  In this paper, we overview our experiences of developing large set of open
source tools in ECSEL JU European project called MegaM@Rt2 whose main objective
is to propose a scalable model-based framework incorporating methods and tools
for the continuous development and runtime support of complex
software-intensive Cyber-Physical Systems (CPSs). We briefly present the
MegaM@Rt2 concepts, discuss our approach for open source, enumerate tools and
give an example of a tools selection for a specific industrial context. Our
goal is to introduce the reader with open source tools for the model-based
engineering of CPSs suitable for diverse industrial applications.


  A directed odd cycle transversal of a directed graph (digraph) $D$ is a
vertex set $S$ that intersects every odd directed cycle of $D$. In the Directed
Odd Cycle Transversal (DOCT) problem, the input consists of a digraph $D$ and
an integer $k$. The objective is to determine whether there exists a directed
odd cycle transversal of $D$ of size at most $k$.
  In this paper, we settle the parameterized complexity of DOCT when
parameterized by the solution size $k$ by showing that DOCT does not admit an
algorithm with running time $f(k)n^{O(1)}$ unless FPT = W[1]. On the positive
side, we give a factor $2$ fixed parameter tractable (FPT) approximation
algorithm for the problem. More precisely, our algorithm takes as input $D$ and
$k$, runs in time $2^{O(k^2)}n^{O(1)}$, and either concludes that $D$ does not
have a directed odd cycle transversal of size at most $k$, or produces a
solution of size at most $2k$. Finally, we provide evidence that there exists
$\epsilon > 0$ such that DOCT does not admit a factor $(1+\epsilon)$
FPT-approximation algorithm.


  The destruction of a regular black hole event horizon might provide us the
possibility to access regions inside black hole event horizon. This paper
investigates the possibility of overcharging a charged Taub-NUT regular black
hole via the scattering of a charged field and the absorption of a charged
particle. For the charged scalar field scattering, both the near-extremal and
extremal charged Taub-NUT regular black holes cannot be overcharged. For the
test charged particle absorption, the result shows that the event horizon of
the extremal charged Taub-NUT regular black hole still exists while the event
horizon of the near-extremal one can be destroyed. However, if the charge and
energy cross the event horizon in a continuous path, the near-extremal charged
Taub-NUT regular black hole might not be overcharged.


  We tackle the problem of automatically reconstructing a complete 3D model of
a scene from a single RGB image. This challenging task requires inferring the
shape of both visible and occluded surfaces. Our approach utilizes
viewer-centered, multi-layer representation of scene geometry adapted from
recent methods for single object shape completion. To improve the accuracy of
view-centered representations for complex scenes, we introduce a novel
"Epipolar Feature Transformer" that transfers convolutional network features
from an input view to other virtual camera viewpoints, and thus better covers
the 3D scene geometry. Unlike existing approaches that first detect and
localize objects in 3D, and then infer object shape using category-specific
models, our approach is fully convolutional, end-to-end differentiable, and
avoids the resolution and memory limitations of voxel representations. We
demonstrate the advantages of multi-layer depth representations and epipolar
feature transformers on the reconstruction of a large database of indoor
scenes.


  We treat random rank-$D$ tensor models as $D$-dimensional quantum field
theories---tensor field theories (TFT)---and review some of their
non-perturbative methods. We classify the correlation functions of complex
tensor field theories by boundary graphs, sketch the derivation of the
Ward-Takahashi identity and stress its relevance in the derivation of the tower
of exact, analytic Schwinger-Dyson equations for all the correlation functions
(with connected boundary) of TFTs with quartic pillow-like interactions.


  A dominating set of a graph $\mathcal{G=(V, E)}$ is a subset of vertices
$S\subseteq\mathcal{V}$ such that every vertex $v\in \mathcal{V} \setminus S$
outside the dominating set is adjacent to a vertex $u\in S$ within the set. The
minimum dominating set problem seeks to find a dominating set of minimum
cardinality and is a well-established NP-hard combinatorial optimization
problem. We propose a novel learning-based heuristic approach to compute
solutions for the minimum dominating set problem using graph convolutional
networks. We conduct an extensive experimental evaluation of the proposed
method on a combination of randomly generated graphs and real-world graph
datasets. Our results indicate that the proposed learning-based approach can
outperform a classical greedy approximation algorithm. Furthermore, we
demonstrate the generalization capability of the graph convolutional network
across datasets and its ability to scale to graphs of higher order than those
on which it was trained. Finally, we utilize the proposed learning-based
heuristic in an iterative greedy algorithm, achieving state-of-the-art
performance in the computation of dominating sets.


  We explore the consequences of very high dimensionality in the dynamical
landscape of protein folding. Consideration of both typical range of
stabilising interactions, and folding rates themselves, leads to a model of the
energy hypersurface that is characterised by the structure of diffusive
"hypergutters" as well as the familiar "funnels". Several general predictions
result: (1) intermediate subspaces of configurations will always be visited;
(2) specific but non-native interactions are important in stabilising these
low-dimensional diffusive searches on the folding pathway; (3) sequential
barriers will commonly be found, even in "two-state"proteins; (4) very early
times will show charactreristic departures from single-exponential kinetics;
(5) contributions of non-native interactions to phi-values are calculable, and
may be significant. The example of a three-helix bundle is treated in more
detail as an illustration. The model also shows that high-dimensional
structures provide conceptual relations between the "folding funnel",
"diffusion-collision", "nucleation-condensation" and "topomer search" models of
protein folding. It suggests that kinetic strategies for fast folding may be
encoded rather generally in non-native, rather than native interactions. The
predictions are related to very recent findings in experiment and simulation.


  We consider entanglement swapping, a key component of quantum network
operations and entanglement distribution. Pure entangled states, which are the
desired input to the swapping protocol, are typically mixed by environmental
interactions causing a reduction in their degree of entanglement. Thus an
understanding of entanglement swapping with partially mixed states is of
importance. Here we present a general analytical solution for entanglement
swapping of arbitrary two-qubit states. Our result provides a comprehensive
method for analyzing entanglement swapping in quantum networks. First, we show
that the concurrence of a partially mixed state is conserved when this state is
swapped with a Bell state. Then, we find upper and lower bounds on the
concurrence of the state resulting from entanglement swapping for various
classes of input states. Finally, we determine a general relationship between
the ranks of the initial states and the rank of the final state after swapping.


  We study the validity of the $L^p$ inequality for the Riesz transform when
$p>2$ and of its reverse inequality when $p<2$ on complete Riemannian manifolds
under the doubling property and some Poincar\'e inequalities.


  We exhibit a simple condition under which a finite involutary semigroup whose
semigroup reduct is inherently nonfinitely based is also inherently nonfinitely
based as a unary semigroup. As applications, we get already known as well as
new examples of inherently nonfinitely based involutory semigroups. We also
show that for finite regular semigroups, our condition is not only sufficient
but also necessary for the property of being inherently nonfinitely based to
persist. This leads to an algorithmic description of regular inherently
nonfinitely based involutory semigroups.


  In this paper we present a method for algorithmic melody generation using a
generative adversarial network without recurrent components. Music generation
has been successfully done using recurrent neural networks, where the model
learns sequence information that can help create authentic sounding melodies.
Here, we use DC-GAN architecture with dilated convolutions and towers to
capture sequential information as spatial image information, and learn
long-range dependencies in fixed-length melody forms such as Irish traditional
reel.


  The Hamiltonian formulation of the lowest-order projectable Horava gravity,
namely the so-called $\lambda$-$R$ gravity, is studied. Since a preferred
foliation has been chosen in projectable Horava gravity, there is no local
Hamiltonian constraint in the theory. In contrast to general relativity, the
constraint algebra of $\lambda$-$R$ gravity forms a Lie algebra. By canonical
transformations, we further obtain the connection-dynamical formalism of the
$\lambda$-R gravity theories with real $su(2)$-connections as configuration
variables. This formalism enables us to extend the scheme of non-perturbative
loop quantum gravity to the $\lambda$-$R$ gravity. While the quantum
kinematical framework is the same as that for general relativity, the
Hamiltonian constraint operator of loop quantum $\lambda$-$R$ gravity can be
well defined in the diffeomorphism-invariant Hilbert space. Moreover, by
introducing a global dust degree of freedom to represent a dynamical time, a
physical Hamiltonian operator with respect to the dust can be defined and the
physical states satisfying all the constraints are obtained.


  We present measurements of second- and higher-order intensity correlation
functions (so-called Hanbury Brown and Twiss experiment) performed at the
free-electron laser (FEL) FLASH in the non-linear regime of its operation. We
demonstrate the high transverse coherence properties of the FEL beam with a
degree of transverse coherence of about 80% and degeneracy parameter of the
order 10^9 that makes it similar to laser sources. Intensity correlation
measurements in spatial and frequency domain gave an estimate of the FEL
average pulse duration of 50 fs. Our measurements of the higher-order
correlation functions indicate that FEL radiation obeys Gaussian statistics,
which is characteristic to chaotic sources.


  This paper focuses on synthesizing control policies for discrete-time
stochastic control systems together with a lower bound on the probability that
the systems satisfy the complex temporal properties. The desired properties of
the system are expressed as linear temporal logic (LTL) specifications over
finite traces. In particular, our approach decomposes the given specification
into simpler reachability tasks based on its automata representation. We then
propose the use of so-called \emph{control barrier certificate} to solve those
simpler reachability tasks along with computing the corresponding controllers
and probability bounds. Finally, we combine those controllers to obtain a
hybrid control policy solving the considered problem. Under some assumptions,
we also provide two systematic approaches for uncountable and finite input sets
to search for control barrier certificates. We demonstrate the effectiveness of
the proposed approach on a room temperature control and lane-keeping of a
vehicle modeled as a four-dimensional single-track kinematic model. We compare
our results with the discretization-based methods in the literature.


  We consider a magnetic flow without conjugate points on a closed manifold $M$
with generating vector field $\G$. Let $h\in C^{\infty}(M)$ and let $\theta$ be
a smooth 1-form on $M$. We show that the cohomological equation \[\G(u)=h\circ
\pi+\theta\] has a solution $u\in C^{\infty}(SM)$ only if $h=0$ and $\theta$ is
closed. This result was proved in \cite{DP2} under the assumption that the flow
of $\G$ is Anosov.


