  In this paper we introduce a notion of fault-tolerance distance between
labeled transition systems. Intuitively, this notion of distance measures the
degree of fault-tolerance exhibited by a candidate system. In practice, there
are different kinds of fault-tolerance, here we restrict ourselves to the
analysis of masking fault-tolerance because it is often a highly desirable goal
for critical systems. Roughly speaking, a system is masking fault-tolerant when
it is able to completely mask the faults, not allowing these faults to have any
observable consequences for the users. We capture masking fault-tolerance via a
simulation relation, which is accompanied by a corresponding game
characterization. We enrich the resulting games with quantitative objectives to
define the notion of masking fault-tolerance distance. Furthermore, we
investigate the basic properties of this notion of masking distance, and we
prove that it is a directed pseudo metric. We have implemented our approach in
a prototype tool that automatically compute the masking distance between a
nominal system and a fault-tolerant version of it. We have used this tool to
measure the masking tolerance of multiple instances of several case studies


  The use of nanoparticles for intracellular drug delivery could reduce the
toxicity and side effects of the drug but, the uptake of these nanocarriers
could induce adverse effects on cells and tissues after their incorporation.
Macrophages play a central role in host defense and are responsible for in vivo
nanoparticle trafficking. Assessment of their defense capacity against
pathogenic micro-organisms after nanoparticle uptake, is necessary to prevent
infections associated with nanoparticle therapies. In this study, the effects
of hollow mesoporous SiO2-CaO nanospheres labeled with fluorescein
isothiocyanate (FITC-NanoMBGs) on the function of peritoneal macrophages was
assessed by measuring their ability to phagocytize Candida albicans expressing
a red fluorescent protein. Two macrophage-fungus ratios (MOI 1 and MOI 5) were
used and two experimental strategies were carried out: a) pretreatment of
macrophages with FITC-NanoMBGs and subsequent fungal infection; b) competition
assays after simultaneous addition of fungus and nanospheres. Macrophage
pro-inflammatory phenotype markers (CD80 expression and interleukin 6
secretion) were also evaluated. Significant decreases of CD80+ macrophage
percentage and interleukin 6 secretion were observed after 30 min, indicating
that the simultaneous incorporation of NanoMBG and fungus favors the macrophage
noninflammatory phenotype. The present study evidences that the uptake of these
nanospheres in all the studied conditions does not alter the macrophage
function. Moreover, intracellular FITCNanoMBGs induce a transitory increase of
the fungal phagocytosis by macrophages at MOI 1 and after a short time of
interaction. In the competition assays, as the intracellular fungus quantity
increased, the intracellular FITC-NanoMBG content decreased in a MOI- and
timedependent manner...


  We establish integral tests and laws of the iterated logarithm at 0 and at
$+\infty$, for the upper envelope of positive self-similar Markov processes.
Our arguments are based on the Lamperti representation, time reversal arguments
and on the study of the upper envelope of their future infimum due to Pardo
\cite{Pa}. These results extend integral test and laws of the iterated
logarithm for Bessel processes due to Dvoretsky and Erd\"os \cite{de} and
stable L\'evy processes conditioned to stay positive with no positive jumps due
to Bertoin \cite{be1}.


  Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for
the 3D visualization of cellular structure and organization at submolecular
resolution. It enables analyzing the native structures of macromolecular
complexes and their spatial organization inside single cells. However, due to
the high degree of structural complexity and practical imaging limitations,
systematic macromolecular structural recovery inside CECT images remains
challenging. Particularly, the recovery of a macromolecule is likely to be
biased by its neighbor structures due to the high molecular crowding. To reduce
the bias, here we introduce a novel 3D convolutional neural network inspired by
Fully Convolutional Network and Encoder-Decoder Architecture for the supervised
segmentation of macromolecules of interest in subtomograms. The tests of our
models on realistically simulated CECT data demonstrate that our new approach
has significantly improved segmentation performance compared to our baseline
approach. Also, we demonstrate that the proposed model has generalization
ability to segment new structures that do not exist in training data.


  We study the size-topology relations in random packings of dry adhesive
polydisperse microspheres with Gaussian and lognormal size distributions
through a geometric tessellation. We find that the dependence of the neighbour
number on the centric particle size is always quasilinear, independent of the
size distribution, the size span or interparticle adhesion. The average local
packing fraction as a function of normalized particle size for different size
variances is well regressed on the same profile, which grows to larger values
as the relative strength of adhesion decreases. As for the local coordination
number-particle size profiles, they converge onto a single curve for all the
adhesive particles, but will gradually transfer to another branch for
non-adhesive particles. Such adhesion induced size-topology relations are
interpreted theoretically by a modified geometrical "granocentric" model, where
the model parameters are dependent on a dimensionless adhesion number. Our
findings, together with the modified theory, provide a more unified perspective
on the substantial geometry of amorphous polydisperse systems, especially those
with fairly loose structures.


  The incomplete plasma dispersion function is a generalization of the plasma
dispersion function in which the defining integral spans a semi-infinite,
rather than infinite, domain. It is useful for describing the linear dielectric
response and wave dispersion in non-Maxwellian plasmas when the distribution
functions can be approximated as Maxwellian over finite, or semi-infinite,
intervals in velocity phase-space. A ubiquitous example is the depleted
Maxwellian electron distribution found near boundary sheaths or double layers,
where the passing interval can be modeled as Maxwellian with a lower
temperature than the trapped interval. The depleted Maxwellian is used as an
example to demonstrate the utility of using the incomplete plasma dispersion
function for calculating modifications to wave dispersion relations.


  The Noether Symmetry Approach can be used to construct spherically symmetric
solutions in $f({\cal R})$ gravity. Specifically, the Noether conserved
quantity is related to the gravitational mass and a gravitational radius that
reduces to the Schwarzschild radius in the limit $f({\cal R})\rightarrow {\cal
R}$. We show that it is possible to construct the $M-R$ relation for neutron
stars depending on the Noether conserved quantity and the associated
gravitational radius. This approach enables the recovery of extreme massive
stars that could not be stable in the standard Tolman-Oppenheimer-Volkoff based
on General Relativity. Examples are given for some power law $f({\cal R})$
gravity models.


  The static and dynamic local spin susceptibility of the organic Mott
insulator $\kappa$-(ET)$_2$Cu$_2$(CN)$_3$, a model material of the spin- 1/2
triangular lattice, is studied by $^{13}$C NMR spectroscopy from room
temperature down to 20 mK. We observe an anomalous field-dependent spectral
broadening with the continuous and bipolar shift distribution, appearing
without the critical spin fluctuations. It is attributable to spatially
nonuniform magnetizations induced in the spin liquid under magnetic fields. The
amplitude of the magnetization levels off below 1 K, while the low-lying spin
fluctuations survive toward the ground state, as indicated by the temperature
profile of the relaxation rates.


  Oral cancer incidence is rapidly increasing worldwide. The most important
determinant factor in cancer survival is early diagnosis. To facilitate large
scale screening, we propose a fully automated pipeline for oral cancer
detection on whole slide cytology images. The pipeline consists of fully
convolutional regression-based nucleus detection, followed by per-cell focus
selection, and CNN based classification. Our novel focus selection step
provides fast per-cell focus decisions at human-level accuracy. We demonstrate
that the pipeline provides efficient cancer classification of whole slide
cytology images, improving over previous results both in terms of accuracy and
feasibility. The complete source code is available at
https://github.com/MIDA-group/OralScreen.


  For each $2 \leq n \leq \infty$, we construct an uncountable family of free
ergodic measure preserving actions $\alpha_t$ of the free group $\Bbb F_n$ on
the standard probability space $(X, \mu)$ such that any two are non orbit
equivalent (in fact, not even stably orbit equivalent). These actions are all
``rigid'' (in the sense of [Po01]), with the II$_1$ factors $L^\infty(X,
\mu)\rtimes_{\alpha_t} \Bbb F_n$ mutually non stably isomorphic (even
non-stably isomorphic) and in the class $\Cal H\Cal T_{_{s}}.$


  One-point functions of local operators are studied, at weak and strong
coupling, for the ABJM theory in the presence of a 1/2 BPS domain wall. In the
underlying quantum spin chain the domain wall is represented by a boundary
state which we show is integrable yielding a compact determinant formula for
one-point functions of generic operators.


  Recently, Sidford, Wang, Wu and Ye (2018) developed an algorithm combining
variance reduction techniques with value iteration to solve discounted Markov
decision processes. This algorithm has a sublinear complexity when the discount
factor is fixed. Here, we extend this approach to mean-payoff problems,
including both Markov decision processes and perfect information zero-sum
stochastic games. We obtain sublinear complexity bounds, assuming there is a
distinguished state which is accessible from all initial states and for all
policies. Our method is based on a reduction from the mean payoff problem to
the discounted problem by a Doob h-transform, combined with a deflation
technique. The complexity analysis of this algorithm uses at the same time the
techniques developed by Sidford et al. in the discounted case and non-linear
spectral theory techniques (Collatz-Wielandt characterization of the
eigenvalue).


  In this paper, we consider a stochastic process that may experience random
reset events which relocate the system to its starting position. We focus our
attention on a one-dimensional, monotonic continuous-time random walk with a
constant drift: the process moves in a fixed direction between the reset
events, either by the effect of the random jumps, or by the action of a
deterministic bias. However, the orientation of its motion is randomly
determined after each restart. As a result of these alternating dynamics,
interesting properties do emerge. General formulas for the propagator as well
as for two extreme statistics, the survival probability and the mean
first-passage time, are also derived. The rigor of these analytical results is
verified by numerical estimations, for particular but illuminating examples.


  The discovery that the number of physically consistent string vacua is on the
order of 10^500 has prompted several statistical studies of string
phenomenology. Contained here is one such study that focuses on the Weakly
Coupled Free Fermionic String (WCFFHS) formalism. Presented here are systematic
extensions of a variation on the NAHE (Nanopoulos, Antoniadis, Hagelin, Ellis)
set of basis vectors. This variation is more conducive to the production of
"mirrored" models, in which the observable and hidden sector particles are
identical. This study is a parallel to the extensions of the NAHE set itself
(referenced herein), and presents statistics related to similar model
properties. Statistical coupling between specific gauge groups and ST SUSYs is
also examined. Finally, a model with completely mirrored gauge groups is
discussed.


