  Let \Omega be a bounded connected, open set of \R^n with Lipschitz boundary.
Let F be a suitable norm in \R^n and let \Delta_F u be the so-colled Finsler
Laplacian. In this paper we prove two inequalities for the first eigenvalue of
\Delta_F with Robin boundary conditions involving a positive function \beta. As
a consequence of our result we obtain the asymptotic behavior of this
eigenvalue when \beta is a positive constant which goes to zero.


  We systematically analyze the influence of the superconducting gap symmetry
and the electronic structure on the dynamical spin susceptibility in
superconducting Na_xCoO_2$\cdot y$H_2O within a three different models: the
single a_{1g}-band model with nearest-neighbor hoppings, the realistic
three-band t_{2g}-model with, and without e'_g pockets present at the Fermi
surface. We show that the magnetic response in the normal state is dominated by
the incommensurate antiferromagnetic spin density wave fluctuations at large
momenta in agreement with experimental temperature dependence of the
spin-lattice relaxation rate. Also, we demonstrate that the presence or the
absence of the e'_g-pockets at the Fermi surface does not affect significantly
this conclusion. In the superconducting state our results for d_{x^2-y^2}- or
d_{xy}-wave symmetries of the superconducting order parameter are consistent
with experimental data and exclude nodeless $d_{x^2-y^2} + id_{xy}$-wave
symmetry. We further point out that the spin-resonance peak proposed earlier is
improbable for the realistic band structure of Na_xCoO_2$\cdot y$H_2O.
Moreover, even if present the resonance peak is confined to the
antiferromagnetic wave vector and disappears away from it.


  Sterile neutrinos with a mass of a few keV can serve as cosmological warm
dark matter. We study the production of keV sterile neutrinos in the early
universe from the decay of a frozen-in scalar. Previous studies focused on
heavy frozen-in scalars with masses above the Higgs mass leading to a hot
spectrum for sterile neutrinos with masses below 8-10 keV. Motivated by the
recent hints for an X-ray line at 3.55 keV, we extend the analysis to lighter
frozen-in scalars, which allow for a cooler spectrum. Below the electroweak
phase transition, several qualitatively new channels start contributing. The
most important ones are annihilation into electroweak vector bosons,
particularly W-bosons as well as Higgs decay into pairs of frozen-in scalars
when kinematically allowed.


  A dynamical system is strongly robustly safe provided that it remains safe in
the presence of a continuous and positive perturbation, named robustness
margin, added to both the argument and the image of the right-hand side (the
dynamics). Therefore, in comparison with existing robust-safety notions, where
the continuous and positive perturbation is added only to the image of the
right-hand side, the proposed notion is shown to be relatively stronger in the
context of set-valued right-hand sides. Furthermore, we distinguish between
strong robust safety and \textit{uniform strong robust safety}, which requires
the existence of a constant robustness margin. The first part of the paper
proposes sufficient conditions for strong robust safety in terms of barrier
functions. The proposed conditions involve only the barrier function and the
system's right-hand side. Furthermore, we establish the equivalence between
strong robust safety and the existence of a smooth barrier certificate. The
second part of the paper proposes scenarios, under which, strong robust safety
implies uniform strong robust safety. Finally, we propose sufficient conditions
for the latter notion in terms of barrier functions.


  The magnetic excitations in the cuprate superconductors might be essential
for an understanding of high-temperature superconductivity. In these cuprate
superconductors the magnetic excitation spectrum resembles an hour-glass and
certain resonant magnetic excitations within are believed to be connected to
the pairing mechanism which is corroborated by the observation of a universal
linear scaling of superconducting gap and magnetic resonance energy. So far,
charge stripes are widely believed to be involved in the physics of hour-glass
spectra. Here we study an isostructural cobaltate that also exhibits an
hour-glass magnetic spectrum. Instead of the expected charge stripe order we
observe nano phase separation and unravel a microscopically split origin of
hour-glass spectra on the nano scale pointing to a connection between the
magnetic resonance peak and the spin gap originating in islands of the
antiferromagnetic parent insulator. Our findings open new ways to theories of
magnetic excitations and superconductivity in cuprate superconductors.


  We introduce a variational method for the approximation of ground states of
strongly interacting spin systems in arbitrary geometries and spatial
dimensions. The approach is based on weighted graph states and superpositions
thereof. These states allow for the efficient computation of all local
observables (e.g. energy) and include states with diverging correlation length
and unbounded multi-particle entanglement. As a demonstration we apply our
approach to the Ising model on 1D, 2D and 3D square-lattices. We also present
generalizations to higher spins and continuous-variable systems, which allows
for the investigation of lattice field theories.


  Based on the canonical correlation analysis we derive series representations
of the probability density function (PDF) and the cumulative distribution
function (CDF) of the information density of arbitrary Gaussian random vectors
as well as a general formula to calculate the central moments. Using the
general results we give closed-form expressions of the PDF and CDF and explicit
formulas of the central moments for important special cases. Furthermore, we
derive recurrence formulas and tight approximations of the general series
representations, which allow very efficient numerical calculations with an
arbitrarily high accuracy as demonstrated with an implementation in Python
publicly available on GitLab. Finally, we discuss the (in)validity of Gaussian
approximations of the information density.


  Segmentation of regions of interest (ROIs) for identifying abnormalities is a
leading problem in medical imaging. Using machine learning for this problem
generally requires manually annotated ground-truth segmentations, demanding
extensive time and resources from radiologists. This work presents a weakly
supervised approach that utilizes binary image-level labels, which are much
simpler to acquire, to effectively segment anomalies in 2D magnetic resonance
images without ground truth annotations. We train a generative adversarial
network (GAN) that converts cancerous images to healthy variants, which are
used along with localization seeds as priors to generate improved weakly
supervised segmentations. The non-cancerous variants can also be used to
evaluate the segmentations in a weakly supervised fashion, which allows for the
most effective segmentations to be identified and then applied to downstream
clinical classification tasks. On the Multimodal Brain Tumor Segmentation
(BraTS) 2020 dataset, our proposed method generates and identifies
segmentations that achieve test Dice coefficients of 83.91%. Using these
segmentations for pathology classification results with a test AUC of 93.32%
which is comparable to the test AUC of 95.80% achieved when using true
segmentations.


  We present the first compelling evidence of shock-heated molecular clouds
associated with the supernova remnant (SNR) N49 in the Large Magellanic Cloud
(LMC). Using $^{12}$CO($J$ = 2-1, 3-2) and $^{13}$CO($J$ = 2-1) line emission
data taken with the Atacama Large Millimeter/Submillimeter Array, we derived
the H$_2$ number density and kinetic temperature of eight $^{13}$CO-detected
clouds using the large velocity gradient approximation at a resolution of
3.5$''$ (~0.8 pc at the LMC distance). The physical properties of the clouds
are divided into two categories: three of them near the shock front show the
highest temperatures of ~50 K with densities of ~500-700 cm$^{-3}$, while other
clouds slightly distant from the SNR have moderate temperatures of ~20 K with
densities of ~800-1300 cm$^{-3}$. The former clouds were heated by supernova
shocks, but the latter were dominantly affected by the cosmic-ray heating.
These findings are consistent with the efficient production of X-ray
recombining plasma in N49 due to thermal conduction between the cold clouds and
hot plasma. We also find that the gas pressure is roughly constant except for
the three shock-engulfed clouds inside or on the SNR shell, suggesting that
almost no clouds have evaporated within the short SNR age of ~4800 yr. This
result is compatible with the shock-interaction model with dense and clumpy
clouds inside a low-density wind bubble.


  We show that the Calabi-Yau dimension of the stable module category of a
selfinjective algebra of finite representation type is determined by the action
of the Nakayama and suspension functors on objects.
  Our arguments are based on recent results of C. Amiot, and hence apply more
generally to triangulated categories having only finitely many indecomposable
objects.


  Error-Correcting Output Codes (ECOCs) offer a principled approach for
combining simple binary classifiers into multiclass classifiers. In this paper,
we investigate the problem of designing optimal ECOCs to achieve both nominal
and adversarial accuracy using Support Vector Machines (SVMs) and binary deep
learning models. In contrast to previous literature, we present an Integer
Programming (IP) formulation to design minimal codebooks with desirable error
correcting properties. Our work leverages the advances in IP solvers to
generate codebooks with optimality guarantees. To achieve tractability, we
exploit the underlying graph-theoretic structure of the constraint set in our
IP formulation. This enables us to use edge clique covers to substantially
reduce the constraint set. Our codebooks achieve a high nominal accuracy
relative to standard codebooks (e.g., one-vs-all, one-vs-one, and dense/sparse
codes). We also estimate the adversarial accuracy of our ECOC-based classifiers
in a white-box setting. Our IP-generated codebooks provide non-trivial
robustness to adversarial perturbations even without any adversarial training.


  We report on the magnetic switching and backhopping effects due to
spin-transfer-torque in magnetic tunnel junctions. Experimental data on the
current-induced switching in junctions with MgO tunnel barrier reveal a random
back-and-forth switching between the magnetization states, which appears when
the current direction favors the parallel magnetic configuration. The effect
depends on the barrier thickness $t_b$, and is not observed in tunnel junctions
with very thin MgO tunnel barriers, $t_b$ $<$ 0.95 nm. Switching dependence on
the bias voltage and barrier thickness is explained in terms of the macrospin
model, with the magnetization dynamics described by the modified
Landau-Lifshitz-Gilbert equation. Numerical simulations indicate that the
competition between in-plane and out-of-plane torque components can result at
high bias voltages in a non-deterministic switching behavior, in agreement with
experimental observations. When the barrier thickness is reduced, the overall
coupling between the magnetic layers across the barrier becomes ferromagnetic,
which suppresses the backhopping effect.


  We use dimensional regularization to evaluate the one loop contribution to
the graviton self-energy from a massless, minimally coupled scalar on a locally
de Sitter background. For noncoincident points our result agrees with the
stress tensor correlators obtained recently by Perez-Nadal, Roura and
Verdaguer. We absorb the ultraviolet divergences using the $R^2$ and $C^2$
counterterms first derived by 't Hooft and Veltman, and we take the $D=4$ limit
of the finite remainder. The renormalized result is expressed as the sum of two
transverse, 4th order differential operators acting on nonlocal, de Sitter
invariant structure functions. In this form it can be used to quantum-correct
the linearized Einstein equations so that one can study how the inflationary
production of infrared scalars affects the propagation of dynamical gravitons
and the force of gravity.