  The penetration of dendrites in ceramic lithium conductors severely
constrains the development of solid-state batteries (SSBs) while its nanoscopic
origin remain unelucidated. We develop an in-situ nanoscale electrochemical
characterization technique to reveal the nanoscopic lithium dendrite growth
kinetics and use it as a guiding tool to unlock the design of interfaces for
dendrite-proof SSBs. Using Li7La3Zr2O12 (LLZO) as a model system, in-situ
nanoscopic dendrite triggering measurements, ex-situ electro-mechanical
characterizations, and finite element simulations are carried out which reveal
the dominating role of Li+ flux detouring and nano-mechanical inhomogeneity on
dendrite penetration. To mitigate such nano-inhomogeneity, an ionic-conductive
homogenizing layer based on poly(propylene carbonate) is designed which in-situ
reacts with lithium to form a highly conformal interphase at mild conditions. A
high critical current density of 1.8mA cm-2 and a low interfacial resistance of
14{\Omega} cm2 is achieved. Practical SSBs based on LiFePO4 cathodes show great
cyclic stability without capacity decay over 300 cycles. Beyond this, highly
reversible electrochemical dendrite healing behavior in LLZO is discovered
using the nano-electrode, based on which a model memristor with a high on/off
ratio of ~10^5 is demonstrated for >200 cycles. This work not only provides a
novel tool to investigate and design interfaces in SSBs but offers also new
opportunities for solid electrolytes beyond energy applications.


  We present a bidirectional quantum communication system based on optical
phase conjugation for achieving fully autocompensating high-dimensional quantum
cryptography. We prove that random phase shifts and couplings among 2N spatial
and polarization optical modes described by SU(2N) transformations due to
perturbations are autocompensated after a single round trip between Alice and
Bob. Bob can use a source of single photons or, alternatively, coherent states
and then Alice attenuates them up to a single photon level, and thus
non-perturbated 1-qudit states are generated for high-dimensional QKD
protocols, as the BB84 one, of a higher security.


  A key yet unresolved question in modern-day astronomy is how galaxies formed
and evolved under the paradigm of the $\Lambda$CDM model. A critical limiting
factor lies in the lack of robust tools to describe the merger history through
a statistical model. In this work, we employ a generative graph network, E(n)
Equivariant Graph Normalizing Flows Model. We demonstrate that, by treating the
progenitors as a graph, our model robustly recovers their distributions,
including their masses, merging redshifts and pairwise distances at redshift
z=2 conditioned on their z=0 properties. The generative nature of the model
enables other downstream tasks, including likelihood-free inference, detecting
anomalies and identifying subtle correlations of progenitor features.


  I show that a finite density of near-zero localised Dirac modes can lead to
the disappearance of the massless excitations predicted by the
finite-temperature version of Goldstone's theorem in the chirally broken phase
of a gauge theory.


  How animals allocate energy to different body functions is still not
completely understood and a challenging topic until recently. Here, we
investigate in more detail the allocation of energy intake to growth,
reproduction or heat production by developing energy budget models for
ectothermic and endothermic vertebrates using a mathematical approach. We
calculated energy intake functions of ectotherms and endotherms derived from
their body mass growth. We show that our energy budget model produces energy
intake patterns and distributions as observed in ectothermic and endothermic
species. Our results comply consistently with some empirical studies that in
endothermic species, like birds and mammals, energy is used for heat production
instead of growth. Our model additionally offers an explanation on known
differences in absolute energy intake between ectothermic fish and reptiles and
endothermic birds and mammals. From a mathematical point of view, the model
comes in two equivalent formulations, a differential and an integral one. It is
derived from a discrete level approach, and it is shown to be well-posed and to
attain a unique solution for (almost) every parameter set. Numerically, the
integral formulation of the model is considered as an inverse problem with
unknown parameters that are estimated using a series of experiments/realistic
data.


  A boson sampler implements a restricted model of quantum computing. It is
defined by the ability to sample from the distribution resulting from the
interference of identical bosons propagating according to programmable,
non-interacting dynamics. Here, we demonstrate a new combination of tools for
implementing boson sampling using ultracold atoms in a two-dimensional,
tunnel-coupled optical lattice. These tools include fast and programmable
preparation of large ensembles of nearly identical bosonic atoms
($99.5^{+0.5}_{-1.6}\;\%$ indistinguishability) by means of rearrangement with
optical tweezers and high-fidelity optical cooling, propagation for variable
evolution time in the lattice with low loss ($5.0(2)\;\%$, independent of
evolution time), and high fidelity detection of the atom positions after their
evolution (typically $99.8(1)\;\%$). With this system, we study specific
instances of boson sampling involving up to $180$ atoms distributed among $\sim
1000$ sites in the lattice. Direct verification of a given boson sampling
distribution is not feasible in this regime. Instead, we introduce and perform
targeted tests to determine the indistinguishability of the prepared atoms, to
characterize the applied family of single particle unitaries, and to observe
expected bunching features due to interference for a large range of atom
numbers. When extended to interacting systems, our work demonstrates the core
capabilities required to directly assemble ground and excited states in
simulations of various Hubbard models.


  We study the problem of teaching multiple learners simultaneously in the
nonparametric iterative teaching setting, where the teacher iteratively
provides examples to the learner for accelerating the acquisition of a target
concept. This problem is motivated by the gap between current single-learner
teaching setting and the real-world scenario of human instruction where a
teacher typically imparts knowledge to multiple students. Under the new problem
formulation, we introduce a novel framework -- Multi-learner Nonparametric
Teaching (MINT). In MINT, the teacher aims to instruct multiple learners, with
each learner focusing on learning a scalar-valued target model. To achieve
this, we frame the problem as teaching a vector-valued target model and extend
the target model space from a scalar-valued reproducing kernel Hilbert space
used in single-learner scenarios to a vector-valued space. Furthermore, we
demonstrate that MINT offers significant teaching speed-up over repeated
single-learner teaching, particularly when the multiple learners can
communicate with each other. Lastly, we conduct extensive experiments to
validate the practicality and efficiency of MINT.


  We apply the theory of elasticity to study the effects of skyrmion mass on
lattice dynamics in quantum Hall systems. We find that massive Skyrme lattices
behave like a Wigner crystal in the presence of a uniform perpendicular
magnetic field. We make a comparison with the microscopic Hartree-Fock results
to characterize the mass of quantum Hall skyrmions at $\nu=1$ and investigate
how the low temperature phase of Skyrme lattices may be affected by the
skyrmion mass.


  We propose a method for inferring the existence of a latent common cause
('confounder') of two observed random variables. The method assumes that the
two effects of the confounder are (possibly nonlinear) functions of the
confounder plus independent, additive noise. We discuss under which conditions
the model is identifiable (up to an arbitrary reparameterization of the
confounder) from the joint distribution of the effects. We state and prove a
theoretical result that provides evidence for the conjecture that the model is
generically identifiable under suitable technical conditions. In addition, we
propose a practical method to estimate the confounder from a finite i.i.d.
sample of the effects and illustrate that the method works well on both
simulated and real-world data.


  We present a study of B--> D0(CP)K- decays, where D0(CP) is reconstructed in
non-CP (K-pi+), CP-even (K-K+,pi-pi+) and CP-odd (KS0 pi0) eigenstates, based
on a sample of about 214 million Y(4S) -> BBbar decays collected with the BABAR
detector at the PEP-II e+e- storage ring. We measure the ratios of branching
fractions R+ = [Br(B- ->D0_CP+ K-)+Br(B+ ->D0_CP+ K+)]/[Br(B- ->D0 K-)] = 0.87
+/- 0.14(stat) +/- 0.06(syst), R- = [Br(B- ->D0_CP- K-)+Br(B+ ->D0_CP-
K+)]/[Br(B- ->D0 K-)] = 0.80 +/- 0.14(stat) +/- 0.08(syst), and the CP
asymmetries A_CP+ = 0.40 +/- 0.15 (stat) +/- 0.08 (syst) and A_CP- = 0.21 +/-
0.17 (stat) +/- 0.07 (syst) All results are preliminary.


  We develop a theory for transit times and mean ages for nonautonomous
compartmental systems. Using the McKendrick-von F\"orster equation, we show
that the mean ages of mass in a compartmental system satisfy a linear
nonautonomous ordinary differential equation that is exponentially stable. We
then define a nonautonomous version of transit time as the mean age of mass
leaving the compartmental system at a particular time and show that our
nonautonomous theory generalises the autonomous case. We apply these results to
study a nine-dimensional nonautonomous compartmental system modeling the
terrestrial carbon cycle, which is a modification of the Carnegie-Ames-Stanford
approach (CASA) model, and we demonstrate that the nonautonomous versions of
transit time and mean age differ significantly from the autonomous quantities
when calculated for that model.


  Interpretability of machine learning models has gained more and more
attention among researchers in the artificial intelligence (AI) and
human-computer interaction (HCI) communities. Most existing work focuses on
decision making, whereas we consider knowledge discovery. In particular, we
focus on evaluating AI-discovered knowledge/rules in the arts and humanities.
From a specific scenario, we present an experimental procedure to collect and
assess human-generated verbal interpretations of AI-generated music
theory/rules rendered as sophisticated symbolic/numeric objects. Our goal is to
reveal both the possibilities and the challenges in such a process of decoding
expressive messages from AI sources. We treat this as a first step towards 1)
better design of AI representations that are human interpretable and 2) a
general methodology to evaluate interpretability of AI-discovered knowledge
representations.


  In elemental Bismuth, 10$^5$ atoms share a single itinerant electron.
Therefore, a moderate magnetic field can confine electrons to the lowest Landau
level. We report on the first study of metallic thermoelectricity in this
regime. The main thermoelectric response is off-diagonal with an oscillating
component several times larger than the non-oscillating background. When the
first Landau level attains the Fermi Energy, both the Nernst and the
Ettingshausen coefficients sharply peak, and the latter attains a
temperature-independent maximum. A qualitative agreement with a theory invoking
current-carrying edge excitations is observed.


  The problem of secret-key based authentication under privacy and storage
constraints on the source sequence is considered. The identifier measurement
channels during authentication are assumed to be controllable via a
cost-constrained action sequence. Single-letter inner and outer bounds for the
key-leakage-storage-cost regions are derived for a generalization of a classic
two-terminal key agreement model with an eavesdropper that observes a sequence
that is correlated with the sequences observed by the legitimate terminals. The
additions to the model are that the encoder observes a noisy version of a
remote source, and the noisy output and the remote source output together with
an action sequence are given as inputs to the measurement channel at the
decoder. Thus, correlation is introduced between the noise components on the
encoder and decoder measurements. The model with a secret key generated by an
encoder is extended to the randomized models, where a secret-key is embedded to
the encoder. The results are relevant for several user and device
authentication scenarios including physical and biometric identifiers with
multiple measurements that provide diversity and multiplexing gains. To
illustrate the behavior of the rate region, achievable (secret-key rate,
storage-rate, cost) tuples are given for binary identifiers and measurement
channels that can be represented as a mixture of binary symmetric subchannels.
The gains from using an action sequence such as a large secret-key rate at a
significantly small hardware cost, are illustrated to motivate the use of
low-complexity transform-coding algorithms with cost-constrained actions.


