  The zero-temperature phase diagram of the spin-$\frac{1}{2}$
$J_{1}$--$J_{2}$--$J_{1}^{\perp}$ model on an $AA$-stacked square-lattice
bilayer is studied using the coupled cluster method implemented to very high
orders. Both nearest-neighbor (NN) and frustrating next-nearest-neighbor
Heisenberg exchange interactions, of strengths $J_{1}>0$ and $J_{2} \equiv
\kappa J_{1}>0$, respectively, are included in each layer. The two layers are
coupled via a NN interlayer Heisenberg exchange interaction with a strength
$J_{1}^{\perp} \equiv \delta J_{1}$. The magnetic order parameter $M$ (viz.,
the sublattice magnetization) is calculated directly in the thermodynamic
(infinite-lattice) limit for the two cases when both layers have
antiferromagnetic ordering of either the N\'{e}el or the striped kind, and with
the layers coupled so that NN spins between them are either parallel (when
$\delta < 0$) or antiparallel (when $\delta > 0$) to one another. Calculations
are performed at $n$th order in a well-defined sequence of approximations,
which exactly preserve both the Goldstone linked cluster theorem and the
Hellmann-Feynman theorem, with $n \leq 10$. The sole approximation made is to
extrapolate such sequences of $n$th-order results for $M$ to the exact limit,
$n \to \infty$. By thus locating the points where $M$ vanishes, we calculate
the full phase boundaries of the two collinear AFM phases in the
$\kappa$--$\delta$ half-plane with $\kappa > 0$. In particular, we provide the
accurate estimate, ($\kappa \approx 0.547,\delta \approx -0.45$), for the
position of the quantum triple point (QTP) in the region $\delta < 0$. We also
show that there is no counterpart of such a QTP in the region $\delta > 0$,
where the two quasiclassical phase boundaries show instead an ``avoided
crossing'' behavior, such that the entire region that contains the nonclassical
paramagnetic phases is singly connected.


  Molino's description of Riemannian foliations on compact manifolds is
generalized to the setting of compact equicontinuous foliated spaces, in the
case where the leaves are dense. In particular, a structural local group is
associated to such a foliated space. As an application, we obtain a partial
generalization of results by Carri\`ere and Breuillard-Gelander, relating the
structural local group to the growth of the leaves.


  As is already known, a spacetime horizon acts like a boundary of a thermal
system an we can associate with it notions as temperature and entropy.
Following the work of M. Akbar, in this paper we will show how it is possible
to interpret the field equation of a charged BTZ black hole near horizon as a
thermodynamic identity dE=TdS+PdA+PhidQ, where Phi is the electric potential
and Q is the electric charge of BTZ black hole. These results indicate that the
field equations for the charged BTZ black hole possess intrinsic thermodynamic
properties near horizon.


  Prolonged focused work periods decrease efficiency with related decline of
attention and performance. Therefore, emergency fleet break scheduling should
consider both area coverage by idle vehicles (related to the fleet's target
arrival time to incidents) and vehicle crews' service requirements for breaks
to avoid fatigue. In this paper, we propose a break assignment problem
considering area coverage (BAPCAC) addressing this issue. Moreover, we
formulate a mathematical model for the BAPCAC problem. Based on available
historical spatio-temporal incident data and service requirements, the BAPCAC
model can be used not only to dimension the size of the fleet at the tactical
level, but also to decide upon the strategies related with break scheduling.
Moreover, the model could be used to compute (suboptimal) locations for idle
vehicles in each time period and arrange vehicles' crews' work breaks
considering given break and coverage constraints.


  The unstable radium nucleus is appealing for probing new physics due to its
high mass, octupole deformation and energy level structure. Ion traps, with
long hold times and low particle numbers, are excellent for work with
radioactive species, such as radium and radium-based molecular ions, where low
activity, and hence low total numbers, is desirable. We address the challenges
associated with the lack of stable isotopes in a tabletop experiment with a
low-activity ($\sim 10 \ \mu\mathrm{Ci}$) source where we laser-cool trapped
radium ions. With a laser-cooled radium ion we measured the $7p\ ^2P_{1/2}^o$
state's branching fractions to the ground state, $7s\ ^2S_{1/2}$, and a
metastable excited state, $6d\ ^2D_{3/2}$, to be $p=0.9104(7)$ and $0.0896(7)$,
respectively. With a nearby tellurium reference line we measured the $7s\
^2S_{1/2} \rightarrow 7p\ ^2P_{1/2}^o$ transition frequency, 640.09663(6) THz.


  This paper deals with existence and uniqueness results for a transient
nonlinear radiative-conductive system in three-dimensional case. This system
describes the heat transfer for a grey, semi-transparent and non-scattering
medium with general boundary conditions. We reformulate the full transient
state system as a fixed-point problem. The existence and uniqueness proof is
based on Banach fixed point theorem.


  We study the effects of O(alpha_s) supersymmetric QCD (SQCD) corrections on
the total production rate and kinematic distributions of polarized and
unpolarized top-pair production in pp and p anti-p collisions. At the Fermilab
Tevatron p anti-p collider, top-quark pairs are mainly produced via
quark-antiquark annihilation, q anti-q -> t anti-t, while at the CERN LHC pp
collider gluon-gluon scattering, g g -> t anti-t, dominates. We compute the
complete set of O(alpha_s) SQCD corrections to both production channels and
study their dependence on the parameters of the Minimal Supersymmetric Standard
Model. In particular, we discuss the prospects for observing strong,
loop-induced SUSY effects in top-pair production at the Tevatron Run II and the
LHC.


  We analyse local models at the point of E_8 in F-theory GUTs and identify
exactly two models with potentially realistic properties concerning proton
stability and a suitable pattern of quark and lepton masses. To this end we
identify a matter parity at the local point. A globally consistent ultraviolet
completion turns out to be problematic for both models. It is impossible to
embed the models in a semilocal scheme via the spectral cover approach. This
seems to severely limit the predictive power of local model building and to
indicate that the full string theory might give us valuable hints for particle
physics model building.


  Mixing effects in the MSSM Higgs sector can give rise to a sizeable
interference between the neutral Higgs bosons. On the other hand, factorising a
more complicated process into production and decay parts by means of the
narrow-width approximation (NWA) simplifies the calculation. The standard NWA,
however, does not account for interference terms. Therefore, we introduce a
generalisation of the NWA (gNWA) which allows for a consistent treatment of
interference effects between nearly mass-degenerate particles. Furthermore, we
apply the gNWA at the tree and 1-loop level to an example process where the
neutral Higgs bosons $h$ and $H$ are produced in the decay of a heavy
neutralino and subsequently decay into a fermion pair. The $h-H$ propagator
mixing is found to agree well with the approximation of Breit-Wigner
propagators times finite wave-function normalisation factors, both leading to a
significant interference contribution. The factorisation of the interference
term based on on-shell matrix elements reproduces the full interference result
within a precision of better than 1% for the considered process. The gNWA also
enables the inclusion of contributions beyond the 1-loop order into the most
precise prediction.


  Nuclear masses are predicted with the Bayesian neural networks by learning
the mass surface of even-even nuclei and the correlation energies to their
neighbouring nuclei. By keeping the known physics in various sophisticated mass
models and performing the delicate design of neural networks, the proposed
Bayesian machine learning (BML) mass model achieves an accuracy of $84$~keV,
which crosses the accuracy threshold of the $100$~keV in the experimentally
known region. It is also demonstrated the corresponding uncertainties of mass
predictions are properly evaluated, while the uncertainties increase by about
$50$~keV each step along the isotopic chains towards the unknown region. The
shell structures in the known region are well described and several important
features in the unknown region are predicted, such as the new magic numbers
around $N = 40$, the robustness of $N = 82$ shell, the quenching of $N = 126$
shell, and the smooth separation energies around $N = 104$.


  21 cm intensity mapping (IM) has the potential to be a strong and unique
probe of cosmology from redshift of order unity to redshift potentially as high
as 30. For post-reionization 21 cm observations, the signal is modulated by the
thermal and dynamical reaction of gas in the galaxies to the passage of
ionization fronts during the Epoch of Reionization. In this work, we
investigate the impact of inhomogeneous reionization on the post-reionization
21 cm power spectrum and the induced shifts of cosmological parameters at
redshifts $3.5 \lesssim z \lesssim 5.5$. We make use of hydrodynamics
simulations that could resolve small-scale baryonic structure evolution to
quantify HI abundance fluctuation, while semi-numerical large box 21cmFAST
simulations capable of displaying inhomogeneous reionization process are
deployed to track the inhomogeneous evolution of reionization bubbles. We
discussed the prospects of capturing this effect in two post-reionization 21 cm
intensity mapping experiments: SKA1-LOW and PUMA. We find the inhomogeneous
reionization effect could impact the HI power spectrum up to tens of percent
level and shift cosmological parameters estimation from sub-percent to tens
percent in the observation of future post-reionization 21 cm intensity mapping
experiments such as PUMA, while SKA1-LOW is likely to miss this effect at the
redshifts of interest given the considered configuration. In particular, the
shift is up to 0.0206 in the spectral index $n_s$ and 0.0192 eV in the sum of
the neutrino masses $\sum m_\nu$ depending on the reionization model and the
observational parameters. We discuss strategies to mitigate and separate these
biases.


  In this paper,we report a detailed analysis of recurrent jets originated from
a location with emerging, canceling and converging negative magnetic field at
the east edge of NOAA active region AR11166 from 2011 March 09 to 10. The event
presented several interesting features. First, a satellite sunspot appeared and
collided with a pre-existing opposite polarity magnetic field and caused a
recurrent solar jet event. Second, the evolution of the jets showed
blowout-like nature and standard characteristics. Third, the satellite sunspot
exhibited a motion toward southeast of AR11166 and merged with the emerging
flux near the opposite polarity sunspot penumbra, which afterward, due to flux
convergence and cancellation episodes, caused recurrent jets. Fourth, three of
the blowout jets associated with coronal mass ejections (CMEs), were observed
from field of view of the Solar Terrestrial Relations Observatory. Fifth,
almost all the blowout jet eruptions were accompanied with flares or with more
intense brightening in the jet base region, while almost standard jets did not
manifest such obvious feature during eruptions. The most important, the blowout
jets were inclined to faster and larger scale than the standard jets. The
standard jets instead were inclined to relative longer-lasting. The obvious
shearing and twisting motions of the magnetic field may be interpreted as due
to the shearing and twisting motions for a blowout jet eruption. From the
statistical results, about 30% blowout jets directly developed into CMEs. It
suggests that the blowout jets and CMEs should have a tight relationship.