  In this letter, we present a novel Gaussian Process Learning-based
Probabilistic Optimal Power Flow (GP-POPF) for solving POPF under renewable and
load uncertainties of arbitrary distribution. The proposed method relies on a
non-parametric Bayesian inference-based uncertainty propagation approach,
called Gaussian Process (GP). We also suggest a new type of sensitivity called
Subspace-wise Sensitivity, using observations on the interpretability of
GP-POPF hyperparameters. The simulation results on 14-bus and 30-bus systems
show that the proposed method provides reasonably accurate solutions when
compared with Monte-Carlo Simulations (MCS) solutions at different levels of
uncertain renewable penetration as well as load uncertainties, while requiring
much less number of samples and elapsed time.


  Five cryogenic resonant gravitational antennas are now in operation. This is
the first time that such a large number of high sensitive antennas are taking
data and an agreement on data exchange has been signed by the responsible
groups. The data exchanged will consist essentially in lists of ''candidate
events''. In this paper the procedure used by the Rome group in order to obtain
''candidate events'' is presented.
  Some methods of analyzing the data of the "network" of the five antennas are
shown.


  Autograding short textual answers has become much more feasible due to the
rise of NLP and the increased availability of question-answer pairs brought
about by a shift to online education. Autograding performance is still inferior
to human grading. The statistical and black-box nature of state-of-the-art
machine learning models makes them untrustworthy, raising ethical concerns and
limiting their practical utility. Furthermore, the evaluation of autograding is
typically confined to small, monolingual datasets for a specific question type.
This study uses a large dataset consisting of about 10 million question-answer
pairs from multiple languages covering diverse fields such as math and
language, and strong variation in question and answer syntax. We demonstrate
the effectiveness of fine-tuning transformer models for autograding for such
complex datasets. Our best hyperparameter-tuned model yields an accuracy of
about 86.5\%, comparable to the state-of-the-art models that are less general
and more tuned to a specific type of question, subject, and language. More
importantly, we address trust and ethical concerns. By involving humans in the
autograding process, we show how to improve the accuracy of automatically
graded answers, achieving accuracy equivalent to that of teaching assistants.
We also show how teachers can effectively control the type of errors made by
the system and how they can validate efficiently that the autograder's
performance on individual exams is close to the expected performance.


  Galactic globular clusters (GCs) are believed to have formed in-situ in the
Galaxy as well as in dwarf galaxies later accreted onto the Milky Way. However,
to date, there is no unambiguous signature to distinguish accreted GCs. Using
specifically designed $N$-body simulations of GCs evolving in a variety of
time-dependent tidal fields (describing the potential of a dwarf galaxy-Milky
Way merger), we analyze the effects imprinted to the internal kinematics of an
accreted GC. In particular, we look at the evolution of the velocity
anisotropy. Our simulations show that at early phases, the velocity anisotropy
is determined by the tidal field of the dwarf galaxy and subsequently the
clusters will adapt to the new tidal environment, losing any signature of their
original environment in a few relaxation times. At 10 Gyr, GCs exhibit a
variety of velocity anisotropy profiles, namely, isotropic velocity
distribution in the inner regions and either isotropy or radial/tangential
anisotropy in the intermediate and outer regions. Independently of an accreted
origin, the velocity anisotropy primarily depends on the strength of the tidal
field cumulatively experienced by a cluster. Tangentially anisotropic clusters
correspond to systems that have experienced stronger tidal fields and are
characterized by higher tidal filling factor, $r_{50}/r_j\gtrsim0.17$, higher
mass loss $\gtrsim60\%$ and relaxation times $t_{rel}\lesssim10^9$ Gyr.
Interestingly, we demonstrate that the presence of tidal tails can
significantly contaminate the measurements of velocity anisotropy when a
cluster is observed in projection. Our characterization of the velocity
anisotropy profiles in different tidal environments provides a theoretical
benchmark for the interpretation of the unprecedented amount of
three-dimensional kinematic data progressively available for Galactic GCs.


  We discuss the problem of a N two-level systems interacting with a single
radiation mode in the strong-coupling regime. The thermodynamic properties of
Dicke model are analyzed developing a perturbative expansion of the partition
function in the high-temperature limit and we use this method to investigate
the connections between the Dicke and the collective one-dimensional Ising
model


  The Kemeny method is one of the popular tools for rank aggregation. However,
computing an optimal Kemeny ranking is NP-hard. Consequently, the computational
task of finding a Kemeny ranking has been studied under the lens of
parameterized complexity with respect to many parameters. We first present a
comprehensive relationship, both theoretical and empirical, among these
parameters. Further, we study the problem of computing all distinct Kemeny
rankings under the lens of parameterized complexity. We consider the target
Kemeny score, number of candidates, average distance of input rankings, maximum
range of any candidate, and unanimity width as our parameters. For all these
parameters, we already have FPT algorithms. We find that any desirable number
of Kemeny rankings can also be found without substantial increase in running
time. We also present FPT approximation algorithms for Kemeny rank aggregation
with respect to these parameters.


  The simplest rigourous, non-circular proof that d(cos x) = -sen x. Some
details omitted if the gap is intuitive, nevertheles, each gap is easily
filled, rigourously. As Ehrenpreis and others have pointed out, the usual text
book `proof' is circular, since it assumes the area of circular sectors is
already known. Various Cours d'Analyse tomes (Hermite, Jordan, etc.) have
correct proofs which, however, are more difficult.


  The non-radiative electron-relaxation dynamics in C$_{60}$ molecule is
studied after selective initial photoexcitations. The methodology includes
nonadibabtic molecular simulation combined with time-dependent density
functional theory (DFT) and semi-classical surface hopping approach. Results of
treating the DFT exchange-correlation (xc) interaction by the non-empirical
Perdew-Burke-Ernzerhof (PBE), hybrid PBE0, and hybrid Becke 3-parameter
Lee-Yang-Parr (B3LYP) functional are compared. Even though some differences in
the details are found, all three functionals produce qualitatively similar
unoccupied band structures in the ground state. The model-dependent differences
in the ultrafast population dynamics, including the occurrences of transient
entrapment of population, are studied systematically. The trend of the results
demonstrates a universal dependence on the structure of unoccupied band
offering a spectroscopic route to probe this structure. Results can be
verified, as well as the best xc model for quantitative accuracy can be
determined, by comparing with ultrafast transient absorption or time-resolved
photoelectron spectroscopy measurements. From the computational standpoint, the
study facilitates method optimization to simulate nonadiabatic relaxation
dynamics in technologically important fullerene derivatives.


  We present a microscopic model for systems showing resistivity saturation. An
essentially exact quantum Monte-Carlo calculation demonstrates that the model
describes saturation. We give a simple explanation for saturation, using charge
conservation and considering the limit where thermally excited phonons have
destroyed the periodicity. Crucial model features are phonons coupling to the
hopping matrix elements and a unit cell with several atoms. We demonstrate the
difference to a model of alkali-doped C60 with coupling to the level positions,
for which there is no saturation.


  The neutron skin of atomic nuclei impacts the structure of neutron-rich
nuclei, the equation of state of nucleonic matter, and the size of neutron
stars. Here we predict the neutron skin of selected light- and medium-mass
nuclei using coupled-cluster theory and the auxiliary field diffusion Monte
Carlo method with two- and three-nucleon forces from chiral effective field
theory. We find a linear correlation between the neutron skin and the isospin
asymmetry in agreement with the liquid-drop model and compare with data. We
also extract the linear relationship that describes the difference between
neutron and proton radii of mirror nuclei and quantify the effect of charge
symmetry breaking terms in the nuclear Hamiltonian. Our results for the
mirror-difference charge radii and binding energies per nucleon agree with
existing data.


  The Casimir effect is a physical manifestation of zero point energy of
quantum vacuum. In a relativistic quantum field theory, Poincar\'e symmetry of
the theory seems, at first sight, to imply that non-zero vacuum energy is
inconsistent with translational invariance of the vacuum. In the setting of two
uniform boundary plates at rest, quantum fields outside the plates have
(1+2)-dimensional Poincar\'e symmtry. Taking a massless scalar field as an
example, we have examined the consistency between the Poincar\'e symmetry and
the existence of the vacuum enegy. We note that, in quantum theory, symmetries
are represented projectively in general and show that the Casimir energy is
connected to central charges appearing in the algebra of generators in the
projective representations.


  Selman and Kautz's work on ``knowledge compilation'' established how
approximation (strengthening and/or weakening) of a propositional
knowledge-base can be used to speed up query processing, at the expense of
completeness. In this classical approach, querying uses Horn over- and
under-approximations of a given knowledge-base, which is represented as a
propositional formula in conjunctive normal form (CNF). Along with the class of
Horn functions, one could imagine other Boolean function classes that might
serve the same purpose, owing to attractive deduction-computational properties
similar to those of the Horn functions. Indeed, Zanuttini has suggested that
the class of affine Boolean functions could be useful in knowledge compilation
and has presented an affine approximation algorithm. Since CNF is awkward for
presenting affine functions, Zanuttini considers both a sets-of-models
representation and the use of modulo 2 congruence equations. In this paper, we
propose an algorithm based on reduced ordered binary decision diagrams
(ROBDDs). This leads to a representation which is more compact than the sets of
models and, once we have established some useful properties of affine Boolean
functions, a more efficient algorithm.


  Using an isospin-dependent transport model, we have studied the isospin and
momentum relaxation times in the heavy residues formed in heavy-ion collisions
at intermediate energies. It is found that only at incident energies below the
Fermi energy, chemical or thermal equilibrium can be reached before dynamical
instability is developed in the heavy residues. Also, the isospin relaxation
time is shorter (longer) than that for momentum at beam energies lower (higher)
than the Fermi energy.