  A search for the pair-production of heavy leptons ($N^0,L^{\pm}$) predicted
by the type-III seesaw theory formulated to explain the origin of small
neutrino masses is presented. The decay channels $N^0\rightarrow
W^{\pm}l^{\mp}$ ($\ell = e, \mu, \tau$) and $L^{\pm}\rightarrow W^\pm \nu$
($\nu=\nu_e, \nu_\mu, \nu_\tau$) are considered. The analysis is performed
using the final state that contains two leptons (electrons or muons), two jets
from a hadronically decaying $W$ boson, and large missing transverse momentum.
The data used in the measurement correspond to an integrated luminosity of
$20.3\,fb^{-1}$ of $pp$ collisions at $\sqrt{s} = 8$ TeV collected by the ATLAS
detector at the LHC. No evidence of heavy lepton pair-production is observed.
Heavy leptons with masses below 325--540 GeV are excluded at the 95% confidence
level, depending on the theoretical scenario considered.


  I summarize recent surveys of protoplanetary disks at millimeter wavelengths
and show that the distribution of luminosity, equivalent to the mass in small
dust grains, declines rapidly. This contrasts with statistics on the lifetime
of disks from infrared observations and the high occurrence of planets from
radial velocity and transit surveys. I suggest that these disparate results can
be reconciled if most of the dust in a disk is locked up in millimeter and
larger sized particles within about 2 Myr. This statistical result on disk
evolution agrees with detailed modeling of a small number of individual disks
and with cosmochemical measurements of rapid planetesimal formation.


  Investigating spin and charge noise in strongly correlated electron systems
is a valuable way to analyze their physical properties and unlock new phases of
matter. In this context, nitrogen-vacancy (NV) center-based magnetometry has
been proven to be a versatile sensor for various classes of magnetic materials
in broad temperature and frequency ranges. Here, we use longitudinal relaxation
time $T_1$ of single NV centers to investigate the spin dynamics of
nanometers-thin flakes of $\alpha$-RuCl$_3$ at room temperature. We observe a
significant reduction in the $T_1$ in the presence of $\alpha$-RuCl$_3$ in
proximity to our NVs, which we attribute to paramagnetic spin noise confined in
the 2D hexagonal plane. Furthermore, the $T_1$ time exhibits an almost linear
increase with an applied external magnetic field. We associate this trend with
the alteration of spin and charge noise in $\alpha$-RuCl$_3$ under an external
magnetic field. These findings suggest that the influence of the
room-temperature spin dynamics of $\alpha$-RuCl$_3$ on the longitudinal
relaxation time of the NV center can be used to gain information on the
material itself and the technique to be used on other 2D materials.


  Linear RMS-flux relation has been well established in different spectral
states of all accreting systems. In this work, we study the evolution of the
frequency-dependent RMS-flux relation of MAXI J1820+070 during the initial
decaying phase of the 2018 outburst with Insight-HXMT over a broad energy range
1-150 keV. As the flux decreases, we first observe a linear RMS-flux relation
at frequencies from 2 mHz to 10 Hz, while such a relation breaks at varying
times for different energies, leading to a substantial reduction in the slope.
Moreover, we find that the low-frequency variability exhibits the highest
sensitivity to the break, which occurs prior to the hard-to-hard state
transition time determined through time-averaged spectroscopy, and the time
deviation increases with energy. The overall evolution of the RMS-flux slope
and intercept suggests the presence of a two-component Comptonization system.
One component is radially extended, explaining the strong disk-corona coupling
before the break, while the other component extends vertically, contributing to
the reduction of the disk-corona coupling after the break. A further vertical
expansion of the latter component is required to accommodate the dynamic
evolution observed in the RMS-flux slope. In conclusion, we suggest that the
RMS-flux slope in 1-150 keV band can be employed as an indicator of the
disk-corona coupling and the hard-to-hard state transition in MAXI J1820+070
could be partially driven by the changes in the corona geometry.


  With a view towards Riemannian or sub-Riemannian manifolds, RCD metric spaces
and specially fractals, this paper makes a step further in the development of a
theory of heat semigroup based $(1,p)$ Sobolev spaces in the general framework
of Dirichlet spaces. Under suitable assumptions that are verified in a variety
of settings, the tools developed by D. Bakry, T. Coulhon, M. Ledoux and L.
Saloff-Coste in the paper "Sobolev inequalities in disguise" allow us to obtain
the whole family of Gagliardo-Nirenberg and Trudinger-Moser inequalities with
optimal exponents. The latter depend not only on the Hausdorff and walk
dimensions of the space but also on other invariants. In addition, we prove
Morrey type inequalities and apply them to study the infimum of the exponents
that ensure continuity of Sobolev functions. The results are illustrated for
fractals using the Vicsek set, whereas several conjectures are made for nested
fractals and the Sierpinski carpet.


  Cooperative multi-agent reinforcement learning is a powerful tool to solve
many real-world cooperative tasks, but restrictions of real-world applications
may require training the agents in a fully decentralized manner. Due to the
lack of information about other agents, it is challenging to derive algorithms
that can converge to the optimal joint policy in a fully decentralized setting.
Thus, this research area has not been thoroughly studied. In this paper, we
seek to systematically review the fully decentralized methods in two settings:
maximizing a shared reward of all agents and maximizing the sum of individual
rewards of all agents, and discuss open questions and future research
directions.


  As scientists worldwide search for answers to the overwhelmingly unknown
behind the deadly pandemic, the literature concerning COVID-19 has been growing
exponentially. Keeping abreast of the body of literature at such a rapidly
advancing pace poses significant challenges not only to active researchers but
also to the society as a whole. Although numerous data resources have been made
openly available, the analytic and synthetic process that is essential in
effectively navigating through the vast amount of information with heightened
levels of uncertainty remains a significant bottleneck. We introduce a generic
method that facilitates the data collection and sense-making process when
dealing with a rapidly growing landscape of a research domain such as COVID-19
at multiple levels of granularity. The method integrates the analysis of
structural and temporal patterns in scholarly publications with the delineation
of thematic concentrations and the types of uncertainties that may offer
additional insights into the complexity of the unknown. We demonstrate the
application of the method in a study of the COVID-19 literature.


  We give necessary and sufficient conditions for a sequence to be exactly
realizable as the sequence of numbers of periodic points in a dynamical system.
Using these conditions, we show that no non-constant polynomial is realizable,
and give some conditions on realizable binary recurrence sequences. Realization
in rate is always possible for sufficiently rapidly-growing sequences, and is
never possible for slowly-growing sequences. Finally, we discuss the
relationship between the growth rate of periodic points and the growth rate of
points with specified least period.


  We compute the $K$-groups of the $C^{*}$-algebra of bounded operators
generated by the Boutet de Monvel operators with classical SG-symbols of order
(0,0) and type 0 on $\mathbb{R}_{+}^{n}$, as defined by Schrohe, Kapanadze and
Schulze. In order to adapt the techniques used in Melo, Nest, Schick and
Schrohe's work on the K-theory of Boutet de Monvel's algebra on compact
manifolds, we regard the symbols as functions defined on the radial
compactifications of $\mathbb{R}_{+}^{n}\times\mathbb{R}^{n}$ and
$\mathbb{R}^{n-1}\times\mathbb{R}^{n-1}$. This allows us to give useful
descriptions of the kernel and the image of the continuous extension of the
boundary principal symbol map, which defines a $C^{*}$-algebra homomorphism. We
are then able to compute the $K$-groups of the algebra using the standard
K-theory six-term cyclic exact sequence associated to that homomorphism.


  The long-range spectral density correlations (spectral rigidities
$\bar{\Delta}_3(\bar n)$ and related spectral compressibilities) of the
$E\otimes (b_1+b_2)$ Jahn-Teller model are found strongly nonuniversal with
respect to the Hamiltonian parameters and inhomogeneous with respect to the
choice of a partial energy segment. However, the spectral compressibilities
found for the partial spectral segments and averaged over a whole relevant part
of the spectrum cumulate close to a well-defined limit pertaining to the
semi-Poisson statistics. This is in accordance with similar tendencies revealed
in the short-range averaged statistical characteristics of this model
investigated in our previous paper [1]. We ascribe the nonuniversal and
inhomogeneous nonintegrability behaviour to the changing degree of the
brokening of the rotation symmetry when changing parameters of our effectively
two-dimensional model. It results in a random distribution of the respective
localized wave functions at all scales up to the size of an available state
space. The multifractal behaviour of the wave functions is implied from the
analysis of their (averaged) fractal dimensions which range up to $1.5\pm 0.1$
(for $\bar{D}_2$). This might imply the concept of the chaos-assisted
tunnelling between the regions of reduced degree of stochasticity through
regions of high degree of stochasticity. It supports the analogy with the
two-dimensional Anderson model with marginal-asymptotically far metal-insulator
transition. The features found allow us to classify the present model as a
member of the class with a multifractal eigenfunction statistics characteristic
for the spectra with weakened level repulsion similar to the Anderson model
close to the metal-insulator transition.


  We record various properties of twisted Becker-Gottlieb transfer maps and
study their multiplicative properties analogous to Becker-Gottlieb transfer. We
show these twisted transfer maps factorise through
Becker-Schultz-Mann-Miller-Miller transfer; some of these might be well known.
We apply this to show that $BSO(2n+1)_+$ splits off $MTO(2n)$, which after
localisation away from $2$, refines to a homotopy equivalence $MTO(2n)\simeq
BO(2n)_+$ as well as $MTO(2n+1)\simeq *$ for all $n\geqslant0$. This reduces
the study of $MTO(n)$ to the $2$-localised case. At the prime $2$ our splitting
allows to identify some algebraically independent classes in mod $2$ cohomology
of $\Omega^\infty MTO(2n)$. We also show that $BG_+$ splits off $MTK$ for some
pairs $(G,K)$ at appropriate set of primes $p$, and investigate the
consequences for characteristic classes, including algebraic independence and
non-divisibility of some universally defined characteristic classes,
generalizing results of Ebert and Randal-Williams.