  We envision a publish/subscribe ontology system that is able to index
millions of user subscriptions and filter them against ontology data that
arrive in a streaming fashion. In this work, we propose a SPARQL extension
appropriate for a publish/subscribe setting; our extension builds on the
natural semantic graph matching of the language and supports the creation of
full-text subscriptions. Subsequently, we propose a main-memory subscription
indexing algorithm which performs both semantic and full-text matching at low
complexity and minimal filtering time. Thus, when ontology data are published
matching subscriptions are identified and notifications are forwarded to users.


  Multi-objective optimization of the textile manufacturing process is an
increasing challenge because of the growing complexity involved in the
development of the textile industry. The use of intelligent techniques has been
often discussed in this domain, although a significant improvement from certain
successful applications has been reported, the traditional methods failed to
work with high-as well as human intervention. Upon which, this paper proposed a
multi-agent reinforcement learning (MARL) framework to transform the
optimization process into a stochastic game and introduced the deep Q-networks
algorithm to train the multiple agents. A utilitarian selection mechanism was
employed in the stochastic game, which (-greedy policy) in each state to avoid
the interruption of multiple equilibria and achieve the correlated equilibrium
optimal solutions of the optimizing process. The case study result reflects
that the proposed MARL system is possible to achieve the optimal solutions for
the textile ozonation process and it performs better than the traditional
approaches.


  Let $F$ be a number field and $D$ a quaternion algebra over $F$. Take a
cuspidal automorphic representation $\pi$ of $D_{\mathbb{A}}^\times$ with
trivial central character and a cusp form $\phi$ in $\pi$. Using the
prehomogeneous zeta function, we find an explicit mean value of the toric
periods of $\phi$ with respect to quadratic algebras over $F$. The result can
also be written as a mean value formula for the central values of automorphic
$L$-functions twisted by quadratic characters.


  The covariant canonical transformation theory applied to the relativistic
Hamiltonian theory of classical matter fields in dynamical space-time yields a
novel (first order) gauge field theory of gravitation. The emerging field
equations necessarily embrace a quadratic Riemann term added to Einstein's
linear equation. The quadratic term endows space-time with inertia generating a
dynamic response of the space-time geometry to deformations relative to (Anti)
de Sitter geometry. A "deformation parameter" is identified, the inverse
dimensionless coupling constant governing the relative strength of the
quadratic invariant in the Hamiltonian, and directly observable via the
deceleration parameter $q_0$. The quadratic invariant makes the system
inconsistent with Einstein's constant cosmological term, $\Lambda =
\mathrm{const}$. In the Friedman model this inconsistency is resolved with the
scaling ansatz of a "cosmological function", $\Lambda(a)$, where $a$ is the
scale parameter of the FLRW metric. %Moreover, the strain generated by the
quadratic term turns out to act as a geometrical stress. The cosmological
function can be normalized such that with the $\Lambda$ CDM parameter set the
present-day observables, the Hubble constant and the deceleration parameter,
can be reproduced. %We analyze the asymptotics of the such normalized Friedman
equations with respect to both, the fundamental parameters (coupling constants)
and the scale $a$. With this parameter set we recover the dark energy scenario
in the late epoch. The proof that inflation in the early phase is caused by the
"geometrical fluid" representing the inertia of space-time is yet pending,
though.


  We present a brief overview of a splinter session on M dwarf stars as planet
hosts that was organized as part of the Cool Stars 17 conference. The session
was devoted to reviewing our current knowledge of M dwarf stars and exoplanets
in order to prepare for current and future exoplanet searches focusing in low
mass stars. We review the observational and theoretical challenges to
characterize M dwarf stars and the importance of accurate fundamental
parameters for the proper characterization of their exoplanets and our
understanding on planet formation.


  The chiral perturbation theory (ChPT) of pions is extended to include vector
mesons as well as pertinent degrees of freedom. By counting the typical
momentum scale of vector mesons as order of $Q$ and vector meson masses as of
the order of $\Lambda_\chi$, a consistent theory could be obtained. The
explicit renormalization procedure of the theory is presented for the form
factors of the pion up to one-loop accuracy. The resulting theory prediction
for the form factors is in good agreement with the experimental data for a wide
range of momentum transfers. The vector-meson dominance mechanism is also
discussed in the systematic framework of ChPT.


  In the standard hot big bang model, the expansion of the early universe is
given by the Friedmann equation with an energy density dominated by
relativistic particles. Since in a variety of models this equation is altered,
we introduce modifications in the Friedmann equation and show that we can
constrain them using big bang nucleosynthesis data. When there is no
neutrino/antineutrino asymmetry these modifications are tightly bounded but in
presence of an asymmetry the bounds become much looser. As an illustration, we
apply our results to a model where the second and third families couple to
gravity differently than the first family (non-universal gravity).


  We discuss two models for describing the behavior of matter at large
densities and intermediate temperatures. In both models a softening of the
equation of state takes place due to the appearance of new degrees of freedom.
The first is a hadronic model in which the softening is due to chiral symmetry
restoration. In the second model the softening is associated with the formation
of clusters of quarks in the mixed phase. We show that both models allow a
significant softening but, in the first case the bulk modulus is mainly
dependent on the density, while in the mixed phase model it also strongly
depends on the temperature. We also show that the bulk modulus is not vanishing
in the mixed phase due to the presence of two conserved charges, the baryon and
the isospin one. Only in a small region of densities and temperatures the
incompressibility becomes extremely small. Finally we compare our results with
recent analysis of heavy ion collisions at intermediate energies.


  In this paper we aim at employing a compactness criterion of Da Prato,
Malliavin, Nualart for square integrable Brownian functionals to construct
unique strong solutions of SDE's under an integrability condition on the drift
coefficient. The obtained solutions turn out to be Malliavin differentiable and
are used to derive a Bismut-Elworthy-Li formula for solutions of the Kolmogorov
equation.


  We demonstrate a simple method for testing the significance of peaks in the
periodogram of red noise data. The procedure was designed to test for spurious
periodicities in X-ray light curves of active galaxies, but can be used quite
generally to test for periodic components against a background noise spectrum
assumed to have a power law shape. The method provides a simple and fast test
of the significance of candidate periodic signals in short, well-sampled time
series such as those obtained from XMM-Newton observations of Seyfert galaxies,
without the need for Monte Carlo simulations. A full account is made of the
number of trials and the uncertainties inherent to the model fitting. Ignoring
these subtle effects can lead to substantially overestimated significances.
These difficulties motivate us to demand high standards of detection (minimum
>99.9 per cent confidence) for periodicities in sources that normally show red
noise spectra. The method also provides a simple means to estimate the power
spectral index, which may be an interesting parameter itself, regardless of the
presence/absence of periodicities.


  Using machine learning, especially deep learning, to facilitate biological
research is a fascinating research direction. However, in addition to the
standard classification or regression problems, in bioinformatics, we often
need to predict more complex structured targets, such as 2D images and 3D
molecular structures. The above complex prediction tasks are referred to as
structured prediction. Structured prediction is more complicated than the
traditional classification but has much broader applications, considering that
most of the original bioinformatics problems have complex output objects. Due
to the properties of those structured prediction problems, such as having
problem-specific constraints and dependency within the labeling space, the
straightforward application of existing deep learning models can lead to
unsatisfactory results. Here, we argue that the following ideas can help
resolve structured prediction problems in bioinformatics. Firstly, we can
combine deep learning with other classic algorithms, such as probabilistic
graphical models, which model the problem structure explicitly. Secondly, we
can design the problem-specific deep learning architectures or methods by
considering the structured labeling space and problem constraints, either
explicitly or implicitly. We demonstrate our ideas with six projects from four
bioinformatics subfields, including sequencing analysis, structure prediction,
function annotation, and network analysis. The structured outputs cover 1D
signals, 2D images, 3D structures, hierarchical labeling, and heterogeneous
networks. With the help of the above ideas, all of our methods can achieve SOTA
performance on the corresponding problems. The success of these projects
motivates us to extend our work towards other more challenging but important
problems, such as health-care problems, which can directly benefit people's
health and wellness.


  Interpretability is becoming an active research topic as machine learning
(ML) models are more widely used to make critical decisions. Tabular data is
one of the most commonly used modes of data in diverse applications such as
healthcare and finance. Much of the existing interpretability methods used for
tabular data only report feature-importance scores -- either locally (per
example) or globally (per model) -- but they do not provide interpretation or
visualization of how the features interact. We address this limitation by
introducing Feature Vectors, a new global interpretability method designed for
tabular datasets. In addition to providing feature-importance, Feature Vectors
discovers the inherent semantic relationship among features via an intuitive
feature visualization technique. Our systematic experiments demonstrate the
empirical utility of this new method by applying it to several real-world
datasets. We further provide an easy-to-use Python package for Feature Vectors.


  Hypergraph states form a family of multiparticle quantum states that
generalizes the well-known concept of Greenberger-Horne-Zeilinger states,
cluster states, and more broadly graph states. We study the nonlocal properties
of quantum hypergraph states. We demonstrate that the correlations in
hypergraph states can be used to derive various types of nonlocality proofs,
including Hardy-type arguments and Bell inequalities for genuine multiparticle
nonlocality. Moreover, we show that hypergraph states allow for an
exponentially increasing violation of local realism which is robust against
loss of particles. Our results suggest that certain classes of hypergraph
states are novel resources for quantum metrology and measurement-based quantum
computation.


  We develop a general theory of 3-dimensional ``orbifold completion'', to
describe (generalised) orbifolds of topological quantum field theories as well
as all their defects. Given a semistrict 3-category $\mathcal{T}$ with adjoints
for all 1- and 2-morphisms (more precisely, a Gray category with duals), we
construct the 3-category $\mathcal{T}_{\textrm{orb}}$ as a Morita category of
certain $E_1$-algebras in $\mathcal{T}$ which encode triangulation invariance.
We prove that in $\mathcal{T}_{\textrm{orb}}$ again all 1- and 2-morphisms have
adjoints, that it contains $\mathcal{T}$ as a full subcategory, and we argue,
but do not prove, that it satisfies a universal property which implies
$(\mathcal{T}_{\textrm{orb}})_{\textrm{orb}} \cong \mathcal{T}_{\textrm{orb}}$.
This is a categorification of the work in [CR]. Orbifold completion by design
allows us to lift the orbifold construction from closed TQFT to the much richer
world of defect TQFTs. We illustrate this by constructing a universal
3-dimensional state sum model with all defects from first principles, and we
explain how recent work on defects between Witt equivalent Reshetikhin--Turaev
theories naturally appears as a special case of orbifold completion.