  We introduce a new data structure for answering connectivity queries in
undirected graphs subject to batched vertex failures. Precisely, given any
graph G and integer k, we can in fixed-parameter time construct a data
structure that can later be used to answer queries of the form: ``are vertices
s and t connected via a path that avoids vertices $u_1,..., u_k$?'' in time
$2^{2^{O(k)}}$. In the terminology of the literature on data structures, this
gives the first deterministic data structure for connectivity under vertex
failures where for every fixed number of failures, all operations can be
performed in constant time.
  With the aim to understand the power and the limitations of our new
techniques, we prove an algorithmic meta theorem for the recently introduced
separator logic, which extends first-order logic with atoms for connectivity
under vertex failures. We prove that the model-checking problem for separator
logic is fixed-parameter tractable on every class of graphs that exclude a
fixed topological minor. We also show a weak converse. This implies that from
the point of view of parameterized complexity, under standard complexity
assumptions, the frontier of tractability of separator logic is almost exactly
delimited by classes excluding a fixed topological minor.
  The backbone of our proof relies on a decomposition theorem of Cygan et al.
[SICOMP '19], which provides a tree decomposition of a given graph into bags
that are unbreakable. Crucially, unbreakability allows to reduce separator
logic to plain first-order logic within each bag individually. We design our
model-checking algorithm using dynamic programming over the tree decomposition,
where the transition at each bag amounts to running a suitable model-checking
subprocedure for plain first-order logic. This approach is robust enough to
provide also efficient enumeration of queries expressed in separator logic.


  With the development of cloud-native technologies, microservice-based
software systems face challenges in accurately localizing root causes when
failures occur. Additionally, the cloud-edge collaborative environment
introduces more difficulties, such as unstable networks and high latency across
network segments. Accurately identifying the root cause of microservices in a
cloud-edge collaborative environment has thus become an urgent problem. In this
paper, we propose MicroCERCL, a novel approach that pinpoints root causes at
the kernel and application level in the cloud-edge collaborative environment.
Our key insight is that failures propagate through direct invocations and
indirect resource-competition dependencies in a cloud-edge collaborative
environment characterized by instability and high latency. This will become
more complex in the hybrid deployment that simultaneously involves multiple
microservice systems. Leveraging this insight, we extract valid contents from
kernel-level logs to prioritize localizing the kernel-level root cause.
Moreover, we construct a heterogeneous dynamic topology stack and train a graph
neural network model to accurately localize the application-level root cause
without relying on historical data. Notably, we released the first benchmark
hybrid deployment microservice system in a cloud-edge collaborative environment
(the largest and most complex within our knowledge). Experiments conducted on
the dataset collected from the benchmark show that MicroCERCL can accurately
localize the root cause of microservice systems in such environments,
significantly outperforming state-of-the-art approaches with an increase of at
least 24.1% in top-1 accuracy.


  Alchemical free energy calculations are widely used in predicting pKa, and
binding free energy calculations in biomolecular systems. These calculations
are carried out using either Free Energy Perturbation (FEP) or Thermodynamic
Integration (TI). Numerous efforts have been made to improve the accuracy and
efficiency of such calculations, especially by boosting conformational
sampling. In this paper, we use a technique that enhances the conformational
sampling by temperature acceleration of collective variables for alchemical
transformations and applies it to the prediction of pKa of the buried Asp 26
residue in thioredoxin protein. We discuss the importance of enhanced sampling
in the pKa calculations. The effect of the solvent models in the computed pKa
values is also presented.


  We present algorithms and experiments for the visualization of directed
graphs that focus on displaying their reachability information. Our algorithms
are based on the concepts of the path and channel decomposition as proposed in
the framework presented in GD 2018 (pp. 579-592) and focus on showing the
existence of paths clearly. In this paper we customize these concepts and
present experimental results that clearly show the interplay between bends,
crossings and clarity. Additionally, our algorithms have direct applications to
the important problem of showing and storing transitivity information of very
large graphs and databases. Only a subset of the edges is drawn, thus reducing
the visual complexity of the resulting drawing, and the memory requirements for
storing the transitivity information. Our algorithms require almost linear
time, $O(kn+m)$, where $k$ is the number of paths/channels, $n$ and $m$ is the
number of vertices and edges, respectively. They produce progressively more
abstract drawings of the input graph. No dummy vertices are introduced and the
vertices of each path/channel are vertically aligned.


  We propose a feedback-assisted direct laser writing method to perform laser
ablation of fiber optics devices in which their light-collection signal is used
to optimize their properties. A femtosecond-pulsed laser beam is used to ablate
a metal coating deposited around a tapered optical fiber, employed to show the
suitability of the approach to pattern devices with small radius of curvature.
During processing, the same pulses generate two-photon fluorescence in the
surrounding environment and the signal is monitored to identify different
patterning regimes over time through spectral analysis. The employed fs beam
mostly interacts with the metal coating, leaving almost intact the underlying
silica and enabling fluorescence to couple with a specific subset of guided
modes, as verified by far-field analysis. Although the method is described here
for tapered optical fibers used to obtain efficient light collection in the
field of optical neural interfaces, it can be easily extended to other
waveguides-based devices and represents a general approach to support the
implementation closed-loop laser ablation system of fiber optics.


  Wetting phenomena, molecular protrusions of lipid bilayers and membrane
stacks under lateral tension provide physical examples for interacting surfaces
with tension. Such surfaces are studied theoretically using functional
renormalization and Monte Carlo simulations. The critical behavior arising from
thermally-excited shape fluctuations is determined both for global quantities
such as the mean separation of these surfaces and for local quantities such as
the probabilities for local contacts.


  We argue that the Path Integral formulation of Feynman can be reconciled via
a Planck scale underpinning for spacetime, with fuzzy spacetime considerations.


  In this paper we study the uniform stability properties of two classes of
parallel server networks with multiple classes of jobs and multiple server
pools of a tree topology. These include a class of networks with a single
non-leaf server pool, such as the 'N' and 'M' models, and networks of any tree
topology with class-dependent service rates. We show that with $\sqrt{n}$
safety staffing, and no abandonment, in the Halfin--Whitt regime, the
diffusion-scaled controlled queueing processes are exponentially ergodic and
their invariant probability distributions are tight, uniformly over all
stationary Markov controls. We use a unified approach in which the same
Lyapunov function is used in the study of the prelimit and diffusion limit.
  A parameter called the spare capacity (safety staffing) of the network plays
a central role in characterizing the stability results: the parameter being
positive is necessary and sufficient that the limiting diffusion is uniformly
exponentially ergodic over all stationary Markov controls. We introduce the
concept of "system-wide work conserving policies", which are defined as
policies that minimize the number of idle servers at all times. This is
stronger than the so-called joint work conservation. We show that, provided the
spare capacity parameter is positive, the diffusion-scaled processes are
geometrically ergodic and the invariant distributions are tight, uniformly over
all "system-wide work conserving policies". In addition, when the spare
capacity is negative we show that the diffusion-scaled processes are transient
under any stationary Markov control, and when it is zero, they cannot be
positive recurrent.


  A thesis submitted for the degree of Doctor of Philosophy of The Australian
National University.
  In this work we introduce several new optimisation methods for problems in
machine learning. Our algorithms broadly fall into two categories: optimisation
of finite sums and of graph structured objectives. The finite sum problem is
simply the minimisation of objective functions that are naturally expressed as
a summation over a large number of terms, where each term has a similar or
identical weight. Such objectives most often appear in machine learning in the
empirical risk minimisation framework in the non-online learning setting. The
second category, that of graph structured objectives, consists of objectives
that result from applying maximum likelihood to Markov random field models.
Unlike the finite sum case, all the non-linearity is contained within a
partition function term, which does not readily decompose into a summation.
  For the finite sum problem, we introduce the Finito and SAGA algorithms, as
well as variants of each.
  For graph-structured problems, we take three complementary approaches. We
look at learning the parameters for a fixed structure, learning the structure
independently, and learning both simultaneously. Specifically, for the combined
approach, we introduce a new method for encouraging graph structures with the
"scale-free" property. For the structure learning problem, we establish
SHORTCUT, a O(n^{2.5}) expected time approximate structure learning method for
Gaussian graphical models. For problems where the structure is known but the
parameters unknown, we introduce an approximate maximum likelihood learning
algorithm that is capable of learning a useful subclass of Gaussian graphical
models.


  The crystal structure and physical properties of the layered material EuMnBi2
have been characterized by measurements on single crystals. EuMnBi2 is
isostructural with the Dirac material SrMnBi2 based on single crystal x-ray
diffraction, crystallizing in the I4/mmm space group (No. 139). Magnetic
susceptibility measurements suggest antiferromagnetic (AFM) ordering of moments
on divalent Eu ions near T_N=22K. For low fields, the ordered Eu moments are
aligned along the c-axis, and a spin-flop is observed near 5.4T at 5K. The
moment is not saturated in an applied field of 13T at 5K, which is uncommon for
compounds containing Eu^{2+}. The magnetic behavior suggests an anisotropy
enhancement via interaction between Eu and the Mn moments that appear to be
order antiferromagnetically below approximately 310K. A large increase in the
magnetoresistance is observed across the spin-flop, with absolute
magnetoresistance reaching approximately 650% at 5K and 12T. Hall effect
measurements reveal a decrease in the carrier density below T_N, which implies
a manipulation of the Fermi surface by magnetism on the sites surrounding the
Bi square nets that lead to Dirac cones in this family of materials.


  A theoretical and phenomenological consideration is given to higher order,
strong field effects in electron/laser interactions. A consistent strong field
theory is the Furry interaction picture of intense field quantum field theory.
In this theory, fermions are embedded in the strong laser field and the Volkov
wavefunction solutions that result, are exact with respect to the strong field.
When these Volkov fermions interact with individual photons from other sources,
the transition probability is enhanced in a series of resonances when the
kinematics allow the virtual fermion to go on-shell. An experiment is proposed
in which, for the first time, resonances could be used to generate high energy
photons from relativistic electrons at rates orders of magnitude in excess of
usual mechanisms.


