  We present an analysis of the UV (IUE) spectra of the central stars of Hb7
and Sp3. Comparison with the IUE spectrum of the standard star HD 93205 leads
to a spectral classification of O3V for these stars, with an effective
temperature of 50,000 K. From the P-Cygni profiles of CIV (1550 A), we derive
stellar wind velocities and mass loss rates of -1317 km/s +/- 300 km/s and
2.9X10^{-8} solar mass yr^{-1} and -1603 km/s +/- 400 km/s and 7X10^{-9} solar
mass yr^{-1} for Hb7 and Sp3 respectively. From all the available data, we
reconstruct the spectral energy distribution of Hb7 and Sp3.


  We have simulated conduction in a two-dimensional electron gas subject to a
weak two-dimensional periodic potential, $V_x \cos(2\pi x/a) + V_y \cos(2\pi
y/a)$. The usual commensurability oscillations in $\rho_{xx}(B)$ are seen with
$V_x$ alone. An increase of $V_y$ suppresses these oscillations, rather than
introducing the additional oscillations in $\rho_{yy}(B)$ expected from
previous perturbation theories. We show that this behavior arises from drift of
the guiding center of cyclotron motion along contours of an effective
potential. Periodic modulation in the magnetic field can be treated in the same
way.


  In this paper we present SimCommSys, a Simulator of Communication Systems
that we are releasing under an open source license. The core of the project is
a set of C++ libraries defining communication system components and a
distributed Monte Carlo simulator. Of principal interest is the error-control
coding component, where various kinds of binary and non-binary codes are
implemented, including turbo, LDPC, repeat-accumulate, and Reed-Solomon. The
project also contains a number of ready-to-build binaries implementing various
stages of the communication system (such as the encoder and decoder), a
complete simulator, and a system benchmark. Finally, SimCommSys also provides a
number of shell and python scripts to encapsulate routine use cases. As long as
the required components are already available in SimCommSys, the user may
simulate complete communication systems of their own design without any
additional programming. The strict separation of development (needed only to
implement new components) and use (to simulate specific constructions)
encourages reproducibility of experimental work and reduces the likelihood of
error. Following an overview of the framework, we provide some examples of how
to use the framework, including the implementation of a simple codec, the
specification of communication systems and their simulation.


  Two kinds of evolving trees are considered here: the exponential trees, where
subsequent nodes are linked to old nodes without any preference, and the
Barab\'asi--Albert scale-free networks, where the probability of linking to a
node is proportional to the number of its pre-existing links. In both cases,
new nodes are linked to $m=1$ nodes. Average node-node distance $d$ is
calculated numerically in evolving trees as dependent on the number of nodes
$N$. The results for $N$ not less than a thousand are averaged over a thousand
of growing trees. The results on the mean node-node distance $d$ for large $N$
can be approximated by $d=2\ln(N)+c_1$ for the exponential trees, and
$d=\ln(N)+c_2$ for the scale-free trees, where the $c_i$ are constant. We
derive also iterative equations for $d$ and its dispersion for the exponential
trees. The simulation and the analytical approach give the same results.


  We study the bounds on minimal lepton flavour violation in the context of
Type-III see-saw imposed by LHC Run I search for events which contain two
charged leptons (either electron or muons of equal or opposite sign), two jets
from a hadronically decaying $W$ boson and large missing transverse momentum.
In this scenario the flavour structure of the couplings of the triplet fermions
to the Standard Model leptons can be reconstructed from the neutrino mass
matrix and lepton number violation is very suppressed. We find that using the
information on charge and flavour of the leptons in the above final state it is
possible to unambiguously rule out this scenario with triplet masses lighter
than 300 GeV at 95% CL. The same analysis allows to exclude triplet masses
masses up to 480 GeV at 95% CL for normal ordering of neutrino masses and
specific values of a Majorana CP phase currently undetermined by neutrino
physics.


  We report the beginning of the astronomical masers investigations in the 3-mm
bandwidth at the radio telescope RT-22 (CrAO, Ukraine). For this purpose the
special complex for maser lines investigation in 85...115 GHz frequency band is
developed. It is made on the base of the low noise cryogenic Shottky-diode
receiver and the high resolution Fourier-spectrometer. The cryogenic receiver
has the DSB noise temperature less than 100K. The spectral channel separation
of the Fourier-spectrometer is about 4kHz and the spectrometer bandwidth is 8
MHz. Results of maser observations of 8$^{0}-7^{1} $A$^{+}$ transition of
methanol (95.169 GHz) towards DR-21(OH), DR-21W and NGC7538 are in good
agreement with early obtained results by other authors. On the basis of the
analysis of the location of masers in the NGC7538 direction we can assume that
the origin of all known class I methanol masers in this region is connected
with existing molecular outflows from young stars.


  Limitations of the delayed feedback control and of its extended versions have
been fully treated in the literature. The oscillating delayed feedback control
appears as a promising scheme to overcome this problem. In this work, two
methods based on oscillatory delayed feedback control schemes for the
continuous time case are dealt with. For both of them, stabilization of an
equilibrium point in the general non-linear scalar case is rigorously proven.
Additionally, their control performance and stability parameters region are
respectively studied.


  We compute exactly the von Neumann entanglement entropy of the eta-pairing
states - a large set of exact excited eigenstates of the Hubbard Hamiltonian.
For the singlet eta-pairing states the entropy scales with the logarithm of the
spatial dimension of the (smaller) partition. For the eta-pairing states with
finite spin magnetization density, the leading term can scale as the volume or
as the area-times-log, depending on the momentum space occupation of the
Fermions with flipped spins. We also compute the corrections to the leading
scaling. In order to study the eigenstate thermalization hypothesis (ETH), we
also compute the entanglement Renyi entropies of such states and compare them
with the corresponding entropies of thermal density matrix in various
ensembles. Such states, which we find violate strong ETH, may provide a useful
platform for a detailed study of the time-dependence of the onset of
thermalization due to perturbations which violate the total pseudospin
conservation.


  Quantum process tomography is a critical capability for building quantum
computers, enabling quantum networks, and understanding quantum sensors. Like
quantum state tomography, the process tomography of an arbitrary quantum
channel requires a number of measurements that scale exponentially in the
number of quantum bits affected. However, the recent field of shadow
tomography, applied to quantum states, has demonstrated the ability to extract
key information about a state with only polynomially many measurements. In this
work, we apply the concepts of shadow state tomography to the challenge of
characterizing quantum processes. We make use of the Choi isomorphism to
directly apply rigorous bounds from shadow state tomography to shadow process
tomography, and we find additional bounds on the number of measurements that
are unique to process tomography. Our results, which include algorithms for
implementing shadow process tomography enable new techniques including
evaluation of channel concatenation and the application of channels to shadows
of quantum states. This provides a dramatic improvement for understanding
large-scale quantum systems.


  GRB 190114C is an unusual gamma-ray burst (GRB) due to its detection at
sub-$TeV$ energies by MAGIC, seen at redshift z = 0.42. This burst is one of
the brightest GRB detected by \fermi. A joint GBM-LAT analysis of the prompt
emission reveals the presence of sub-$GeV$ spectral cutoff when the LAT
\emph{low-energy events} (LLE) data is also examined. A similar high-energy
cutoff was likewise reported in GRB 160509A and GRB 100724B earlier, as well as
handful of other sources. The cutoff can be explained by the intrinsic opacity
due to pair production within the emitting region. GRB 190114C shows a
transition from non-thermal to a quasi-thermal-like spectrum and a radiation
component that can be attributed to afterglow. Based on spectral analysis, we
constrain the site of the prompt emission and $Lorentz$ factor. Knowing that
sub-$TeV$ photons are detected in MAGIC, we perceive that the observed spectrum
is indeed an overlap from two emission sites, where the emission observed in
\fermi is more consistent with prompt emission produced via photospheric
dissipation along with a concurrent component from the external shock.


  Kolmogorov flow in two dimensions - the two-dimensional Navier-Stokes
equations with a sinusoidal body force - is considered over extended periodic
domains to reveal localised spatiotemporal complexity. The flow response
mimicks the forcing at small forcing amplitudes but beyond a critical value
develops a long wavelength instability. The ensuing state is described by a
Cahn-Hilliard-type equation and as a result coarsening dynamics are observed
for random initial data. After further bifurcations, this regime gives way to
multiple attractors, some of which possess spatially-localised time dependence.
Co-existence of such attractors in a large domain gives rise to interesting
collisional dynamics which is captured by a system of 5 (1-space and 1-time)
PDEs based on a long wavelength limit. The coarsening regime reinstates itself
at yet higher forcing amplitudes in the sense that only longest-wavelength
solutions remain attractors. Eventually, there is one global longest-wavelength
attractor which possesses two localised chaotic regions - a kink and antikink -
which connect two steady one-dimensional flow regions of essentially half the
domain width each. The wealth of spatiotemporal complexity uncovered presents a
bountiful arena in which to study the existence of simple invariant localised
solutions which presumably underpin all of the observed behaviour.


  We prove a quantitative version of the non-uniform hyperbolicity of the
Teichm\"uller geodesic flow. Namely, at each point of any Teichm\"uller flow
line, we bound the infinitesimal spectral gap for variations of the Hodge norm
along the flow line in terms of an easily estimated geometric quantity on the
flat surface, which is greater than or equal to the flat systole. As
applications, we strengthen results of Trevi\~no and Smith regarding unique
ergodicity of measured foliations, and give an estimate for the spectral gaps
of pseudo-Anosov homeomorphisms based on the location of their axes in the
moduli space of quadratic differentials.


  Model reduction for fluid flow simulation continues to be of great interest
across a number of scientific and engineering fields. Here, we explore the use
of Neural Ordinary Differential Equations, a recently introduced family of
continuous-depth, differentiable networks (Chen et al 2018), as a way to
propagate latent-space dynamics in reduced order models. We compare their
behavior with two classical non-intrusive methods based on proper orthogonal
decomposition and radial basis function interpolation as well as dynamic mode
decomposition. The test problems we consider include incompressible flow around
a cylinder as well as real-world applications of shallow water hydrodynamics in
riverine and estuarine systems. Our findings indicate that Neural ODEs provide
an elegant framework for stable and accurate evolution of latent-space dynamics
with a promising potential of extrapolatory predictions. However, in order to
facilitate their widespread adoption for large-scale systems, significant
effort needs to be directed at accelerating their training times. This will
enable a more comprehensive exploration of the hyperparameter space for
building generalizable Neural ODE approximations over a wide range of system
dynamics.


