  There exists observational evidence that the interstellar medium has a
fractal structure in a wide range of spatial scales. The measurement of the
fractal dimension (Df) of interstellar clouds is a simple way to characterize
this fractal structure, but several factors, both intrinsic to the clouds and
to the observations, may contribute to affect the values obtained. In this work
we study the effects that opacity and noise have on the determination of Df. We
focus on two different fractal dimension estimators: the perimeter-area based
dimension (Dper) and the mass-size dimension (Dm). We first use simulated
fractal clouds to show that opacity does not affect the estimation of Dper.
However, Dm tends to increase as opacity increases and this estimator fails
when applied to optically thick regions. In addition, very noisy maps can
seriously affect the estimation of both Dper and Dm, decreasing the final
estimation of Df. We apply these methods to emission maps of Ophiuchus, Perseus
and Orion molecular clouds in different molecular lines and we obtain that the
fractal dimension is always in the range 2.6 < Df < 2.8 for these regions.
These results support the idea of a relatively high (> 2.3) average fractal
dimension for the interstellar medium, as traced by different chemical species.


  Relevant methods of variable selection have been proposed in model-based
clustering and classification. These methods are making use of backward or
forward procedures to define the roles of the variables. Unfortunately, these
stepwise procedures are terribly slow and make these variable selection
algorithms inefficient to treat large data sets. In this paper, an alternative
regularization approach of variable selection is proposed for model-based
clustering and classification. In this approach, the variables are first ranked
with a lasso-like procedure in order to avoid painfully slow stepwise
algorithms. Thus, the variable selection methodology of Maugis et al (2009b)
can be efficiently applied on high-dimensional data sets.


  In this work, we propose a distributed rate allocation algorithm that
minimizes the average decoding delay for multimedia clients in inter-session
network coding systems. We consider a scenario where the users are organized in
a mesh network and each user requests the content of one of the available
sources. We propose a novel distributed algorithm where network users determine
the coding operations and the packet rates to be requested from the parent
nodes, such that the decoding delay is minimized for all the clients. A rate
allocation problem is solved by every user, which seeks the rates that minimize
the average decoding delay for its children and for itself. Since the
optimization problem is a priori non-convex, we introduce the concept of
equivalent packet flows, which permits to estimate the expected number of
packets that every user needs to collect for decoding. We then decompose our
original rate allocation problem into a set of convex subproblems, which are
eventually combined to obtain an effective approximate solution to the delay
minimization problem. The results demonstrate that the proposed scheme
eliminates the bottlenecks and reduces the decoding delay experienced by users
with limited bandwidth resources. We validate the performance of our
distributed rate allocation algorithm in different video streaming scenarios
using the NS-3 network simulator. We show that our system is able to take
benefit of inter-session network coding for simultaneous delivery of video
sessions in networks with path diversity.


  We study the center structure of full dynamical QCD at finite temperatures
and nonzero values of the background magnetic field using continuum
extrapolated lattice data. We concentrate on two particular observables
characterizing center clusters: their fractality and the probability for
percolation. For temperatures below and around the transition region, the
fractal dimension is found to be significantly smaller than three, leading to a
vanishing mean free path inside the cluster structure. This finding might be
relevant for center symmetry-based models of heavy-ion collisions. In addition,
the percolation probability is employed to define the transition temperature
and to map out the QCD phase diagram in the magnetic field-temperature plane.


  We present a theoretical analysis of magnetic toroidal moments in periodic
systems, in the limit in which the toroidal moments are caused by a time and
space reversal symmetry breaking arrangement of localized magnetic dipole
moments. We summarize the basic definitions for finite systems and address the
question of how to generalize these definitions to the bulk periodic case. We
define the toroidization as the toroidal moment per unit cell volume, and we
show that periodic boundary conditions lead to a multivaluedness of the
toroidization, which suggests that only differences in toroidization are
meaningful observable quantities. Our analysis bears strong analogy to the
modern theory of electric polarization in bulk periodic systems, but we also
point out some important differences between the two cases. We then discuss the
instructive example of a one-dimensional chain of magnetic moments, and we show
how to properly calculate changes of the toroidization for this system.
Finally, we evaluate and discuss the toroidization (in the local dipole limit)
of four important example materials: BaNiF_4, LiCoPO_4, GaFeO_3, and BiFeO_3.


  The advancement of knowledge distillation has played a crucial role in
enabling the transfer of knowledge from larger teacher models to smaller and
more efficient student models, and is particularly beneficial for online and
resource-constrained applications. The effectiveness of the student model
heavily relies on the quality of the distilled knowledge received from the
teacher. Given the accessibility of unlabelled remote sensing data,
semi-supervised learning has become a prevalent strategy for enhancing model
performance. However, relying solely on semi-supervised learning with smaller
models may be insufficient due to their limited capacity for feature
extraction. This limitation restricts their ability to exploit training data.
To address this issue, we propose an integrated approach that combines
knowledge distillation and semi-supervised learning methods. This hybrid
approach leverages the robust capabilities of large models to effectively
utilise large unlabelled data whilst subsequently providing the small student
model with rich and informative features for enhancement. The proposed
semi-supervised learning-based knowledge distillation (SSLKD) approach
demonstrates a notable improvement in the performance of the student model, in
the application of road segmentation, surpassing the effectiveness of
traditional semi-supervised learning methods.


  Circumscription is a representative example of a nonmonotonic reasoning
inference technique. Circumscription has often been studied for first order
theories, but its propositional version has also been the subject of extensive
research, having been shown equivalent to extended closed world assumption
(ECWA). Moreover, entailment in propositional circumscription is a well-known
example of a decision problem in the second level of the polynomial hierarchy.
This paper proposes a new Boolean Satisfiability (SAT)-based algorithm for
entailment in propositional circumscription that explores the relationship of
propositional circumscription to minimal models. The new algorithm is inspired
by ideas commonly used in SAT-based model checking, namely counterexample
guided abstraction refinement. In addition, the new algorithm is refined to
compute the theory closure for generalized close world assumption (GCWA).
Experimental results show that the new algorithm can solve problem instances
that other solutions are unable to solve.


  A new method is developed to deal with the problem that a complex
decentralized control system needs to keep centralized control performance. The
systematic procedure emphasizes quickly finding the decentralized
subcontrollers that matching the closed-loop performance and robustness
characteristics of the centralized controller, which is featured by the fact
that GA is used to optimize the design of centralized H-infinity controller
K(s) and decentralized engine subcontroller KT(s), and that only one interface
variable needs to satisfy decentralized control system requirement according to
the proposed selection principle. The optimization design is motivated by the
implementation issues where it is desirable to reduce the time in trial and
error process and accurately find the best decentralized subcontrollers. The
method is applied to decentralized control system design for a short takeoff
and landing fighter. By comparing the simulation results of the decentralized
control system with those of the centralized control system, the target of the
decentralized control attains the performance and robustness of centralized
control is validated.


  In this paper, a comparison analysis between geometric impedance controls
(GICs) derived from two different potential functions on SE(3) for robotic
manipulators is presented. The first potential function is defined on the Lie
group, utilizing the Frobenius norm of the configuration error matrix. The
second potential function is defined utilizing the Lie algebra, i.e., log-map
of the configuration error. Using a differential geometric approach, the
detailed derivation of the distance metric and potential function on SE(3) is
introduced. The GIC laws are respectively derived from the two potential
functions, followed by extensive comparison analyses. In the qualitative
analysis, the properties of the error function and control laws are analyzed,
while the performances of the controllers are quantitatively compared using
numerical simulation.


  Bayesian nonparametric regression under a rescaled Gaussian process prior
offers smoothness-adaptive function estimation with near minimax-optimal error
rates. Hierarchical extensions of this approach, equipped with stochastic
variable selection, are known to also adapt to the unknown intrinsic dimension
of a sparse true regression function. But it remains unclear if such extensions
offer variable selection consistency, i.e., if the true subset of important
variables could be consistently learned from the data. It is shown here that
variable consistency may indeed be achieved with such models at least when the
true regression function has finite smoothness to induce a polynomially larger
penalty on inclusion of false positive predictors. Our result covers the high
dimensional asymptotic setting where the predictor dimension is allowed to grow
with the sample size. The proof utilizes Schwartz theory to establish that the
posterior probability of wrong selection vanishes asymptotically. A necessary
and challenging technical development involves providing sharp upper and lower
bounds to small ball probabilities at all rescaling levels of the Gaussian
process prior, a result that could be of independent interest.


  This paper investigates a reconciliation method in order to establish an
errorless secret key in a QKD protocol. Classical key distribution protocols
are no longer unconditionally secure because computational complexity of
mathematical problems forced hardships. In this context, QKD protocols offer a
highest level of security because they are based on the quantum laws of
physics. But, the protocol performances can be lowered by multiples errors. It
appears clearly that reconciliation should be performed in such a situation in
order to remove the errors as for the legitimate partners. The proposed method
accomplishes reconciliation by using QTC in the special problem of
sideinformation source coding (Slepian-Wolf coding model). Our theoretical
hypothesis are sustained by experimental results that confirm the advantage of
our method in resolving reconciliation problem compared to a recent related
work. Indeed, the integration of our method generates an important progess in
security and a large decrease of the QBER. The gain is obtained with a
reasonable complexity increase. Also, the novelty of our work is that it tested
the reconciliation method on a real photonic system under VPItransmissionMaker.


  Data augmentation can be a simple yet powerful tool for autonomous robots to
fully utilise available data for selfsupervised identification of atypical
scenes or objects. State-of-the-art augmentation methods arbitrarily embed
"structural" peculiarity on typical images so that classifying these artefacts
can provide guidance for learning representations for the detection of
anomalous visual signals. In this paper, however, we argue that learning such
structure-sensitive representations can be a suboptimal approach to some
classes of anomaly (e.g., unhealthy fruits) which could be better recognised by
a different type of visual element such as "colour". We thus propose Channel
Randomisation as a novel data augmentation method for restricting neural
networks to learn encoding of "colour irregularity" whilst predicting
channel-randomised images to ultimately build reliable fruit-monitoring robots
identifying atypical fruit qualities. Our experiments show that (1) this
colour-based alternative can better learn representations for consistently
accurate identification of fruit anomalies in various fruit species, and also,
(2) unlike other methods, the validation accuracy can be utilised as a
criterion for early stopping of training in practice due to positive
correlation between the performance in the self-supervised
colour-differentiation task and the subsequent detection rate of actual
anomalous fruits. Also, the proposed approach is evaluated on a new
agricultural dataset, Riseholme-2021, consisting of 3.5K strawberry images
gathered by a mobile robot, which we share online to encourage active
agri-robotics research.


