  In this Letter, we study the transient electron transfer phenomena of
single-electron devices with alternating external gate voltages. We obtain a
high frequency limit for pumping electrons one at a time in single-electron
devices. Also, we find that in general the electrical current is not
proportional to the frequency of the external signals in the single-electron
devices, due to the strong quantum coherence tunneling effect.


  We review a class of non-topological defects in the standard electroweak
model, and their implications. Starting with the semilocal string, which
provides a counterexample to many well known properties of topological
vortices, we discuss electroweak strings and their stability with and without
external influences such as magnetic fields. Other known properties of
electroweak strings and monopoles are described in some detail and their
potential relevance to future particle accelerator experiments and to baryon
number violating processes is considered. We also review recent progress on the
cosmology of electroweak defects and the connection with superfluid helium,
where some of the effects discussed here could possibly be tested.


  We study the tensor product of an associative and a nonassociative cyclic
algebra. The condition for the tensor product to be a division algebra equals
the classical one for the tensor product of two associative cyclic algebras by
Albert or Jacobson, if the base field contains a suitable root of unity.
Stronger conditions are obtained in special cases. Applications to space-time
block coding are discussed.


  This contribution examines two radically different explanations of our
phenomenal intuitions, one reductive and one strongly non-reductive, and
identifies two germane ideas that could benefit many other theories of
consciousness. Firstly, the ability of sophisticated agent architectures with a
purely physical implementation to support certain functional forms of qualia or
proto-qualia appears to entail the possibility of machine consciousness with
qualia, not only for reductive theories but also for the nonreductive ones that
regard consciousness as ubiquitous in Nature. Secondly, analysis of
introspective psychological material seems to hint that, under the threshold of
our ordinary waking awareness, there exist further 'submerged' or 'subliminal'
layers of consciousness which constitute a hidden foundation and support and
another source of our phenomenal intuitions. These 'submerged' layers might
help explain certain puzzling phenomena concerning subliminal perception, such
as the apparently 'unconscious' multisensory integration and learning of
subliminal stimuli.


  Given a complete hereditary cotorsion pair $(\mathcal{A}, \mathcal{B})$ in an
abelian category $\mathcal{C}$ satisfying certain conditions, we study the
completeness of the induced cotorsion pairs $(\Phi(\mathcal{A}),
\Phi(\mathcal{A})^{\perp})$ and $(^{\perp}\Psi(\mathcal{B}), \Psi(\mathcal{B})
)$ in the category $\mbox{Rep}(Q, \mathcal{C})$ of $\mathcal{C}$-valued
representations of a given quiver $Q$. We show that if $Q$ is left rooted, then
the cotorsion pair $(\Phi(\mathcal{A}), \Phi(\mathcal{A})^{\perp})$ is
complete, and if $Q$ is right rooted, then the cotorsion pair
$(^{\perp}\Psi(\mathcal{B}), \Psi(\mathcal{B}) )$ is complete. Besides, we work
on the infinite line quiver $A_{\infty}^{\infty}$, which is neither left rooted
nor right rooted. We prove that these cotorsion pairs in
$\mbox{Rep}(A_{\infty}^{\infty}, R)$ are complete, as well.


  Recent progress in AI and Reinforcement learning has shown great success in
solving complex problems with high dimensional state spaces. However, most of
these successes have been primarily in simulated environments where failure is
of little or no consequence. Most real-world applications, however, require
training solutions that are safe to operate as catastrophic failures are
inadmissible especially when there is human interaction involved. Currently,
Safe RL systems use human oversight during training and exploration in order to
make sure the RL agent does not go into a catastrophic state. These methods
require a large amount of human labor and it is very difficult to scale up. We
present a hybrid method for reducing the human intervention time by combining
model-based approaches and training a supervised learner to improve sample
efficiency while also ensuring safety. We evaluate these methods on various
grid-world environments using both standard and visual representations and show
that our approach achieves better performance in terms of sample efficiency,
number of catastrophic states reached as well as overall task performance
compared to traditional model-free approaches


  Multiphysics problems involving two or more coupled physical phenomena are
ubiquitous in science and engineering. This work develops a new partitioned
exponential approach for the time integration of multiphysics problems. After a
possible semi-discretization in space, the class of problems under
consideration is modeled by a system of ordinary differential equations where
the right-hand side is a summation of two component functions, each
corresponding to a given set of physical processes.
  The partitioned-exponential methods proposed herein evolve each component of
the system via an exponential integrator, and information between partitions is
exchanged via coupling terms. The traditional approach to constructing
exponential methods, based on the variation-of-constants formula, is not
directly applicable to partitioned systems. Rather, our approach to developing
new partitioned-exponential families is based on a general-structure additive
formulation of the schemes. Two method formulations are considered, one based
on a linear-nonlinear splitting of the right hand component functions, and
another based on approximate Jacobians. The paper develops classical
(non-stiff) order conditions theory for partitioned exponential schemes based
on particular families of T-trees and B-series theory. Several practical
methods of third order are constructed that extend the Rosenbrock-type and
EPIRK families of exponential integrators. Several implementation optimizations
specific to the application of these methods to reaction-diffusion systems are
also discussed. Numerical experiments reveal that the new
partitioned-exponential methods can perform better than traditional
unpartitioned exponential methods on some problems.


  The generating function for restricted partitions is a finite product with a
Laurent expansion at each root of unity. The question of the behavior of these
Laurent coefficients as the size of the product increases goes back to
Rademacher and his work on partitions. Building on the methods of Drmota,
Gerhold and previous results of the author, we complete this description and
give the full asymptotic expansion of each coefficient at every root of unity.
These techniques are also shown to give the asymptotics of Sylvester waves.


  The Venereau polynomials v-n:=y+x^n(xz+y(yu+z^2)), n>= 1, on A4 have all
fibers isomorphic to the affine space A3. Moreover, for all n>= 1 the map (v-n,
x) : A4 -> A2 yields a flat family of affine planes over A2. In the present
note we show that over the punctured plane A2\0, this family is a fiber bundle.
This bundle is trivial if and only if v-n is a variable of the ring C[x][y,z,u]
over C[x].
  It is an open question whether v1 and v2 are variables of the polynomial ring
C[x,y,z,u]. S. Venereau established that v-n is indeed a variable of
C[x][y,z,u] over C[x] for n>= 3. In this note we give an alternative proof of
Venereau's result based on the above equivalence. We also discuss some other
equivalent properties, as well as the relations to the Abhyankar-Sathaye
Embedding Problem and to the Dolgachev-Weisfeiler Conjecture on triviality of
flat families with fibers affine spaces.


  We present the results of the observations performed in the occasion of the
June 21, 2001 total solar eclipse, looking for visible photons emitted trough a
possible radiative decay of solar neutrinos. We establish lower limits for the
neutrino proper lifetimes larger than 1000 s/eV, for neutrino masses larger
than 0.01 eV.


  The mass of the top quark is measured in a data set corresponding to 4.6
fb$^{-1}$ of proton-proton collisions with centre-of-mass energy $\sqrt{s}=7$
TeV collected by the ATLAS detector at the LHC. Events consistent with hadronic
decays of top-antitop quark pairs with at least six jets in the final state are
selected. The substantial background from multijetproduction is modelled with
data-driven methods that utilise the number of identified $b$-quark jets and
the transverse momentum of the sixth leading jet,which have minimal
correlation. The top-quark mass is obtained from template fits to the ratio of
three-jet to dijet mass. The three-jet mass is calculated from the three jets
produced in a top-quark decay. Using these three jets the dijet mass is
obtained from the two jets produced in the $W$ boson decay. The top-quark mass
obtained from this fit is thus less sensitive to the uncertainty in the energy
measurement of the jets. A binned likelihood fit yields a top-quark mass of
$m_{t} = 175.1 \pm 1.4$ (stat) $\pm 1.2$ (syst) GeV.


  We consider the fate of Bose-Einstein condensation (BEC) with time-reversal
symmetry and inversion symmetry in a spin-orbit coupled bilayer system. When
these two symmetry operators commute, all the single particle bands are exactly
two-fold degenerate in the momentum space. The scattering in the two-fold
degenerate rings can relax the spin-momentum locking effect resulting from
spin-orbit coupling, thus we can realize the spin polarized plane wave phase
even when the inter-particle interaction dominates. When these two operators
anti-commute, the lowest two bands may have the same minimal energy, which have
totally different spin structures. As a result, the competition between
different condensates in these two energetically degenerate rings can give rise
to interesting stripe phases with atoms condensed at two or four colinear
momenta. We find that the crossover between these two cases is accompanied by
the excited band condensation when the interference energy can overcome the
increased single particle energy in the excited band. This effect is not based
on strong interaction, thus can be realized even with moderate interaction
strength.


  We propose a method of construction of a cubic interaction in massless Higher
Spin gauge theory both in flat and in AdS space-times of arbitrary dimensions.
We consider a triplet formulation of the Higher Spin gauge theory and
generalize the Higher Spin symmetry algebra of the free model to the
corresponding algebra for the case of cubic interaction. The generators of this
new algebra carry indexes which label the three Higher Spin fields involved
into the cubic interaction. The method is based on the use of oscillator
formalism and on the Becchi-Rouet-Stora-Tyutin (BRST) technique. We derive
general conditions on the form of cubic interaction vertex and discuss the
ambiguities of the vertex which result from field redefinitions. This method
can in principle be applied for constructing the Higher Spin interaction vertex
at any order. Our results are a first step towards the construction of a
Lagrangian for interacting Higher Spin gauge fields that can be holographically
studied.


  Symmetric conformal Killing tensors and (skew-symmetric) conformal
Yano-Killing tensors for Euclidean Taub-NUT metric are given in explicit form.
Relations between Yano and CYK tensors in terms of conformal rescaling are
discussed.