  We show that the stochastic dynamics of a large class of one-dimensional
interacting particle systems may be presented by integrable quantum spin
Hamiltonians. Generalizing earlier work \cite{Stin95a,Stin95b} we present an
alternative description of these processes in terms of a time-dependent
operator algebra with quadratic relations. These relations generate the Bethe
ansatz equations for the spectrum and turn the calculation of time-dependent
expectation values into the problem of either finding representations of this
algebra or of solving functional equations for the initial values of the
operators. We use both strategies for the study of two specific models: (i) We
construct a two-dimensional time-dependent representation of the algebra for
the symmetric exclusion process with open boundary conditions. In this way we
obtain new results on the dynamics of this system and on the eigenvectors and
eigenvalues of the corresponding quantum spin chain, which is the isotropic
Heisenberg ferromagnet with non-diagonal, symmetry-breaking boundary fields.
(ii) We consider the non-equilibrium spin relaxation of Ising spins with
zero-temperature Glauber dynamics and an additional coupling to an
infinite-temperature heat bath with Kawasaki dynamics. We solve the functional
equations arising from the algebraic description and show non-perturbatively on
the level of all finite-order correlation functions that the coupling to the
infinite-temperature heat bath does not change the late-time behaviour of the
zero-temperature process. The associated quantum chain is a non-hermitian
anisotropic Heisenberg chain related to the seven-vertex model.


  Nowadays, experimental techniques allow scientists to have access to large
amounts of data. In order to obtain reliable information from the complex
systems which produce these data, appropriate analysis tools are needed}. The
Kalman filter is a {frequently used} technique to infer, assuming a model of
the system, the parameters of the model from uncertain observations. A
well-known implementation of the Kalman filter, the Unscented Kalman filter
(UKF), was recently shown to be able to infer the connectivity of a set of
coupled chaotic oscillators. {I}n this work, we test whether the UKF can also
reconstruct the connectivity of {small groups of} coupled neurons when their
links are either electrical or chemical {synapses}. {In particular, w}e
consider Izhikevich neurons, and aim to infer which neurons influence each
other, considering {simulated spike trains as the experimental observations
used by the UKF}. First, we {verify} that the UKF can recover the parameters of
a single neuron, even when the parameters vary in time. Second, we analyze
small neural ensembles and}} demonstrate that the UKF allows inferring the
connectivity between the neurons, even for heterogeneous, directed, and
{temporally evolving} networks. {Our results show that time-dependent parameter
and coupling estimation is possible in this nonlinearly coupled system.


  Datacenters execute large computational jobs, which are composed of smaller
tasks. A job completes when all its tasks finish, so stragglers -- rare, yet
extremely slow tasks -- are a major impediment to datacenter performance.
Accurately predicting stragglers would enable proactive intervention, allowing
datacenter operators to mitigate stragglers before they delay a job. While much
prior work applies machine learning to predict computer system performance,
these approaches rely on complete labels -- i.e., sufficient examples of all
possible behaviors, including straggling and non-straggling -- or strong
assumptions about the underlying latency distributions -- e.g., whether
Gaussian or not. Within a running job, however, none of this information is
available until stragglers have revealed themselves when they have already
delayed the job. To predict stragglers accurately and early without labeled
positive examples or assumptions on latency distributions, this paper presents
NURD, a novel Negative-Unlabeled learning approach with Reweighting and
Distribution-compensation that only trains on negative and unlabeled streaming
data. The key idea is to train a predictor using finished tasks of
non-stragglers to predict latency for unlabeled running tasks, and then
reweight each unlabeled task's prediction based on a weighting function of its
feature space. We evaluate NURD on two production traces from Google and
Alibaba, and find that compared to the best baseline approach, NURD produces
2--11 percentage point increases in the F1 score in terms of prediction
accuracy, and 2.0--8.8 percentage point improvements in job completion time.


  Let $A$ be a simple separable unital locally approximately subhomogeneous
C*-algebra (locally ASH algebra). It is shown that $A\otimes Q$ can be
tracially approximated by unital Elliott-Thomsen algebras with trivial
$\textrm{K}_1$-group, where $Q$ is the universal UHF algebra. In particular, it
follows that $A$ is classifiable by the Elliott invariant if $A$ is Jiang-Su
stable.


  We derive the dispersionless Hirota equations of the universal Whitham
hierarchy from the kernel formula approach proposed by Carroll and Kodama.
Besides, we also verify the associativity equations in this hierarchy from the
dispersionless Hirota equations and give a realization of the associative
algebra with structure constants expressed in terms of the residue formulas.


  Accounting for phase variability is a critical challenge in functional data
analysis. To separate it from amplitude variation, functional data are
registered, i.e., their observed domains are deformed elastically so that the
resulting functions are aligned with template functions. At present, most
available registration approaches are limited to datasets of complete and
densely measured curves with Gaussian noise. However, many real-world
functional data sets are not Gaussian and contain incomplete curves, in which
the underlying process is not recorded over its entire domain. In this work, we
extend and refine a framework for joint likelihood-based registration and
latent Gaussian process-based generalized functional principal component
analysis that is able to handle incomplete curves. Our approach is accompanied
by sophisticated open-source software, allowing for its application in diverse
non-Gaussian data settings and a public code repository to reproduce all
results. We register data from a seismological application comprising spatially
indexed, incomplete ground velocity time series with a highly volatile Gamma
structure. We describe, implement and evaluate the approach for such incomplete
non-Gaussian functional data and compare it to existing routines.


  Local differential privacy has recently surfaced as a strong measure of
privacy in contexts where personal information remains private even from data
analysts. Working in a setting where both the data providers and data analysts
want to maximize the utility of statistical analyses performed on the released
data, we study the fundamental trade-off between local differential privacy and
utility. This trade-off is formulated as a constrained optimization problem:
maximize utility subject to local differential privacy constraints. We
introduce a combinatorial family of extremal privatization mechanisms, which we
call staircase mechanisms, and show that it contains the optimal privatization
mechanisms for a broad class of information theoretic utilities such as mutual
information and $f$-divergences. We further prove that for any utility function
and any privacy level, solving the privacy-utility maximization problem is
equivalent to solving a finite-dimensional linear program, the outcome of which
is the optimal staircase mechanism. However, solving this linear program can be
computationally expensive since it has a number of variables that is
exponential in the size of the alphabet the data lives in. To account for this,
we show that two simple privatization mechanisms, the binary and randomized
response mechanisms, are universally optimal in the low and high privacy
regimes, and well approximate the intermediate regime.


  Extracting governing stochastic differential equation models from elusive
data is crucial to understand and forecast dynamics for complex systems. We
devise a method to extract the drift term and estimate the diffusion
coefficient of a governing stochastic dynamical system, from its time-series
data of the most probable transition trajectory. By the Onsager-Machlup theory,
the most probable transition trajectory satisfies the corresponding
Euler-Lagrange equation, which is a second order deterministic ordinary
differential equation involving the drift term and diffusion coefficient. We
first estimate the coefficients of the Euler-Lagrange equation based on the
data of the most probable trajectory, and then we calculate the drift and
diffusion coefficients of the governing stochastic dynamical system. These two
steps involve sparse regression and optimization. Finally, we illustrate our
method with an example and some discussions.


  We consider a hyperbolic free boundary problem by means of minimizing time
discretized functionals of Crank-Nicolson type. The feature of this functional
is that it enjoys energy conservation in the absence of free boundaries, which
is an essential property for numerical calculations. The existence and
regularity of minimizers is shown and an energy estimate is derived. These
results are then used to show the existence of a weak solution to the free
boundary problem in the 1-dimensional setting.


  We investigate the elastic and isotropic aggregate properties of
ferromagnetic bcc iron as a function of temperature and pressure by computing
the Helmholtz free energies for the volume-conserving strained structures using
the first-principles linear response linear-muffin-tin-orbital method and the
generalized-gradient approximation. We include the electronic excitation
contributions to the free energy from the band structures, and phonon
contributions from quasi-harmonic lattice dynamics. We make detailed
comparisons between our calculated elastic moduli and their temperature and
pressure dependences with available experimental and theoretical data.


  We consider a practically motivated variant of the canonical online fair
allocation problem: a decision-maker has a budget of perishable resources to
allocate over a fixed number of rounds. Each round sees a random number of
arrivals, and the decision-maker must commit to an allocation for these
individuals before moving on to the next round. The goal is to construct a
sequence of allocations that is envy-free and efficient. Our work makes two
important contributions toward this problem: we first derive strong lower
bounds on the optimal envy-efficiency trade-off that demonstrate that a
decision-maker is fundamentally limited in what she can hope to achieve
relative to the no-perishing setting; we then design an algorithm achieving
these lower bounds which takes as input $(i)$ a prediction of the perishing
order, and $(ii)$ a desired bound on envy. Given the remaining budget in each
period, the algorithm uses forecasts of future demand and perishing to
adaptively choose one of two carefully constructed guardrail quantities. We
demonstrate our algorithm's strong numerical performance - and
state-of-the-art, perishing-agnostic algorithms' inefficacy - on simulations
calibrated to a real-world dataset.


  User programs recover from hardware exceptions and respond to signals by
executing custom handlers that they register specifically for such events. We
present SIGY attack, which abuses this programming model on Intel SGX to break
the confidentiality and integrity guarantees of enclaves. SIGY uses the
untrusted OS to deliver fake hardware events and injects fake signals in an
enclave at any point. Such unintended execution of benign program-defined
handlers in an enclave corrupts its state and violates execution integrity. 7
runtimes and library OSes (OpenEnclave, Gramine, Scone, Asylo, Teaclave,
Occlum, EnclaveOS) are vulnerable to SIGY. 8 languages supported in Intel SGX
have programming constructs that are vulnerable to SIGY. We use SIGY to
demonstrate 4 proof of concept exploits on webservers (Nginx, Node.js) to leak
secrets and data analytics workloads in different languages (C and Java) to
break execution integrity.