  We study the asymptotic growth of the diameter of a graph obtained by adding
sparse "long" edges to a square box in $\Z^d$. We focus on the cases when an
edge between $x$ and $y$ is added with probability decaying with the Euclidean
distance as $|x-y|^{-s+o(1)}$ when $|x-y|\to\infty$. For $s\in(d,2d)$ we show
that the graph diameter for the graph reduced to a box of side $L$ scales like
$(\log L)^{\Delta+o(1)}$ where $\Delta^{-1}:=\log_2(2d/s)$. In particular, the
diameter grows about as fast as the typical graph distance between two vertices
at distance $L$. We also show that a ball of radius $r$ in the intrinsic metric
on the (infinite) graph will roughly coincide with a ball of radius
$\exp\{r^{1/\Delta+o(1)}\}$ in the Euclidean metric.


  More than six million people died of the COVID-19 by April 2022. The heavy
casualties have put people on great and urgent alert and people try to find all
kinds of information to keep them from being inflected by the coronavirus. This
research tries to find out whether the mobile health text information sent to
peoples devices is correct as smartphones becoming the major information source
for people. The proposed method uses various mobile information retrieval and
data mining technologies including lexical analysis, stopword elimination,
stemming, and decision trees to classify the mobile health text information to
one of the following classes: (i) true, (ii) fake, (iii) misinformative, (iv)
disinformative, and (v) neutral. Experiment results show the accuracy of the
proposed method is above the threshold value 50 percentage, but is not optimal.
It is because the problem, mobile text misinformation identification, is
intrinsically difficult.


  This work investigates the possibilities enabled by federated learning
concerning IoT malware detection and studies security issues inherent to this
new learning paradigm. In this context, a framework that uses federated
learning to detect malware affecting IoT devices is presented. N-BaIoT, a
dataset modeling network traffic of several real IoT devices while affected by
malware, has been used to evaluate the proposed framework. Both supervised and
unsupervised federated models (multi-layer perceptron and autoencoder) able to
detect malware affecting seen and unseen IoT devices of N-BaIoT have been
trained and evaluated. Furthermore, their performance has been compared to two
traditional approaches. The first one lets each participant locally train a
model using only its own data, while the second consists of making the
participants share their data with a central entity in charge of training a
global model. This comparison has shown that the use of more diverse and large
data, as done in the federated and centralized methods, has a considerable
positive impact on the model performance. Besides, the federated models, while
preserving the participant's privacy, show similar results as the centralized
ones. As an additional contribution and to measure the robustness of the
federated approach, an adversarial setup with several malicious participants
poisoning the federated model has been considered. The baseline model
aggregation averaging step used in most federated learning algorithms appears
highly vulnerable to different attacks, even with a single adversary. The
performance of other model aggregation functions acting as countermeasures is
thus evaluated under the same attack scenarios. These functions provide a
significant improvement against malicious participants, but more efforts are
still needed to make federated approaches robust.


  In thin films of smectic-C liquid crystals, localized regions containing
additional smectic layers form circular inclusions that carry a topological
charge. Such inclusions nucleate a companion topological defect. These
inclusion-defect pairs are modeled as topological dipoles within the context of
a one-coupling constant approximation to the 2D Frank free energy. Deviations
of the dipole direction from a preferred orientation cause the dipoles to
acquire a logarithmic charge. Thermal fluctuations of the dipole direction are
calculated and found to be large, scaling as the logarithm of the system size.
In addition to dipole-dipole interactions arising from the topological charges,
we also find that the thermal fluctuations of the dipole directions are coupled
through a preference for global charge neutrality of the logarithmic charges.


  This is a brief introductory review of the AdS/CFT correspondence and of the
ideas that led to its formulation. Emphasis is placed on dualities between
conformal large $N$ gauge theories in 4 dimensions and string backgrounds of
the form $AdS_5\times X_5$. Attempts to generalize this correspondence to
asymptotically free theories are also included.


  Interactions are ubiquitous across biological systems. These interactions can
be abstracted as patterns of connections among distinct units such as genes,
proteins, individual organisms, or species which form a hierarchy of biological
organisation. Connections in this hierarchy are arranged in a nested structure:
gene and protein networks shape phenotypic traits and together constitute
individuals, individuals are embedded within populations, populations within
communities, and communities within ecosystems. This pervasive "nestedness" of
networks can result in propagation of the effects from within-level
interactions at one level to units at higher or lower levels of organization.
The concept of nested biological networks is implicit in a variety of
disciplines ranging from the study of genetic circuits regulating phenotypic
trait expression (Babu et al. 2004), to the study of predator-prey interactions
influencing community composition (Poisot et al. 2016). However, studies
typically only address interactions within and among directly neighbouring
hierarchical levels, such as genotypes and phenotypic traits, or populations
and communities. Here, we formalise nested networks as having nodes that can
contain, or be embedded in, other nodes, and where edges can bridge connections
between sets of embedded nodes. We then argue that explicitly accounting for
network nestedness across levels of organization will encourage integrative
thinking on new interdisciplinary research fronts. We focus on two phenomena in
particular: (i) indirect connections among units can arise from the structure
of connections at higher or lower levels of organisation, (ii) the propagation
of effects across neighbouring hierarchical levels of organization. This
framework of nested interaction networks provides a tool for researchers across
disciplines to conceptualize their work as elements on a common scaffold.


  We present an in-depth study of co-following on Twitter based on the
observation that two Twitter users whose followers have similar friends are
also similar, even though they might not share any direct links or a single
mutual follower. We show how this observation contributes to (i) a better
understanding of language-agnostic user classification on Twitter, (ii)
eliciting opportunities for Computational Social Science, and (iii) improving
online marketing by identifying cross-selling opportunities.
  We start with a machine learning problem of predicting a user's preference
among two alternative choices of Twitter friends. We show that co-following
information provides strong signals for diverse classification tasks and that
these signals persist even when (i) the most discriminative features are
removed and (ii) only relatively "sparse" users with fewer than 152 but more
than 43 Twitter friends are considered.
  Going beyond mere classification performance optimization, we present
applications of our methodology to Computational Social Science. Here we
confirm stereotypes such as that the country singer Kenny Chesney
(@kennychesney) is more popular among @GOP followers, whereas Lady Gaga
(@ladygaga) enjoys more support from @TheDemocrats followers.
  In the domain of marketing we give evidence that celebrity endorsement is
reflected in co-following and we demonstrate how our methodology can be used to
reveal the audience similarities between Apple and Puma and, less obviously,
between Nike and Coca-Cola. Concerning a user's popularity we find a
statistically significant connection between having a more "average"
followership and having more followers than direct rivals. Interestingly, a
\emph{larger} audience also seems to be linked to a \emph{less diverse}
audience in terms of their co-following.


  We consider Morrey's open question whether rank-one convexity already implies
quasiconvexity in the planar case. For some specific families of energies,
there are precise conditions known under which rank-one convexity even implies
polyconvexity. We will extend some of these findings to the more general family
of energies $W:\operatorname{GL}^+(n)\rightarrow\mathbb{R}$ with an additive
volumetric-isochoric split, i.e. \[
  W(F)=W_{\rm iso}(F)+W_{\rm vol}(\det F)=\widetilde W_{\rm
iso}\bigg(\frac{F}{\sqrt{\det F}}\bigg)+W_{\rm vol}(\det F)\,, \] which is the
natural finite extension of isotropic linear elasticity. Our approach is based
on a condition for rank-one convexity which was recently derived from the
classical two-dimensional criterion by Knowles and Sternberg and consists of a
family of one-dimensional coupled differential inequalities. We identify a
number of \enquote{least} rank-one convex energies and, in particular, show
that for planar volumetric-isochorically split energies with a concave
volumetric part, the question of whether rank-one convexity implies
quasiconvexity can be reduced to the open question of whether the rank-one
convex energy function \[
  W_{\rm magic}^+(F)=\frac{\lambda_{\rm max}}{\lambda_{\rm
min}}-\log\frac{\lambda_{\rm max}}{\lambda_{\rm min}}+\log\det
F=\frac{\lambda_{\rm max}}{\lambda_{\rm min}}-2\log\lambda_{\rm min} \] is
quasiconvex. In addition, we demonstrate that under affine boundary conditions,
$W_{\rm magic}^+(F)$ allows for non-trivial inhomogeneous deformations with the
same energy level as the homogeneous solution, and show a surprising connection
to the work of Burkholder and Iwaniec in the field of complex analysis.


  We conducted a survey of seven magnetic O and eleven B-type stars with masses
above $8M_{\odot}$ using the Very Large Array in the 1cm, 3cm and 13cm bands.
The survey resulted in a detection of two O and two B-type stars. While the
detected O-type stars - HD 37742 and HD 47129 - are in binary systems, the
detected B-type stars, HD 156424 and ALS 9522, are not known to be in binaries.
All four stars were detected at 3cm, whereas three were detected at 1cm and
only one star was detected at 13cm. The detected B-type stars are significantly
more radio luminous than the non-detected ones, which is not the case for
O-type stars. The non-detections at 13cm are interpreted as due to thermal
free-free absorption. Mass-loss rates were estimated using 3cm flux densities
and were compared with theoretical mass-loss rates, which assume free-free
emission. For HD 37742, the two values of the mass-loss rates were in good
agreement, possibly suggesting that the radio emission for this star is mainly
thermal. For the other three stars, the estimated mass-loss rates from radio
observations were much higher than those expected from theory, suggesting
either a possible contribution from non- thermal emission from the magnetic
star or thermal or non-thermal emission due to interacting winds of the binary
system, especially for HD 47129. All the detected stars are predicted to host
centrifugal magnetospheres except HD 37742, which is likely to host a dynamical
magnetosphere. This suggests that non-thermal radio emission is favoured in
stars with centrifugal magnetospheres.