  We examine the effect of realistic pinhole geometries used in neutron imaging
on the size of the image produced in the image plane. Using a simple inverted
parabola shape for the neutron source, an MCNP simulation shows that the area
of the images are only very slightly changed by pinhole blurring. Using a
metric, A80, which is the area of the image that contains 80% of the neutrons,
we find a maximum of 1.5% increase in the metric. In comparing a `perfect'
cylinder bore hole approximation for the pinhole with a full stepped collimator
tapered pinhole we find a difference in A80 of 0.8%. In addition, we find very
little difference in the prediction of a full MCNP simulation compared to a
simple ray-tracing calculation that includes neutron absorption in the pinhole
system but does not include neutron scattering. We note that more sophisticated
assumptions for the neutron source shape than made here could lead to larger
increases in the image size, and increases in A80 of the order of 5-8% could be
realistic.


  We discuss same-sign dilepton resonances in the simplified model approach.
The relevant $SU(3}_Q^J$ quantum numbers are $1_2^{0,1,2}$. For simplicity, we
only consider a spin 0 scalar, which is typically referred to as a doubly
charged Higgs in the literature. We consider the three simplest cases where the
doubly charged Higgs resides in a singlet, doublet or triplet $SU(2)_L$
representation. We discuss production and decay of such a doubly charged Higgs,
summarize the current direct search limits, and obtain mass limits in the cases
of singlet and doublet for the first time. We also present a complete set of
updated indirect search limits. We study the discovery potential at the Large
Hadron Collider (LHC) with center of mass energies 7 and 14 TeV for the
dominant Drell-Yan pair production with $H^{\pm\pm}$ decay in the $ee$ and
$\mu\mu$ channels. We find that at 7 TeV, the LHC with 10 $fb^{-1}$ luminosity
can probe mass of the doubly charged Higgs up to 380 GeV assuming 100% decay to
leptons. At 14 TeV, the LHC with 100 $fb^{-1}$ luminosity can reach a mass of
up to 800 GeV.


  We construct three sequences of regular surfaces of general type with
unbounded numerical invariants whose canonical map is 2-to-1 onto a canonically
embedded surface. Only sporadic examples of surfaces with these properties were
previously known.


  Estimation of perceptual quality in audio and speech is possible using a
variety of methods. The combined v3 release of ViSQOL and ViSQOLAudio (for
speech and audio, respectively,) provides improvements upon previous versions,
in terms of both design and usage. As an open source C++ library or binary with
permissive licensing, ViSQOL can now be deployed beyond the research context
into production usage. The feedback from internal production teams at Google
has helped to improve this new release, and serves to show cases where it is
most applicable, as well as to highlight limitations. The new model is
benchmarked against real-world data for evaluation purposes. The trends and
direction of future work is discussed.


  We formulate and study a low-order nonlinear coupled ocean-atmosphere model
with an emphasis on the impact of radiative and heat fluxes and of the
frictional coupling between the two components. This model version extends a
previous 24-variable version by adding a dynamical equation for the passive
advection of temperature in the ocean, together with an energy balance model.
  The bifurcation analysis and the numerical integration of the model reveal
the presence of low-frequency variability (LFV) concentrated on and near a
long-periodic, attracting orbit. This orbit combines atmospheric and oceanic
modes, and it arises for large values of the meridional gradient of radiative
input and of frictional coupling. Chaotic behavior develops around this orbit
as it loses its stability; this behavior is still dominated by the LFV on
decadal and multi-decadal time scales that is typical of oceanic processes.
Atmospheric diagnostics also reveals the presence of predominant low- and
high-pressure zones, as well as of a subtropical jet; these features recall
realistic climatological properties of the oceanic atmosphere.
  Finally, a predictability analysis is performed. Once the decadal-scale
periodic orbits develop, the coupled system's short-term instabilities --- as
measured by its Lyapunov exponents --- are drastically reduced, indicating the
ocean's stabilizing role on the atmospheric dynamics. On decadal time scales,
the recurrence of the solution in a certain region of the invariant subspace
associated with slow modes displays some extended predictability, as reflected
by the oscillatory behavior of the error for the atmospheric variables at long
lead times.


  In this work we study how nonminimally coupled theories of gravity modify the
usual Friedmann equation, and develop two methods to treat these. The ambiguity
in the form of the Lagrangian density of a perfect fluid is emphasized, and the
impact of different dominant matter species is assessed. The Cosmological
Constant problem is also discussed.


  In 2001, Knight constructed a stochastic process modeling the one dimensional
interaction of two particles, one being Newtonian in the sense that it obeys
Newton's laws of motion, and the other particle being Brownian. We construct a
multi-particle analog, using Skorohod map estimates in proving a propagation of
chaos and characterizing the hydrodynamic limit as the solution to a PDE with
free boundary condition. Stochastic methods are used to show existence and
uniqueness for the free boundary problem, and also present an algorithm of
approximating the solution.


  We show how spectral submanifold (SSM) theory can be used to extract
forced-response curves, including isolas, without any numerical simulation in
high-degree-of-freedom, periodically forced mechanical systems. We use
multivariate recurrence relations to construct the SSMs, achieving a major
speed-up relative to earlier autonomous SSM algorithms. The increase in
computational efficiency promises to close the current gap between studying
lower-dimensional academic examples and analyzing larger systems obtained from
finite-element modeling, as we illustrate on a discretization of a
damped-forced beam model.


  Physical (and weak) regularity conditions are used to determine and classify
all the possible types of spherically symmetric dust spacetimes in general
relativity. This work unifies and completes various earlier results. The
junction conditions are described for general non-comoving (and non-null)
surfaces, and the limits of kinematical quantities are given on all comoving
surfaces where there is Darmois matching. We show that an inhomogeneous
generalisation of the Kantowski-Sachs metric may be joined to the
Lemaitre-Tolman-Bondi metric. All the possible spacetimes are explicitly
divided into four groups according to topology, including a group in which the
spatial sections have the topology of a 3-torus. The recollapse conjecture (for
these spacetimes) follows naturally in this approach.


  We systematically explore the landscape of nonrelativistic effective field
theories with a local $S$-matrix and enhanced symmetries and soft behavior. The
exploration is carried out using both conventional quantum field theory methods
based on symmetry arguments, and recently developed on-shell recursion
relations. We show that, in contrary to relativistic theories, enhancement of
the soft limit of scattering amplitudes in nonrelativistic theories is
generally not a byproduct of symmetry alone, but requires additional low-energy
data. Sufficient conditions for enhanced scattering amplitudes can be derived
by combining symmetries and dispersion relations of the scattered particles.
This has direct consequences for the infrared dynamics that different types of
nonrelativistic Nambu-Goldstone bosons can exhibit. We then use a bottom-up
soft bootstrap approach to narrow down the landscape of nonrelativistic
effective field theories that possess a consistent low-energy $S$-matrix. We
recover two exceptional theories of a complex Schr\"odinger-type scalar, namely
the $\mathbb{C} P^1$ nonlinear sigma model and the
Schr\"odinger-Dirac-Born-Infeld theory. Moreover, we use soft recursion to
prove a no-go theorem ruling out the existence of other exceptional
Schr\"odinger-type theories. We also prove that all exceptional theories of a
single real scalar with a linear dispersion relation are necessarily
Lorentz-invariant. Soft recursion allows us to obtain some further general
bounds on the landscape of nonrelativistic effective theories with enhanced
soft limits. Finally, we present a novel theory of a complex scalar with a
technically natural quartic dispersion relation. Altogether, our work
represents the first step of a program to extend the developments in the study
of scattering amplitudes to theories without Lorentz invariance.


  Nucleosynthesis in evolved (RGB and AGB) low-mass stars is reviewed under the
light of the reaction rates recommended in the NACRE compilation (Angulo et al.
1999). We use a parametric model of stellar nucleosynthesis to investigate the
uncertainties that still exist nowadays on the nuclear data and to give a
critical point of view on the resulting evolution of the chemical abundances.
We discuss in particular (i) the NeNa and MgAl modes of hydrogen burning in the
context of the chemical anomalies observed in RGB globular cluster stars, (ii)
the helium combustion in a thermal pulse of an AGB star.


  Machine learning is currently dominated by largely experimental work focused
on improvements in a few key tasks. However, the impressive accuracy numbers of
the best performing models are questionable because the same test sets have
been used to select these models for multiple years now. To understand the
danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by
creating a new test set of truly unseen images. Although we ensure that the new
test set is as close to the original data distribution as possible, we find a
large drop in accuracy (4% to 10%) for a broad range of deep learning models.
Yet more recent models with higher original accuracy show a smaller drop and
better overall performance, indicating that this drop is likely not due to
overfitting based on adaptivity. Instead, we view our results as evidence that
current accuracy numbers are brittle and susceptible to even minute natural
variations in the data distribution.


  It is proved that every separable $C^*$-algebra of real rank zero contains an
AF-sub-$C^*$-algebra such that the inclusion mapping induces an isomorphism of
the ideal lattices of the two $C^*$-algebras and such that every projection in
a matrix algebra over the large $C^*$-algebra is equivalent to a projection in
a matrix algebra over the AF-sub-$C^*$-algebra. This result is proved at the
level of monoids, using that the monoid of Murray-von Neumann equivalence
classes of projections in a $C^*$-algebra of real rank zero has the refinement
property. As an application of our result, we show that given a unital
$C^*$-algebra $A$ of real rank zero and a natural number $n$, then there is a
unital $^*$-homomorphism $M_{n_1} \oplus ... \oplus M_{n_r} \to A$ for some
natural numbers $r,n_1, ...,n_r$ with $n_j \ge n$ for all $j$ if and only if
$A$ has no representation of dimension less than $n$.