  Vector-boson fusion processes are an important tool for the study of
electroweak symmetry breaking at hadron colliders, since they allow to
distinguish a light Higgs boson scenario from strong weak boson scattering. We
here consider the channels WW->ZZ and ZZ->ZZ as part of electroweak Z boson
pair production in association with two tagging jets. We present the
calculation of the NLO QCD corrections to the cross sections for p p -> e+ e-
mu+ mu- + 2 jets and p p -> e+ e- nu_mu nubar_mu + 2 jets via vector-boson
fusion at order alpha_s alpha^6, which is performed in the form a NLO
parton-level Monte Carlo program. The corrections to the integrated cross
sections are found to be modest, while the shapes of some kinematical
distributions change appreciably at NLO. Residual scale uncertainties typically
are at the few percent level.


  The supernova remnant (SNR) G288.8-6.3 was recently discovered as a faint
radio shell at large Galactic latitude using observations with ASKAP in the EMU
survey. Here, we make the first detailed investigation of the $\gamma$-ray
emission from the G288.8-6.3 region, aiming to characterise the high-energy
emission in the GeV regime from the newly discovered SNR, dubbed Ancora. 15
years of Fermi-Large Area Telescope (LAT) data were analysed at energies
between 400 MeV and 1 TeV and the excess seen in the region was modelled using
different spatial and spectral models. We detect spatially extended
$\gamma$-ray emission coinciding with the radio SNR, with detection
significance up to 8.8 $\sigma$. A radial disk spatial model in combination
with a power-law spectral model with an energy flux of $(4.80 \pm 0.91) \times
10^{-6}$ $\text{MeV}$ $\text{cm}^{-2}$ $\text{s}^{-1}$, with the spectrum
extending up to around 5 GeV was found to be the preferred model.
Morphologically, hotspots seen above 1 GeV are well-correlated with the bright
western part of the radio shell. The emission is more likely to be of leptonic
origin given the estimated gas density in the region and the estimated distance
and age of the SNR, but a hadronic scenario cannot be ruled out. Ancora is the
eighth SNR detected at high Galactic latitude with Fermi-LAT. This new
population of remnants has the potential to constrain the physics of particle
diffusion and escape from SNRs into the Galaxy.


  The effective action of the radion in the Randall-Sundrum model is analysed.
Fine tunings are needed to obtain the observed mass hierarchy and an invisible
radion. since the kinetic terms are important for determining the radion mass,
the finite quantum corrections from massless conformally coupled fermions are
analysed and found to vanish at one loop order.


  Reasoning about distance is indispensable for establishing or avoiding
contact in manipulation tasks. To this end, we present an online approach for
learning implicit representations of signed distance using piecewise polynomial
basis functions. Starting from an arbitrary prior shape, our method
incrementally constructs a continuous and smooth distance representation from
incoming surface points, with analytical access to gradient information. The
underlying model does not store training data for prediction, and its
performance can be balanced through interpretable hyperparameters such as
polynomial degree and number of segments. We assess the accuracy of the
incrementally learned model on a set of household objects and compare it to
neural network and Gaussian process counterparts. The utility of intermediate
results and analytical gradients is further demonstrated in a physical
experiment. For code and video, see https://sites.google.com/view/pp-sdf/.


  We derive and present a collection of properties about the Steklov averages,
including some results about the derivation with respect to spatial variables,
and with respect to time, and a form of the fundamental theorem of the
calculus.


  Dynamical interactions in dense star clusters are considered one of the most
effective formation channels of binary black holes (BBHs). Here, we present
direct $N-$body simulations of two different star cluster families: low-mass
($\sim{500-800}$ M$_\odot$) and relatively high-mass star clusters ($\ge{5000}$
M$_\odot$). We show that the formation channels of BBHs in low- and high-mass
star clusters are extremely different and lead to two completely distinct
populations of BBH mergers. Low-mass clusters host mainly low-mass BBHs born
from binary evolution, while BBHs in high-mass clusters are relatively massive
(chirp mass up to $\sim{100}$ M$_\odot$) and driven by dynamical exchanges.
Tidal disruption dramatically quenches the formation and dynamical evolution of
BBHs in low-mass clusters on a very short timescale ($\lesssim{100}$ Myr),
while BBHs in high-mass clusters undergo effective dynamical hardening until
the end of our simulations (1.5 Gyr). In high-mass clusters we find that 8\% of
BBHs have primary mass in the pair-instability mass gap, all of them born via
stellar collisions, while only one BBH with primary mass in the mass gap forms
in low-mass clusters. These differences are crucial for the interpretation of
the formation channels of gravitational-wave sources.


  We study the quasi-ergodicity of compact strong Feller semigroups $U_t$, $t >
0$, on $L^2(M,\mu)$; we assume that $M$ is a locally compact Polish space
equipped with a locally finite Borel measue $\mu$. The operators $U_t$ are
ultracontractive and positivity preserving, but not necessarily self-adjoint or
normal. We are mainly interested in those cases where the measure $\mu$ is
infinite and the semigroup is not intrinsically ultracontractive. We relate
quasi-ergodicity on $L^p(M,\mu)$ and uniqueness of the quasi-stationary measure
with the finiteness of the heat content of the semigroup (for large values of
$t$) and with the progressive uniform ground state domination property. The
latter property is equivalent to a variant of quasi-ergodicity which
progressively propagates in space as $t \uparrow \infty$; the propagation rate
is determined by the decay of $U_t \mathbb{1}_M(x)$. We discuss several
applications and illustrate our results with examples. This includes a complete
description of quasi-ergodicity for a large class of semigroups corresponding
to non-local Schr\"odinger operators with confining potentials.


  We prove that all cubulated groups are semistable at infinity. In doing so we
prove two further results about cubulations of groups. The first of these
states that any one-ended cubulated group has a cubulation for which all
halfspaces are one-ended. The second states that any cubulated group has a
cubulation for which all quarterspaces are deep -- analogous to the fact that
passing to the essential core of a given cubulation ensures that all halfspaces
are deep.


  Sample size dependent critical current density has been observed in magnesium
diboride superconductors. At high fields, larger samples provide higher
critical current densities, while at low fields, larger samples give rise to
lower critical current densities. The explanation for this surprising result is
proposed in this study based on the electric field generated in the
superconductors. The dependence of the current density on the sample size has
been derived as a power law $j\propto R^{1/n}$ ($n$ is the $n$ factor
characterizing $E-j$ curve $E=E_c(j/j_c)^n$). This dependence provides one with
a new method to derive the $n$ factor and can also be used to determine the
dependence of the activation energy on the current density.


  Quantum diffusion is a major topic in condensed-matter physics, and the
Caldeira-Leggett model has been one of the most successful approaches to study
this phenomenon. Here, we generalize this model by coupling the bath to the
system through a Weyl fractional derivative. The Weyl fractional Langevin
equation is then derived without imposing a non-Ohmic macroscopic spectral
function for the bath. By investigating the short- and long-time behavior of
the mean squared displacement (MSD), we show that this model is able to
describe a large variety of anomalous diffusion. Indeed, we find ballistic,
sub-ballistic, and super-ballistic behavior for short times, whereas for long
times we find saturation, and sub- and super-diffusion.


  Spherical confinement can act either stabilizing or destabilizing on the
collapsed state of a semi-flexible polymer. General free-energy arguments
suggest that the order of the unconstrained collapse transition is the
distinguishing factor: First order implies stabilization, second order causes
destabilization. We confirm this conjecture by Monte Carlo simulations of a
coarse-grained model for semiflexible polymers whose chain stiffness tunes the
transition order. The resulting physical picture is potentially relevant for
other systems under strong confinement such as proteins and DNA.


  Despite several advances in recent years, learning causal structures
represented by directed acyclic graphs (DAGs) remains a challenging task in
high dimensional settings when the graphs to be learned are not sparse. In this
paper, we propose to exploit a low rank assumption regarding the (weighted)
adjacency matrix of a DAG causal model to help address this problem. We utilize
existing low rank techniques to adapt causal structure learning methods to take
advantage of this assumption and establish several useful results relating
interpretable graphical conditions to the low rank assumption. Specifically, we
show that the maximum rank is highly related to hubs, suggesting that
scale-free networks, which are frequently encountered in practice, tend to be
low rank. Our experiments demonstrate the utility of the low rank adaptations
for a variety of data models, especially with relatively large and dense
graphs. Moreover, with a validation procedure, the adaptations maintain a
superior or comparable performance even when graphs are not restricted to be
low rank.


  Federated learning (FL) is a distributed learning paradigm that facilitates
collaborative training of a shared global model across devices while keeping
data localized. The deployment of FL in numerous real-world applications faces
delays, primarily due to the prevalent reliance on supervised tasks. Generating
detailed labels at edge devices, if feasible, is demanding, given resource
constraints and the imperative for continuous data updates. In addressing these
challenges, solutions such as federated semi-supervised learning (FSSL), which
relies on unlabeled clients' data and a limited amount of labeled data on the
server, become pivotal. In this paper, we propose FedAnchor, an innovative FSSL
method that introduces a unique double-head structure, called anchor head,
paired with the classification head trained exclusively on labeled anchor data
on the server. The anchor head is empowered with a newly designed label
contrastive loss based on the cosine similarity metric. Our approach mitigates
the confirmation bias and overfitting issues associated with pseudo-labeling
techniques based on high-confidence model prediction samples. Extensive
experiments on CIFAR10/100 and SVHN datasets demonstrate that our method
outperforms the state-of-the-art method by a significant margin in terms of
convergence rate and model accuracy.


  Light sub-GeV halo dark matter (DM) particles up-scattered by high-energy
cosmic-rays (CRs) (referred to as CRDM) can be energetic and become detectable
by conventional DM direct detection experiments. We perform a refined analysis
on the exclusion bounds of the spin-independent DM-nucleon scattering cross
section $\sigma_{\chi p}$ in this approach. For the exclusion lower bounds, we
determine the parameter of the effective distance $D_\text{eff}$ for CRDM
production using spatial-dependent CR fluxes and including the contributions
from the major heavy CR nuclear species. We obtain $D_\text{eff}\simeq 9$ kpc
for CRDM particles with kinetic energy above $\sim 1~\text{GeV}$, which pushes
the corresponding exclusion lower bounds down to $\sigma_{\chi p} \sim 4\times
10^{-32}~\text{cm}^2$ for DM particle mass at MeV scale and below. For the
exclusion upper bounds from Earth attenuation, previous estimations neglecting
the nuclear form factor leaded to typical exclusion upper bounds of
$\sigma_{\chi p}\sim\mathcal{O}(10^{-28})~\text{cm}^2$ from the XENON1T data.
Using both the analytic and numerical approaches, we show that for CRDM
particles, the presence of the nuclear form factor strongly suppresses the
effect of Earth attenuation. Consequently, the cross section that can be
excluded by the XENON1T data can be a few orders of magnitude higher, which
closes the gap in the cross sections excluded by the XENON1T experiment and
that by the astrophysical measurements such that for the cosmic microwave
background (CMB), galactic gas cloud cooling, and structure formation, etc..


