potentials.


  Some recent beyond Standard Model phenomenology is based on new strongly
interacting dynamics of $SU(N)$ gauge fields coupled to various numbers of
fermions. When $N=3$ these systems are analogues of QCD, although the fermion
masses are typically different from -- and heavier than -- the ones of real
world QCD. Many quantities needed for phenomenology from these models have been
computed on the lattice. We are writing a guide for these phenomenologists,
telling them about lattice results. We'll tell you (some of) what they are
interested in knowing.


  We study the S-integral points on the complement of a union of hyperplanes in
projective space, where S is a finite set of places of a number field k. In the
classical case where S consists of the set of archimedean places of k, we
completely characterize, in terms of the hyperplanes and the field k, when the
(S-)integral points are not Zariski-dense.


  Scientific writing is a challenging task, particularly for novice researchers
who often rely on feedback from experienced peers. Recent work has primarily
focused on improving surface form and style rather than manuscript content. In
this paper, we propose a novel task: automated focused feedback generation for
scientific writing assistance. We present SWIF$^{2}$T: a Scientific WrIting
Focused Feedback Tool. It is designed to generate specific, actionable and
coherent comments, which identify weaknesses in a scientific paper and/or
propose revisions to it. Our approach consists of four components - planner,
investigator, reviewer and controller - leveraging multiple Large Language
Models (LLMs) to implement them. We compile a dataset of 300 peer reviews
citing weaknesses in scientific papers and conduct human evaluation. The
results demonstrate the superiority in specificity, reading comprehension, and
overall helpfulness of SWIF$^{2}$T's feedback compared to other approaches. In
our analysis, we also identified cases where automatically generated reviews
were judged better than human ones, suggesting opportunities for integration of
AI-generated feedback in scientific writing.


  In this paper, we study the application of quasi-Newton methods for solving
empirical risk minimization (ERM) problems defined over a large dataset.
Traditional deterministic and stochastic quasi-Newton methods can be executed
to solve such problems; however, it is known that their global convergence rate
may not be better than first-order methods, and their local superlinear
convergence only appears towards the end of the learning process. In this
paper, we use an adaptive sample size scheme that exploits the superlinear
convergence of quasi-Newton methods globally and throughout the entire learning
process. The main idea of the proposed adaptive sample size algorithms is to
start with a small subset of data points and solve their corresponding ERM
problem within its statistical accuracy, and then enlarge the sample size
geometrically and use the optimal solution of the problem corresponding to the
smaller set as an initial point for solving the subsequent ERM problem with
more samples. We show that if the initial sample size is sufficiently large and
we use quasi-Newton methods to solve each subproblem, the subproblems can be
solved superlinearly fast (after at most three iterations), as we guarantee
that the iterates always stay within a neighborhood that quasi-Newton methods
converge superlinearly. Numerical experiments on various datasets confirm our
theoretical results and demonstrate the computational advantages of our method.


  Joint optimization of scheduling and estimation policies is considered for a
system with two sensors and two non-collocated estimators. Each sensor produces
an independent and identically distributed sequence of random variables, and
each estimator forms estimates of the corresponding sequence with respect to
the mean-squared error sense. The data generated by the sensors is transmitted
to the corresponding estimators, over a bandwidth-constrained wireless network
that can support a single packet per time slot. The access to the limited
communication resources is determined by a scheduler who decides which sensor
measurement to transmit based on both observations. The scheduler has an
energy-harvesting battery of limited capacity, which couples the
decision-making problem in time. Despite the overall lack of convexity of the
team decision problem, it is shown that this system admits globally optimal
scheduling and estimation strategies under the assumption that the
distributions of the random variables at the sensors are symmetric and
unimodal. Additionally, the optimal scheduling policy has a structure
characterized by a threshold function that depends on the time index and energy
level. A recursive algorithm for threshold computation is provided.


  Little groups for preon branes (i.e. configurations of branes with maximal
(n-1)/n fraction of survived supersymmetry) for dimensions d=2,3,...,11 are
calculated for all massless, and partially for massive orbits. For massless
orbits little groups are semidirect product of d-2 translational group
$T_{d-2}$ on a subgroup of (SO(d-2) $\times$ R-invariance) group. E.g. at d=9
the subgroup is exceptional $G_2$ group. It is also argued, that 11d Majorana
spinor invariants, which distinguish orbits, are actually invariant under
d=2+10 Lorentz group. Possible applications of these results include
construction of field theories in generalized space-times with brane charges
coordinates, different problems of group's representations decompositions,
spin-statistics issues.