  The notion of entanglement is the most well-known nonclassical correlation in
quantum mechanics and a fundamental resource in quantum information and
computation. This correlation, which is displayed by certain classes of quantum
states, is of utmost importance when dealing with protocols such as quantum
teleportation, cryptography, and quantum key distribution. In this paper, we
exploit a classification of tripartite entanglement by introducing the concepts
of entangled hypergraph and evolving entangled hypergraph as data structures
suitable to model quantum protocols which use entanglement. Finally, we present
a few examples to provide applications of this model.


  Associated with any composition beta=(a,b,...) is a corresponding fence poset
F(beta) whose covering relations are
  x_1 < x_2 < ... < x_{a+1} > x_{a+2} > ... > x_{a+b+1} < x_{a+b+2} < ... The
distributive lattice L(beta) of all lower order ideals of F(beta) is important
in the theory of cluster algebras. In addition, its rank generating function
r(q;beta) is used to define q-analogues of rational numbers. Oguz and
Ravichandran recently showed that its coefficients satisfy an interlacing
condition, proving a conjecture of McConville, Smyth and Sagan, which in turn
implies a previous conjecture of Morier-Genoud and Ovsienko that r(q;beta) is
unimodal. We show that, when beta has an odd number of parts, then the
polynomial is also partially symmetric: the number of ideals of F(beta) of size
k equals the number of filters of size k, when k is below a certain value. Our
proof is completely bijective. Oguz and Ravichandran also introduced a circular
version of fences and proved, using algebraic techniques, that the distributive
lattice for such a poset is rank symmetric. We give a bijective proof of this
result as well. We end with some questions and conjectures raised by this work.


  A prediction for the contributions of chiral loops and the L$\sigma$M model
to the radiative $\phi\to\pi^0\eta\gamma$ decay mode is presented. The
L$\sigma$M is used as an appropriate framework for describing the pole effects
of the $a_0(980)$ scalar resonance. As a result, a better agreement with
present available data is achieved for the higher part of the $\pi^0\eta$
invariant mass spectrum. For the branching ratio, a value of
$B(\phi\to\pi^0\eta\gamma)=(0.75$--$0.95)\times 10^{-4}$ is found.


  We define a deformation of the triply graded Khovanov-Rozansky homology of a
link $L$ depending on a choice of parameters $y_c$ for each component of $L$,
which satisfies link-splitting properties similar to the Batson-Seed invariant.
Keeping the $y_c$ as formal variables yields a link homology valued in triply
graded modules over $\mathbb{Q}[x_c,y_c]_{c\in \pi_0(L)}$. We conjecture that
this invariant restores the missing $Q\leftrightarrow TQ^{-1}$ symmetry of the
triply graded Khovanov-Rozansky homology, and in addition satisfies a number of
predictions coming from a conjectural connection with Hilbert schemes of points
in the plane. We compute this invariant for all positive powers of the full
twist and match it to the family of ideals appearing in Haiman's description of
the isospectral Hilbert scheme.


  Proactive dialogue system is able to lead the conversation to a goal topic
and has advantaged potential in bargain, persuasion and negotiation. Current
corpus-based learning manner limits its practical application in real-world
scenarios. To this end, we contribute to advance the study of the proactive
dialogue policy to a more natural and challenging setting, i.e., interacting
dynamically with users. Further, we call attention to the non-cooperative user
behavior -- the user talks about off-path topics when he/she is not satisfied
with the previous topics introduced by the agent. We argue that the targets of
reaching the goal topic quickly and maintaining a high user satisfaction are
not always converge, because the topics close to the goal and the topics user
preferred may not be the same. Towards this issue, we propose a new solution
named I-Pro that can learn Proactive policy in the Interactive setting.
Specifically, we learn the trade-off via a learned goal weight, which consists
of four factors (dialogue turn, goal completion difficulty, user satisfaction
estimation, and cooperative degree). The experimental results demonstrate I-Pro
significantly outperforms baselines in terms of effectiveness and
interpretability.


  Materials exhibiting multiple stable phases can be used as functional
components in electronic and optical applications if the phase transition is
controllable. Group-VI transition metal dichalcogenides (TMDs, MX$_2$, where
M=Mo, W and X = S, Se) are known to undergo charge induced transitions from
semi-conducting H phases to metallic T phases. This occurs, for example, when
bulk TMDs are exfoliated with the aid of alkali ion intercalants. However, it
is difficult to experimentally decouple the effect of composition-dependent
phase transition barriers from indirect effects related to the exfoliation
process. Here, using first-principles calculations, we study the energetics of
transition between the different structural polytypes of four group-VI TMDs
upon lithium adsorption. We find that both the activation barrier from the H
phase to the metallic phase in charged monolayers and the reverse barrier in
neutral monolayers are required to explain experimental results. We show that
the high proportion of metallic phase found in WS$_2$ monolayers after alkali
treatment can be explained by high transition barriers to revert back to the H
phase once in a neutral state. The calculated barriers however cannot explain
the low proportion of metallic phase found in MoS$_2$ monolayers in some
experiments and so non-electronic effects must also play a role.


  In this paper we propose and study an optimization problem over a matrix
group orbit that we call \emph{Group Orbit Optimization} (GOO). We prove that
GOO can be used to induce matrix decomposition techniques such as singular
value decomposition (SVD), LU decomposition, QR decomposition, Schur
decomposition and Cholesky decomposition, etc. This gives rise to a unified
framework for matrix decomposition and allows us to bridge these matrix
decomposition methods. Moreover, we generalize GOO for tensor decomposition. As
a concrete application of GOO, we devise a new data decomposition method over a
special linear group to normalize point cloud data. Experiment results show
that our normalization method is able to obtain recovery well from distortions
like shearing, rotation and squeezing.


  The technical state of software, i.e., its technical debt (TD) and
maintainability are of increasing interest as ever more software is developed
and deployed. Since td and maintainability are neither uniformly defined, not
easy to understand, nor directly measurable, practitioners are likely to apply
readily available tools to assess TD or maintainability and they may rely on
the reported results without properly understanding what they embody. In this
paper, we: a) methodically identify 11 readily available tools that measure TD
or maintainability, b) present an in-depth investigation on how each of these
tools measures and computes TD or maintainability, and c) compare these tools
and their characteristics. We find that contemporary tools focus mainly on
internal qualities of software, i.e., quality of source code, that they define
and measure TD or maintainability in widely different ways, that most of the
tools measure TD or maintainability opaquely, and that it is not obvious why
the measure of one tool is more trustworthy or representative than the one of
another.


  We consider the semiclassical theory in a joint phase space of spin and
orbital degrees of freedom. The method is developed from the path integrals
using the spin-coherent-state representation, and yields the trace formula for
the density of states. We discuss special cases, such as weak and strong
spin-orbit coupling, and relate the present theory to the earlier approaches.


  Gate bias stress instability in organic field-effect transistors (OFETs) is a
major conceptual and device issue. This effect manifests itself by an
undesirable shift of the transfer characteristics and is associated with long
term charge trapping. We study the role of the dielectric and the semiconductor
separately by producing OFETs with the same semiconductor (pentacene) combined
with different dielectrics (SiO$_2$ and Cytop). We show, it is possible to
fabricate devices which are immune to gate bias stress. For other material
combinations, charge trapping occurs in the semiconductor alone, or in the
dielectric.


  Analytical and geometrical properties of generalized power-law (GPL) density
profiles are investigated in detail. In particular, a one-to-one correspondence
is found between mathematical parameters and geometrical parameters. Then GPL
density profiles are compared with simulated dark haloes (SDH) density
profiles, and nonlinear least-absolute values and least-squares fits involving
the above mentioned five parameters (RFSM5 method) are prescribed. More
specifically, the sum of absolute values or squares of absolute logarithmic
residuals is evaluated on a large number of points making a 5-dimension
hypergrid, through a few iterations. The size is progressively reduced around a
fiducial minimum, and superpositions on nodes of earlier hypergrids are
avoided. An application is made to a sample of 17 SDHs on the scale of cluster
of galaxies, within a flat $\Lambda$CDM cosmological model (Rasia et al. 2004).
In dealing with the mean SDH density profile, a virial radius, averaged over
the whole sample, is assigned, which allows the calculation of the remaining
parameters. Using a RFSM5 method provides a better fit with respect to other
methods. No evident correlation is found between SDH dynamical state (relaxed
or merging) and asymptotic inner slope of the logarithmic density profile or
(for SDH comparable virial masses) scaled radius. Mean values and standard
deviations of some parameters are calculated, and a comparison with previous
results is made with regard to the scaled radius. A certain degree of
degeneracy is found in fitting GPL to SDH density profiles. If it is intrinsic
to the RFSM5 method or it could be reduced by the next generation of
high-resolution simulations, still remains an open question.


  We derive the reciprocal cluster mean-field method to study the
strongly-interacting bosonic Harper-Hofstadter-Mott model. The system exhibits
a rich phase diagram featuring band insulating, striped superfluid, and
supersolid phases. Furthermore, for finite hopping anisotropy we observe
gapless uncondensed liquid phases at integer fillings, which are analyzed by
exact diagonalization. The liquid phases at fillings 1 and 3 exhibit the same
band fillings as the fermionic integer quantum Hall effect, while the phase at
filling 2 is CT-symmetric with zero charge response. We discuss how these
phases become gapped on a quasi-one-dimensional cylinder, leading to a
quantized Hall response, which we characterize by introducing a suitable
measure for non-trivial many-body topological properties. Incompressible
metastable states at fractional filling are also observed, indicating competing
fractional quantum Hall phases. The combination of reciprocal cluster
mean-field and exact diagonalization yields a promising method to analyze the
properties of bosonic lattice systems with non-trivial unit cells in the
thermodynamic limit.


  Let $X$ be a zero-dimensional locally compact Hausdorff space not necessarily
metric and $G$ a compactly generated topological group not necessarily abelian
or countable. We define recurrence at a point for any continuous action of $G$
on $X$, and then, show that if $\overline{Gx}$ is compact for all $x\in X$, the
conditions (i) this dynamics is pointwise recurrent, (ii) $X$ is a union of
$G$-minimal sets, (iii) the $G$-orbit closure relation is closed in $X\times
X$, and (iv) $X\ni x\mapsto \overline{Gx}\in 2^X$ is continuous, are pairwise
equivalent. Consequently, if this dynamics is distal, then it is
equicontinuous.


