  Multi-view approaches to people-tracking have the potential to better handle
occlusions than single-view ones in crowded scenes. They often rely on the
tracking-by-detection paradigm, which involves detecting people first and then
connecting the detections. In this paper, we argue that an even more effective
approach is to predict people motion over time and infer people's presence in
individual frames from these. This enables to enforce consistency both over
time and across views of a single temporal frame. We validate our approach on
the PETS2009 and WILDTRACK datasets and demonstrate that it outperforms
state-of-the-art methods.


  Quantum chromodynamics and the electroweak theory at low energies are
prominent instances of the combination of a short-range and a long-range
interaction. For the description of light nuclei, the large nucleon-nucleon
scattering lengths produced by the strong interaction, and the reduction of the
weak interaction to the Coulomb potential, play a crucial role. Helium-3 is the
first bound nucleus comprised of more than one proton in which this combination
of forces can be studied.
  We demonstrate a proper renormalization of Helium-3 using the pionless
effective field theory as the formal representation of the nuclear regime as
strongly interacting fermions. The theory is found consistent at leading and
next-to-leading order without isospin-symmetry-breaking 3-nucleon interactions
and a non-perturbative treatment of the Coulomb interaction. The conclusion
highlights the significance of the regularization method since a comparison to
previous work is contradictory if the difference in those methods is not
considered.
  With a perturbative Coulomb interaction, as suggested by dimensional
analysis, we find the Helium-3 system properly renormalized, too.
  For both treatments, renormalization-scheme independence of the effective
field theory is demonstrated by regulating the potential and a variation of the
associated cutoff.


  We calculate NLTE line-driven wind models of selected O stars in the spectral
range of O4 to O9 in the Small Magellanic Cloud (SMC). We compare predicted
basic wind properties, i.e. the terminal velocity and the mass-loss rate with
values derived from observation. We found relatively good agreement between
theoretical and observed terminal velocities. On the other hand, predicted
mass-loss rates and mass-loss rates derived from observation are in a good
agreement only for higher mass-loss rates. Theoretical mass-loss rates lower
than approximately 10^{-7} M_o/year are significantly higher than those derived
from observation. These results confirm the previously reported problem of weak
winds, since our calculated mass-loss rates are in a fair agreement with
predictions of Vink et al. (2001). We study multicomponent models for these
winds. For this purpose we develop a more detailed description of wind
decoupling. We show that the instability connected with the decoupling of
individual wind elements may occur for low-density winds. In the case of winds
with very low observed mass-loss rate the multicomponent effects are important
for the wind structure, however this is not able to consistently explain the
difference between predicted mass-loss rate and mass-loss rate derived from
observation for these stars. Similar to previous studies, we found the
dependence of wind parameters on the metallicity. We conclude that the wind
mass-loss rate significantly increases with metallicity as dM/dt ~ Z^0.67,
whereas the wind terminal velocity on average depends on metallicity only
slightly, namely v_infty ~ Z^0.06 (for studied stars).


  We examine the autocorrelation function of the $^{235}$U(n,f) reaction with a
view to quantify the presence of intermediate structure in the cross section.
Fluctuations due to compound nucleus resonances on the eV energy scale are
clearly visible up to $\sim100$ keV neutron energies. Structure on the one-keV
energy scale is not present as a systematic feature of the correlation
function, although it is present in the data covering the region around 20 keV.


  The game of Cops and Robber is traditionally played on a finite graph. The
purpose of this paper is to introduce and analyse the game that is played on an
arbitrary geodesic space (a compact, path-connected space endowed with
intrinsic metric). It is shown that the game played on metric graphs is
essentially the same as the discrete game played on abstract graphs and that
for every compact geodesic surface there is an integer $c$ such that $c$ cops
can win the game against one robber, and $c$ only depends on the genus $g$ of
the surface. It is shown that $c=3$ for orientable surfaces of genus $0$ or $1$
and nonorientable surfaces of crosscap number $1$ or $2$ (with any number of
boundary components) and that $c=O(g)$ and that $c=\Omega(\sqrt{g})$ when the
genus $g$ is larger. The main motivation for discussing this game is to view
the cop number (the minimum number of cops needed to catch the robber) as a new
geometric invariant describing how complex is the geodesic space.


  Here, Darboux's classical results about transformations with differential
substitutions for hyperbolic equations are extended to the case of parabolic
equations of the form $L u = \big(D^2_{x} + a(x,y) D_x + b(x,y) D_y +
c(x,y)\big)u=0$.
  We prove a general Theorem that provides a way to determine transformations
for parabolic equations shown above. It turnes out that transforming operators
$M$ of some higher order can be always represented as a composition of some
first-order operators that consecutively define a series of transformations.
  Existence of inverse transformations implies some differential constrains on
the coefficients of the initial operator. We show that these relations can
imply famous integrable equations, in particular, the Boussinesq equation.


  The Boltzmann equation is an integro-differential equation which describes
the density function of the distribution of the velocities of the molecules of
dilute monoatomic gases under the assumption that the energy is only
transferred via collisions between the molecules. In 1956 Kac studied the
Boltzmann equation and defined a property of the density function that he
called the "Boltzmann property" which describes the behavior of the density
function at a given fixed time as the number of particles tends to infinity.
The Boltzmann property has been studied extensively since then, and now it is
simply called chaos, or Kac's chaos. On the other hand, in ergodic theory,
chaos usually refers to the mixing properties of a dynamical system as time
tends to infinity. A relationship is derived between Kac's chaos and the notion
of mixing.


  In this paper, we propose a network scenario where the baseband processes of
the virtual small cells powered solely by energy harvesters and batteries can
be opportunistically executed in a grid-connected edge computing server,
co-located at the macro base station site. We state the corresponding energy
minimization problem and propose multi-agent Reinforcement Learning (RL) to
solve it. Distributed Fuzzy Q-Learning and Q-Learning on-line algorithms are
tailored for our purposes. Coordination among the multiple agents is achieved
by broadcasting system level information to the independent learners. The
evaluation of the network performance confirms that coordination via
broadcasting may achieve higher system level gains than un-coordinated
solutions and cumulative rewards closer to the off-line bounds. Finally, our
analysis permits to evaluate the benefits of continuous state/action
representation for the learning algorithms in terms of faster convergence,
higher cumulative reward and more adaptation to changing environments.


  On a static spacetime, the solutions of the Dirac equation are generated by a
time-independent Hamiltonian. We study this Hamiltonian and characterize the
split into positive and negative energy. We use it to find explicit expressions
for advanced and retarded fundamental solutions and for the propagator.
Finally, we use a fermion Fock space based on the positive/negative energy
split to define a Dirac quantum field operator whose commutator is the
propagator.


  To strike a balance between energy efficiency and data quality control, this
paper proposes a sensor censoring scheme for distributed sparse signal recovery
via compressive-sensing based wireless sensor networks. In the proposed
approach, each sensor node employs a sparse sensing vector with known support
for data compression, meanwhile enabling making local inference about the
unknown support of the sparse signal vector of interest. This naturally leads
to a ternary censoring protocol, whereby each sensor (i) directly transmits the
real-valued compressed data if the sensing vector support is detected to be
overlapped with the signal support, (ii) sends a one-bit hard decision if empty
support overlap is inferred, (iii) keeps silent if the measurement is judged to
be uninformative. Our design then aims at minimizing the error probability that
empty support overlap is decided but otherwise is true, subject to the
constraints on a tolerable false-alarm probability that non-empty support
overlap is decided but otherwise is true, and a target censoring rate. We
derive a closed-form formula of the optimal censoring rule; a low complexity
implementation using bi-section search is also developed. In addition, the
average communication cost is analyzed. To aid global signal reconstruction
under the proposed censoring framework, we propose a modified l_1-minimization
based algorithm, which exploits certain sparse nature of the hard decision
vector received at the fusion center. Analytic performance guarantees,
characterized in terms of the restricted isometry property, are also derived.
Computer simulations are used to illustrate the performance of the proposed
scheme.


  In "Self-adjoint Operators as Functions I: Lattices, Galois Connections, and
the Spectral Order" [arXiv:1208.4724], it was shown that self-adjoint operators
affiliated with a von Neumann algebra N can equivalently be described as
certain real-valued functions on the projection lattice P(N) of the algebra,
which we call q-observable functions. Here, we show that q-observable functions
can be interpreted as generalised quantile functions for quantum observables
interpreted as random variables. More generally, when L is a complete
meet-semilattice, we show that L-valued cumulative distribution functions and
the corresponding L-quantile functions form a Galois connection. An ordinary
CDF can be written as an L-CDF composed with a state. For classical
probability, one picks L=B(\Omega), the complete Boolean algebra of measurable
subsets modulo null sets of a measurable space \Omega. For quantum probability,
one uses L=P(N), the projection lattice of a nonabelian von Neumann algebra N.
Moreover, using some constructions from the topos approach to quantum theory,
we show that there is a joint sample space for all quantum observables, despite
no-go results such as the Kochen-Specker theorem. Specifically, the spectral
presheaf \Sigma\ of a von Neumann algebra N, which is not a mere set, but a
presheaf (i.e., a 'varying set'), plays the role of the sample space. The
relevant meet-semilattice L in this case is the complete bi-Heyting algebra of
clopen subobjects of \Sigma. We show that using the spectral presheaf \Sigma\
and associated structures, quantum probability can be formulated in a way that
is structurally very similar to classical probability.


  We discuss the observational manifestations of an isolated stellar mass black
hole - the recently discovered microlens MOA-2011-BLG-191/OGLE-2011-BLG-0462.
The data available for this object are used to calculate the density,
temperature, and sound speed in its local interstellar medium, as well as
estimate its velocity. We obtain the accretion rate and luminosity of the
object, and construct its theoretical spectrum. A comparison of the spectrum
with the sensitivity levels of current and future instruments in different
frequency ranges has shown that direct detection of the emission from this
black hole is possible for several future observing missions.