  The fluxes of electrons, positrons, gammas, Cherenkov photons and muons in
individual extensive air showers induced by the primary protons and helium,
oxygen and iron nuclei at the level of observation have been estimated with
help of the code CORSICA 6.616. The comparison show that the values of the
function Xi**2 per one degree of freedom changes from 1.1 for iron nuclei to
0.9 for primary protons. As this difference is small all readings of detectors
of the Vavilov-Cherenkov radiation have been used. At last, readings of
underground detectors of muons with energies above 1 GeV have been exploited to
make definite conclusion about chemical composition.


  We determine the range of thicknesses and refractive indices for which
omnidirectional reflection from quasiperiodic multilayers occurs. By resorting
to the notion of area under the transmittance curve, we assess in a systematic
way the performance of the different quasiperiodic Fibonacci multilayers.


  We present a simple yet general and efficient approach to representation of
computational meshes. Meshes are represented as sets of mesh entities of
different topological dimensions and their incidence relations. We discuss a
straightforward and efficient storage scheme for such mesh representations and
efficient algorithms for computation of arbitrary incidence relations from a
given initial and minimal set of incidence relations. The general
representation may harbor a wide range of computational meshes, and may also be
specialized to provide simple user interfaces for particular meshes, including
simplicial meshes in one, two and three space dimensions where the mesh
entities correspond to vertices, edges, faces and cells. It is elaborated on
how the proposed concepts and data structures may be used for assembly of
variational forms in parallel over distributed finite element meshes.
Benchmarks are presented to demonstrate efficiency in terms of CPU time and
memory usage.


  The problem of Turing instabilities for a reaction-diffusion system defined
on a complex Cartesian product networks is considered. To this end we operate
in the linear regime and expand the time dependent perturbation on a basis
formed by the tensor product of the eigenvectors of the discrete Laplacian
operators, associated to each of the individual networks that build the
Cartesian product. The dispersion relation which controls the onset of the
instability depends on a set of discrete wave- lenghts, the eigenvalues of the
aforementioned Laplacians. Patterns can develop on the Cartesian network, if
they are supported on at least one of its constituive sub-graphs. Multiplex
networks are also obtained under specific prescriptions. In this case, the
criteria for the instability reduce to compact explicit formulae. Numerical
simulations carried out for the Mimura-Murray reaction kinetics confirm the
adequacy of the proposed theory.


  We consider the quantum completeness problem, i.e. the problem of confining
quantum particles, on a non-complete Riemannian manifold $M$ equipped with a
smooth measure $\omega$, possibly degenerate or singular near the metric
boundary of $M$, and in presence of a real-valued potential $V\in
L^2_{\mathrm{loc}}(M)$. The main merit of this paper is the identification of
an intrinsic quantity, the effective potential $V_{\mathrm{eff}}$, which allows
to formulate simple criteria for quantum confinement. Let $\delta$ be the
distance from the possibly non-compact metric boundary of $M$. A simplified
version of the main result guarantees quantum completeness if $V\ge -c\delta^2$
far from the metric boundary and \[
  V_{\mathrm{eff}}+V\ge \frac3{4\delta^2}-\frac{\kappa}{\delta}, \qquad
\text{close to the metric boundary}. \] These criteria allow us to: (i) obtain
quantum confinement results for measures with degeneracies or singularities
near the metric boundary of $M$; (ii) generalize the
Kalf-Walter-Schmincke-Simon Theorem for strongly singular potentials to the
Riemannian setting for any dimension of the singularity; (iii) give the first,
to our knowledge, curvature-based criteria for self-adjointness of the
Laplace-Beltrami operator; (iv) prove, under mild regularity assumptions, that
the Laplace-Beltrami operator in almost-Riemannian geometry is essentially
self-adjoint, partially settling a conjecture formulated in [Boscain, Laurent -
Ann. Inst. Fourier, 2013] .


  Monte Carlo simulations of the frustrated 2D XY model were carried out at
small commensurate values of the frustration $f$. For $f=1/30$ a single
transition was observed at which phase coherence (finite helicity modulus) and
vortex lattice orientational order vanish together. For $f=1/56$ a new phase in
which phase coherence is absent but orientational order persists was observed.
Where comparison is possible, the results are in detailed agreement with the
behavior of the lattice Coulomb gas model of vortices. It is argued that the
helicity modulus of the frustrated 2D XY model vanishes for any finite
temperature in the limit of weak frustration $f$.