  A. Reid showed that if $\Gamma_1$ and $\Gamma_2$ are arithmetic lattices in
$G = \operatorname{PGL}_2(\mathbb R)$ or in $\operatorname{PGL}_2(\mathbb C)$
which give rise to isospectral manifolds, then $\Gamma_1$ and $\Gamma_2$ are
commensurable (after conjugation). We show that for $d \geq 3$ and ${\mathcal
S} = \operatorname{PGL}_d(\mathbb R) / \operatorname{PGO}_d(\mathbb R)$, or
${\mathcal S} = \operatorname{PGL}_d(\mathbb C) / \operatorname{PU}_d(\mathbb
C)$, the situation is quite different: there are arbitrarily large finite
families of isospectral non-commensurable compact manifolds covered by
$\mathcal S$.
  The constructions are based on the arithmetic groups obtained from division
algebras with the same ramification points but different invariants.


  Density functional theory (DFT) calculations confirm that the structurally
ordered Fe$_2$VAl Heusler alloy is nonmagnetic narrow-gap semiconductor. This
compound is apt to form various disordered modifications with high
concentration of antisite defects. We study the effect of structural disorder
on the electronic structure, magnetic, and electronic transport properties of
the full Heusler alloy Fe$_2$VAl and its off-stoichiometric equivalent
Fe$_2$VAl$_{1.35}$. Data analysis in relation to {\it ab initio} calculations
indicates an appearance of antisite disorder mainly due to Fe--V and Fe--Al
stoichiometric variations. The data for weakly magnetic Fe$_2$VAl$_{1.35}$ are
discussed in respect to Ni$_2$VAl. Fe$_2$VAl$_{1.35}$ can be classified as a
nearly ferromagnetic metal with a pronounced spin glassy contribution, which,
however, does not give a predominant effect on its thermoelectric properties.
The figure of merit $ZT$ is at 300 K about 0.05 for the Fe sample and 0.02 for
Ni one, respectively. However, it is documented that the narrow $d$ band
resulting from Fe/V site exchange can be responsible for the unusual
temperature dependencies of the physical properties of the Fe2TiAl$_{1.35}$
alloy, characteristic of strongly correlated electron systems. As an example,
the magnetic susceptibility of Fe$_2$VAl$_{1.35}$ exhibits singularity
characteristic of a Griffiths phase, appearing as an inhomogeneous electronic
state below $T_G\sim 200$ K. We also performed numerical analysis which
supports the Griffiths phase scenario.


  Spatio-temporal coherency is a major challenge in synthesizing high quality
videos, particularly in synthesizing human videos that contain rich global and
local deformations. To resolve this challenge, previous approaches have
resorted to different features in the generation process aimed at representing
appearance and motion. However, in the absence of strict mechanisms to
guarantee such disentanglement, a separation of motion from appearance has
remained challenging, resulting in spatial distortions and temporal jittering
that break the spatio-temporal coherency. Motivated by this, we here propose
LEO, a novel framework for human video synthesis, placing emphasis on
spatio-temporal coherency. Our key idea is to represent motion as a sequence of
flow maps in the generation process, which inherently isolate motion from
appearance. We implement this idea via a flow-based image animator and a Latent
Motion Diffusion Model (LMDM). The former bridges a space of motion codes with
the space of flow maps, and synthesizes video frames in a warp-and-inpaint
manner. LMDM learns to capture motion prior in the training data by
synthesizing sequences of motion codes. Extensive quantitative and qualitative
analysis suggests that LEO significantly improves coherent synthesis of human
videos over previous methods on the datasets TaichiHD, FaceForensics and
CelebV-HQ. In addition, the effective disentanglement of appearance and motion
in LEO allows for two additional tasks, namely infinite-length human video
synthesis, as well as content-preserving video editing.


  We study a double scaling limit for a solution of the discrete Painlev\'e II
equation with boundary conditions. The location of the right boundary point is
in the critical regime where the discrete Painlev\'e equation turns into the
continuous Painlev\'e II equation. Our main results it that, instead of the
Hastings-McLeod solution (which would occur when the right boundary point is at
infinity), the solution to the discrete equation converges in a double scaling
limit to a tronqu\'ee solution of the Painlev\'e II equation that behaves like
the Hastings-McLeod solution at minus infinity and has a pole at a prescribed
location. Our proof of the double scaling limit is based on finding an
approximation that is sufficiently close in order to apply the Kantorovich
theorem for Netwons method. To meet the criteria for this theorem, we will
establish a lower bound for the solutions to the Painlev\'e II equation that
occur (including the Hastings-McLeod solution).


  The hyperdeterminant of a polynomial (interpreted as a symmetric tensor)
factors into several irreducible factors with multiplicities. Using geometric
techniques these factors are identified along with their degrees and their
multiplicities. The analogous decomposition for the \mu-discriminant of
polynomial is found.


  In this paper, two kinds of ultrasonic radiation rod with surface treatment
(ion nitriding and vacuum carburizing) are selected to carry out finite element
analysis on ultrasonic vibration system and casting system, and explore the
influence of surface treatment on vibration energy transmission of radiation
rod. The cavitation field of radiation rod with different surface treatment in
water was obtained through the cavitation erosion area of aluminum foil in
water by using the aluminum foil cavitation experiment, so as to verify the
simulation results of sound pressure field in aluminum melt. The results show
that the surface treatment weakens the vibration response of the radiating rod,
reduces the longitudinal amplitude of the radiating rod, and reduces the
amplitude of sound pressure transmitted into the aluminum melt.


  Anisotropy of the quadratic Zeeman effect for the $^3{\rm H}_6 \rightarrow \,
^3{\rm H}_4$ transition at 793 nm wavelength in $^{169}$Tm$^{3+}$-doped
Y$_3$Al$_5$O$_{12}$ is studied, revealing shifts ranging from near zero up to +
4.69 GHz/T$^2$ for ions in magnetically inequivalent sites. This large range of
shifts is used to spectrally resolve different subsets of ions and study
nuclear spin relaxation as a function of temperature, magnetic field strength,
and orientation in a site-selective manner. A rapid decrease in spin lifetime
is found at large magnetic fields, revealing the weak contribution of
direct-phonon absorption and emission to the nuclear spin-lattice relaxation
rate. We furthermore confirm theoretical predictions for the phonon coupling
strength, finding much smaller values than those estimated in the limited
number of past studies of thulium in similar crystals. Finally, we observe a
significant -- and unexpected -- magnetic field dependence of the two-phonon
Orbach spin relaxation process at higher field strengths, which we explain
through changes in the electronic energy-level splitting arising from the
quadratic Zeeman effect.


  Luttinger's theorem is a fundamental result in the theory of interacting
Fermi systems: it states that the volume inside the Fermi surface is left
invariant by interactions, if the number of particles is held fixed. Although
this is traditionally justified using perturbation theory, it can be viewed as
arising from a momentum balance argument that examines the response of the
ground state to the insertion of a single flux quantum [M. Oshikawa, Phys. Rev.
Lett. 84, 3370 (2000)]. This reveals that the Fermi sea volume is a
topologically protected quantity. Extending this approach, I show that spinless
or spin-rotation-preserving fermionic systems in non-symmorphic crystals
possess generalized topological 'Luttinger invariants' that can be nonzero even
in cases where the Fermi sea volume vanishes. A nonzero Luttinger invariant
then forces energy bands to touch, leading to semimetals whose gaplessness is
thus rooted in topology; opening a gap without symmetry breaking automatically
triggers fractionalization. The existence of these invariants is linked to the
inability of non-symmorphic crystals to host band insulating ground states
except at special fillings. I exemplify the use of these new invariants by
showing that they distinguish various classes of two- and three-dimensional
semimetals.


  Videos are multimodal in nature. Conventional video recognition pipelines
typically fuse multimodal features for improved performance. However, this is
not only computationally expensive but also neglects the fact that different
videos rely on different modalities for predictions. This paper introduces
Hierarchical and Conditional Modality Selection (HCMS), a simple yet efficient
multimodal learning framework for efficient video recognition. HCMS operates on
a low-cost modality, i.e., audio clues, by default, and dynamically decides
on-the-fly whether to use computationally-expensive modalities, including
appearance and motion clues, on a per-input basis. This is achieved by the
collaboration of three LSTMs that are organized in a hierarchical manner. In
particular, LSTMs that operate on high-cost modalities contain a gating module,
which takes as inputs lower-level features and historical information to
adaptively determine whether to activate its corresponding modality; otherwise
it simply reuses historical information. We conduct extensive experiments on
two large-scale video benchmarks, FCVID and ActivityNet, and the results
demonstrate the proposed approach can effectively explore multimodal
information for improved classification performance while requiring much less
computation.


  Blockmodeling of a given problem represented by an $N\times N$ adjacency
matrix can be found by swapping rows and columns of the matrix (i.e.
multiplying matrix from left and right by a permutation matrix). Although
classical matrix permutations can be efficiently done by swapping pointers for
the permuted rows (or columns) of the matrix, by changing row-column order, a
permutation changes the location of the matrix elements, which determines the
membership of a group in the matrix based blockmodeling. Therefore, a brute
force initial estimation of a fitness value for a candidate solution involving
counting the memberships of the elements may require going through all the sum
of the rows (or the columns).
  Similarly permutations can be also implemented efficiently on quantum
computers, e.g. a NOT gate on a qubit. In this paper, using permutation
matrices and qubit measurements, we show how to solve blockmodeling on quantum
computers. In the model, the measurement outcomes of a small group of qubits
are mapped to indicate the fitness value. However, if the number of qubits in
the considered group is much less than $n=log(N)$, it is possible to find or
update the fitness value based on the state tomography in $O(poly(log(N)))$.
Therefore, when the number of iterations is less than $log(N)$ time and the
size of the considered qubit group is small, we show that it may be possible to
reach the solution very efficiently.