  Methods to estimate the Hausdorff dimension of invariant sets of scattering
systems are presented. Based on the levels' hierarchical structure of the time
delay function, these techniques can be used in systems whose
future-invariant-set codimensions are approximately equal to or greater than
one. The discussion is illustrated by a numerical example of a scatterer built
with four hard spheres located at the vertices of a regular tetrahedron.


  Artificial neural network potentials (NNPs) have emerged as effective tools
for understanding atomic interactions at the atomic scale in various phenomena.
Recently, we developed highly transferable NNPs for {\alpha}-iron and
{\alpha}-iron/hydrogen binary systems (Physical Review Materials 5 (11),
113606, 2021). These potentials allowed us to investigate deformation and
fracture in {\alpha}-iron under the influence of hydrogen. However, the
computational cost of the NNP remains relatively high compared to empirical
potentials, limiting their applicability in addressing practical issues related
to hydrogen embrittlement. In this work, building upon our prior research on
iron-hydrogen NNP, we developed a new NNP that not only maintains the excellent
transferability but also significantly improves computational efficiency (more
than 40 times faster). We applied this new NNP to study the impact of hydrogen
on the cracking of iron and the deformation of polycrystalline iron. We
employed large-scale through-thickness {110}<110> crack models and large-scale
polycrystalline {\alpha}-iron models. The results clearly show that hydrogen
atoms segregated at crack tips promote brittle-cleavage failure followed by
crack growth. Additionally, hydrogen atoms at grain boundaries facilitate the
nucleation of intergranular nanovoids and subsequent intergranular fracture. We
anticipate that this high-efficiency NNP will serve as a valuable tool for
gaining atomic-scale insights into hydrogen embrittlement.


  The SNO+ experiment is located 2 km underground at SNOLAB in Sudbury, Canada.
A low background search for neutrinoless double beta ($0\nu\beta\beta$) decay
will be conducted using 780 tonnes of liquid scintillator loaded with 3.9
tonnes of natural tellurium, corresponding to 1.3 tonnes of $^{130}$Te. This
paper provides a general overview of the SNO+ experiment, including detector
design, construction of process plants, commissioning efforts, electronics
upgrades, data acquisition systems, and calibration techniques. The SNO+
collaboration is reusing the acrylic vessel, PMT array, and electronics of the
SNO detector, having made a number of experimental upgrades and essential
adaptations for use with the liquid scintillator. With low backgrounds and a
low energy threshold, the SNO+ collaboration will also pursue a rich physics
program beyond the search for $0\nu\beta\beta$ decay, including studies of geo-
and reactor antineutrinos, supernova and solar neutrinos, and exotic physics
such as the search for invisible nucleon decay. The SNO+ approach to the search
for $0\nu\beta\beta$ decay is scalable: a future phase with high
$^{130}$Te-loading is envisioned to probe an effective Majorana mass in the
inverted mass ordering region.


  In recent years, the exponential increase in the demand of wireless data
transmission rises the urgency for accurate spectrum sensing approaches to
improve spectrum efficiency. The unreliability of conventional spectrum sensing
methods by using measurements from a single secondary user (SU) has motivated
research on cooperative spectrum sensing (CSS). In this work, we propose a
vertical federated learning (VFL) framework to exploit the distributed features
across multiple SUs without compromising data privacy. However, the repetitive
training process in VFL faces the issue of high communication latency. To
accelerate the training process, we propose a truncated vertical federated
learning (T-VFL) algorithm, where the training latency is highly reduced by
integrating the standard VFL algorithm with a channel-aware user scheduling
policy. The convergence performance of T-VFL is provided via mathematical
analysis and justified by simulation results. Moreover, to guarantee the
convergence performance of the T-VFL algorithm, we conclude three design rules
on the neural architectures used under the VFL framework, whose effectiveness
is proved through simulations.


  We provide additional methods for the evaluation of the integral
\begin{eqnarray} N_{0,4}(a;m) & := & \int_{0}^{\infty} \frac{dx} {\left( x^{4}
+ 2ax^{2} + 1 \right)^{m+1}} \end{eqnarray} where $m \in {\mathbb{N}}$ and $a
\in (-1, \infty)$ in the form \begin{eqnarray} N_{0,4}(a;m) & = &
\frac{\pi}{2^{m+3/2} (a+1)^{m+1/2} } P_{m}(a) \end{eqnarray} where $P_{m}(a)$
is a polynomial in $a$. The first one is based on a method of Schwinger to
evaluate integrals appearing in Feynman diagrams, the second one is a byproduct
of an expression for a rational integral in terms of Schur functions. Finally,
the third proof, is obtained from an integral representation involving modified
Bessel functions.


  We present an extension of the Tomonaga-Luttinger model in which left and
right-moving particles have different Fermi velocities. We derive expressions
for one-particle Green's functions, momentum-distributions, density of states,
charge compressibility and conductivity as functions of both the velocity
difference $\epsilon$ and the strength of the interaction $\beta$. This allows
us to identify a novel restricted region in the parameter space in which the
system keeps the main features of a Luttinger liquid but with an unusual
behavior of the density of states and the static charge compressibility
$\kappa$. In particular $\kappa$ diverges on the boundary of the restricted
region, indicating the occurrence of a phase transition.


  We consider a mixed function of type $H(z,\bar z)=f(z)\bar g(z)$ where $f,g$
are non-degenerate but they are not assumed to be convenient. We assume that
$f=0$ and $g=0$ and $f=g=0$ are non-degenerate and locally tame. We will show
that $H$ has a tubular Milnor fibration and a spherical Milnor fibration. We
show also two fibrations are equivalent.


  We introduce network science as a framework for studying the string
landscape. Two large networks of string geometries are constructed, where nodes
are extra-dimensional six-manifolds and edges represent topological transitions
between them. We show that a standard bubble cosmology model on the networks
has late-time behavior determined by the largest eigenvector of $-(\mathbf{L} +
\mathbf{D})$, where $\mathbf{L}$ and $\mathbf{D}$ are the Laplacian and degree
matrices of the networks, which provides a dynamical mechanism for vacuum
selection in the string landscape.


  Gibbs-type random probability measures, or Gibbs-type priors, are arguably
the most "natural" generalization of the celebrated Dirichlet prior. Among them
the two parameter Poisson-Dirichlet prior certainly stands out for the
mathematical tractability and interpretability of its predictive probabilities,
which made it the natural candidate in several applications. Given a sample of
size $n$, in this paper we show that the predictive probabilities of any
Gibbs-type prior admit a large $n$ approximation, with an error term vanishing
as $o(1/n)$, which maintains the same desirable features as the predictive
probabilities of the two parameter Poisson-Dirichlet prior.


  This summary was prepared for the proceedings of the GR15 conference in Pune,
India in Dec. 1997.


  Graph diffusion problems such as the propagation of rumors, computer viruses,
or smart grid failures are ubiquitous and societal. Hence it is usually crucial
to identify diffusion sources according to the current graph diffusion
observations. Despite its tremendous necessity and significance in practice,
source localization, as the inverse problem of graph diffusion, is extremely
challenging as it is ill-posed: different sources may lead to the same graph
diffusion patterns. Different from most traditional source localization
methods, this paper focuses on a probabilistic manner to account for the
uncertainty of different candidate sources. Such endeavors require overcoming
challenges including 1) the uncertainty in graph diffusion source localization
is hard to be quantified; 2) the complex patterns of the graph diffusion
sources are difficult to be probabilistically characterized; 3) the
generalization under any underlying diffusion patterns is hard to be imposed.
To solve the above challenges, this paper presents a generic framework: Source
Localization Variational AutoEncoder (SL-VAE) for locating the diffusion
sources under arbitrary diffusion patterns. Particularly, we propose a
probabilistic model that leverages the forward diffusion estimation model along
with deep generative models to approximate the diffusion source distribution
for quantifying the uncertainty. SL-VAE further utilizes prior knowledge of the
source-observation pairs to characterize the complex patterns of diffusion
sources by a learned generative prior. Lastly, a unified objective that
integrates the forward diffusion estimation model is derived to enforce the
model to generalize under arbitrary diffusion patterns. Extensive experiments
are conducted on 7 real-world datasets to demonstrate the superiority of SL-VAE
in reconstructing the diffusion sources by excelling other methods on average
20% in AUC score.


  Zero-interaction authentication (ZIA) refers to a form of user-transparent
login mechanism using which a terminal (e.g., a desktop computer) can be
unlocked by the mere proximity of an authentication token (e.g., a smartphone).
Given its appealing usability, ZIA has already been deployed in many real-world
applications. However, ZIA contains one major security weakness - unauthorized
physical access to the token, e.g., during lunch-time or upon theft, allows the
attacker to have unfettered access to the terminal.
  In this paper, we address this gaping vulnerability with ZIA systems by
(un)locking the authentication token with the user's walking pattern as she
approaches the terminal to access it. Since a user's walking or gait pattern is
believed to be unique, only that user (no imposter) would be able to unlock the
token to gain access to the terminal in a ZIA session. While walking-based
biometrics schemes have been studied in prior literature for other application
settings, our main novelty lies in the careful use of: (1) multiple sensors
available on the current breed of devices (e.g., accelerometer, gyroscope and
magnetometer), and (2) multiple devices carried by the user (in particular, an
"in-pocket" smartphone and a "wrist-worn" smartwatch), that all capture unique
facets of user's walking pattern. Our contributions are three-fold. First, we
introduce, design and implement WUZIA ("Walk-Unlock ZIA"), a multi-modal
walking biometrics approach tailored to enhance the security of ZIA systems
(still with zero interaction). Second, we demonstrate that WUZIA offers a high
degree of detection accuracy, based on multi-sensor and multi-device fusion.
Third, we show that WUZIA can resist active attacks that attempt to mimic a
user's walking pattern, especially when multiple devices are used.


  We review the current issues of nonequilibrium phase transitions, in
particular, in the early universe. Phase transitions cannot maintain thermal
equilibrium and become nonequilibrium when the thermal relaxation time scale is
greater than the dynamical time scale. Nonequilibrium phase transitions would
have happened in certain evolution stages of the early universe because the
rapid expansion quenched matter fields. We apply the recently introduced
Liouville-von Neumann method, another canonical method, to nonequilibrium phase
transitions in the Minkowski spacetime and find the scaling behavior of domain
sizes. Topological defects are thus determined by the dynamical processes of
nonequilibrium phase transitions. However, the expansion of the universe
freezes domain growth in the comoving frame and thus leads to a scale-invariant
domain size. We also discuss the physical implications of nonequilibrium phase
transitions in the early universe.