  Strong magnetic fields are expected to significantly modify the pulsation
frequencies of waves propagating in the cores of red giants or in the radiative
envelopes of intermediate- and high-mass main-sequence stars. We calculate the
g-mode frequencies of stars with magnetic dipole fields which are aligned with
their rotational axes, treating both the Lorentz and Coriolis forces
non-perturbatively. We provide a compact asymptotic formula for the g-mode
period spacing, and universally find that strong magnetism decreases this
period spacing substantially more than is predicted by perturbation theory.
These results are validated with explicit numerical mode calculations for
realistic stellar models. The approach we present is highly versatile: once the
eigenvalues $\lambda$ of a certain differential operator are precomputed as a
function of the magnetogravity and rotational frequencies (in units of the mode
frequency), the non-perturbative impact of the Coriolis and Lorentz forces is
understood under a broad domain of validity, and is readily incorporated into
asteroseismic modeling.


  At first glance, the ruins of the Roman Theatre in the Italian town of
Volterra have little in common with cassette tapes containing Atari games. One
is certainly considered an important historical landmark, while the consensus
on the importance of the other is partial at best. Still, both are remnants of
times vastly different from the present and are at risk of oblivion. Unearthed
architectural structures are exposed to the elements just as the deteriorating
signals stored on magnetic tapes. However, the rate of deterioration is much
faster with the magnetic media, as their life expectancy is counted in decades,
whereas the Roman Theater, which is already in ruin, measures its lifespan in
centuries. Hence, both would benefit from some form of digital preservation and
reconstruction. In this panel, we discuss how to sustainably preserve tangible
and intangible cultural artifacts for future generations.


  This paper proposes a hierarchical Bayesian model for probabilistic
estimation of the electric vehicle battery capacity fade. Since the battery
aging factors such as temperature, current, and state of charge are not fixed,
and they change in different times, locations and by the different users,
deterministic models with constant parameters cannot accurately evaluate the
battery capacity fade. Therefore, a probabilistic presentation of the capacity
fade including uncertainties of the measurements or observations of the
variables can be a proper solution. We have developed a hierarchical Bayesian
Network model for the electric vehicle battery capacity fade considering
multiple external variables. The mathematical expression of the model is
extracted based on Bayes theorem, the probability distributions for all
variables and their dependencies are carefully chosen where the Metropolis
Hastings Markov Chain Monte Carlo sampling method is applied to generate the
posterior distributions. The model is trained with 85 percent of experimental
data to obtain its unseen parameters and tested with other 15 percent of data
to prove its accuracy. Also, three case studies for different drivers,
different grid services frequencies, and different climates are explored to
show model flexibility with different input data. The developed model needs
training data for parameter tuning in different conditions. However, after
training, it has more than 95 percent precision in estimating the battery
capacity fade percentage.


  We study transitions of hadronic matter (HM) to 3-flavor quark matter (3QM),
regarding the conversion processes as combustion and describing them
hydrodynamically. Under the assumption that HM is metastable with their free
energies being larger than those of 3QM but smaller than those of 2-flavor
quark matter (2QM), we consider in this paper the conversion induced by
diffusions of seed 3QM. This is a sequel to our previous paper, in which the
shock-induced conversion was studied in the same frame work. We not only pay
attention to the jump condition on both sides of the conversion front but the
structures inside the front are also considered by taking into account what
happens during the conversion processes on the time scale of weak interactions.
We employ for HM the Shen's EOS, which is based on the relativistic mean field
theory, and the bag model-based EOS for QM just as in the previous paper. We
demonstrated in that paper that in this combination of EOS's the combustion
will occur for a wide range of the bag constant and strong coupling constant in
the so-called endothermic regime, in which the Hugoniot curve for combustion
runs below the initial state. We find that weak deflagration nearly always
occurs and that weak detonation is possible only when the diffusion constant is
(unrealistically) large and the critical strange fraction is small. The
velocities of the conversion front are ~ $10^3-10^7$ cm/s depending on the
initial temperature and density as well as the parameters in the QM EOS and
become particularly small when the final state is in the mixed phase. Finally
we study linear stability of the laminar weak-deflagration front and find that
it is unstable in the exothermic regime (Darrius-Landau instability) but stable
in the endothermic regime, which is quite contrary to the ordinary combustions.


  The status of searches for gravitational microlensing events of the stars in
our galaxy and in other galaxies of the Local Group, the interpretation of the
results, some theory, and prospects for the future are reviewed. The searches
have already unveiled about 100 events, at least two of them caused by
binaries, and have already proven to be useful for studies of the Galactic
structure. The events detected so far are probably attributable to the effects
of ordinary stars, and possibly to sub-stellar brown dwarfs; however a firm
conclusion cannot be reached yet because the analysis published to date is
based on a total of only 16 events. The current searches, soon to be upgraded,
will probably allow determination of the mass function of stars and brown
dwarfs in the next few years; these efforts will also provide good statistical
information about binary systems, in particular their mass ratios. They may
also reveal the nature of dark matter and allow us to detect planets and
planetary mass objects.


  We apply recently developed convex programs to find the minimal-area
Riemannian metric on $2n$-sided polygons ($n\geq 3$) with length conditions on
curves joining opposite sides. We argue that the Riemannian extremal metric
coincides with the conformal extremal metric on the regular $2n$-gon. The
hexagon was considered by Calabi. The region covered by the maximal number $n$
of geodesics bands extends over most of the surface and exhibits positive
curvature. As $n\to \infty$ the metric, away from the boundary, approaches the
well-known round extremal metric on $\mathbb{RP}_2$. We extend Calabi's
isosystolic variational principle to the case of regions with more than three
bands of systolic geodesics. The extremal metric on $\mathbb{RP}_2$ is a
stationary point of this functional applied to a surface with infinite number
of systolic bands.


  In usual scale-free networks of Barabasi-Albert type, a newly added node
selects randomly m neighbors from the already existing network nodes,
proportionally to the number of links these had before. Then the number N(k) of
nodes with k links each decays as 1/k^gamma where gamma=3 is universal, i.e.
independent of m. Now we use a limited directedness in the construction of the
network, as a result of which the exponent gamma decreases from 3 to 2 for
increasing m.


  We investigate the oscillatory properties of the quiet solar chromosphere in
relation to the underlying photosphere, with particular regard to the effects
of the magnetic topology. We perform a Fourier analysis on a sequence of
line-of-sight velocities measured simultaneously in a photospheric (Fe I 709.0
nm) and a chromospheric line (Ca II 854.2 nm). The velocities were obtained
from full spectroscopic data acquired at high spatial resolution with the
Interferometric BIdimensional Spectrometer (IBIS). The field of view
encompasses a full supergranular cell, allowing us to discriminate between
areas with different magnetic characteristics. We show that waves with
frequencies above the acoustic cut-off propagate from the photosphere to upper
layers only in restricted areas of the quiet Sun. A large fraction of the quiet
chromosphere is in fact occupied by ``magnetic shadows'', surrounding network
regions, that we identify as originating from fibril-like structures observed
in the core intensity of the Ca II line. We show that a large fraction of the
chromospheric acoustic power at frequencies below the acoustic cut-off,
residing in the proximity of the magnetic network elements, directly propagates
from the underlying photosphere. This supports recent results arguing that
network magnetic elements can channel low-frequency photospheric oscillations
into the chromosphere, thus providing a way to input mechanical energy in the
upper layers.


  We introduce NebulOS, a Big Data platform that allows a cluster of Linux
machines to be treated as a single computer. With NebulOS, the process of
writing a massively parallel program for a datacenter is no more complicated
than writing a Python script for a desktop computer. The platform enables most
pre-existing data analysis software to be used, as scale, in a datacenter
without modification. The shallow learning curve and compatibility with
existing software greatly reduces the time required to develop distributed data
analysis pipelines. The platform is built upon industry-standard, open-source
Big Data technologies, from which it inherits several fault tolerance features.
NebulOS enhances these technologies by adding an intuitive user interface,
automated task monitoring, and other usability features. We present a summary
of the architecture, provide usage examples, and discuss the system's
performance scaling.


  The recent discovery of superconductivity under high pressure in the two-leg
ladder compound BaFe$_2$S$_3$ [H. Takahashi et al., Nature Materials 14, 1008
(2015)] opens a broad avenue of research, because it represents the first
report of pairing tendencies in a quasi one-dimensional iron-based high
critical temperature superconductor. Similarly as in the case of the cuprates,
ladders and chains can be far more accurately studied using many-body
techniques and model Hamiltonians than their layered counterparts, particularly
if several orbitals are active. In this publication, we derive a two-orbital
Hubbard model from first principles that describes individual ladders of
BaFe$_2$S$_3$. The model is studied with the density matrix renormalization
group. These first reported results are exciting for two reasons: (i) at
half-filling, ferromagnetic order emerges as the dominant magnetic pattern
along the rungs of the ladder, and antiferromagnetic order along the legs, in
excellent agreement with neutron experiments; (ii) pairs form in the strong
coupling regime, as found by studying the binding energy of two holes doped on
the half-filled system. In addition, Orbital Selective Mott Phase
characteristics develop with doping, with only one Wannier orbital receiving
the hole carriers while the other remains half-filled. These results suggest
that the analysis of models for iron-based two-leg ladders could clarify the
origin of pairing tendencies and other exotic properties of iron-based high
critical temperature superconductors.


  Content on the Internet is heterogeneous and arises from various domains like
News, Entertainment, Finance and Technology. Understanding such content
requires identifying named entities (persons, places and organizations) as one
of the key steps. Traditionally Named Entity Recognition (NER) systems have
been built using available annotated datasets (like CoNLL, MUC) and demonstrate
excellent performance. However, these models fail to generalize onto other
domains like Sports and Finance where conventions and language use can differ
significantly. Furthermore, several domains do not have large amounts of
annotated labeled data for training robust Named Entity Recognition models. A
key step towards this challenge is to adapt models learned on domains where
large amounts of annotated training data are available to domains with scarce
annotated data.
  In this paper, we propose methods to effectively adapt models learned on one
domain onto other domains using distributed word representations. First we
analyze the linguistic variation present across domains to identify key
linguistic insights that can boost performance across domains. We propose
methods to capture domain specific semantics of word usage in addition to
global semantics. We then demonstrate how to effectively use such domain
specific knowledge to learn NER models that outperform previous baselines in
the domain adaptation setting.