  Mode-based clustering methods define clusters to be the basins of attraction
of the modes of a density estimate. The most common version is mean shift clus-
tering which uses a gradient ascent algorithm to find the basins. Rodriguez and
Laio (2014) introduced a new method that is faster and simpler than mean shift
clustering. Furthermore, they define a clustering diagram that provides a sim-
ple, two-dimensional summary of the mode clustering information. We study the
statistical properties of this diagram and we propose some improvements and
extensions. In particular, we show a connection between the diagram and robust
linear regression.


  By analogy with the kinetic theory of gases, most turbulence modeling strate-
gies rely on an eddy viscosity to model the unresolved turbulent fluctuations.
How- ever, the ratio of unresolved to resolved scales - very much like a degree
of rarefaction - is not taken into account by the popular conventional schemes,
based on the Navier- Stokes equations. This paper investigates the simulation
of turbulent flow with a gas- kinetic scheme. In so doing, the modeling of
turbulence becomes part of the numerical scheme: the degree of rarefaction is
automatically taken into account; the turbulent stress tensor is not
constrained into a linear relation with the strain rate. Instead it is modeled
on the basis of the analogy between particles and eddies, without any as-
sumptions on the type of turbulence or flow class. The implementation of a
turbulent gas-kinetic scheme into a finite-volume solver is put forward, with
turbulent kinetic energy and dissipation supplied by an allied turbulence
model. A number of flow cases, challenging for conventional RANS methods, are
investigated; results show that the shock-turbulent boundary layer is captured
in a much more convincing way by the gas-kinetic scheme.


  Rabi, Rivest, and Sherman alter the standard notion of noninvertibility to a
new notion they call strong noninvertibility, and show -- via explicit
cryptographic protocols for secret-key agreement ([RS93,RS97] attribute this to
Rivest and Sherman) and digital signatures [RS93,RS97] -- that strongly
noninvertible functions would be very useful components in protocol design.
Their definition of strong noninvertibility has a small twist (``respecting the
argument given'') that is needed to ensure cryptographic usefulness. In this
paper, we show that this small twist has a large, unexpected consequence:
Unless P=NP, some strongly noninvertible functions are invertible.


  We study the scaling behavior of the size of minimum dominating set (MDS) in
scale-free networks, with respect to network size $N$ and power-law exponent
$\gamma$, while keeping the average degree fixed. We study ensembles generated
by three different network construction methods, and we use a greedy algorithm
to approximate the MDS. With a structural cutoff imposed on the maximal degree
($k_{\max}=\sqrt{N}$) we find linear scaling of the MDS size with respect to
$N$ in all three network classes. Without any cutoff ($k_{\max}=N-1$) two of
the network classes display a transition at $\gamma \approx 1.9$, with linear
scaling above, and vanishingly weak dependence below, but in the third network
class we find linear scaling irrespective of $\gamma$. We find that the partial
MDS, which dominates a given $z<1$ fraction of nodes, displays essentially the
same scaling behavior as the MDS.


  We study the consequences of deterministic chaos for diffusion-controlled
reaction. As an example, we analyze a diffusive-reactive deterministic
multibaker and a parameter-dependent variation of it. We construct the
diffusive and the reactive modes of the models as eigenstates of the
Frobenius-Perron operator. The associated eigenvalues provide the dispersion
relations of diffusion and reaction and, hence, they determine the reaction
rate. For the simplest model we show explicitly that the reaction rate behaves
as phenomenologically expected for one-dimensional diffusion-controlled
reaction. Under parametric variation, we find that both the diffusion
coefficient and the reaction rate have fractal-like dependences on the system
parameter.


  Maximum run-length limited codes are constraint codes used in communication
and data storage systems. Insertion/deletion correcting codes correct insertion
or deletion errors caused in transmitted sequences and are used for combating
synchronization errors. This paper investigates the maximum run-length limited
single insertion/deletion correcting (RLL-SIDC) codes. More precisely, we
construct efficiently encodable and decodable RLL-SIDC codes. Moreover, we
present its encoding algorithm and show the redundancy of the code.


  Digital Twin is a breaking technology that allows creating virtual
representations of complex physical systems based on updated information of the
system and its physical laws. However, making the Digital Twin behavior
matching with the real system can be challenging due to the number of unknown
parameters in each twin. Its search can be done using optimization-based
techniques, producing a family of models based on different system datasets,
so, a discrimination criterion is required to determine the best Digital Twin
model. This paper presents an information theory-based discrimination criterion
to determine the best Digital Twin model resulting from a behavioral matching
process. The information gain of a model is employed as a discrimination
criterion. Box-Jenkins models are used to define the family of models for each
behavioral matching result. The proposed method is compared with other
information-based metrics as well as the $\nu$gap metric. As a study case, the
discrimination method is applied to the Digital Twin for a real-time vision
feedback infrared temperature uniformity control system. Obtained results show
that information-based methodologies are useful for selecting an accurate
Digital Twin model representing the system among a family of plants


  The upcoming generation of galaxy surveys will probe the distribution of
matter in the universe with unprecedented accuracy. Measurements of the matter
power spectrum at different scales and redshifts will provide stringent
constraints on the cosmological parameters. However, on non-linear scales this
will require an accurate evaluation of the covariance matrix. Here, we compute
the covariance matrix of the 3D matter density power spectrum for the
concordance $\Lambda$CDM cosmology from an ensemble of N-body simulations of
the Dark Energy Universe Simulation - Parallel Universe Runs (DEUS-PUR). This
consists of 12288 realisations of a $(656\,h^{-1}\,\textrm{Mpc})^3$ simulation
box with $256^3$ particles. We combine this set with an auxiliary sample of 96
simulations of the same volume with $1024^3$ particles. We find N-body mass
resolution effect to be an important source of systematic errors on the
covariance at high redshift and small intermediate scales. We correct for this
effect by introducing an empirical statistical method which provide an accurate
determination of the covariance matrix over a wide range of scales including
the Baryon Oscillations interval. Contrary to previous studies that used
smaller N-body ensembles, we find the power spectrum distribution to
significantly deviate from expectations of a Gaussian random density field at
$k\gtrsim 0.25\,h\,\textrm{Mpc}^{-1}$ and $z<0.5$. This suggests that in the
case of finite volume surveys an unbiased estimate of the ensemble averaged
band power at these scales and redshifts may require a careful assessment of
non-Gaussian errors more than previously considered.


  The amplitude and phase of Shubnikov-de Haas oscillations have been analyzed
in detail for two-dimensional electron gases subjected to a weak unidirectional
periodic potential modulation. The amplitude is suppressed, accompanied by
inversion of the phase, at the maximum bandwidth conditions at low magnetic
fields. The suppression is gradually taken over by the enhancement with the
increase of the magnetic fields. The suppression and the enhancement are
attributable to the collisional and the diffusion contribution of the modulated
potential to the conductivity, respectively, the former (the latter) being
dominant at low (high) magnetic fields. A theoretical calculation that takes
the two types of contributions into account shows semi-quantitative agreement
with experimental traces.


  To any simple graph \(G\), the clique graph operator \(K\) assigns the graph
\(K(G)\) which is the intersection graph of the maximal complete subgraphs of
\(G\). The iterated clique graphs are defined by \(K^{0}(G)=G\) and
\(K^{n}(G)=K(K^{n-1}(G))\) for \(n\geq 1\). We associate topological concepts
to graphs by means of the simplicial complex \(\mathrm{Cl}(G)\) of complete
subgraphs of \(G\). Hence we say that the graphs \(G_{1}\) and \(G_{2}\) are
homotopic whenever \(\mathrm{Cl}(G_{1})\) and \(\mathrm{Cl}(G_{2})\) are. A
graph \(G\) such that \(K^{n}(G)\simeq G\) for all \(n\geq1\) is called
\emph{\(K\)-homotopy permanent}. A graph is \emph{Helly} if the collection of
maximal complete subgraphs of \(G\) has the Helly property. Let \(G\) be a
Helly graph. Escalante (1973) proved that \(K(G)\) is Helly, and Prisner (1992)
proved that \(G\simeq K(G)\), and so Helly graphs are \(K\)-homotopy permanent.
We conjecture that if a graph \(G\) satisfies that \(K^{m}(G)\) is Helly for
some \(m\geq1\), then \(G\) is \(K\)-homotopy permanent. If a connected graph
has maximum degree at most four and is different from the octahedral graph, we
say that it is a \emph{low degree graph}. It was recently proven that all low
degree graphs \(G\) satisfy that \(K^{2}(G)\) is Helly. In this paper, we show
that all low degree graphs have the homotopy type of a wedge or circumferences,
and that they are \(K\)-homotopy permanent.


  The (zeroth-order) energy of a particle in the background of a black hole is
given by Carter's integrals. However, exact calculations of a particle's {\it
self-energy} (first-order corrections) are still beyond our present reach in
many situations. In this paper we use Hawking's area theorem in order to derive
bounds on the self-energy of a particle in the vicinity of a black hole.
Furthermore, we show that self-energy corrections {\it must} be taken into
account in order to guarantee the validity of Penrose cosmic censorship
conjecture.


  We derive the exact solution of low energy limit of Bogoliubov equations for
excitations of Bose-Einstein condensate in the presence of arbitrary potential
barrier and maximum current of condensate. Using this solution, we give the
explicit expression for the transmission coefficient against the potential
barrier, which shows partial transmission in the low energy limit. The
wavefunctions of excitations in the low energy limit do not coincide with that
of the condensate. The absence of the perfect transmission in the critical
current state originates from local enhancement of density fluctuations around
the potential barrier.


  In this paper we look for a correlations between intermediate $p_T$ particle
pairs and the $v_2$ of the remaining low $p_T$ particles. We find that the
shape of the flow vector distribution, which is calculated from all low $p_T$
tracks, depends in a non-trivial way on the angular separation between the high
$p_T$ particle pairs in the event. Our analysis is based on 200 GeV Au+Au
collision measured with the STAR detector.