  We prove a decomposition theorem for orthocomplemented state property
systems. More specifically we prove that an orthocomplemented state property
system is isomorphic to the direct union of the non classical components of
this state property system over the state space of the classical state property
system of this state property system.


  Misinformation has emerged as a major societal threat in recent years in
general; specifically in the context of the COVID-19 pandemic, it has wrecked
havoc, for instance, by fuelling vaccine hesitancy. Cost-effective, scalable
solutions for combating misinformation are the need of the hour. This work
explored how existing information obtained from social media and augmented with
more curated fact checked data repositories can be harnessed to facilitate
automated rebuttal of misinformation at scale. While the ideas herein can be
generalized and reapplied in the broader context of misinformation mitigation
using a multitude of information sources and catering to the spectrum of social
media platforms, this work serves as a proof of concept, and as such, it is
confined in its scope to only rebuttal of tweets, and in the specific context
of misinformation regarding COVID-19. It leverages two publicly available
datasets, viz. FaCov (fact-checked articles) and misleading (social media
Twitter) data on COVID-19 Vaccination.


  The Kontsevich integral of a knot is a powerful invariant which takes values
in an algebra of trivalent graphs with legs. Given a Lie algebra, the
Kontsevich integral determines an invariant of knots (the so-called colored
Jones function) with values in the symmetric algebra of the Lie algebra.
Recently A. Kricker and the author constructed a rational form of the
Kontsevich integral which takes values in an algebra of trivalent graphs with
beads. After replacing beads by an exponential legs, this rational form
recovers the Kontsevich integral. The goal of the paper is to explain the
relation between beads and functions defined on a Lie group. As an application,
we provide a rational form for the colored Jones function of a knot,
conjectured by Rozansky. This is a revised version.


  Short-pulse fibre lasers are a complex dynamical system possessing a broad
space of operating states that can be accessed through control of cavity
parameters. Determination of target regimes is a multi-parameter global
optimisation problem. Here, we report the implementation of a genetic algorithm
to intelligently locate optimum parameters for stable single-pulse mode-locking
in a Figure-8 fibre laser, and fully automate the system turn-on procedure.
Stable ultrashort pulses are repeatably achieved by employing a compound
fitness function that monitors both temporal and spectral output properties of
the laser. Our method of encoding photonics expertise into an algorithm and
applying machine-learning principles paves the way to self-optimising `smart'
optical technologies.


  As a first step toward a multi-phase equation of state for dense water, we
develop a temperature-dependent equation of state for dense water covering the
liquid and plasma regimes and extending to the super-ionic and gas regimes.
This equation of state covers the complete range of conditions encountered in
planetary modeling.
  We use first principles quantum molecular dynamics simulations and its
Thomas-Fermi extension to reach the highest pressures encountered in giant
planets several times the size of Jupiter. Using these results, as well as the
data available at lower pressures, we obtain a parametrization of the Helmholtz
free energy adjusted over this extended temperature and pressure domain. The
parametrization ignores the entropy and density jumps at phase boundaries but
we show that it is sufficiently accurate to model interior properties of most
planets and exoplanets.
  We produce an equation of state given in analytical form that is readily
usable in planetary modeling codes and dynamical simulations (a fortran
implementation can be found at http://www.ioffe.ru/astro/H2O/). The EOS
produced is valid for the entire density range relevant to planetary modeling,
for densities where quantum effects for the ions can be neglected, and for
temperatures below 50,000K. We use this equation of state to calculate the
mass-radius relationship of exoplanets up to 5,000M_Earth, explore temperature
effects in ocean and wet Earth-like planets, and quantify the influence of the
water EOS for the core on the gravitational moments of Jupiter.


  The purpose of this expository note is to revisit Morawetz's method for
obtaining a lower bound on the rate of exponential decay of waves for the
Dirichlet problem outside star-shaped obstacles, and to discuss the uniqueness
of the sphere as the extremizer of the sharp lower bound proved by Ralston.


  We report measurements of the frequency-dependent shear moduli of aging
colloidal systems that evolve from a purely low-viscosity liquid to a
predominantly elastic glass or gel. Using microrheology, we measure the local
complex shear modulus $G^{*}(\omega)$ over a very wide range of frequencies (1
Hz- 100 kHz). The combined use of one- and two-particle microrheology allows us
to differentiate between colloidal glasses and gels - the glass is homogenous,
whereas the colloidal gel shows a considerable degree of heterogeneity on
length scales larger than 0.5 micrometer. Despite this characteristic
difference, both systems exhibit similar rheological behavior which evolve in
time with aging, showing a crossover from a single power-law frequency
dependence of the viscoelastic modulus to a sum of two power laws. The
crossover occurs at a time $t_{0}$, which defines a mechanical transition
point. We found that the data acquired during the aging of different samples
can be collapsed onto a single master curve by scaling the aging time with
$t_{0}$. This raises questions about the prior interpretation of two power laws
in terms of a superposition of an elastic network embedded in a viscoelastic
background.
  Keywords: Aging, colloidal glass, passive microrheology


  The boundary layers of weakly-magnetized white dwarfs (WDs) accreting at
rates <=10^16 g/s are radially extended, hot, optically-thin, and they advect
some of their internally-dissipated energy (Narayan & Popham 1993). Motivated
by this, I construct here idealized spectral models of an Advection-Dominated
Accretion Flow (ADAF) around a WD, for application to quiescent Dwarf Novae
(DN). The Bremsstrahlung cooling of the gas in the ADAF, with temperatures
ranging from a few keV to a few tens of keV, can account for the X-ray emission
properties of quiescent DN. If the energy advected by the flow is thermalized
in the WD atmosphere, the resulting emission from the entire stellar surface
(blackbody of temperature T_eff ~ 5 eV) outshines the X-ray luminosity
substantially. This extreme-UV component provides a flux in the 0.055-0.28 keV
band which is sufficient to power the strong HeII lambda4686 emission lines of
quiescent DN by photoionization of the disk material. Reprocessing of the ADAF
X-ray emission by a cold outer thin disk could also lead to an observable iron
Kalpha fluorescence emission line, which can be used to probe the geometry of
the accretion flow. Existing observational data indicate that the presence of
ADAFs in quiescent DN is not ubiquitous, while future observations, in
particular with the X-ray satellites Chandra and XMM-Newton, have the potential
to detect signatures of the hot flow in promising candidates.


  We find a new $CP$-violation effect in charm decays into neutral kaons, which
results from the interference between two tree (Cabibbo-favored and doubly
Cabibbo-suppressed) amplitudes with the mixing of final-state mesons. This
effect, estimated to be of an order of $10^{-3}$, is much larger than the
direct $CP$ asymmetries in these decays, but missed in the literature. It can
be revealed by measuring the difference of the time-dependent $CP$ asymmetries
in the $D^{+}\to\pi^{+}K_S^0$ and $D_{s}^{+}\to K^{+} K_S^0$ modes, which are
accessible at the LHCb and Belle II experiments. If confirmed, the new effect
has to be taken into account, as the above direct $CP$ asymmetries are used to
search for new physics.


  We have investigated a method for substituting oxygen with nitrogen in EuO
thin films, which is based on molecular beam epitaxy distillation with NO gas
as the oxidizer. By varying the NO gas pressure, we produce crystalline,
epitaxial EuO_(1-x)N_x films with good control over the films' nitrogen
concentration. In-situ x-ray photoemission spectroscopy reveals that nitrogen
substitution is connected to the formation Eu3+ 4f6 and a corresponding
decrease in the number of Eu2+ 4f7, indicating that nitrogen is being
incorporated in its 3- oxidation state. While small amounts of Eu3+ in
over-oxidized Eu_(1-delta)O thin films lead to a drastic suppression of the
ferromagnetism, the formation of Eu3+ in EuO_(1-x)N_x still allows the
ferromagnetic phase to exist with an unaffected Tc, thus providing an ideal
model system to study the interplay between the magnetic f7 (J=7/2) and the
non-magnetic f6 (J=0) states close to the Fermi level.


  We study the sharp interface limit and well-posedness of a phase field model
for self-climb of prismatic dislocation loops in periodic settings. The model
is set up in a Cahn-Hilliard/Allen-Cahn framework featured with degenerate
phase-dependent diffusion mobility with an additional stablizing function.
Moreover, a nonlocal climb force is added to the chemical potential. We
introduce a notion of weak solutions for the nonlinear model. The existence
result is obtained by approximations of the proposed model with nondegenerate
mobilities. Lastly, the numerical simulations are performed to validate the
phase field model and the simulation results show the big difference for the
prismatic dislocation loops in the evolution time and the pattern with and
without self-climb contribution.


  The Minimum Current Corona (MCC) model provides a way to estimate stored
coronal energy using the number of field lines connecting regions of positive
and negative photospheric flux. This information is quantified by the net flux
connecting pairs of opposing regions in a connectivity matrix. Changes in the
coronal magnetic field, due to processes such as magnetic reconnection,
manifest themselves as changes in the connectivity matrix. However, the
connectivity matrix will also change when flux sources emerge or submerge
through the photosphere, as often happens in active regions. We have developed
an algorithm to estimate the changes in flux due to emergence and submergence
of magnetic flux sources. These estimated changes must be accounted for in
order to quantify storage and release of magnetic energy in the corona. To
perform this calculation over extended periods of time, we must additionally
have a consistently labeled connectivity matrix over the entire observational
time span. We have therefore developed an automated tracking algorithm to
generate a consistent connectivity matrix as the photospheric source regions
evolve over time. We have applied this method to NOAA Active Region 11112,
which underwent a GOES M2.9 class flare around 19:00 on Oct.16th, 2010, and
calculated a lower bound on the free magnetic energy buildup of ~8.25 x 10^30
ergs over 3 days.


  A well-posed initial-boundary value problem is formulated for the model
problem of the vector wave equation subject to the divergence-free constraint.
Existence, uniqueness and stability of the solution is proved by reduction to a
system evolving the constraint quantity statically, i.e., the second time
derivative of the constraint quantity is zero. A new set of
radiation-controlling constraint-preserving boundary conditions is constructed
for the free evolution problem. Comparison between the new conditions and the
standard constraint-preserving boundary conditions is made using the
Fourier-Laplace analysis and the power series decomposition in time. The new
boundary conditions satisfy the Kreiss condition and are free from the
ill-posed modes growing polynomially in time.