  It is argued that the natural kinematics of the pilot-wave theory is
Aristotelian. Despite appearances, Galilean invariance is not a fundamental
symmetry of the low-energy theory. Instead, it is a fictitious symmetry that
has been artificially imposed. It is concluded that the search for a
Lorentz-invariant extension is physically misguided.


  We describe trlib, a library that implements a variant of Gould's Generalized
Lanczos method (Gould et al. in SIAM J. Opt. 9(2), 504-525, 1999) for solving
the trust region problem.
  Our implementation has several distinct features that set it apart from
preexisting ones. We implement both conjugate gradient (CG) and Lanczos
iterations for assembly of Krylov subspaces. A vector- and matrix-free reverse
communication interface allows the use of most general data structures, such as
those arising after discretization of function space problems. The hard case of
the trust region problem frequently arises in sequential methods for nonlinear
optimization. In this implementation, we made an effort to fully address the
hard case in an exact way by considering all invariant Krylov subspaces.
  We investigate the numerical performance of trlib on the full subset of
unconstrained problems of the CUTEst benchmark set. In addition to this,
interfacing the PDE discretization toolkit FEniCS with trlib using the
vector-free reverse communication interface is demonstrated for a family of
PDE-constrained control trust region problems adapted from the OPTPDE
collection.


  We present the design of, and a first analysis of data from, the atmospheric
seeing monitor at the Australia Telescope Compact Array (ATCA). The seeing
monitor has been operational almost continuously since May 2004 and every 10min
delivers a measurement of the atmospheric phase stability at the observatory.
Its measurements can be used by observers to help deciding whether it is worth
carrying out observations at millimetre wavelengths or whether a
longer-wavelength backup project should be observed. We present a statistical
analysis of the data recorded since September 2004 to characterize the annual
variations in atmospheric path length fluctuations. Our analysis shows that in
terms of phase stability, nights in spring, summer, and autumn are as good as,
or better than, days in winter. We also find that the data imply that the
turbulence in the lower few hundred metres of the atmosphere is predominantly
responsible for the atmospheric seeing.


  Mixtures of hard hyperspheres in odd space dimensionalities are studied with
an analytical approximation method. This technique is based on the so-called
Rational Function Approximation and provides a procedure for evaluating
equations of state, structure factors, radial distribution functions, and
direct correlations functions of additive mixtures of hard hyperspheres with
any number of components and in arbitrary odd-dimension space. The method gives
the exact solution of the Ornstein--Zernike equation coupled with the
Percus--Yevick closure, thus extending to arbitrary odd dimension the solution
for hard-sphere mixtures [J. L. Lebowitz, Phys.\ Rev.\ \textbf{133}, 895
(1964)]. Explicit evaluations for binary mixtures in five dimensions are
performed. The results are compared with computer simulations and a good
agreement is found.


  The electric and magnetic fields of an infinite straight wire carrying a
steady current which is turned on abruptly are determined using Jefimenko's
equations, starting from the standard assumption that the wire is electrically
neutral in its rest frame. Some nontrivial aspects of the solution are
discussed in detail.


  Parity violation at LEP or SLC can be measured through the charge asymmetry.
An optimal method of moments is developed here to measure this asymmetry, as
well as similar asymmetries. This method is equivalent to the likelihood fit.
It is simpler in use, as it gives analytical formulas for both the asymmetry
and its statistical error. These formulas give the dependence of the accuracy
on the experimental angular acceptance explicitly.


  Psychological traumas are thought to be present in a wide range of
conditions, including post-traumatic stress disorder, disorganised attachment,
personality disorders, dissociative identity disorder and psychosis. This work
presents a new psychotherapy for psychological traumas, based on a functional
model of the mind, built with elements borrowed from the fields of computer
science, artificial intelligence and neural networks. The model revolves around
the concept of hierarchical value and explains the emergence of dissociation
and splitting in response to emotional pain. The key intuition is that traumas
are caused by too strong negative emotions, which are in turn made possible by
a low-value self, which is in turn determined by low-value self-associated
ideas. The therapeutic method compiles a list of patient's traumas, identifies
for each trauma a list of low-value self-associated ideas, and provides for
each idea a list of counterexamples, to raise the self value and solve the
trauma. Since the psychotherapy proposed has not been clinically tested,
statements on its effectiveness are premature. However, since the conceptual
basis is solid and traumas are hypothesised to be present in many psychological
disorders, the potential gain may be substantial.


  Visual and audio modalities are two symbiotic modalities underlying videos,
which contain both common and complementary information. If they can be mined
and fused sufficiently, performances of related video tasks can be
significantly enhanced. However, due to the environmental interference or
sensor fault, sometimes, only one modality exists while the other is abandoned
or missing. By recovering the missing modality from the existing one based on
the common information shared between them and the prior information of the
specific modality, great bonus will be gained for various vision tasks. In this
paper, we propose a Cross-Modal Cycle Generative Adversarial Network (CMCGAN)
to handle cross-modal visual-audio mutual generation. Specifically, CMCGAN is
composed of four kinds of subnetworks: audio-to-visual, visual-to-audio,
audio-to-audio and visual-to-visual subnetworks respectively, which are
organized in a cycle architecture. CMCGAN has several remarkable advantages.
Firstly, CMCGAN unifies visual-audio mutual generation into a common framework
by a joint corresponding adversarial loss. Secondly, through introducing a
latent vector with Gaussian distribution, CMCGAN can handle dimension and
structure asymmetry over visual and audio modalities effectively. Thirdly,
CMCGAN can be trained end-to-end to achieve better convenience. Benefiting from
CMCGAN, we develop a dynamic multimodal classification network to handle the
modality missing problem. Abundant experiments have been conducted and validate
that CMCGAN obtains the state-of-the-art cross-modal visual-audio generation
results. Furthermore, it is shown that the generated modality achieves
comparable effects with those of original modality, which demonstrates the
effectiveness and advantages of our proposed method.


  We present a decomposition of the generalized binomial coefficients
associated with Jack polynomials into two factors: a stem, which is described
explicitly in terms of hooks of the indexing partitions, and a leaf, which
inherits various recurrence properties from the binomial coefficients and
depends exclusively on the skew diagram. We then derive a direct combinatorial
formula for the leaf in the special case where the two indexing partitions
differ by at most two rows. This formula also exhibits an unexpected symmetry
with respect to the lengths of the two rows.


  We lift metrics over words to metrics over word-to-word transductions, by
defining the distance between two transductions as the supremum of the
distances of their respective outputs over all inputs. This allows to compare
transducers beyond equivalence.
  Two transducers are close (resp. $k$-close) with respect to a metric if their
distance is finite (resp. at most $k$). Over integer-valued metrics computing
the distance between transducers is equivalent to deciding the closeness and
$k$-closeness problems. For common integer-valued edit distances such as,
Hamming, transposition, conjugacy and Levenshtein family of distances, we show
that the closeness and the $k$-closeness problems are decidable for functional
transducers. Hence, the distance with respect to these metrics is also
computable.
  Finally, we relate the notion of distance between functions to the notions of
diameter of a relation and index of a relation in another. We show that
computing edit distance between functional transducers is equivalent to
computing diameter of a rational relation and both are a specific instance of
the index problem of rational relations.


  In this paper, a legless capsule robot (capsubot) comprised of a sealed
external body, an internal body, and a rotational actuator is proposed. The aim
of this robot is to move in a two-dimensional viscous environment. After the
robot is designed and modeled, a four-stage angular velocity profile is
proposed to move the robot in one direction. In addition, a camera is used to
obtain the position of the robot in the environment while the orientation and
the velocity of the robot is estimated with Kalman filter by fusing the IMU,
gyro, and the magnetometer sensors. Furthermore, to control the robot a state
feedback control is implemented. Finally, experimental results are provided to
demonstrate the performance of the robots and the proposed algorithms.


  We present dynamical models of the narrow line region (NLR) outflows in the
nearby Seyfert galaxies Mrk 3, Mrk 78, NGC 1068, and NGC 4151 using
observations from the Hubble Space Telescope and Apache Point Observatory. We
employ long-slit spectroscopy to map the spatially-resolved outflow and
rotational velocities of the ionized gas. We also perform surface brightness
decompositions of host galaxy images to constrain the enclosed stellar mass
distributions as functions of distance from the supermassive black holes
(SMBHs). Assuming that the NLR gas is accelerated by AGN radiation pressure,
and subsequently decelerated by the host galaxy and SMBH gravitational
potentials, we derive outflow velocity profiles where the gas is launched in
situ at multiple distances from the SMBH. We find a strong correlation between
the turnover (from acceleration to deceleration) radii from our models, with
the turnovers seen in the observed velocities and spatially-resolved mass
outflow rates for the AGN with bolometric luminosities $>$ 10$^{44}$ erg
sec$^{-1}$. This consistency indicates that radiation pressure is the dominant
driving mechanism behind the NLR outflows in these moderate-luminosity AGN,
with a force multiplier $\sim$500 yielding the best agreement between the
modeled and observed turnover radii. However, in Meena2021 we found that this
trend may not hold at lower luminosities, where our modeled turnover distance
for NGC 4051 is much smaller than in the observed kinematics. This result may
indicate that either additional force(s) are responsible for accelerating the
NLR outflows in low-luminosity AGN, or higher spatial resolution observations
are required to quantify their turnover radii.


  This work presents, to the best of our knowledge of the literature, the first
analytic model to address the performance of an LRU (Least Recently Used)
implementing cache under non-stationary traffic conditions, i.e., when the
popularity of content evolves with time. We validate the accuracy of the model
using Monte Carlo simulations. We show that the model is capable of accurately
estimating the cache hit probability, when the popularity of content is
non-stationary.
  We find that there exists a dependency between the performance of an LRU
implementing cache and i) the lifetime of content in a system, ii) the volume
of requests associated with it, iii) the distribution of content request
volumes and iv) the shape of the popularity profile over time.