  We study the incidence of nuclear obscuration on a complete sample of 1310
AGN selected on the basis of their rest-frame 2-10 keV X-ray flux from the
XMM-COSMOS survey, in the redshift range 0.3<z<3.5. We classify the AGN as
obscured or un-obscured on the basis of either the optical spectral properties
and the overall SED or the shape of the X-ray spectrum. The two classifications
agree in about 70% of the objects, and the remaining 30% can be further
subdivided into two distinct classes: at low luminosities X-ray un-obscured AGN
do not always show signs of broad lines or blue/UV continuum emission in their
optical spectra, most likely due to galaxy dilution effects; at high
luminosities broad line AGN may have absorbed X-ray spectra, which hints at an
increased incidence of small-scale (sub-parsec) dust-free obscuration. We
confirm that the fraction of obscured AGN is a decreasing function of the
intrinsic X-ray luminosity, while the incidence of absorption shows significant
evolution only for the most luminous AGN, which appear to be more commonly
obscured at higher redshift. We find no significant difference between the mean
stellar masses and star formation rates of obscured and un-obscured AGN hosts.
We conclude that the physical state of the medium responsible for obscuration
in AGN is complex, and mainly determined by the radiation environment (nuclear
luminosity) in a small region enclosed within the gravitational sphere of
influence of the central black hole, but is largely insensitive to the wider
scale galactic conditions.


  The primate visual system has inspired the development of deep artificial
neural networks, which have revolutionized the computer vision domain. Yet
these networks are much less energy-efficient than their biological
counterparts, and they are typically trained with backpropagation, which is
extremely data-hungry. To address these limitations, we used a deep
convolutional spiking neural network (DCSNN) and a latency-coding scheme. We
trained it using a combination of spike-timing-dependent plasticity (STDP) for
the lower layers and reward-modulated STDP (R-STDP) for the higher ones. In
short, with R-STDP a correct (resp. incorrect) decision leads to STDP (resp.
anti-STDP). This approach led to an accuracy of $97.2\%$ on MNIST, without
requiring an external classifier. In addition, we demonstrated that R-STDP
extracts features that are diagnostic for the task at hand, and discards the
other ones, whereas STDP extracts any feature that repeats. Finally, our
approach is biologically plausible, hardware friendly, and energy-efficient.


  This article provides an assessment of the causal effect of customer
relationship management (CRM) applications on one-to-one marketing
effectiveness. We use a potential outcomes based propensity score approach to
assess this causal effect. We find that firms using CRM systems have greater
levels of one-to-one marketing effectiveness. We discuss the strengths and
challenges of using the propensity score approach to design and execute CRM
related observational studies. We also discuss the applicability of the
framework in this paper to study typical causal questions in business and
electronic commerce research at the firm, individual and economy levels, and to
clarify the assumptions that researchers must make to infer causality from
observational data.


  Coherent discourse is distinguished from a mere collection of utterances by
the satisfaction of a diverse set of constraints, for example choice of
expression, logical relation between denoted events, and implicit compatibility
with world-knowledge. Do neural language models encode such constraints? We
design an extendable set of test suites addressing different aspects of
discourse and dialogue coherence. Unlike most previous coherence evaluation
studies, we address specific linguistic devices beyond sentence order
perturbations, allowing for a more fine-grained analysis of what constitutes
coherence and what neural models trained on a language modelling objective do
encode. Extending the targeted evaluation paradigm for neural language models
(Marvin and Linzen, 2018) to phenomena beyond syntax, we show that this
paradigm is equally suited to evaluate linguistic qualities that contribute to
the notion of coherence.


  We compute the open Gromov-Witten disk invariants and the relative quantum
cohomology of the Chiang Lagrangian $L_\triangle \subset \mathbb{C}P^3$. Since
$L_\triangle$ is not fixed by any anti-symplectic involution, the invariants
may augment straightforward $J$-holomorphic disk counts with correction terms
arising from the formalism of Fukaya $A_\infty$-algebras and bounding cochains.
These correction terms are shown in fact to be non-trivial for many invariants.
Moreover, examples of non-vanishing mixed disk and sphere invariants are
obtained.
  We characterize a class of open Gromov-Witten invariants, called basic, which
coincide with straightforward counts of $J$-holomorphic disks. Basic invariants
for the Chiang Lagrangian are computed using the theory of axial disks
developed by Evans-Lekili and Smith in the context of Floer cohomology. The
open WDVV equations give recursive relations which determine all invariants
from the basic ones. The denominators of all invariants are observed to be
powers of $2$ indicating a non-trivial arithmetic structure of the open WDVV
equations. The magnitude of invariants is not monotonically increasing with
degree. Periodic behavior is observed with periods $8$ and $16.$


  Extending the method successful for one-loop integrals, the computation of
two-loop diagrams with general internal masses is discussed. For the two-loop
vertex of non-planar type, as an example, we show a calculation related to
\(Z^0\rightarrow t \bar t\) vertex.


  In this chapter, we will explore the cloud-outsourced privacy-preserving
computation of a controller on encrypted measurements from a (possibly
distributed) system, taking into account the challenges introduced by the
dynamical nature of the data. The privacy notion used in this work is that of
cryptographic multi-party privacy, i.e., the computation of a functionality
should not reveal anything more than what can be inferred only from the inputs
and outputs of the functionality. The main theoretical concept used towards
this goal is Homomorphic Encryption, which allows the evaluation of sums and
products on encrypted data, and, when combined with other cryptographic
techniques, such as Secret Sharing, results in a powerful tool for solving a
wide range of secure multi-party problems. We will rigorously define these
concepts and discuss how multi-party privacy can be enforced in the
implementation of a Model Predictive Controller, which encompasses computing
stabilizing control actions by solving an optimization problem on encrypted
data.


  For any given spacetime the choice of time coordinate is undetermined. A
particular choice is the absolute time associated with a preferred vector
field. Using the absolute time Hamilton's equations are
  $- (\delta H_{c})/(\delta q)=\dot{\pi}+\Theta\pi,
  $+ (\delta H_{c})/(\delta \pi)=\dot{q}$,
  where $\Theta = V^{a}_{.;a}$ is the expansion of the vector field. Thus there
is a hitherto unnoticed term in the expansion of the preferred vector field.
Hamilton's equations can be used to describe fluid motion. In this case the
absolute time is the time associated with the fluid's co-moving vector. As
measured by this absolute time the expansion term is present. Similarly in
cosmology, each observer has a co-moving vector and Hamilton's equations again
have an expansion term. It is necessary to include the expansion term to
quantize systems such as the above by the canonical method of replacing Dirac
brackets by commutators. Hamilton's equations in this form do not have a
corresponding sympletic form. Replacing the expansion by a particle number
$N\equiv exp(-\int\Theta d \ta)$ and introducing the particle numbers conjugate
momentum $\pi^{N}$ the standard sympletic form can be recovered with two extra
fields N and $\pi^N$. Briefly the possibility of a non-standard sympletic form
and the further possibility of there being a non-zero Finsler curvature
corresponding to this are looked at.


  Spin excitation spectrum of the double-exchange model is studied in the
presence of randomness. Spin wave approximation in the ground state shows that
the randomness significantly modifies the spectrum from the cosine-like one in
the pure system to that with anomalies such as broadening, anti-crossing and
gap opening. The origin of anomalies is speculated to be modulation of
effective ferromagnetic coupling by the Friedel oscillation. These anomalies
qualitatively reproduce the spin excitation spectrum in colossal
magnetoresistance manganites whose Curie temperatures are relatively low. Our
results suggest that randomness control is an important notion to understand
effects of the A-site substitution which has previously been understood as the
bandwidth control.


  We show that top-quark pairs are produced in an essentially unique spin
configuration in polarized $e^+e^-$ colliders at all energies above the
threshold region. Since the directions of the electroweak decay products of
polarized top-quarks are strongly correlated to the top-quark spin axis, this
unique spin configuration leads to a distinctive topology for top-quark pair
events which can be used to constrain anomalous couplings to the top-quark. A
significant interference effect between the {\it longitudinal} and {\it
transverse} W-bosons in the decay of polarized top-quarks is also discussed.
These results are obtained at leading order in perturbation theory but
radiative corrections are expected to be small.


  We have searched the core-jets pairs in the VLBI scales ($<$1 kpc), from
several VLBI catalogues, and find out 6 possible binary black hole (BBH)
candidates. We present the search result and analyze the candidates
preliminarily, and further study with multi-band VLBI observation and optical
line investigation would be carried out in the future.


  Requirements traceability is an important activity to reach an effective
requirements management method in the requirements engineering.
Requirement-to-Code Traceability Links (RtC-TLs) shape the relations between
requirement and source code artifacts. RtC-TLs can assist engineers to know
which parts of software code implement a specific requirement. In addition,
these links can assist engineers to keep a correct mental model of software,
and decreasing the risk of code quality degradation when requirements change
with time mainly in large sized and complex software. However, manually
recovering and preserving of these TLs puts an additional burden on engineers
and is error-prone, tedious, and costly task. This paper introduces YamenTrace,
an automatic approach and implementation to recover and visualize RtC-TLs in
Object-Oriented software based on Latent Semantic Indexing (LSI) and Formal
Concept Analysis (FCA). The originality of YamenTrace is that it exploits all
code identifier names, comments, and relations in TLs recovery process.
YamenTrace uses LSI to find textual similarity across software code and
requirements. While FCA employs to cluster similar code and requirements
together. Furthermore, YamenTrace gives a visualization of recovered TLs. To
validate YamenTrace, it applied on three case studies. The findings of this
evaluation prove the importance and performance of YamenTrace proposal as most
of RtC-TLs were correctly recovered and visualized.


  For the last decade, several probes have pointed to a cosmological tension
between the amplitude of density fluctuations extrapolated from the cosmic
microwave background within the standard cosmological model and the one
encapsulated by the $S_8$ parameter from large scale structure. The origin of
this $S_8$ tension has not yet been elucidated and may hint at systematics in
the data, unaccounted effects from baryonic physics, or new physics beyond the
standard model of cosmology. Baryonic physics may in principle provide a
nonlinear solution to the tension by suppressing the matter power spectrum more
strongly on nonlinear scales than is traditionally assumed. Such a solution
would not worsen the Hubble tension, contrary to many other proposed solutions
to the $S_8$ tension. However, no realistic baryonic feedback in hydrodynamical
simulations provides the needed suppression as a function of redshift. Here, we
point out that a scale-dependence of local-type primordial non-Gaussianities
(PNG), with significant PNG at scales of a few Mpc, can provide the needed
suppression, since such PNG can suppress the power spectrum at slightly larger
scales than baryons do. We demonstrate this by devising collisionless numerical
simulations of structure formation in boxes of 0.5 Gpc/$h$ with scale-dependent
local-type PNG. Our simple models show that, as a proof of principle,
scale-dependent PNG, with a Gaussian random field for primordial density
fluctuations on large scales and $f_{\rm NL} \simeq -300$ at $\lesssim 10$ Mpc
scales, together with state-of-the-art baryonification of the matter power
spectrum, can in principle solve the $S_8$ tension. The $S_8$ tension would
then be a smoking-gun of non-trivial inflationary physics.


