  Novel experimental techniques are required to make the next big leap in
neutron electric dipole moment experimental sensitivity, both in terms of
statistics and systematic error control. The nEDM experiment at the Spallation
Neutron Source (nEDM@SNS) will implement the scheme of Golub & Lamoreaux [Phys.
Rep., 237, 1 (1994)]. The unique properties of combining polarized ultracold
neutrons, polarized $^3$He, and superfluid $^4$He will be exploited to provide
a sensitivity to $\sim 10^{-28}\,e{\rm \,\cdot\, cm}$. Our cryogenic apparatus
will deploy two small ($3\,{\rm L}$) measurement cells with a high density of
ultracold neutrons produced and spin analyzed in situ. The electric field
strength, precession time, magnetic shielding, and detected UCN number will all
be enhanced compared to previous room temperature Ramsey measurements. Our
$^3$He co-magnetometer offers unique control of systematic effects, in
particular the Bloch-Siegert induced false EDM. Furthermore, there will be two
distinct measurement modes: free precession and dressed spin. This will provide
an important self-check of our results. Following five years of "critical
component demonstration," our collaboration transitioned to a "large scale
integration" phase in 2018. An overview of our measurement techniques,
experimental design, and brief updates are described in these proceedings.


  Symplectic four-manifolds give rise to Lefschetz fibrations, which are
determined by monodromy representations of free groups in mapping class groups.
We study the topology of Lefschetz fibrations by analysing the action of the
monodromy on the universal cover of a smooth fibre. We give new and simple
proofs that Lefschetz fibrations arising from pencils (i.e. with exceptional
sections) never split as non-trivial fibre sums, and that no simple closed
curve can be invariant to isotopy under the monodromy representation.


  Cloud computing environment simulators enable cost-effective experimentation
of novel infrastructure designs and management approaches by avoiding
significant costs incurred from repetitive deployments in real Cloud platforms.
However, widely used Cloud environment simulators compromise on usability due
to complexities in design and configuration, along with the added overhead of
programming language expertise. Existing approaches attempting to reduce this
overhead, such as script-based simulators and Graphical User Interface (GUI)
based simulators, often compromise on the extensibility of the simulator.
Simulator extensibility allows for customization at a fine-grained level, thus
reducing it significantly affects flexibility in creating simulations. To
address these challenges, we propose an architectural framework to enable
human-readable script-based simulations in existing Cloud environment
simulators while minimizing the impact on simulator extensibility. We implement
the proposed framework for the widely used Cloud environment simulator, the
CloudSim toolkit, and compare it against state-of-the-art baselines using a
practical use case. The resulting framework, called CloudSim Express, achieves
extensible simulations while surpassing baselines with over a 71.43% reduction
in code complexity and an 89.42% reduction in lines of code.


  We investigate the determination of the Higgs-boson propagator poles in the
MSSM. Based upon earlier works, we point out that in case of a large hierarchy
between the electroweak scale and one or more SUSY masses a numerical
determination with $\overline{\text{DR}}$ Higgs field renormalization induces
higher order terms which would cancel in a more complete calculation. The
origin of these terms is the momentum dependence of contributions involving at
least one of the heavy particles. We present two different methods to avoid
their appearance. In the first approach, the poles are determined by expanding
around the one-loop solutions. In the second approach, a "heavy-OS" Higgs field
renormalization is employed in order to absorb the momentum dependence of heavy
contributions. We will find that the first approach leads to an unwanted
behaviour of the Higgs boson mass predictions close to crossing points where
two Higgs bosons that mix with each other are almost mass degenerate. These
problems are avoided in the second approach, which became the default approach
used in the public code FeynHiggs. Despite the discussion being very specific
to the MSSM, the argumentation and the methods presented in this work are
straightforwardly applicable to the determination of propagator poles in other
models involving a large mass hierarchy.