  CNNs, RNNs, GCNs, and CapsNets have shown significant insights in
representation learning and are widely used in various text mining tasks such
as large-scale multi-label text classification. However, most existing deep
models for multi-label text classification consider either the non-consecutive
and long-distance semantics or the sequential semantics, but how to consider
them both coherently is less studied. In addition, most existing methods treat
output labels as independent methods, but ignore the hierarchical relations
among them, leading to useful semantic information loss. In this paper, we
propose a novel hierarchical taxonomy-aware and attentional graph capsule
recurrent CNNs framework for large-scale multi-label text classification.
Specifically, we first propose to model each document as a word order preserved
graph-of-words and normalize it as a corresponding words-matrix representation
which preserves both the non-consecutive, long-distance and local sequential
semantics. Then the words-matrix is input to the proposed attentional graph
capsule recurrent CNNs for more effectively learning the semantic features. To
leverage the hierarchical relations among the class labels, we propose a
hierarchical taxonomy embedding method to learn their representations, and
define a novel weighted margin loss by incorporating the label representation
similarity. Extensive evaluations on three datasets show that our model
significantly improves the performance of large-scale multi-label text
classification by comparing with state-of-the-art approaches.


  We perform a comparative study of the time dependence of the holographic
quantum complexity of some space like singular bulk gravitational backgrounds.
This is done by considering the two available notions of complexity, one that
relates it to the maximal spatial volume and the other that relates it to the
classical action of the Wheeler-de Witt patch. We calculate and compare the
leading and the next to leading terms and find some universal features. The
complexity decreases towards the singularity for both definitions, for all
types of singularities studied. In addition the leading terms have the same
quantitative behavior for both definitions in restricted number of cases and
the behaviour itself is different for different singular backgrounds. The
quantitative details of the next to leading terms, such as their specific form
of time dependence, are found not to be universal. They vary between the
different cases and between the different bulk definitions of complexity. We
also address some technical points inherent to the calculation.


  Effective Field Theory (EFT) provides a powerful framework that exploits a
separation of scales in physical systems to perform systematically improvable,
model-independent calculations. Particularly interesting are few-body systems
with short-range interactions and large two-body scattering length. Such
systems display remarkable universal features. In systems with more than two
particles, a three-body force with limit cycle behavior is required for
consistent renormalization already at leading order. We will review this EFT
and some of its applications in the physics of cold atoms. Recent extensions of
this approach to the four-body system and N-boson droplets in two spatial
dimensions will also be discussed.


  This is a comprehensive report on the phase transition between two turbulent
states of electroconvection in nematic liquid crystals, which was recently
found by the authors to be in the directed percolation (DP) universality class
[K. A. Takeuchi et al., Phys. Rev. Lett. 99, 234503 (2007)]. We further
investigate both static and dynamic critical behavior of this phase transition,
measuring a total of 12 critical exponents, 5 scaling functions, and 8 scaling
relations, all in full agreement with those characterizing the DP class in 2+1
dimensions. Developing an experimental technique to create a seed of
topological-defect turbulence by pulse laser, we confirm in particular the
rapidity symmetry, which is a basic but nontrivial consequence of the
field-theoretic approach to DP. This provides the first clear experimental
realization of this outstanding, truly out-of-equilibrium universality class,
dominating most phase transitions into an absorbing state.


  The multi-state constraint Kalman filter (MSCKF) has been proven to be more
efficient than graph optimization for visual-based odometry while with similar
accuracy. However, it has not yet been properly considered and studied for
LiDAR-based odometry. In this paper, we propose a novel tightly coupled
LiDAR-inertial odometry based on the MSCKF framework, named MSC-LIO. An
efficient LiDAR same-plane-point (LSPP) tracking method, without explicit
feature extraction, is present for frame-to-frame data associations. The
tracked LSPPs are employed to build an LSPP measurement model, which constructs
a multi-state constraint. Besides, we propose an effective point-velocity-based
LiDAR-IMU time-delay (LITD) estimation method, which is derived from the
proposed LSPP tracking method. Extensive experiments were conducted on both
public and private datasets. The results demonstrate that the proposed MSC-LIO
yields higher accuracy and efficiency than the state-of-the-art methods. The
ablation experiment results indicate that the data-association efficiency is
improved by nearly 3 times using the LSPP tracking method. Besides, the
proposed LITD estimation method can effectively and accurately estimate the
LITD.


  We construct a novel preorder on the set of nodes of a simple undirected
graph. We prove that the preorder (induced by the topology of the graph) is
preserved, e.g., by the logistic dynamical system (both in discrete and
continuous time). Moreover, the underlying equivalence relation of the preorder
corresponds to the coarsest equitable partition (CEP). This will further imply
that the logistic dynamical system on a graph preserves its coarsest equitable
partition. The results provide a nontrivial invariant set for the logistic and
the like dynamical systems, as we show. We note that our construct provides a
functional characterization for the CEP as an alternative to the pure set
theoretical iterated degree sequences characterization. The construct and
results presented might have independent interest for analysis on graphs or
qualitative analysis of dynamical systems over networks.


  Social media systems allow Internet users a congenial platform to freely
express their thoughts and opinions. Although this property represents
incredible and unique communication opportunities, it also brings along
important challenges. Online hate speech is an archetypal example of such
challenges. Despite its magnitude and scale, there is a significant gap in
understanding the nature of hate speech on social media. In this paper, we
provide the first of a kind systematic large scale measurement study of the
main targets of hate speech in online social media. To do that, we gather
traces from two social media systems: Whisper and Twitter. We then develop and
validate a methodology to identify hate speech on both these systems. Our
results identify online hate speech forms and offer a broader understanding of
the phenomenon, providing directions for prevention and detection approaches.


  We introduce descent methods to the study of strong approximation on
algebraic varieties. We apply them to two classes of varieties defined by
P(t)=N_{K/k}(z): firstly for quartic extensions of number fields K/k and
quadratic polynomials P(t) in one variable, and secondly for k=Q, an arbitrary
number field K and P(t) a product of linear polynomials over Q in at least two
variables. Finally, we illustrate that a certain unboundedness condition at
archimedean places is necessary for strong approximation.


  We introduce a new resource, AlloVera, which provides mappings from 218
allophones to phonemes for 14 languages. Phonemes are contrastive phonological
units, and allophones are their various concrete realizations, which are
predictable from phonological context. While phonemic representations are
language specific, phonetic representations (stated in terms of (allo)phones)
are much closer to a universal (language-independent) transcription. AlloVera
allows the training of speech recognition models that output phonetic
transcriptions in the International Phonetic Alphabet (IPA), regardless of the
input language. We show that a "universal" allophone model, Allosaurus, built
with AlloVera, outperforms "universal" phonemic models and language-specific
models on a speech-transcription task. We explore the implications of this
technology (and related technologies) for the documentation of endangered and
minority languages. We further explore other applications for which AlloVera
will be suitable as it grows, including phonological typology.


  We investigate the neutrino helicity-flip process under supernova core
conditions, where the left-handed neutrinos being produced can be converted
into right-handed neutrinos sterile with respect to the weak interaction due to
the interaction of magnetic moments with plasma electrons and protons. Instead
of the uniform ball model for the SN core used in previous analyses, realistic
models for radial distributions and time evolution of physical parameters in
the supernova core are considered. We have obtained new upper limits on the
Dirac neutrino magnetic moment averaged over flavours and time from the
condition that the influence of the right-handed neutrino emission on the total
cooling time scale should be limited.


  Tables are everywhere, from scientific journals, papers, websites, and
newspapers all the way to items we buy at the supermarket. Detecting them is
thus of utmost importance to automatically understanding the content of a
document. The performance of table detection has substantially increased thanks
to the rapid development of deep learning networks. The goals of this survey
are to provide a profound comprehension of the major developments in the field
of Table Detection, offer insight into the different methodologies, and provide
a systematic taxonomy of the different approaches. Furthermore, we provide an
analysis of both classic and new applications in the field. Lastly, the
datasets and source code of the existing models are organized to provide the
reader with a compass on this vast literature. Finally, we go over the
architecture of utilizing various object detection and table structure
recognition methods to create an effective and efficient system, as well as a
set of development trends to keep up with state-of-the-art algorithms and
future research. We have also set up a public GitHub repository where we will
be updating the most recent publications, open data, and source code. The
GitHub repository is available at
https://github.com/abdoelsayed2016/table-detection-structure-recognition.


  In this paper, we study the stability of general kernel bundles on
$\mathbb{P}^n$. Let $a,b,d>0$ be integers. A kernel bundle $E_{a,b}$ on
$\mathbb{P}^n$ is defined as the kernel of a surjective map
$\phi:\mathcal{O}_{\mathbb{P}^n}(-d)^a\rightarrow
\mathcal{O}_{\mathbb{P}^n}^b$. Here $\phi$ is represented by a $b\times a$
matrix $(f_{ij})$ where the entries $f_{ij}$ are polynomials of degree $d$. We
give sufficient conditions for semistability of a general kernel bundle on
$\mathbb{P}^n$, in terms of its Chern class.


  An algebraical background of the Lattice Conformal Field Theory is refined
with the help of a novel $q$-exponential identity.


  An intriguing and unexpected result for students learning numerical analysis
is that Newton's method, applied to the simple polynomial z^3 - 1 = 0 in the
complex plane, leads to intricately interwoven basins of attraction of the
roots. As an example of an interesting open question that may help to stimulate
student interest in numerical analysis, we investigate the question of whether
a damping method, which is designed to increase the likelihood of convergence
for Newton's method, modifies the fractal structure of the basin boundaries.
The overlap of the frontiers of numerical analysis and nonlinear dynamics
provides many other problems that can help to make numerical analysis courses
interesting.