  We have investigated the properties of the ergosphere and the energy
extraction by Penrose process in a Konoplya-Zhidenko rotating non-Kerr black
hole spacetime. We find that the ergosphere becomes thin and the maximum
efficiency of energy extraction decreases as the deformation parameter
increases. For the case with $a<M$, the positive deformation parameter yields
that the maximum efficiency is lower than that in the Kerr black hole with the
same rotation parameter. However, for the superspinning case with $a>M$, we
find that the maximum efficiency can reach so high that it is almost unlimited
as the positive deformation parameter is close to zero, which is a new feature
of energy extraction in such kind of rotating non-Kerr black hole spacetime.


  We consider a one-dimensional directional array of diffusively coupled
oscillators. They are perturbed by the injection of a small additive noise,
typically orders of magnitude smaller than the oscillation amplitude, and the
system is studied in a region of the parameters that would yield deterministic
synchronization. Non normal directed couplings seed a coherent amplification of
the perturbation: this latter manifests as a modulation, transversal to the
limit cycle, which gains in potency node after node. If the lattice extends
long enough, the initial synchrony gets eventually lost and the system moves
toward a non trivial attractor, which can be analytically characterized as an
asymptotic splay state. The noise assisted instability, ultimately vehiculated
and amplified by the non normal nature of the imposed couplings, eventually
destabilizes also this second attractor. This phenomenon yields spatiotemporal
patterns, which cannot be anticipated by a conventional linear stability
analysis.


  In this paper, we use reduction by extended actions to give a construction of
transitive Courant algebroids from string classes. We prove that T-duality
commutes with the reductions and thereby determine global conditions for the
existence of T-duals in heterotic string theory. In particular we find that
T-duality exchanges string structures and gives an isomorphism of transitive
Courant algebroids. Consequently we derive the T-duality transformation for
generalised metrics and show that the heterotic Einstein equations are
preserved. The presence of string structures significantly extends the domain
of applicability of T-duality and this is illustrated by several classes of
examples.


  Why is the lithium abundance constant in the so-called lithium plateau while
all predictions suggest that it should vary from star to star? Can we find a
"lithium attractor" which would remain stable in halo stars while fundamental
parameters (M_*, Teff, [Fe/H]) vary?


  Based on an idea in [Gan--Savin, Represent. Theory (2005)], we give a
classification of minimal representations of connected simple real Lie groups
not of type $A$. Actually, we prove that there exist no new minimal
representations up to infinitesimal equivalence.


  CodEval is a code evaluation tool that integrates with the Canvas Learning
Management System to automatically evaluates students' work within a few
minutes of the submission. This early feedback allows students to catch and
correct problems in their submissions before their submission is graded and
gives them a clear idea of the quality of their submission. CodEval handles the
tedious aspects of grading, such as compiling and running tests, leaving
graders more time to spend on the qualitative aspect of grading.
  Before using CodEval, instructors would not have a clear view of the
student's comprehension of the concept evaluated by the assignment until after
the due date. CodeEval helps instructors identify and address the gaps in
students' understanding and thus helps more students successfully complete the
assignment.
  We implemented CodEval using Python using the public Canvas API. Any
instructor or grader for a Canvas course can use CodEval to automatically
evaluate submissions for programming assignments. We developed a syntax to
express requirements of submissions such as compilation parameters, inputs,
outputs, command-line arguments, timeouts, exit codes, functions used, files
generated, output validators, and more. We have made CodEval open source.
  CodEval is an easy tool for students, graders, and instructors and seamlessly
integrates with Canvas. We share our experience with using CodEval in two
classes with a total of 90 students and multiple coding assignments.


  Context. Departures from local thermodynamic equilibrium (LTE) distort the
calcium abundance derived from stellar spectra in various ways, depending on
the lines used and the stellar atmospheric parameters. The collection of atomic
data adopted in non-LTE (NLTE) calculations must be sufficiently complete and
accurate. Aims. We derive NLTE abundances from high-quality observations and
reliable stellar parameters using a model atom built afresh for this work, and
check the consistency of our results over a wide wavelength range with
transitions of atomic and singly ionised calcium. Methods. We built and tested
Ca i and Ca ii model atoms with state-of-the-art radiative and collisional
data, and tested their performance deriving the Ca abundance in three benchmark
stars: Procyon, the Sun, and Arcturus. We have excellent-quality observations
and accurate stellar parameters for these stars. Two methods to derive the LTE
/ NLTE abundances were used and compared. The LTE / NLTE centre-to-limb
variation (CLV) of Ca lines in the Sun was also investigated. Results. The two
methods used give similar results in all three stars. Several discrepancies
found in LTE do not appear in our NLTE results; in particular the agreement
between abundances in the visual and infra-red (IR) and the Ca i and Ca ii
ionisation balance is improved overall, although substantial line-to-line
scatter remains. The CLV of the calcium lines around 6165 Angs can be partially
reproduced. We suspect differences between our modelling and CLV results are
due to inhomogeneities in the atmosphere that require 3D modelling.


  Let $\mu$ be a Borel probability measure associated with an iterated function
system consisting of a countably infinite number of contracting similarities
and an infinite probability vector. In this paper, we study the quantization
dimension of the measure $\mu$ with respect to the geometric mean error. The
quantization for infinite systems is different from the well-known finite case
investigated by Graf and Luschgy. That is, many tools which are used in the
finite setting, for example, existence of finite maximal antichains, fail in
the infinite case. We prove that the quantization dimension of the measure
$\mu$ is equal to its Hausdorff dimension which extends a well-known result of
Graf and Luschgy for the finite case to an infinite setting. In the last
section, we discuss the stability of quantization dimension for infinite
systems.


  Sublimative outgassing of comets produces torques that alter the rotation
state of their nuclei. Recently, parameterized sublimative torque models have
been developed to study rotation state changes of individual comet nuclei and
populations of cometary bodies. However, these models simplify the interactions
between the escaping gas and cometary surface into only a few parameters that
hide the details of these complex interactions. Here we directly compare the
X-parameter model (Samarasinha & Mueller, 2013) with the SYORP model (Steckloff
& Jacobson, 2016) to tease out insights into the details of the gas-surface
interactions driving sublimative torques. We find that, for both of these
models to accurately model sublimative torques, the number of sublimating
molecules that contribute to the net torque is largely independent of the
detailed shape and activity of the nucleus, but rather depends primarily on the
size of the nucleus and the effective heliocentric distance of the comet. We
suggest that cometary activity must be largely restricted to regions of steep
gravitational surface slopes (above the angle of repose), where mass wasting
can refresh activity by shedding mantles of refractory materials and exposing
fresh volatiles. We propose a new classification scheme for comets based on the
frequency of this mass-wasting process (relative to the timescale of activity
fading): quasi-equilibrium, episodic, quasi-dormant, and extinct.


  The cosmological constraining power of modern galaxy cluster catalogs can be
improved by obtaining low-scatter mass proxy measurements for even a small
fraction of sources. In the context of large upcoming surveys that will reveal
the cluster population down to the group scale and out to high redshifts,
efficient strategies for obtaining such mass proxies will be valuable. In this
work, we use high-quality weak lensing and X-ray mass estimates for massive
clusters in current X-ray selected catalogs to revisit the scaling relations of
the projected, center-excised X-ray luminosity ($L_{ce}$), which previous work
suggests correlates tightly with total mass. Our data confirm that this is the
case, with $L_{ce}$ having an intrinsic scatter at fixed mass comparable to
that of gas mass, temperature or $Y_X$. Compared to these other proxies,
however, $L_{ce}$ is less susceptible to systematic uncertainties due to
background modeling, and can be measured precisely with shorter exposures. This
opens up the possibility of using $L_{ce}$ to estimate masses for large numbers
of clusters discovered by new X-ray surveys (e.g. eROSITA) directly from the
survey data, as well as for clusters discovered at other wavelengths, with
relatively short follow-up observations. We describe a simple procedure for
making such estimates from X-ray surface brightness data, and comment on the
spatial resolution required to apply this method as a function of cluster mass
and redshift. We also explore the potential impact of Chandra and XMM-Newton
follow-up observations over the next decade on dark energy constraints from new
cluster surveys.


  This article has two main objectives. The first one is to show
Kuttler-Sigillito's type inequalities involving the mixed Neumann-Dirichlet,
mixed Steklov-Dirichlet, and mixed Robin-Dirichlet eigenvalue problems. We will
provide examples on e.g. squares and balls for the inequalities presented. Next
we will show a Rellich identity for the mixed Neumann-Dirichlet problem. This
identity will be used to prove a Rellich-Christianson identity for the
Neumann-Dirichlet problem.


  The classical Remez inequality bounds the supremum of a bounded-degree
polynomial on an interval $X$ by its supremum on any subset $Y\subset X$ of
positive Lebesgue measure.
  There are many multivariate generalizations of the Remez inequality, but most
have constants that depend strongly on dimension.
  Here we show that a broad class of domains $X$ and test sets $Y$ -- termed
\emph{norm designs} -- enjoy dimension-free Remez-type estimates.
  Instantiations of this theorem allow us for example \emph{a}) to bound the
supremum of an $n$-variate degree-$d$ polynomial on the solid cube $[0,1]^n$ by
its supremum on the regular grid $\{0,1/d,2/d,\ldots, 1\}^n$ independent of
dimension; and \emph{b}) in the case of a degree-$d$ polynomial
$f:\mathbf{Z}_K^n\to\mathbf{C}$ on the $n$-fold product of cyclic groups of
order $K$, to show the supremum of $f$ does not increase by more than
$\mathcal{O}(\log K)^{2d}$ when $f$ is extended to the polytorus as
$f:\mathbf{T}^n\to\mathbf{C}$.