  The photovoltaic and bolometric photoresponse in gapped bilayer graphene was
investigated by optical and transport measurements. A pulse coincidence
technique at 1.5 um was used to measure the response times as a function of
temperature. The bolometric and photovoltaic response times were found to be
identical implying that the photovoltaic response is also governed by hot
electron thermal relaxation. Response times of tau ~ 100 - 20 ps were found for
temperatures from 3 - 100 K. Above 10 K, the relaxation time was observed to be
tau = 25 +/- 5 ps, independent of temperature within noise.


  In this paper, we show that an oscillator in proper time can mimic a point
mass at rest in general relativity. The spacetime outside this proper time
oscillator is static and satisfies the Schwarzschild solution.


  In this paper we consider, by means of a precise spectral analysis, the 3D
Navier-Stokes equations endowed with Navier slip-with-friction boundary
conditions. We study the problem in a very simple geometric situation as the
region between two parallel planes, with periodicity along the two planes. This
setting, which is often used in the theory of boundary layers, requires some
special treatment for what concerns the functional setting and allows us to
characterize in a rather explicit manner eigenvalues and eigenfunctions of the
associated Stokes problem. These, will be then used in order to identify
infinite dimensional classes of data leading to global strong solutions for the
corresponding evolution Navier-Stokes equations.


  This paper explores the usage of multimodal image-to-text models to enhance
text-based item retrieval. We propose utilizing pre-trained image captioning
and tagging models, such as instructBLIP and CLIP, to generate text-based
product descriptions which are combined with existing text descriptions. Our
work is particularly impactful for smaller eCommerce businesses who are unable
to maintain the high-quality text descriptions necessary to effectively perform
item retrieval for search and recommendation use cases. We evaluate the
searchability of ground-truth text, image-generated text, and combinations of
both texts on several subsets of Amazon's publicly available ESCI dataset. The
results demonstrate the dual capability of our proposed models to enhance the
retrieval of existing text and generate highly-searchable standalone
descriptions.


  So far, only the results on 3 qubit spaces (both on superconducting and
ion-trap realisations of quantum processors) have beaten the classical
unstructured search in the expected number of oracle calls using optimal
protocols in both settings. We present experimental results on running
unstructured search in spaces defined by 4, 5 and 6 qubits on ion-trapped
quantum processor. Our best circuits obtained respectively 66\%, 26\% and 6\%
average probability of measuring the marked element. In the case of 4 and 5
qubit spaces we obtained fewer expected number of oracle calls required to find
a marked element than any classical approach. Viability of the theoretical
result by Grover at these qubit counts is, to authors' knowledge demonstrated
experimentally for the first time. Also at 6 qubits, a circuit using a single
oracle call returned a measured probability of success exceeding any possible
classical approach. These results were achieved using a variety of unstructured
search algorithms in conjunction with recent developments in reducing the
number of entangling gates. The latter are currently considered to be a
dominating source of errors in quantum computations. Some of these improvements
have been made possible by using mid-circuit measurements. To our knowledge the
latter feature is currently available only on the H0 quantum processor we run
on.


  We perform an improved cosmic microwave background (CMB) analysis to search
for dark matter-proton scattering with a momentum-transfer cross section of the
form $\sigma_0 v^n$ for $n=-2$ and $n=-4$. In particular, we present a new and
robust prescription for incorporating the relative bulk velocity between the
dark matter and baryon fluids into the standard linear Boltzmann calculation.
Using an iterative procedure, we self-consistently include the effects of the
bulk velocities in a cosmology in which dark matter interacts with baryons.
With this prescription, we derive CMB bounds on the cross section, excluding
$\sigma_0 > 2.3 \times 10^{-33}~\mathrm{cm}^2$ for $n=-2$ and $\sigma_0 > 1.7
\times 10^{-41}~\mathrm{cm}^2$ for $n=-4$ at $95\%$ confidence, for dark matter
masses below 10 MeV. Furthermore, we investigate how these constraints change
when only a subcomponent of dark matter is interacting. We show that Planck
limits vanish if $\lesssim 0.4\%$ of dark matter is tightly coupled to baryons.
We discuss the implications of our results for present and future cosmological
observations.