  We establish the equivalence of the Maxwell-Chern-Simons-Proca model to a
doublet of Maxwell-Chern-Simons models at the level of polarization vectors of
the basic fields using both Lagrangian and Hamiltonian formalisms. The analysis
reveals a U(1) invariance of the polarization vectors in the momentum space.
Its implications are discussed. We also study the role of Wigner's little group
as a generator of gauge transformations in three space-time dimensions.


  The scattering angle due to the target volume can be split into two interior
angles in the tomographic setups based on the muon scattering, and we call this
property as the triangular correlation where the sum of these two interior
angles is equal to the scattering angle. In this study, we apply this
triangular correlation for a multi-block material configuration that consist of
concrete, stainless steel, and uranium. By changing the order of this material
set, we employ the GEANT4 simulations and we show that the triangular
correlation is valid in the multi-block material setups, thereby providing the
possibility of supportive information for the coarse prediction of the material
order in such configurations.


  Minimal spanning forests on infinite graphs are weak limits of minimal
spanning trees from finite subgraphs. These limits can be taken with free or
wired boundary conditions and are denoted FMSF (free minimal spanning forest)
and WMSF (wired minimal spanning forest), respectively. The WMSF is also the
union of the trees that arise from invasion percolation started at all
vertices. We show that on any Cayley graph where critical percolation has no
infinite clusters, all the component trees in the WMSF have one end a.s. In
$\mathbb{Z}^d$ this was proved by Alexander [Ann. Probab. 23 (1995) 87--104],
but a different method is needed for the nonamenable case. We also prove that
the WMSF components are ``thin'' in a different sense, namely, on any graph,
each component tree in the WMSF has $p_{\mathrm{c}}=1$ a.s., where
$p_{\mathrm{c}}$ denotes the critical probability for having an infinite
cluster in Bernoulli percolation. On the other hand, the FMSF is shown to be
``thick'': on any connected graph, the union of the FMSF and independent
Bernoulli percolation (with arbitrarily small parameter) is a.s. connected. In
conjunction with a recent result of Gaboriau, this implies that in any Cayley
graph, the expected degree of the FMSF is at least the expected degree of the
FSF (the weak limit of uniform spanning trees). We also show that the number of
infinite clusters for Bernoulli($p_{\mathrm{u}}$) percolation is at most the
number of components of the FMSF, where $p_{\mathrm{u}}$ denotes the critical
probability for having a unique infinite cluster. Finally, an example is given
to show that the minimal spanning tree measure does not have negative
associations.


  We investigate the relationship between the MgII $\lambda2798$ emission-line
and the 3000 {\AA} continuum variations using a sample of 68
intermediate-redshift ($z\sim$ 0.65$-$1.50) broad-line quasars spanning a
bolometric luminosity range of 44.49 erg s$^{-1} \leq \rm{log}$$L_{\rm{bol}}
\leq 46.31$ erg s$^{-1}$ (Eddington ratio from $\sim$ 0.026 to 0.862). This
sample is constructed from SDSS-DR7Q and BOSS-DR12Q, each with at least 2
spectroscopic epochs in SDSS-I/II/III surveys. Additionally, we adopt the
following signal-to-noise ratio (S/N) selection criteria: a) for MgII and the
3000 {\AA} continuum, S/N $\geq$ 10; b) for narrow lines, S/N $\geq$ 5. All our
quasar spectra are recalibrated based on the assumption of constant narrow
emission-line fluxes. In an analysis of spectrum-to-spectrum variations, we
find a fairly close correlation (Spearman $\rho = 0.593$) between the
variations in broad MgII and in the continuum. This is consistent with the idea
that MgII is varying in response to the continuum emission variations. Adopting
the modified weighted least squares regression method, we statistically
constrain the slopes (i.e., the responsivity $\alpha$ of the broad MgII)
between the variations in both components for the sources in different
luminosity bins after eliminating intrinsic biases introduced by the rescaling
process itself. It is shown that the responsivity is quite small (average
$\bar{\alpha} \approx$ 0.464) and anti-correlates with the quasar luminosity.
Our results indicate that high signal-to-noise flux measurements are required
to robustly detect the intrinsic variability and the time lag of MgII line.


  We study the origin of "frozen" states in coupled Jaynes-Cummings-Hubbard
arrays in the presence of losses. For the case of half the array initially
populated with photons while the other half is left empty we show the emergence
of self-localized photon or "frozen" states for specific values of the local
atom-photon coupling. We analyze the dynamics in the quantum regime and
discover important additional features appear not captured by a semiclassical
treatment, which we analyze for different array sizes and filling fractions. We
trace the origin of this interaction-induced photon "freezing" to the
suppression of excitation of propagating modes in the system at large
interaction strengths. We discuss in detail the possibility to experimentally
probe the relevant transition by analyzing the emitted photon correlations. We
find a strong signature of the effect in the emitted photons statistics.


  We evaluate the place of Eta Carinae amongst the class of luminous blue
variables (LBVs) and show that the LBV phenomenon is not restricted to
extremely luminous objects like Eta Car, but extends luminosities as low as
log(L/Lsun) = 5.4 - corresponding to initial masses ~25 Msun, and final masses
as low as ~10-15 Msun. We present a census of S Doradus variability, and
discuss basic LBV properties, their mass-loss behaviour, and whether at maximum
light they form pseudo-photospheres. We argue that those objects that exhibit
giant Eta Car-type eruptions are most likely related to the more common type of
S Doradus variability. Alternative atmospheric models as well as
sub-photospheric models for the instability are presented, but the true nature
of the LBV phenomenon remains as yet elusive. We end with a discussion on the
evolutionary status of LBVs - highlighting recent indications that some LBVs
may be in a direct pre-supernova state, in contradiction to the standard
paradigm for massive star evolution.


  Melanin is a ubiquitous natural pigment that exhibits broadband absorption
and high refractive index. Despite its widespread use in structural color
production, how the absorbing material, melanin, affects the generated color is
unknown. Using a combined molecular dynamics and finite-difference time-domain
computational approach, this paper investigates structural color generation in
one-component melanin nanoparticle-based supra-assemblies (called supraballs)
as well as binary mixtures of melanin and silica (non-absorbing)
nanoparticle-based supraballs. Experimentally produced one-component melanin
and one-component silica supraballs, with thoroughly characterized primary
particle characteristics using neutron scattering, produce reflectance profiles
similar to the computational analogues, confirming that the computational
approach correctly simulates both absorption and multiple scattering from the
self-assembled nanoparticles. These combined approaches demonstrate that
melanin's broadband absorption increases the primary reflectance peak
wavelength, increases saturation, and decreases lightness factor. In addition,
the dispersity of nanoparticle size more strongly influences the optical
properties of supraballs than packing fraction, as evidenced by production of a
larger range of colors when size dispersity is varied versus packing fraction.
For binary melanin and silica supraballs, the chemistry-based stratification
allows for more diverse color generation and finer saturation tuning than does
the degree of mixing/demixing between the two chemistries.


  We have designed an introductory laboratory course that engaged first-year
undergraduate students in two complementary types of iteration: (1) iterative
improvement of experiments through cycles of modeling systems, designing
experiments, analyzing data, and refining models and designs; and (2) iterative
improvement of self through cycles of reflecting on progress, soliciting
feedback, and implementing changes to study habits and habits of mind. The
course consisted of three major activities: a thermal expansion activity, which
spanned the first half of the semester; final research projects, which spanned
the second half of the semester; and guided student reflections, which took
place throughout the duration of the course. We describe our curricular designs
and report examples of student work that demonstrate students' iterative
improvements in multiple contexts.


  We use the example of the cuboctahedron, a highly frustrated molecule with 12
sites, to study the approach to the classical limit. We compute magnetic
susceptibility, specific heat, and magnetic cooling rate at high magnetic
fields and low temperatures for different spin quantum numbers s. Remarkably
big deviations of these quantities from their classical counterparts are
observed even for values of s which are usually considered to be almost
classical.


  In this paper, we propose a new technique to uniquely classify and identify
multiple users accessing a single application using keystroke dynamics. This
problem is usually encountered when multiple users have legitimate access to
shared computers and accounts, where, at times, one user can inadvertently be
logged in on another user's account. Since the login processes are usually
bypassed at this stage, we rely on keystroke dynamics in order to tell users
apart. Our algorithm uses the quantile transform and techniques from
localization to classify and identify users. Specifically, we use an algorithm
known as ordinal Unfolding based Localization (UNLOC), which uses only ordinal
data obtained from comparing distance proxies, by "locating" users in a reduced
PCA/Kernel-PCA/t-SNE space based on their typing patterns. Our results are
validated with the help of benchmark keystroke datasets and show that our
algorithm outperforms other methods.


  We study a variation of Carlsson and Zomorodian's persistent homology. As an
application, we analyze the persistent cellular sheaf cohomology of network
coding sheaves on deteriorating networks.


  Quantum tomography is one of the major challenges of large-scale quantum
information research due to the exponential time complexity. In this work, we
develop and apply a Bayesian state estimation method to experimentally
demonstrate quantum overlapping tomography [Phys. Rev. Lett. \textbf{124},
100401 (2020)], a scheme intent on characterizing critical information of a
many-body quantum system in logarithmic time complexity. By comparing the
measurement results of full state tomography and overlapping tomography, we
show that overlapping tomography gives accurate information of the system with
much fewer state measurements than full state tomography.