  Composing for a film requires developing an understanding of the film, its
characters and the film aesthetic choices made by the director. We propose
using existing visual analysis systems as a core technology for film music
generation. We extract film features including main characters and their
emotions to develop a computer understanding of the film's narrative arc. This
arc is combined with visually analyzed director aesthetic choices including
pacing and levels of movement. Two systems are presented, the first using a
robotic film composer and marimbist to generate film scores in real-time
performance. The second software-based system builds on the results from the
robot film composer to create narrative driven film scores.


  Informationization is a prevailing trend in today's world. The increasing
demand for information in decision-making processes poses significant
challenges for investigation activities, particularly in terms of effectively
allocating limited resources to plan investigation programs. This paper
addresses the investigation path planning problem by formulating it as a
multi-traveling salesman problem (MTSP). Our objective is to minimize costs,
and to achieve this, we propose a chaotic artificial fish swarm algorithm based
on multiple population differential evolution (DE-CAFSA). To overcome the
limitations of the artificial fish swarm algorithm, such as low optimization
accuracy and the inability to consider global and local information, we
incorporate adaptive field of view and step size adjustments, replace random
behavior with the 2-opt operation, and introduce chaos theory and sub-optimal
solutions to enhance optimization accuracy and search performance.
Additionally, we integrate the differential evolution algorithm to create a
hybrid algorithm that leverages the complementary advantages of both
approaches. Experimental results demonstrate that DE-CAFSA outperforms other
algorithms on various public datasets of different sizes, as well as showcasing
excellent performance on the examples proposed in this study.


  Fabrication technology, which allows a substantial decrease of the plasmonic
propagation loss for both plasmon-friendly metals like Au, Cu or Al and
plasmon-unfriendly metals like Co, Fe or Cr, has been developed and
experimentally demonstrated. Optimization of the optical confinement is used to
reduce the propagation loss below 1 dB per plasmonic device.


  We propose an explanation of the new resonances observed in $D_s^{(*)+}
\pi^0$ decays. We suggest that the data can be explained by the mixing of
conventional p-wave excited $D_s^+$ mesons with 4-quark states. The narrow
states observed in $D_s^+ \pi^0$ and $D_s^{*+}\pi^0$ are primarily p-wave
$D_{sJ}^{*}$ states, while the predominantly 4-quark states are shifted above
$D^{(*)} K$ threshold and should be broad. Ranges for the mixing parameter and
mass of the 4-quark state in this scenario are given. Other experimental
consequences are discussed.


  Data generation is recognized as a potent strategy for unsupervised domain
adaptation (UDA) pertaining semantic segmentation in adverse weathers.
Nevertheless, these adverse weather scenarios encompass multiple possibilities,
and high-fidelity data synthesis with controllable weather is under-researched
in previous UDA works. The recent strides in large-scale text-to-image
diffusion models (DM) have ushered in a novel avenue for research, enabling the
generation of realistic images conditioned on semantic labels. This capability
proves instrumental for cross-domain data synthesis from source to target
domain owing to their shared label space. Thus, source domain labels can be
paired with those generated pseudo target data for training UDA. However, from
the UDA perspective, there exists several challenges for DM training: (i)
ground-truth labels from target domain are missing; (ii) the prompt generator
may produce vague or noisy descriptions of images from adverse weathers; (iii)
existing arts often struggle to well handle the complex scene structure and
geometry of urban scenes when conditioned only on semantic labels. To tackle
the above issues, we propose ControlUDA, a diffusion-assisted framework
tailored for UDA segmentation under adverse weather conditions. It first
leverages target prior from a pre-trained segmentor for tuning the DM,
compensating the missing target domain labels; It also contains UDAControlNet,
a condition-fused multi-scale and prompt-enhanced network targeted at
high-fidelity data generation in adverse weathers. Training UDA with our
generated data brings the model performances to a new milestone (72.0 mIoU) on
the popular Cityscapes-to-ACDC benchmark for adverse weathers. Furthermore,
ControlUDA helps to achieve good model generalizability on unseen data.


  In this paper, we present a deep autoencoder based energy method (DAEM) for
the bending, vibration and buckling analysis of Kirchhoff plates. The DAEM
exploits the higher order continuity of the DAEM and integrates a deep
autoencoder and the minimum total potential principle in one framework yielding
an unsupervised feature learning method. The DAEM is a specific type of
feedforward deep neural network (DNN) and can also serve as function
approximator. With robust feature extraction capacity, the DAEM can more
efficiently identify patterns behind the whole energy system, such as the field
variables, natural frequency and critical buckling load factor studied in this
paper. The objective function is to minimize the total potential energy. The
DAEM performs unsupervised learning based on random generated points inside the
physical domain so that the total potential energy is minimized at all points.
For vibration and buckling analysis, the loss function is constructed based on
Rayleigh's principle and the fundamental frequency and the critical buckling
load is extracted. A scaled hyperbolic tangent activation function for the
underlying mechanical model is presented which meets the continuity requirement
and alleviates the gradient vanishing/explosive problems under bending
analysis. The DAEM can be easily implemented and we employed the Pytorch
library and the LBFGS optimizer. A comprehensive study of the DAEM
configuration is performed for several numerical examples with various
geometries, load conditions, and boundary conditions.


  We present an analytic model, based on previous works which tackled the
reionization history of the universe, which allows us to describe the
generation of the secondary CMB anisotropies due to the kinetic SZ effect. We
take into account the "patchy pattern" of reionization (HII bubbles), the
cross-correlations of these ionized regions, the small-scale fluctuations of
the matter density field and the contribution from collapsed objects. For an
open universe, we find that the power-spectrum $l(l+1)C_l/(2\pi)$ exhibits a
plateau of height $10^{-13}$ in the range $10^3 < l < 10^6$. We find that for
large wavenumbers $l > 10^4$ the signal is dominated by the contribution from
collapsed halos while for $l < 10^4$ it is governed by the large-scale
correlations of HII bubbles. This implies that one cannot discriminate
reionization by stars from a quasar-driven scenario since the size of ionized
regions never dominates the behaviour of the anisotropies. Moreover, the
secondary CMB anisotropies arise from a broad range of redshifts (7.5<z<10 for
the IGM and 0<z<7 for galactic halos). The signal expected in our model might
bias the cosmological parameter estimation from CMB experiments such as Planck
and could be detected by future mm-wavelength interferometers (e.g., ALMA).


  Given a natural number $n \geq 1$, the odometer semigroup $O_n$, also known
as the adding machine or the Baumslag-Solitar monoid with two generators, is a
well-known object in group theory. This paper examines the odometer semigroup
in relation to representations of bounded linear operators. We focus on
noncommutative operators and prove that contractive representations of $O_n$
always admit to nicer representations of $O_n$. We give a complete description
of representations of $O_n$ on the Fock space and relate it to the odometer
lifting and subrepresentations of $O_n$. Along the way, we also classify Nica
covariant representations of $O_n$.


  To infer a diffusion network based on observations from historical diffusion
processes, existing approaches assume that observation data contain exact
occurrence time of each node infection, or at least the eventual infection
statuses of nodes in each diffusion process. They determine potential influence
relationships between nodes by identifying frequent sequences, or statistical
correlations, among node infections. In some real-world settings, such as the
spread of epidemics, tracing exact infection times is often infeasible due to a
high cost; even obtaining precise infection statuses of nodes is a challenging
task, since observable symptoms such as headache only partially reveal a node's
true status. In this work, we investigate how to effectively infer a diffusion
network from observation data with uncertainty. Provided with only
probabilistic information about node infection statuses, we formulate the
problem of diffusion network inference as a constrained nonlinear regression
w.r.t. the probabilistic data. An alternating maximization method is designed
to solve this regression problem iteratively, and the improvement of solution
quality in each iteration can be theoretically guaranteed. Empirical studies
are conducted on both synthetic and real-world networks, and the results verify
the effectiveness and efficiency of our approach.


  We present optical and near-infrared images and spectra of ESO248-G10, the
host of giant radio galaxy MRC B0319-454. From near-infrared colours, the
active nucleus is shown to be reddened by hot dust emission or dust extinction.
Star forming regions are identified beyond a radius of 5" (8.1 kpc) where hot
gas is prevalent. The optical spectrum shows gas rotation at speeds of up to
350km/s out to >=32kpc along the radio axis. A model is proposed in which the
giant elliptical is triaxial with the radio axis along the short axis, and the
figure rotation is around the long axis. From the model, the angles of the
principal axes are psi=34 degrees, theta=65 degrees, and phi=19 degrees with
axis ratios of B/A=0.75 and C/A=0.69. A second velocity feature from 5" to 15"
to the north-east of the nucleus is proposed to be a merging gas-rich galaxy
inducing star formation while settling into an orbit about the figure rotation
axis.


  We present a yield simulator to predict the number and characteristics of
planets, false positives and false alarms in transit surveys. The simulator is
based on a galactic model and the planet occurrence rates measured by the
Kepler mission. It takes into account the observation window function and
measured noise levels of the investigated survey. Additionally, it includes
vetting criteria to identify false positives. We apply this simulator to the
Next Generation Transit Survey (NGTS), a wide-field survey designed to detect
transiting Neptune-sized exoplanets. We find that red noise is the main
limitation of NGTS up to 14th magnitude, and that its obtained level determines
the expected yield. Assuming a red noise level of 1 mmag, the simulation
predicts the following for a four-year survey: 4+-3 Super-Earths, 19+-5 Small
Neptunes, 16+-4 Large Neptunes, 55+-8 Saturn-sized planets and 150+-10
Jupiter-sized planets, along with 4688+-45 eclipsing binaries and 843+-75
background eclipsing binaries. We characterize the properties of these objects
to enhance the early identification of false positives and discuss follow-up
strategies for transiting candidates.