  The Skyrmion number of paraxial optical Skyrmions can be defined solely via
their polarization singularities and associated winding numbers, using a
mathematical derivation that exploits Stokes's theorem. It is demonstrated that
this definition provides a robust way to extract the Skyrmion number from
experimental data, as illustrated for a variety of optical (N\'eel-type)
Skyrmions and bimerons, and their corresponding lattices. This method generates
not only an increase in accuracy, but also provides an intuitive geometrical
approach to understanding the topology of such quasi-particles of light, and
their robustness against smooth transformations.


  This paper discusses a statistical anomaly-detection method for
model-independent searches for new physics in collision events produced at the
Large Hadron Collider (LHC). The method requires calculations of $Z$-scores for
a large number of Lorenz-invariant variables to identify events that deviate
from those expected for the Standard Model (SM).


  The presence of R-parity ($\mathcal{R}_p$) violation offers intersting decay
channels for the gluinos. In this work we present a new search strategy for the
gluinos in the presence of semileptonic $\mathcal{R}_p$ violating couplings
$\lambda^{'}_{133}$ and $\lambda^{'}_{233}$. We consider two scenarios (i)
$\lambda^{'}$ induced 3-body decay of gluinos to a top quark ($t$), a bottom
quark ($b$) and a light lepton ($\ell$) (ii) cascade decay of gluinos to top
quarks and neutralinos ($\widetilde{\chi}_1^0$) followed by the decay of
$\widetilde{\chi}_1^0$ to $t$, $b$ and $\ell$ through $\lambda^{'}$ couplings.
We present two different search procedures which are common to both the
scenarios. While the first one involves the traditional approach with
multi-leptons and $b$-tagged jets, the second one employs the more recent
technique to reconstruct highly energetic hadronically decaying top quarks. We
perform a detailed simulation of the signal as well as all the relevant
Standard Model backgrounds to show that the second procedure offers slightly
better sensitivity for gluino discovery. In both the procedures, a $\geq$
5$\sigma$ discovery is possible for the gluino mass in the range 1.5 -1.7 TeV
at 14 TeV LHC with 50 fb$^{-1}$ integrated luminosity.


  X-ray and UV line emission in X-ray binaries can be accounted for by a hot
corona. Such a corona forms through irradiation of the outer disk by radiation
produced in the inner accretion flow. The same irradiation can produce a strong
outflow from the disk at sufficiently large radii. Outflowing gas has been
recently detected in several X-ray binaries via blue-shifted absorption lines.
However, the causal connection between winds produced by irradiation and the
blue-shifted absorption lines is problematic, particularly in the case of GRO
J1655-40. Observations of this source imply wind densities about two orders of
magnitude higher than theoretically predicted. This discrepancy does not mean
that these `thermal disk-winds' cannot explain blue-shifted absorption in other
systems, nor that they are unimportant as a sink of matter. Motivated by the
inevitability of thermal disk-winds and wealth of data taken with current
observatories such as Chandra, XMM-Newton and Suzaku, as well as the future
AstroH mission, we decided to investigate the requirements to produce very
dense winds. Using physical arguments, hydrodynamical simulations and
absorption line calculations, we found that modification of the heating and
cooling rates by a factor of a few results in an increase of the wind density
of up to an order of magnitude and the wind velocity by a factor of about two.
Therefore, the mass loss rate from the disk can be one, if not even two orders
of magnitude higher than the accretion rate onto the central object. Such a
high mass loss rate is expected to destabilize the disk and perhaps provides a
mechanism for state change.


  We explore an extended cosmological scenario where the dark matter is an
admixture of cold and additional non-cold species. The mass and temperature of
the non-cold dark matter particles are extracted from a number of cosmological
measurements. Among others, we consider tomographic weak lensing data and Milky
Way dwarf satellite galaxy counts. We also study the potential of these
scenarios in alleviating the existing tensions between local measurements and
Cosmic Microwave Background (CMB) estimates of the $S_8$ parameter, with
$S_8=\sigma_8\sqrt{\Omega_m}$, and of the Hubble constant $H_0$. In principle,
a sub-dominant, non-cold dark matter particle with a mass $m_X\sim$~keV, could
achieve the goals above. However, the preferred ranges for its temperature and
its mass are different when extracted from weak lensing observations and from
Milky Way dwarf satellite galaxy counts, since these two measurements require
suppressions of the matter power spectrum at different scales. Therefore,
solving simultaneously the CMB-weak lensing tensions and the small scale crisis
in the standard cold dark matter picture via only one non-cold dark matter
component seems to be challenging.


  An efficient and robust restart strategy is important for any Krylov-based
method for eigenvalue problems. The tensor infinite Arnoldi method (TIAR) is a
Krylov-based method for solving nonlinear eigenvalue problems (NEPs). This
method can be interpreted as an Arnoldi method applied to a linear and infinite
dimensional eigenvalue problem where the Krylov basis consists of polynomials.
We propose new restart techniques for TIAR and analyze efficiency and
robustness. More precisely, we consider an extension of TIAR which corresponds
to generating the Krylov space using not only polynomials but also structured
functions that are sums of exponentials and polynomials, while maintaining a
memory efficient tensor representation. We propose two restarting strategies,
both derived from the specific structure of the infinite dimensional Arnoldi
factorization. One restarting strategy, which we call semi-explicit TIAR
restart, provides the possibility to carry out locking in a compact way. The
other strategy, which we call implicit TIAR restart, is based on the
Krylov-Schur restart method for linear eigenvalue problem and preserves its
robustness. Both restarting strategies involve approximations of the tensor
structured factorization in order to reduce complexity and required memory
resources. We bound the error in the infinite dimensional Arnoldi factorization
showing that the approximation does not substantially influence the robustness
of the restart approach. We illustrate the approaches by applying them to solve
large scale NEPs that arise from a delay differential equation and a wave
propagation problem. The advantages in comparison to other restart methods are
also illustrated.


  This white paper is a condensation of a report by a committee appointed
jointly by the Nuclear Science and Physics Divisions at Lawrence Berkeley
National Laboratory (LBNL). The goal of this study was to identify the most
promising technique(s) for resolving the neutrino mass hierarchy. For the most
part, we have relied on calculations and simulations presented by the
proponents of the various experiments. We have included evaluations of the
opportunities and challenges for these experiments based on what is available
already in the literature.


  Studying the stellar kinematics of galaxies is a key tool in the
reconstruction of their evolution. However, the current measurements of the
stellar kinematics are complicated by several factors, including dust
extinction and the presence of multiple stellar populations. We use integral
field spectroscopic data of four galaxies from the TIMER survey to explore and
compare the kinematics measured in different spectral regions that are
sensitive to distinct stellar populations. We derive the line-of-sight velocity
and velocity dispersion of both a young (<2 Gyr) and an old stellar population
from the spectral regions around the H$\beta$ line and the Ca II Triplet. In
addition we obtain colour excess, mean age, and metallicity. We report a
correlation of the colour excess with the difference in the kinematic
parameters of the H$\beta$ line and the Ca II Triplet range, which are
dominated by young and old stellar populations, respectively. Young stellar
populations, located primarily in nuclear rings, have higher velocity
dispersions than old ones. These differences in the rings are typically 10 km/s
in velocity dispersion, but up to a mean value of 24 km/s in the most extreme
case. Trends with age exist in the nuclear rings but are less significant than
those with dust extinction. We report different degrees of correlation of these
trends among the galaxies in the sample, which are related to the size of the
Voronoi bins in their rings. No clear trends for the difference of
line-of-sight velocity are observed. The absence of these trends can be
explained as a consequence of the masking process of the H$\beta$ line during
the kinematic extraction, as confirmed by dedicated simulations. Our study
demonstrates that kinematic differences caused by different stellar populations
can be identified in the central regions of nearby galaxies even from
intermediate resolution spectroscopy.


  We present a method of embedding a Monolithic Active Pixel Sensor (MAPS) into
a flexible printed circuit board (FPC) and its interconnection by means of
through-hole copper plating. The resulting assembly, baptised "MAPS foil", is a
flexible, light, protected, and fully integrated detector module. By using
widely available printed circuit board manufacturing techniques, the production
of these devices can be scaled easily in size and volume, making it a
compelling candidate for future large-scale applications.
  A first series of prototypes that embed the ALPIDE chip has been produced,
functionally tested, and shown to be working.


  This paper has been withdrawn by the author. An assumption made in the
analysis is not valid under the experimental conditions.


  We examine physical aspects for the electric version of a recently proposed
logarithmic electrodynamics, for which the electric field of a point-like
charge is finite at the origin. It is shown that this electrodynamics displays
the vacuum birefringence phenomenon in the presence of external magnetic field.
Afterwards we compute the lowest-order modification to the interaction energy
by means of the gauge-invariant but path-dependent variables formalism. These
are shown to result in a long-range ($1/r^3$-type) correction, in addition to a
linear and another logarithmic correction, to the Coulomb potential.


  In supervised machine learning, the assumption that training data is labelled
correctly is not always satisfied. In this paper, we investigate an instance of
labelling error for classification tasks in which the dataset is corrupted with
out-of-distribution (OOD) instances: data that does not belong to any of the
target classes, but is labelled as such. We show that detecting and relabelling
certain OOD instances, rather than discarding them, can have a positive effect
on learning. The proposed method uses an auxiliary classifier, trained on data
that is known to be in-distribution, for detection and relabelling. The amount
of data required for this is shown to be small. Experiments are carried out on
the FSDnoisy18k audio dataset, where OOD instances are very prevalent. The
proposed method is shown to improve the performance of convolutional neural
networks by a significant margin. Comparisons with other noise-robust
techniques are similarly encouraging.


  The light-matter interaction described by Rabi model and Jaynes-Cummings (JC)
model is investigated by parity breaking as well as the scaling behavior of
ground-state population-inversion expectation. We show that the parity breaking
leads to different scaling behaviors in the two models, where the Rabi model
demonstrates scaling invariance, but the JC model behaves in cusp-like way. Our
study helps further understanding rotating-wave approximation and could present
more subtle physics than any other characteristic parameter for the difference
between the two models. More importantly, our results could be
straightforwardly applied to the understanding of quantum phase transitions in
spin-boson model. Furthermore, the scaling behavior is observable using
currently available techniques in light-matter interaction.