  Spin kinetics in $n$-type InAs quantum wells under intense terahertz laser
fields is investigated by developing fully microscopic kinetic spin Bloch
equations via the Floquet-Markov theory and the nonequilibrium Green's function
approach, with all the relevant scattering, such as the electron-impurity,
electron-phonon, and electron-electron Coulomb scattering explicitly included.
We find that a {\em finite} steady-state terahertz spin polarization induced by
the terahertz laser field, first predicted by Cheng and Wu [Appl. Phys. Lett.
{\bf 86}, 032107 (2005)] in the absence of dissipation, exists even in the
presence of all the scattering. We further discuss the effects of the terahertz
laser fields on the spin relaxation and the steady-state spin polarization. It
is found that the terahertz laser fields can {\em strongly} affect the spin
relaxation via hot-electron effect and the terahertz-field-induced effective
magnetic field in the presence of spin-orbit coupling. The two effects compete
with each other, giving rise to {\em non-monotonic} dependence of the spin
relaxation time as well as the amplitude of the steady state spin polarization
on the terahertz field strength and frequency. The terahertz field dependences
of these quantities are investigated for various impurity densities, lattice
temperatures, and strengths of the spin-orbit coupling. Finally, the importance
of the electron-electron Coulomb scattering on spin kinetics is also addressed.


  In an upward-planar L-drawing of a directed acyclic graph (DAG) each edge $e$
is represented as a polyline composed of a vertical segment with its lowest
endpoint at the tail of $e$ and of a horizontal segment ending at the head of
$e$. Distinct edges may overlap, but not cross. Recently, upward-planar
L-drawings have been studied for $st$-graphs, i.e., planar DAGs with a single
source $s$ and a single sink $t$ containing an edge directed from $s$ to $t$.
It is known that a plane $st$-graph, i.e., an embedded $st$-graph in which the
edge $(s,t)$ is incident to the outer face, admits an upward-planar L-drawing
if and only if it admits a bitonic $st$-ordering, which can be tested in linear
time.
  We study upward-planar L-drawings of DAGs that are not necessarily
$st$-graphs. On the combinatorial side, we show that a plane DAG admits an
upward-planar L-drawing if and only if it is a subgraph of a plane $st$-graph
admitting a bitonic $st$-ordering. This allows us to show that not every tree
with a fixed bimodal embedding admits an upward-planar L-drawing. Moreover, we
prove that any acyclic cactus with a single source (or a single sink) admits an
upward-planar L-drawing, which respects a given outerplanar embedding if there
are no transitive edges. On the algorithmic side, we consider DAGs with a
single source (or a single sink). We give linear-time testing algorithms for
these DAGs in two cases: (i) when the drawing must respect a prescribed
embedding and (ii) when no restriction is given on the embedding, but it is
biconnected and series-parallel.


  This paper considers the oscillations modeled by a forced Van der Pol
generalized oscillator. These oscillations are described by a nonlinear
differential equation of the form $
\ddot{x}+x-\varepsilon\left(1-ax^2-b\dot{x}^2\right)\dot{x}=E\sin{{\Omega}t}.$
The amplitudes of the forced harmonic, primary resonance superharmonic and
subharmonic oscillatory states are obtained using the harmonic balance
technique and the multiple time scales methods. We obtain also the hysteresis
and jump phenomena in the system oscillations. Bifurcation sequences displayed
by the model for each type of oscillatory states are performed numerically
through the fourth-order Runge- Kutta scheme.


  We report on the generation of frequency combs from the recently-discovered
phenomenon of high-order sideband generation (HSG). A near-band gap
continuous-wave (cw) laser with frequency $f_\text{NIR}$ was transmitted
through an epitaxial layer containing GaAs/AlGaAs quantum wells that were
driven by quasi-cw in-plane electric fields $F_\text{THz}$ between 4 and 50
kV/cm oscillating at frequencies $f_\text{THz}$ between 240 and 640 GHz.
Frequency combs with teeth at $f_\text{sideband}=f_\text{NIR}+nf_\text{THz}$
($n$ even) were produced, with maximum reported $n>120$, corresponding to a
maximum comb span $>80$ THz. Comb spectra with the identical product
$f_\text{THz}\times F_\text{THz}$ were found to have similar spans and shapes
in most cases, as expected from the picture of HSG as a scattering-limited
electron-hole recollision phenomenon. The HSG combs were used to measure the
frequency and linewidth of our THz source as a demonstration of potential
applications.


  The modeling of jet substructure significantly differs between Parton Shower
Monte Carlo (PSMC) programs. Despite this, we observe that machine learning
classifiers trained on different PSMCs learn nearly the same function. This
means that when these classifiers are applied to the same PSMC for testing,
they result in nearly the same performance. This classifier universality
indicates that a machine learning model trained on one simulation and tested on
another simulation (or data) will likely be optimal. Our observations are based
on detailed studies of shallow and deep neural networks applied to simulated
Lorentz boosted Higgs jet tagging at the LHC.


  This paper studies the performance of partial-Rake (PRake) receivers in
impulse-radio ultrawideband wireless networks when an energy-efficient power
control scheme is adopted. Due to the large bandwidth of the system, the
multipath channel is assumed to be frequency-selective. By making use of
noncooperative game-theoretic models and large-system analysis tools, explicit
expressions are derived in terms of network parameters to measure the effects
of self-interference and multiple-access interference at a receiving access
point. Performance of the PRake receivers is thus compared in terms of achieved
utilities and loss to that of the all-Rake receiver. Simulation results are
provided to validate the analysis.


  Intriguingly conducting perovskite interfaces between ordinary band
insulators are widely explored, whereas similar interfaces with Mott insulators
are still not quite understood. Here we address the (001), (110), and (111)
interfaces between the LaTiO$_{3}$ Mott- and large band gap KTaO$_{3}$
insulators. Based on first-principles calculations, we reveal a mechanism of
interfacial conductivity, which is distinct from a formerly studied one
applicable to interfaces between polar wide band insulators. Here the key
factor causing conductivity is the matching of oxygen octahedra tilting in
KTaO$_{3}$ and LaTiO$_{3}$ which, due to a small gap in the LaTiO$_{3}$ results
in its sensitivity to the crystal structure, yields metalization of its
overlayer and following charge transfer from Ti to Ta. Our findings, also
applicable to other Mott insulators interfaces, shed light on the emergence of
conductivity observed in LaTiO$_{3}$/KTaO$_{3}$~(110) where the ''polar``
arguments are not applicable and on the emergence of superconductivity in these
structures.


  We analyze the peculiar velocity field for 2400 flat spiral galaxies selected
from an infrared sky survey (2MFGC). The distances to the galaxies have been
determined from the Tully-Fisher relation in the photometric J band with a
dispersion of 0.45 mag. The bulk motion of this sample relative to the cosmic
microwave background (3K) frame has an amplitude of 199+/-37 km/s in the
direction l = 290 +/- 11, b = +1 +/- 9 degree. The amplitude of the dipole
motion tends to decrease with distance in accordance with the expected
convergence of bulk flows in the 3K frame. We believe that external massive
attractors similar to the Shapley cluster concentration are responsible for
\~60% of the local flow velocity in the z = 0.03 volume.


  It has been observed that machine learning algorithms exhibit biased
predictions against certain population groups. To mitigate such bias while
achieving comparable accuracy, a promising approach is to introduce surrogate
functions of the concerned fairness definition and solve a constrained
optimization problem. However, it is intriguing in previous work that such
fairness surrogate functions may yield unfair results and high instability. In
this work, in order to deeply understand them, taking a widely used fairness
definition--demographic parity as an example, we show that there is a
surrogate-fairness gap between the fairness definition and the fairness
surrogate function. Also, the theoretical analysis and experimental results
about the gap motivate us that the fairness and stability will be affected by
the points far from the decision boundary, which is the large margin points
issue investigated in this paper. To address it, we propose the general sigmoid
surrogate to simultaneously reduce both the surrogate-fairness gap and the
variance, and offer a rigorous fairness and stability upper bound.
Interestingly, the theory also provides insights into two important issues that
deal with the large margin points as well as obtaining a more balanced dataset
are beneficial to fairness and stability. Furthermore, we elaborate a novel and
general algorithm called Balanced Surrogate, which iteratively reduces the gap
to mitigate unfairness. Finally, we provide empirical evidence showing that our
methods consistently improve fairness and stability while maintaining accuracy
comparable to the baselines in three real-world datasets.


  We study the electronic state of an inverse spinel compound MnMg$_{2}$O$_{4}$
based on first-principles calculations. The high-spin state is realized in Mn
ions on the diamond lattice, resulting in that this material is found to be a
half-metallic semimetal with the minority spin-gap about 3eV, and also with
line nodes in the Brillouin zone. The intrinsic anomalous Hall conductivity
(AHC) is also computed as a function of the chemical potential of the system
assuming the rigid band structure, and is found to exhibit a peak structure
with a maximum value of 200 S/cm at only 15 meV above the Fermi level. The
relation between the large AHC and Berry curvature in the Brillouin zone is
also discussed.


  The search for electromagnetic counterparts for gravitational waves events is
one of the main topics of multi-messenger Astrophysics. Among these searches is
the one for high energy gamma-ray emission with the H.E.S.S. Imaging
Atmospheric Cherenkov Telescopes in Namibia. During their second Observation
Run O2, the Advanced Virgo detector in Italy and the two advanced LIGO
detectors in Washington and Louisiana while conducting joint observations,
detected for the first time, on August $14^{th}$, 2017 a transient GW signal
due to the coalescence of two stellar masses black holes, an event labeled
GW170814. The alert announcing the event was issued two hours later and
H.E.S.S. observations could be scheduled for the nights of $16^{th}$, $17^{th}$
and $18^{th}$ August 2017. Three days after the binary BH merger, on August
$17^{th}$, the coalescence of two neutron star was detected for the first time,
followed by a GRB detection by Fermi's GBM starting a new era in
multi-messenger Astronomy. Observations started 5.3 h after the merge and
contained the counterpart SSS17a that was identified several hours later. It
stands as the first data obtained by a ground-based pointing instrument on this
object. In this contribution, we will present the results of the search of
high-energy gamma ray emission as electromagnetic counterpart of these two GW
events. No significant gamma ray emission was detected for either event.
Nevertheless upper limit maps were derived constraining, for the first time,
the non-thermal, high-energy emission on the remnant of a three detector binary
black hole coalescence (GW170814), and a binary neutron star coalescence
(GW170817).