  Deep learning models, such as convolutional neural networks, have long been
applied to image and multi-media tasks, particularly those with structured
data. More recently, there has been more attention to unstructured data that
can be represented via graphs. These types of data are often found in health
and medicine, social networks, and research data repositories. Graph
convolutional neural networks have recently gained attention in the field of
deep learning that takes advantage of graph-based data representation with
automatic feature extraction via convolutions. Given the popularity of these
methods in a wide range of applications, robust uncertainty quantification is
vital. This remains a challenge for large models and unstructured datasets.
Bayesian inference provides a principled approach to uncertainty quantification
of model parameters for deep learning models. Although Bayesian inference has
been used extensively elsewhere, its application to deep learning remains
limited due to the computational requirements of the Markov Chain Monte Carlo
(MCMC) methods. Recent advances in parallel computing and advanced proposal
schemes in MCMC sampling methods has opened the path for Bayesian deep
learning. In this paper, we present Bayesian graph convolutional neural
networks that employ tempered MCMC sampling with Langevin-gradient proposal
distribution implemented via parallel computing. Our results show that the
proposed method can provide accuracy similar to advanced optimisers while
providing uncertainty quantification for key benchmark problems.


  Context: GR290 (M33/V532=Romano's Star) is a suspected post-LBV star located
in M33 galaxy that shows a rare Wolf-Rayet spectrum during its minimum light
phase. In spite of many studies, its atmospheric structure, its circumstellar
environment and its place in the general context of massive stars evolution is
poorly known. Aims: Detailed study of its wind and mass loss, and study of the
circumstellar environment associated to the star. Methods: Long-slit spectra of
GR290 were obtained during its present minimum luminosity phase with the GTC
together with contemporaneous BVRI photometry. The data were compared with
non-LTE model atmosphere synthetic spectra computed with CMFGEN and with CLOUDY
models for ionized interstellar medium regions. Results: The current $m_V=18.8$
mag, is the faintest at which this source has ever been observed. The non-LTE
models indicate effective temperature $T_{eff}$=27-30 kK at radius
$R_{2/3}$=27-21 Rsun and mass loss rate $\dot{M}=1.5\times10^{-5}$ Msun
yr$^{-1}$. The terminal wind speed $V_\infty$=620 ${\rm km~s^{-1}}$ is faster
than ever before recorded while the current luminosity $L_*=(3.1-3.7)\times
10^5$ Lsun is the lowest ever deduced. It is overabundant in He and N and
underabundant in C and O. It is surrounded by an unresolved compact HII region
with dimensions $\leq$4 pc, from where H-Balmer, HeI lines and [OIII] and [NII]
are detected. In addition, we find emission from a more extended interstellar
medium (ISM) region which appears to be asymmetric, with a larger extent to the
East (16-40 pc) than to the West. Conclusions: In the present long lasting
visual minimum, GR290 is in a lower bolometric luminosity state with higher
mass loss rate. The nearby nebular emission seems to suggest that the star has
undergone significant mass loss over the past $10^4-10^5$ years and is nearing
the end stages of its evolution.


  The motion of electrons in or near solids, liquids and gases can be tracked
by forcing their ejection with attosecond x-ray pulses, derived from
femtosecond lasers. The momentum of these emitted electrons carries the imprint
of the electronic state. Aberration corrected transmission electron microscopes
have observed individual atoms, and have sufficient energy sensitivity to
quantify atom bonding and electronic configurations. Recent developments in
ultrafast electron microscopy and diffraction indicate that spatial and
temporal information can be collected simultaneously. In the present work, we
push the capability of femtosecond transmission electron microscopy (fs-TEM)
towards that of the state of the art in ultrafast lasers and electron
microscopes. This is anticipated to facilitate unprecedented elucidation of
physical, chemical and biological structural dynamics on electronic time and
length scales. The fs-TEM numerically studied employs a nanotip source,
electrostatic acceleration to 70 keV, magnetic lens beam transport and
focusing, a condenser-objective around the sample and a terahertz temporal
compressor, including space charge effects during propagation. With electron
emission equivalent to a 20 fs laser pulse, we find a spatial resolution below
10 nm and a temporal resolution of below 10 fs will be feasible for pulses
comprised of on average 20 electrons. The influence of a transverse electric
field at the sample is modelled, indicating that a field of 1 V/$\mu$m can be
resolved.


  Total cross section for pp->pp eta process has been calculated in meson
exchange model. Eta production T-matrices are from our PWA. Initial and final
state interactions are disregarded for the moment, due to large uncertainties.
Preliminary results do not show rho meson exchange dominance.


  Let $(Z,\omega)$ be a connected Kahler manifold with an holomorphic action of
the complex reductive Lie group $U^{\mathbb C}$, where $U$ is a compact
connected Lie group acting in a hamiltonian fashion. Let $G$ be a closed
compatible Lie group of $U^{\mathbb C}$ and let $M$ be a $G$-invariant
connected submanifold of $Z$. Let $x\in M$. If $G$ is a real form of
$U^{\mathbb C}$, we investigate conditions such that $G\cdot x$ compact implies
$U^{\mathbb C} \cdot x$ is compact as well. The vice-versa is also
investigated. We also characterize $G$-invariant real submanifolds such that
the norm square of the gradient map is constant. As an application, we prove a
splitting result for real connected submanifolds of $(Z,\omega)$ generalizing a
result proved in \cite{pg}, see also \cite{bg,bs}.


  We study the stability of an inflaton condensate in the presence of
attractive inflaton self-interactions, in order to determine analytical
conditions on the self-interaction couplings under which the condensate
undergoes fragmentation. As an application of our results, we consider the
stability of the inflaton condensate in $\alpha$-attractor inflation. We show
that the stability of the condensate depends upon the value of $\alpha$. For
the T-model with $n = 1$, we find that it is unstable for $\alpha \lesssim
10^{-4}$. In this case it is expected that inflation will be followed by an
oscillon-dominated era.


  Accurate representation of the physical layer is required for analysis and
simulation of multi-hop networking in sensor, ad hoc, and mesh networks. This
paper investigates, models, and analyzes the correlations that exist in shadow
fading between links in multi-hop networks. Radio links that are geographically
proximate often experience similar environmental shadowing effects and thus
have correlated fading. We describe a measurement procedure and campaign to
measure a large number of multi-hop networks in an ensemble of environments.
The measurements show statistically significant correlations among shadowing
experienced on different links in the network, with correlation coefficients up
to 0.33. We propose a statistical model for the shadowing correlation between
link pairs which shows strong agreement with the measurements, and we compare
the new model with an existing shadowing correlation model of Gudmundson
(1991). Finally, we analyze multi-hop paths in three and four node networks
using both correlated and independent shadowing models and show that
independent shadowing models can underestimate the probability of route failure
by a factor of two or greater.


  The lifecycle of dust in the interstellar medium (ISM) is heavily influenced
by outflows from asymptotic giant branch (AGB) and red supergiant (RSG) stars,
a large fraction of which is contributed by a few very dusty sources. We
compute the dust input to the Small Magellanic Cloud (SMC) by fitting the
multi-epoch mid-infrared spectral energy distributions (SEDs) of AGB/RSG
candidates with models from the {\em G}rid of {\em R}SG and {\em A}GB {\em
M}odel{\em S} (GRAMS) grid, allowing us to estimate the luminosities and
dust-production rates (DPRs) of the entire population. By removing
contaminants, we guarantee a high-quality dataset with reliable DPRs and a
complete inventory of the dustiest sources. We find a global AGB/RSG
dust-injection rate of $(1.3\pm 0.1)\times 10^{-6}$ \msunperyr, in agreement
with estimates derived from mid-infrared colours and excess fluxes. As in the
LMC, a majority (66\%) of the dust arises from the extreme AGB stars, which
comprise only $\approx$7\% of our sample. A handful of far-infrared sources,
whose 24 \mic\ fluxes exceed their 8 \mic\ fluxes, dominate the dust input.
Their inclusion boosts the global DPR by $\approx$1.5$\times$, making it
necessary to determine whether they are AGB stars. Model assumptions, rather
than missing data, are the major sources of uncertainty; depending on the
choice of dust shell expansion speed and dust optical constants, the global DPR
can be up to $\approx$10 times higher. Our results suggest a non-stellar origin
for the SMC dust, barring as yet undiscovered evolved stars with very high
DPRs.


  Online influence maximization aims to maximize the influence spread of a
content in a social network with unknown network model by selecting a few seed
nodes. Recent studies followed a non-adaptive setting, where the seed nodes are
selected before the start of the diffusion process and network parameters are
updated when the diffusion stops. We consider an adaptive version of
content-dependent online influence maximization problem where the seed nodes
are sequentially activated based on real-time feedback. In this paper, we
formulate the problem as an infinite-horizon discounted MDP under a linear
diffusion process and present a model-based reinforcement learning solution.
Our algorithm maintains a network model estimate and selects seed users
adaptively, exploring the social network while improving the optimal policy
optimistically. We establish $\widetilde O(\sqrt{T})$ regret bound for our
algorithm. Empirical evaluations on synthetic network demonstrate the
efficiency of our algorithm.


  These notes provide a short, focused introduction to modelling stochastic
gene expression, including a derivation of the master equation, the recovery of
deterministic dynamics, birth-and-death processes, and Langevin theory. The
notes were last updated around 2010 and written for lectures given at summer
schools held at McGill University's Centre for Non-linear Dynamics in 2004,
2006, and 2008.


  In this paper, we have derived the field equations in an extended theory of
gravity in an anisotropic space time background and in the presence of magnetic
field. The physical and geometrical parameters of the models are determined
with respect to the Hubble parameter using some algebraic approaches. A time
varying scale factor has been introduced to analyze the behavior of the model.
From some diagnostic approach, we found that the model behaves as
$\Lambda_{CDM}$ model at late time of cosmic evolution.