  A least-squares neural network (LSNN) method was introduced for solving
scalar linear and nonlinear hyperbolic conservation laws (HCLs) in [7, 6]. This
method is based on an equivalent least-squares (LS) formulation and uses ReLU
neural network as approximating functions, making it ideal for approximating
discontinuous functions with unknown interface location. In the design of the
LSNN method for HCLs, the numerical approximation of differential operators is
a critical factor, and standard numerical or automatic differentiation along
coordinate directions can often lead to a failed NN-based method. To overcome
this challenge, this paper rewrites HCLs in their divergence form of space and
time and introduces a new discrete divergence operator. As a result, the
proposed LSNN method is free of penalization of artificial viscosity.
Theoretically, the accuracy of the discrete divergence operator is estimated
even for discontinuous solutions. Numerically, the LSNN method with the new
discrete divergence operator was tested for several benchmark problems with
both convex and non-convex fluxes, and was able to compute the correct physical
solution for problems with rarefaction, shock or compound waves. The method is
capable of capturing the shock of the underlying problem without oscillation or
smearing, even without any penalization of the entropy condition, total
variation, and/or artificial viscosity.


  The carrier density and temperature dependence of the Hall mobility in mono-,
bi- and tri-layer graphene has been systematically studied. We found that as
the carrier density increases, the mobility decreases for mono-layer graphene,
while it increases for bi-layer/tri-layer graphene. This can be explained by
the different density of states in mono-layer and bi-layer/tri-layer graphenes.
In mono-layer, the mobility also decreases with increasing temperature
primarily due to surface polar substrate phonon scattering. In
bi-layer/tri-layer graphene, on the other hand, the mobility increases with
temperature because the field of the substrate surface phonons is effectively
screened by the additional graphene layer(s) and the mobility is dominated by
Coulomb scattering.
  We also find that the temperature dependence of the Hall coefficient in
mono-, bi- and tri-layer graphene can be explained by the formation of electron
and hole puddles in graphene. This model also explains the temperature
dependence of the minimum conductance of mono-, bi- and tri-layer graphene. The
electrostatic potential variations across the different graphene samples are
extracted.


  The security issues of passive optical networks (PONs) have always been a
concern due to broadcast transmission. Physical-layer security enhancement for
the coherent PON should be as significant as improving transmission
performance. In this paper, we propose the advanced encryption standard (AES)
algorithm and geometric constellation shaping four-level pulse amplitude
modulation (GCS-PAM4) pilot-based key distribution for secure coherent PON. The
first bit of the GCS-PAM4 pilot is used for the hardware-efficient carrier
phase recovery (CPR), while the second bit is utilized for key distribution
without occupying the additional overhead. The key bits are encoded by the
polar code to ensure error-free distribution. Frequent key updates are
permitted for every codeword to improve the security of coherent PON. The
experimental results of the 200-Gbps secure coherent PON using digital
subcarrier multiplexing with 16-ary quadrature amplitude modulation show that
the GCS-PAM4 pilot-based key distribution could be error-free at upstream
transmission without occupying the additional overhead and the eavesdropping
would be prevented by AES algorithm at downstream transmission. Moreover, there
is almost no performance penalty on the CPR using the GCS-PAM4 pilot compared
to the binary phase shift keying pilot.


  Brane inflation in string theory leads to a new realization of power law
inflation which can give rise to significant non-gaussianity. This can happen
for any throat geometry if the scalar potential is appropriate. This note
presents a consistency relation connecting the running of the nonlinearity
parameter characterizing the non-gaussianity and the scalar and tensor indices.
The relationship is valid assuming that the throat geometry and scalar
potential support power law inflation, regardless of the level of
non-gaussianity.


  The HAWC observatory reconstructs atmospheric showers induced by very
high-energy primary cosmic- or gamma-rays. Cosmic-rays with hadronic nature are
several orders of magnitude more frequent than primary gammas and therefore it
is necessary to perform the best gamma-hadron discrimination for each event to
be able to survey the gamma-ray sky. Single vertical muons cause very
characteristic signals in the 300 HAWC detectors. Particularly, those events
that produce signals in the four PMTs of a tank corresponding to muons
traversing the tank in the central region. In such cases exist the correlation
that the largest signal is also the earliest to arrive, this due to the
geometry of the Cherenkov light emission and the fact that relativistic muons
travel faster than the light in the medium. This pattern is different from that
of electrons, positrons and low energy gammas showering in the first meter of
the tank water, which is typical of electromagnetic showers. In this work, we
explored whether in air-showers it is possible to identify muon signals and
whether this can help in the gamma/hadron discrimination of the recorded
events.


  Large Language Models (LLMs) have demonstrated great potential for code
generation and other software engineering tasks. Vulnerability detection is of
crucial importance to maintaining the security, integrity, and trustworthiness
of software systems. Precise vulnerability detection requires reasoning about
the code, making it a good case study for exploring the limits of LLMs'
reasoning capabilities. Although recent work has applied LLMs to vulnerability
detection using generic prompting techniques, their full capabilities for this
task and the types of errors they make when explaining identified
vulnerabilities remain unclear.
  In this paper, we surveyed eleven LLMs that are state-of-the-art in code
generation and commonly used as coding assistants, and evaluated their
capabilities for vulnerability detection. We systematically searched for the
best-performing prompts, incorporating techniques such as in-context learning
and chain-of-thought, and proposed three of our own prompting methods. Our
results show that while our prompting methods improved the models' performance,
LLMs generally struggled with vulnerability detection. They reported 0.5-0.63
Balanced Accuracy and failed to distinguish between buggy and fixed versions of
programs in 76% of cases on average. By comprehensively analyzing and
categorizing 287 instances of model reasoning, we found that 57% of LLM
responses contained errors, and the models frequently predicted incorrect
locations of buggy code and misidentified bug types. LLMs only correctly
localized 6 out of 27 bugs in DbgBench, and these 6 bugs were predicted
correctly by 70-100% of human participants. These findings suggest that despite
their potential for other tasks, LLMs may fail to properly comprehend critical
code structures and security-related concepts. Our data and code are available
at https://figshare.com/s/78fe02e56e09ec49300b.


  The increasing uptake of distributed energy resources (DERs) in distribution
systems and the rapid advance of technology have established new scenarios in
the operation of low-voltage networks. In particular, recent trends in
cryptocurrencies and blockchain have led to a proliferation of peer-to-peer
(P2P) energy trading schemes, which allow the exchange of energy between the
neighbors without any intervention of a conventional intermediary in the
transactions. Nevertheless, far too little attention has been paid to the
technical constraints of the network under this scenario. A major challenge to
implementing P2P energy trading is that of ensuring that network constraints
are not violated during the energy exchange. This paper proposes a methodology
based on sensitivity analysis to assess the impact of P2P transactions on the
network and to guarantee an exchange of energy that does not violate network
constraints. The proposed method is tested on a typical UK low-voltage network.
The results show that our method ensures that energy is exchanged between users
under the P2P scheme without violating the network constraints, and that users
can still capture the economic benefits of the P2P architecture.


  Technical systems have evolved over time into large and complex Interwoven
Systems consisting of several to a huge number of (possibly heterogeneous)
subsystems that have interdependencies. The resultant mutual influences among
subsystems have made them so complex that they are no longer manageable by
humans and it is assumed to intensify rapidly. Identifying such mutual
influences is the first step towards mastering the complexity of such systems.
This paper presents mutual influences in Interwoven Systems by describing
real-world examples and a methodology to detect them in the context of Organic
Computing. The methodology is evaluated with the help of an example. Further, a
taxonomy of Organic Computing applications helpful for selecting suitable
methods for detecting hidden mutual influences is described briefly.


  We discuss the equivalence between light-front time-ordered-perturbation
theory and covariant quantum field theory in light-front quantization, in the
case of quantum electrodynamics at one-loop level. In particular, we review the
one-loop calculation of the vertex correction, fermion self-energy and vacuum
polarization. We apply the procedure of integration by residue over the
light-front energy in the loop to show how the perturbative expansion in
covariant terms can be reduced to a sum of propagating and instantaneous
diagrams of light-front time-ordered perturbation theory. The detailed proof of
equivalence between the two formulations of the theory resolves the
controversial question on which form should be used for the gauge-field
propagator in the light-cone gauge in the covariant approach.


  We show that there exist $k$-colorable matroids that are not
$(b,c)$-decomposable when $b$ and $c$ are constants. A matroid is
$(b,c)$-decomposable, if its ground set of elements can be partitioned into
sets $X_1, X_2, \ldots, X_l$ with the following two properties. Each set $X_i$
has size at most $ck$. Moreover, for all sets $Y$ such that $|Y \cap X_i| \leq
1$ it is the case that $Y$ is $b$-colorable. A $(b,c)$-decomposition is a
strict generalization of a partition decomposition and, thus, our result
refutes a conjecture from arXiv:1911.10485v2 .


  Recent work has witnessed a paradigm shift from Seq2Seq to Seq2Edit in the
field of text editing, with the aim of addressing the slow autoregressive
inference problem posed by the former. Despite promising results, Seq2Edit
approaches still face several challenges such as inflexibility in generation
and difficulty in generalizing to other languages. In this work, we propose a
novel non-autoregressive text editing method to circumvent the above issues, by
modeling the edit process with latent CTC alignments. We make a crucial
extension to CTC by introducing the copy operation into the edit space, thus
enabling more efficient management of textual overlap in editing. We conduct
extensive experiments on GEC and sentence fusion tasks, showing that our
proposed method significantly outperforms existing Seq2Edit models and achieves
similar or even better results than Seq2Seq with over $4\times$ speedup.
Moreover, it demonstrates good generalizability on German and Russian. In-depth
analyses reveal the strengths of our method in terms of the robustness under
various scenarios and generating fluent and flexible outputs.


  The composition of planetary solids and gases is largely rooted in the
processing of volatile elements in protoplanetary disks. To shed light on the
key processes, we carry out a comparative analysis of the gas-phase carbon
abundance in two systems with a similar age and disk mass, but different
central stars: HD 100546 and TW Hya. We combine our recent detections of
C$^{0}$ in these disks with observations of other carbon reservoirs (CO,
C$^{+}$, C$_{2}$H) and gas mass and warm gas tracers (HD, O$^{0}$), as well as
spatially resolved ALMA observations and the spectral energy distribution. The
disks are modelled with the DALI 2D physical-chemical code. Stellar abundances
for HD 100546 are derived from archival spectra. Upper limits on HD emission
from HD 100546 place an upper limit on the total disk mass of
$\leq0.1\,M_{\odot}$. The gas-phase carbon abundance in the atmosphere of this
warm Herbig disk is at most moderately depleted compared to the interstellar
medium, with [C]/[H]$_{\rm gas}=(0.1-1.5)\times 10^{-4}$. HD 100546 itself is a
$\lambda\,$Bo\"{o}tis star, with solar abundances of C and O but a strong
depletion of rock-forming elements. In the gas of the T Tauri disk TW Hya, both
C and O are strongly underabundant, with [C]/[H]$_{\rm gas}=(0.2-5.0)\times
10^{-6}$ and C/O${>}1$. We discuss evidence that the gas-phase C and O
abundances are high in the warm inner regions of both disks. Our analytical
model, including vertical mixing and a grain size distribution, reproduces the
observed [C]/[H]$_{\rm gas}$ in the outer disk of TW Hya and allows to make
predictions for other systems.