  Identifying genes associated with complex human diseases is one of the main
challenges of human genetics and computational medicine. To answer this
question, millions of genetic variants get screened to identify a few of
importance. To increase the power of identifying genes associated with diseases
and to account for other potential sources of protein function aberrations, we
propose a novel factor-graph based model, where much of the biological
knowledge is incorporated through factors and priors. Our extensive simulations
show that our method has superior sensitivity and precision compared to
variant-aggregating and differential expression methods. Our integrative
approach was able to identify important genes in breast cancer, identifying
genes that had coding aberrations in some patients and regulatory abnormalities
in others, emphasizing the importance of data integration to explain the
disease in a larger number of patients.


  Let $\text{AGL}(1,\Bbb F_q)$ denote the affine linear group of dimension one
over the finite field $\Bbb F_q$. We determine the M\"obius function of the
lattice of subgroups of $\text{AGL}(1,\Bbb F_q)$.


  We present a Fokker-Planck description of supercooled colloidal systems
exhibiting slow relaxation dynamics. By assuming the existence of a local
quasi-equilibrium state during the relaxation of the system, we derive a
non-Markovian Fokker-Planck equation for the non-stationary conditional
probability. A generalized Stokes-Einstein relation containing the temperature
of the system instead of the temperature of the bath is obtained. Our results
explain experiments showing that the diffusion coefficient is not proportional
to the inverse of the effective viscosity at frequencies related to the
diffusion time scale.


  Properties of $\sim$ 6 000 Ly-$\alpha$ absorbers observed in 19 high
resolution spectra of QSOs are investigated using the model of formation and
evolution of DM structure elements based on the Zel'dovich theory. This model
asserts that absorbers are formed in the course of both linear and nonlinear
adiabatic or shock compression of dark matter (DM) and gaseous matter. It
allows us to link the column density and overdensity of DM and gaseous
components with the observed column density of neutral hydrogen, redshifts and
Doppler parameters of absorbers and demonstrates that at high redshifts we
observe a self similar period of structure evolution with the Gaussian initial
perturbations. We show that the colder absorbers are associated with rapidly
expanded regions of a galactic scale which represent large amplitude negative
density perturbations.
  We extend and improve the method of measuring the power spectrum of initial
perturbations proposed in Demia\'nski & Doroshkevich (2003b). Our method links
the observed separations and the DM column density of absorbers with the
correlation function of the initial velocity field. We recover the cold dark
matter (CDM) like power spectrum at scales 10> D > 0.15Mpc/h with a precision
of ~15%. However at scales $\sim 3 - 150 h^{-1}$kpc the measured and CDM--like
spectra are different. This result suggests a possible complex inflation with
generation of excess power at small scales.


  Quantum Krylov subspace diagonalization (QKSD) is an emerging method used in
place of quantum phase estimation in the early fault-tolerant era, where
limited quantum circuit depth is available. In contrast to the classical Krylov
subspace diagonalization (KSD) or the Lanczos method, QKSD exploits the quantum
computer to efficiently estimate the eigenvalues of large-size Hamiltonians
through a faster Krylov projection. However, unlike classical KSD, which is
solely concerned with machine precision, QKSD is inherently accompanied by
errors originating from a finite number of samples. Moreover, due to difficulty
establishing an artificial orthogonal basis, ill-conditioning problems are
often encountered, rendering the solution vulnerable to noise. In this work, we
present a nonasymptotic theoretical framework to assess the relationship
between sampling noise and its effects on eigenvalues. We also propose an
optimal solution to cope with large condition numbers by eliminating the
ill-conditioned bases. Numerical simulations of the one-dimensional Hubbard
model demonstrate that the error bound of finite samplings accurately predicts
the experimental errors in well-conditioned regions.


  It is a well known fact that finite time optimal controllers, such as MPC
does not necessarily result in closed loop stable systems. Within the MPC
community it is common practice to add a final state constraint and/or a final
state penalty in order to obtain guaranteed stability. However, for more
advanced controller structures it can be difficult to show stability using
these techniques. Additionally in some cases the final state constraint set
consists of so many inequalities that the complexity of the MPC problem is too
big for use in certain fast and time critical applications. In this paper we
instead focus on deriving a tool for a-postiori analysis of the closed loop
stability for linear systems controlled with MPC controllers. We formulate an
optimisation problem that gives a sufficient condition for stability of the
closed loop system and we show that the problem can be written as a Mixed
Integer Linear Programming Problem (MILP)


  In contemporary machine learning workloads, numerous hyper-parameter search
algorithms are frequently utilized to efficiently discover high-performing
hyper-parameter values, such as learning and regularization rates. As a result,
a range of parameter schedules have been designed to leverage the capability of
adjusting hyper-parameters during training to enhance loss performance. These
schedules, however, introduce new hyper-parameters to be searched and do not
account for the current loss values of the models being trained.
  To address these issues, we propose Population Descent (PopDescent), a
progress-aware hyper-parameter tuning technique that employs a memetic,
population-based search. By merging evolutionary and local search processes,
PopDescent proactively explores hyper-parameter options during training based
on their performance. Our trials on standard machine learning vision tasks show
that PopDescent converges faster than existing search methods, finding model
parameters with test-loss values up to 18% lower, even when considering the use
of schedules. Moreover, we highlight the robustness of PopDescent to its
initial training parameters, a crucial characteristic for hyper-parameter
search techniques.


  We present a systematic first-principles study of the electronic surface
states and resonances occuring in thin films of Pd of various thicknesses
deposited on a single ferromagnetic monolayer of Fe on top of Ir(111)
substrate. This system is of interest since one Pd layer deposited on
Fe/Ir(111) hosts small magnetic skyrmions. The latter are topological magnetic
objects with swirling spin-textures with possible implications in the context
of spintronic devices since they have the potential to be used as magnetic bits
for information technology. The stabilization, detection and manipulation of
such non-collinear magnetic entities require a quantitative investigation and a
fundamental understanding of their electronic structure. Here we investigate
the nature of the unoccupied electronic states in Pd/Fe/Ir(111), which are
essential in the large spin-mixing magnetoresistance (XMR) signature captured
using non spin-polarized scanning tunnelling microscopy [Crum et al., Nat.
Commun. {\bf 6} 8541 (2015); Hanneken et al., Nat. Nanotech. {\bf 10}, 1039
(2015)]. To provide a complete analysis, we investigate bare Fe/Ir(111) and
Pd$_{n=2,7}$/Fe/Ir(111) surfaces. Our results demonstrate the emergence of
surface and interface states after deposition of Pd monolayers, which are
strongly impacted by the large spin-orbit coupling of Ir surface.


  Seidel-Smith and Manolescu constructed knot homology theories using
symplectic fibrations whose total spaces were certain varieties of matrices.
These knot homology theories were associated to $SL(n) $ and tensor products of
the standard and dual representations. In this paper, we place their geometric
setups in a natural, general framework. For any complex reductive group and any
sequence of minuscule dominant weights, we construct a fibration of affine
varieties over a configuration space. The middle cohomology of these varieties
is isomorphic to the space of invariants in the corresponding tensor product of
representations. Our construction uses the Beilinson-Drinfeld Grassmannian and
the geometric Satake correspondence.


  In [Distrance-regular Cayley graphs on dihedral groups, J. Combin. Theory Ser
B 97 (2007) 14--33], Miklavi\v{c} and Poto\v{c}nik proposed the problem of
characterizing distance-regular Cayley graphs, which can be viewed as an
extension of the problem of identifying strongly regular Cayley graphs, or
equivalently, regular partial difference sets. In this paper, all
distance-regular Cayley graphs over $\mathbb{Z}_{p^s}\oplus\mathbb{Z}_{p}$ with
$p$ being an odd prime are determined. It is shown that every such graph is
isomorphic to a complete graph, a complete multipartite graph, or the line
graph of a transversal design $TD(r,p)$ with $2\leq r\leq p-1$.


  Pregnant women have increased susceptibility to malaria infection. In these
women, malaria parasites are frequently found sequestered in the placental
intervillous spaces, a condition referred to as placental malaria (PM).
Placental malaria threatens the health of the mother and the child's life by
causing still births and reduction in gestational age. An estimated 24 million
pregnant women in Sub-Saharan Africa are at risk. Mechanisms responsible for
increased susceptibility in pregnant women are not fully understood. Pregnancy
malaria studies have been limited by the lack of a suitable animal model. This
research aimed to develop a baboon (Papio anubis) model for studying PM. The
pregnancies of three adult female baboons were synchronized and their
gestational levels confirmed by ultrasonography. On the 150th day of gestation
the pregnant baboons were infected with Plasmodium knowlesi H strain parasites
together with four nulligravid control baboons. Parasitaemia was monitored from
two days post inoculation until the 159th day of gestation when caesarean
section was done on one baboon in order to obtain the placenta. Two baboons
aborted their conceptus. Smears prepared from placental blood demonstrated the
presence of Plasmodium knowlesi parasites in all the three sampled placentas.
These new findings show that P. knowlesi sequesters in the baboon placenta. In
addition, this study has characterized haemoglobin, eosinophil, Immunoglobulin
G and Immunoglobulin M profiles in this model. Thus a non human primate
(baboon) model for studying PM has been established. The established baboon -
P. knowlesi model for studying human placental/pregnancy malaria now offers an
opportunity for circumventing the obstacles experienced during human studies
like having inadequate tissue for analysis, inaccurate estimation of
gestational age, moral, ethical and financial limitations.


  In this paper, we consider two types of the scattering problems (relativistic
case), namely, the stationary scattering problem, where the distance $r$ tends
to infinity, and the dynamical scattering problem, where the time $t$ tends to
infinity. Using our results on Lippmann-Schwinger equation in the relativistic
case, we found the connection between the stationary scattering problem (the
scattering amplitude) and the dynamical scattering problem (the scattering
operator). This result is the quantum mechanical analog of the ergodic formulas
in the classical mechanics.