  For an elastic system that is non-conservative but autonomous, subjected for
example to time-independent loading by a steadily flowing fluid (air or water),
a dangerous bifurcation, such as a sub-critical bifurcation, or a cyclic fold,
will trigger a dynamic jump to one or more remote stable attractors. When there
is more than one candidate attractor, the one onto which the structure settles
can then be indeterminate, being sensitive to infinitesimally small variations
in starting conditions or parameters. In this paper we develop and study an
archetypal model to explore the nonlinear dynamic interactions between
galloping at an incipient sub-critical Hopf bifurcation of a structure with
shell-like buckling behaviour that is gravity-loaded to approach a sub-critical
pitch-fork bifurcation. For the fluid forces, we draw on the aerodynamic
coefficients determined experimentally by Novak for the flow around a bluff
body of rectangular cross-section. Meanwhile, for the structural component, we
consider a variant of the propped-cantilever model that is widely used to
illustrate the sub-critical pitchfork: within this model a symmetry-breaking
imperfection makes the behaviour generic. The compound bifurcation
corresponding to simultaneous galloping and buckling is the so-called
Takens-Bodganov Cusp. We make a full unfolding of this codimension-3
bifurcation for our archetypal model to explore the adjacent phase-space
topologies and their indeterminacies.


  Null and timelike equatorial orbits are investigated in a family of hairy
black holes in the cubic Galileon theory. These include rotating
generalizations of static black hole metrics supporting a time-dependent scalar
field. Depending on the coupling and rotation, the properties of the geodesics
expectedly deviate from general relativity. In particular, it is found that
stable circular geodesics only exist below a critical coupling, which is
related to the existence of an outermost stable circular orbit. Focusing on the
strong-field region, images of an accretion disk are also produced to highlight
tendencies that would constrain the model given further accurate observations
of supermassive black holes.


  Interferometry of the cosmic 21-cm signal is set to revolutionize our
understanding of the Epoch of Reionization (EoR), eventually providing 3D maps
of the early Universe. Initial detections however will be low signal-to-noise,
limited by systematics. To confirm a putative 21-cm detection, and check the
accuracy of 21-cm data analysis pipelines, it would be very useful to
cross-correlate against a genuine cosmological signal. The most promising
cosmological signals are wide-field maps of Lyman alpha emitting galaxies
(LAEs), expected from the Subaru Hyper-Suprime Cam (HSC) Ultra-Deep field. Here
we present estimates of the correlation between LAE maps at z~7 and the 21-cm
signal observed by both the Low Frequency Array (LOFAR) and the planned Square
Kilometer Array Phase 1 (SKA1). We adopt a systematic approach, varying both:
(i) the prescription of assigning LAEs to host halos; and (ii) the large-scale
structure of neutral and ionized regions (i.e. EoR morphology). We find that
the LAE-21cm cross-correlation is insensitive to (i), thus making it a robust
probe of the EoR. A 1000h observation with LOFAR would be sufficient to
discriminate at >1 standard deviation a fully ionized Universe from one with a
mean neutral fraction of xHI~0.50, using the LAE-21cm cross-correlation
function on scales of R~3-10 Mpc. Unlike LOFAR, whose detection of the LAE-21cm
cross-correlation is limited by noise, SKA1 is mostly limited by ignorance of
the EoR morphology. However, the planned 100h wide-field SKA1-Low survey will
be sufficient to discriminate an ionized Universe from one with xHI~0.25, even
with maximally pessimistic assumptions.


  We constrain the rest-frame FUV (1546\AA), NUV (2345\AA) and U-band (3690\AA)
luminosity functions (LFs) and luminosity densities (LDs) with unprecedented
precision from $z\sim0.2$ to $z\sim3$ (FUV, NUV) and $z\sim2$ (U-band). Our
sample of over 4.3 million galaxies, selected from the CFHT Large Area $U$-band
Deep Survey (CLAUDS) and HyperSuprime-Cam Subaru Strategic Program (HSC-SSP)
data lets us probe the very faint regime (down to
$M_\mathrm{FUV},M_\mathrm{NUV},M_\mathrm{U} \simeq -15$ at low redshift) while
simultaneously detecting very rare galaxies at the bright end down to comoving
densities $<10^{-5}$ Mpc$^{-3}$. Our FUV and NUV LFs are well fitted by single
Schechter functions, with faint-end slopes that are very stable up to $z\sim2$.
We confirm, but self-consistently and with much better precision than previous
studies, that the LDs at all three wavelengths increase rapidly with lookback
time to $z\sim1$, and then much more slowly at $1<z<2$--$3$. Evolution of the
FUV and NUV LFs and LDs at $z<1$ is driven almost entirely by the fading of the
characteristic magnitude, $M^\star_{UV}$, while at $z>1$ it is due to the
evolution of both $M^\star_{UV}$ and the characteristic number density
$\phi^\star_{UV}$. In contrast, the U-band LF has an excess of faint galaxies
and is fitted with a double-Schechter form; $M^\star_\mathrm{U}$, both
$\phi^\star_\mathrm{U}$ components, and the bright-end slope evolve throughout
$0.2<z<2$, while the faint-end slope is constant over at least the measurable
$0.05<z<0.6$. We present tables of our Schechter parameters and LD measurements
that can be used for testing theoretical galaxy evolution models and
forecasting future observations.