  We study the first year of the eBOSS quasar sample in the redshift range
$0.9<z<2.2$ which includes 68,772 homogeneously selected quasars. We show that
the main source of systematics in the evaluation of the correlation function
arises from inhomogeneities in the quasar target selection, particularly
related to the extinction and depth of the imaging data used for targeting. We
propose a weighting scheme that mitigates these systematics. We measure the
quasar correlation function and provide the most accurate measurement to date
of the quasar bias in this redshift range, $b_Q = 2.45 \pm 0.05$ at $\bar
z=1.55$, together with its evolution with redshift. We use this information to
determine the minimum mass of the halo hosting the quasars and the
characteristic halo mass, which we find to be both independent of redshift
within statistical error. Using a recently-measured quasar-luminosity-function
we also determine the quasar duty cycle. The size of this first year sample is
insufficient to detect any luminosity dependence to quasar clustering and this
issue should be further studied with the final $\sim$500,000 eBOSS quasar
sample.


  This paper calculates the quantized energy levels of the hydrogen atom, using
a metaplectic-c prequantization bundle and a definition of a quantized energy
level that was introduced by the author in a previous paper. The calculation
makes use of a computational technique also demonstrated in that paper. The
result is consistent with the standard quantum mechanical prediction. Unlike
other treatments of the hydrogen atom, this approach does not require the
construction of the symplectic reduction, but takes place over a regular level
set of the energy function. The Ligon-Schaaf regularization map is used to
transform the problem into one of determining the quantized energy levels of a
free particle on the tangent bundle for S3.


  Convolutional neural networks (CNNs) have shown very promising performance in
recent years for different problems, including object recognition, face
recognition, medical image analysis, etc. However, generally the trained CNN
models are tested over the test set which is very similar to the trained set.
The generalizability and robustness of the CNN models are very important
aspects to make it to work for the unseen data. In this letter, we study the
performance of CNN models over the high and low frequency information of the
images. We observe that the trained CNN fails to generalize over the high and
low frequency images. In order to make the CNN robust against high and low
frequency images, we propose the stochastic filtering based data augmentation
during training. A satisfactory performance improvement has been observed in
terms of the high and low frequency generalization and robustness with the
proposed stochastic filtering based data augmentation approach. The
experimentations are performed using ResNet50 model over the CIFAR-10 dataset
and ResNet101 model over Tiny-ImageNet dataset.


  This document describes the Data Access Layer Interface (DALI). DALI defines
the base web service interface common to all Data Access Layer (DAL) services.
This standard defines the behaviour of common resources, the meaning and use of
common parameters, success and error responses, and DAL service registration.
The goal of this specification is to define the common elements that are shared
across DAL services in order to foster consistency across concrete DAL service
specifications and to enable standard re-usable client and service
implementations and libraries to be written and widely adopted.


  State redistribution allows one party to optimally send part of her state to
another party. Here we show that this can be derived simply from two
applications of coherent state-merging. This provides a protocol whereby a
middle party acts as a relay station to help another party more efficiently
transfer quantum states. This also gives a protocol for state splitting and the
reverse Shannon theorem (assisted or unassisted by side information), and
allows one to use less classical communication for partial state-merging using
a sub-protocol we call ebit repackaging. Thus state-merging generates the other
primitives of quantum communication theory, reducing the hierarchy between
members of the first family of quantum protocols.


  The upcoming Fermilab E989 experiment will measure the muon anomalous
magnetic moment $a_{\mu}$ . This measurement is motivated by the previous
measurement performed in 2001 by the BNL E821 experiment that reported a 3-4
standard deviation discrepancy between the measured value and the Standard
Model prediction. The new measurement at Fermilab aims to improve the precision
by a factor of four reducing the total uncertainty from 540 parts per billion
(BNL E821) to 140 parts per billion (Fermilab E989). This paper gives the
status of the experiment.


  According to Einstein-Maxwell-Dilaton theory, the dilaton field $\psi$ can be
produced by electromagnetic fields with non-zero Maxwell invariant. So
electromagnetic wave propagating in an external electromagnetic field is a
typical source of dilaton radiation. For study dilaton photoproduction in
astrophysical conditions it's interesting to consider plane elliptically
polarized electromagnetic wave propagating in the electromagnetic field of
magnetic dipole ${\bf m}$ of pulsars and magnetars. The dilation field equation
is solved in case $|\psi| \ll 1$. The angular distribution dilaton radiation is
studied in every point of space. It's shown that spectral composition of
dilatons is similar to spectral composition of plane electromagnetic wave.
Amount of dilaton energy radiated in time and all directions is greatest in
condition $(B_1^2-B_2^2)(m_x^2-m_y^2)\geq 0,$ where $B_1$ and $B_2$ are
electromagnetic wave amplitudes along the axes of polarization ellipse. This
condition is valid for many neutron star systems.


  The archetypical strongly correlated Mott-phenomena compound V2O3 is known to
show a paramagnetic metal-insulator transition driven by doping with chromium
atoms and/or (negative) pressure. Via charge self-consistent density-functional
theory+dynamical mean-field theory calculations we demonstrate that these two
routes cannot be understood as equivalent. To this end, the explicit
description of Cr-doped V2O3 by means of supercell calculations and the virtual
crystal approximation is performed. Already the sole introduction of chromium's
additional electron to the system is shown to modify the overall correlated
electronic structure substantially. Correlation-induced charge transfers
between Cr and the remaining V ions occur and the transition-metal orbital
polarization is increased by the electron doping, in close agreement with
experimental findings.


  Distributed medium access control (MAC) protocols are essential for the
proliferation of low cost, decentralized wireless local area networks (WLANs).
Most MAC protocols are designed with the presumption that nodes comply with
prescribed rules. However, selfish nodes have natural motives to manipulate
protocols in order to improve their own performance. This often degrades the
performance of other nodes as well as that of the overall system. In this work,
we propose a class of protocols that limit the performance gain which nodes can
obtain through selfish manipulation while incurring only a small efficiency
loss. The proposed protocols are based on the idea of a review strategy, with
which nodes collect signals about the actions of other nodes over a period of
time, use a statistical test to infer whether or not other nodes are following
the prescribed protocol, and trigger a punishment if a departure from the
protocol is perceived. We consider the cases of private and public signals and
provide analytical and numerical results to demonstrate the properties of the
proposed protocols.


  The determination of the sample size required by a crossover trial typically
depends on the specification of one or more variance components. Uncertainty
about the value of these parameters at the design stage means that there is
often a risk a trial may be under- or over-powered. For many study designs,
this problem has been addressed by considering adaptive design methodology that
allows for the re-estimation of the required sample size during a trial. Here,
we propose and compare several approaches for this in multi-treatment crossover
trials. Specifically, regulators favour re-estimation procedures to maintain
the blinding of the treatment allocations. We therefore develop blinded
estimators for the within and between person variances, following simple or
block randomisation. We demonstrate that, provided an equal number of patients
are allocated to sequences that are balanced for period, the proposed
estimators following block randomisation are unbiased. We further provide a
formula for the bias of the estimators following simple randomisation. The
performance of these procedures, along with that of an unblinded approach, is
then examined utilising three motivating examples, including one based on a
recently completed four-treatment four-period crossover trial. Simulation
results show that the performance of the proposed blinded procedures is in many
cases similar to that of the unblinded approach, and thus they are an
attractive alternative.


  The most popular model for short gamma-ray bursts (sGRBs) involves the
coalescence of binary neutron stars. Because the progenitor is actually hidden
from view, we must consider under which circumstances such merging systems are
capable of producing a successful sGRB. Soon after coalescence, winds are
launched from the merger remnant. In this paper, we use realistic wind profiles
derived from global merger simulations in order to investigate the interaction
of sGRB jets with these winds using numerical simulations. We analyze the
conditions for which these axisymmetric winds permit relativistic jets to
breakout and produce a sGRB. We find that jets with luminosities comparable to
those observed in sGRBs are only successful when their half-opening angles are
below ~20{\deg}. This jet collimation mechanism leads to a simple physical
interpretation of the luminosities and opening angles inferred for sGRBs. If
wide, low luminosity jets are observed, they might be indicative of a different
progenitor avenue such as the merger of a neutron star with a black hole. We
also use the observed durations of sGRB to place constraints on the lifetime of
the wind phase, which is determined by the time it takes the jet to breakout.
In all cases we find that the derived limits argue against completely stable
remnants for binary neutron star mergers that produce sGRBs.


  Analogues of classical combinatorial identities for elementary and
homogeneous symmetric functions with coefficients in Yanigian are discussed. As
a corollary, similar relations are deduced for shifted Schur functions.


  Let $\mathcal S^2$ be the Stepanov space and let $ \lambda_n\uparrow\infty$.
Let $(a_n)_{n\ge 1}$ be satisfying Wiener's condition $A:= \sum_{n\ge 1}
\big(\sum_{k\, :\, n\le \lambda_k \le n+1}|a_k|\big)^2 <\infty$. We prove that
$\big\| \sup_{N\ge 1} \big|\sum_{n=1}^Na_n{\rm e}^{i\lambda_n t}\big| \,
\big\|_{\mathcal S^2}\le C\, A^{1/2} $ where $C>0$ denotes a universal
constant. Moreover, the series $\sum_{n\ge 1} a_n{\rm e}^{it\lambda_n }$
converges for $\lambda$-a.e. $t\in \mathbb R$. This contains as a special case
Hedenmalm and Saksman result for Dirichlet series. We also obtain maximal
inequalities for corresponding series of dilates. Let $1\le p,q\le 2$ be such
that $1/p+1/q=3/2$. Then for any sequence $(\alpha_n)_{n\ge 1}$ and
$(\beta_n)_{n\ge 1}$ of complex numbers such that $K:=\sum_{n\ge 1}
\big(\sum_{k\,:\, n\le \lambda_k< n+1}|\alpha_k|\,\big)^p <\infty$ and
$L:=\sum_{n\ge 1} \big(\sum_{k\,:\, n\le \mu_k< n+1} |\beta_k|\,\big)^q
<\infty$, we have $$ \Big\|\sup_{N\ge 1} \big|\sum_{n=1}^N \alpha_n D(\lambda_n
t)\big|\, \Big\|_{\mathcal S^2} \le C\, K^{1/p}\, L^{1/q }
  $$ where $D(t)= \sum_{n\ge 1}\beta_n {\rm e}^{i\mu_n t}$ is defined in
$\mathcal S^2$. Moreover, the series $\sum_{n\ge 1} \alpha_n D(\lambda_nt)$
converges in $\mathcal S^2$ and for $\lambda$-a.e. $t\in \mathbb R$. We further
show that if $\{\lambda_k, k\ge 1\}$ satisfies the following condition $$\sum_{
k\not=\ell\,,\, k'\not=\ell'\atop
(k,\ell)\neq(k',\ell')}\big(1-|(\lambda_k-\lambda_\ell)-(\lambda_{k'}-\lambda_{\ell'})
|\big)_+^2 \, <\infty,$$ then the series $\sum_{k} a_k {\rm e}^{i\lambda_kt}$
converges on a set of positive Lebesgue measure, only if the series
$\sum_{k=1}^\infty |a_k|^2$ converges. The above condition is in particular
fulfilled when $\{\lambda_k, k\ge 1\}$ is a Sidon sequence.