  Interpretability has become an essential topic for artificial intelligence in
some high-risk domains such as healthcare, bank and security. For commonly-used
tabular data, traditional methods trained end-to-end machine learning models
with numerical and categorical data only, and did not leverage human
understandable knowledge such as data descriptions. Yet mining human-level
knowledge from tabular data and using it for prediction remain a challenge.
Therefore, we propose a concept and argumentation based model (CAM) that
includes the following two components: a novel concept mining method to obtain
human understandable concepts and their relations from both descriptions of
features and the underlying data, and a quantitative argumentation-based method
to do knowledge representation and reasoning. As a result of it, CAM provides
decisions that are based on human-level knowledge and the reasoning process is
intrinsically interpretable. Finally, to visualize the purposed interpretable
model, we provide a dialogical explanation that contain dominated reasoning
path within CAM. Experimental results on both open source benchmark dataset and
real-word business dataset show that (1) CAM is transparent and interpretable,
and the knowledge inside the CAM is coherent with human understanding; (2) Our
interpretable approach can reach competitive results comparing with other
state-of-art models.


  Information Theory concepts and methodologies conform the background of how
communication systems are studied and understood. They are mainly focused on
the source-channel-receiver problem and on the asymptotic limits of accuracy
and communication rates, which are the classical problems studied by Shannon.
However, the impact of Information Theory on networks (acting as the channel)
is just starting. Here, we present an approach to understand how information
flows in any connected complex network. Our approach is based on defining
linear conservative flows that travel through the network from source to
receiver. This framework allows us to have an analytical description of the
problem and also linking the topological invariants of the network, such as the
node degree, with the information flow. In particular, our approach is able to
deal with information transmission in modular networks (networks containing
community structures) or multiplex networks (networks with multiple layers),
which are nowadays of paramount importance.


  By doping Rh to the Fe sites in SrFe2As2, superconductivity is induced when
the antiferromagnetic (AF) order is suppressed. The maximum superconducting
transition temperature was found at about 22 K with the doping level of x =
0.25. It is found that the resistivity anomaly associating with the AF order
reveals a sharp drop for the parent phase and the samples with low doping (x =
0.05), while it evolves into an uprising at a higher doping level (x = 0.1). A
general phase diagram is depicted which exhibits the similarity with that of Co
doping. Regarding the close maximum superconducting transition temperatures in
doping Co, Rh and Ir, we argue that the superconductivity is intimately related
to the suppression of the AF order, rather than the electron-phonon coupling.


  This paper is a brief review of the status of the search for astrophysical
neutrinos of high energy. Its emphasis is on the search for a hard spectrum of
neutrinos from the whole Northern sky above the steeply falling background of
atmospheric neutrinos. Current limits are so low that they are beginning to
constrain models of the origin of extragalactic cosmic rays. Systematic effects
stemming from incomplete knowledge of the background of atmospheric neutrinos
are discussed.


  Sponsored search is an important monetization channel for search engines, in
which an auction mechanism is used to select the ads shown to users and
determine the prices charged from advertisers. There have been several pieces
of work in the literature that investigate how to design an auction mechanism
in order to optimize the revenue of the search engine. However, due to some
unrealistic assumptions used, the practical values of these studies are not
very clear. In this paper, we propose a novel \emph{game-theoretic machine
learning} approach, which naturally combines machine learning and game theory,
and learns the auction mechanism using a bilevel optimization framework. In
particular, we first learn a Markov model from historical data to describe how
advertisers change their bids in response to an auction mechanism, and then for
any given auction mechanism, we use the learnt model to predict its
corresponding future bid sequences. Next we learn the auction mechanism through
empirical revenue maximization on the predicted bid sequences. We show that the
empirical revenue will converge when the prediction period approaches infinity,
and a Genetic Programming algorithm can effectively optimize this empirical
revenue. Our experiments indicate that the proposed approach is able to produce
a much more effective auction mechanism than several baselines.


  We investigate the n-body problem on a sphere with a general interaction
potential that depends on the mutual distances. We focus on the equilibrium
configurations, especially on the Dziobek equilibrium configurations, which is
an analogy of Dziobek central configurations of the classical n-body problem.
We obtain a criterion and then reduce it to two sets of equations. Then we
apply these equations to the curved n-body problem in S^3. In the end, we find
the derivative of the Cayley-Menger determinant.