  Mass measurements and absorption line studies indicate that the stellar
initial mass function (IMF) is bottom-heavy in the central regions of many
early-type galaxies, with an excess of low mass stars compared to the IMF of
the Milky Way. Here we test this hypothesis using a method that is independent
of previous techniques. Low mass stars have strong chromospheric activity
characterized by non-thermal emission at short wavelengths. Approximately half
of the UV flux of M dwarfs is contained in the $\lambda{}1215.7$ Ly$\alpha$
line, and we show that the total Ly$\alpha$ emission of an early-type galaxy is
a sensitive probe of the IMF with a factor of $\sim 2$ flux variation in
response to plausible variations in the number of low mass stars. We use the
Cosmic Origins Spectrograph on the Hubble Space Telescope to measure the
Ly$\alpha$ line in the centers of the massive early-type galaxies NGC1407 and
NGC2695. We detect Ly$\alpha$ emission in both galaxies and demonstrate that it
originates in stars. We find that the Ly$\alpha$ to i-band flux ratio is a
factor of 2.0$\pm$0.4 higher in NGC1407 than in NGC2695, in agreement with the
difference in their IMFs as previously determined from gravity-sensitive
optical absorption lines. Although a larger sample of galaxies is required for
definitive answers, these initial results support the hypothesis that the IMF
is not universal but varies with environment.


  This paper presents our proposed approach to the Discharge Me! shared task,
collocated with the 23th Workshop on Biomedical Natural Language Processing
(BioNLP). In this work, we develop an LLM-based framework for solving the
Discharge Summary Documentation (DSD) task, i.e., generating the two critical
target sections `Brief Hospital Course' and `Discharge Instructions' in the
discharge summary. By streamlining the recent instruction-finetuning process on
LLMs, we explore several prompting strategies for optimally adapting LLMs to
specific generation task of DSD. Experimental results show that providing a
clear output structure, complimented by a set of comprehensive
Chain-of-Thoughts (CoT) questions, effectively improves the model's reasoning
capability, and thereby, enhancing the structural correctness and faithfulness
of clinical information in the generated text. Source code is available at:
https://github.com/antangrocket1312/Discharge_LLM


  Studies of discrete languages emerging when neural agents communicate to
solve a joint task often look for evidence of compositional structure. This
stems for the expectation that such a structure would allow languages to be
acquired faster by the agents and enable them to generalize better. We argue
that these beneficial properties are only loosely connected to
compositionality. In two experiments, we demonstrate that, depending on the
task, non-compositional languages might show equal, or better, generalization
performance and acquisition speed than compositional ones. Further research in
the area should be clearer about what benefits are expected from
compositionality, and how the latter would lead to them.


  After the discovery of space masers based on OH radicals (Weaver et al, 1965)
and H2O (Cheung et al, 1969) such microwave lasers have been found to work in
more than 100 molecular species (Elitzur, 1992; Townes, 1997), as well as in
highly excited H atoms (Strelnitski et al,1996). In the IR region (10 microns),
the effect of stimulated emission of radiation in the CO2 molecule has been
discovered in the Martian and Venus' atmospheres (Betz et al, 1976; Mumma et
al, 1981). We report here on the discovery of laser action in the range 0.9-2
micr. in several spectral lines of Fe II, which are associated with transitions
from "pseudo-metastable" states populated by spontaneous transitions from
Ly-alpha pumped Fe II levels. The intense Ly-alpha radiation is formed in the
HII region of gas condensations close to the star Eta Carinae. The laser
transitions form together with spontaneous transitions closed radiative cycles,
one of which includes the extremely bright 2507/09 A lines. Closed radiative
cycles, together with an accidental mixing of energy levels, may provide an
explanation of the abnormal intensities of these UV non-lasing lines. Using the
complicated energy level diagram of Fe II we present those peculiar features,
which are essential for the inverted population and laser effect: the pumping,
the level mixing, and the "bottle neck" for spontaneous decay. The laser action
is a new indicator of non-equilibrium and spatially non-homogeneous physical
conditions as well as a high brightness temperature of Ly-alpha in ejecta from
eruptive stars. Such conditions are very difficult to probe by existing
methods, and we propose some future experiments. The fact, that the lasing
near-IR lines appear in the spectrum with about the same inten- sity as
non-lasing lines is discussed and compared with the situation in masers.


  The canonical undestanding of stellar convection has recently been put under
doubt due to helioseismic results and global 3D convection simulations. This
"convective conundrum" is manifested by much higher velocity amplitudes in
simulations at large scales in comparison to helioseismic results, and the
difficulty in reproducing the solar differential rotation and dynamo with
global 3D simulations. Here some aspects of this conundrum are discussed from
the viewpoint of hydrodynamic Cartesian 3D simulations targeted at testing the
rotational influence and surface forcing on deep convection. More specifically,
the dominant scale of convection and the depths of the convection zone and the
weakly subadiabatic -- yet convecting -- Deardorff zone are discussed in
detail.


  We show that for young stars which are still accreting and for which
measurements of stellar age, disc mass and accretion rate are available,
nominal disc age (Disc Age = Disc Mass / Accretion Rate) is approximately equal
to the stellar age, at least within the considerable observational scatter. We
then consider theoretical models of proto-stellar discs through analytic and
numerical models. A variety of viscosity prescriptions including empirical
power laws, magnetohydrodynamic turbulence and gravitational instability were
considered within models describing the disc phenomena of dead zones,
photoevaporation and planet formation. These models are generally poor fits to
the observational data, showing values of 'Disc Age' which are too high by
factors of 3 - 10. We then ask whether a systematic error in the measurement of
one of the observational quantities might provide a reasonable explanation for
this discrepancy. We show that for the observed systems only disc mass shows a
systematic dependence on the value of 'Disc Age / Stellar Age' and we note that
a systematic underestimate of the value of disc mass by a factor of around 3 -
5, would account for the discrepancy between theory and observations.


  The Unbounded Subset-Sum Problem (USSP) is defined as: given sum $s$ and a
set of integers $W\leftarrow \{p_1,\dots,p_n\}$ output a set of non-negative
integers $\{y_1,\dots,y_n\}$ such that $p_1y_1+\dots+p_ny_n=s$. The USSP is an
NP-complete problem that does not have any known polynomial-time solution.
There is a pseudo-polynomial algorithm for the USSP problem with
$O((p_{1})^{2}+n)$ time complexity and $O(p_{1})$ memory complexity, where
$p_{1}$ is the smallest element of $W$ \cite{PH}. This algorithm is polynomial
in term of the number of inputs, but exponential in the size of $p_1$.
Therefore, this solution is impractical for the large-scale problems.
  In this paper, first we propose an efficient polynomial-time algorithm with
$O(n)$ computational complexity for solving the specific case of the USSP where
$ s> \sum_{i=1}^{k-1}q_iq_{i+1}-q_i-q_{i+1}$, $q_i$'s are the elements of a
small subset of $W$ in which $gcd$ of its elements divides $s$ and $2\le k \le
n$. Second, we present another algorithm for smaller values of $s$ with
$O(n^2)$ computational complexity that finds the answer for some inputs with a
probability between $0.5$ to $1$. Its success probability is directly related
to the number of subsets of $W$ in which $gcd$ of their elements divides $s$.
This algorithm can solve the USSP problem with large inputs in the
polynomial-time, no matter how big inputs are, but, in some special cases where
$s$ is small, it cannot find the answer.


  We analyze radiation reaction for synchrotron radiation by computing, via a
multipole expansion, the near field evaluated on the world-line of the charge.
We find that the temporal component of the self four-force agrees with the
radiated power, which one calculates in the radiation zone. This is the case
for each mode in the multipole decomposition. We also find agreement with the
Abraham-Lorentz-Dirac equation.


  We analyze the Parkes quarter-Jansky flat-spectrum sample of QSOs in terms of
space density, including the redshift distribution, the radio luminosity
function, and the evidence for a redshift cutoff. With regard to the luminosity
function, we note the strong evolution in space density from the present day to
epochs corresponding to redshifts ~ 1. We draw attention to a selection effect
due to spread in spectral shape that may have misled other investigators to
consider the apparent similarities in shape of luminosity functions in
different redshift shells as evidence for luminosity evolution. To examine the
evolution at redshifts beyond 3, we develop a model-independent method based on
the V_max test using each object to predict expectation densities beyond z=3.
With this we show that a diminution in space density at z > 3 is present at a
significance level >4 sigma. We identify a severe bias in such determinations
from using flux-density measurements at epochs significantly later than that of
the finding survey. The form of the diminution is estimated, and is shown to be
very similar to that found for QSOs selected in X-ray and optical wavebands.
The diminution is also compared with the current estimates of star-formation
evolution, with less conclusive results. In summary we suggest that the
reionization epoch is little influenced by powerful flat-spectrum QSOs, and
that dust obscuration does not play a major role in our view of the QSO
population selected at radio, optical or X-ray wavelengths.


  We analyze the energy configuration of a charged black hole in the
Teleparallel Framework of General Relativity. We obtain the energy-momentum
tensor of the gravitational field in a stationary frame, and we calculate its
contribution to the total energy of the system. We study the same gravitational
field measured by an accelerated frame and we analyze how the energy-momentum
tensor is transformed. We found that in the accelerated frame, a Poyinting-like
flux appears for the gravitational field but not for the electromagnetic field.


  The MAPS prototype of the proposed ALICE Forward Calorimeter (FoCal) is the
highest granularity electromagnetic calorimeter, with 39 million pixels of $30
\times 30 \mu$m$^{2}$. Particle showers can be studied with unprecedented
detail with this prototype. Electromagnetic showers at energies between 2 GeV
and 244 GeV have been studied and compared to Geant4 simulations. Simulation
models can be tested in more detail than ever before and the differences
observed between FoCal data and Geant4 simulations illustrates that
improvements in electromagnetic models are still possible.


  In this paper, the quasinormal modes of gravitational perturbation around
some well-known regular black holes were evaluated by using the WKB
approximation as well as the asymptotic iteration method. Through numerical
calculation, we make a detailed analysis of the gravitational QNM frequencies
by varying the characteristic parameters of the gravitational perturbation and
the spacetime charge parameters of the regular black holes. It is found that
the imaginary part of quasinormal modes as a function of the charge parameter
has different monotonic behaviors for different black hole spacetimes.
Moreover, the asymptotic expressions of gravitational QNMs for $l \gg 1$ are
obtained by using the eikonal limit method. We demonstrate that the
gravitational perturbation is stable in all these spacetimes.