  The c2x code fills two distinct roles. Its first role is as a converter
between the binary format .check files from the Castep electronic structure
code and various visualisation programs. Its second role is to manipulate and
analyse the input and output files from a variety of electronic structure
codes, including Castep, Onetep and Vasp, as well as the widely-used `Gaussian
cube' file format. Analysis includes symmetry analysis, and arbitrary cell
transformations. It continues to be under development, with growing
functionality, and is written in a form which would make it easy to extend it
to working directly with files from other electronic structure codes. Data
which c2x is capable of extracting from Castep's binary checkpoint files
include charge densities, spin densities, wavefunctions, relaxed atomic
positions, forces, the Fermi level, the total energy, and symmetry operations.
It can recreate .cell input files from checkpoint files. Volumetric data can be
output in formats usable by many common visualisation programs, and c2x will
itself calculate integrals, expand data into supercells, and interpolate data
via combinations of Fourier and trilinear interpolation. It can extract data
along arbitrary lines (such as between atoms) as 1D output. C2x is able to
convert between several common formats for describing molecules and crystals,
including the .cell format of Castep. It can construct supercells, reduce cells
to their primitive form, and add specified k-point meshes. It uses the spglib
library to report symmetry information, which it can add to .cell files. C2x is
a command-line utility, so is readily included in scripts. It is available
under the GPL and can be obtained from http://www.c2x.org.uk. It is believed to
be the only open-source code which can read Castep's .check files, so it will
have utility in other projects.


  We model the Galactic population of detached binaries that harbor black holes
with (0.5-1.7) solar mass companions -- the remnants of case B mass-exchange
that rapidly cross Hertzsprung gap after the termination of the Roche-lobe
overflow or as He-shell burning stars. Several such binaries can be currently
present in the Galaxy. The range of black hole masses in them is about 4 to 10
solar ones, the orbital periods are tens to hundreds day. The unique BH-binary
LB-1 fits well into this extremely rare class of double stars.


  In a variety of different systems, high-Z ion species show a marked tendency
to accumulate in regions of high plasma density. It has previously been
suggested that the apparent universality of this behavior could be explained
thermodynamically, in terms of the maximum-entropy state attainable when the
system must obey an ambipolarity condition. However, the previous treatment did
not allow for the possibility of temperature gradients. Here, tools from
non-equilibrium (Onsager) thermodynamics are used to show that ambipolarity
continues to play a key role in producing this behavior in the presence of
temperature gradients, and to recover well-known temperature screening effects
that appear in these cases.


  Recent studies of luminous infrared-selected active galactic nuclei (AGN)
suggest that the reddest, most obscured objects display a higher angular
clustering amplitude, and thus reside in higher-mass dark matter halos. This is
a direct contradiction to the prediction of the simplest
unification-by-orientation models of AGN and quasars. However, clustering
measurements depend strongly on the "mask" that removes low-quality data and
describes the sky and selection function. We find that applying a robust,
conservative mask to WISE-selected quasars yields a weaker but still
significant difference in the bias between obscured and unobscured quasars.
These findings are consistent with results from previous Spitzer surveys, and
removes any scale dependence of the bias. For obscured quasars with $\langle z
\rangle = 0.99$ we measure a bias of $b_q = 2.67 \pm 0.16$, corresponding to a
halo mass of $\log (M_h / M_{\odot} h^{-1}) = 13.3 \pm 0.1$, while for
unobscured sources with $\langle z \rangle = 1.04$ we find $b_q = 2.04 \pm
0.17$ with a halo mass $\log (M_h / M_{\odot} h^{-1} )= 12.8 \pm 0.1$. This
improved measurement indicates that WISE-selected obscured quasars reside in
halos only a few times more massive than the halos of their unobscured
counterparts, a reduction in the factor of $\sim$10 larger halo mass as has
been previously reported using WISE-selected samples. Additionally, an
abundance matching analysis yields lifetimes for both obscured and unobscured
quasar phases on the order of a few 100 Myr ($\sim$ 1\% of the Hubble time) ---
however, the obscured phase lasts roughly twice as long, in tension with many
model predictions.


  The aim of this work is to analyse the formation mechanisms of large-scale
coherent structures in the flow around a wall-mounted square cylinder, due to
their impact on pollutant transport within cities. To this end, we assess
causal relations between the modes of a reduced-order model obtained by
applying proper-orthogonal decomposition to high-fidelity-simulation data of
the flow case under study. The causal relations are identified using
conditional transfer entropy, which is an information-theoretical quantity that
estimates the amount of information contained in the past of one variable about
another. This allows for an understanding of the origins and evolution of
different phenomena in the flow, with the aim of identifying the modes
responsible for the formation of the main vortical structures. Our approach
unveils that vortex-breaker modes are the most causal modes, in particular,
over higher-order modes, and no significant causal relationships were found for
vortex-generator modes. We validate this technique by determining the causal
relations present in the nine-equation model of near-wall turbulence developed
by Moehlis et al. (New J. Phys, vol. 6, 2004, p. 56), which are in good
agreement with literature results for turbulent channel flows.


  Prior works have shown that semi-supervised learning algorithms can leverage
unlabeled data to improve over the labeled sample complexity of supervised
learning (SL) algorithms. However, existing theoretical analyses focus on
regimes where the unlabeled data is sufficient to learn a good decision
boundary using unsupervised learning (UL) alone. This begs the question: Can
SSL algorithms simultaneously improve upon both UL and SL? To this end, we
derive a tight lower bound for 2-Gaussian mixture models that explicitly
depends on the labeled and the unlabeled dataset size as well as the
signal-to-noise ratio of the mixture distribution. Surprisingly, our result
implies that no SSL algorithm can improve upon the minimax-optimal statistical
error rates of SL or UL algorithms for these distributions. Nevertheless, we
show empirically on real-world data that SSL algorithms can still outperform UL
and SL methods. Therefore, our work suggests that, while proving performance
gains for SSL algorithms is possible, it requires careful tracking of
constants.


  The CAMbridge Emission Line Surveyor (CAMELS) is a pathfinder program to
demonstrate on-chip spectrometry at millimetre wavelengths. CAMELS will observe
at frequencies from 103-114.7 GHz, providing 512 channels with a spectral
resolution of R = 3000. In this paper we describe the science goals of CAMELS,
the current system level design for the instrument and the work we are doing on
the detailed designs of the individual components. In addition, we will discuss
our efforts to understand the impact that the design and calibration of the
filter bank on astronomical performance. The shape of the filter channels, the
degree of overlap and the nature of the noise all effect how well the
parameters of a spectral line can be recovered. We have developed a new and
rigorous method for analysing performance, based on the concept of Fisher
information. This can in be turn coupled to a detailed model of the science
case, allowing design trade-offs to be properly investigated.


  Phonon spectra of detwinned {SrFe$_2$As$_2$} crystals, as measured by
inelastic x-ray scattering, show clear anisotropy accompanying the
magneto-structural transition at 200 K. We model the mode splitting using
magnetic DFT calculations, including a phenomenological reduction in
force-constant anisotropy that can be attributed to magnetic fluctuations. This
serves as a starting point for a general model of phonons in this material
applicable to both magnetic and non-magnetic phase. Using this model, the
measured splitting in the magnetic phase below $\it T_{N}$, and the measured
phonon linewidth, we set a lower bound on the mean magnetic fluctuation
frequency above $\it T_{N}$ at 210 K.


  The commissioning of two cryomodule components is underway at Fermilab's
Superconducting Radio Frequency (SRF) Accelerator Test Facility. The research
at this facility supports the next generation high intensity linear
accelerators such as the International Linear Collider (ILC), a new high
intensity injector (Project X) and other future machines. These components,
Cryomodule #1 (CM1) and Capture Cavity II (CC2), which contain 1.3 GHz cavities
are connected in series in the beamline and through cryogenic plumbing. Studies
regarding characterization of ground motion, technical and cultural noise
continue. Mechanical transfer functions between the foundation and critical
beamline components have been measured and overall system displacement
characterized. Baseline motion measurements given initial operation of
cryogenic, vacuum systems and other utilities are considered.


  This paper studies the problem of non-cooperative radio resource scheduling
in a vehicle-to-vehicle communication network. The technical challenges lie in
high vehicle mobility and data traffic variations. Over the discrete scheduling
slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in
the coverage of a road side unit (RSU) for the limited frequency to transmit
queued packets. The frequency allocation at the beginning of each slot by the
RSU is regulated following a sealed second-price auction. Each VUE-pair aims to
optimize the expected long-term performance. Such interactions among VUE-pairs
are modelled as a stochastic game with a semi-continuous global network state
space. By defining a partitioned control policy, we transform the stochastic
game into an equivalent game with a global queue state space of finite size. We
adopt an oblivious equilibrium (OE) to approximate the Markov perfect
equilibrium (MPE), which characterizes the optimal solution to the equivalent
game. The OE solution is theoretically proven to be with an asymptotic Markov
equilibrium property. Due to the lack of a priori knowledge of network
dynamics, we derive an online algorithm to learn the OE policies. Numerical
simulations validate the theoretical analysis and show the effectiveness of the
proposed online learning algorithm.


  In this fifth paper of the series, we use the parametrized, spherically
symmetric explosion method PUSH to investigate the impact of eight different
nuclear equations of state (EOS). We present and discuss the explosion
properties and the detailed nucleosynthesis yields, and predict the remnant
(neutron star or black hole) for all our simulations. For this, we perform two
sets of simulations. One, a complete study of non-rotating stars from 11 to 40
M$_{\odot}$ at three different metallicities using the SFHo EOS. And two, a
suite of simulations for four progenitors (16 M$_{\odot}$ at three
metallicities and 25 M$_{\odot}$ at solar metallicity) for eight different
nuclear EOS. We compare our predicted explosion energies and yields to observed
supernovae and to the metal-poor star HD~84937. We find EOS-dependent
differences in the explosion properties and the nucleosynthesis yields.
However, when comparing to observations, these differences are not large enough
to rule out any EOS considered in this work.


  Segmental structure is a common pattern in many types of sequences such as
phrases in human languages. In this paper, we present a probabilistic model for
sequences via their segmentations. The probability of a segmented sequence is
calculated as the product of the probabilities of all its segments, where each
segment is modeled using existing tools such as recurrent neural networks.
Since the segmentation of a sequence is usually unknown in advance, we sum over
all valid segmentations to obtain the final probability for the sequence. An
efficient dynamic programming algorithm is developed for forward and backward
computations without resorting to any approximation. We demonstrate our
approach on text segmentation and speech recognition tasks. In addition to
quantitative results, we also show that our approach can discover meaningful
segments in their respective application contexts.