  We present a TTS neural network that is able to produce speech in multiple
languages. The proposed network is able to transfer a voice, which was
presented as a sample in a source language, into one of several target
languages. Training is done without using matching or parallel data, i.e.,
without samples of the same speaker in multiple languages, making the method
much more applicable. The conversion is based on learning a polyglot network
that has multiple per-language sub-networks and adding loss terms that preserve
the speaker's identity in multiple languages. We evaluate the proposed polyglot
neural network for three languages with a total of more than 400 speakers and
demonstrate convincing conversion capabilities.


  General relativity predicts mass and spin growth of an inspiralling black
hole due to an energy-momentum flux flowing through the black-hole horizon. The
leading-order terms of this horizon flux introduce 2.5 and 3.5 post-Newtonian
corrections to inspiral motions of binary black holes. The corrections may be
measurable by gravitational waves detectors. Since the proper improvements to
general relativity is still a mystery, it is possible that the true modified
gravity theory introduces negligible direct corrections to the geodesics of
test masses, while near horizon corrections are observable. We introduce a
parameterization to describe arbitrary mass and spin growth of inspiralling
black holes. Comparing signals of gravitational waves and a waveform model with
parameterized horizon flux corrections, deviations from general relativity can
be constrained. We simulate a set of gravitational waves signals following an
astrophysical distribution with horizon flux modifications. Then, we perform a
Bayesian analysis to obtain the expected constraints from the simulated
response of the Advanced LIGO-Virgo detector network to the simulated signals.
We show that the constraint can be improved by stacking multiple detections.
The constraints of modified horizon flux can be used to test a specific class
of modified gravity theories which predict dominant corrections near black-hole
horizons over other types of corrections to general relativity. To support
Hawking's area theorem at 90\% confidence level, over 10000 LIGO-Virgo
detections are required. Within the lifetime of LIGO and Einstein Telescope, a
future ground-based gravitational wave detector, near horizon corrections of
modified gravity theories are potentially detectable if one of the modified
gravity theory is true and the theory predicts a strong correction.


  We investigate the evolution of population density vector,
$\bold{u}=\left(u^1,\cdots,u^k\right)$, of $k$-species whose diffusion is
controlled by its absolute value $\left|\bold{u}\right|$. More precisely we
study the properties and asymptotic large time behaviour of solution
$\bold{u}=\left(u^1,\cdots,u^k\right)$ of degenerate parabolic system
\begin{equation*}
\left(u^i\right)_t=\nabla\cdot\left(\left|\bold{u}\right|^{m-1}\nabla
u^i\right) \qquad \mbox{for $m>1$ and $i=1,\cdots,k$}. \end{equation*} Under
some regularity assumption, we prove that the function $u^i$ which describes
the population density of $i$-th species with population $M_i$ converges to
$\frac{M_i}{\left|\bold{M}\right|}\mathcal{B}_{\left|\bold{M}\right|}$ in space
with two different approaches where $\mathcal{B}_{\left|\bold{M}\right|}$ is
the Barenblatt solution of the porous medium equation with $L^1$-mass
$\left|\bold{M}\right|=\sqrt{M_1^2+\cdots+M_k^2}$. \indent As an application of
the asymptotic behaviour, we establish a suitable harnack type inequality which
makes the spatial average of $u^i$ under control by the value of $u^i$ at one
point. We also find an 1-directional travelling wave type solutions and the
properties of solutions which has travelling wave behaviour at infinity.


  To learn how to behave, the current revolutionary generation of AIs must be
trained on vast quantities of published images, written works, and sounds, many
of which fall within the core subject matter of copyright law. To some, the use
of copyrighted works as training sets for AI is merely a transitory and
non-consumptive use that does not materially interfere with owners' content or
copyrights protecting it. Companies that use such content to train their AI
engine often believe such usage should be considered "fair use" under United
States law (sometimes known as "fair dealing" in other countries). By contrast,
many copyright owners, as well as their supporters, consider the incorporation
of copyrighted works into training sets for AI to constitute misappropriation
of owners' intellectual property, and, thus, decidedly not fair use under the
law. This debate is vital to the future trajectory of AI and its applications.
  In this article, we analyze the arguments in favor of, and against, viewing
the use of copyrighted works in training sets for AI as fair use. We call this
form of fair use "fair training". We identify both strong and spurious
arguments on both sides of this debate. In addition, we attempt to take a
broader perspective, weighing the societal costs (e.g., replacement of certain
forms of human employment) and benefits (e.g., the possibility of novel
AI-based approaches to global issues such as environmental disruption) of
allowing AI to make easy use of copyrighted works as training sets to
facilitate the development, improvement, adoption, and diffusion of AI.
Finally, we suggest that the debate over AI and copyrighted works may be a
tempest in a teapot when placed in the wider context of massive societal
challenges such as poverty, equality, climate change, and loss of biodiversity,
to which AI may be part of the solution.


  This paper presents a wearable electrotactile feedback system to enhance
contact information for mid-air interactions with virtual objects. In
particular, we propose the use of electrotactile feedback to render the
interpenetration distance between the user's finger and the virtual content is
touched. Our approach consists of modulating the perceived intensity (frequency
and pulse width modulation) of the electrotactile stimuli according to the
registered interpenetration distance. In a user study (N=21), we assessed the
performance of four different interpenetration feedback approaches:
electrotactile-only, visual-only, electrotactile and visual, and no
interpenetration feedback. First, the results showed that contact precision and
accuracy were significantly improved when using interpenetration feedback.
Second, and more interestingly, there were no significant differences between
visual and electrotactile feedback when the calibration was optimized and the
user was familiarized with electrotactile feedback. Taken together, these
results suggest that electrotactile feedback could be an efficient replacement
of visual feedback for enhancing contact information in virtual reality
avoiding the need of active visual focus and the rendering of additional visual
artefacts.


  The problem of extending a function $f$ defined on a training data
$\mathcal{C}$ on an unknown manifold $\mathbb{X}$ to the entire manifold and a
tubular neighborhood of this manifold is considered in this paper. For
$\mathbb{X}$ embedded in a high dimensional ambient Euclidean space
$\mathbb{R}^D$, a deep learning algorithm is developed for finding a local
coordinate system for the manifold {\bf without eigen--decomposition}, which
reduces the problem to the classical problem of function approximation on a low
dimensional cube. Deep nets (or multilayered neural networks) are proposed to
accomplish this approximation scheme by using the training data. Our methods do
not involve such optimization techniques as back--propagation, while assuring
optimal (a priori) error bounds on the output in terms of the number of
derivatives of the target function. In addition, these methods are universal,
in that they do not require a prior knowledge of the smoothness of the target
function, but adjust the accuracy of approximation locally and automatically,
depending only upon the local smoothness of the target function. Our ideas are
easily extended to solve both the pre--image problem and the out--of--sample
extension problem, with a priori bounds on the growth of the function thus
extended.


  The doping effect of Sr and transition metals Mn, Fe, Co into the direct-gap
semiconductor LaZnAsO has been investigated. Our results indicate that the
single phase ZrCuSiAs-type tetragonal crystal structure is preserved in
(La1-xSrx)(Zn1-xTMx)AsO (TM = Mn, Fe, Co) with the doping level up to x = 0.1.
While the system remains semiconducting, doping with Sr and Mn results in
ferromagnetic order with TC ~ 30K, and doping with Sr and Fe results in a spin
glass like state below ~6K with a saturation moment of ~0.02 muB/Fe, an order
of magnitude smaller than the ~0.4 muB/Mn of Sr and Mn doped samples. The same
type of magnetic state is observed neither for (Zn,Fe) substitution without
carrier doping, nor for Sr and Co doped specimens.


  The ability to navigate light signals in two-dimensional networks of
waveguide arrays is a prerequisite for the development of all-optical
integrated circuits for information processing and networking. In this article,
we present a theoretical analysis of bending losses in linear photonic lattices
with engineered couplings, and discuss possible ways for their minimization. In
contrast to previous work in the field, the lattices under consideration
operate in the linear regime, in the sense that discrete solitons cannot exist.
The present results suggest that the functionality of linear waveguide networks
can be extended to operations that go beyond the recently demonstrated
point-to-point transfer of signals, such as blocking, routing, logic functions,
etc.


  We study analytically and numerically the non-equilibrium dynamics of an
isolated interacting many-body quantum system following a random quench. We
model the system Hamiltonian by Embedded Gaussian Orthogonal Ensemble (EGOE) of
random matrices with one plus few-body interactions for fermions. EGOE are
paradigmatic models to study the crossover from integrability to chaos in
interacting many-body quantum systems. We obtain a generic formulation, based
on spectral variances, for describing relaxation dynamics of survival
probabilities as a function of rank of interactions. Our analytical results are
in good agreement with numerics.


  The ultrafast-rotating ($P_\mathrm{rot}\approx0.44 d$) fully convective
single M4 dwarf V374 Peg is a well-known laboratory for studying intense
stellar activity in a stable magnetic topology. As an observable proxy for the
stellar magnetic field, we study the stability of the light curve, and thus the
spot configuration. We also measure the occurrence rate of flares and coronal
mass ejections (CMEs). We analyse spectroscopic observations, $BV(RI)_C$
photometry covering 5 years, and additional $R_C$ photometry that expands the
temporal base over 16 years. The light curve suggests an almost rigid-body
rotation, and a spot configuration that is stable over about 16 years,
confirming the previous indications of a very stable magnetic field. We
observed small changes on a nightly timescale, and frequent flaring, including
a possible sympathetic flare. The strongest flares seem to be more concentrated
around the phase where the light curve indicates a smaller active region.
Spectral data suggest a complex CME with falling-back and re-ejected material,
with a maximal projected velocity of $\approx$675km/s. We observed a CME rate
much lower than expected from extrapolations of the solar flare-CME relation to
active stars.