  We report the results of 1D particle-in-cell simulations of ultrarelativistic
shock waves in proton-electron-positron plasmas. We consider magnetized shock
waves, in which the upstream medium carries a large scale magnetic field,
directed transverse to the flow. Relativistic cyclotron instability of each
species as the incoming particles encounter the increasing magnetic field
within the shock front provides the basic plasma heating mechanism. The most
significant new results come from simulations with mass ratio $m_p/m_\pm =
100$. We show that if the protons provide a sufficiently large fraction of the
upstream flow energy density (including particle kinetic energy and Poynting
flux), a substantial fraction of the shock heating goes into the formation of
suprathermal power-law spectra of pairs. Cyclotron absorption by the pairs of
the high harmonic ion cyclotron waves, emitted by the protons, provides the
non-thermal acceleration mechanism. As the proton fraction increases, the
non-thermal efficiency increases and the pairs' power-law spectra harden.
  We suggest that the varying power law spectra observed in synchrotron sources
powered by magnetized winds and jets might reflect the correlation of the
proton to pair content enforced by the underlying electrodynamics of these
sources' outflows, and that the observed correlation between the X-ray spectra
of rotation powered pulsars with the X-ray spectra of their nebulae might
reflect the same correlation.


  The development of materials with specific structural properties is of huge
practical interest, for example, for medical applications or for the
development of light weight structures in aeronautics. In this article, we
combine shape optimization and homogenization for the optimal design of the
microstructure in scaffolds. Given the current microstructure, we apply the
isogeometric boundary element method to compute the effective tensor and to
update the microstructure by using the shape gradient in order to match the
desired effective tensor. Extensive numerical studies are presented to
demonstrate the applicability and feasibility of the approach.


  We show many consequences of the proof of Basel problem by Boo Rim Choe
(1987) to central binomial series, multiple zeta values and some other multiple
sums. The main idea is to evaluate integrals involving powers of arcsine
function.


  Most real networks are characterized by connectivity patterns that evolve in
time following complex, non-Markovian, dynamics. Here we investigate the impact
of this ubiquitous feature by studying the Susceptible-Infected-Recovered (SIR)
and Susceptible-Infected-Susceptible (SIS) epidemic models on activity driven
networks with and without memory (i.e., Markovian and non-Markovian). We show
that while memory inhibits the spreading process in SIR models, where the
epidemic threshold is moved to larger values, it plays the opposite effect in
the case of the SIS, where the threshold is lowered. The heterogeneity in tie
strengths, and the frequent repetition of connections that it entails, allows
in fact less virulent SIS-like diseases to survive in tightly connected local
clusters that serve as reservoir for the virus. We validate this picture by
evaluating the threshold of both processes in a real temporal network. Our
findings confirm the important role played by non-Markovian network dynamics on
dynamical processes


  We study the graphite intercalated compound CaC$_6$ by means of Eliashberg
theory, focusing on the anisotropy properties. An analysis of the
electron-phonon coupling is performed, and we define a minimal 6-band
anisotropy structure. Comparing with Superconducting Density Functional Theory
(SCDFT) the condition under which Eliashberg theory is able to reproduce the
SCDFT gap structure is determined, and we discuss the role of Coulomb
interactions. The Engelsberg-Schrieffer polaron structure is computed by
solving the Eliashberg equation on the Matsubara axis and analytically
continuing it to the full complex plane. This reveals the polaronic
quasiparticle bands anisotropic features as well as the interplay with
superconductivity.


  What is the path associated with the fastest Brownian particle that reaches a
narrow window located on the boundary of a domain? Although the distribution of
the fastest arrival times has been well studied in dimension 1, much less is
known in higher dimensions. Based on the Wiener path-integral, we derive a
variational principle for the path associated with the fastest arrival
particle. Specifically, we show that in a large ensemble of independent
Brownian trajectories, the first moment of the shortest arrival time is
associated with the minimization of the energy-action and the optimal
trajectories are geodesics. Escape trajectories concentrate along these
geodesics, as confirmed by stochastic simulations when an obstacle is
positioned in front of the narrow window. To conclude paths in stochastic
dynamics and their time scale can differ significantly from the mean
properties, usually at the basis of the Smoluchowski's theory of chemical
reactions.