  The Reeb graph of a scalar function defined on a domain gives a topologically
meaningful summary of that domain. Reeb graphs have been shown in the past
decade to be of great importance in geometric processing, image processing,
computer graphics, and computational topology. The demand for analyzing large
data sets has increased in the last decade. Hence the parallelization of
topological computations needs to be more fully considered. We propose a
parallel augmented Reeb graph algorithm on triangulated meshes with and without
a boundary. That is, in addition to our parallel algorithm for computing a Reeb
graph, we describe a method for extracting the original manifold data from the
Reeb graph structure. We demonstrate the running time of our algorithm on
standard datasets. As an application, we show how our algorithm can be utilized
in mesh segmentation algorithms.


  Abelian projection is reanalysed in the frame of the SU(N) Higgs model. The
extension to QCD is discussed. It is shown that dual superconductivity of the
vacuum is an intrinsic property independent of the choice of the abelian
projection.


  We study the Hankel transforms of sequences whose generating function can be
expressed as a C-fraction. In particular, we relate the index sequence of the
non-zero terms of the Hankel transform to the powers appearing in the monomials
defining the C-fraction. A closed formula for the Hankel transforms studied is
given. As every power-series can be represented by a C-fraction, this gives in
theory a closed form formula for the Hankel transform of any sequence. The
notion of multiplicity is introduced to differentiate between Hankel
transforms.


  Continuing our work on the nature and existence of fluctuation-dissipation
relations (FDR) in linear and nonlinear open quantum systems [1-3], here we
consider such relations when a linear system is in a nonequilibrium steady
state (NESS). With the model of two-oscillators (considered as a short harmonic
chain with the two ends) each connected to a thermal bath of different
temperatures we find that when the chain is fully relaxed due to interaction
with the baths, the relation that connects the noise kernel and the imaginary
part of the dissipation kernel of the chain in one bath does not assume the
conventional form for the FDR in equilibrium cases. There exists an additional
term we call the `bias current' that depends on the difference of the bath's
initial temperatures and the inter-oscillator coupling strength. We further
show that this term is related to the steady heat flow between the two baths
when the system is in NESS. The ability to know the real-time development of
the inter-heat exchange (between the baths and the end-oscillators) and the
intra-heat transfer (within the chain) and their dependence on the parameters
in the system offers possibilities for quantifiable control and in the design
of quantum heat engines or thermal devices.


  We present first results from follow-up of targets in the northern hemisphere
Beta Pictoris and AB Doradus moving group candidate list of Schlieder, Lepine,
and Simon (2012). We obtained high-resolution, near-infrared spectra of 27
candidate members to measure their radial velocities and confirm consistent
group kinematics. We identify 15 candidates with consistent predicted and
measured radial velocities, perform analyses of their 6-dimensional
(U,V,W,X,Y,Z) Galactic kinematics, and compare to known group member
distributions. Based on these analyses, we propose that 7 Beta Pic and 8 AB Dor
candidates are likely new group members. Four of the likely new Beta Pic stars
are binaries; one a double lined spectroscopic system. Three of the proposed AB
Dor stars are binaries. Counting all binary components, we propose 22 likely
members of these young, moving groups. The majority of the proposed members are
M2 to M5 dwarfs, the earliest being of type K2. We also present preliminary
parameters for the two new spectroscopic binaries identified in the data, the
proposed Beta Pic member and a rejected Beta Pic candidate. Our candidate
selection and follow-up has thus far identified more than 40 low-mass, likely
members of these two moving groups. These stars provide a new sample of nearby,
young targets for studies of local star formation, disks and exoplanets via
direct imaging, and astrophysics in the low-mass regime.


  Pseudo-valuation domains have been studied since their introduction in 1978
by Hedstrom and Houston. Related objects, boundary valuation domains, were
introduced by Maney in 2004. Here, it is shown that the class of atomic
pseudo-valuation domains coincides with the class of boundary valuation
domains. It is also shown that power series rings and generalized power series
rings give examples of pseudo-valuation domains whose congruence lattices can
be characterized. The paper also introduces, and makes use of, a sufficient
condition on the group of divisibility of a domain to guarantee that it is a
pseudo-valuation domain.


  We suggest a disordered traffic flow model that captures many features of
traffic flow. It is an extension of the Nagel-Schreckenberg (NaSch) stochastic
cellular automata for single line vehicular traffic model. It incorporates
random acceleration and deceleration terms that may be greater than one unit.
Our model leads under its intrinsic dynamics, for high values of braking
probability $p$, to a constant flow at intermediate densities without
introducing any spatial inhomogeneities. For a system of fast drivers $p\to 0$,
the model exhibits a density wave behavior that was observed in car following
models with optimal velocity. The gap of the disordered model we present
exhibits, for high values of $p$ and random deceleration, at a critical
density, a power law distribution which is a hall mark of a self organized
criticality phenomena.


  Relativistic and non-relativistic fluid equations can exhibit finite time
singular solutions including density singularities appearing in collapse or
compression systems and gradient singularities in shock waves. However, only
the non-relativistic fluid equations have been shown to exhibit finite-time
velocity singularities, v tending to infinity in this regime. As this limit
violates the cosmic speed limit v<c, where c is the speed of light, it is
unclear how velocity singularity source terms in the non-relativistic equations
are modified in the relativistic equations. This paper uses a driven system to
demonstrate that singular points within the relativistic fluid equations can
generate finite time relativistic velocity singularities v tending to c.


  Superconducting EuFe2(As0.82P0.18)2 single crystals are investigated by
infrared spectroscopy in a wide frequency range. Below Tc=28K a superconducting
gap forms at 2\Delta_{0} = 9.5 meV = 3.8 k_B T_c causing the reflectivity to
sharply rise to unity at low frequency. In the range of the gap the optical
conductivity can be perfectly described by BCS theory with an $s$-wave gap and
no nodes. From our analysis of the temperature dependent conductivity and
spectral weight at T>T_c, we deduce an increased interband coupling between
hole- and electron-sheets on the Fermi surface when $T$ approaches T_c.


  We present the results from analyses of Suzaku observations of the supergiant
X-ray binaries IGR J16207-5129 and IGR J17391-3021. For IGR J16207-5129, we
provide the first broadband (0.5--60 keV) spectrum from which we confirm a
large intrinsic column density (nH = 16e22 /cm2), and constrain the cutoff
energy for the first time (Ec = 19 keV). We observed a prolonged (> 30 ks)
attenuation of the X-ray flux which we tentatively attribute to an eclipse of
the probable neutron star by its massive companion. For IGR J17391-3021, we
witnessed a transition from quiescence to a low-activity phase punctuated by
weak flares whose peak luminosities in the 0.5--10 keV band are only a factor
of 5 times that of the pre-flare emission. The weak flaring is accompanied by
an increase in the absorbing column which suggests the accretion of obscuring
clumps of wind. Placing this observation in the context of the recent Swift
monitoring campaign, we now recognize that these low-activity epochs constitute
the most common emission phase for this system, and perhaps in other SFXTs as
well.


  Predictions of antineutrino fluxes produced by fission isotopes in a nuclear
reactor have recently received increased scrutiny due to observed differences
in predicted and measured inverse beta decay (IBD) yields, referred to as the
'reactor antineutrino flux anomaly.' In this paper, global fits are applied to
existing IBD yield measurements to produce constraints on antineutrino
production by individual plutonium and uranium fission isotopes. We find that
fits including measurements from highly 235U-enriched cores and fits including
Daya Bay's new fuel evolution result produce discrepant best-fit IBD yields for
235U and 239Pu. This discrepancy can be alleviated in a global analysis of all
datasets through simultaneous fitting of 239Pu, 235U, and 238U yields. The
measured IBD yield of U238 in this analysis is (7.02 +/- 1.65) x 10^-43
cm2/fission, nearly two standard deviations below existing predictions. Future
hypothetical IBD yield measurements by short-baseline reactor experiments are
examined to determine their possible impact on global understanding of isotopic
IBD yields. It is found that future improved short-baseline IBD yield
measurements at both high-enriched and low-enriched cores can significantly
improve constraints for 235U, 238U, and 239Pu, providing comparable or superior
precision to existing conversion- and summation-based antineutrino flux
predictions. Systematic and experimental requirements for these future
measurements are also investigated.


  In distribution networks, there are slow controlling devices and fast
controlling devices for Volt-VAR regulation. These slow controlling devices,
such as capacitors or voltage regulators, cannot be operated frequently and
should be scheduled tens of minutes ahead (Hereafter named as slow control).
Since of the uncertainties in predicting the load and distributed generation,
the voltage violations cannot be eliminated by fast controlling devices with
improper schedule of the slow controlling devices. In this paper we propose
dual time-scale coordination for the Volt-VAR control scheme, corresponding to
slow and fast control. In the case of slow control, a robust voltage and
reactive power optimization model is developed. This guarantees that subsequent
fast controls can maintain the system's voltage security if the uncertain
parameters vary within predefined limits. This nonconvex optimization problem
is relaxed to a mix integer second order conic problem, and the dual form of
its sub-problem is also derived. Then a column-and-constraint generation
algorithm was used to solve the robust convexified model. A conventional
deterministic optimization model can be used to determine the fast control
mechanism. Numerical tests were conducted on a real distribution feeder in
China, a balanced IEEE 69-bus and unbalanced 123-bus benchmark distribution
networks. The simulation results show that solving the deterministic model is
not always feasible and voltage violation may occur. The robust model was shown
to be effective with respect to all possible scenarios in the uncertainty set,
with little compromise in terms of network losses.


  We present a general procedure for calculating the exact partition function
of an Ising model on a periodic chain in presence of magnetic field considering
both open and closed boundary conditions. Using same procedure on a
quasiperiodic (Fibonacci) chain we have established a recurrence relation among
partition functions of different Fibonacci generations from n-th to (n+6)-th.
In the large N limit we find $(2\tau + 1){F_{n+1}}={F_{n+2}}$; where $\tau$ is
the golden mean and $F_n$ stands for free energy/spin for the n-th generation.
We have also studied chemical potential in both cases.