  Can topological quantum entanglement between anyons in one topological medium
"stray" into a different, topologically distinct medium? In other words, can
quantum information encoded non-locally in the combined state of non-Abelian
anyons be shared between two distinct topological media? We consider a setup
with two p-wave superconductors of opposite chirality and demonstrate that such
scenario is indeed possible. The information encoded in the fermionic parity of
two Majorana zero modes, originally within the same superconducting domain, can
be shared between the domains or moved entirely from one domain to another
provided that vortices can tunnel between them in a controlled fashion.


  Multi-core neuromorphic systems typically use on-chip routers to transmit
spikes among cores. These routers require significant memory resources and
consume a large part of the overall system's energy budget. A promising
alternative approach to using standard CMOS and SRAM-based routers is to
exploit the features of memristive crossbar arrays and use them as programmable
switch-matrices that route spikes. However, the scaling of these crossbar
arrays presents physical challenges, such as "IR drop" on the metal lines due
to the parasitic resistance, and leakage current accumulation on multiple
active memristors in their "off" state. While reliability challenges of this
type have been extensively studied in synchronous systems for compute-in-memory
matrix-vector multiplication (MVM) accelerators and storage class memory,
little effort has been devoted so far to characterizing the scaling limits of
memristor-based crossbar routers. Here, we study the challenges of memristive
crossbar arrays, when used as routing channels to transmit spikes in
asynchronous Spiking Neural Network (SNN) hardware. We validate our analytical
findings with experimental results obtained from a 4K-ReRAM chip which
demonstrates its functionality as a routing crossbar. We determine the
functionality bounds on the routing due to the IR drop and leak problem, based
on theoretical modeling, circuit simulations for a 22nm FDSOI technology, and
experimental measurements. This work highlights the limitations of this
approach and provides useful guidelines for engineering the memristor device
properties in memristive crossbar routers for multi-core asynchronous
neuromorphic systems.


  We propose a novel formulation of the "effectiveness problem" in
communications, put forth by Shannon and Weaver in their seminal work [2], by
considering multiple agents communicating over a noisy channel in order to
achieve better coordination and cooperation in a multi-agent reinforcement
learning (MARL) framework. Specifically, we consider a multi-agent partially
observable Markov decision process (MA-POMDP), in which the agents, in addition
to interacting with the environment can also communicate with each other over a
noisy communication channel. The noisy communication channel is considered
explicitly as part of the dynamics of the environment and the message each
agent sends is part of the action that the agent can take. As a result, the
agents learn not only to collaborate with each other but also to communicate
"effectively" over a noisy channel. This framework generalizes both the
traditional communication problem, where the main goal is to convey a message
reliably over a noisy channel, and the "learning to communicate" framework that
has received recent attention in the MARL literature, where the underlying
communication channels are assumed to be error-free. We show via examples that
the joint policy learned using the proposed framework is superior to that where
the communication is considered separately from the underlying MA-POMDP. This
is a very powerful framework, which has many real world applications, from
autonomous vehicle planning to drone swarm control, and opens up the rich
toolbox of deep reinforcement learning for the design of multi-user
communication systems.


  Due to the increasing demand on user data rates, future wireless
communication networks require higher spectral efficiency. To reach higher
spectral efficiencies, wireless network technologies collaborate and construct
a seamless interconnection between multiple tiers of architectures at the cost
of increased co-channel interference. To evaluate the performance of the
co-channel transmission based communication, we propose a new metric for area
spectral efficiency (ASE) of interference limited Ad-hoc network by assuming
that the nodes are randomly distributed according to a Poisson point processes
(PPP). We introduce a utility function, U = ASE/delay and derive the optimal
ALOHA transmission probability p and the SIR threshold \tau that jointly
maximize the ASE and minimize the local delay. Finally numerical results has
been conducted to confirm that the joint optimization based on the U metric
achieves a significant performance gain compared to conventional systems.