  Within this study, we propose a new approach for natural language processing
using Bayesian networks to predict and analyze the context and how this
approach can be applied to the Community Question Answering domain. We discuss
how Bayesian networks can detect semantic relationships and dependencies
between entities, and this is connected to different score-based approaches of
structure-learning. We compared the Bayesian networks with different score
metrics, such as the BIC, BDeu, K2 and Chow-Liu trees. Our proposed approach
out-performs the baseline model at the precision metric. We also discuss the
influence of penalty terms on the structure of Bayesian networks and how they
can be used to analyze the relationships between entities. In addition, we
examine the visualization of directed acyclic graphs to analyze semantic
relationships. The article further identifies issues with detecting certain
semantic classes that are separated in the structure of directed acyclic
graphs. Finally, we evaluate potential improvements for the Bayesian network
approach.


  In this paper, we study the interactions of electromagnetic waves with a
non-dispersive dynamic medium that is temporally dependent. Electromagnetic
fields under material time-modulation conserve their momentum but not their
energy. We assume a time-variation of the permittivity, permeability and
conductivity and derive the appropriate time-domain solutions based on the
causality state at a past observation time. We formulate a time-transitioning
state matrix and connect the unusual energy transitions of electromagnetic
fields in time-varying media with the exceptional point theory. This
state-matrix approach allows us to analyze further the electromagnetic waves in
terms of parity and time-reversal symmetries and signify parity-time symmetric
wave-states without the presence of a spatially symmetric distribution of gain
and loss, or any inhomogeneities and material periodicity. This paper provides
a useful arsenal to study electromagnetic wave phenomena under time-varying
media and points out novel physical insights connecting the resulting energy
transitions and electromagnetic modes with exceptional point physics and
operator symmetries.


  Tiny machine learning (TinyML) is a rapidly growing field aiming to
democratize machine learning (ML) for resource-constrained microcontrollers
(MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask
whether TinyML applications can benefit from aggregating their knowledge.
Federated learning (FL) enables decentralized agents to jointly learn a global
model without sharing sensitive local data. However, a common global model may
not work for all devices due to the complexity of the actual deployment
environment and the heterogeneity of the data available on each device. In
addition, the deployment of TinyML hardware has significant computational and
communication constraints, which traditional ML fails to address. Considering
these challenges, we propose TinyReptile, a simple but efficient algorithm
inspired by meta-learning and online learning, to collaboratively learn a solid
initialization for a neural network (NN) across tiny devices that can be
quickly adapted to a new device with respect to its data. We demonstrate
TinyReptile on Raspberry Pi 4 and Cortex-M4 MCU with only 256-KB RAM. The
evaluations on various TinyML use cases confirm a resource reduction and
training time saving by at least two factors compared with baseline algorithms
with comparable performance.


  This paper introduces an extension of the backpropagation algorithm that
enables us to have layers with constrained weights in a deep network. In
particular, we make use of the Riemannian geometry and optimization techniques
on matrix manifolds to step outside of normal practice in training deep
networks, equipping the network with structures such as orthogonality or
positive definiteness. Based on our development, we make another contribution
by introducing the Stiefel layer, a layer with orthogonal weights. Among
various applications, Stiefel layers can be used to design orthogonal filter
banks, perform dimensionality reduction and feature extraction. We demonstrate
the benefits of having orthogonality in deep networks through a broad set of
experiments, ranging from unsupervised feature learning to fine-grained image
classification.


  The purpose of this paper is to give a twisted version of the Eynard-Orantin
topological recursion by a 2D Topological Quantum Field Theory. We define a
kernel for a 2D TQFT and use an algebraic definition for a topological
recursion to define how to twist a standard topological recursion by a 2D TQFT.
The A-model side enumerative problem consists of counting cell graphs where in
addition vertices are decorated by elements in a Frobenius algebra, and which
are a twisted version of the generalized Catalan numbers of
Dumitrescu-Mulase-Safnuk-Sorkin. We show that the function which counts these
decorated graphs satisfies a twisted version of the same type of recursion of
Catalan numbers with respect to the edge-contraction axioms of
Dumitrescu-Mulase. The path we follow to pass from the A-model side to the
remodelled B-model side is to use a discrete Laplace transform based on the
ideas of the group of Mulase. We show that a twisted version by a 2D TQFT of
the Eynard-Orantin differentials satisfies a twisted generalization of the
topological recursion formula. We shall illustrate these results with a toy
model for the theory arising from the orbifold cohomology of the classifying
space of a finite group. In this example, the graphs are drawn on an orbifold
punctured Riemann surface and defined out of the moduli space of stable
morphisms from twisted curves to the classifying space of a finite group. In
particular we show that the cotangent class intersection numbers on this moduli
space satisfy a twisted Eynard-Orantin topological recursion and we derive an
orbifold DVV equation as a consequence of it. This proves from a different
perspective a result of Jarvis-Kimura, which states that the cotangent class
intersection numbers on that moduli space satisfy the Virasoro constraint
condition.