  The electron shakeoff of 19F and 35Cl atoms resulting from the \b{eta}+ decay
of 19Ne+ and 35Ar+ ions has been investigated using a Paul trap coupled to a
time of flight recoil-ion spectrometer. The charge-state distributions of the
recoiling daughter nuclei were compared to theoretical calculations based on
the sudden approximation and accounting for subsequent Auger processes. The
excellent agreement obtained for 35Cl is not reproduced in 19F. The shortcoming
is attributed to the inaccuracy of the independent particle model employed to
calculate the primary shakeoff probabilities in systems with rather low atomic
numbers. This calls for more elaborate calculations, including explicitly the
electron-electron correlations.


  Suspicions about medical murder sometimes arise due to a surprising or
unexpected series of events, such as an apparently unusual number of deaths
among patients under the care of a particular nurse. But also a single
disturbing event might trigger suspicion about a particular nurse, and this
might then lead to investigation of events which happened when she was thought
to be present. In either case, there is a statistical challenge of
distinguishing event clusters that arise from criminal acts from those that
arise coincidentally from other causes. We show that an apparently striking
association between a nurse's presence and a high rate of deaths in a hospital
ward can easily be completely spurious. In short: in a medium-care hospital
ward where many patients are suffering terminal illnesses, and deaths are
frequent, most deaths occur in the morning. Most nurses are on duty in the
morning, too. There are less deaths in the afternoon, and even less at night;
correspondingly, less nurses are on duty in the afternoon, even less during the
night. Consequently, a full time nurse works the most hours when the most
deaths occur. The death rate is higher when she is present than when she is
absent.


  We consider non-supersymmetric four-dimensional closed string theories
constructed out of tensor products of N=2 minimal models. Generically such
theories have closed string tachyons, but these may be removed either by
choosing a non-trivial partition function or a suitable Klein bottle
projection. We find large numbers of examples of both types.


  This paper introduces the R package slm which stands for Stationary Linear
Models. The package contains a set of statistical procedures for linear
regression in the general context where the error process is strictly
stationary with short memory. We work in the setting of Hannan (1973), who
proved the asymptotic normality of the (normalized) least squares estimators
(LSE) under very mild conditions on the error process. We propose different
ways to estimate the asymptotic covariance matrix of the LSE, and then to
correct the type I error rates of the usual tests on the parameters (as well as
confidence intervals). The procedures are evaluated through different sets of
simulations, and two examples of real datasets are studied.


  The present work is concerned with the stabilization of a general class of
time-varying linear parabolic equations by means of a finite-dimensional
receding horizon control (RHC). The stability and suboptimality of the
unconstrained receding horizon framework is studied. The analysis allows the
choice of the squared $\ell_1$-norm as control cost. This leads to a nonsmooth
infinite-horizon problem which provides stabilizing optimal controls with a low
number of active actuators over time. Numerical experiments are given which
validate the theoretical results and illustrate the qualitative differences
between the $\ell_1$- and $\ell_2$-control costs.


  An original interferometer-based setup for measurements of length of
needle-like samples is developed, and thermal expansion of o-TaS$_3$ crystals
is studied. Below the Peierls transition the temperature hysteresis of length
$L$ is observed, the width of the hysteresis loop $\delta L/L$ being up to $5
\cdot 10^{-5}$. The behavior of the loop is anomalous: the length changes so
that it is in front of its equilibrium value. The hysteresis loop couples with
that of conductivity. The sign and the value of the length hysteresis are
consistent with the strain dependence of the charge-density waves (CDW) wave
vector. With lowering temperature down to 100 K the CDW elastic modulus grows
achieving a value comparable with the lattice Young modulus. Our results could
be helpful in consideration of different systems with intrinsic
superstructures.


  Numerical calculations of light propagation in random media demand the
multiply scattered Stokes intensities to be written in a common fixed
reference. A particularly useful way to perform automatically these basis
transformations is to write the scattered intensities in the
Chandrasekhar-Sekera representation. This representation produces side effects
so that numerical tests are necessary to deal with the limiting situations of
the small-particle (Rayleigh) and forward/backward scattering. Here a new set
of basis functions is presented to describe the scattering of light by
spherical particles (Mie scattering) in the Chandrasekhar-Sekera
representation. These basis functions can be implemented in a new algorithm to
calculate the Mie scattering amplitudes, which leads straightforwardly to all
the scattering quantities. In contrast to the traditional implementation, this
set of basis functions implies to natural numerical convergence to the above
mentioned limiting cases, which are thoroughly discussed.


  The one-particle reduced density-matrix (1-RDM) functional theory is a
promising alternative to density-functional theory (DFT) that uses the 1-RDM
rather than the electronic density as a basic variable. However, long-standing
challenges such as the lack of Kohn--Sham scheme and the complexity of the pure
$N$-representability conditions are still impeding its wild utilization.
Fortunately, ensemble $N$-representability conditions derived in the natural
orbital basis are known and trivial, such that almost every functionals of the
1-RDM are actually natural orbital functionals which do not perform well for
all the correlation regimes. In this work, we propose a variational
minimization scheme in the ensemble $N$-representable domain that is not
restricted to the natural orbital representation of the 1-RDM. We show that
splitting the minimization into the diagonal and off-diagonal part of the 1-RDM
can open the way toward the development of functionals of the orbital
occupations, which remains a challenge for the generalization of
site-occupation functional theory in chemistry. Our approach is tested on the
uniform Hubbard model using the M\"uller and the T\"ows--Pastor functionals, as
well as on the dihydrogen molecule using the M\"uller functional.


  The sociotechnological system is a system constituted of human individuals
and their artifacts: technological artifacts, institutions, conceptual and
representational systems, worldviews, knowledge systems, culture and the whole
biosphere as a volutionary niche. In our view the sociotechnological system as
a super-organism is shaped and determined both by the characteristics of the
agents involved and the characteristics emergent in their interactions at
multiple scales. Our approach to sociotechnological dynamics will maintain a
balance between perspectives: the individual and the collective. Accordingly,
we analyze dynamics of the Web as a sociotechnological system made of people,
computers and digital artifacts (Web pages, databases, search engines, etc.).
Making sense of the sociotechnological system while being part of it, is also a
constant interplay between pragmatic and value based approaches. The first is
focusing on the actualities of the system while the second highlights the
observer's projections. In our attempt to model sociotechnological dynamics and
envision its future, we take special care to make explicit our values as part
of the analysis. In sociotechnological systems with a high degree of
reflexivity (coupling between the perception of the system and the system's
behavior), highlighting values is of critical importance. In this essay, we
choose to see the future evolution of the web as facilitating a basic value,
that is, continuous open-ended intelligence expansion. By that we mean that we
see intelligence expansion as the determinant of the 'greater good' and 'well
being' of both of individuals and collectives at all scales. Our working
definition of intelligence here is the progressive process of sense-making of
self, other, environment and universe. Intelligence expansion, therefore, means
an increasing ability of sense-making.


  We discuss the possible existence of a spin-gap phase in the low-doping
regime of strongly-correlated two-dimensional electrons within the gauge field
description of the t-J model. The spin-gap phase was recently shown by Ubbens
and Lee to be destroyed by gauge field quantum fluctuations for a single-layer
2D system in the absence of disorder and for a full gap. We show that the same
conclusion applies both in the dirty limit and for the case of a gapless spinon
condensate.


  We have systematically calculated the pseudoscalar Goldstone boson and
charmed baryon scattering lengths to the third order with heavy baryon chiral
perturbation theory. The scattering lengths can reveal the interaction of $K
\Lambda_c$, $K \Sigma_c$, $\pi \Omega_c$ etc. For each channel, we take into
account of the interaction between the external particle and every possible
charmed baryon in the triplet, sextet, and excited sextet. We use dimensional
regularization and modified minimal subtraction to get rid of the divergences
in the loop-diagram corrections. We notice that the convergence of chiral
expansion for most of the channels becomes better after we let the low energy
constants absorb the analytic contributions of the loop-diagram corrections and
keep the nonanalytic terms only.


  The relativistic extension of non-relativistic hydrodynamics suffers from
notorious difficulties. In non-relativistic hydrodynamics where difficulties
also abound, it has proved a useful supplement to study lattice models which
can imitate viscous fluid flow. In this paper we construct a relativistic
spacetime lattice and construct a dynamics of points, thus a relativistic
cellular automaton over it, to model relativistic fluid flow. A simple example
is also explicitly studied, and some numerical results with figures are shown
in the last section.


  Simulation tools and testbeds have been proposed to assess the performance of
control designs and wireless protocols in isolation. A cyber-physical system
(CPS), however, integrates control with network elements, which must be
evaluated together under real-world conditions to assess control performance,
stability, and associated costs. We present an approach to evaluate CPS relying
on embedded devices and low-power wireless technology. Using one or multiple
inverted pendulums as physical system, our approach supports a spectrum of
realistic CPS scenarios that impose different requirements onto the control and
networking elements. Moreover, our approach allows one to flexibly combine
simulated and real pendulums, promoting adoption, scalability, repeatability,
and integration with existing wireless testbed infrastructures. A case study
demonstrates implementation, execution, and measurements using the proposed
evaluation approach.