  Large organizations that collect data about populations (like the US Census
Bureau) release summary statistics that are used by multiple stakeholders for
resource allocation and policy making problems. These organizations are also
legally required to protect the privacy of individuals from whom they collect
data. Differential Privacy (DP) provides a solution to release useful summary
data while preserving privacy. Most DP mechanisms are designed to answer a
single set of queries. In reality, there are often multiple stakeholders that
use a given data release and have overlapping but not-identical queries. This
introduces a novel joint optimization problem in DP where the privacy budget
must be shared among different analysts.
  We initiate study into the problem of DP query answering across multiple
analysts. To capture the competing goals and priorities of multiple analysts,
we formulate three desiderata that any mechanism should satisfy in this setting
-- The Sharing Incentive, Non-Interference, and Adaptivity -- while still
optimizing for overall error. We demonstrate how existing DP query answering
mechanisms in the multi-analyst settings fail to satisfy at least one of the
desiderata. We present novel DP algorithms that provably satisfy all our
desiderata and empirically show that they incur low error on realistic tasks.


  Heavy neutrinos with masses in the MeV range can in principle simultaneously
explain the light neutrino masses and the origin of baryonic matter in the
universe. The strongest constraints on their properties come from their
potential impact on the formation of light elements in the early universe.
Since these constraints rely on assumptions about the cosmic history,
independent checks in the laboratory are highly desirable. In this paper, we
discuss the opportunity to search for heavy neutrinos within the MeV mass range
in short and medium baseline reactor neutrino experiments, using the SoLid,
JUNO and TAO experiments as examples. This kind of experiments can give the
currently strongest upper bound on the mixing between the light electron
neutrinos and the heavy neutrino in the 2-9 MeV mass range.


  In this paper we analyse two-player games by their response graphs. The
response graph has nodes which are strategy profiles, with an arc between
profiles if they differ in the strategy of a single player, with the direction
of the arc indicating the preferred option for that player. Response graphs,
and particularly their sink strongly connected components, play an important
role in modern techniques in evolutionary game theory and multi-agent learning.
We show that the response graph is a simple and well-motivated model of
strategic interaction which captures many non-trivial properties of a game,
despite not depending on cardinal payoffs. We characterise the games which
share a response graph with a zero-sum or potential game respectively, and
demonstrate a duality between these sets. This allows us to understand the
influence of these properties on the response graph. The response graphs of
Matching Pennies and Coordination are shown to play a key role in all
two-player games: every non-iteratively-dominated strategy takes part in a
subgame with these graph structures. As a corollary, any game sharing a
response graph with both a zero-sum game and potential game must be
dominance-solvable. Finally, we demonstrate our results on some larger games.


  In the modular invariant flavor model of $\rm A_4$, we study the hierarchical
structure of lepton/quark flavors at nearby fixed points of $\tau=i$ and
$\tau=\omega$ of the modulus, which are in the fundamental domain of $\rm
PSL(2,\mathbb{Z})$. These fixed points correspond to the residual symmetries
$\mathbb{Z}_2^{S}=\{I, S \}$ and $\mathbb{Z}_3^{S}=\{ I, ST, (ST)^2 \}$ of $\rm
A_4$, where $S$ and $T$ are generators of the $A_4$ group. The infinite $\tau=
i \infty$ also preserves the residual symmetry of the subgroup
$\mathbb{Z}^T_3=\{ I,T,T^2 \}$ of $\rm A_4$. We study typical two-type mass
matrices for charged leptons and quarks in terms of modular forms of weights
$2$, $4$ and $6$ while the neutrino mass matrix with the modular forms of
weight $4$ through the Weinberg operator. Linear modular forms are obtained
approximately by performing Taylor expansion of modular forms around fixed
points. By using them, the flavor structure of the lepton and quark mass
matrices are examined at nearby fixed points. The hierarchical structure of
these mass matrices is clearly shown in the diagonal base of $S$, $T$ and $ST$.
The observed PMNS and CKM mixing matrices can be reproduced at nearby fixed
points in some cases of mass matrices. By scanning model parameters numerically
at nearby fixed points, our discussion are confirmed for both the normal
hierarchy and inverted one of neutrino masses. Predictions are given for the
sum of neutrino masses and the CP violating Dirac phase of leptons at each
nearby fixed point.


  Classical locally recoverable codes, which permit highly efficient recovery
from localized errors as well as global recovery from larger errors, provide
some of the most useful codes for distributed data storage in practice. In this
paper, we initiate the study of quantum locally recoverable codes (qLRCs). In
the long term, like their classical counterparts, such qLRCs may be used for
large-scale quantum data storage. Our results also have concrete implications
for quantum LDPC codes, which are applicable to near-term quantum
error-correction.
  After defining quantum local recoverability, we provide an explicit
construction of qLRCs based on the classical LRCs of Tamo and Barg (2014),
which we show have (1) a close-to-optimal rate-distance tradeoff (i.e. near the
Singleton bound), (2) an efficient decoder, and (3) permit good spatial
locality in a physical implementation. Although the analysis is significantly
more involved than in the classical case, we obtain close-to-optimal parameters
by introducing a "folded" version of our quantum Tamo-Barg (qTB) codes, which
we then analyze using a combination of algebraic techniques. We furthermore
present and analyze two additional constructions using more basic techniques,
namely random qLRCs, and qLRCs from AEL distance amplification. Each of these
constructions has some advantages, but neither achieves all 3 properties of our
folded qTB codes described above.
  We complement these constructions with Singleton-like bounds that show our
qLRC constructions achieve close-to-optimal parameters. We also apply these
results to obtain Singleton-like bounds for qLDPC codes, which to the best of
our knowledge are novel. We then show that even the weakest form of a stronger
locality property called local correctability, which permits more robust local
recovery and is achieved by certain classical codes, is impossible quantumly.


  We present a search for prompt gamma-ray counterparts to compact binary
coalescence gravitational wave (GW) candidates from Advanced LIGO's first
observing run (O1). As demonstrated by the multimessenger observations of
GW170817/GRB 170817A, electromagnetic and GW observations provide complementary
information about the astrophysical source and, in the case of weaker
candidates, may strengthen the case for an astrophysical origin. Here we
investigate low-significance GW candidates from the O1 compact-binary
coalescence searches using the Fermi Gamma-ray Burst Monitor (GBM), leveraging
its all-sky and broad energy coverage. Candidates are ranked and compared to
background to measure significance. Those with false alarm rates of less than
10^-5 Hz (about one per day) are used as the search sample for gamma-ray
follow-up. No GW candidates were found to be coincident with gamma-ray
transients independently identified by blind searches of the GBM data. In
addition, GW candidate event times were followed up by a separate targeted
search of GBM data. Among the resulting GBM events, the two with lowest false
alarm rates were the gamma-ray transient GW150914-GBM presented in Connaughton
et al. (2016) and a solar flare in chance coincidence with a GW candidate.


  This article investigates the question, if it is economically feasible for a
large publishing house to waive article processing charges for the group of 47
so called least developed countries (LDC). As an example Springer-Nature is
selected. The analysis is based on the Web of Science, OpenAPC and the Jisc
collections Springer compact journal list. As a result, it estimates an average
yearly publication output of 520 publications (or a share of 0,26% of the
worldwide publication output in Springer-Nature journals) for the LDC country
group. The loss of revenues for Springer-Nature would be 1,1 million $ if a
waiver would be applied for all of these countries. Given that money is
indispensable for development in the case of LDC (e.g. life expectancy, health,
education), it is not only desirable but also possible in economic terms for a
publisher like Springer-Nature to waive APCs for these countries without much
loss in revenues.


  We identify a diffeomorphism Lie algebra in the self-dual sector of
Yang-Mills theory, and show that it determines the kinematic numerators of
tree-level MHV amplitudes in the full theory. These amplitudes can be computed
off-shell from Feynman diagrams with only cubic vertices, which are dressed
with the structure constants of both the Yang-Mills colour algebra and the
diffeomorphism algebra. Therefore, the latter algebra is the dual of the colour
algebra, in the sense suggested by the work of Bern, Carrasco and Johansson. We
further study perturbative gravity, both in the self-dual and in the MHV
sectors, finding that the kinematic numerators of the theory are the BCJ
squares of the Yang-Mills numerators.


  We complete the classification, initiated by the second named author, of
homogeneous singular Riemannian foliations of spheres that are lifts of
foliations produced from Clifford systems.


  In this letter, we quantify the impact of device limitations on the
classification accuracy of an artificial neural network, where the synaptic
weights are implemented in a Ferroelectric FET (FeFET) based in-memory
processing architecture. We explore a design-space consisting of the resolution
of the analog-to-digital converter, number of bits per FeFET cell, and the
neural network depth. We show how the system architecture, training models and
overparametrization can address some of the device limitations.


  A change of the particle density (by gas puff, pellets or impurity seeding)
during the plasma discharge in tokamak produces a radial current and implicitly
a torque and rotation that can modify the state of confinement. After
ionization the newly born ions will evolve toward the periodic neoclassical
orbits (trapped or circulating) but the first part of their excursion, which
precedes the periodicity, is an effective radial current. It is short,
spatially finite and unique for each new ion, but multiplied by the rate of
ionization and it can produce a substantial total radial current. The
associated torque induces rotation which modify the transport processes. We
derive the magnitude of the radial current induced by ionization by three
methods: the analysis of a simple physical picture, a numerical model and the
neoclassical drift-kinetic treatment. The results of the three approaches are
in agreement and show that the current can indeed be substantial. Many well
known experimental observations can be reconsidered under this perspective. In
reactor-grade plasma the confinement can be strongly influenced by adequate
particle fuelling.


  Current theories of perception suggest that the brain represents features of
the world as probability distributions, but can such uncertain foundations
provide the basis for everyday vision? Perceiving objects and scenes requires
knowing not just how features (e.g., colors) are distributed but also where
they are and which other features they are combined with. Using a Bayesian
computational model, we recovered probabilistic representations used by human
observers to search for odd stimuli among distractors. Importantly, we found
that the brain integrates information between feature dimensions and spatial
locations, leading to more precise representations compared to when information
integration is not possible. We also uncovered representational asymmetries and
biases, showing their spatial organization and explain how this structure
argues against "summary statistics" accounts of visual representations. Our
results confirm that probabilistically encoded visual features are bound with
other features and to particular locations, providing a powerful demonstration
of how probabilistic representations can be a foundation for higher-level
vision.