  We analyze three different high-order nonlinear Schr\"{o}dinger equation
(HONLSE) models that have been used in the literature to describe the evolution
of slowly modulated gravity waves on the surface of ideal finite-depth fluid.
We demonstrate that the inclusion of the fifth-order nonlinear term to the
HONLSE model introduces only a small correction to the amplitude of the bright
HONLSE soliton solutions obtained without this term. Such soliton slutions
behave as quasi-solitons in this more general case.


  In this paper we study existence, nonexistence and classification of radial
positive solutions of some weighted fully nonlinear equations involving Pucci
extremal operators. Our results are entirely based on the analysis of the
dynamics induced by an autonomous quadratic system which is obtained after a
suitable transformation. This method allows to treat both regular and singular
solutions in a unified way, without using energy arguments. In particular we
recover known results on regular solutions for the fully nonlinear non weighted
problem by alternative proofs. We also slightly improve the classification of
the solutions for the extremal operator $\mathcal{M}^-$.


  ZigBee has been recently drawing a lot of attention as a promising solution
for ubiquitous computing. The ZigBee devices are normally resource-limited,
making the network susceptible to a variety of security threats. This paper
presents a severe attack on ZigBee networks termed as ghost, which leverages
the underlying vulnerabilities of the IEEE 802.15.4 security suites to deplete
the energy of the devices. We manifest that the impact of ghost is severe as it
can reduce the lifetime of devices from years to days and facilitate a variety
of threats including denial of service and replay attacks. We highlight that
merely deploying a standard suite of advanced security techniques does not
necessarily guarantee improved security, but instead might be leveraged by
adversaries to cause severe disruption in the network. We propose several
recommendations on how to localize and withstand the ghost and other related
attacks in ZigBee networks. Extensive simulations are provided to show the
impact of the ghost and the performance of the proposed recommendations.
Moreover, physical experiments also have been conducted and the observations
confirm the severity of the impact by the ghost attack. We believe that the
presented work will aid the researchers to improve the security of ZigBee
further.


  I present a construction of real or complex selfdual conformal 4-manifolds
(of signature (2,2) in the real case) from a natural gauge field equation on a
real or complex projective surface, the gauge group being the group of
diffeomorphisms of a real or complex 2-manifold. The 4-manifolds obtained are
characterized by the existence of a foliation by selfdual null surfaces of a
special kind. The classification by Dunajski and West of selfdual conformal
4-manifolds with a null conformal vector field is the special case in which the
gauge group reduces to the group of diffeomorphisms commuting with a vector
field, and I analyse the presence of compatible scalar-flat K\"ahler,
hypercomplex and hyperk\"ahler structures from a gauge-theoretic point of view.
In an appendix, I discuss the twistor theory of projective surfaces, which is
used in the body of the paper, but is also of independent interest.


  A simple normal form for Hardy operators is introduced that unifies and
simplifies the theory of weighted Hardy inequalities. A straightforward
transition to normal form is given that applies to the various Hardy operators
and their duals, whether defined on Lebesgue spaces of sequences, of functions
on the half-line, or of functions on $\mathbb R^n$ or more general metric
spaces. This is done by introducing an abstract formulation of Hardy operators,
more general than any of these, and showing that the normal form transition
applies to all operators formulated in this way.
  The transition to normal form is shown to preserve boundedness, compactness,
and operator norm. To a large extent the transition can be carried out via
well-behaved linear operators.
  Known results for boundedness and compactness of Hardy operators are given
simple proofs and extended, via the transition, to this general setting.
  New estimates for the best constant in Hardy inequalities are established and
a large class of Hardy inequalities is identified in which the best constants
are is known precisely.