  Here we present HAMR-Jr, a \SI{22.5}{\milli\meter}, \SI{320}{\milli\gram}
quadrupedal microrobot. With eight independently actuated degrees of freedom,
HAMR-Jr is, to our knowledge, the most mechanically dexterous legged robot at
its scale and is capable of high-speed locomotion
(\SI{13.91}{bodylengths~\second^{-1}}) at a variety of stride frequencies
(\SI{1}{}-\SI{200}{\hertz}) using multiple gaits. We achieved this using a
design and fabrication process that is flexible, allowing scaling with minimum
changes to our workflow. We further characterized HAMR-Jr's open-loop
locomotion and compared it with the larger scale HAMR-VI microrobot to
demonstrate the effectiveness of scaling laws in predicting running
performance.


  III-V solar cells dominate the high efficiency charts, but with significantly
higher cost than other solar cells. Ultrathin III-V solar cells can exhibit
lower production costs and immunity to short carrier diffusion lengths caused
by radiation damage, dislocations, or native defects. Nevertheless, solving the
incomplete optical absorption of sub-micron layers presents a challenge for
light-trapping structures. Simple photonic crystals have high diffractive
efficiencies, which are excellent for narrow-band applications. Random
structures a broadband response instead but suffer from low diffraction
efficiencies. Quasirandom (hyperuniform) structures lie in between providing
high diffractive efficiency over a target wavelength range, broader than simple
photonic crystals, but narrower than a random structure. In this work, we
present a design method to evolve a simple photonic crystal into a quasirandom
structure by modifying the spatial-Fourier space in a controlled manner. We
apply these structures to an ultrathin GaAs solar cell of only 100 nm. We
predict a photocurrent for the tested quasirandom structure of 25.3 mA/cm$^2$,
while a planar structure would be limited to 16.1 mA/cm$^2$. The modified
spatial-Fourier space in the quasirandom structure increases the amount of
resonances, with a progression from discrete number of peaks to a continuum in
the absorption. The enhancement in photocurrent is stable under angle
variations because of this continuum. We also explore the robustness against
changes in the real-space distribution of the quasirandom structures using
different numerical seeds, simulating variations in a self-assembly method.


  We show how generalized quark distributions in the nucleon describe the
density of polarized quarks in the impact parameter plane, both for
longitudinal and transverse polarization of the quark and the nucleon. This
density representation entails positivity bounds including chiral-odd
distributions, which tighten the known bounds in the chiral-even sector. Using
the quark equations of motion, we derive relations between the moments of
chiral-odd generalized parton distributions of twist two and twist three. We
exhibit the analogy between polarized quark distributions in impact parameter
space and transverse momentum dependent distribution functions.


  The relation between ecological conditions and geomorphological factors is
considered the basis for species distribution in Romania. In this context, the
location of each species within parts of the mountain slopes is difficult on a
medium to brad scale level. The paper presents methodology to combine
vegetation data, obtained from IKONOS satellite images, and Digital Elevation
Model obtained from digitized topographic maps. The study area is a northern
slope of the Stanisoarei Mountains with a gradient of species from beech mixed
and coniferous stands.


  The purpose of this paper is to extend the non-invasive global/local
iterative coupling technique [15] to the case of large structures undergoing
nonlinear time-dependent evolutions at all scales. It appears that, due to the
use of legacy codes, the use of different time grids at the global and local
levels is mandatory in order to reach a satisfying level of precision. In this
paper two strategies are proposed and compared for elastoviscoplastic models.
The questions of the precision and performance of those schemes with respect to
a monolithic approach is addressed. The methods are first exposed on a 2D
example and then applied on a 3D part of industrial complexity.


  We study the transition between parallel and intersecting branes on a torus.
Spontaneous symmetry breaking of nonabelian gauge symmetry is understood as
brane separation, and a more general intermediate deformation is discussed. We
argue that there exists supersymmetry preserving transition and we can always
have parallel branes as a final state. The transition is interpreted due to
dynamics of the F- and D-string junctions and their generalization to (F, D$p$)
bound states. The gauge group and coupling unification is achieved, also as a
result of supersymmetry. From the tadpole cancelation condition, we naturally
have some class of intersecting brane models as broken phases of Type I theory
with SO(32) gauge group.