  Blind deconvolution problems are severely ill-posed because neither the
underlying signal nor the forward operator are not known exactly.
Conventionally, these problems are solved by alternating between estimation of
the image and kernel while keeping the other fixed. In this paper, we show that
this framework is flawed because of its tendency to get trapped in local minima
and, instead, suggest the use of a kernel estimation strategy with a non-blind
solver. This framework is employed by a diffusion method which is trained to
sample the blur kernel from the conditional distribution with guidance from a
pre-trained non-blind solver. The proposed diffusion method leads to
state-of-the-art results on both synthetic and real blur datasets.


  We briefly summarize recent results of five TeV SNRs from radio and X-ray
observations. We focus on remeasuring kinematic distances of 5 TeV SNRs, i.e.
HESS J1732-347/SNR G353.6-0.7 (3.2 kpc), HESS J1834-087/G23.3-0.3 (also W41,
4.0 kpc), HESS J1833-105/G21.5-0.9 (4.8 kpc), HESS J1846-029/G29.7-0.3 (Kes 75,
6.3 kpc) and TeV SNR G54.1-0.3 (6.5 kpc), and studying non-thermal X-ray
emissions from two old SNRs (G353.6-0.7 and W41). These not only allow
constraining the TeV SNR basic physical properties, but also help reveal
acceleration mechanisms of TeV Gamma-rays in the SNRs which are either related
with the SNRs or the pulsar wind nebulae.


  In this paper we present a stepwise construction of the path integral over
relativistic orbits in Euclidean spacetime. It is shown that the apparent
problems of this path integral, like the breakdown of the naive
Chapman-Kolmogorov relation, can be solved by a careful analysis of the
overcounting associated with local and global symmetries. Based on this, the
direct calculation of the quantum propagator of the relativistic point particle
in the path integral formulation results from a simple and purely geometric
construction.
  paperpitch https://www.youtube.com/watch?v=tNMbCgPbq2A


  We study dynamics of a test charged particle moving in a weakly charged
extreme Kerr throat. Dynamical equations of the particle motion are solved in
quadratures. We show explicitly that the Killing tensor of the Kerr spacetime
becomes reducible in the extreme Kerr throat geometry. Special types of motion
of particles and light are discussed.


  We consider a system consisting of multiple mobile robots in which the user
can at any time issue relocation tasks ordering one of the robots to move from
its current location to a given destination location. In this paper, we deal
with the problem of finding a trajectory for each such relocation task that
avoids collisions with other robots. The chosen robot plans its trajectory so
as to avoid collision with other robots executing tasks that were issued
earlier. We prove that if all possible destinations of the relocation tasks
satisfy so-called valid infrastructure property, then this mechanism is
guaranteed to always succeed and provide a trajectory for the robot that
reaches the destination without colliding with any other robot. The
time-complexity of the approach on a fixed space-time discretization is only
quadratic in the number of robots. We demonstrate the applicability of the
presented method on several real-world maps and compare its performance against
a popular reactive approach that attempts to solve the collisions locally.
Besides being dead-lock free, the presented approach generates trajectories
that are significantly faster (up to 48% improvement) than the trajectories
resulting from local collision avoidance.


  Far-field Wireless Power Transfer (WPT) and Simultaneous Wireless Information
and Power Transfer (SWIPT) have attracted significant attention in the RF and
communication communities. Despite the rapid progress, the problem of waveform
design to enhance the output DC power of wireless energy harvester has received
limited attention so far. In this paper, we bridge communication and RF design
and derive novel multisine waveforms for multi-antenna wireless power transfer.
The waveforms are adaptive to the channel state information and result from a
posynomial maximization problem that originates from the non-linearity of the
energy harvester. They are shown through realistic simulations to provide
significant gains (in terms of harvested DC power) over state-of-the-art
waveforms under a fixed transmit power constraint.


  Permutation Matrices are a well known class of matrices which encode the
elements of the symmetric group on $d$ elements as a square $d\times d$ matrix.
Motivated by [4], we define a similar class of matrices which are a
generalization of Permutation Matrices. We give explicit formulas for the
multiplication of these matrices. Lastly, we discuss the spectral radius,
eigenvalues, and periodicity before giving a form of Birkhoff-Von Neumann's
Theorem for Left Stochastic Matrices.


  Dynamic features, such as chromospheric jets, transition region network jets,
coronal plumes and coronal jets, are abundant in the network regions of the
solar polar coronal holes. We investigate the relationship between
chromospheric jets and coronal activities (e.g., coronal plumes and jets).We
analyze observations of a polar coronal hole including the filtergrams that
were taken by the New Vacuum Solar Telescope (NVST) at the H{\alpha}-0.6
{\AA}to study the H{\alpha} jets,and the Atmospheric Imaging Assembly (AIA) 171
{\AA} images to follow the evolution of coronal activities. H{\alpha} jets are
persistent in the network regions, only some regions (denoted as R1-R5) are
rooted with discernible coronal plumes.With an automated method, we identify
and track 1 320 H{\alpha} jets in the network regions. We find that the average
lifetime, height and ascending speed of the H{\alpha} jets are 75.38 s, 2.67
Mm, 65.60 km s$^{-1}$, respectively. The H{\alpha} jets rooted in R1-R5 are
higher and faster than those in the others. We also find that propagating
disturbances (PDs) in coronal plumes have a close connection with the H{\alpha}
jets. The speeds of 28 out of 29 H{\alpha} jets associated with PDs are about
50 km s$^{-1}$ . In a case of coronal jet, we find that the speeds of both the
coronal jet and the H{\alpha} jet are over 150 km s$^{-1}$, suggesting that
both cool and hot jets can be coupled together. Based on our analyses, it is
evident that more dynamic H{\alpha} jets could release the energies to the
corona, which might be the results of the development of Kelvin-Helmholtz
instability (KHi) or small-scaled magnetic activities. We suggest that
chromospheric jets, transition region network jets and ray-like features in the
corona are coherent phenomena, and they are important tunnels for cycling
energy and mass in the solar atmosphere.


  Constructing fast samplers for unconditional diffusion and flow-matching
models has received much attention recently; however, existing methods for
solving inverse problems, such as super-resolution, inpainting, or deblurring,
still require hundreds to thousands of iterative steps to obtain high-quality
results. We propose a plug-and-play framework for constructing efficient
samplers for inverse problems, requiring only pre-trained diffusion or
flow-matching models. We present Conditional Conjugate Integrators, which
leverage the specific form of the inverse problem to project the respective
conditional diffusion/flow dynamics into a more amenable space for sampling.
Our method complements popular posterior approximation methods for solving
inverse problems using diffusion/flow models. We evaluate the proposed method's
performance on various linear image restoration tasks across multiple datasets,
employing diffusion and flow-matching models. Notably, on challenging inverse
problems like 4$\times$ super-resolution on the ImageNet dataset, our method
can generate high-quality samples in as few as 5 conditional sampling steps and
outperforms competing baselines requiring 20-1000 steps. Our code and models
will be publicly available at https://github.com/mandt-lab/CI2RM.


  We derive integral tests for the existence and absence of arbitrage in a
financial market with one risky asset which is either modeled as stochastic
exponential of an Ito process or a positive diffusion with Markov switching. In
particular, we derive conditions for the existence of the minimal martingale
measure. We also show that for Markov switching models the minimal martingale
measure preserves the independence of the noise and we study how the minimal
martingale measure can be modified to change the structure of the switching
mechanism. Our main mathematical tools are new criteria for the martingale and
strict local martingale property of certain stochastic exponentials.


  We consider the sample of Tran (2001) to study the X-ray properties of
Seyfert 2 galaxies with and without polarized broad lines.


  In this paper, we focus on the mean-field backward stochastic differential
equations (BSDEs) driven by a fractional Brownian motion with Hurst parameter H
greater then 1/2. First, the existence and uniqueness of these equations are
established under Lipschitz condition. Then, a comparison theorem for such
mean-field BSDEs is obtained. Finally, as an application, we connect this
mean-field BSDE with a nonlocal partial differential equation (PDE).


  We study the entropy of a set of identical hard objects, of general shape,
with each object pivoted on the vertices of a d-dimensional regular lattice of
lattice spacing a, but can have arbitrary orientations. When the pivoting point
is situated asymmetrically on the object, we show that there is a range of
lattice spacings a, where in any orientation, a particle can overlap with at
most one of its neighbors. In this range, the entropy of the system of
particles can be expressed exactly in terms of the grand partition function of
coverings of the base lattice by dimers at a finite negative activity. The
exact entropy in this range is fully determined by the second virial
coefficient. Calculation of the partition function is also shown to be
reducible to that of the same model with discretized orientations. We determine
the exact functional form of the probability distribution function of
orientations at a site. This depends on the density of dimers for the given
activity in the dimer problem, that we determine by summing the corresponding
Mayer series numerically. These results are verified by numerical simulations.


  We study mutual alignment and interactions between colloidal particles of
dissimilar shapes and dimensions when dispersed in a nematic host fluid. Convex
pentagonal and concave starfruit-shaped nanoprisms and microspheres induce
dipolar or quadrupolar director structures. The ensuing elastic pair
interactions between microspheres and nanoprisms are highly dependent on the
nanoparticle shape being omnidirectionally attractive for convex prisms but
strongly anisotropic for concave prisms. Elastic deformations due to spherical
particles cause well-defined alignment of complex-shaped nanoparticles at
distances much larger than the microsphere size. We characterize distance and
angular dependencies of elastic pair interaction forces, torques, and binding
energies. The studied elasticity-mediated self-assembly of metal and dielectric
nanoparticles with dissimilar shapes and sizes opens new possibilities for
self-assembly based fabrication of structured mesoscopic composites with
predesigned properties.