  We formulate a model describing the doping ($x$) and temperature ($T$)
dependence of the $ab$-plane and c-axis penetration depth of a cuprate
superconductor. The model incorporates the suppression of the superfluid
density with underdoping as the system approaches the Mott-Hubbard insulating
state by augmenting a d-wave BCS model with a phenomenological charge
renormalization factor that is vanishingly small for states away from the nodes
of the d-wave pair potential but close to unity in the vicinity of the nodes.
The c-axis penetration depth is captured within a model of incoherent electron
tunneling between the CuO$_2$ planes. Application of this model to the recent
experimental data on the high-purity single crystals of
YBa$_2$Cu$_3$O$_{6+\delta}$ implies existence of a ``nodal protectorate'', a
$k$-space region in the vicinity of the nodes whose size decreases in
proportion to $x$, in which d-wave quasiparticles remain sharp even as the
system teeters on the brink of becoming an insulator. The superfluid density,
which is extremely small for these samples, also appears to come exclusively
from these protected nodal regions.


  In this article, we consider the space of c\`{a}dl\`{a}g loops on a Polish
space $S$. The loop space can be equipped with a ``Skorokhod'' metric.
Moreover, it is Polish under this metric. Our main result is to prove that the
Borel-$\sigma$-field on the space of loops is generated by a class of loop
functionals: the multi-occupation field. This result generalizes the result in
the discrete case, see \cite{loop}.


  Constructing large-scaled medical knowledge graphs can significantly boost
healthcare applications for medical surveillance, bring much attention from
recent research. An essential step in constructing large-scale MKG is
extracting information from medical reports. Recently, information extraction
techniques have been proposed and show promising performance in biomedical
information extraction. However, these methods only consider limited types of
entity and relation due to the noisy biomedical text data with complex entity
correlations. Thus, they fail to provide enough information for constructing
MKGs and restrict the downstream applications. To address this issue, we
propose Biomedical Information Extraction, a hybrid neural network to extract
relations from biomedical text and unstructured medical reports. Our model
utilizes a multi-head attention enhanced graph convolutional network to capture
the complex relations and context information while resisting the noise from
the data. We evaluate our model on two major biomedical relationship extraction
tasks, chemical-disease relation and chemical-protein interaction, and a
cross-hospital pan-cancer pathology report corpus. The results show that our
method achieves superior performance than baselines. Furthermore, we evaluate
the applicability of our method under a transfer learning setting and show that
BioIE achieves promising performance in processing medical text from different
formats and writing styles.


  We study the dynamics of a polymer when it is quenched from a $\theta$
solvent into a good or bad solvent by means of a Langevin equation. The
variation of the radius of gyration is studied as a function of time. For the
first stage of collapse or swelling, the characteristic time-scale is found to
be independent of the number of monomers. Other scaling laws are derived for
the diffusion regime at larger times. Although the present model is solved only
for homopolymers and doesn't include hydrodynamic interactions, these results
may be a first step towards the understanding of the early stages of protein
folding.


  We discuss possible notions of conformal Lie algebras, paying particular
attention to graded conformal Lie algebras with $d$-dimensional space isotropy:
namely, those with a $\mathfrak{co}(d)$ subalgebra acting in a prescribed way
on the additional generators. We classify those Lie algebras up to isomorphism
for all $d\geq 2$ following the same methodology used recently to classify
kinematical Lie algebras, as deformations of the `most abelian' graded
conformal algebra. We find 17 isomorphism classes of Lie algebras for $d\neq 3$
and 23 classes for $d=3$. Lie algebra contractions define a partial order in
the set of isomorphism classes and this is illustrated via the corresponding
Hesse diagram. The only metric graded conformal Lie algebras are the simple Lie
algebras, isomorphic to either $\mathfrak{so}(d{+}1,2)$ or
$\mathfrak{so}(d{+}2,1)$. We also work out the central extensions of the graded
conformal algebras and study their invariant inner products. We find that
central extensions of a Lie algebra in $d=3$ and two Lie algebras in $d=2$ are
metric. We then discuss several other notions of conformal Lie algebras
(generalised conformal, Schr\"odinger and Lifshitz Lie algebras) and we present
some partial results on their classification. We end with some open problems
suggested by our results.


  Robots such as autonomous underwater vehicles (AUVs) and autonomous surface
vehicles (ASVs) have been used for sensing and monitoring aquatic environments
such as oceans and lakes. Environmental sampling is a challenging task because
the environmental attributes to be observed can vary both spatially and
temporally, and the target environment is usually a large and continuous domain
whereas the sampling data is typically sparse and limited. The challenges
require that the sampling method must be informative and efficient enough to
catch up with the environmental dynamics. In this paper we present a planning
and learning method that enables a sampling robot to perform persistent
monitoring tasks by learning and refining a dynamic "data map" that models a
spatiotemporal environment attribute such as ocean salinity content. Our
environmental sampling framework consists of two components: to maximize the
information collected, we propose an informative planning component that
efficiently generates sampling waypoints that contain the maximal information;
To alleviate the computational bottleneck caused by large-scale data
accumulated, we develop a component based on a sparse Gaussian Process whose
hyperparameters are learned online by taking advantage of only a subset of data
that provides the greatest contribution. We validate our method with both
simulations running on real ocean data and field trials with an ASV in a lake
environment. Our experiments show that the proposed framework is both accurate
in learning the environmental data map and efficient in catching up with the
dynamic environmental changes.


  We have studied charged nuclear fragments produced by 200 - 400 MeV/nucleon
carbon ions, interacting with water and polycarbonate, using a newly developed
emulsion detector. Total and partial charge-changing cross sections for the
production of B, Be, and Li fragments were measured and compared with both
previously published measurements, and model predictions. This study is of
importance for validating and improving carbon ion therapy treatment planning
systems, and for estimating the radiological risks for personnel on space
missions, since carbon is a significant component of the Galactic Cosmic Rays.


  The robust construction of the ledger data structure is an essential
ingredient for the safe operation of a distributed ledger. While in traditional
linear blockchain systems, permission to append to the structure is
leader-based, in Directed Acyclic Graph-based ledgers, the writing access can
be organised leaderless. However, this leaderless approach relies on fair
treatment of non-referenced blocks, i.e. tips, by honest block issuers.
  We study the impact of a deviation from the standard tip selection by a
subset of block issuers with the aim of halting the confirmation of honest
blocks entirely. We provide models on this so-called orphanage of blocks and
validate these through open-sourced simulation studies. A critical threshold
for the adversary issuance rate is shown to exist, above which the tip pool
becomes unstable, while for values below the orphanage decrease exponentially.
We study the robustness of the protocol with an expiration time on tips, also
called garbage collection, and modification of the parent references per block.


  In the recent literature, on the one hand, many 3D multi-object tracking
(MOT) works have focused on tracking accuracy and neglected computation speed,
commonly by designing rather complex cost functions and feature extractors. On
the other hand, some methods have focused too much on computation speed at the
expense of tracking accuracy. In view of these issues, this paper proposes a
robust and fast camera-LiDAR fusion-based MOT method that achieves a good
trade-off between accuracy and speed. Relying on the characteristics of camera
and LiDAR sensors, an effective deep association mechanism is designed and
embedded in the proposed MOT method. This association mechanism realizes
tracking of an object in a 2D domain when the object is far away and only
detected by the camera, and updating of the 2D trajectory with 3D information
obtained when the object appears in the LiDAR field of view to achieve a smooth
fusion of 2D and 3D trajectories. Extensive experiments based on the typical
datasets indicate that our proposed method presents obvious advantages over the
state-of-the-art MOT methods in terms of both tracking accuracy and processing
speed. Our code is made publicly available for the benefit of the community.


  With the rise of short video platforms represented by TikTok, the trend of
users expressing their creativity through photos and videos has increased
dramatically. However, ordinary users lack the professional skills to produce
high-quality videos using professional creation software. To meet the demand
for intelligent and user-friendly video creation tools, we propose the Dynamic
Visual Composition (DVC) task, an interesting and challenging task that aims to
automatically integrate various media elements based on user requirements and
create storytelling videos. We propose an Intelligent Director framework,
utilizing LENS to generate descriptions for images and video frames and
combining ChatGPT to generate coherent captions while recommending appropriate
music names. Then, the best-matched music is obtained through music retrieval.
Then, materials such as captions, images, videos, and music are integrated to
seamlessly synthesize the video. Finally, we apply AnimeGANv2 for style
transfer. We construct UCF101-DVC and Personal Album datasets and verified the
effectiveness of our framework in solving DVC through qualitative and
quantitative comparisons, along with user studies, demonstrating its
substantial potential.


  The present note relies on the recently published conceptual design report of
the LHeC and extends the first contribution to the European strategy debate in
emphasising the role of the LHeC to complement and complete the high luminosity
LHC programme. The brief discussion therefore focuses on the importance of high
precision PDF and $\alpha_s$ determinations for the physics beyond the Standard
Model (GUTs, SUSY, Higgs). Emphasis is also given to the importance of high
parton density phenomena in nuclei and their relevance to the heavy ion physics
programme at the LHC.


  The Collatz Conjecture (also known as the 3x+1 Problem) proposes that the
following algorithm will, after a certain number of iterations, always yield
the number 1: given a natural number, multiply by three and add one if the
number is odd, halve the resulting number, then repeat. In this article, for
each $N$ for which the Collatz Conjecture holds we define the $N^{th}$ Collatz
polynomial to be the monic polynomial with constant term $N$ and $k^{th}$ term
(for $k > 1$) the $k^{th}$ iterate of $N$ under the Collatz function. In
particular, we bound the moduli of the roots of these polynomials, prove
theorems on when they have rational integer roots, and suggest further
applications and avenues of research.