  A deformation of pure Yang-Mill theory by a phantom field similar to the
Faddeev-Popov ghost is considered. In this theory an {\em Ersatz}-supersymmetry
is identified which results in cancellation of quantum corrections up to
two-loop order. A quadruplet built from two complex fields in the adjoint --
the Faddeev-Popov ghost $c^a$ and the phantom $\Phi^a$, all with the wrong
statistics -- balances four gauge fields $a_\mu^a$. At this level, the
instanton measure and the $\beta$ function is fully determined by
quasiclassics. In a simple $\phi^4$ theory with a phantom added I identify a
strictly conserved {\em Ersatz}-supercurrent. In the latter theory unitarity of
amplitudes persists despite the presence of the phantom. In deformed Yang-Mills
it is likely (although not proven) to persist too in all amplitudes with only
gluon external legs. It remains to be seen whether this construction is just a
device facilitating some loop calculations or broader applications can be
found.


  We consider the initial-boundary value problem for systems of quasilinear
wave equations on domains of the form $[0,T] \times \Sigma$, where $\Sigma$ is
a compact manifold with smooth boundaries $\partial\Sigma$. By using an
appropriate reduction to a first order symmetric hyperbolic system with maximal
dissipative boundary conditions, well posedness of such problems is established
for a large class of boundary conditions on $\partial\Sigma$. We show that our
class of boundary conditions is sufficiently general to allow for a well posed
formulation for different wave problems in the presence of constraints and
artificial, nonreflecting boundaries, including Maxwell's equations in the
Lorentz gauge and Einstein's gravitational equations in harmonic coordinates.
Our results should also be useful for obtaining stable finite-difference
discretizations for such problems.


  We consider the dynamics towards the initial singularity of Bianchi type IX
vacuum and orthogonal perfect fluid models with a linear equation of state. The
`Bianchi type IX attractor theorem' states that the past asymptotic behavior of
generic type IX solutions is governed by Bianchi type I and II vacuum states
(Mixmaster attractor). We give a comparatively short and self-contained new
proof of this theorem. The proof we give is interesting in itself, but more
importantly it illustrates and emphasizes that type IX is special, and to some
extent misleading when one considers the broader context of generic models
without symmetries.


  String theory suggests the simultaneous presence of many ultralight axions
possibly populating each decade of mass down to the Hubble scale 10^-33eV.
Conversely the presence of such a plenitude of axions (an "axiverse") would be
evidence for string theory, since it arises due to the topological complexity
of the extra-dimensional manifold and is ad hoc in a theory with just the four
familiar dimensions. We investigate how upcoming astrophysical experiments will
explore the existence of such axions over a vast mass range from 10^-33eV to
10^-10eV. Axions with masses between 10^-33eV to 10^-28eV cause a rotation of
the CMB polarization that is constant throughout the sky. The predicted
rotation angle is of order \alpha~1/137. Axions in the mass range 10^-28eV to
10^-18eV give rise to multiple steps in the matter power spectrum, that will be
probed by upcoming galaxy surveys. Axions in the mass range 10^-22eV to
10^-10eV affect the dynamics and gravitational wave emission of rapidly
rotating astrophysical black holes through the Penrose superradiance process.
When the axion Compton wavelength is of order of the black hole size, the
axions develop "superradiant" atomic bound states around the black hole
"nucleus". Their occupation number grows exponentially by extracting rotational
energy from the ergosphere, culminating in a rotating Bose-Einstein axion
condensate emitting gravitational waves. This mechanism creates mass gaps in
the spectrum of rapidly rotating black holes that diagnose the presence of
axions. The rapidly rotating black hole in the X-ray binary LMC X-1 implies an
upper limit on the decay constant of the QCD axion f_a<2*10^17GeV, much below
the Planck mass. This reach can be improved down to the grand unification scale
f_a<2*10^16GeV, by observing smaller stellar mass black holes.


  We propose a sampling algorithm to perform system identification from a set
of input-output graph signal pairs. The dynamics of the systems we study are
given by a partially known adjacency matrix and a generic parametric graph
filter of unknown parameters. The methodology we employ is built upon the
principles of annealed Langevin diffusion. This enables us to draw samples from
the posterior distribution instead of following the classical approach of point
estimation using maximum likelihood. We investigate how to harness the prior
information inherent in a dataset of graphs of different sizes through the
utilization of graph neural networks. We demonstrate, via numerical experiments
involving both real-world and synthetic networks, that integrating prior
knowledge into the estimation process enhances estimation performance.


  There exists just one regular polytope of rank larger than 3 whose full
automorphism group is a projective general linear group PGL_2(q), for some
prime-power q. This polytope is the 4-simplex and the corresponding group is
PGL_2(5), which is isomorphic to S_5.


  Multi-wavelength scattered light imaging of debris disks may inform dust
properties including typical size and mineral composition. Existing studies
have investigated a small set of individual systems across a variety of imaging
instruments and filters, calling for uniform comparison studies to
systematically investigate dust properties. We obtain the surface brightness of
dust particles in debris disks by post-processing coronagraphic imaging
observations, and compare the multi-wavelength reflectance of dust. For a
sample of resolved debris disks, we perform a systematic analysis on the
reflectance properties of their birth rings. We reduced the visible and
near-infrared images of 23 debris disk systems hosted by A through M stars
using two coronagraphs onboard the Hubble Space Telescope: the STIS instrument
observations centering at 0.58 $\mu$m, and the NICMOS instrument at 1.12 $\mu$m
or 1.60 $\mu$m. For proper recovery of debris disks, we used classical
reference differential imaging for STIS, and adopted non-negative matrix
factorization with forward modeling for NICMOS. By dividing disk signals by
stellar signals to take into account of intrinsic stellar color effects, we
systematically obtained and compared the reflectance of debris birth rings at
~90 deg scattering angle. Debris birth rings typically exhibit a blue color at
~90 deg scattering angle. As the stellar luminosity increases, the color tends
to be more neutral. A likely L-shaped color-albedo distribution indicates a
clustering of scatterer properties. The observed color trend correlates with
the expected blow-out size of dust particles. The color-albedo clustering
likely suggests different populations of dust in these systems. More detailed
radiative transfer models with realistic dust morphology will contribute to
explaining the observed color and color-albedo distribution of debris systems.


  Parabolic subalgebras $\frak{p}$ of semisimple Lie algebras define a
$\Bbb{Z}$-grading of the Lie algebra. If there exists a nilpotent element in
the first graded part of $\frak{g}$ on which the adjoint group of $\frak{p}$
acts with a dense orbit, the parabolic subalgebra is said to be nice. The
corresponding nilpotent element is also called admissible. Nice parabolic
subalgebras of simple Lie algebras have been classified. In the case of Borel
subalgebras a Richardson element of $\frak{g}_1$ is exactly one that involves
all simple root spaces. It is however difficult to write down such nilpotent
elements for general parabolic subalgebras. In this paper we give an explicit
construction of admissible elements in $\frak{g}_1$ that uses as few root
spaces as possible.


  The nature and the extent of the Great Attractor has been the subject of much
debate, not in the least due to the unfortunate position of its central part
being behind the Milky Way. We here present the latest results from our deep
optical galaxy search in the southern Milky Way. A full view of the southern
hemisphere is emerging, revealing ACO 3627 as the most prominent concentration
of galaxies in the southern sky. Our follow-up spectroscopic observations
support the idea that ACO 3627 is the dominant component of a ``great
wall''-structure, similar to Coma in the (northern) Great Wall.


  The randomized play-the-winner rule (RPW) is a response-adaptive design
proposed by Wei and Durham (1978) for sequentially randomizing patients to
treatments in a two-treatment clinical trial so that more patients are assigned
to the better treatment as the clinical trial goes on. The elephant random walk
(ERW) proposed by Schutz and Trimper (2004) is a non-Markovian discrete-time
random walk on $\mathbb Z$ which has a link to a famous saying that elephants
can always remember where they have been. The asymptotic behaviors of RPW rule
and ERW have been studied in litterateurs independently, and their asymptotic
behaviors are very similar. In this paper, we link RPW rule and ERW with the
recursive stochastic algorithm. With the help of a recursive stochastic
algorithm, we obtain the Gaussian approximation of the ERW and
multi-dimensional varying-memory ERW with random step sizes. By the Gaussian
approximation, the central limit theorem, precise law of the iterated
logarithm, and almost sure central limit theorem are obtained for the
multi-dimensional ERW, the multi-dimensional ERW with random step sizes, and
their centers of mass for all the diffusive, critical, and superdiffusive
regimes. Based on the Gaussian approximation and the small ball probabilities
for a new kind of Gaussian process, the precise Chung type laws of the iterated
logarithm of the multi-dimensional ERW with random step sizes and its mass of
center are also obtained for both the diffusive regime and superdiffusive
regime.


  Buildings consume 60% of global electricity. However, current building
management systems (BMSs) are highly expensive and difficult to justify for
small to medium-sized buildings. As such, the Internet of Things (IoT), which
can monitor and collect a large amount of data on different contexts of a
building and feed the data to the processor of the BMS, provides a new
opportunity to integrate intelligence into the BMS to monitor and manage the
energy consumption of the building in a cost-effective manner. Although an
extensive literature is available on IoT based BMS and applications of signal
processing techniques for some aspects of building energy management
separately, detailed study on their integration to address the overall BMS is
quite limited. As such, the proposed paper will address this gap by providing
an overview of an IoT based BMS leveraging signal processing and machine
learning techniques. It is demonstrated how to extract high-level building
occupancy information through simple and low-cost IoT sensors and studied the
impact of human activities on energy usage of a building, which can be
exploited to design energy conservation measures to reduce the building's
energy consumption.


  We apply the TrOCR framework to real-world, historical manuscripts and show
that TrOCR per se is a strong model, ideal for transfer learning. TrOCR has
been trained on English only, but it can adapt to other languages that use the
Latin alphabet fairly easily and with little training material. We compare
TrOCR against a SOTA HTR framework (Transkribus) and show that it can beat such
systems. This finding is essential since Transkribus performs best when it has
access to baseline information, which is not needed at all to fine-tune TrOCR.


  The idea that wave-function collapse is a physical process stems from a
misunderstanding of probability and the role it plays in quantum mechanics.