  Context: The presence of a turbulent magnetic field in the quiet Sun has been
unveiled observationally using different techniques. The magnetic field is
quasi-isotropic and has field strengths weaker than 100G. It is pervasive and
may host a local dynamo. Aims: We aim to determine the length scale of the
turbulent magnetic field in the quiet Sun. Methods: The Stokes V area asymmetry
is sensitive to minute variations in the magnetic topology along the line of
sight. Using data provided by Hinode-SOT/SP instrument, we performed a
statistical study of this quantity.We classified the different magnetic regimes
and infer properties of the turbulent magnetic regime. In particular we
measured the correlation length associated to these fields for the first time.
Results: The histograms of Stokes V area asymmetries reveal three different
regimes: one organized, quasi-vertical and strong field (flux tubes or other
structures of the like); a strongly asymmetric group of profiles found around
field concentrations; and a turbulent isotropic field. For the last, we confirm
its isotropy and measure correlation lengths from hundreds of kilometers down
to 10km, at which point we lost sensitivity. A crude attempt to measure the
power spectra of these turbulent fields is made. Conclusions: In addition to
confirming the existence of a turbulent field in the quiet Sun, we give further
prove of its isotropy.We also measure correlation lengths down to 10km. The
combined results show magnetic fields with a large span of length scales, as
expected from a turbulent cascade.


  Most software companies have extensive test suites and re-run parts of them
continuously to ensure recent changes have no adverse effects. Since test
suites are costly to execute, industry needs methods for test case
prioritisation (TCP). Recently, TCP methods use machine learning (ML) to
exploit the information known about the system under test (SUT) and its test
cases. However, the value added by ML-based TCP methods should be critically
assessed with respect to the cost of collecting the information. This paper
analyses two decades of TCP research, and presents a taxonomy of 91 information
attributes that have been used. The attributes are classified with respect to
their information sources and the characteristics of their extraction process.
Based on this taxonomy, TCP methods validated with industrial data and those
applying ML are analysed in terms of information availability, attribute
combination and definition of data features suitable for ML. Relying on a high
number of information attributes, assuming easy access to SUT code and
simplified testing environments are identified as factors that might hamper
industrial applicability of ML-based TCP. The TePIA taxonomy provides a
reference framework to unify terminology and evaluate alternatives considering
the cost-benefit of the information attributes.


  We present an analytic calculation of the thermonuclear depletion of the
light elements lithium, beryllium, and boron in fully convective, low-mass
stars. Under the presumption that the pre--main-sequence star is always fully
mixed during contraction, we find that the burning of these rare light elements
can be computed analytically, even when the star is degenerate. Using the
effective temperature as a free parameter, we constrain the properties of
low-mass stars from observational data, independently of the uncertainties
associated with modeling their atmospheres and convection. Our analytic
solution explains the dependence of the age at a given level of elemental
depletion on the stellar effective temperature, nuclear cross sections, and
chemical composition. Most importantly, our results allow observers to
translate lithium non-detections in young cluster members into a
model-independent minimum age for that cluster. Using this procedure, we have
found lower limits to the ages of the Pleiades (100 Myr) and Alpha Persei (60
Myr) clusters. Recent experimental work on the low energy resonance in the
^10B(p,\alpha)^7Be reaction has greatly enhanced estimates of the destruction
rate of ^10B, making it possible for stars with M>0.1 M_sun to deplete both
^10B and ^11B before reaching the main sequence. Moreover, there is an
interesting range of masses, 0.085 M_sun < M < 0.13 M_sun, where boron
depletion occurs on the main sequence in less than a Hubble time, providing a
potential ``clock'' for dating low-mass stars.


  Retrieving indexed documents, not by their topical content but their writing
style opens the door for a number of applications in information retrieval
(IR). One application is to retrieve textual content of a certain author X,
where the queried IR system is provided beforehand with a set of reference
texts of X. Authorship verification (AV), which is a research subject in the
field of digital text forensics, is suitable for this purpose. The task of AV
is to determine if two documents (i.e. an indexed and a reference document)
have been written by the same author X. Even though AV represents a unary
classification problem, a number of existing approaches consider it as a binary
classification task. However, the underlying classification model of an AV
method has a number of serious implications regarding its prerequisites,
evaluability, and applicability. In our comprehensive literature review, we
observed several misunderstandings regarding the differentiation of unary and
binary AV approaches that require consideration. The objective of this paper
is, therefore, to clarify these by proposing clear criteria and new properties
that aim to improve the characterization of existing and future AV approaches.
Given both, we investigate the applicability of eleven existing unary and
binary AV methods as well as four generic unary classification algorithms on
two self-compiled corpora. Furthermore, we highlight an important issue
concerning the evaluation of AV methods based on fixed decision criterions,
which has not been paid attention in previous AV studies.


  We present observations of the Type Ia supernova 2003du and report the
detectionof an unusual, high-velocity component in the Ca II infrared triplet,
similar tofeatures previously observed in SN 2000cx and SN 2001el. This feature
exhibits a large expansion velocity (~18,000 km/s) which is nearly constant
between -7 and +2 days relative to maximum light, and disappears shortly
thereafter. Otherthan this feature, the spectral evolution and light curve
resemble those of a normal SN Ia.
  We find that the Ca II feature can plausibly be caused by a dense shell
formed when circumstellar material of solar abundance is overrun by the rapidly
expanding outermost layers of the SN ejecta. Model calculations show that the
optical and infrared spectra are remarkably unaffected by the circumstellar
interaction. In particular, no hydrogen lines are detectable in either
absorption or emission. The only qualitatively different features are the
strong, high-velocity feature in the Ca II IR-triplet, and a somewhat weaker O
I feature near 7,300 AA. The morphology and time evolution of these features
provide an estimate for the amount of accumulated matter and an indication of
the mixing in the dense shell. We apply these diagnostic tools to SN 2003du and
infer that about 2 x 10^{-2} M_sun of solar abundance material may have
accumulated in a circumstellar shell prior to the observations. Furthermore,
the early light curve data imply that the circumstellar material was originally
very close to the progenitor system, perhaps from an accretion disk, Roche lobe
or common envelope.


  We propose capacitively driven low-swing global interconnect circuit using a
receiver that utilizes magnetoelectric (ME) effect induced magnetization
switching to reduce the energy consumption. Capacitively driven wire has
recently been shown to be effective in improving the performance of global
interconnects. Such techniques can reduce the signal swing in the interconnect
by using a capacitive divider network and does not require an additional
voltage supply. However, the large reduction in signal swing makes it necessary
to use differential signaling and amplification for successful regeneration at
the receiver, which add area and static power. ME effect induced magnetization
reversal has recently been proposed which shows the possibility of using a low
voltage to switch a nanomagnet adjacent to a multi-ferroic oxide. Here, we
propose an ME effect based receiver that uses the low voltage at the receiving
end of the global wire to switch a nanomagnet. The nanomagnet is also used as
the free layer of a magnetic tunnel junction (MTJ), the resistance of which is
tuned through the ME effect. This change in MTJ resistance is converted to full
swing binary signals by using simple digital components. This process allows
capacitive low swing interconnection without differential signaling or
amplification, which leads to significant energy efficiency. Our simulation
results indicate that for 5-10 mm long global wires in IBM 45 nm technology,
capacitive ME design consumes 3x lower energy compared to full-swing CMOS
design and 2x lower energy compared to differential amplifier based low-swing
capacitive CMOS design.


  In this paper we consider the algebraic structures related to invariants of
topological structures introduced respectively in [CEKL22] and [ADEM19]. Our
main results is to show how all these structures are closely related to each
other using the language of binary operations.


  A new supersymmetric equation is proposed for the Sawada-Kotera equation. The
integrability of this equation is shown by the existence of Lax representation
and infinite conserved quantities and a recursion operator.


  We present new SOFIA [CII] and ALMA CO(J=1-0) observations of the nearby
asymmetric barred spiral galaxy NGC 7479. The data, which cover the whole bar
of the galaxy and the counter-arms visible in the radio continuum, are analyzed
in conjunction with a wealth of existing visible, infrared, radio, and X-ray
data. As in most normal galaxies, the [CII] emission is generally consistent
with emission from cooling gas excited by photoelectric heating in
photo-dissociation regions. However, anomalously high [CII]/CO ratios are seen
at the two ends of the counter-arms. Both ends show shell-like structures,
possibly bubbles, in H-alpha emission. In addition, the southern end has [CII]
to infrared emission ratios inconsistent with normal star formation. Because
there is little HI emission at this location, the [CII] emission probably
originates in warm shocked molecular gas heated by the interaction of the radio
jet forming the counter-arms with the interstellar medium in the galaxy. At two
other locations, the high [CII]/CO ratios provide evidence for the existence of
patches of CO-dark molecular gas. The [CII] and CO observations also reveal
resolved velocity components along the bar. In particular, the CO emission can
be separated into two components associated to gas along the leading edge of
the bar and gas trailing the bar. The trailing gas component that amounts to
approximately 40% of the gas around the bar region may be related to a minor
merger.


  Recent rapid loss of the Arctic sea ice motivates the study of the Arctic sea
ice thickness. Global climate model that describes the ice's thickness
evolution requires an accurate spatial temperature profile of the Arctic sea
ice. However, measuring the complete temperature profile is not feasible within
and throughout the Arctic icecap. Instead, measuring the ice's thickness is
doable with the acquisition of data from submarine and satellite devices. In
this paper, we develop a backstepping observer algorithm to estimate the
temperature profile for the Arctic sea ice model via available measurements of
sea ice thickness and sea ice surface temperature. The observer is designed in
a rigorous manner to drive the temperature profile estimation error to zero,
for a salinity-free sea ice model. Moreover, the proposed observer is used to
estimate the temperature profile of the original sea ice model with salinity
via numerical simulation. In comparison with the straightforward open-loop
algorithm, the simulation results illustrate that our observer design achieves
ten times faster convergence of the estimated temperature.


  General Relativity and the $\Lambda$CDM framework are currently the standard
lore and constitute the concordance paradigm. Nevertheless, long-standing open
theoretical issues, as well as possible new observational ones arising from the
explosive development of cosmology the last two decades, offer the motivation
and lead a large amount of research to be devoted in constructing various
extensions and modifications. All extended theories and scenarios are first
examined under the light of theoretical consistency, and then are applied to
various geometrical backgrounds, such as the cosmological and the spherical
symmetric ones. Their predictions at both the background and perturbation
levels, and concerning cosmology at early, intermediate and late times, are
then confronted with the huge amount of observational data that astrophysics
and cosmology are able to offer recently. Theories, scenarios and models that
successfully and efficiently pass the above steps are classified as viable and
are candidates for the description of Nature. This work is a Review of the
recent developments in the fields of gravity and cosmology, presenting the
state of the art, high-lighting the open problems, and outlining the directions
of future research. Its realization was performed in the framework of the COST
European Action ``Cosmology and Astrophysics Network for Theoretical Advances
and Training Actions''.