  With the growth of digitized comics, image understanding techniques are
becoming important. In this paper, we focus on object detection, which is a
fundamental task of image understanding. Although convolutional neural networks
(CNN)-based methods archived good performance in object detection for
naturalistic images, there are two problems in applying these methods to the
comic object detection task. First, there is no large-scale annotated comics
dataset. The CNN-based methods require large-scale annotations for training.
Secondly, the objects in comics are highly overlapped compared to naturalistic
images. This overlap causes the assignment problem in the existing CNN-based
methods. To solve these problems, we proposed a new annotation dataset and a
new CNN model. We annotated an existing image dataset of comics and created the
largest annotation dataset, named Manga109-annotations. For the assignment
problem, we proposed a new CNN-based detector, SSD300-fork. We compared
SSD300-fork with other detection methods using Manga109-annotations and
confirmed that our model outperformed them based on the mAP score.


  In this paper we develop a tool to identify functions which have no iterative
roots of any order. Using this, we prove that when $X$ is $[0,1]^m$,
$\mathbb{R}^m$ or $S^1$, every non-empty open set of the space $\mathcal{C}(X)$
of continuous self-maps on $X$ endowed with the compact-open topology contains
a map that does not have even discontinuous iterative roots of order $n\ge 2$.
This, in particular, proves that the complement of $\{f^n: f\in
\mathcal{C}(X)~\text{and}~n\ge 2\}$, the set of non-iterates, is dense in
$\mathcal{C}(X)$ for these $X$.


  The Renyi entropy is a generalisation of the Shannon entropy that is
sensitive to the fine details of a probability distribution. We present results
for the Renyi entropy of the totally asymmetric exclusion process (TASEP). We
calculate explicitly an entropy whereby the squares of configuration
probabilities are summed, using the matrix product formalism to map the problem
to one involving a six direction lattice walk in the upper quarter plane. We
derive the generating function across the whole phase diagram, using an
obstinate kernel method. This gives the leading behaviour of the Renyi entropy
and corrections in all phases of the TASEP. The leading behaviour is given by
the result for a Bernoulli measure and we conjecture that this holds for all
Renyi entropies. Within the maximal current phase the correction to the leading
behaviour is logarithmic in the system size. Finally, we remark upon a special
property of equilibrium systems whereby discontinuities in the Renyi entropy
arise away from phase transitions, which we refer to as secondary transitions.
We find no such secondary transition for this nonequilibrium system, supporting
the notion that these are specific to equilibrium cases.


  This work designs an image restoration deep network relying on unfolded
Chambolle-Pock primal-dual iterations. Each layer of our network is built from
Chambolle-Pock iterations when specified for minimizing a sum of a
$\ell_2$-norm data-term and an analysis sparse prior. The parameters of our
network are the step-sizes of the Chambolle-Pock scheme and the linear operator
involved in sparsity-based penalization, including implicitly the
regularization parameter. A backpropagation procedure is fully described.
Preliminary experiments illustrate the good behavior of such a deep primal-dual
network in the context of image restoration on BSD68 database.


  This paper explores the contributions of Answer Set Programming (ASP) to the
study of an established theory from the field of Second Language Acquisition:
Input Processing. The theory describes default strategies that learners of a
second language use in extracting meaning out of a text, based on their
knowledge of the second language and their background knowledge about the
world. We formalized this theory in ASP, and as a result we were able to
determine opportunities for refining its natural language description, as well
as directions for future theory development. We applied our model to automating
the prediction of how learners of English would interpret sentences containing
the passive voice. We present a system, PIas, that uses these predictions to
assist language instructors in designing teaching materials. To appear in
Theory and Practice of Logic Programming (TPLP).


  We are concerned with blow-up solutions of the 5-dimensional energy critical
heat equation $u_t=\Delta u + | u |^{\frac{4}{3}}u$. Our main result is to show
that the existence of type 2 solutions blows up at 2 points with 2 different
blow-up rates. The inner-outer gluing method has been employed.


  We present results that compare the accuracy of the AM05 density functional
to a set of chemical reaction energies. The reactions were generated from the
singlet species in the well-known G2 test suite. Our results show that, in
general, the AM05 functional performs nearly as well as the other "pure"
density functionals, but none of these perform as well as the hybrid B3LYP
functional. These results are nonetheless encouraging because the AM05
functional arises from very simple assumptions, and does not require the
calculation of the Hartree-Fock exchange integrals.


  Evaporative mass flux is governed by interfacial state of liquid and vapor
phases. For closely similar pressures and mass fluxes of liquid water into its
own vapor, discontinuity between interfacial liquid and vapor temperatures in
the range of 0.14-28 K is reported. This controversial discontinuity has
resulted in an obstacle on understanding and theoretical modeling of
evaporation. Here, through study of vapor transport by Boltzmann transport
equation solved through Direct Simulation Monte Carlo Method, we demonstrated
that the measured discontinuities were strongly affected by boundary condition
on the vapor side of the interface and do not reflect the interfacial state.
The temperature discontinuity across the evaporating interface is < 0.1 K for
all these studies. To accurately capture the interfacial state, the vapor heat
flux should be suppressed.


  In this article we systematically study the general properties and the
single-point moments of the inverse of the Gaussian multiplicative chaos.


  Using a similarity Hamiltonian renormalization procedure, we determine an
effective spin-1/2 representation of the Bose-Hubbard model at half-integer
filling and at a finite on-site interaction energy U. By means of bosonization,
we are able to recast the effective Hamiltonian as that of a spin-1/2 XXZ
magnetic chain with pertinently renormalized coupling and anisotropy
parameters. We use this mapping to provide analytical estimates of the
correlation functions of the Bose-Hubbard model. We then compare such results
with those based on DMRG numerical simulations of the Bose-Hubbard model for
various values of U and for a number L of lattice sites as low as L ~ 30. We
find an excellent agreement up to 10% between the output of analytical and
numerical computations, even for relatively small values of U. Our analysis
implies that, also at finite U, the 1D Bose-Hubbard model with suitably chosen
parameters may be seen as a quantum simulator of the XXZ chain.


  Multi-object tracking (MOT) is a fundamental problem in computer vision with
numerous applications, such as intelligent surveillance and automated driving.
Despite the significant progress made in MOT, pedestrian attributes, such as
gender, hairstyle, body shape, and clothing features, which contain rich and
high-level information, have been less explored. To address this gap, we
propose a simple, effective, and generic method to predict pedestrian
attributes to support general Re-ID embedding. We first introduce AttMOT, a
large, highly enriched synthetic dataset for pedestrian tracking, containing
over 80k frames and 6 million pedestrian IDs with different time, weather
conditions, and scenarios. To the best of our knowledge, AttMOT is the first
MOT dataset with semantic attributes. Subsequently, we explore different
approaches to fuse Re-ID embedding and pedestrian attributes, including
attention mechanisms, which we hope will stimulate the development of
attribute-assisted MOT. The proposed method AAM demonstrates its effectiveness
and generality on several representative pedestrian multi-object tracking
benchmarks, including MOT17 and MOT20, through experiments on the AttMOT
dataset. When applied to state-of-the-art trackers, AAM achieves consistent
improvements in MOTA, HOTA, AssA, IDs, and IDF1 scores. For instance, on MOT17,
the proposed method yields a +1.1 MOTA, +1.7 HOTA, and +1.8 IDF1 improvement
when used with FairMOT. To encourage further research on attribute-assisted
MOT, we will release the AttMOT dataset.


  Recent advances in the integration of vehicular sensor network (VSN)
technology, and crowd sensing leveraging pervasive sensors called onboard units
(OBUs), like smartphones and radio frequency IDentifications to provide sensing
services, have attracted increasing attention from both industry and academy.
Nowadays, existing vehicular sensing applications lack good mechanisms to
improve the maximum achievable throughput and minimizing service time of
participating sensing OBUs in vehicular sensor networks. To fill these gaps, in
this paper, first, we introduce real imperfect link states to the calculation
of Markov chains. Second, we incorporate the result of different link states
for multiple types of vehicles with the calculations of uplink throughput and
service time. Third, in order to accurately calculate the service time of an
OBU, we introduce the steady state probability to calculate the exact time of a
duration for back-off decrement, rather than using the traditional relative
probability. Additionally, to our best knowledge, we first explore a
multichannel scheduling strategy of uplink data access in a single roadside
unit (RSU) by using a non-cooperative game in a RSU coverage region to maximize
the uplink throughput and minimize service time under saturated and unsaturated
traffic loads. To this end, we conduct a theoretical analysis and find the
equilibrium point of the scheduling. The numerical results show that the
solution of the equilibrium points are consistent with optimization problems.


  An orthogonal representation of a graph $G$ over a field $\mathbb{F}$ is an
assignment of a vector $u_v \in \mathbb{F}^t$ to every vertex $v$ of $G$, such
that $\langle u_v,u_v \rangle \neq 0$ for every vertex $v$ and $\langle
u_v,u_{v'} \rangle = 0$ whenever $v$ and $v'$ are adjacent in $G$. The locality
of the orthogonal representation is the largest dimension of a subspace spanned
by the vectors associated with a closed neighborhood in the graph. We introduce
a novel graph parameter, called the local orthogonality dimension, defined for
a given graph $G$ and a given field $\mathbb{F}$, as the smallest possible
locality of an orthogonal representation of $G$ over $\mathbb{F}$. We
investigate the usefulness of topological methods for proving lower bounds on
the local orthogonality dimension. We prove that graphs for which topological
methods imply a lower bound of $t$ on their chromatic number have local
orthogonality dimension at least $\lceil t/2 \rceil +1$ over every field,
strengthening a result of Simonyi and Tardos on the local chromatic number. We
show that for certain graphs this lower bound is tight, whereas for others, the
local orthogonality dimension over the reals is equal to the chromatic number.
More generally, we prove that for every complement of a line graph, the local
orthogonality dimension over $\mathbb{R}$ coincides with the chromatic number.
This strengthens a recent result by Daneshpajouh, Meunier, and Mizrahi, who
proved that the local and standard chromatic numbers of these graphs are equal.
As another extension of their result, we prove that the local and standard
chromatic numbers are equal for some additional graphs, from the family of
Kneser graphs. We also show an $\mathsf{NP}$-hardness result for the local
orthogonality dimension and present an application of this graph parameter to
the index coding problem from information theory.