  We study the structure of quantum ground states of simplex solid models,
which are generalizations of the valence bond construction for quantum
antiferromagnets originally proposed by Affleck, Kennedy, Lieb, and Tasaki
(AKLT) [Phys. Rev. Lett. 59, 799 (1987)]. Whereas the AKLT states are created
by application of bond singlet operators for SU(2) spins, the simplex solid
construction is based on N-simplex singlet operators for SU(N) spins. In both
cases, a discrete one-parameter family of translationally-invariant models with
exactly solvable ground states is defined on any regular lattice, and the equal
time ground state correlations are given by the finite temperature correlations
of an associated classical model on the same lattice, owing to the product form
of the wave functions when expressed in a CP^{N-1} coherent state
representation. We study these classical companion models via a mix of Monte
Carlo simulations, mean-field arguments, and low-temperature effective field
theories. Our analysis reveals that the ground states of SU(4) edge- and SU(8)
face-sharing cubic lattice simplex solid models are long range ordered for
sufficiently large values of the discrete parameter, whereas the ground states
of the SU(3) models on the kagome (2D) and hyperkagome (3D) lattices are always
quantum disordered. The kagome simplex solid exhibits strong local order absent
in its three-dimensional hyperkagome counterpart, a contrast that we
rationalize with arguments similar to those leading to `order by disorder'.


  In this paper we describe a new connection between UPB (unextendable product
bases) and P (positive) maps which are not CP (completely positive). We show
that inner automorphisms of the set of P maps which are not CP, produce
extremal extensions of these maps that help in entanglement detection. By
constructing such an extension of the well-known Choi map, we strengthen its
power to unearth PPT (positive under partial transpose) entangled states. We
further show that the class of maps generated from the Choi map via an inner
automorphism naturally detects the entanglement of states in the orthogonal
complement of certain UPB. This brings out a hitherto undiscovered connection
between the Choi map and UPB. We also show that certain other recently
considered extremal extensions are obtainable by such extensions of the Choi
map.


  We demonstrate how shape-induced strain can be used to control
antiferromagnetic order in NiO/Pt thin films. For rectangular elements
patterned along the easy and hard magnetocrystalline anisotropy axes of our
film, we observe different domain structures and we identify magnetoelastic
interactions that are distinct for different domain configurations. We
reproduce the experimental observations by modeling the magnetoelastic
interactions, considering spontaneous strain induced by the domain
configuration, as well as elastic strain due to the substrate and the shape of
the patterns. This allows us to demonstrate and explain how the variation of
the aspect ratio of rectangular elements can be used to control the
antiferromagnetic ground state domain configuration. Shape-dependent strain
does not only need to be considered in the design of antiferromagnetic devices,
but can potentially be used to tailor their properties, providing an additional
handle to control antiferromagnets.


  Detection of the faint 21 cm line emission from the Cosmic Dawn and Epoch of
Reionisation will require not only exquisite control over instrumental
calibration and systematics to achieve the necessary dynamic range of
observations but also validation of analysis techniques to demonstrate their
statistical properties and signal loss characteristics. A key ingredient in
achieving this is the ability to perform high-fidelity simulations of the kinds
of data that are produced by the large, many-element, radio interferometric
arrays that have been purpose-built for these studies. The large scale of these
arrays presents a computational challenge, as one must simulate a detailed sky
and instrumental model across many hundreds of frequency channels, thousands of
time samples, and tens of thousands of baselines for arrays with hundreds of
antennas. In this paper, we present a fast matrix-based method for simulating
radio interferometric measurements (visibilities) at the necessary scale. We
achieve this through judicious use of primary beam interpolation, fast
approximations for coordinate transforms, and a vectorised outer product to
expand per-antenna quantities to per-baseline visibilities, coupled with
standard parallelisation techniques. We validate the results of this method,
implemented in the publicly-available matvis code, against a high-precision
reference simulator, and explore its computational scaling on a variety of
problems.


  We introduce a general definition of homogeneous Besov spaces on a stratified
Lie group $G$, based on a Littlewood-Paley-type decomposition of Schwartz
functions with all moments vanishing. We show that under mild and intuitive
conditions the spaces thus defined are independent of the decomposition
employed. A corollary of this is that previously constructed versions of
homogeneous Besov spaces on $G$, relying on the spectral calculus of a
sub-Laplacian of the group, are consistent, i.e., independent of the choice of
sub-Laplacian.
  We further prove characterizations of homogeneous Besov spaces using
continuous wavelet transforms, with a large variety of analysing wavelets to
choose from.


  The early-time optical spectrum of the Type Ic supernova (SN) 2002ap was
characterized by unusually broad features, leading some authors to designate it
a "hypernova." We present optical spectropolarimetry of this object 16 and 37
days after the estimated date of explosion. After correcting for interstellar
polarization, we find evidence for a high level of intrinsic continuum
polarization at both epochs: p >~ 1.3% on day 16 and p >~ 1.0% on day 37.
Prominent line polarization is also seen, especially in the trough of the Ca II
near-infrared triplet during the second epoch. When interpreted in terms of the
oblate, electron-scattering model atmospheres of Hoeflich (1991), our results
imply an asymmetry of at least 20% (day 16) and 17% (day 37). The data suggest
a fair degree of axisymmetry, although the polarization angle of the dominant
polarization axes are different by about 55 deg. between the two epochs,
implying a complex morphology for the thinning ejecta. In particular, there
exists some spectropolarimetric evidence for a different distribution of Ca
relative to iron-group elements. We also present flux spectra of SN 2002ap
taken 131 and 140 days after the explosion. The spectra are characterized by a
very weak continuum and broad emission lines, indicating that SN 2002ap has
entered the nebular phase. The spectral features are substantially similar to
those of ``normal'' SNe Ic in the nebular phase, and the emission lines are not
significantly broader. However, some of the broad lines are very sharply
peaked, and may possess a narrow component (probably unresolved by our spectra,
FWHM <~ 400 km/s that is redshifted by ~ 580$ km/s with respect to the systemic
velocity of the host galaxy.


  Simplified analytic methods are frequently used to model the light curves of
supernovae and other energetic transients and to extract physical quantities,
such as the ejecta mass and amount of radioactive heating. The applicability
and quantitative accuracy of these models, however, have not been clearly
delineated. Here we carry out a systematic study comparing certain analytic
models to numerical radiation transport calculations. We show that the neglect
of time-dependent diffusion limits the accuracy of common Arnett-like analytic
models, and that the widely-applied Arnett's rule for inferring radioactive
mass does not hold in general, with an error that increases for models with
longer diffusion times or more centralized heating. We present new analytic
relations that accurately relate the peak time and luminosity of an observed
light curve to the physical ejecta and heating parameters. We further show that
recombination and the spatial distribution of heating modify the peak of the
light curve and that these effects can be accounted for by varying a single
dimensionless parameter in the new relations. The results presented should be
useful for estimating the physical properties of a wide variety of transient
phenomena.


  We introduce a new model of aggregation of particles where in addition to
diffusion and aggregation upon contact, a single unit of mass can dissociate
from a conglomerate. This dissociation move conserves the total mass and leads
to a striking behaviour in the steady state. As the parameters are varied, the
system undergoes a dynamical phase transition in all dimensions. In one phase
the mass distribution decays exponentially for large mass whereas in the other
phase there is a power law distribution of masses which coexists with an
infinite mass aggregate. The model is investigated analytically within mean
field theory, and numerically in one dimension.


  We report on enhanced fluorescence of lead sulfide quantum dots interacting
with leaky modes of slab-type silicon photonic crystals. The photonic crystal
slabs were fabricated supporting leaky modes in the near infrared wavelength
range. Lead sulfite quantum dots which are resonant the same spectral range
were prepared in a thin layer above the slab. We selectively excited the leaky
modes by tuning wavelength and angle of incidence of the laser source and
measured distinct resonances of enhanced fluorescence. By an appropriate
experiment design, we ruled out directional light extraction effects and
determined the impact of enhanced excitation. Three-dimensional numerical
simulations consistently explain the experimental findings by strong near-field
enhancements in the vicinity of the photonic crystal surface. Our study
provides a basis for systematic tailoring of photonic crystals used in
biological applications such as biosensing and single molecule detection, as
well as quantum dot solar cells and spectral conversion applications.


  Using the Secular Light Curve (SLC) formalism (Ferr\'in, 2010), we have
catalogued 88 probable members of the Taurid Complex (TC). 51 of them have
useful SLCs and 34 of these (67%) exhibit cometary activity. This high
percentage of active asteroids gives support to the hypothesis of a catastrophe
that took place during the Upper Paleolithic (Clube and Napier, 1984), when a
large short-period comet, arriving in the inner Solar System from the Kuiper
Belt, experienced, starting from 20 thousand years ago, a series of
fragmentations that produced the present 2P/Encke comet, together with a large
number of other members of the TC. The fragmentation of the progenitor body was
facilitated by its heterogeneous structure (very similar to a rubble pile) and
this also explains the current coexistence in the complex of fragments of
different composition and origin. We have found that (2212) Hephaistos and
169P/NEAT are active and members of the TC with their own sub-group. Other
components of the complex are groups of meteoroids, that often give rise to
meteor showers when they enter the terrestrial atmosphere, and very probably
also the two small asteroids that in 1908 and 2013 exploded in the terrestrial
atmosphere over Tunguska and Chelyabinsk, respectively. What we see today of
the TC are the remnants of a very varied and numerous complex of objects,
characterized by an intense past of collisions with the Earth which may
continue to represent a danger for our planet.


  We consider two distinguished asymptotic limits of the Euler equations in a
gravitational field, namely the incompressible and Boussinesq limits. Both
these limits can be obtained as singular limits of the Euler equations under
appropriate scaling of the Mach and Froude numbers. We propose and analyse an
asymptotic preserving (AP) time discretisation for the numerical approximation
of the Euler system in these asymptotic regimes. A key step in the construction
of the AP scheme is a semi-implicit discretisation of the fluxes and the source
term. The non-stiff convective terms are treated explicitly whereas the stiff
pressure-gradient and source term are implicit. The implicit terms are combined
to get a nonlinear elliptic equation. We show that the overall scheme is
consistent with the respective limit system when the Mach number goes to zero.
A linearised stability analysis confirms the $L^2$-stability of the proposed
scheme. The results of numerical experiments validate the theoretical findings.


  At the end of the 19th century light was regarded as an electromagnetic wave
propagating in a material medium called ether. The speed c appearing in
Maxwell's wave equations was the speed of light with respect to the ether.
Therefore, according to the Galilean addition of velocities, the speed of light
in the laboratory would differ from c. The measure of such difference would
reveal the motion of the laboratory (the Earth) relative to the ether (a sort
of absolute motion). However the Earth's absolute motion was never evidenced.
  Galileo addition of velocities is based on the assumption that lengths and
time intervals are invariant (independent of the state of motion). This way of
thinking the spacetime emanates from our daily experience and lies at the heart
of Newton's Classical Mechanics. Nevertheless, in 1905 Einstein defied Galileo
addition of velocities by postulating that light travels at the same speed c in
any inertial frame. In doing so, Einstein extended the principle of relativity
to the electromagnetic phenomena described by Maxwell's laws. In Einstein's
Special Relativity the ether does not exist and the absolute motion is devoid
of meaning. The invariance of the speed of light forced the replacement of
Galileo transformations with Lorentz transformations. Thus, relativistic length
contractions and time dilations entered our understanding of the spacetime.
Newtonian mechanics had to be reformulated, which led to the discovery of the
mass-energy equivalence.