  The problem of robust controller synthesis for plants affected by structured
uncertainty, captured by integral quadratic constraints, is discussed. The
solution is optimized towards a worst-case white noise rejection specification,
which is a generalization of the standard $\mathcal{H}_2$-norm to the robust
setting including possibly non-LTI uncertainty. Arbitrary structural
constraints can be imposed on the control solution, making this method suitable
for distributed systems. The nonsmooth optimization algorithm used to solve the
robust synthesis problem operates directly in the frequency domain, eliminating
scalability issues for complex systems and providing local optimality
certificates. The method is evaluated using a literature example and a
real-world system using a novel implementation of a robust
$\mathcal{H}_2$-performance bound.


  Mg10Ir19B16, a previously unreported compound in the Mg-Ir-B chemical system,
is found to be superconducting at temperatures near 5 K. The fact that the
compound exhibits a range of superconducting temperatures between 4 and 5 K
suggests that a range of stoichiometries is allowed, though no structural
evidence for this is observed. The compound has a large, noncentrosymmetric,
body centered cubic unit cell with a = 10.568 Angstrom, displaying a structure
type for which no previous superconductors have been reported.


  Low-decoherence regime plays a key role in constructing multi-particle
quantum systems and has therefore been constantly pursued in order to build
quantum simulators and quantum computers in a scalable fashion. Quantum error
correction and quantum topological computing have been proved being able to
protect quantumness but haven't been experimentally realized yet. Recently,
topological boundary states are found inherently stable and are capable of
protecting physical fields from dissipation and disorder, which inspires the
application of such a topological protection on quantum correlation. Here, we
present an experimental demonstration of topological protection of two-photon
quantum states on a photonic chip. By analyzing the quantum correlation of
photons out from the topologically nontrivial boundary state, we obtain a high
cross-correlation and a strong violation of Cauchy-Schwarz inequality up to 30
standard deviations. Our results, together with our integrated implementation,
provide an alternative way of protecting quantumness, and may inspire many more
explorations in 'quantum topological photonics', a crossover between
topological photonics and quantum information.


  In this paper we construct complete convex hypersurfaces in $\mathbb R^{n+1}$
which translate under the flow by powers $\alpha \in (0, \frac1{n+2})$ of the
Gauss curvature. The level set of each solution is asymptotic to a shrinking
soliton for the flow by power $\frac \alpha {1-\alpha}$ of the Gauss curvature
in $\mathbb R^n$. For example, our construction reveals the existence of
translators whose level set converges to the sphere, simplex, hypercube and so
on. The translating solitons exist as a family whose parameters correspond to
Jacobi fields, solutions to linearized equation around the asymptotic profile.


  In the field of numerical approximation, specialists considering highly
complex problems have recently proposed various ways to simplify their
underlying problems. In this field, depending on the problem they were tackling
and the community that are at work, different approaches have been developed
with some success and have even gained some maturity, the applications can now
be applied to information analysis or for numerical simulation of PDE's. At
this point, a crossed analysis and effort for understanding the similarities
and the differences between these approaches that found their starting points
in different backgrounds is of interest. It is the purpose of this paper to
contribute to this effort by comparing some constructive reduced
representations of complex functions. We present here in full details the
Adaptive Cross Approximation (ACA) and the Empirical Interpolation Method (EIM)
together with other approaches that enter in the same category.


  Following the increasing interest and adoption of FaaS systems, benchmarking
frameworks for determining non-functional properties have also emerged. While
existing (microbenchmark) frameworks only evaluate single aspects of FaaS
platforms, a more holistic, application-driven approach is still missing. In
this paper, we design and present BeFaaS, an extensible application-centric
benchmarking framework for FaaS environments that focuses on the evaluation of
FaaS platforms through realistic and typical examples of FaaS applications.
BeFaaS includes a built-in e-commerce benchmark, is extensible for new workload
profiles and new platforms, supports federated benchmark runs in which the
benchmark application is distributed over multiple providers, and supports a
fine-grained result analysis. Our evaluation compares three major FaaS
providers in single cloud provider setups and shows that BeFaaS is capable of
running each benchmark automatically with minimal configuration effort and
providing detailed insights for each interaction.


  We present a numerical determination of the scaling functions of the
magnetization, the suscep- tibility, and the Binders cumulant, for two
nonequilibrium model systems with varying range of interactions. We consider
Monte Carlo simulations of the block voter model (BVM) on square lat- tices and
of the majority-vote model (MVM) on random graphs. In both cases, the
satisfactory data collapse obtained for several system sizes and interaction
ranges, supports the hypothesis that these functions are universal. Our
analysis yields an accurate estimation of the long-range exponents, which
govern the decay of the critical amplitudes with the range of interaction, and
is consistent with the assumption that the static exponents are Ising-like for
the BVM and classical for the MVM.


  Orbital angular momentum (OAM) has gained great interest due to its most
attractive feature of high dimensionality, and several ground-breaking
demonstrations in communication based on OAM multiplexing have been carried
out. Accordingly, a rapid data-density growth from OAM multiplexing has posed a
great challenge to the signal-processing layer. Meanwhile, in another area,
optical signal-processing circuit based on photon-phonon conversion has
received considerable attention and made rapid progress. Here, with an aim of
finding the intersection between OAM multiplexing and photon-phonon conversion,
we report on the observation of reversible OAM photon-phonon conversion. A
specific OAM state can be flexibly and controllably interconverted between
photonic and phononic domains via Brillouin photon-phonon coupling within the
decay time of acoustic signal, in which OAM and spin angular momentum are
independently conserved. Our result demonstrates the controllable OAM transfer
between photons and phonons, shows the potential of using OAM multiplexing to
extend the capacity of photon-phonon conversion based signal-processing scheme,
and may trigger the development of OAM-multiplexed photon-phonon circuit.


  We report on recent results on charmless hadronic and radiative rare $B_s^0$
decays, based on the data collected by the Belle experiment at the KEKB
$e^+e^-$ collider. These include the studies of $B_s^0\rightarrow
K^0\bar{K^0}$, $B_s^0\rightarrow\phi\gamma$ and $B_s^0\rightarrow\gamma\gamma$.


  Let $f\colon X\to Y$ be a surjective morphism of Fano manifolds of Picard
number 1 whose VMRTs at a general point are not dual defective. Suppose that
the tangent bundle $T_X$ is big. We show that $f$ is an isomorphism unless $Y$
is a projective space. As applications, we study the bigness of the tangent
bundles of complete intersections, del Pezzo manifolds, and Mukai manifolds, as
well as their dynamical rigidity.


  The free closed semialgebraic set $D_f$ determined by a hermitian
noncommutative polynomial $f$ is the closure of the connected component of
$\{(X,X^*)\mid f(X,X^*)>0\}$ containing the origin. When $L$ is a hermitian
monic linear pencil, the free closed semialgebraic set $D_L$ is the feasible
set of the linear matrix inequality $L(X,X^*)\geq 0$ and is known as a free
spectrahedron. Evidently these are convex and it is well-known that a free
closed semialgebraic set is convex if and only it is a free spectrahedron. The
main result of this paper solves the basic problem of determining those $f$ for
which $D_f$ is convex. The solution leads to an efficient algorithm that not
only determines if $D_f$ is convex, but if so, produces a minimal hermitian
monic pencil $L$ such that $D_f=D_L$. Of independent interest is a subalgorithm
based on a Nichtsingul\"arstellensatz presented here: given a linear pencil
$L'$ and a hermitian monic pencil $L$, it determines if $L'$ takes invertible
values on the interior of $D_L$. Finally, it is shown that if $D_f$ is convex
for an irreducible hermitian polynomial $f$, then $f$ has degree at most two,
and arises as the Schur complement of an $L$ such that $D_f=D_L$.


  We study confined scattering of a quantum particle by two centers fixed on
the longitudinal axis of a harmonic waveguide-like trap. The conditions of
confinement-induced resonances (CIRs) appearing in these systems, when
scattering cross section approaches the unitary limit, are derived for a
regularized pseudopotential describing particle interaction with scattering
centers. In the limit of a single center, the position of CIR for even state
tends to the well-known result obtained by Olshanii. Our result can be
applicable to confined atomic scattering by fixed impurities, like ions or
Rydberg atoms, with possible extension to $N$ impurities, or by two-atomic
molecules.


  Auxiliary information can be exploited in machine learning models using the
paradigm of evidence based conditional inference. Multi-modal techniques in
Deep Neural Networks (DNNs) can be seen as perturbing the latent feature
representation for incorporating evidence from the auxiliary modality. However,
they require training a specialized network which can map sparse evidence to a
high dimensional latent space vector. Designing such a network, as well as
collecting jointly labeled data for training is a non-trivial task. In this
paper, we present a novel multi-task learning (MTL) based framework to perform
evidence based conditional inference in DNNs which can overcome both these
shortcomings. Our framework incorporates evidence as the output of secondary
task(s), while modeling the original problem as the primary task of interest.
During inference, we employ a novel Bayesian formulation to change the joint
latent feature representation so as to maximize the probability of the observed
evidence. Since our approach models evidence as prediction from a DNN, this can
often be achieved using standard pre-trained backbones for popular tasks,
eliminating the need for training altogether. Even when training is required,
our MTL architecture ensures the same can be done without any need for jointly
labeled data. Exploiting evidence using our framework, we show an improvement
of 3.9% over the state-of-the-art, for predicting semantic segmentation given
the image tags, and 2.8% for predicting instance segmentation given image
captions.


  In this paper we propose an expression for the entanglement entropy of
several intervals in a stationary state of a free, translational invariant
Hamiltonian in a fermionic chain. We check numerically the accuracy of our
proposal and conjecture a new formula for the asymptotic behaviour of principal
sub-matrices of a Toeplitz matrix.


  Following the idea of Lusztig, Atiyah-Hirzebruch and Kosniowski, we note that
the Dolbeault-type operators on compact, almost-complex manifolds are rigid.
When the circle action has isolated fixed points, this rigidity result will
produce many identities concerning the weights on the fixed points. In
particular, it gives a criterion to detemine whether or not a symplectic circle
action with isolated fixed points is Hamiltonian. As applications, we simplify
the proofs of some known results related to symplectic circle actions, due to
Godinho, Tolman-Weitsman and Pelayo-Tolman, and generalize some of them to more
general cases.