  U(1) gauge fields are decomposed into a monopole and photon part across the
phase transition from the confinement to the Coulomb phase. We analyze the
leading Lyapunov exponents of such gauge field configurations on the lattice
which are initialized by quantum Monte Carlo simulations. We observe that the
monopole field carries the same Lyapunov exponent as the original U(1) field.
As a long awaited result, we show that monopoles are created and annihilated in
pairs as a function of real time in excess to a fixed average monopole number.


  We propose a generative Causal Adversarial Network (CAN) for learning and
sampling from conditional and interventional distributions. In contrast to the
existing CausalGAN which requires the causal graph to be given, our proposed
framework learns the causal relations from the data and generates samples
accordingly. The proposed CAN comprises a two-fold process namely Label
Generation Network (LGN) and Conditional Image Generation Network (CIGN). The
LGN is a GAN-based architecture which learns and samples from the causal model
over labels. The sampled labels are then fed to CIGN, a conditional GAN
architecture, which learns the relationships amongst labels and pixels and
pixels themselves and generates samples based on them. This framework is
equipped with an intervention mechanism which enables. the model to generate
samples from interventional distributions. We quantitatively and qualitatively
assess the performance of CAN and empirically show that our model is able to
generate both interventional and conditional samples without having access to
the causal graph for the application of face generation on CelebA data.


  A tracking detector is proposed for lepton flavor violation experiments ($\mu
\to e$ conversion, $\mu \to e + \gamma$, $\mu \to 3e $) consisting of identical
chambers which can be reconfigured to meet the requirements for all three
experiments. A pattern recognition and track reconstruction procedure based on
the Kalman filter technique is presented for this detector.
  The pattern recognition proceeds in two stages. At the first stage only hit
straw tube center coordinates, without drift time information, are used to
reduce the background to a manageable level. At the second stage the drift time
information is incorporated and a deterministic annealing filter is applied to
reach the final level of background suppression. The final track momentum
reconstruction is provided by a combinatorial drop filter which is effective in
hit-to-track assignment.
  The momentum resolution of the tracker in measuring monochromatic leptons is
found to be $\sigma_{p}$ = 0.17 and 0.26 MeV for the $\mu \to e$ conversion and
$\mu^+ \to e^+ + \gamma$ processes, respectively. The tracker reconstruction
resolution for the total scalar lepton momentum is $\sigma_{p} = $ 0.33 MeV for
the $\mu \to 3e$ process. The obtained tracker resolutions allow an increase in
sensitivity to the branching ratios for these processes by a few orders of
magnitude over current experimental limits.


  We study high-dimensional estimators with the trimmed $\ell_1$ penalty, which
leaves the $h$ largest parameter entries penalty-free. While optimization
techniques for this nonconvex penalty have been studied, the statistical
properties have not yet been analyzed. We present the first statistical
analyses for $M$-estimation and characterize support recovery, $\ell_\infty$
and $\ell_2$ error of the trimmed $\ell_1$ estimates as a function of the
trimming parameter $h$. Our results show different regimes based on how $h$
compares to the true support size. Our second contribution is a new algorithm
for the trimmed regularization problem, which has the same theoretical
convergence rate as the difference of convex (DC) algorithms, but in practice
is faster and finds lower objective values. Empirical evaluation of $\ell_1$
trimming for sparse linear regression and graphical model estimation indicate
that trimmed $\ell_1$ can outperform vanilla $\ell_1$ and non-convex
alternatives. Our last contribution is to show that the trimmed penalty is
beneficial beyond $M$-estimation, and yields promising results for two deep
learning tasks: input structures recovery and network sparsification.