  The technological possibilities of a realistic eavesdropper are discussed.
Two eavesdropping strategies taking profit of multiphoton pulses in faint laser
QKD are presented. We conclude that, as long as storage of Qubits is
technically impossible, faint laser QKD is not limited by this security issue,
but mostly by the detector noise.


  We discuss the role of the trace part of metric fluctuations $h_{MN}$ in the
Randall-Sundrum scenario of gravity. Without the matter, this field
($h=\eta^{MN}h_{MN}$) is a gauge-dependent term, and thus it can be gauged
away. But, including the uniform source $\tilde{T}_{MN}$, this field satisfies
the linearized equation $\Box_4 h =16\pi G_5 T^{\mu}_{\mu}$. This may
correspond to the scalar $\xi^5$ in the bending of the brane due to the
localized source. Considering the case of longitudinal perturbations ($h_{5\mu}
=h_{55}=0$), one finds the source relation
$\tilde{T}^{\mu}_{\mu}=2\tilde{T}_{55}$, which leads to the ghost states in the
massive modes. In addition, if one requires $T_{44}=2(T_{22}+T_{33})$, it is
found that in the limit of $m^2_h \to 0$ we have the massless spin-2
propagation without the ghost state. This exactly corresponds to the same
situation as in the intermediate scales of Gregory-Rubakov-Sibiryakov (GRS)
model.


  The notion of almost symmetric numerical semigroup was given by V. Barucci
and R. Fr\"oberg. We characterize almost symmetric numerical semigroups by
symmetry of pseudo-Frobenius numbers. We give a criterion for $H^*$ (the dual
of $M$) to be almost symmetric numerical semigroup. Using these results we give
a formula for multiplicity of an opened modular numerical semigroups. Finally,
we show that if $H_1$ or $H_2$ is not symmetric, then the gluing of $H_1$ and
$H_2$ is not almost symmetric.


  This paper studies chemical kinetic systems which decompose into weakly
reversible complex factorizable (CF) systems. Among power law kinetic systems,
CF systems (denoted as PL-RDK systems) are those where branching reactions of a
reactant complex have identical rows in the kinetic order matrix. Mass action
and generalized mass action systems (GMAS) are well-known examples. Schmitz's
global carbon cycle model is a previously studied non-complex factorizable (NF)
power law system (denoted as PL-NDK). We derive novel conditions for the
existence of weakly reversible CF-decompositions and present an algorithm for
verifying these conditions. We discuss methods for identifying independent
decompositions, i.e., those where the stoichiometric subspaces of the
subnetworks form a direct sum, as such decompositions relate positive
equilibria sets of the subnetworks to that of the whole network. We then use
the results to determine the positive equilibria sets of PL-NDK systems which
admit an independent weakly reversible decomposition into PL-RDK systems of PLP
type, i.e., the positive equilibria are log-parametrized, which is a broad
generalization of a Deficiency Zero Theorem of Fortun et al. (2019).


  We describe all isometries of the $q$-numerical radius on the space
${\mathcal K}(\mathcal H)$ of compact operators on a (possibly
infinite-dimensional) Hilbert space $\mathcal H$.


  Learning-based methods especially with convolutional neural networks (CNN)
are continuously showing superior performance in computer vision applications,
ranging from image classification to restoration. For image classification,
most existing works focus on very clean images such as images in Caltech-256
and ImageNet datasets. However, in most realistic scenarios, the acquired
images may suffer from degradation. One important and interesting problem is to
combine image classification and restoration tasks to improve the performance
of CNN-based classification networks on degraded images. In this report, we
explore the influence of degradation types and levels on four widely-used
classification networks, and the use of a restoration network to eliminate the
degradation's influence. We also propose a novel method leveraging a fidelity
map to calibrate the image features obtained by pre-trained classification
networks. We empirically demonstrate that our proposed method consistently
outperforms the pre-trained networks under all degradation levels and types
with additive white Gaussian noise (AWGN), and it even outperforms the
re-trained networks for degraded images under low degradation levels. We also
show that the proposed method is a model-agnostic approach that benefits
different classification networks. Our results reveal that the proposed method
is a promising solution to mitigate the effect caused by image degradation.


  We introduce a new class of nonlocal kinetic equations and nonlocal
Fokker-Planck equations associated with an effective generalized
thermodynamical formalism. These equations have a rich physical and
mathematical structure that can describe phase transitions and blow-up
phenomena. On general grounds, our formalism can have applications in different
domains of physics, astrophysics, hydrodynamics and biology. We find an
aesthetic connexion between topics (stars, vortices, bacteries,...) which were
previously disconnected. The common point between these systems is the
(attractive) long-range nature of the interactions.


  Coronavirus Disease 2019 (COVID-19) has rapidly spread in 2020, emerging a
mass of studies for lung infection segmentation from CT images. Though many
methods have been proposed for this issue, it is a challenging task because of
infections of various size appearing in different lobe zones. To tackle these
issues, we propose a Graph-based Pyramid Global Context Reasoning (Graph-PGCR)
module, which is capable of modeling long-range dependencies among disjoint
infections as well as adapt size variation. We first incorporate graph
convolution to exploit long-term contextual information from multiple lobe
zones. Different from previous average pooling or maximum object probability,
we propose a saliency-aware projection mechanism to pick up infection-related
pixels as a set of graph nodes. After graph reasoning, the relation-aware
features are reversed back to the original coordinate space for the down-stream
tasks. We further construct multiple graphs with different sampling rates to
handle the size variation problem. To this end, distinct multi-scale long-range
contextual patterns can be captured. Our Graph-PGCR module is plug-and-play,
which can be integrated into any architecture to improve its performance.
Experiments demonstrated that the proposed method consistently boost the
performance of state-of-the-art backbone architectures on both of public and
our private COVID-19 datasets.


  This study explores the use of origami composite structures as active
aerodynamic control surfaces. Towards this goal, two origami concepts were
designed leveraging a combination of analytical and finite element modeling,
and computational fluid dynamics simulations. Wind tunnel tests were performed
at different dynamic pressures in conjunction with two different active control
laws to test the capability of obtaining desired drag values. The experiments
revealed excellent structural rigidity and folding characteristics under
aerodynamic loading. Future work will focus on developing advanced origami
designs that allow for more deterministic folding as well as improved weight,
stiffness, and fatigue characteristics in the use of materials. Upon completion
of these improvements, it is anticipated that full-scale testing on a vehicle
could be meaningfully conducted.


  We study the properties of coupled linear and nonlinear resonances. The
fundamental phenomena and the level crossing scenarios are introduced for a
nonlinear two-level system with one decaying state, describing the dynamics of
a Bose-Einstein condensate in a mean-field approximation (Gross-Pitaevskii or
nonlinear Schroedinger equation). An important application of the discussed
concepts is the dynamics of a condensate in tilted optical lattices. In
particular the properties of resonance eigenstates in double-periodic lattices
are discussed, in the linear case as well as within mean-field theory. The
decay is strongly altered, if an additional period-doubled lattice is
introduced. Our analytic study is supported by numerical computations of
nonlinear resonance states, and future applications of our findings for
experiments with ultracold atoms are discussed.


  Today digital revolution is having a dramatic impact on the pharmaceutical
industry and the entire healthcare system. The implementation of machine
learning, extreme-scale computer simulations, and big data analytics in the
drug design and development process offers an excellent opportunity to lower
the risk of investment and reduce the time to the patient.
  Within the LIGATE project, we aim to integrate, extend, and co-design
best-in-class European components to design Computer-Aided Drug Design (CADD)
solutions exploiting today's high-end supercomputers and tomorrow's Exascale
resources, fostering European competitiveness in the field.
  The proposed LIGATE solution is a fully integrated workflow that enables to
deliver the result of a virtual screening campaign for drug discovery with the
highest speed along with the highest accuracy. The full automation of the
solution and the possibility to run it on multiple supercomputing centers at
once permit to run an extreme scale in silico drug discovery campaign in few
days to respond promptly for example to a worldwide pandemic crisis.


  Increasing experimental evidence suggests that axonal action potential
conduction velocity is a highly adaptive parameter in the adult central nervous
system. Yet, the effects of this newfound plasticity on global brain dynamics
is poorly understood. In this work, we analyzed oscillations in biologically
plausible neuronal networks with different conduction velocity distributions.
Changes of 1-2 (ms) in network mean signal transmission time resulted in
substantial network oscillation frequency changes ranging in 0-120 (Hz). Our
results suggest that changes in axonal conduction velocity may significantly
affect both the frequency and synchrony of brain rhythms, which have well
established connections to learning, memory, and other cognitive processes.


  Over the past few years, the futures market has been successfully developing
in the North-West region. Futures markets are one of the most effective and
liquid-visible trading mechanisms. A large number of buyers are forced to
compete with each other and raise their prices. A large number of sellers make
them reduce prices. Thus, the gap between the prices of offers of buyers and
sellers is reduced due to high competition, and this is a good criterion for
the liquidity of the market. This high degree of liquidity contributed to the
fact that futures trading took such an important role in commerce and finance.
A multi-step, non-cooperative n persons game is formalized and studied


  In this paper, we have carried out the detail studies of pre-cancer by
wavelet coherency and multifractal based detrended fluctuation analysis (MFDFA)
on differential interference contrast (DIC) images of stromal region among
different grades of pre-cancer tissues. Discrete wavelet transform (DWT)
through Daubechies basis has been performed for identifying fluctuations over
polynomial trends for clear characterization and differentiation of tissues.
Wavelet coherence plots are performed for identifying the level of correlation
in time scale plane between normal and various grades of DIC samples. Applying
MFDFA on refractive index variations of cervical tissues, we have observed that
the values of Hurst exponent (correlation) decreases from healthy (normal) to
pre-cancer tissues. The width of singularity spectrum has a sudden degradation
at grade-I in comparison of healthy (normal) tissue but later on it increases
as cancer progresses from grade-II to grade-III.