  Few-shot learning-the ability to train models with access to limited data-has
become increasingly popular in the natural language processing (NLP) domain, as
large language models such as GPT and T0 have been empirically shown to achieve
high performance in numerous tasks with access to just a handful of labeled
examples. Smaller language models such as BERT and its variants have also been
shown to achieve strong performance with just a handful of labeled examples
when combined with few-shot learning algorithms like pattern-exploiting
training (PET) and SetFit. The focus of this work is to investigate the
performance of alternative few-shot learning approaches with BERT-based models.
Specifically, vanilla fine-tuning, PET and SetFit are compared for numerous
BERT-based checkpoints over an array of training set sizes. To facilitate this
investigation, applications of few-shot learning are considered in software
engineering. For each task, high-performance techniques and their associated
model checkpoints are identified through detailed empirical analysis. Our
results establish PET as a strong few-shot learning approach, and our analysis
shows that with just a few hundred labeled examples it can achieve performance
near that of fine-tuning on full-sized data sets.


  The heterogeneous network is a robust data abstraction that can model
entities of different types interacting in various ways. Such heterogeneity
brings rich semantic information but presents nontrivial challenges in
aggregating the heterogeneous relationships between objects - especially those
of higher-order indirect relations. Recent graph neural network approaches for
representation learning on heterogeneous networks typically employ the
attention mechanism, which is often only optimized for predictions based on
direct links. Furthermore, even though most deep learning methods can aggregate
higher-order information by building deeper models, such a scheme can diminish
the degree of interpretability. To overcome these challenges, we explore an
architecture - Layer-stacked ATTention Embedding (LATTE) - that automatically
decomposes higher-order meta relations at each layer to extract the relevant
heterogeneous neighborhood structures for each node. Additionally, by
successively stacking layer representations, the learned node embedding offers
a more interpretable aggregation scheme for nodes of different types at
different neighborhood ranges. We conducted experiments on several benchmark
heterogeneous network datasets. In both transductive and inductive node
classification tasks, LATTE can achieve state-of-the-art performance compared
to existing approaches, all while offering a lightweight model. With extensive
experimental analyses and visualizations, the framework can demonstrate the
ability to extract informative insights on heterogeneous networks.


  Existing inertial motion capture techniques use the human root coordinate
frame to estimate local poses and treat it as an inertial frame by default. We
argue that when the root has linear acceleration or rotation, the root frame
should be considered non-inertial theoretically. In this paper, we model the
fictitious forces that are non-neglectable in a non-inertial frame by an
auto-regressive estimator delicately designed following physics. With the
fictitious forces, the force-related IMU measurement (accelerations) can be
correctly compensated in the non-inertial frame and thus Newton's laws of
motion are satisfied. In this case, the relationship between the accelerations
and body motions is deterministic and learnable, and we train a neural network
to model it for better motion capture. Furthermore, to train the neural network
with synthetic data, we develop an IMU synthesis by simulation strategy to
better model the noise model of IMU hardware and allow parameter tuning to fit
different hardware. This strategy not only establishes the network training
with synthetic data but also enables calibration error modeling to handle bad
motion capture calibration, increasing the robustness of the system. Code is
available at https://xinyu-yi.github.io/PNP/.


  In this paper, we define the phases of a complex sectorial matrix to be its
canonical angles, which are uniquely determined from a sectorial decomposition
of the matrix. Various properties of matrix phases are studied, including those
of compressions, Schur complements, matrix products, and sums. In particular,
by exploiting a notion known as the compound numerical range, we establish a
majorization relation between the phases of the eigenvalues of $AB$ and the
phases of $A$ and $B$. This is then applied to investigate the rank of $I + AB$
with phase information of $A$ and $B$, which plays a crucial role in feedback
stability analysis. A pair of problems: banded sectorial matrix completion and
decomposition is studied. The phases of the Kronecker and Hadamard products are
also discussed.


  QCD strings are color-electric flux tubes between quarks with a finite
thickness and thus a finite curvature stiffness. Contrary to an earlier
rigid-string by Polyakov and Kleinert, and motivated by the properties of a
magnetic flux tubes in type-II superconductors we put forward the hypothesis
that QCD strings have a {\em negative\/ stiffness. We set up a new string model
with this property and show that it is free of the three principal problems of
rigid-strings --- particle states with negative norm, nonexistence of a
lowest-energy state, and wrong high-temperature behavior of string tension ---
thus making it a better candidate for a string description of quark forces than
previous models.


  A new class of survival frailty models based on the Generalized
Inverse-Gaussian (GIG) distributions is proposed. We show that the GIG frailty
models are flexible and mathematically convenient like the popular gamma
frailty model. Furthermore, our proposed class is robust and does not present
some computational issues experienced by the gamma model. By assuming a
piecewise-exponential baseline hazard function, which gives a semiparametric
flavour for our frailty class, we propose an EM-algorithm for estimating the
model parameters and provide an explicit expression for the information matrix.
Simulated results are addressed to check the finite sample behavior of the
EM-estimators and also to study the performance of the GIG models under
misspecification. We apply our methodology to a TARGET (Therapeutically
Applicable Research to Generate Effective Treatments) data about survival time
of patients with neuroblastoma cancer and show some advantages of the GIG
frailties over existing models in the literature.


  The mass evolution of a coronal mass ejection (CME) is an important parameter
characterizing the drag force acting on a CME as it propagates through
interplanetary space. Spacecraft measure in-situ plasma densities of CMEs
during crossing events, but for investigating the mass evolution, we also need
to know the CME geometry, more specific, its volume. Having derived the CME
volume and mass from remote sensing data using 3D reconstructed CME geometry,
we can calculate the CME density and compare it with in-situ proton density
measurements near Earth. From that we may draw important conclusions on a
possible mass increase as the CME interacts with the ambient solar wind in the
heliosphere. In this paper we will describe in detail the method for deriving
the CME volume using the graduated cylindrical shell (GCS) model (Thernisien et
al., 2006,2009, see Figure 1). We show that, assuming self-similar expansion,
one can derive the volume of the CME from two GCS parameters and that it
furthermore can be expressed as a function of distance.


  We study information aggregation in networks when agents interact to learn a
binary state of the world. Initially each agent privately observes an
independent signal which is "correct" with probability $\frac{1}{2}+\delta$ for
some $\delta > 0$. At each round, a node is selected uniformly at random to
update their public opinion to match the majority of their neighbours (breaking
ties in favour of their initial private signal). Our main result shows that for
sparse and connected binomial random graphs $\mathcal G(n,p)$ the process
stabilizes in a "correct" consensus in $\mathcal O(n\log^2 n/\log\log n)$ steps
with high probability. In fact, when $\log n/n \ll p = o(1)$ the process
terminates at time $\hat T = (1+o(1))n\log n$, where $\hat T$ is the first time
when all nodes have been selected at least once. However, in dense binomial
random graphs with $p=\Omega(1)$, there is an information cascade where the
process terminates in the "incorrect" consensus with probability bounded away
from zero.


  Thanks to recent advancements in accelerating non-linear model predictive
control (NMPC), it is now feasible to deploy whole-body NMPC at real-time rates
for humanoid robots. However, enforcing inequality constraints in real time for
such high-dimensional systems remains challenging due to the need for
additional iterations. This paper presents an implementation of whole-body NMPC
for legged robots that provides low-accuracy solutions to NMPC with general
equality and inequality constraints. Instead of aiming for highly accurate
optimal solutions, we leverage the alternating direction method of multipliers
to rapidly provide low-accuracy solutions to quadratic programming subproblems.
Our extensive simulation results indicate that real robots often cannot benefit
from highly accurate solutions due to dynamics discretization errors, inertial
modeling errors and delays. We incorporate control barrier functions (CBFs) at
the initial timestep of the NMPC for the self-collision constraints, resulting
in up to a 26-fold reduction in the number of self-collisions without adding
computational burden. The controller is reliably deployed on hardware at 90 Hz
for a problem involving 32 timesteps, 2004 variables, and 3768 constraints. The
NMPC delivers sufficiently accurate solutions, enabling the MIT Humanoid to
plan complex crossed-leg and arm motions that enhance stability when walking
and recovering from significant disturbances.


  We present a new mechanism for generating primordial statistical anisotropy
of curvature perturbations. We introduce a vector field which has a non-minimal
kinetic term and couples with a waterfall field in hybrid inflation model. In
such a system, the vector field gives fluctuations of the end of inflation and
hence induces a subcomponent of curvature perturbations. Since the vector has a
preferred direction, the statistical anisotropy could appear in the
fluctuations. We present the explicit formula for the statistical anisotropy in
the primordial power spectrum and the bispectrum of curvature perturbations.
Interestingly, there is the possibility that the statistical anisotropy does
not appear in the power spectrum but does appear in the bispectrum. We also
find that the statistical anisotropy provides the shape dependence to the
bispectrum.


  We propose a logic of interactive proofs as a framework for an intuitionistic
foundation for interactive computation, which we construct via an interactive
analog of the Goedel-McKinsey-Tarski-Artemov definition of Intuitionistic Logic
as embedded into a classical modal logic of proofs, and of the Curry-Howard
isomorphism between intuitionistic proofs and typed programs. Our interactive
proofs effectuate a persistent epistemic impact in their intended communities
of peer reviewers that consists in the induction of the (propositional)
knowledge of their proof goal by means of the (individual) knowledge of the
proof with the interpreting reviewer. That is, interactive proofs effectuate a
transfer of propositional knowledge (knowable facts) via the transmission of
certain individual knowledge (knowable proofs) in multi-agent distributed
systems. In other words, we as a community can have the formal common knowledge
that a proof is that which if known to one of our peer members would induce the
knowledge of its proof goal with that member. Last but not least, we prove
non-trivial interactive computation as definable within our simply typed
interactive Combinatory Logic to be nonetheless equipotent to non-interactive
computation as defined by simply typed Combinatory Logic.


  We present a new method for evolving the equations of magnetohydrodynamics
(both Newtonian and relativistic) that is capable of maintaining a
divergence-free magnetic field ($\nabla \cdot \mathbf{B} = 0$) on adaptively
refined, conformally moving meshes. The method relies on evolving the magnetic
vector potential and then using it to reconstruct the magnetic fields. The
advantage of this approach is that the vector potential is not subject to a
constraint equation in the same way the magnetic field is, and so can be
refined and moved in a straightforward way. We test this new method against a
wide array of problems from simple Alfven waves on a uniform grid to general
relativistic MHD simulations of black hole accretion on a nested,
spherical-polar grid. We find that the code produces accurate results and in
all cases maintains a divergence-free magnetic field to machine precision.