  In this paper we would like to present a few characteristics of the Resistive
Plate Chambers (RPC) made of a particular grade of bakelite paper laminates
(P-120, NEMA LI-1989 Grade XXX), produced and commercially available in India.
This particular grade is used for high voltage insulation in humid conditions.
The chambers are tested with cosmic rays in the streamer mode using argon,
tetrafluroethane and isobutane in 34:59:7 mixing ratio. In the first set of
detectors made with such grade, a thin coating of silicone fluid on the inner
surfaces of the bakelite was found to be necessary for operation of the
detector. Those silicone coated RPCs were found to give satisfactory
performance with stable efficiency of >90% continuously for a long period as
reported earlier. Results of the crosstalk measurement of these silicone coated
RPC will be presented in this paper. Very recently RPCs made with the same
grade of bakelite but having better surface finish, are found to give
equivalent performance even without any coating inside. Preliminary results of
this type of RPCs are also being presented.


  Functional connectomes capture brain interactions via synchronized
fluctuations in the functional magnetic resonance imaging signal. If measured
during rest, they map the intrinsic functional architecture of the brain. With
task-driven experiments they represent integration mechanisms between
specialized brain areas. Analyzing their variability across subjects and
conditions can reveal markers of brain pathologies and mechanisms underlying
cognition. Methods of estimating functional connectomes from the imaging signal
have undergone rapid developments and the literature is full of diverse
strategies for comparing them. This review aims to clarify links across
functional-connectivity methods as well as to expose different steps to perform
a group study of functional connectomes.


  Reverse engineering of undocumented protocols is a common task in security
analyses of networked services. The communication itself, captured in traffic
traces, contains much of the necessary information to perform such a protocol
reverse engineering. The comprehension of the format of unknown messages is of
particular interest for binary protocols that are not human-readable. One major
challenge is to discover probable fields in a message as the basis for further
analyses. Given a set of messages, split into segments of bytes by an existing
segmenter, we propose a method to refine the approximation of the field
inference. We use principle component analysis (PCA) to discover linearly
correlated variance between sets of message segments. We relocate the
boundaries of the initial coarse segmentation to more accurately match with the
true fields. We perform different evaluations of our method to show its benefit
for the message format inference and subsequent analysis tasks from literature
that depend on the message format. We can achieve a median improvement of the
message format accuracy across different real-world protocols by up to 100 %.


  We develop an exact non-perturbative framework to compute steady-state
properties of quantum-impurities subject to a finite bias. We show that the
steady-state physics of these systems is captured by nonequilibrium scattering
eigenstates which satisfy an appropriate Lippman-Schwinger equation.
Introducing a generalization of the equilibrium Bethe-Ansatz - the
Nonequilibrium Bethe-Ansatz (NEBA), we explicitly construct the scattering
eigenstates for the Interacting Resonance Level model and derive exact,
nonperturbative results for the steady-state properties of the system.


  We consider the propagation of a generic scalar field around a rotating and
charged black hole. Using the partial wave method, we find, numerically, the
total and partial absorption cross sections for different incidence angles. We
investigate the low- and high-frequency limits, finding semi-analytical
approximations for the absorption cross section, which we compare with our
numerical results. Finally, we consider the superradiant regime, showing that,
for charged fields, planar waves can be superradiantly scattered.


  Automatic search of neural architectures for various vision and natural
language tasks is becoming a prominent tool as it allows to discover
high-performing structures on any dataset of interest. Nevertheless, on more
difficult domains, such as dense per-pixel classification, current automatic
approaches are limited in their scope - due to their strong reliance on
existing image classifiers they tend to search only for a handful of additional
layers with discovered architectures still containing a large number of
parameters. In contrast, in this work we propose a novel solution able to find
light-weight and accurate segmentation architectures starting from only few
blocks of a pre-trained classification network. To this end, we progressively
build up a methodology that relies on templates of sets of operations, predicts
which template and how many times should be applied at each step, while also
generating the connectivity structure and downsampling factors. All these
decisions are being made by a recurrent neural network that is rewarded based
on the score of the emitted architecture on the holdout set and trained using
reinforcement learning. One discovered architecture achieves 63.2% mean IoU on
CamVid and 67.8% on CityScapes having only 270K parameters. Pre-trained models
and the search code are available at
https://github.com/DrSleep/nas-segm-pytorch.


  The anisotropy of the cosmic microwave background radiation contains
information about the contents and history of the universe. We report new
limits on cosmological parameters derived from the angular power spectrum
measured in the first Antarctic flight of the BOOMERANG experiment. Within the
framework of inflation-motivated adiabatic cold dark matter models, and using
only weakly restrictive prior probabilites on the age of the universe and the
Hubble expansion parameter $h$, we find that the curvature is consistent with
flat and that the primordial fluctuation spectrum is consistent with scale
invariant, in agreement with the basic inflation paradigm. We find that the
data prefer a baryon density $\Omega_b h^2$ above, though similar to, the
estimates from light element abundances and big bang nucleosynthesis. When
combined with large scale structure observations, the BOOMERANG data provide
clear detections of both dark matter and dark energy contributions to the total
energy density $\Omega_{\rm {tot}}$, independent of data from high redshift
supernovae.


  To investigate the different vortex structure between two chiral pairing p_x
+(-) i p_y, we calculate the pair potential, the internal field, the local
density of states, and free energy in the vortex lattice state based on the
quasiclassical Eilenberger theory, and analyze the magnetic field dependence.
The induced opposite chiral component of the pair potential plays an important
role in the vortex structure. It also produces H^{1/2}-behavior of the
zero-energy density of states at higher field. These results are helpful when
we understand the vortex states in Sr2RuO4.


  CyberPhysical systems (CPS) must be closely monitored to identify and
potentially mitigate emergent problems that arise during their routine
operations. However, the multivariate time-series data which they typically
produce can be complex to understand and analyze. While formal product
documentation often provides example data plots with diagnostic suggestions,
the sheer diversity of attributes, critical thresholds, and data interactions
can be overwhelming to non-experts who subsequently seek help from discussion
forums to interpret their data logs. Deep learning models, such as Long
Short-term memory (LSTM) networks can be used to automate these tasks and to
provide clear explanations of diverse anomalies detected in real-time
multivariate data-streams. In this paper we present RESAM, a requirements
process that integrates knowledge from domain experts, discussion forums, and
formal product documentation, to discover and specify requirements and design
definitions in the form of time-series attributes that contribute to the
construction of effective deep learning anomaly detectors. We present a
case-study based on a flight control system for small Uncrewed Aerial Systems
and demonstrate that its use guides the construction of effective anomaly
detection models whilst also providing underlying support for explainability.
RESAM is relevant to domains in which open or closed online forums provide
discussion support for log analysis.


  A model for an anisotropic Bianchi type VI universe in a Scale Covariant
theory of gravitation (Canuto et al. 1977) is analyzed. A finite singularity is
found in the model at the initial time $t=0$. All the physical parameters are
studied and thoroughly discussed. The model behaves like a big bang singular
model of the universe. This universe model is found to be consistent with the
observations on the present day universe.


  We study on the third quantization of a Kaluza-Klein toy model. In this model
time ($x$) is defined by the scale factor of universe, and the space coordinate
($y$) is defined by the ratio of the scales of the ordinary space and the
internal space. We calculate the number density of the universes created from
nothing and examine whether the compactification can be explained statistically
by the idea of the third quantization.


  State estimation is important for a variety of tasks, from forecasting to
substituting for unmeasured states in feedback controllers. Performing
real-time state estimation for PDEs using provably and rapidly converging
observers, such as those based on PDE backstepping, is computationally
expensive and in many cases prohibitive. We propose a framework for
accelerating PDE observer computations using learning-based approaches that are
much faster while maintaining accuracy. In particular, we employ the
recently-developed Fourier Neural Operator (FNO) to learn the functional
mapping from the initial observer state and boundary measurements to the state
estimate. By employing backstepping observer gains for previously-designed
observers with particular convergence rate guarantees, we provide numerical
experiments that evaluate the increased computational efficiency gained with
FNO. We consider the state estimation for three benchmark PDE examples
motivated by applications: first, for a reaction-diffusion (parabolic) PDE
whose state is estimated with an exponential rate of convergence; second, for a
parabolic PDE with exact prescribed-time estimation; and, third, for a pair of
coupled first-order hyperbolic PDEs that modeling traffic flow density and
velocity. The ML-accelerated observers trained on simulation data sets for
these PDEs achieves up to three orders of magnitude improvement in
computational speed compared to classical methods. This demonstrates the
attractiveness of the ML-accelerated observers for real-time state estimation
and control.


  Action anticipation is critical in scenarios where one needs to react before
the action is finalized. This is, for instance, the case in automated driving,
where a car needs to, e.g., avoid hitting pedestrians and respect traffic
lights. While solutions have been proposed to tackle subsets of the driving
anticipation tasks, by making use of diverse, task-specific sensors, there is
no single dataset or framework that addresses them all in a consistent manner.
In this paper, we therefore introduce a new, large-scale dataset, called
VIENA2, covering 5 generic driving scenarios, with a total of 25 distinct
action classes. It contains more than 15K full HD, 5s long videos acquired in
various driving conditions, weathers, daytimes and environments, complemented
with a common and realistic set of sensor measurements. This amounts to more
than 2.25M frames, each annotated with an action label, corresponding to 600
samples per action class. We discuss our data acquisition strategy and the
statistics of our dataset, and benchmark state-of-the-art action anticipation
techniques, including a new multi-modal LSTM architecture with an effective
loss function for action anticipation in driving scenarios.