  Digital Twins (DT) virtually model cyber-physical objects using Internet of
Things (IoT) components (e.g., sensors) to gather and process senstive
information stored in the cloud. Trustworthiness of the streamed data is
crucial which requires quantum safety and breach resiliency. Digital signatures
are essential for scalable authentication and non-repudiation. Yet, NIST PQC
signature standards are exorbitantly costly for low-end IoT without considering
forward security. Moreover, Post-Quantum (PQ) signatures lack aggregation,
which is highly desirable to reduce the transmission and storage burdens in
DTs. Hence, there is an urgent need for lightweight digital signatures that
offer compromise resiliency and compactness while permitting an effective
transition into the PQ era for DTs.
  We create a series of highly lightweight digital signatures called
Hardware-ASsisted Efficient Signature (HASES) that meets the above
requirements. The core of HASES is a hardware-assisted cryptographic commitment
construct oracle (CCO) that permits verifiers to obtain expensive commitments
without signer interaction. We created three HASES schemes: PQ-HASES is a
forward-secure PQ signature, LA-HASES is an efficient aggregate Elliptic-Curve
signature, and HY-HASES is a novel hybrid scheme that combines PQ-HASES and
LA-HASES with novel strong nesting and sequential aggregation. HASES does not
require a secure-hardware on the signer. We proved that HASES schemes are
secure and implemented them on commodity hardware and an 8-bit AVR ATmega2560.
Our experiments confirm that PQ-HASES and LA-HASES are two magnitudes of times
more signer efficient than their PQ and conventional-secure counterparts,
respectively. HY-HASES outperforms NIST PQC and conventional signature
combinations, offering a standardcompliant transitional solution for emerging
DTs. We open-source HASES schemes for public testing and adaptation.


  We study the effect of weak lensing by cosmic (super-)strings on the
anisotropies of cosmic microwave background (CMB). In developing a method to
calculate the lensing convergence field due to strings, and thereby temperature
and polarization angular power spectra of CMB, we clarify how the nature of
strings, characterized by the intercommuting probability, can influence these
CMB anisotropies. Assuming that the power spectrum is dominated by
Poisson-distributed string segments, we find that the convergence spectrum
peaks at low multipoles and is mostly contributed from strings located at
relatively low redshifts. As the intercommuting probability decreases, the
spectra of the convergence and hence the lensed temperature and polarizations
are gained because the number density of string segments becomes larger. An
observationally important feature of the string-induced CMB polarizations is
that the string-lensed spectra decay more slowly on small scales compared with
primordial scalar perturbations from standard inflation.


  We study different properties of an anti-Hermitian Yukawa interaction,
motivated by a scenario of radiative anomalous generation of masses for the
right-handed sterile neutrinos. The model, involving either a pseudo-scalar or
a scalar, is consistent both at the classical and quantum levels, and
particular attention is given to its properties under improper Lorentz
transformations. The path integral is consistently defined with a Euclidean
signature, and we discuss the energetics of the model, which show that no
dynamical mass generation can occur, unless extra interactions are considered.


  This paper proposes a Voltage Stability Index (VSI) suitable for unbalanced
polyphase power systems. To this end, the grid is represented by a polyphase
multiport network model (i.e., compound hybrid parameters), and the aggregate
behavior of the devices in each node by Th\'evenin Equivalents (TEs) and
Polynomial Models (PMs), respectively. The proposed VSI is a generalization of
the known L-index, which is achieved through the use of compound electrical
parameters, and the incorporation of TEs and PMs into its formal definition.
Notably, the proposed VSI can handle unbalanced polyphase power systems,
explicitly accounts for voltage-dependent behavior (represented by PMs), and is
computationally inexpensive. These features are valuable for the operation of
both transmission and distribution systems. Specifically, the ability to handle
the unbalanced polyphase case is of particular value for distribution systems.
In this context, it is proven that the compound hybrid parameters required for
the calculation of the VSI do exist under practical conditions (i.e., for lossy
grids). The proposed VSI is validated against state-of-the-art methods for
voltage stability assessment using a benchmark system which is based on the
IEEE 34-node feeder.


  We prove a restriction of an analogue of the Robinson--Schensted--Knuth
correspondence for semi-skyline augmented fillings, due to Mason, to multisets
of cells of a staircase possibly truncated by a smaller staircase at the upper
left end corner, or at the bottom right end corner.
  The restriction to be imposed on the pairs of semi-skyline augmented fillings
is that the pair of shapes, rearrangements of each other, satisfies an
inequality in the Bruhat order, w.r.t. the symmetric group, where one shape is
bounded by the reverse of the other. For semi-standard Young tableaux the
inequality means that the pair of their right keys is such that one key is
bounded by the Sch\"utzenberger evacuation of the other. This bijection is then
used to obtain %recover an expansion formula of the non-symmetric Cauchy
kernel, over staircases or truncated staircases, in the basis of Demazure
characters of type $A$, and the basis of Demazure atoms. The expansion implies
Lascoux expansion formula, when specialised to staircases or truncated
staircases, and make explicit, in the latter, the Young tableaux in the
Demazure crystal by interpreting Demazure operators via elementary bubble
sorting operators acting on weak compositions.


  Most existing robotic datasets capture static scene data and thus are limited
in evaluating robots' dynamic performance. To address this, we present a mobile
robot oriented large-scale indoor dataset, denoted as THUD (Tsinghua University
Dynamic) robotic dataset, for training and evaluating their dynamic scene
understanding algorithms. Specifically, the THUD dataset construction is first
detailed, including organization, acquisition, and annotation methods. It
comprises both real-world and synthetic data, collected with a real robot
platform and a physical simulation platform, respectively. Our current dataset
includes 13 larges-scale dynamic scenarios, 90K image frames, 20M 2D/3D
bounding boxes of static and dynamic objects, camera poses, and IMU. The
dataset is still continuously expanding. Then, the performance of mainstream
indoor scene understanding tasks, e.g. 3D object detection, semantic
segmentation, and robot relocalization, is evaluated on our THUD dataset. These
experiments reveal serious challenges for some robot scene understanding tasks
in dynamic scenes. By sharing this dataset, we aim to foster and iterate new
mobile robot algorithms quickly for robot actual working dynamic environment,
i.e. complex crowded dynamic scenes.


  "You don't look like a librarian" is a phrase we often hear in the astronomy
department or observatory library. Astronomy librarians are a breed apart, and
are taking on new and non-traditional roles as information technology evolves.
This talk will explore the future of librarians and librarianship through the
lens of the recent talks given at the sixth "Libraries and Information Services
in Astronomy" conference held in Pune, India in February 2010. We will explore
the librarian's universe, illustrating how librarians use new technologies to
perform such tasks as bibliometrics, how we are re-fashioning our library
spaces in an increasingly digital world and how we are confronting the brave
new world of open access, to name but a few topics.


  Smart devices of everyday use (such as smartphones and wearables) are
increasingly integrated with sensors that provide immense amounts of
information about a person's daily life such as behavior and context. The
automatic and unobtrusive sensing of behavioral context can help develop
solutions for assisted living, fitness tracking, sleep monitoring, and several
other fields. Towards addressing this issue, we raise the question: can a
machine learn to recognize a diverse set of contexts and activities in a
real-life through joint learning from raw multi-modal signals (e.g.
accelerometer, gyroscope and audio etc.)? In this paper, we propose a
multi-stream temporal convolutional network to address the problem of
multi-label behavioral context recognition. A four-stream network architecture
handles learning from each modality with a contextualization module which
incorporates extracted representations to infer a user's context. Our empirical
evaluation suggests that a deep convolutional network trained end-to-end
achieves an optimal recognition rate. Furthermore, the presented architecture
can be extended to include similar sensors for performance improvements and
handles missing modalities through multi-task learning without any manual
feature engineering on highly imbalanced and sparsely labeled dataset.


  In this paper we analyze the long-time behaviour of 3 dimensional Ricci flow
with surgery. We prove that under the topological condition that the initial
manifold only has non-aspherical or hyperbolic components in its geometric
decomposition, there are only finitely many surgeries and the curvature is
bounded by $C t^{-1}$ for large $t$. This answers an open question in
Perelman's work, which was made more precise by Lott and Tian, for this class
of initial topologies.
  More general classes will be discussed in subsequent papers using similar
methods.


  In this paper we focus on artificial intelligence (AI) for governance, not
governance for AI, and on just one aspect of governance, namely ethics audit.
Different kinds of ethical audit bots are possible, but who makes the choices
and what are the implications? In this paper, we do not provide
ethical/philosophical solutions, but rather focus on the technical aspects of
what an AI-based solution for validating the ethical soundness of a target
system would be like. We propose a system that is able to conduct an ethical
audit of a target system, given certain socio-technical conditions. To be more
specific, we propose the creation of a bot that is able to support
organisations in ensuring that their software development lifecycles contain
processes that meet certain ethical standards.


  Many-body approaches to open quantum systems have recently become powerful
tools for investigating the detailed role of dissipative environments in
diverse non-equilibrium molecular and condensed matter processes. Here, we
report the development of an efficient algorithm that utilises a time-dependent
variational principle for matrix product states to evolve large
system-environment states. By thus capturing all system-environment
correlations, we reproduce the highly non-perturbative, quantum-critical
dynamics of the zero temperature spin-boson model, and then exploit the
many-body information to output a complete time-frequency spectrum of the
environmental excitations. We highlight how theoretical 'environmental spectra'
could yield valuable insights into a wide range of complex dissipative
processes, by showing that correlated motion of modes entangled with the spin
can appear with persistent vibrational coherence, in spite of incoherent spin
relaxation.


  We discuss the occurrence of subcritical star formation in the outskirts of
some galactic discs and across LSB galaxies, contrary to the picture that star
formation happens only when the gas surface density is above a critical
threshold density. This raises the question of whether the Toomre Q-criterion
for gas alone is valid for correlating the gas distribution to star formation
or rather some effective Q-parameter, taking account of components such as
stars, dark matter and magnetic fields, is more representative of the
correlation between gas instability and star formation activity. As a potential
candidate, we investigate the role of dark matter in triggering subcritical
star formation, particularly at the outskirts where dark matter is dominant.
Indeed, our axisymmetric analysis favours the picture that dark matter
contributes to disc instability at the subcritical regime where gas seems
stable when dark matter is neglected.