  For the degree corrected stochastic block model in the presence of arbitrary
or even adversarial outliers, we develop a convex-optimization-based clustering
algorithm that includes a penalization term depending on the positive deviation
of a node from the expected number of edges to other inliers. We prove that
under mild conditions, this method achieves exact recovery of the underlying
clusters. Our synthetic experiments show that our algorithm performs well on
heterogeneous networks, and in particular those with Pareto degree
distributions, for which outliers have a broad range of possible degrees that
may enhance their adversarial power. We also demonstrate that our method allows
for recovery with significantly lower error rates compared to existing
algorithms.


  Although less than 1% of all radio pulsars are detected at optical
wavelengths, their optical emission can yield a wealth of information that is
either very difficult or plainly impossible to obtain at other wavelengths.


  In this paper, the content service provision of information-centric vehicular
networks (ICVNs) is investigated from the aspect of mobile edge caching,
considering the dynamic driving-related context information. To provide
up-to-date information with low latency, two schemes are designed for cache
update and content delivery at the roadside units (RSUs). The roadside unit
centric (RSUC) scheme decouples cache update and content delivery through
bandwidth splitting, where the cached content items are updated regularly in a
round-robin manner. The request adaptive (ReA) scheme updates the cached
content items upon user requests with certain probabilities. The performance of
both proposed schemes are analyzed, whereby the average age of information
(AoI) and service latency are derived in closed forms. Surprisingly, the
AoI-latency trade-off does not always exist, and frequent cache update can
degrade both performances. Thus, the RSUC and ReA schemes are further optimized
to balance the AoI and latency. Extensive simulations are conducted on SUMO and
OMNeT++ simulators, and the results show that the proposed schemes can reduce
service latency by up to 80\% while guaranteeing content freshness in heavily
loaded ICVNs.


  With the aim of building machine learning systems that incorporate standards
of fairness and accountability, we explore explicit subgroup sample complexity
bounds. The work is motivated by the observation that classifier predictions
for real world datasets often demonstrate drastically different metrics, such
as accuracy, when subdivided by specific sensitive variable subgroups. The
reasons for these discrepancies are varied and not limited to the influence of
mitigating variables, institutional bias, underlying population distributions
as well as sampling bias. Among the numerous definitions of fairness that
exist, we argue that at a minimum, principled ML practices should ensure that
classification predictions are able to mirror the underlying sub-population
distributions. However, as the number of sensitive variables increase,
populations meeting at the intersectionality of these variables may simply not
exist or may not be large enough to provide accurate samples for
classification. In these increasingly likely scenarios, we make the case for
human intervention and applying situational and individual definitions of
fairness. In this paper we present lower bounds of subgroup sample complexity
for metric-fair learning based on the theory of Probably Approximately Metric
Fair Learning. We demonstrate that for a classifier to approach a definition of
fairness in terms of specific sensitive variables, adequate subgroup population
samples need to exist and the model dimensionality has to be aligned with
subgroup population distributions. In cases where this is not feasible, we
propose an approach using individual fairness definitions for achieving
alignment. We look at two commonly explored UCI datasets under this lens and
suggest human interventions for data collection for specific subgroups to
achieve approximate individual fairness for linear hypotheses.


  We study the impact of different galaxy statistics and empirical metallicity
scaling relations on the merging rates and on the properties of compact objects
binaries. First, we analyze the similarities and differences of using the star
formation rate functions or the stellar mass functions as galaxy statistics for
the computation of the cosmic star formation rate density. Then we investigate
the effects of adopting the Fundamental Metallicity Relation or a classic Mass
Metallicity Relation to assign metallicity to galaxies with given properties.
We find that when the Fundamental Metallicity Relation is exploited, the bulk
of the star formation occurs at relatively high metallicities even at high
redshift; the opposite holds when the Mass Metallicity Relation is employed,
since in this case the metallicity at which most of the star formation takes
place strongly decreases with redshift. We discuss the various reasons and
possible biases originating this discrepancy. Finally, we show the impact that
these different astrophysical prescriptions have on the merging rates and on
the properties of compact objects binaries; specifically, we present results
for the redshift dependent merging rates and for the chirp mass and time delay
distributions of the merging binaries.


  We consider unitary dynamical evolutions on n qubits caused by time dependent
pair-interaction Hamiltonians and show that the running time of a parallelized
two-qubit gate network simulating the evolution is given by the time integral
over the chromatic index of the interaction graph. This defines a complexity
measure of continuous and discrete quantum algorithms which are in exact
one-to-one correspondence. Furthermore we prove a lower bound on the growth of
large-scale entanglement depending on the chromatic index.


  We introduce a singular chain intersection homology theory which generalizes
that of King and which agrees with the Deligne sheaf intersection homology of
Goresky and MacPherson on any topological stratified pseudomanifold, compact or
not, with constant or local coefficients, and with traditional perversities or
superperversities (those satisfying p(2)>0). For the case p(2)=1, these latter
perversitie were introduced by Cappell and Shaneson and play a key role in
their superduality theorem for embeddings. We further describe the
sheafification of this singular chain complex and its adaptability to broader
classes of stratified spaces.


  Most video person re-identification (re-ID) methods are mainly based on
supervised learning, which requires cross-camera ID labeling. Since the cost of
labeling increases dramatically as the number of cameras increases, it is
difficult to apply the re-identification algorithm to a large camera network.
In this paper, we address the scalability issue by presenting deep
representation learning without ID information across multiple cameras.
Technically, we train neural networks to generate both ID-discriminative and
camera-invariant features. To achieve the ID discrimination ability of the
embedding features, we maximize feature distances between different person IDs
within a camera by using a metric learning approach. At the same time,
considering each camera as a different domain, we apply adversarial learning
across multiple camera domains for generating camera-invariant features. We
also propose a part-aware adaptation module, which effectively performs
multi-camera domain invariant feature learning in different spatial regions. We
carry out comprehensive experiments on three public re-ID datasets (i.e.,
PRID-2011, iLIDS-VID, and MARS). Our method outperforms state-of-the-art
methods by a large margin of about 20\% in terms of rank-1 accuracy on the
large-scale MARS dataset.


  Based on a microscopic approach, we propose a Lagrangian for the combined
system of a rotating dielectric nanoparticle above a plane surface in the
presence of electromagnetic vacuum fluctuations. In the framework of canonical
quantization, the electromagnetic vacuum field is quantized in the presence of
dielectric fields describing the nanoparticle and a semi-infinite dielectric
with planar interface. The radiative heat power absorbed by the rotating
nanoparticle is obtained and the result is in agreement with previous results
when the the rotational frequency of the nanoparticle is zero or much smaller
than the relaxation frequency of the dielectrics. The well known near field
effect is reexamined and discussed in terms of the rotational frequency. The
radiative heat power absorbed by the nanoparticle for well-known peak
frequencies, is plotted in terms of the rotational frequency showing an
interesting effect resembling a phase transition around a critical frequency,
determined by the relaxation frequency of the dielectrics.


  Entanglement distribution will form the backbone of many future distributed
quantum technologies, especially the quantum internet. The act of purifying
multiple noisy entangled states into a single one of higher quality has no
analogue in classical networking and as such, this transforms the way in which
we will consider future algorithms for routing entanglement. We outline the
differences that arise because of this, demonstrate some elementary formalisms
for `multi-path entanglement routing', and discuss the philosophical
differences that arise when comparing this regime to conventional digital
network theory. We also present a software package, QuNet, that uses novel
`quantum cost-vector analysis' to simulate and benchmark routing in multi-user
entanglement networks in a way that is is highly scalable in network size and
the number of competing users. Our software accommodates both ground- and
space-based networks, and implements efficient multi-user time-optimisation for
mitigating congestion when quantum memories are available.


  In this paper we show preliminary results concerning the chemical and
energetic enrichment of the ICM by means of supernova-driven wind models in
elliptical galaxies. These models are obtained by taking into account new
prescriptions about supernova remnant evolution in the interstellar medium. We
find that models, which can reproduce the Fe abundance and the [$\alpha$/Fe]
ratios observed in the ICM, predict that the SN energy input can provide about
0.3 keV per ICM particle. We have obtained this result by assuming that each SN
explosion inject on the average into the ISM no more than 20% of its initial
blast wave energy. The predicted energy per particle is not enough to break the
cluster self-similarity but is more than predicted in previous models.


  This paper aims to characterize the capacity limits of a wireless cellular
network with a full-duplex (FD) base-station (BS) and half-duplex user
terminals, in which three independent messages are communicated: the uplink
message $m_1$ from the uplink user to the BS, the downlink message $m_2$ from
the BS to the downlink user, and the device-to-device (D2D) message $m_3$ from
the uplink user to the downlink user. From an information theoretical
perspective, the overall network can be viewed as a generalization of the FD
relay broadcast channel with a side message transmitted from the relay to the
destination. We begin with a simpler case that involves the uplink and downlink
transmissions of $(m_1,m_2)$ only, and propose an achievable rate region based
on a novel strategy that uses the BS as a FD relay to facilitate the
interference cancellation at the downlink user. We also prove a new converse,
which is strictly tighter than the cut-set bound, and characterize the capacity
region of the scalar Gaussian FD network without a D2D message to within a
constant gap. This paper further studies a general setup wherein
$(m_1,m_2,m_3)$ are communicated simultaneously. To account for the D2D
message, we incorporate Marton's broadcast coding into the previous scheme to
obtain a larger achievable rate region than the existing ones in the
literature. We also improve the cut-set bound by means of genie and show that
by using one of the two simple rate-splitting schemes, the capacity region of
the scalar Gaussian FD network with a D2D message can already be reached to
within a constant gap. Finally, a generalization to the vector Gaussian channel
case is discussed. Simulation results demonstrate the advantage of using the BS
as relay in enhancing the throughput of the FD cellular network.


  We introduce a ZFC method that enables us to build spaces (in fact special
dense subspaces of certain Cantor cubes) in which we have "full control" over
all dense subsets.
  Using this method we are able to construct, in ZFC, for each uncountable
regular cardinal $\lambda$ a 0-dimensional $T_2$, hence Tychonov, space which
is $\mu$-resolvable for all $\mu < \lambda$ but not $\lambda$-resolvable. This
yields the final (negative) solution of a celebrated problem of Ceder and
Pearson raised in 1967: Are $\omega$-resolvable spaces maximally resolvable?
This method enables us to solve several other open problems concerning
resolvability as well.