  The Cardioid distribution is a relevant model for circular data. However,
this model is not suitable for scenarios were there is asymmetry or
multimodality. In order to overcome this gap, an extended Cardioid model is
proposed, which is called Exponentiated Cardioid (EC) distribution. Besides,
some of its properties are derived, such as trigonometric moments, kurtosis and
skewness. A discussion about the modality and and expressions for the quantiles
through approximations of the studied model are also presented. To fit the EC
model, two estimation methods are presented based on maximum likelihood and
quantile least squares procedures. The performance of proposed estimators is
evaluated in a Monte Carlo simulation study, adopting both bias and mean square
error as comparison criteria. Finally, the proposed model is applied to a
dataset in the wind direction context. Results indicate that the EC
distribution may outperform Cardioid and the von Mises distributions.


  This work concerns the Perona-Malik equation, which plays essential role in
image processing. The first part gives a survey of results on existance,
uniqueness and stability of solutions, the second part introduces
discretisations of equation and deals with an analysis of discrete problem. In
the last part I present some numerical results, in particular with algorithms
applied to real images.


  As systems are getting more autonomous with the development of artificial
intelligence, it is important to discover the causal knowledge from
observational sensory inputs. By encoding a series of cause-effect relations
between events, causal networks can facilitate the prediction of effects from a
given action and analyze their underlying data generation mechanism. However,
missing data are ubiquitous in practical scenarios. Directly performing
existing casual discovery algorithms on partially observed data may lead to the
incorrect inference. To alleviate this issue, we proposed a deep learning
framework, dubbed Imputated Causal Learning (ICL), to perform iterative missing
data imputation and causal structure discovery. Through extensive simulations
on both synthetic and real data, we show that ICL can outperform
state-of-the-art methods under different missing data mechanisms.


  The origin of large-scale magnetic fields in the Universe is still unknown.
Observations suggest the presence of nGauss primordial magnetic fields at the
last scattering surface. The presence of such fields will affect the evolution
of cosmological perturbations and potentially leave an imprint on the CMB
anisotropies. Here, we show that the B-mode power spectrum carries a clear
signature of the stochastic primordial magnetic fields up to a few nGauss.
Specifically, the presence of nGauss primordial magnetic fields changes the BB
power spectrum at all scales. At large scales, the tensor modes contribute to
the B-mode spectrum, while non-vanishing vector modes contribute at small
scales. We show that the B-mode of CMB carry a distinct signature of the
primordial magnetic fields. To validate our prediction, we use BICEP2 and
POLARBEAR data and find that B-mode observations from the experiments are
consistent with non-zero primordial fields. We also use the BICEP2 observations
to constrain the primordial magnetic field. We provide a detailed analysis of
the B-mode polarization of the CMB with primordial magnetic fields and
investigate the implications for the upcoming CMB missions.


  Cubical type theory provides a constructive justification of homotopy type
theory. A crucial ingredient of cubical type theory is a path lifting operation
which is explained computationally by induction on the type involving several
non-canonical choices. We present in this article two canonicity results, both
proved by a sconing argument: a homotopy canonicity result, every natural
number is path equal to a numeral, even if we take away the equations defining
the lifting operation on the type structure, and a canonicity result, which
uses these equations in a crucial way. Both proofs are done internally in a
presheaf model.


  A rigorous proof is given for the convergence of the solutions of a viscous
Cahn-Hilliard system to the solution of the regularized version of the
forward-backward parabolic equation, as the coefficient of the diffusive term
goes to 0. Non-homogenous Neumann boundary condition are handled for the
chemical potential and the subdifferential of a possible non-smooth double-well
functional is considered in the equation. An error estimate for the difference
of solutions is also proved in a suitable norm and with a specified rate of
convergence.


  We consider the interaction between a spherical plasma sheet and a planar
plasma sheet due to the vacuum fluctuations of electromagnetic fields. We use
the mode summation approach to derive the Casimir interaction energy and study
its asymptotic behaviors. In the small separation regime, we confirm the
proximity force approximation and calculate the first correction beyond the
proximity force approximation. This study has potential application to model
Casimir interaction between objects made of materials that can be modeled by
plasma sheets such as graphene sheets.


  We address the issue of safety in reinforcement learning. We pose the problem
in an episodic framework of a constrained Markov decision process. Existing
results have shown that it is possible to achieve a reward regret of
$\tilde{\mathcal{O}}(\sqrt{K})$ while allowing an
$\tilde{\mathcal{O}}(\sqrt{K})$ constraint violation in $K$ episodes. A
critical question that arises is whether it is possible to keep the constraint
violation even smaller. We show that when a strictly safe policy is known, then
one can confine the system to zero constraint violation with arbitrarily high
probability while keeping the reward regret of order
$\tilde{\mathcal{O}}(\sqrt{K})$. The algorithm which does so employs the
principle of optimistic pessimism in the face of uncertainty to achieve safe
exploration. When no strictly safe policy is known, though one is known to
exist, then it is possible to restrict the system to bounded constraint
violation with arbitrarily high probability. This is shown to be realized by a
primal-dual algorithm with an optimistic primal estimate and a pessimistic dual
update.


  P-wave interaction in cold atoms may give rise to exotic topological
superfluids. However, the realization of p-wave interaction in cold atom system
is experimentally challenging. Here we propose a simple scheme to synthesize
effective $p$-wave interaction in conventional $s$-wave interacting quantum
gases. The key idea is to load atoms into spin-dependent optical lattice
potential. Using two concrete examples involving spin-1/2 fermions, we show how
the original system can be mapped into a model describing spinless fermions
with nearest neighbor p-wave interaction, whose ground state can be a
topological superfluid that supports Majorana fermions under proper conditions.
Our proposal has the advantage that it does not require spin-orbit coupling or
loading atoms onto higher orbitals, which is the key in earlier proposals to
synthesize effective $p$-wave interaction in $s$-wave quantum gases, and may
provide a completely new route for realizing $p$-wave topological superfluids.


  In this paper we propose the toy model of the closed string tachyon effective
action that has marginal tachyon profile as its exact solution in case of
constant or linear dilaton background. Then we will apply this model for
description of two dimensional bosonic string theory. We will find that the
background configuration with the spatial dependent linear dilaton, flat
spacetime metric and marginal tachyon profile is the exact solution of our
model even if we take into account backreaction of tachyon on dilaton and
metric.


  The Skenderis-van Rees prescription, which allows the calculation of
time-ordered correlation functions of local operators in CFT's using
holographic methods is studied and applied for excited states. Calculation of
correlators and matrix elements of local CFT operators between generic in/out
states are carried out in global Lorentzian AdS. We find the precise form of
such states, obtain an holographic formula to compute the inner product between
them, and using the consistency with other known prescriptions, we argue that
the in/out excited states built according to the Skenderis-Van Rees
prescription correspond to {\it coherent} states in the (large-$N$) AdS-Hilbert
space. This is confirmed by explicit holographic computations. The outcome of
this study has remarkable implications on generalizing the Hartle-Hawking
construction for wave functionals of excited states in AdS quantum gravity.


  Hydrogels provide a solution-mimicking environment for the interaction with
living systems that make them desirable for various biomedical and
technological applications. Because relevant biological processes in living
tissues occur at the biomolecular scale, hydrogel nanopatterning can be
leveraged to attain novel material properties and functionalities. However, the
fabrication of high aspect ratio (HAR) nanostructures in hydrogels capable of
self-standing in aqueous environments, with fine control of the size and shape
distribution, remains challenging. Here, we report the synthesis of
nanostructures with a HAR in bacterial cellulose (BC) hydrogel via directed
plasma nanosynthesis using argon ions. The nanostructures in BC are
reproducible, stable to sterilization, and liquid immersion. Using in-situ
surface characterization and semi-empirical modeling, we discovered that
pattern formation was linked to the formation of graphite-like clusters
composed of a mixture of C-C and C=C bonds. Moreover, our model predicts that
reactive species at the onset of the argon irradiation accelerate the bond
breaking of weak bonds, contributing to the formation of an amorphous carbon
layer and nanopattern growth.


  Gravitational domain wall solutions in gauged supergravity are often
constructed within truncations that do not include vectors. As a consequence
the gauge group is only a global symmetry of this truncation. The consistency
of the truncation requires the restriction to solutions with vanishing Noether
charge under this global symmetry, since otherwise vector fields are sourced.
We show that this has interesting consequences for the orbit structure of the
solutions under the global symmetries. We investigate this for
$\text{CSO}(p,q,r)$-gaugings in various dimensions with scalar fields truncated
to the $\text{SL}(n,\mathbb{R})/\text{SO}(n)$ subcoset. We prove that the seed
solution $-$ which generates all other solutions using only global
transformations $-$ has a diagonal coset matrix. This means that there exists a
transformation at the boundary of the geometry that diagonalises the coset
matrix and that this same transformation also diagonalises the whole flow as a
consequence of the vanishing charge.


  We study the eigenvalue problem of the rational Calogero model with the
coupling of the inverse-square interaction as a complex number. We show that
although this model is manifestly non-invariant under the combined parity and
time-reversal symmetry ${\cal{PT}}$, the eigenstates corresponding to the zero
value of the generalized angular momentum have real energies.


  The phenomenon of radicalization is investigated within a mixed population
composed of core and sensitive subpopulations. The latest includes first to
third generation immigrants. Respective ways of life may be partially
incompatible. In case of a conflict core agents behave as inflexible about the
issue. In contrast, sensitive agents can decide either to live peacefully
adjusting their way of life to the core one, or to oppose it with eventually
joining violent activities. The interplay dynamics between peaceful and
opponent sensitive agents is driven by pairwise interactions. These
interactions occur both within the sensitive population and by mixing with core
agents. The update process is monitored using a Lotka-Volterra-like Ordinary
Differential Equation. Given an initial tiny minority of opponents that coexist
with both inflexible and peaceful agents, we investigate implications on the
emergence of radicalization. Opponents try to turn peaceful agents to opponents
driving radicalization. However, inflexible core agents may step in to bring
back opponents to a peaceful choice thus weakening the phenomenon. The required
minimum individual core involvement to actually curb radicalization is
calculated.It is found to be a function of both the majority or minority status
of the sensitive subpopulation with respect to the core subpopulation and the
degree of activeness of opponents. The results highlight the instrumental role
core agents can have to hinder radicalization within the sensitive
subpopulation. Some hints are outlined to favor novel public policies towards
social integration.