  A non-uniqueness result for the canonical structure in quantum theory shows
that the classical part of the Copenhagen interpretation contains physically
important information not contained in its quantum part. As a consequence, we
cannot compute the symmetry group of a quantum theory considering only the
quantum part. The unavoidable vagueness of the classical part therefore leads
to a similar vagueness in the definition of the symmetry group. This makes it
at least problematic, if not impossible, to establish the true symmetry group
of a quantum theory in the Copenhagen interpretation. Different from the old
measurement problem, the symmetry group is to important physically to be
rejected as a metaphysical pseudoproblem.


  The SCUBA polarized 850 microns thermal emission data of the region OMC-2 in
Orion A are added to and homogeneously reduced with data already available in
the region OMC-3. The data set shows that OMC-2 is a region generally less
polarized than OMC-3. Where coincident, most of the 850 microns polarization
pattern is similar to that measured in 350 microns polarization data. Only 850
microns polarimetry data have been obtained in and around MMS7, FIR1 & FIR2,
and in the region south of FIR6. A realignment of the polarization vectors with
the filament can be seen near FIR1 in the region south of OMC-3. An analysis
shows that the energy injected by CO outflows and H2 jets associated to OMC-2
and OMC-3 does not appear to alter the polarization patterns at a scale of the
14'' resolution beam. A second order structure function analysis of the
polarization position angles shows that OMC-2 is a more turbulent region than
OMC-3. OMC-3 appears to be a clear case of a magnetically dominated region with
respect to the turbulence. However for OMC-2 it is not clear that this is the
case. A more in-depth analysis of five regions displayed along OMC-2/3
indicates a decrease of the mean polarization degree and an increase of the
turbulent angular dispersion from north to south. A statistical analysis
suggests the presence of two depolarization regimes in our maps. One regime
including the effects of the cores, the other one excluding it.


  Despite recent progress, the complete understanding of the perturbations of
charged, rotating black holes as described by the Kerr-Newman metric remains an
open and fundamental problem in relativity. In this study, we explore the
existence of families of quasinormal modes of Kerr-Newman black holes whose
decay rates limit to zero at extremality, called zero-damped modes in past
studies. We review the nearly extremal and WKB approximation methods for
spin-weighted scalar fields (governed by the Dudley-Finley equation) and give
an accounting of the regimes where scalar zero-damped and damped modes exist.
Using Leaver's continued fraction method, we verify that these approximations
give accurate predictions for the frequencies in their regimes of validity. In
the nonrotating limit, we argue that gravito-electromagnetic perturbations of
nearly extremal Reissner-Nordstr\"{o}m black holes have zero-damped modes in
addition to the well-known spectrum of damped modes. We provide an analytic
formula for the frequencies of these modes, verify their existence using a
numerical search, and demonstrate the accuracy of our formula. These results,
along with recent numerical studies, point to the existence of a simple
universal equation for the frequencies of zero-damped gravito-electromagnetic
modes of Kerr-Newman black holes, whose precise form remains an open question.


  Micropayment channels are the most prominent solution to the limitation on
transaction throughput in current blockchain systems. However, in practice
channels are risky because participants have to be online constantly to avoid
fraud, and inefficient because participants have to open multiple channels and
lock funds in them. To address the security issue, we propose a novel mechanism
that involves watchtowers incentivized to watch the channels and reveal a
fraud. Our protocol does not require participants to be online constantly
watching the blockchain. The protocol is secure, incentive compatible and
lightweight in communication. Furthermore, we present an adaptation of our
protocol implementable on the Lightning protocol. Towards efficiency, we
examine specific topological structures in the blockchain transaction graph and
generalize the construction of channels to enable topologies better suited to
specific real-world needs. In these cases, our construction reduces the
required amount of signatures for a transaction and the total amount of locked
funds in the system.


  In this paper we study polynomial maps of vector spaces and their
eigenvectors and eigenvalues. The new quantity called complanart is defined.
Complanarts determine complanarity of solution vectors of systems of polynomial
equations. Evaluation of complanart is reduced to evaluation of resultants. As
in linear case, the pattern of eigenvectors defines the phase diagram of
associated differential equation. Theory of such differential equations arise
naturally as extension of Lyapunov's theory of stability for solutions of
differential equations. The results of this work have a number of potential
applications: from solving non-linear differential equations and calculating
non-linear exponents to taking non-Gaussian integrals.


  In conversational speech, the acoustic signal provides cues that help
listeners disambiguate difficult parses. For automatically parsing spoken
utterances, we introduce a model that integrates transcribed text and
acoustic-prosodic features using a convolutional neural network over energy and
pitch trajectories coupled with an attention-based recurrent neural network
that accepts text and prosodic features. We find that different types of
acoustic-prosodic features are individually helpful, and together give
statistically significant improvements in parse and disfluency detection F1
scores over a strong text-only baseline. For this study with known sentence
boundaries, error analyses show that the main benefit of acoustic-prosodic
features is in sentences with disfluencies, attachment decisions are most
improved, and transcription errors obscure gains from prosody.


  An important novelty of 5G is its role in transforming the industrial
production into Industry 4.0. Specifically, Ultra-Reliable Low Latency
Communications (URLLC) will, in many cases, enable replacement of cables with
wireless connections and bring freedom in designing and operating
interconnected machines, robots, and devices. However, not all industrial links
will be of URLLC type; e.g. some applications will require high data rates.
Furthermore, these industrial networks will be highly heterogeneous, featuring
various communication technologies. We consider network slicing as a mechanism
to handle the diverse set of requirements to the network. We present methods
for slicing deterministic and packet-switched industrial communication
protocols at an abstraction level that is decoupled from the specific
implementation of the underlying technologies. Finally, we show how network
calculus can be used to assess the end-to-end properties of the network slices.


  We study thermalization, hydrodynamization, and chemical equilibration in
out-of-equilibrium Quark-Gluon Plasma starting from various initial conditions
using QCD effective kinetic theory, valid at weak coupling. In non-expanding
systems gauge bosons rapidly lose information of the initial state and achieve
kinetic equilibrium among themselves, while fermions approach the equilibrium
distribution only at later time. In systems undergoing rapid longitudinal
expansion, both gluons and quarks are kept away from equilibrium by the
expansion, but the evolution is well described by fluid dynamics even before
local thermal equilibrium is reached. For realistic couplings we determine the
ordering between the separate hydrodynamization, chemical equilibration and
thermalization time scales to be
$\tau_\text{hydro}<\tau_\text{chem}<\tau_\text{therm}$.


  Hierarchical mergers of galaxies can form binary black holes (BBHs) since
many or most galaxies have central massive black holes (BHs). It is possible
that some BBHs exist in active galactic nuclei (AGNs). We argue that each BH
may be surrounded by an accretion disc with a different inclination angle to
the line of sight (due to different BH spin directions and the
Bardeen-Petterson effect). The observed Fe K_alpha line profile from a BBH
system is a combination of the lines from the inner regions of the two discs,
which is significantly affected by the inclination angles of the two discs. The
Fe K_alpha line profile associated with BBHs may have an unusual shape with
double or more peaks as well as short-term variability, which can be
distinguished from the Fe K_alpha line properties of some other possible
models. We suggest that with the improvement of resolution in X-ray astronomy,
Fe K_alpha line profiles be a potential tool to probe the existence of massive
BBHs in AGNs. The Fe K_alpha line profile associated with BBHs may also provide
a tool to investigate the dynamics in strong gravitation field (e.g. providing
evidence of the Bardeen-Petterson effect).


  We present a new analysis on the issue of the location of the observed
microlensing events in direction of the Large Magellanic Cloud (LMC). This is
carried out starting from a recently drawn coherent picture of the geometrical
structure and dynamics of the LMC disk and by considering different
configurations for the LMC bar. In this framework it clearly emerges that the
spatial distribution of the events observed so far shows a near--far asymmetry.
This turns out to be compatible with the optical depth calculated for the LMC
halo objects. In this perspective, our main conclusion, supported by a
statistical analysis on the outcome of an evaluation of the microlensing rate,
is that self lensing can not account for all the observed events. Finally we
propose a general inequality to calculate quickly an upper limit to the optical
depth along a line of view through the LMC center.


  In this paper we consider some properties of a space B(X) of Borel functions
on a set of reals X, with pointwise topology, that are stronger than
separability.


  The growing number of Milky Way satellites detected in recent years has
introduced a new focus for stellar abundance analysis. Abundances of stars in
satellites have been used to probe the nature of these systems and their
chemical evolution. However, for most satellites, only centrally located stars
have been examined. This paper presents an analysis of three stars in the
Tucana V system, one in the inner region and two at $\sim$10\arcmin\ (7--10
half-light radii) from the center. We find a remarkable chemical diversity
between the stars. One star exhibits enhancements in rapid neutron-capture
elements (an $r$-I star), and another is highly enhanced in C, N, and O but
with low neutron-capture abundances (a CEMP-no star). The metallicities of the
stars analyzed span more than 1~dex from $\mathrm{[Fe/H]}=-3.55$ to $-2.46$.
This, combined with a large abundance range of other elements like Ca, Sc, and
Ni, confirms that Tuc~V is an ultra-faint dwarf (UFD) galaxy. The variation in
abundances, highlighted by [Mg/Ca] ratios ranging from $+0.89$ to $-0.75$,
among the stars, demonstrates that the chemical enrichment history of Tuc~V was
very inhomogeneous. Tuc~V is only the second UFD galaxy in which stars located
at large distances from the galactic center have been analyzed, along with
Tucana~II. The chemical diversity seen in these two galaxies, driven by the
composition of the non-central member stars, suggests that distant member stars
are important to include when classifying faint satellites and that these
systems may have experienced more complex chemical enrichment histories than
previously anticipated.