  Central to the enigma of the cuprates is ubiquitous electronic inhomogeneity
arising from a variety of electronic orders that coexist with
superconductivity, the individual signatures of which have been impossible to
disentangle despite four decades of intense research. This strong nanoscale
inhomogeneity complicates interpretation of measurements both by probes which
average over this inhomogeneity and those, like scanning tunneling microscopy
(STM), which should be able to spatially resolve variations driven by both
order and inhomogeneity. Here, we develop a novel technique that directly
acknowledges this electronic inhomogeneity and extracts statistically
significant features from scanning tunneling spectroscopic data. Applying our
novel technique to single and bilayer Bi-based cuprates spanning a large doping
range, we peer through local inhomogeneities and find that the gap breaks
translational and rotational symmetries and varies periodically in a four-fold
pattern. Our direct observation of a symmetry breaking gap in the single
particle tunneling spectra adds strong credence to the pair density wave
hypotheses supposed to exist in these materials. We also discuss various
implications of our observations and, in particular, how they can explain the
origin of the low energy checkerboard pattern.


  We present a new theoretical approach for collisional absorption of laser
energy in dense plasmas which accommodates arbitrary frequencies and high
intensities of the laser field. We establish a connection between laser
absorption by inverse Bremstrahlung and the stopping power. This relation is
then applied to include strong correlations beyond the mean field approach. The
results show an excellent agreement with molecular dynamics simulations up to
very high coupling strength.


  Gallium selenide (GaSe) is a layered compound, which has been exploited in
nonlinear optical applications and photodetectors due to its anisotropic
structure and pseudo-direct optical gap. Theoretical studies predicted that its
two-dimensional (2D) form is a potential photocatalyst for water splitting
reactions. Herein, we first report the photoelectrochemical (PEC)
characterization of GaSe nanoflakes (single-/few-layer flakes), produced via
liquid phase exfoliation, for hydrogen evolution reaction (HER) and oxygen
evolution reaction (OER) in both acidic and alkaline media. In 0.5 M H2SO4, the
GaSe photoelectrodes display the best PEC performance, i.e. a ratiometric
power-saved metric for HER ({\Phi}saved,HER) of 0.09% and a ratiometric
power-saved metric for OER ({\Phi}saved,OER) of 0.25%. When used as PEC-type
photodetectors, GaSe photoelectrodes show a responsivity of ~0.16 A W-1 upon
455 nm illumination at light intensity of 63.5 uW cm-2 and applied potential of
-0.3 V vs. reversible hydrogen electrode (RHE). The stability analysis of the
GaSe photodetectors evidences a durable operation over tens of cathodic linear
sweep voltammetry scans in 0.5 M H2SO4 for HER. Viceversa, degradation effects
have been observed in both alkaline and anodic operation due to highly
oxidizing environment and O2-induced (photo-)oxidation effects. Our results
provide new insight into PEC properties of GaSe nanoflakes for their
exploitation in photoelectrocatalysis, PEC-type photodetectors and
(bio)sensors.


  We consider the optical generation and verification of entanglement in atomic
ensembles under non-uniform interaction between the ensemble and an optical
mode. We show that for a wide range of parameters a system of non-uniformly
coupled atomic spins can be described as an ensemble of uniformly coupled spins
with a reduced effective atom-light coupling and a reduced effective atom
number, with a reduction factor of order unity given by the ensemble-mode
geometry. This description is valid even for complex entangled states with
arbitrary phase-space distribution functions as long as the detection does not
resolve single spins. Furthermore, we derive an analytic formula for the
observable entanglement in the case, of relevance in practice, where the
ensemble-mode coupling differs between state generation and measurement.


  We study the behavior of entanglement between different degrees of freedom of
scattering fermions, based on an exemplary QED scattering process
$e^+e^-\longrightarrow\mu^+\mu^-$. The variation of entanglement entropy
between two fermions from an initial state to the final state was computed,
with respect to different entanglement between the ingoing particles. This
variation of entanglement entropy is found to be proportional to an area
quantity, the total cross section. We also study the spin-momentum and
helicity-momentum entanglements within one particle in the aforementioned
scattering process. The calculations of the relevant variations of mutual
information in the same inertial frame reveals that, for a maximally entangled
initial state, the scattering between the particles does not affect the degree
of both of these entanglements of one particle in the final state. It is also
found that the increasing degree of entanglement between two ingoing particles
would restrict the generation of entanglement between spin (helicity) and
momentum of one outgoing particle. And the entanglement between spin and
momentum within one particle in the final state is shown to always be stronger
than that for helicity-momentum for a general initial entanglement state,
implying significantly distinct properties of entanglement for the helicity and
spin perceived by an inertial observer.


  Policies to mitigate climate change and biodiversity loss often assume that
protecting carbon-rich forests provides co-benefits in terms of biodiversity,
due to the spatial congruence of carbon stocks and biodiversity at
biogeographic scales. However, it remains unclear whether this holds at the
scales relevant for management, with particularly large knowledge gaps for
temperate forests and for taxa other than trees. We built a comprehensive
dataset of Central European temperate forest structure and multi-taxonomic
diversity (beetles, birds, bryophytes, fungi, lichens, and plants) across 352
plots. We used Boosted Regression Trees to assess the relationship between
above-ground live carbon stocks and (a) taxon-specific richness, (b) a unified
multidiversity index. We used Threshold Indicator Taxa ANalysis to explore
individual species' responses to changing above-ground carbon stocks and to
detect change-points in species composition along the carbon-stock gradient.
Our results reveal an overall weak and highly variable relationship between
richness and carbon stock at the stand scale, both for individual taxonomic
groups and for multidiversity. Similarly, the proportion of win-win and
trade-off species (i.e. species favored or disadvantaged by increasing carbon
stock, respectively) varied substantially across taxa. Win-win species
gradually replaced trade-off species with increasing carbon, without clear
thresholds along the above-ground carbon gradient, suggesting that
community-level surrogates (e.g. richness) might fail to detect critical
changes in biodiversity. Collectively, our analyses highlight that leveraging
co-benefits between carbon and biodiversity in temperate forest may require
stand-scale management that prioritizes either biodiversity or carbon-in order
to maximize co-benefits at broader scales. Importantly, this contrasts with
tropical forests, where climate [...]


  Tracer tests in natural porous media sometimes show abnormalities that
suggest considering a fractional variant of the Advection Diffusion Equation
supplemented by a time derivative of non-integer order. We are describing an
inverse method for this equation: it finds the order of the fractional
derivative and the coefficients that achieve minimum discrepancy between
solution and tracer data. Using an adjoint equation divides the computational
effort by an amount proportional to the number of freedom degrees, which
becomes large when some coefficients depend on space. Method accuracy is
checked on synthetical data, and applicability to actual tracer test is
demonstrated.


  The WFPC2 Camera on board HST has been used to obtain high-resolution images
of NGC 3610, a dynamically young elliptical galaxy. These observations
supersede shorter, undithered HST observations where an intermediate-age
population of globular clusters was first discovered. The new observations show
the bimodal color distribution of globular clusters more clearly, with peaks at
(V-I)o = 0.95 and 1.17. The luminosity function (LF) of the blue, metal-poor
population of clusters in NGC 3610 turns over, consistent with a Gaussian
distribution with a peak Mv ~= -7.0, similar to old globular-cluster
populations in ellipticals. The red, metal-rich population of clusters has a LF
that is more extended toward both the bright and faint ends, as expected for a
cluster population of inter-mediate age. It is well fit by a power law with an
exponent of alpha = -1.78 +-0.05, or -1.90+-0.07 when corrected for
observational scatter. A Kolmogorov-Smirnov test confirms the significant
difference between the LFs of the red and blue clusters, with a probability of
less than 0.1% that they come from the same population. A comparison with the
Fall & Zhang cluster disruption models shows marginal agreement with the
observed LF, although there are differences in detail. In particular, there is
no clear evidence of the predicted turnover at the faint end. A by-product of
the analysis is the demonstration that, at any given metallicity, the peak of
the LF should remain nearly constant from 1.5 Gyr to 12 Gyr, since the effect
of the disruption of faint clusters is almost perfectly balanced by the fading
of the clusters. This may help explain the apparent universality of the peak of
the globular cluster luminosity function. (Abridged)


  Powerful radio galaxies at high redshift are highly useful in studies of
early evolution of AGN-hosting galaxies because their observed optical and near
infrared light are dominated by their stellar population rather than the
nonthermal continuum emitted by the central engine of AGNs. In addition, the
presence of AGN activity in them implies that a supermassive black hole has
been already made in their nuclei. These properties allow us to investigate a
possible starburst-AGN connection in early universe and then provide some
crucial hints for the formation mechanism of supermassive black holes. Taking
observational properties of high-z powerful radio galaxies into account, we
discuss a possible formation mechanism of supermassive black holes in their
nuclei.


  Sample-based approximate query processing (AQP) suffers from many pitfalls
such as the inability to answer very selective queries and unreliable
confidence intervals when sample sizes are small. Recent research presented an
intriguing solution of combining materialized, pre-computed aggregates with
sampling for accurate and more reliable AQP. We explore this solution in detail
in this work and propose an AQP physical design called PASS, or
Precomputation-Assisted Stratified Sampling. PASS builds a tree of partial
aggregates that cover different partitions of the dataset. The leaf nodes of
this tree form the strata for stratified samples. Aggregate queries whose
predicates align with the partitions (or unions of partitions) are exactly
answered with a depth-first search, and any partial overlaps are approximated
with the stratified samples. We propose an algorithm for optimally partitioning
the data into such a data structure with various practical approximation
techniques.


  A concept of boundedness of L-index in joint variables (see in Bordulyak M.T.
The space of entire in $\mathbb{C}^n$ functions of bounded L-index, Mat. Stud.,
4 (1995), 53--58. (in Ukrainian)) is generalised for analytic in a bidisc
function. We proved criteria of boundedness of $\mathbf{L}$-index in joint
variables which describe local behaviour of partial derivative and give an
estimate of maximum modulus on a skeleton of polydisc. Some improvements of
known sufficient conditions of boundednees of $\mathbf{L}$-index in joint
variables are obtained.