  Full-waveform inversion (FWI) is today a standard process for the inverse
problem of seismic imaging. PDE-constrained optimization is used to determine
unknown parameters in a wave equation that represent geophysical properties.
The objective function measures the misfit between the observed data and the
calculated synthetic data, and it has traditionally been the least-squares
norm. In a sequence of papers, we introduced the Wasserstein metric from
optimal transport as an alternative misfit function for mitigating the
so-called cycle skipping, which is the trapping of the optimization process in
local minima. In this paper, we first give a sharper theorem regarding the
convexity of the Wasserstein metric as the objective function. We then focus on
two new issues. One is the necessary normalization of turning seismic signals
into probability measures such that the theory of optimal transport applies.
The other, which is beyond cycle skipping, is the inversion for parameters
below reflecting interfaces. For the first, we propose a class of
normalizations and prove several favorable properties for this class. For the
latter, we demonstrate that FWI using optimal transport can recover geophysical
properties from domains where no seismic waves travel through. We finally
illustrate these properties by the realistic application of imaging salt
inclusions, which has been a significant challenge in exploration geophysics.


  Recent works have shown that agents facing independent instances of a
stochastic $K$-armed bandit can collaborate to decrease regret. However, these
works assume that each agent always recommends their individual best-arm
estimates to other agents, which is unrealistic in envisioned applications
(machine faults in distributed computing or spam in social recommendation
systems). Hence, we generalize the setting to include $n$ honest and $m$
malicious agents who recommend best-arm estimates and arbitrary arms,
respectively. We first show that even with a single malicious agent, existing
collaboration-based algorithms fail to improve regret guarantees over a
single-agent baseline. We propose a scheme where honest agents learn who is
malicious and dynamically reduce communication with (i.e., "block") them. We
show that collaboration indeed decreases regret for this algorithm, assuming
$m$ is small compared to $K$ but without assumptions on malicious agents'
behavior, thus ensuring that our algorithm is robust against any malicious
recommendation strategy.


  Most active galactic nuclei at the center of the nearby galaxies have
super-massive black holes accreting at sub-Eddington rates through hot
accretion flows or radiatively inefficient accretion flows, which efficiently
produce jets. The association of radio and X-ray flares with the knot ejection
from M81* inspires us to model its multiwavelength spectral energy distribution
(SED) during these flares to constrain the physical parameters of the jet.
Moreover, we construct a long-term light curve in X-rays to identify the flares
in the available data and constrain the jet parameters during those periods.
The jet activity may vary on short and long time scales, which may produce
flares in different frequency bands. The SEDs from radio to X-ray during the
quiescent as well as flaring states are found to be satisfactorily explained by
synchrotron emission of relativistic electrons from a single zone. The
variation in the values of the jet parameters during the different states is
shown and compared with high-synchrotron peaked blazars.


  We analyze matrix-valued transfer operators. We prove that the fixed points
of transfer operators form a finite dimensional $C^*$-algebra. For matrix
weights satisfying a low-pass condition we identify the minimal projections in
this algebra as correlations of scaling functions, i.e., limits of cascade
algortihms.


  In this paper, we show the photonic nanojet is the central bright maxima in
focused near-field diffraction pattern. Using a simplified Huygens-Fresnel
model, and using numerical simulations, we generate photonic nanojets from
curved interfaces between media of different refractive indices and study the
length, width, and peak intensity of the resultant photonic nanojet. We confirm
that photonic nanojets are not sub-diffraction optical modes and are adequately
described using diffraction theory. We also show how modifying the
optogeometric environment can be used to tailor relevant features of the
nanojets in general.


  We show that any pair $X, Y$ of independent, non-compactly supported random
variables on $[0,\infty)$ satisfies $\liminf_{m\to\infty} \mathbb{P}(\min(X,Y)
>m \,| \,X+Y> 2m) =0$. We conjecture multi-variate and weighted generalizations
of this result, and prove them under the additional assumption that the random
variables are identically distributed.


  We present formulas for the coefficients of 2-, 3-, 4- and 5-point master
integrals for one-loop massive amplitudes. The coefficients are derived from
unitarity cuts in D dimensions. The input parameters can be read off from any
unitarity-cut integrand, as assembled from tree-level expressions, after simple
algebraic manipulations. The formulas presented here are suitable for
analytical as well as numerical evaluation. Their validity is confirmed in two
known cases of helicity amplitudes contributing to gg -> gg and gg -> gH, where
the masses of the Higgs and the fermion circulating in the loop are kept as
free parameters.


  Text-video retrieval aims to find the most relevant cross-modal samples for a
given query. Recent methods focus on modeling the whole spatial-temporal
relations. However, since video clips contain more diverse content than
captions, the model aligning these asymmetric video-text pairs has a high risk
of retrieving many false positive results. In this paper, we propose
Probabilistic Token Aggregation (ProTA) to handle cross-modal interaction with
content asymmetry. Specifically, we propose dual partial-related aggregation to
disentangle and re-aggregate token representations in both low-dimension and
high-dimension spaces. We propose token-based probabilistic alignment to
generate token-level probabilistic representation and maintain the feature
representation diversity. In addition, an adaptive contrastive loss is proposed
to learn compact cross-modal distribution space. Based on extensive
experiments, ProTA achieves significant improvements on MSR-VTT (50.9%), LSMDC
(25.8%), and DiDeMo (47.2%).


  We prove the chain rule in the more general framework of the Wiener-Poisson
space, allowing us to obtain the so-called Nourdin-Peccati bound. From this
bound we obtain a second-order Poincare-type inequality that is useful in terms
of computations. For completeness we survey these results on the Wiener space,
the Poisson space, and the Wiener-Poisson space. We also give several
applications to central limit theorems with relevant examples: linear
functionals of Gaussian subordinated fields (where the subordinated field can
be processes like fractional Brownian motion or the solution of the
Ornstein-Uhlenbeck SDE driven by fractional Brownian motion), Poisson
functionals in the first Poisson chaos restricted to infinitely many \small"
jumps (particularly fractional Levy processes) and the product of two
Ornstein-Uhlenbeck processes (one in the Wiener space and the other in the
Poisson space). We also obtain bounds for their rate of convergence to
normality.


  Sugar beet pectin is a byproduct of the sugar industry with a particularly
low gelling power which hinders its application as gelling agent and thickener.
Here we consider the use of freeze casting to shape sugar beet pectin into
lightweight foams. Freeze casting processing conditions such as the applied
thermal gradient and the polysaccharide concentration were explored to obtain
macroporous lightweight foams with different textures. The precise control over
the foams texture and pore anisotropy was decisive for their performance as
liquid transport devices by capillary ascension and for their mechanical
performance. Overall, the obtained results show that the formation of highly
anisotropic structures using freeze casting can be instrumental in the
upcycling of polysaccharide industrial byproducts.


  Graphical interfaces and interactive visualisations are typical mediators
between human users and data analytics systems. HCI researchers and developers
have to be able to understand both human needs and back-end data analytics.
Participants of our tutorial will learn how visualisation and interface design
can be combined with data analytics to provide better visualisations. In the
first of three parts, the participants will learn about visualisations and how
to appropriately select them. In the second part, restrictions and
opportunities associated with different data analytics systems will be
discussed. In the final part, the participants will have the opportunity to
develop visualisations and interface designs under given scenarios of data and
system settings.


  We explicitly compute the critical exponents associated with logarithmic
corrections (the so-called hatted exponents) starting from the renormalization
group equations and the mean field behavior for a wide class of models at the
upper critical behavior (for short and long range $\phi^n$-theories) and below
it. This allows us to check the scaling relations among these critical
exponents obtained by analysing the complex singularities (Lee-Yang and Fisher
zeroes) of these models. Moreover, we have obtained an explicit method to
compute the $\hat{\coppa}$ exponent [defined by $\xi\sim L (\log
L)^{\hat{\coppa}}$] and, finally, we have found a new derivation of the scaling
law associated with it.


  Affine rank minimization algorithms typically rely on calculating the
gradient of a data error followed by a singular value decomposition at every
iteration. Because these two steps are expensive, heuristic approximations are
often used to reduce computational burden. To this end, we propose a recovery
scheme that merges the two steps with randomized approximations, and as a
result, operates on space proportional to the degrees of freedom in the
problem. We theoretically establish the estimation guarantees of the algorithm
as a function of approximation tolerance. While the theoretical approximation
requirements are overly pessimistic, we demonstrate that in practice the
algorithm performs well on the quantum tomography recovery problem.


  This paper formally shows that an exponentially rising current is optimal in
terms of resistive ohmic loss for driving a semiconductor laser into the
gain-switching mode. A metric to quantify the quality of laser operation that
measures the similarity of a generated optical pulse to the delta function is
proposed. Several circuit implementations to approximate exponentially rising
current are developed, including using a driver circuit with BJT output stage,
a network of RLC circuits, and a saturating inductor. An experimental
comparison between a state-of-the-art sinewave resonant driver circuit and a
directly driven laser is performed that favors the latest variant of the
driver.


  The recent proliferation of 3D content that can be consumed on hand-held
devices necessitates efficient tools for transmitting large geometric data,
e.g., 3D meshes, over the Internet. Detailed high-resolution assets can pose a
challenge to storage as well as transmission bandwidth, and level-of-detail
techniques are often used to transmit an asset using an appropriate bandwidth
budget. It is especially desirable for these methods to transmit data
progressively, improving the quality of the geometry with more data. Our key
insight is that the geometric details of 3D meshes often exhibit similar local
patterns even across different shapes, and thus can be effectively represented
with a shared learned generative space. We learn this space using a
subdivision-based encoder-decoder architecture trained in advance on a large
collection of surfaces. We further observe that additional residual features
can be transmitted progressively between intermediate levels of subdivision
that enable the client to control the tradeoff between bandwidth cost and
quality of reconstruction, providing a neural progressive mesh representation.
We evaluate our method on a diverse set of complex 3D shapes and demonstrate
that it outperforms baselines in terms of compression ratio and reconstruction
quality.