  This research strives for natural language moment retrieval in long,
untrimmed video streams. The problem is not trivial especially when a video
contains multiple moments of interests and the language describes complex
temporal dependencies, which often happens in real scenarios. We identify two
crucial challenges: semantic misalignment and structural misalignment. However,
existing approaches treat different moments separately and do not explicitly
model complex moment-wise temporal relations. In this paper, we present Moment
Alignment Network (MAN), a novel framework that unifies the candidate moment
encoding and temporal structural reasoning in a single-shot feed-forward
network. MAN naturally assigns candidate moment representations aligned with
language semantics over different temporal locations and scales. Most
importantly, we propose to explicitly model moment-wise temporal relations as a
structured graph and devise an iterative graph adjustment network to jointly
learn the best structure in an end-to-end manner. We evaluate the proposed
approach on two challenging public benchmarks DiDeMo and Charades-STA, where
our MAN significantly outperforms the state-of-the-art by a large margin.


  Let $V$ be a finite tree with radially decaying weights. We show that there
exists a set $E \subset \mathbb{R}^2$ for which the following two problems are
equivalent: (1) Given a (real-valued) function $\phi$ on the leaves of $V$,
extend it to a function $\Phi$ on all of $V$ so that $||\Phi||_{L^{1,p}(V)}$
has optimal order of magnitude. Here, $L^{1,p}(V)$ is a weighted Sobolev space
on $V$. (2) Given a function $f:E \rightarrow \mathbb{R}$, extend it to a
function $F \in L^{2,p}(\mathbb{R}^2)$ so that $||F||_{L^{2,p}(\mathbb{R}^2)}$
has optimal order of magnitude.


  Pillars are important structural units used to ensure mining safety in
underground hard rock mines. Therefore, precise predictions regarding the
stability of underground pillars are required. One common index that is often
used to assess pillar stability is the Safety Factor (SF). Unfortunately, such
crisp boundaries in pillar stability assessment using SF are unreliable. This
paper presents a novel application of Artificial Neural Network-Backpropagation
(ANN-BP) and Deep Ensemble Learning for pillar stability classification. There
are three types of ANN-BP used for the classification of pillar stability
distinguished by their activation functions: ANN-BP ReLU, ANN-BP ELU, and
ANN-BP GELU. This research also presents a new labeling alternative for pillar
stability by considering its suitability with the SF. Thus, pillar stability is
expanded into four categories: failed with a suitable safety factor, intact
with a suitable safety factor, failed without a suitable safety factor, and
intact without a suitable safety factor. There are five inputs used for each
model: pillar width, mining height, bord width, depth to floor, and ratio. The
results showed that the ANN-BP model with Ensemble Learning could improve
ANN-BP performance with an average accuracy of 86.48% and an F_2-score of
96.35% for the category of failed with a suitable safety factor.


  In these lectures we cover basics of the theory of heights starting with the
heights in the projective space, heights of polynomials, and heights of the
algebraic curves. We define the minimal height of binary forms and moduli
height for algebraic curves and prove that the moduli height of superelliptic
curves $\mathcal H (f) \leq c_0 \tilde H (f)$ where $c_0$ is a constant and
$\tilde H$ the minimal height of the corresponding binary form. For genus $g=2$
and 3 such constant is explicitly determined. Furthermore, complete lists of
curves of genus 2 and genus 3 hyperelliptic curves with height 1 are computed.


  We have investigated CuNi/Nb/CuNi trilayers, as have been recently used as
the core structure of a spin-valve like device [J. Y. Gu et al., Phys. Rev.
Lett. 89, 267001 (2002)] to study the effect of magnetic configurations of the
CuNi layers on the critical temperature, Tc, of the superconducting Nb. After
reproducing a Tc shift of a few mK, we have gone on to explore the performance
limits of the structure. The results showed the Tc shift we found to be quite
close to the basic limits of this particular materials system. The ratio
between the thickness and the coherence length of the superconductor and the
interfacial transparency were the main features limiting the Tc shift.


  We study the spin and charge dynamics of electrons in n-doped II--VI
semiconductor multiple quantum wells when one or more quantum wells are doped
with Mn. The interplay between strongly nonlinear inter-well charge transport
and the large tunable spin-splitting induced by exchange interactions with
spin-polarized Mn ions produces interesting new spin dependent features. The
tunneling current between quantum wells can be strongly spin polarized and,
under certain conditions, can develop self sustained oscillations under a
finite dc voltage. The spin polarization oscillates in both magnetic and
nonmagnetic quantum wells and the time average in magnetic wells can differ
from its zero-voltage value. Our numerical simulations demonstrate that the
amplitude of the spin polarization oscillations depends on the distribution of
magnetic wells within the sample. We discuss how the spin polarized current and
the spin polarization of the quantum wells can be tailored experimentally.


  Recent advances like StyleGAN have promoted the growth of controllable facial
editing. To address its core challenge of attribute decoupling in a single
latent space, attempts have been made to adopt dual-space GAN for better
disentanglement of style and content representations. Nonetheless, these
methods are still incompetent to obtain plausible editing results with high
controllability, especially for complicated attributes. In this study, we
highlight the importance of interaction in a dual-space GAN for more
controllable editing. We propose TransEditor, a novel Transformer-based
framework to enhance such interaction. Besides, we develop a new dual-space
editing and inversion strategy to provide additional editing flexibility.
Extensive experiments demonstrate the superiority of the proposed framework in
image quality and editing capability, suggesting the effectiveness of
TransEditor for highly controllable facial editing.


  The Phoenix Deep Survey is a multi-wavelength galaxy survey based on deep 1.4
GHz radio imaging (Hopkins et al., 2003). The primary goal of this survey is to
investigate the properties of star formation in galaxies and to trace the
evolution in those properties to a redshift z=1, covering a significant
fraction of the age of the Universe. By compiling a sample of star-forming
galaxies based on selection at radio wavelengths we eliminate possible biases
due to dust obscuration, a significant issue when selecting objects at optical
and ultraviolet wavelengths. In this paper, we present the catalogs and results
of deep optical (UBVRI) and near-infrared (Ks) imaging of the deepest region of
the existing decimetric radio imaging. The observations and data-processing are
summarised and the construction of the optical source catalogs described,
together with the details of the identification of candidate optical
counterparts to the radio catalogs. Based on our UBVRIKs imaging, photometric
redshift estimates for the optical counterparts to the radio detections are
explored.


  This paper aims to recall some of the main contributions of Roberto Petronzio
to physics, with a particular regard to the period we have been working
together. His seminal contributions cover an extremely wide range of topics:
the foundation of the perturbative approach to QCD, various aspects of weak
interaction theory, from basic questions (e.g. the mass of the Higgs) to
lattice weak interaction, lattice QCD from the beginning to most recent
computations.


  We construct two reduced quantum theories for the Oppenheimer-Snyder model,
respectively taking the point of view of the comoving and the exterior
stationary observer, using affine coherent states quantization. Investigations
of the quantum corrected dynamics reveal that both observers can see a bounce,
although for the exterior observer certain quantization ambiguities have to be
chosen correctly. The minimal radius for this bounce as seen from the
stationary observer is then shown to always be outside of the photon sphere.
Possible avenues to lower this minimal radius and reclaim black holes as an
intermediate state in the collapse are discussed. We demonstrate further that
switching between the observers at the level of the quantum theories can be
achieved by modifying the commutation relations.


  Suppose that $C=(C_1,..., C_m)$ is a configuration of 2-dimensional
symplectic submanifolds in a symplectic 4-manifold $(X,\omega)$ with connected,
negative definite intersection graph $\Gamma_C$. We show that by replacing an
appropriate neighborhood of $\cup C_i$ with a smoothing $W_S$ of a normal
surface singularity $(S, 0)$ with resolution graph $\Gamma_C$, the resulting
4-manifold admits a symplectic structure. This operation generalizes the
rational blow-down operation of Fintushel-Stern for other configurations, and
therefore extends Symington's result about symplectic rational blow-downs.


  The nucleon properties and structure should be modified by short-range
correlations (SRC) among nucleons. By analyzing SRC ratio data, we extract the
mass of nucleon in the SRC pair and the expected number of pn-SRC pairs in
deuterium, under the assumption that the SRC nucleon mass is universal in
different nuclei. The nucleon mass of a two-nucleon SRC pair is
$m_{\text{SRC}}=852\pm 18$ MeV, and the number of pn-SRC pairs in deuterium is
$n^{\text{d}}_{\text{SRC}}=0.021\pm 0.005$. The mass deficit of the strongly
overlapping nucleon can be explained by the trace anomaly contribution to the
mass in QCD or alternatively by the vacuum energy in the MIT bag model.


  We consider tunneling from the spin-polarized tip into the Luttinger liquid
edge state of quantum spin Hall system. This problem arose in context of the
spin and charge fractionalization of an injected electron. Renormalization of
the dc conductances of the system is calculated in the fermionic approach and
scattering states formalism. In lowest order of tunneling amplitude we confirm
previous results for the scaling dependence of conductances. Going beyond the
lowest order we show that interaction affects not only the total tunneling
rate, but also the asymmetry of the injected current. The helical edge state
forbids the backscattering, which leads to the possibility of two stable fixed
points in renormalization group sense, in contrast to the Y-junction between
the usual quantum wires.


  Neurites, the long cellular protrusions that form the routes of the neuronal
network are capable to actively extend during early morphogenesis or to
regenerate after trauma. To perform this task, they rely on their cytoskeleton
for mechanical support. In this paper, we present a three-component active gel
model that describes neurites in the three robust mechanical states observed
experimentally: collapsed, static, and motile. These states arise from an
interplay between the physical forces driven by growth of the microtubule-rich
inner core of the neurite and the acto-myosin contractility of its surrounding
cortical membrane. In particular, static states appear as a mechanical
traction/compression balance of these two parallel structures. The model
predicts how the response of a neurite to a towing force depends on the force
magnitude and recovers the response of neurites to several drug treatments that
modulate the cytoskeleton active and passive properties.


  Ultrastrong coupling may allow faster operations for the development of
quantum technologies at the expenses of increased sensitivity to new kind of
intrinsic errors. We study state transfer in superconducting circuit QED
architectures in the ultrastrong coupling regime. Using optimal control methods
we find a protocol resilient to the main source of errors, coming from the
interplay of the dynamical Casimir effect with cavity losses.