  Distributionally robust optimization (DRO) is an effective approach for
data-driven decision-making in the presence of uncertainty. Geometric
uncertainty due to sampling or localized perturbations of data points is
captured by Wasserstein DRO (WDRO), which seeks to learn a model that performs
uniformly well over a Wasserstein ball centered around the observed data
distribution. However, WDRO fails to account for non-geometric perturbations
such as adversarial outliers, which can greatly distort the Wasserstein
distance measurement and impede the learned model. We address this gap by
proposing a novel outlier-robust WDRO framework for decision-making under both
geometric (Wasserstein) perturbations and non-geometric (total variation (TV))
contamination that allows an $\varepsilon$-fraction of data to be arbitrarily
corrupted. We design an uncertainty set using a certain robust Wasserstein ball
that accounts for both perturbation types and derive minimax optimal excess
risk bounds for this procedure that explicitly capture the Wasserstein and TV
risks. We prove a strong duality result that enables tractable convex
reformulations and efficient computation of our outlier-robust WDRO problem.
When the loss function depends only on low-dimensional features of the data, we
eliminate certain dimension dependencies from the risk bounds that are
unavoidable in the general setting. Finally, we present experiments validating
our theory on standard regression and classification tasks.


  We carry out an ab initio calculation of the neutrino flux-folded inclusive
cross sections, measured on $^{12}$C by the MiniBooNE and T2K collaborations in
the charged-current quasielastic (CCQE) regime. The calculation is based on
realistic two- and three-nucleon interactions, and on a realistic nuclear
electroweak current with one-and two-nucleon terms that are constructed
consistently with these interactions and reproduce low-energy electroweak
transitions. Numerically exact quantum Monte Carlo methods are utilized to
compute the nuclear weak response functions, by fully retaining many-body
correlations in the initial and final states and interference effects between
one- and two-body current contributions. We employ a nucleon axial form factor
of the dipole form with $\Lambda_A = 1.0$ or $1.15$ GeV, the latter more in
line with a very recent lattice QCD determination. The calculated cross
sections are found to be in good agreement with the neutrino data of MiniBooNE
and T2K, and antineutrino MiniBooNE data, yielding a consistent picture of
nuclei and their electroweak properties across a wide regime of energy and
momenta.


  Intelligent Transportation Systems (ITS) are pushing an increasing interest
and development when dealing with eco-driving systems. In this framework, this
paper presents a method to define speed profiles specifically designed for
Green Light Optimal Speed Advisory (GLOSA) systems on buses. GLOSA aims to
optimize traffic flow by providing vehicles with real-time speed
recommendations synchronized with traffic signal timings. Leveraging
statistical analysis of experimental data collected from an urban bus, the
study develops a methodology to extract meaningful insights into bus behaviour
and traffic dynamics. The proposed approach considers road topology, scheduled
bus stops, and signal timings to define simple although suitable speed profiles
considering the peculiarities of the motion of a bus in an urban scenario.
Through extensive data collection robust statistical data are defined, allowing
the definition of vehicle motion profile for effectively develop and implement
GLOSA systems. This research contributes to the advancement of Intelligent
Transportation Systems by providing realistic data and practical insights for
optimizing bus operations in urban environments.


  Heralded noiseless amplification based on single-photon sources and linear
optics is ideally suited for long-distance quantum communication tasks based on
discrete variables. We experimentally demonstrate such an amplifier, operating
at telecommunication wavelengths. Coherent amplification is performed with a
gain of G=1.98+/-0.2, for a state with a maximum expected gain G=2. We also
demonstrate that there is no need for a stable phase reference between the
initial signal state and the local auxiliary photons used by the amplifier.
These results highlight the potential of heralded quantum amplifiers for
long-distance quantum communication, and bring device-independent quantum key
distribution one step closer.


  Sensor placement for the purpose of detecting/tracking news outbreak and
preventing rumor spreading is a challenging problem in a large scale online
social network (OSN). This problem is a kind of subset selection problem:
choosing a small set of items from a large population so to maximize some
prespecified set function. However, it is known to be NP-complete. Existing
heuristics are very costly especially for modern OSNs which usually contain
hundreds of millions of users. This paper aims to design methods to find
\emph{good solutions} that can well trade off efficiency and accuracy. We first
show that it is possible to obtain a high quality solution with a probabilistic
guarantee from a "{\em candidate set}" of the underlying social network. By
exploring this candidate set, one can increase the efficiency of placing social
sensors. We also present how this candidate set can be obtained using "{\em
graph sampling}", which has an advantage over previous methods of not requiring
the prior knowledge of the complete network topology. Experiments carried out
on two real datasets demonstrate not only the accuracy and efficiency of our
approach, but also effectiveness in detecting and predicting news outbreak.


  In this paper, we investigate the impact of the Distributed generation (DG)
on Power Transmission Grid and pricing strategy for different geographical
locations in different states, generally distributed generation (DG) on
distribution feeders is known to have an impact on voltage regulation and power
quality. A DG might provide voltage and power support in some cases, but most
cases cause short term instability voltage, depending on the several variables,
including relative DG size and location, power network and load character, so
This paper also addressing to the following questions: the different between DG
and conventional power station? What are penetration levels of different DG to
the power grid and compliances to the IEEE relevant standards, discusses
unexpected issues with a special emphasis on power protection coordination
problems, potential advantage and disadvantage to using DG while Distributed
Generation (DG) allows collection of energy from many sources and may provide
lower environmental pollution then we will discuss what would be the main
Factors affecting Electricity Prices of DG units at different states.


  In 1979 Valiant showed that the complexity class VP_e of families with
polynomially bounded formula size is contained in the class VP_s of families
that have algebraic branching programs (ABPs) of polynomially bounded size.
Motivated by the problem of separating these classes we study the topological
closure VP_e-bar, i.e. the class of polynomials that can be approximated
arbitrarily closely by polynomials in VP_e. We describe VP_e-bar with a
strikingly simple complete polynomial (in characteristic different from 2)
whose recursive definition is similar to the Fibonacci numbers. Further
understanding this polynomial seems to be a promising route to new formula
lower bounds.
  Our methods are rooted in the study of ABPs of small constant width. In 1992
Ben-Or and Cleve showed that formula size is polynomially equivalent to width-3
ABP size. We extend their result (in characteristic different from 2) by
showing that approximate formula size is polynomially equivalent to approximate
width-2 ABP size. This is surprising because in 2011 Allender and Wang gave
explicit polynomials that cannot be computed by width-2 ABPs at all! The
details of our construction lead to the aforementioned characterization of
VP_e-bar.
  As a natural continuation of this work we prove that the class VNP can be
described as the class of families that admit a hypercube summation of
polynomially bounded dimension over a product of polynomially many affine
linear forms. This gives the first separations of algebraic complexity classes
from their nondeterministic analogs.


  We explicitly compute up to the fifth mass-level the partition function of
ten-dimensional pure spinor worldsheet variables including the spin dependence.
After adding the contribution from the (x^{\mu}, \theta^{\alpha}, p_{\alpha})
matter variables, we reproduce the massive superstring spectrum. Even though
pure spinor variables are bosonic, the pure spinor partition function contains
fermionic states which first appear at the second mass-level. These fermionic
states come from functions which are not globally defined in pure spinor space,
and are related to the b ghost in the pure spinor formalism. This result
clarifies the proper definition of the Hilbert space for pure spinor variables.


  We investigate minimal solutions of the Allen-Cahn equation on a
Gromov-hyperbolic graph. Under some natural conditions on the graph, we show
the existence of non-constant uniformly-bounded minimal solutions with
prescribed asymptotic behaviours. For a phase field model on a hyperbolic
graph, such solutions describe energy-minimising steady-state phase transitions
that converge towards prescribed phases given by the asymptotic directions on
the graph.


  Automatic image annotation is one of the most challenging problems in machine
vision areas. The goal of this task is to predict number of keywords
automatically for images captured in real data. Many methods are based on
visual features in order to calculate similarities between image samples. But
the computation cost of these approaches is very high. These methods require
many training samples to be stored in memory. To lessen this burden, a number
of techniques have been developed to reduce the number of features in a
dataset. Manifold learning is a popular approach to nonlinear dimensionality
reduction. In this paper, we investigate Diffusion maps manifold learning
method for web image auto-annotation task. Diffusion maps manifold learning
method is used to reduce the dimension of some visual features. Extensive
experiments and analysis on NUS-WIDE-LITE web image dataset with different
visual features show how this manifold learning dimensionality reduction method
can be applied effectively to image annotation.


  The analysis of electrocardiogram (ECG) signals can be time consuming as it
is performed manually by cardiologists. Therefore, automation through machine
learning (ML) classification is being increasingly proposed which would allow
ML models to learn the features of a heartbeat and detect abnormalities. The
lack of interpretability hinders the application of Deep Learning in
healthcare. Through interpretability of these models, we would understand how a
machine learning algorithm makes its decisions and what patterns are being
followed for classification. This thesis builds Convolutional Neural Network
(CNN) and Long Short-Term Memory (LSTM) classifiers based on state-of-the-art
models and compares their performance and interpretability to shallow
classifiers. Here, both global and local interpretability methods are exploited
to understand the interaction between dependent and independent variables
across the entire dataset and to examine model decisions in each sample,
respectively. Partial Dependence Plots, Shapley Additive Explanations,
Permutation Feature Importance, and Gradient Weighted Class Activation Maps
(Grad-Cam) are the four interpretability techniques implemented on time-series
ML models classifying ECG rhythms. In particular, we exploit Grad-Cam, which is
a local interpretability technique and examine whether its interpretability
varies between correctly and incorrectly classified ECG beats within each
class. Furthermore, the classifiers are evaluated using K-Fold cross-validation
and Leave Groups Out techniques, and we use non-parametric statistical testing
to examine whether differences are significant. It was found that Grad-CAM was
the most effective interpretability technique at explaining predictions of
proposed CNN and LSTM models. We concluded that all high performing classifiers
looked at the QRS complex of the ECG rhythm when making predictions.