  With the growth of deep neural networks (DNN), the number of DNN parameters
has drastically increased. This makes DNN models hard to be deployed on
resource-limited embedded systems. To alleviate this problem, dynamic pruning
methods have emerged, which try to find diverse sparsity patterns during
training by utilizing Straight-Through-Estimator (STE) to approximate gradients
of pruned weights. STE can help the pruned weights revive in the process of
finding dynamic sparsity patterns. However, using these coarse gradients causes
training instability and performance degradation owing to the unreliable
gradient signal of the STE approximation. In this work, to tackle this issue,
we introduce refined gradients to update the pruned weights by forming dual
forwarding paths from two sets (pruned and unpruned) of weights. We propose a
novel Dynamic Collective Intelligence Learning (DCIL) which makes use of the
learning synergy between the collective intelligence of both weight sets. We
verify the usefulness of the refined gradients by showing enhancements in the
training stability and the model performance on the CIFAR and ImageNet
datasets. DCIL outperforms various previously proposed pruning schemes
including other dynamic pruning methods with enhanced stability during
training.


  On the basis of the Schwinger boson representation for the Elliott model
developed in (III), the Holstein-Primakoff representation for the su(3)-algebra
is presented. The basic idea is the same as that adopted in (II), and as an
example, the case of the lowest approximation is shown.


  Polarization information of light in a scene is valuable for various image
processing and computer vision tasks. A division-of-focal-plane polarimeter is
a promising approach to capture the polarization images of different
orientations in one shot, while it requires color-polarization demosaicking. In
this paper, we propose a two-step color-polarization demosaicking
network~(TCPDNet), which consists of two sub-tasks of color demosaicking and
polarization demosaicking. We also introduce a reconstruction loss in the YCbCr
color space to improve the performance of TCPDNet. Experimental comparisons
demonstrate that TCPDNet outperforms existing methods in terms of the image
quality of polarization images and the accuracy of Stokes parameters.


  The methods of effective field theory are used to study generic theories of
inflation with a single inflaton field and to perform a general analysis of the
associated non-Gaussianities. We investigate the amplitudes and shapes of the
various generic three-point correlators, the bispectra, which may be generated
by different classes of single-field inflationary models. Besides the
well-known results for the DBI-like models and the ghost inflationary theories,
we point out that curvature-related interactions may give rise to large
non-Gaussianities in the form of bispectra characterized by a flat shape which,
quite interestingly, is independently produced by several interaction terms. In
a subsequent work, we will perform a similar general analysis for the
non-Gaussianities generated by the generic four-point correlator, the
trispectrum.


  Graph neural networks (GNNs) have received tremendous attention due to their
power in learning effective representations for graphs. Most GNNs follow a
message-passing scheme where the node representations are updated by
aggregating and transforming the information from the neighborhood. Meanwhile,
they adopt the same strategy in aggregating the information from different
feature dimensions. However, suggested by social dimension theory and spectral
embedding, there are potential benefits to treat the dimensions differently
during the aggregation process. In this work, we investigate to enable
heterogeneous contributions of feature dimensions in GNNs. In particular, we
propose a general graph feature gating network (GFGN) based on the graph signal
denoising problem and then correspondingly introduce three graph filters under
GFGN to allow different levels of contributions from feature dimensions.
Extensive experiments on various real-world datasets demonstrate the
effectiveness and robustness of the proposed frameworks.


  We use a matching procedure of sum rules relating the electromagnetic form
factors to generalized parton distributions (GPDs) and AdS modes. In this way,
in the framework of an AdS/QCD hard-wall model, the helicity-independent GPDs
of quarks for the nucleon in the zero skewness case are calculated.


  One of the most common assumptions in the study of neutron star models and
their oscillations is that the pressure is isotopic, however there are
arguments that this may not be correct. Thus in the present paper we make a
first step towards studying the nonradial oscillations of neutron stars with an
anisotropic pressure. We adopt the so-called Cowling approximation where the
spacetime metric is kept fixed and the oscillation spectrum for the first few
fluid modes is obtained. The effect of the anisotropy on the frequencies is
apparent, although with the present results it might be hard to distinguish it
from the changes in the frequencies caused by different equations of state.


  We have used the FORS-1 camera on the VLT to study the main sequence (MS) of
the globular cluster NGC 6218 in the V and R bands. The observations cover an
area of 3.4 x 3.4 around the cluster centre and probe the stellar population
out to the cluster's half-mass radius (r_h ~ 2.2). The colour-magnitude diagram
(CMD) that we derive in this way reveals a narrow and well defined MS extending
down to the 5 sigma detection limit at V~25, or about 6 magnitudes below the
turn-off, corresponding to stars of ~ 0.25 Msolar. The luminosity function (LF)
obtained with these data shows a marked radial gradient, in that the ratio of
lower- and higher-mass stars increases monotonically with radius. The mass
function (MF) measured at the half-mass radius, and as such representative of
the clusters global properties, is surprisingly flat. Over the range 0.4 - 0.8
Msolar, the number of stars per unit mass follows a power-law distribution of
the type dN/dm \propto m^{0}, where, for comparison, Salpeter's IMF would be
dN/dm \propto m^{-2.35}. We expect that such a flat MF does not represent the
cluster's IMF but is the result of severe tidal stripping of the stars from the
cluster due to its interaction with the Galaxy's gravitational field. Our
results cannot be reconciled with the predictions of recent theoretical models
that imply a relatively insignificant loss of stars from NGC 6218 as measured
by its expected very long time to disruption. They are more consistent with the
orbital parameters based on the Hipparcos reference system that imply a much
higher degree of interaction of this cluster with the Galaxy than assumed by
those models. Our results indicate that, if the orbit of a cluster is known,
the slope of its MF could be useful in discriminating between the various
models of the Galactic potential.


  Using results of laboratory experiments, direct numerical simulations,
geomagnetic and solar observations, it is shown that high moments of helicity
distribution can dominate power spectra of the magnetic field generated by the
magnetohydrodynamic (MHD) dynamo in swirling turbulence even for the cases with
zero global helicity. The notion of helical distributed chaos has been used for
this purpose.


  This paper argues for a modal view of probability. The syntax and semantics
of one particularly strong probability logic are discussed and some examples of
the use of the logic are provided. We show that it is both natural and useful
to think of probability as a modal operator. Contrary to popular belief in AI,
a probability ranging between 0 and 1 represents a continuum between
impossibility and necessity, not between simple falsity and truth. The present
work provides a clear semantics for quantification into the scope of the
probability operator and for higher-order probabilities. Probability logic is a
language for expressing both probabilistic and logical concepts.


  D. J. Antonio et al. report an x-ray diffraction experiment on uranium
dioxide at high-magnetic field and low temperature. The authors have apparently
not realized that the diffraction data actually shows unambiguously the
presence of a rhombohedral distortion induced by the field at low temperature
and the presence of more than one domain. Our note correctly analyses their
data. The crystallographic analysis we perform is based on simple arguments
with respect to plane spacings in slightly distorted cubic materials. Similar
studies have been reported in many materials over the last 50 years. Our
analysis explains quantitatively the observations, the presence of the two
peaks (the appearance of which the authors regard as unexpected) and their
different variations with respect to the applied field, which is a simple
consequence of the conservation of atomic volume.


  This paper presents an analytical study of TM-polarized surface plasmon
polaritons (SPPs) in nonlinear multi-layer structures containing graphene
sheets. In the general structure, each graphene sheet has been sandwiched
between two different nonlinear magnetic materials. To show the richness of the
proposed general waveguide, two novel nonlinear structures have been introduced
and investigated as special cases of the general structure. It will be shown
that the propagation features of these structures can be tuned by changing the
chemical potential of the graphene and the incident mode power. A large value
of the effective index, i.e. 240 for the chemical potential of 0.2 ev and the
incident power of 3 is obtained for the second structure at the frequency of 40
THz. The analytical results confirm that the integration of nonlinear magnetic
materials with graphene sheets can control and enhance the propagating features
and the self-focused of the field in the nonlinear layer. This integration
gives more degrees of freedom to the designer to propose new THz components
such as lasers and switches in the THz region.


  Due to underlying symmetries the ground states of magnetic adatoms may be
highly stable, which opens perspectives for application as single-atom memory.
A specific example is a single holmium atom (with $J=8$) on a platinum (111)
surface for which exceptionally long lifetimes were observed in recent scanning
tunneling microscopy studies. For control and read-out the atom must be coupled
to electronic contacts. Hence the spin dynamics of the system is governed by a
quantum master equation. Our analysis shows that in general it cannot be
reduced to a classical master equation in the basis of the unperturbed
crystal-field Hamiltonian. Rather, depending on parameters and control fields,
"environment induced superselection" principles choose the appropriate set of
basis states, which in turn determines the specific relaxation channels and
lifetimes. Our simulations suggest that in ideal situations the lifetimes
should be even longer than observed in the experiment. We, therefore,
investigate the influence of various perturbations. We also study the
initialization process of the state of the Ho atom by applied voltage pulses
and conclude that fast, high fidelity preparation, on a $100\,\text{ns}$
timescale, should be possible.


  As galaxy surveys become more precise and push to smaller scales, the need
for accurate covariances beyond the classical Gaussian formula becomes more
acute. Here, I investigate the analytical implementation and impact of
non-Gaussian covariance terms that I previously derived for galaxy clustering.
Braiding covariance is such a class of terms and it gets contribution both from
in-survey and super-survey modes. I present an approximation for braiding
covariance which speeds up the numerical computation. I show that including
braiding covariance is a necessary condition for including other non-Gaussian
terms: the in-survey 2-, 3- and 4-halo covariance, which yield covariance
matrices with negative eigenvalues if considered on their own. I then quantify
the impact on parameter constraints, with forecasts for a Euclid-like survey.
Compared to the Gaussian case, braiding and in-survey covariances significantly
increase the error bars on cosmological parameters, in particular by 50% for w.
The Halo Occupation Distribution (HOD) error bars are also affected between 12%
and 39%. Accounting for super-sample covariance (SSC) also increases parameter
errors, by 90% for w and between 7% and 64% for HOD. In total, non-Gaussianity
increases the error bar on w by 120% (between 15% and 80% for other
cosmological parameters), and the error bars on HOD parameters between 17% and
85%. Accounting for the 1-halo trispectrum term on top of SSC is not sufficient
for capturing the full non-Gaussian impact: braiding and the rest of in-survey
covariance have to be accounted for. Finally, I discuss why the inclusion of
non-Gaussianity generally eases up parameter degeneracies, making cosmological
constraints more robust to astrophysical uncertainties. The data and a Python
notebook reproducing the results and plots of the article are available at
\url{https://github.com/fabienlacasa/BraidingArticle}. [Abridged]


  Motivated and inspired by the recent work of Colding [5] and
Colding-Minicozzi [6] we derive several families of monotonicity formulas for
manifolds with nonnegative Bakry-Emery Ricci curvature, extending the formulas
in [5, 6].