  New paradigm shifts and 6G technological revolution in vehicular services
have emerged toward unmanned driving, automated transportation, and
self-driving vehicles. As the technology for autonomous vehicles becomes
mature, real challenges come from reliable, safe, real-time connected
transportation operations to achieve ubiquitous and prompt information
exchanges with massive connected and autonomous vehicles. This article aims at
introducing novel wireless distributed architectures that embed the edge
computing capability inside software-defined vehicular networking
infrastructure. Such edge networks consist of open-loop grant-free
communications and computing-based control frameworks, which enable dynamic
eco-routing with ultra-low latency and mobile data-driven orchestration. Thus,
this work advances the frontiers of machine learning potentials and
next-generation mobile system realization in vehicular networking applications.


  COVID-19--a viral infectious disease--has quickly emerged as a global
pandemic infecting millions of people with a significant number of deaths
across the globe. The symptoms of this disease vary widely. Depending on the
symptoms an infected person is broadly classified into two categories namely,
asymptomatic and symptomatic. Asymptomatic individuals display mild or no
symptoms but continue to transmit the infection to otherwise healthy
individuals. This particular aspect of asymptomatic infection poses a major
obstacle in managing and controlling the transmission of the infectious
disease. In this paper, we attempt to mathematically model the spread of
COVID-19 in India under various intervention strategies. We consider SEIR type
epidemiological models, incorporated with India specific social contact matrix
representing contact structures among different age groups of the population.
Impact of various factors such as presence of asymptotic individuals, lockdown
strategies, social distancing practices, quarantine, and hospitalization on the
disease transmission is extensively studied. Numerical simulation of our model
is matched with the real COVID-19 data of India till May 15, 2020 for the
purpose of estimating the model parameters. Our model with zone-wise lockdown
is seen to give a decent prediction for July 20, 2020.


  A thermostatted dynamical model with five degrees of freedom is used to test
both the Evans-Searles and the Gallavotti-Cohen fluctuation relations (ESFR and
GCFR respectively). In the absence of an external driving field, the model
generates a time independent ergodic equilibrium state with two conjugate pairs
of Lyapunov exponents. Each conjugate pair sums to zero. The fluctuation
relations are tested numerically both near and far from equilibrium. As
expected from previous work, near equilibrium the ESFR is verified by the
simulation data while the GCFR is not confirmed by the data. Far from
equilibrium where a positive exponent in one of these conjugate pairs becomes
negative, we test a conjecture regarding the GCFR made by Gallavotti and
co-workers. They conjectured that where the number of nontrivial Lyapunov
exponents that are positive becomes less than the number of such negative
exponents, then the form of the GCFR needs to be corrected. We show that there
is no evidence for this conjecture in the empirical data. In fact as the field
increases, the uncorrected form of the GCFR appears to become more accurate.
The real reason for this observation is likely to be that as the field
increases, the argument of the GCFR more and more accurately approximates the
argument of the ESFR. Since the ESFR works for arbitrary field strengths, the
uncorrected GCFR appears to become ever more accurate as the field increases.
The final point of evidence against the conjecture is that when the smallest
positive exponent changes sign, the conjecture predicts a discontinuous change
in the "correction factor" for GCFR. We see no evidence for a discontinuity at
this field strength; only a gradual improvement of degree of agreement as the
field increases.


  We present some lambda calculus with explicit substitutions and named
variables. The characteristic feature of this calculus is as follows: renaming
of bound variables when performing substitutions is done using special
reductions and may be delayed.


  We study a lossy source coding problem for an arbitrarily varying remote
source (AVRS) which was proposed in a prior work. An AVRS transmits symbols,
each generated in an independent and identically distributed manner, which are
sought to be estimated at the decoder. These symbols are remotely generated,
and the encoder and decoder observe noise corrupted versions received through a
two-output noisy channel. This channel is an arbitrarily varying channel
controlled by a jamming adversary. We assume that the adversary knows the
coding scheme as well as the source data non-causally, and hence, can employ
malicious jamming strategies correlated to them. Our interest lies in studying
the rate distortion function for codes with a stochastic encoder, i.e, when the
encoder can privately randomize while the decoder is deterministic. We provide
upper and lower bounds on this rate distortion function.


  We classify (*)-subgroups of compact Lie groups of adjoint type, and
associate a twisted root system to every (*)-subgroup. We study the structure
of twisted root system in several aspects: properties of the small Weyl group
W_{small} and its normal subgroups W_{tiny} and W_{f}; properties of finite
root datum; structure of strips of infinite roots.


  Given a von Neumann algebra $M$ with a faithful normal semi-finite trace
$\tau,$ we consider the non commutative Arens algebra $L^{\omega}(M,
\tau)=\bigcap\limits_{p\geq1}L^{p}(M, \tau)$ and the related algebras
$L^{\omega}_2(M, \tau)=\bigcap\limits_{p\geq2}L^{p}(M, \tau)$ and
$M+L^{\omega}_2(M, \tau)$ which are proved to be complete metrizable locally
convex *-algebras. The main purpose of the present paper is to prove that any
derivation of the algebra $M+L^{\omega}_2(M, \tau)$ is inner and all
derivations of the algebras $L^{\omega}(M,\tau)$ and $L^{\omega}_2(M, \tau)$
are spatial and implemented by elements of $M+L^{\omega}_2(M, \tau).$


  A function $f:\RR \to \RR$ is called \emph{vertically rigid} if $graph(cf)$
is isometric to $graph (f)$ for all $c \neq 0$. We prove Jankovi\'c's
conjecture by showing that a continuous function is vertically rigid if and
only if it is of the form $a+bx$ or $a+be^{kx}$ ($a,b,k \in \RR$). We answer a
question of Cain, Clark and Rose by showing that there exists a Borel
measurable vertically rigid function which is not of the above form. We discuss
the Lebesgue and Baire measurable case, consider functions bounded on some
interval and functions with at least one point of continuity. We also introduce
horizontally rigid functions, and show that a certain structure theorem can be
proved without assuming any regularity.


  The work provides possible mechanisms of physics of thunderstorm clouds. We
tried to consider the problems of the hail clouds in a completely different
view. The article is based on the latest scientific achievement of modern
theoretical physics, chemistry, physics of thunderstorms clouds and gives
opportunities for new ways searching of impact on hail clouds.


  Emergent functionalities of structural and topological defects in
ferroelectric materials underpin an extremely broad spectrum of applications
ranging from domain wall electronics to high dielectric and electromechanical
responses. Many of these have been discovered and quantified via local scanning
probe microscopy methods. However, the search for these functionalities has
until now been based by either trial and error or using auxiliary information
such as topography or domain wall structure to identify potential objects of
interest based on the intuition of operator or preexisting hypotheses, with
subsequent manual exploration. Here, we report the development and
implementation of a machine learning framework that actively discovers
relationships between local domain structure and polarization switching
characteristics in ferroelectric materials encoded in the hysteresis loop. The
latter and descriptors such as nucleation bias, coercive bias, hysteresis loop
area, or more complex functionals of hysteresis loop shape and corresponding
uncertainties are used to guide the discovery via automated piezoresponse force
microscopy (PFM) and spectroscopy experiments. As such, this approach combines
the power of machine learning methods to learn the correlative relationships
between high dimensional data, and human-based physics insights encoded in the
acquisition function. For ferroelectric, this automated workflow demonstrates
that the discovery path and sampling points of on-field and off-field
hysteresis loops are largely different, indicating the on-field and off-field
hysteresis loops are dominated by different mechanisms. The proposed approach
is universal and can be applied to a broad range of modern imaging and
spectroscopy methods ranging from other scanning probe microscopy modalities to
electron microscopy and chemical imaging.


  The community detection problem requires to cluster the nodes of a network
into a small number of well-connected "communities". There has been substantial
recent progress in characterizing the fundamental statistical limits of
community detection under simple stochastic block models. However, in
real-world applications, the network structure is typically dynamic, with nodes
that join over time. In this setting, we would like a detection algorithm to
perform only a limited number of updates at each node arrival. While standard
voting approaches satisfy this constraint, it is unclear whether they exploit
the network information optimally. We introduce a simple model for networks
growing over time which we refer to as streaming stochastic block model
(StSBM). Within this model, we prove that voting algorithms have fundamental
limitations. We also develop a streaming belief-propagation (StreamBP)
approach, for which we prove optimality in certain regimes. We validate our
theoretical findings on synthetic and real data.


  The past year has seen movement on several fronts for improving software
citation, including the Center for Open Science's Transparency and Openness
Promotion (TOP) Guidelines, the Software Publishing Special Interest Group that
was started at January's AAS meeting in Seattle at the request of that
organization's Working Group on Astronomical Software, a Sloan-sponsored
meeting at GitHub in San Francisco to begin work on a cohesive research
software citation-enabling platform, the work of Force11 to "transform and
improve" research communication, and WSSSPE's ongoing efforts that include
software publication, citation, credit, and sustainability.
  Brief reports on these efforts were shared at the BoF, after which
participants discussed ideas for improving software citation, generating a list
of recommendations to the community of software authors, journal publishers,
ADS, and research authors. The discussion, recommendations, and feedback will
help form recommendations for software citation to those publishers represented
in the Software Publishing Special Interest Group and the broader community.


  Computational analysis methods including machine learning have a significant
impact in the fields of genomics and medicine. High-throughput gene expression
analysis methods such as microarray technology and RNA sequencing produce
enormous amounts of data. Traditionally, statistical methods are used for
comparative analysis of gene expression data. However, more complex analysis
for classification of sample observations, or discovery of feature genes
requires sophisticated computational approaches. In this review, we compile
various statistical and computational tools used in analysis of expression
microarray data. Even though the methods are discussed in the context of
expression microarrays, they can also be applied for the analysis of RNA
sequencing and quantitative proteomics datasets. We discuss the types of
missing values, and the methods and approaches usually employed in their
imputation. We also discuss methods of data normalization, feature selection,
and feature extraction. Lastly, methods of classification and class discovery
along with their evaluation parameters are described in detail. We believe that
this detailed review will help the users to select appropriate methods for
preprocessing and analysis of their data based on the expected outcome.