  This paper provides a self-contained first introduction to description logics
(DLs). The main concepts and features are explained with examples before syntax
and semantics of the DL SROIQ are defined in detail. Additional sections review
light-weight DL languages, discuss the relationship to the Web Ontology
Language OWL and give pointers to further reading.


  We introduce a novel class of spatial complex variable function Gaussian
(CVF-Gaussian) beam, which is the product of an arbitrary analytic complex
variable function and a Gaussian function, in strongly nonlocal nonlinear media
(SNNM). The CVF-Gaussian beam rotates generally during propagation. By choosing
the input power of the beam, we can obtain the CVF-Gaussian breather or the
CVF-Gaussian soliton. We reveal that stable CVF-Gaussian beam can exist in SNNM
with different forms, including rotating dipole, and rotating elliptic donut. A
distribution factor which is the parameter for the description of the
transverse distribution of the CVF-Gaussian beam is discussed.


  In automatic speech processing systems, speaker diarization is a crucial
front-end component to separate segments from different speakers. Inspired by
the recent success of deep neural networks (DNNs) in semantic inferencing,
triplet loss-based architectures have been successfully used for this problem.
However, existing work utilizes conventional i-vectors as the input
representation and builds simple fully connected networks for metric learning,
thus not fully leveraging the modeling power of DNN architectures. This paper
investigates the importance of learning effective representations from the
sequences directly in metric learning pipelines for speaker diarization. More
specifically, we propose to employ attention models to learn embeddings and the
metric jointly in an end-to-end fashion. Experiments are conducted on the
CALLHOME conversational speech corpus. The diarization results demonstrate
that, besides providing a unified model, the proposed approach achieves
improved performance when compared against existing approaches.


  In this paper we demonstrate a framework for efficient development of textual
domain specific languages and supporting tools. We use a redundance-free and
compact definition of a readable concrete syntax and a comprehensible abstract
syntax as both representations significantly overlap in their structure. To
further improve the usability of the abstract syntax this definition format
integrates additional concepts like associations and inheritance into the
well-understood grammar-based approach. Modularity concepts like language
inheritance and embedding are used to simplify the development of languages
based on already existing ones. In addition, the generation of editors and a
template approach for code generation is explained.


  Interactive global illumination remains a challenge in radiometrically- and
geometrically-complex scenes. Specialized sampling strategies are effective for
specular and near-specular transport because the scattering has relatively low
directional variance per scattering event. In contrast, the high variance from
transport paths comprising multiple rough glossy or diffuse scattering events
remains notoriously difficult to resolve with a small number of samples. We
extend unidirectional path tracing to address this by combining screen-space
reservoir resampling and sparse world-space probes, significantly improving
sample efficiency for transport contributions that terminate on diffuse
scattering events. Our experiments demonstrate a clear improvement -- at equal
time and equal quality -- over purely path traced and purely probe-based
baselines. Moreover, when combined with commodity denoisers, we are able to
interactively render global illumination in complex scenes.


  In this work we present an approach for generating alternative text (or
alt-text) descriptions for images shared on social media, specifically Twitter.
More than just a special case of image captioning, alt-text is both more
literally descriptive and context-specific. Also critically, images posted to
Twitter are often accompanied by user-written text that despite not necessarily
describing the image may provide useful context that if properly leveraged can
be informative. We address this task with a multimodal model that conditions on
both textual information from the associated social media post as well as
visual signal from the image, and demonstrate that the utility of these two
information sources stacks. We put forward a new dataset of 371k images paired
with alt-text and tweets scraped from Twitter and evaluate on it across a
variety of automated metrics as well as human evaluation. We show that our
approach of conditioning on both tweet text and visual information
significantly outperforms prior work, by more than 2x on BLEU@4.


  We evaluate in this work the hydrodynamic transport coefficients of a
granular binary mixture in $d$ dimensions. In order to eliminate the observed
disagreement (for strong dissipation) between computer simulations and
previously calculated theoretical transport coefficients for a monocomponent
gas, we obtain explicit expressions of the seven Navier-Stokes transport
coefficients with the use of a new Sonine approach in the Chapman-Enskog
theory. Our new approach consists in replacing, where appropriate in the
Chapman-Enskog procedure, the Maxwell-Boltzmann distribution weight function
(used in the standard first Sonine approximation) by the homogeneous cooling
state distribution for each species. The rationale for doing this lies in the
fact that, as it is well known, the non-Maxwellian contributions to the
distribution function of the granular mixture become more important in the
range of strong dissipation we are interested in. The form of the transport
coefficients is quite common in both standard and modified Sonine
approximations, the distinction appearing in the explicit form of the different
collision frequencies associated with the transport coefficients. Additionally,
we numerically solve by means of the direct simulation Monte Carlo method the
inelastic Boltzmann equation to get the diffusion and the shear viscosity
coefficients for two and three dimensions. As in the case of a monocomponent
gas, the modified Sonine approximation improves the estimates of the standard
one, showing again the reliability of this method at strong values of
dissipation.


  We present the results from a broadband (1 to 3 GHz), spectro-polarimetry
study of the integrated emission from 100 extragalactic radio sources with the
ATCA, selected to be highly linearly polarized at 1.4 GHz. We use a general
purpose, polarization model-fitting procedure that describes the Faraday
rotation measure (RM) and intrinsic polarization structure of up to three
distinct polarized emission regions or 'RM components' of a source. Overall,
37%/52%/11% of sources are best fit by one/two/three RM components. However,
these fractions are dependent on the signal-to-noise ratio (S/N) in
polarization (more RM components more likely at higher S/N). In general, our
analysis shows that sources with high integrated degrees of polarization at 1.4
GHz have low Faraday depolarization, are typically dominated by a single RM
component, have a steep spectral index, and a high intrinsic degree of
polarization. After classifying our sample into radiative-mode and jet-mode
AGN, we find no significant difference between the Faraday rotation or Faraday
depolarization properties of jet-mode and radiative-mode AGN. However, there is
a statistically significant difference in the intrinsic degree of polarization
between the two types, with the jet-mode sources having more intrinsically
ordered magnetic field structures than the radiative-mode sources. We also find
a preferred perpendicular orientation of the intrinsic magnetic field structure
of jet-mode AGN with respect to the jet direction, while no clear preference is
found for the radiative-mode sources.


  Artificial Intelligence (AI) is a double-edged sword: on one hand, AI
promises to provide great advances that could benefit humanity, but on the
other hand, AI poses substantial (even existential) risks. With advancements
happening daily, many people are increasingly worried about AI's impact on
their lives. To ensure AI progresses beneficially, some researchers have
proposed "wellbeing" as a key objective to govern AI. This article addresses
key challenges in designing AI for wellbeing. We group these challenges into
issues of modeling wellbeing in context, assessing wellbeing in context,
designing interventions to improve wellbeing, and maintaining AI alignment with
wellbeing over time. The identification of these challenges provides a scope
for efforts to help ensure that AI developments are aligned with human
wellbeing.


  In a "publish-or-perish culture", the ranking of scientific journals plays a
central role in assessing performance in the current research environment. With
a wide range of existing methods and approaches to deriving journal rankings,
meta-rankings have gained popularity as a means of aggregating different
information sources. In this paper, we propose a method to create a consensus
meta-ranking using heterogeneous journal rankings. Using a parametric model for
paired comparison data we estimate quality scores for 58 journals in the OR/MS
community, which together with a shrinkage procedure allows for the
identification of clusters of journals with similar quality. The use of paired
comparisons provides a flexible framework for deriving a consensus score while
eliminating the problem of data missingness.


  We show that certain generating sets of Dykema and Radulescu for $L(F_r)$
have free Hausdorff dimension r and nondegenerate free Hausdorff r-entropy


  We demonstrate our simple strategy for renormalization with QED at one-loop
level, basing on an elaboration of the effective field theory philosophy. No
artificial regularization or deformation of the original theory is introduced
here and hence no manipulation of infinities, ambiguities arise instead of
infinities. Ward identities first come to reduce the number of ambiguities, the
residual ones could in principle be removed by imposing physical boundary
conditions. Renormalization group equations arise as "decoupling theorems" in
the underlying theory perspective. In addition, a technical theorem concerning
routing of external momenta is also presented and illustrated with the
self-energy and vertex function as examples.


  Recommendation systems are recognised as being hugely important in industry,
and the area is now well understood. At News UK, there is a requirement to be
able to quickly generate recommendations for users on news items as they are
published. However, little has been published about systems that can generate
recommendations in response to changes in recommendable items and user
behaviour in a very short space of time. In this paper we describe a new
algorithm for updating collaborative filtering models incrementally, and
demonstrate its effectiveness on clickstream data from The Times. We also
describe the architecture that allows recommendations to be generated on the
fly, and how we have made each component scalable. The system is currently
being used in production at News UK.


  The existence of an extremal code of length 72 is a long-standing open
problem. Let C be a putative extremal code of length 72 and suppose that C has
an automorphism g of order 6. We show that C, as an F_2<g>-module, is the
direct sum of two modules, one easily determinable and the other one which has
a very restrictive structure. We use this fact to do an exhaustive search and
we do not find any code. This proves that the automorphism group of an extremal
code of length 72 does not contain elements of order 6.


  Combining Bayesian nonparametrics and a forward model selection strategy, we
construct parsimonious Bayesian deep networks (PBDNs) that infer
capacity-regularized network architectures from the data and require neither
cross-validation nor fine-tuning when training the model. One of the two
essential components of a PBDN is the development of a special infinite-wide
single-hidden-layer neural network, whose number of active hidden units can be
inferred from the data. The other one is the construction of a greedy
layer-wise learning algorithm that uses a forward model selection criterion to
determine when to stop adding another hidden layer. We develop both Gibbs
sampling and stochastic gradient descent based maximum a posteriori inference
for PBDNs, providing state-of-the-art classification accuracy and interpretable
data subtypes near the decision boundaries, while maintaining low computational
complexity for out-of-sample prediction.