  Lie groupoids generalize transformation groups, and so provide a natural
language for studying orbifolds and other noncommutative geometries. In this
paper, we investigate a connection between orbifolds and equivariant stable
homotopy theory using such groupoids. A different sort of twisted sector, along
with a classical theorem of tom Dieck, allows for a natural definition of
stable orbifold homotopy groups, and motivates defining extended unstable
orbifold homotopy groups generalizing previous definitions.


  In many situations, we would like to hear desired sound events (SEs) while
being able to ignore interference. Target sound extraction (TSE) tackles this
problem by estimating the audio signal of the sounds of target SE classes in a
mixture of sounds while suppressing all other sounds. We can achieve this with
a neural network that extracts the target SEs by conditioning it on clues
representing the target SE classes. Two types of clues have been proposed,
i.e., target SE class labels and enrollment audio samples (or audio queries),
which are pre-recorded audio samples of sounds from the target SE classes.
Systems based on SE class labels can directly optimize embedding vectors
representing the SE classes, resulting in high extraction performance. However,
extending these systems to extract new SE classes not encountered during
training is not easy. Enrollment-based approaches extract SEs by finding sounds
in the mixtures that share similar characteristics to the enrollment audio
samples. These approaches do not explicitly rely on SE class definitions and
can thus handle new SE classes. In this paper, we introduce a TSE framework,
SoundBeam, that combines the advantages of both approaches. We also perform an
extensive evaluation of the different TSE schemes using synthesized and real
mixtures, which shows the potential of SoundBeam.


  Holes in germanium (Ge) exhibit strong spin-orbit interaction, which can be
exploited for fast and all-electrical manipulation of spin states. Here, we
report transport experiments in a tunable Ge hut wire hole double quantum dot.
We observe the signatures of Pauli spin blockade (PSB) with a large
singlet-triplet energy splitting of ~1.1 meV and extract the g factor. By
analyzing the the PSB leakage current, we obtain a spin-orbit length l_so of ~
40-100 nm. Furthermore, we demonstrate the electric dipole spin resonance.
These results lay a solid foundation for implementing high quality tunable hole
spin-orbit qubits.


  Optical multi-slit spectra have been obtained for 47 globular clusters
surrounding the brightest Virgo elliptical NGC 4472 (M49). Including data from
the literature, we analyze velocities for a total sample of 57 clusters and
present the first tentative evidence for kinematic differences between the red
and blue cluster populations which make up the bimodal colour distribution of
this galaxy. The redder clusters are more centrally concentrated and have a
velocity dispersion of 240 km/sec compared with 320 km/sec for the blue
clusters. The origin of this difference appears to be a larger component of
systematic rotation in the blue cluster system. The larger rotation in the more
extended blue cluster system is indicative of efficient angular momentum
transport, as provided by galaxy mergers. Masses estimated from the globular
cluster velocities are consistent with the mass distribution estimated from
X-ray data, and indicate that the M/L rises to 50 times solar at 2.5 R_e.


  Magnetic materials for specific applications require an accurate control and
complete comprehension of their magnetic properties. In particular,
nanoparticles embedded in a polycrystalline matrix emerge as good candidates
for applications due to the possibility of tuning the magnetic properties
through interface interaction effects. Here, iron/w\"ustite composite is
prepared using high energy mechanical milling from iron powder and water. The
sample is analyzed by X-ray diffraction, dynamic laser light scattering,
M\"ossbauer spectroscopy, field cooling and zero field cooling curves,
magnetization curves, and magnetic hyperthermia. Based on the results, we
identify that the produced sample is like Fe nanoparticles embedded in a
w\"ustite matrix, with high stability in time, and shows noticeable features
such as exchange bias effect at low temperatures and promising temperatures
reached in a short time interval when considered magnetic hyperthermia, $\sim
46\,^\circ$C, becoming an interesting candidate for biological applications,
such as the one employed for cancer therapy.


  Let $k$ be a commutative $\mathbb{Q}$-algebra. We study families of functors
between categories of finitely generated $R$-modules which are defined for all
commutative $k$-algebras $R$ simultaneously and are compatible with base
changes. These operations turn out to be Schur functors associated to
$k$-linear representations of symmetric groups. This result is closely related
to Macdonald's classification of polynomial functors.


  This paper explores advances in reconfiguration properties of SRAM-based
FPGAs, namely Partial Dynamic Reconfiguration, to improve the resilience of
critical systems that take advantage of this technology. Commercial
of-the-shelf state-of-the-art FPGA devices use SRAM cells for the configuration
memory, which allow an increase in both performance and capacity. The fast
access times and unlimited number of writes of this technology, reduces
reconfiguration delays and extends the device lifetime but, at the same time,
makes them more sensitive to radiation effects, in the form of Single Event
Upsets. To overcome this limitation, manufacturers have proposed a few fault
tolerant approaches, which rely on space/time redundancy and configuration
memory content recovery - scrubbing. In this paper, we first present radiation
effects on these devices and investigate the applicability of the most commonly
used fault tolerant approaches, and then propose an approach to improve FPGA
resilience, through the use of a less intrusive failure prediction scrubbing.
It is expected that this approach relieves the system designer from
dependability concerns and reduces both time intrusiveness and overall power
consumption.


  Automata-logic connections are pillars of the theory of regular languages.
Such connections are harder to obtain for transducers, but important results
have been obtained recently for word-to-word transformations, showing that the
three following models are equivalent: deterministic two-way transducers,
monadic second-order (MSO) transducers, and deterministic one-way automata
equipped with a finite number of registers. Nested words are words with a
nesting structure, allowing to model unranked trees as their depth-first-search
linearisations. In this paper, we consider transformations from nested words to
words, allowing in particular to produce unranked trees if output words have a
nesting structure. The model of visibly pushdown transducers allows to describe
such transformations, and we propose a simple deterministic extension of this
model with two-way moves that has the following properties: i) it is a simple
computational model, that naturally has a good evaluation complexity; ii) it is
expressive: it subsumes nested word-to-word MSO transducers, and the exact
expressiveness of MSO transducers is recovered using a simple syntactic
restriction; iii) it has good algorithmic/closure properties: the model is
closed under composition with a unambiguous one-way letter-to-letter transducer
which gives closure under regular look-around, and has a decidable equivalence
problem.


  Let $X,X_1,\ldots,X_n$ be independent identically distributed random
variables. In this paper we study the behavior of concentration functions of
weighted sums $\sum_{k=1}^{n} X_k a_k$ with respect to the arithmetic structure
of coefficients~$a_k$ in the context of the Littlewood--Offord problem.
Concentration results of this type received renewed interest in connection with
distributions of singular values of random matrices. Recently, Tao and Vu
proposed an Inverse Principle in the Littlewood--Offord problem. We discuss the
relations between the Inverse Principle of Tao and Vu as well as that of Nguyen
and Vu and a similar principle formulated for sums of arbitrary independent
random variables in the work of Arak from the 1980's. This paper is a shortened
and edited version of the preprint arXiv:1506.09034. Here we present the
results without proofs.


  We present the extended ALMA MaNGA QUEnching and STar formation survey, a
combination of the original 46 ALMaQUEST galaxies plus new ALMA observations
for a further 20 interacting galaxies. Three well-studied scaling relations are
fit to the 19,999 star-forming spaxels in the extended sample, namely the
resolved Schmidt-Kennicutt (rSK) relation, the resolved star forming main
sequence (rSFMS) and the resolved molecular gas main sequence (rMGMS). We
additionally investigate the relationship between the dynamical equilibrium
pressure (PDE) and star formation rate surface density (Sigma_SFR), which we
refer to as the resolved PDE (rPDE) relation. Contrary to previous studies that
have focussed on normal star-forming galaxies and found an approximately linear
rPDE relation, the presence of more vigourously star-forming galaxies in the
extended ALMaQUEST sample reveals a marked turnover in the relation at high
pressures. Although the scatter around the linear fit to the rPDE relation is
similar to the other three relations, a random forest analysis, which can
extract non-linear dependences, finds that PDE is unambiguously more important
than either Sigma_H2 or Sigma_star for predicting Sigma_SFR. We compare the
observed rPDE relation to the prediction of the pressure-regulated
feedback-modulated (PRFM) model of star formation, finding that galaxies
residing on the global SFMS do indeed closely follow the rPDE relation
predicted by the PRFM theory. However, galaxies above and below the global SFMS
show significant deviations from the model. Galaxies with high SFR are instead
consistent with models that include other contributions to turbulence in
addition to the local star formation feedback.


  Missing data are frequently observed by practitioners and researchers in the
building energy modeling community. In this regard, advanced data-driven
solutions, such as Deep Learning methods, are typically required to reflect the
non-linear behavior of these anomalies. As an ongoing research question related
to Deep Learning, a model's applicability to limited data settings can be
explored by introducing prior knowledge in the network. This same strategy can
also lead to more interpretable predictions, hence facilitating the field
application of the approach. For that purpose, the aim of this paper is to
propose the use of Physics-informed Denoising Autoencoders (PI-DAE) for missing
data imputation in commercial buildings. In particular, the presented method
enforces physics-inspired soft constraints to the loss function of a Denoising
Autoencoder (DAE). In order to quantify the benefits of the physical component,
an ablation study between different DAE configurations is conducted. First,
three univariate DAEs are optimized separately on indoor air temperature,
heating, and cooling data. Then, two multivariate DAEs are derived from the
previous configurations. Eventually, a building thermal balance equation is
coupled to the last multivariate configuration to obtain PI-DAE. Additionally,
two commonly used benchmarks are employed to support the findings. It is shown
how introducing physical knowledge in a multivariate Denoising Autoencoder can
enhance the inherent model interpretability through the optimized physics-based
coefficients. While no significant improvement is observed in terms of
reconstruction error with the proposed PI-DAE, its enhanced robustness to
varying rates of missing data and the valuable insights derived from the
physics-based coefficients create opportunities for wider applications within
building systems and the built environment.