  In this work we study the self-integral of a function-measure kernel and its
importance on stochastic integration. A continuous-function measure kernel $K$
over $D \subset \mathbb{R}^{d}$ is a function of two variables which acts as a
continuous function in the first variable and as a real Radon measure in the
second. Some analytical properties of such kernels are studied, particularly in
the case of cross-positive-definite type kernels. The self-integral of $K$ over
a bounded set $D$ is the "integral of $K$ with respect to itself". It is
defined using Riemann sums and denoted $\int_{D}K(x,dx)$. Some examples where
such notion is well-defined are presented. This concept turns out to be crucial
for unique-definiteness of stochastic integrals, that is, when the integral is
independent of the way of approaching it. If $K$ is the cross-covariance kernel
between a mean-square continuous stochastic process $Z$ and a random measure
with measure covariance structure $M$, $\int_{D}K(x,dx)$ is the expectation of
the stochastic integral $\int_{D} ZdM$ when both are uniquely-defined. It is
also proven that when $Z$ and $M$ are jointly Gaussian, self-integrability
properties on $K$ are necessary and sufficient to guarantee the
unique-definiteness of $\int_{D}ZdM$. Results on integrations over subsets, as
well as potential $\sigma$-additive structures are obtained. Three applications
of these results are proposed, involving tensor products of Gaussian random
measures, the study of a uniquely-defined stochastic integral with respect to
fractional Brownian motion with Hurst index $H > \frac{1}{2}$, and the
non-uniquely-defined stochastic integrals with respect to orthogonal random
measures. The studied stochastic integrals are defined without use of
martingale-type conditions, providing a potential filtration-free approach to
stochastic calculus grounded on covariance structures.


  We extend results by Stotland and Di Ventra on the phenomenon of resistive
switching aided by noise. We further the analysis of the mechanism underlying
the beneficial role of noise and study the EPIR (Electrical Pulse Induced
Resistance) ratio dependence with noise power. In the case of internal noise we
find an optimal range where the EPIR ratio is both maximized and independent of
the preceding resistive state. However, when external noise is considered no
beneficial effect is observed.


  We study the properties of tidal disruption event (TDE) host galaxies in the
context of a catalog of ~500,000 galaxies from the Sloan Digital Sky Survey. We
explore whether selection effects can account for the overrepresentation of
TDEs in E+A/post-starburst galaxies by creating matched galaxy samples.
Accounting for possible selection effects due to black hole (BH) mass, redshift
completeness, strong AGN presence, bulge colors, and surface brightness can
reduce the apparent overrepresentation of TDEs in E+A host galaxies by a factor
of ~4 (from ~$\times$100-190 to ~$\times$25-48), but cannot fully explain the
preference. We find that TDE host galaxies have atypical photometric properties
compared to similar, "typical" galaxies. In particular, TDE host galaxies tend
to live in or near the "green valley" between star-forming and passive
galaxies, and have bluer bulge colors ($\Delta (g-r) \approx 0.3$ mag), lower
half-light surface brightnesses (by ~1 mag/arcsec$^2$), higher Sersic indices
($\Delta n_{\rm g} \approx 3$), and higher bulge-to-total-light ratios ($\Delta
B/T \approx 0.5$) than galaxies with matched BH masses. We find that TDE host
galaxies appear more centrally concentrated and that all have high galaxy
Sersic indices and $B/T$ fractions---on average in the top 10% of galaxies of
the same BH mass---suggesting a higher nuclear stellar density. We identify a
region in Sersic index and BH mass parameter space that contains ~2% of our
reference catalog galaxies but $\ge\!60\%$ of TDE host galaxies. The unique
photometric properties of TDE host galaxies may be useful for selecting
candidate TDEs for spectroscopic follow-up observations in large transient
surveys.


  Obtaining a stable magnetohydrodynamical (MHD) formalism in SPH - i.e.
smoothed particle magnetohydrodynamics (SPMHD) - has proven remarkably
difficult. To implement MHD requires two steps: a modification to the momentum
equation and an induction equation, and both present challenges. We first
provide an overview of how SPMHD is implemented, and then discuss how this
implementation fails and the limitation of various corrective methods - with
particular reference to the effects of particle disorder. Although there are
many problems for which, with careful choice of corrective measures, good
results can be obtained, we then show that, at the very limits of the state of
the art, the ability to perform stable MHD calculations in SPH is curtailed by
numerical issues.


  In this article, we describe a simple class of models of absorbed diffusion
processes with parameter, whose conditional law exhibits a transcritical
bifurcation. Our proofs are based on the description of the set of
quasi-stationary distributions for general two-clusters reducible processes.