  Actin is a filamentary protein which has many remarkable properties making it
an ideal system for the study of the dynamics and mechanics of semi-flexible
polymer solutions and gels; actin has a persistence length of over 10 microns
and can polymerize to lengths of several tens of microns, this permits the use
of video microscopy and other optical methods in the study polymer dynamics.
Many associated proteins exist which can be used to regulate length or
crosslink filaments. We discuss the dynamics and rheology of these solutions.


  We compare X-ray and weak-lensing masses for four galaxy clusters that
comprise the top-ranked shear-selected cluster system in the Deep Lens Survey.
The weak-lensing observations of this system, which is associated with A781,
are from the Kitt Peak Mayall 4-m telescope, and the X-ray observations are
from both Chandra and XMM-Newton. For a faithful comparison of masses, we adopt
the same matter density profile for each method, which we choose to be an NFW
profile. Since neither the X-ray nor weak-lensing data are deep enough to well
constrain both the NFW scale radius and central density, we estimate the scale
radius using a fitting function for the concentration derived from cosmological
hydrodynamic simulations and an X-ray estimate of the mass assuming
isothermality. We keep this scale radius in common for both X-ray and
weak-lensing profiles, and fit for the central density, which scales linearly
with mass. We find that for three of these clusters, there is agreement between
X-ray and weak-lensing NFW central densities, and thus masses. For the other
cluster, the X-ray central density is higher than that from weak-lensing by 2
sigma. X-ray images suggest that this cluster may be undergoing a merger with a
smaller cluster. This work serves as an additional step towards understanding
the possible biases in X-ray and weak-lensing cluster mass estimation methods.
Such understanding is vital to efforts to constrain cosmology using X-ray or
weak-lensing cluster surveys to trace the growth of structure over cosmic time.


  Learning to learn is a powerful paradigm for enabling models to learn from
data more effectively and efficiently. A popular approach to meta-learning is
to train a recurrent model to read in a training dataset as input and output
the parameters of a learned model, or output predictions for new test inputs.
Alternatively, a more recent approach to meta-learning aims to acquire deep
representations that can be effectively fine-tuned, via standard gradient
descent, to new tasks. In this paper, we consider the meta-learning problem
from the perspective of universality, formalizing the notion of learning
algorithm approximation and comparing the expressive power of the
aforementioned recurrent models to the more recent approaches that embed
gradient descent into the meta-learner. In particular, we seek to answer the
following question: does deep representation combined with standard gradient
descent have sufficient capacity to approximate any learning algorithm? We find
that this is indeed true, and further find, in our experiments, that
gradient-based meta-learning consistently leads to learning strategies that
generalize more widely compared to those represented by recurrent models.


  We revisit the cluster expansion for Ising lattice gauge theory on
$\mathbb{Z}^m, \, m \ge 3,$ with Wilson action, at a fixed inverse temperature
\( \beta\) in the low-temperature regime. We prove existence and analyticity of
the infinite volume limit of the free energy and compute the first few terms in
its expansion in powers of $e^{-\beta}$. We further analyze Wilson loop
expectations and derive an estimate that shows how the lattice scale geometry
of a loop is reflected in the large $\beta$ asymptotic expansion. Specializing
to axis parallel rectangular loops $\gamma_{T,R}$ with side-lengths $T$ and
$R$, we consider the limiting function
  $$
  V_\beta(R) := \lim_{T \to \infty} - \frac{1}{T} \log \, \langle
W_{\gamma_{T,R}} \rangle_\beta,
  $$
  known as the static quark potential in the physics literature. We verify
existence of the limit (with an estimate on the convergence rate) and compute
the first few terms in the expansion in powers of $e^{-\beta}$. As a
consequence, a strong version of the perimeter law follows. We also treat $-
\log \, \langle W_{\gamma_{T,R}} \rangle_\beta / (T+R)$ as $T, R$ tend to
infinity simultaneously and give analogous estimates.


  Existing coordinated cyber-attack detection methods have low detection
accuracy and efficiency and poor generalization ability due to difficulties
dealing with unbalanced attack data samples, high data dimensionality, and
noisy data sets. This paper proposes a model for cyber and physical data fusion
using a data link for detecting attacks on a Cyber-Physical Power System
(CPPS). Two-step principal component analysis (PCA) is used for classifying the
system's operating status. An adaptive synthetic sampling algorithm is used to
reduce the imbalance in the categories' samples. The loss function is improved
according to the feature intensity difference of the attack event, and an
integrated classifier is established using a classification algorithm based on
the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation
results show that the proposed method provides higher accuracy, recall, and
F-Score than comparable algorithms.


  The paper presents the DEF-AI-MIA COV19D Competition, which is organized in
the framework of the 'Domain adaptation, Explainability, Fairness in AI for
Medical Image Analysis (DEF-AI-MIA)' Workshop of the 2024 Computer Vision and
Pattern Recognition (CVPR) Conference. The Competition is the 4th in the
series, following the first three Competitions held in the framework of ICCV
2021, ECCV 2022 and ICASSP 2023 International Conferences respectively. It
includes two Challenges on: i) Covid-19 Detection and ii) Covid-19 Domain
Adaptation. The Competition use data from COV19-CT-DB database, which is
described in the paper and includes a large number of chest CT scan series.
Each chest CT scan series consists of a sequence of 2-D CT slices, the number
of which is between 50 and 700. Training, validation and test datasets have
been extracted from COV19-CT-DB and provided to the participants in both
Challenges. The paper presents the baseline models used in the Challenges and
the performance which was obtained respectively.


  Let $L$ be a distributive lattice and $R[L]$ the associated Hibi ring. We
show that if $L$ is planar, then any bounded Hibi subring of $R[L]$ has a
quadratic Gr\"obner basis. We characterize all planar distributive lattices $L$
for which any proper rank bounded Hibi subring of $R[L]$ has a linear
resolution. Moreover, if $R[L]$ is linearly related for a lattice $L$, we find
all the rank bounded Hibi subrings of $R[L]$ which are linearly related too.


  Pyro-current measurements have been widely used to study ferroelectric
properties in multiferroic materials. However, determination of intrinsic
polarization by this method is not straightforward because of leakage current
and trapped charge carriers. Here, we demonstrate the formation of internal
electric field due to thermally stimulated charge carriers and its influence on
ferroelectric polarization in a polycrystalline sample of the well known
multiferroic TbMnO$_3$. While an electric field ($E_{ext}$) poling across the
ferroelectric transition ($T_C$ $\sim$ 26 K) is essential to obtain
depolarization current at $T_C$, the sample poled only in the paraelectric
state ($T_{pole}$ = 130 $-$ 50 K) also exhibits a pyro-current peak at $T_C$
but with the same polarity ($-$ $I_{pyro}$) as that of the external field ($-$
$E_{ext}$). We demonstrate that these unusual behavior of pyro-current are
caused by a positive internal electric field ($+$ $E_{int}$) which in turn is
created by thermally stimulated free charge carriers during the poling process
in the paraelectric state. We also show that a combination of DC-biased current
and pyro-current measurements is a promising method to study the intrinsic
ferroelectric properties in multiferroic materials.


  Non-smoothness at optimal points is a common phenomenon in many eigenvalue
optimization problems. We consider two recent algorithms to minimize the
largest eigenvalue of a Hermitian matrix dependent on one parameter, both
proven to be globally convergent unaffected by non-smoothness. One of these
models the eigenvalue function with a piece-wise quadratic function, and
effective in dealing with non-convex problems. The other projects the Hermitian
matrix into subspaces formed of eigenvectors, and effective in dealing with
large-scale problems. We generalize the latter slightly to cope with
non-smoothness. For both algorithms, we analyze the rate-of-convergence in the
non-smooth setting, when the largest eigenvalue is multiple at the minimizer
and zero is strictly in the interior of the generalized Clarke derivative, and
prove that both algorithms converge rapidly. The algorithms are applied to, and
the deduced results are illustrated on the computation of the inner numerical
radius, the modulus of the point on the boundary of the field of values closest
to the origin, which carries significance for instance for the numerical
solution of a definite generalized symmetric eigenvalue problem.


  Defects in low-bandgap polymer based photodetectors play a critical role in
determining switching characteristics in the near infra-red spectral-regime
relevant to communication wavelength. We carry out detailed studies of the
bandwidth limiting factors of the long-live transient photocurrent at different
incident wavelength as a function of temperature, light pulse width, bias
voltage. The results indicate that dominant transport mechanism of the
photogenerated carriers is limited by recombination at low temperature and
detrapping rate at high temperature. A general trend of a slower response of
photocarriers originating from the band-tail region is observed.


  Modeling of physical systems includes extensive use of software packages that
implement the accurate finite element method for solving differential equations
considered along with the appropriate initial and boundary conditions. When the
problem size becomes large, time needed to solve the resulting linear systems
may range from hours to weeks, and if the input parameters need to be adjusted,
even slightly, the simulations has to be re-done from scratch. Recent advances
in machine learning algorithms and their successful applications in various
fields demonstrate that, if properly chosen and trained, these models can
significantly improve conventional techniques. In this note we discuss
possibilities to complement the finite element studies with machine learning
and provide several basic examples.


  The influence of optical feedback on semiconductor lasers has been a widely
studied field of research due to fundamental interests as well as the
optimization of optical data transmission and computing. Recent publications
have shown that it is possible to induce a periodic pulsed like output in
quantum-dot and quantum-well laser diodes utilizing the locking of the external
cavity modes and the relaxation oscillation frequency. We present an in-depth
analysis of this effect. We choose submonolayer quantum dots as a gain system,
as these provide a relatively strong amplitude-phase coupling, which has proven
to be very beneficial for these locking effects to occur. Introducing a new
theoretical model we can correctly reproduce the essential features of the gain
system and validate them by comparison to our experimental results. From this
starting point we can further explore how the staircase behavior of the
oscillation frequency with increasing pump current can be influenced by
changing various laser parameters. The staircase behavior is induced by a
reordering of the Hopf bifurcations giving birth to the regular pulsed-like
oscillations.


  We study the asymptotic behaviour of regularized determinants of certain
Laplace type operators with respect to singular deformations of the underlying
manifold which are obtained by stretching a tubular neighborhood of an embedded
separating hypersurface to a cylinder of infinite length. Using the asymptotic
expansions, we derive adiabatic splitting formulas for regularized determinants
of Laplace type operators.