  A quasi-metric is a distance function which satisfies the triangle inequality
but is not symmetric: it can be thought of as an asymmetric metric. The central
result of this thesis, developed in Chapter 3, is that a natural correspondence
exists between similarity measures between biological (nucleotide or protein)
sequences and quasi-metrics.
  Chapter 2 presents basic concepts of the theory of quasi-metric spaces and
introduces a new examples of them: the universal countable rational
quasi-metric space and its bicompletion, the universal bicomplete separable
quasi-metric space. Chapter 4 is dedicated to development of a notion of the
quasi-metric space with Borel probability measure, or pq-space. The main result
of this chapter indicates that `a high dimensional quasi-metric space is close
to being a metric space'.
  Chapter 5 investigates the geometric aspects of the theory of database
similarity search in the context of quasi-metrics. The results about
$pq$-spaces are used to produce novel theoretical bounds on performance of
indexing schemes.
  Finally, the thesis presents some biological applications. Chapter 6
introduces FSIndex, an indexing scheme that significantly accelerates
similarity searches of short protein fragment datasets. Chapter 7 presents the
prototype of the system for discovery of short functional protein motifs called
PFMFind, which relies on FSIndex for similarity searches.


  Since the low quality of document images will greatly undermine the chances
of success in automatic text recognition and analysis, it is necessary to
assess the quality of document images uploaded in online business process, so
as to reject those images of low quality. In this paper, we attempt to achieve
document image quality assessment and our contributions are twofold. Firstly,
since document image quality assessment is more interested in text, we propose
a text line based framework to estimate document image quality, which is
composed of three stages: text line detection, text line quality prediction,
and overall quality assessment. Text line detection aims to find potential text
lines with a detector. In the text line quality prediction stage, the quality
score is computed for each text line with a CNN-based prediction model. The
overall quality of document images is finally assessed with the ensemble of all
text line quality. Secondly, to train the prediction model, a large-scale
dataset, comprising 52,094 text line images, is synthesized with diverse
attributes. For each text line image, a quality label is computed with a
piece-wise function. To demonstrate the effectiveness of the proposed
framework, comprehensive experiments are evaluated on two popular document
image quality assessment benchmarks. Our framework significantly outperforms
the state-of-the-art methods by large margins on the large and complicated
dataset.


  Studying the environments of 0.4<z<1.2 UV-selected galaxies, as examples of
extreme star-forming galaxies (with star formation rates in the range of 3-30
M_sol/yr), we explore the relationship between high rates of star-formation,
host halo mass and pair fractions. We study the large-scale and small-scale
environments of local Ultraviolet Luminous Galaxies (UVLGs) by measuring
angular correlation functions. We cross-correlate these systems with other
galaxy samples: a volume-limited sample (ALL), a Blue Luminous Galaxy sample
(BLG) and a Luminous Red Galaxy sample (LRG). We determine the UVLG comoving
correlation length to be r_0=4.8(+11.6/-2.4) h^-1 Mpc at <z> =1.0, which is
unable to constrain the halo mass for this sample. However, we find that UVLGs
form close (separation < 30 kpc) pairs with the ALL sample, but do not
frequently form pairs with LRGs. A rare subset of UVLGs, those with the highest
FUV surface brightnesses, are believed to be local analogs of high redshift
Lyman Break Galaxies (LBGs) and are called Lyman Break Analogs (LBAs). LBGs and
LBAs share similar characteristics (i.e., color, size, surface brightness,
specific star formation rates, metallicities, and dust content). Recent HST
images of z~0.2 LBAs show disturbed morphologies, signs of mergers and
interactions. UVLGs may be influenced by interactions with other galaxies and
we discuss this result in terms of other high star-forming, merging systems.


  We discuss the resonant activation phenomenon on a Josephson junction due to
the coupling of the Josephson system with axions. We show how such an effect
can be exploited for axion detection. A nonmonotonic behavior, with a minimum,
of the mean switching time from the superconducting to the resistive state
versus the ratio of the axion energy and the Josephson plasma energy is found.
We demonstrate how variations in switching times make it possible to detect the
presence of the axion field. An experimental protocol for observing axions
through their coupling with a Josephson system is proposed.


  Quantum technologies are moving towards the development of novel hardware
devices based on quantum bits (qubits). In parallel to the development of
quantum devices, efficient simulation tools are needed in order to design and
benchmark quantum algorithms and applications before deployment on quantum
hardware. In this context, we present a first attempt to perform circuit-based
quantum simulation using the just-in-time (JIT) compilation technique on
multiple hardware architectures and configurations based on single-node central
processing units (CPUs) and graphics processing units (GPUs). One of the major
challenges in scientific code development is to balance the level of complexity
between algorithms and programming techniques without losing performance or
degrading code readability. In this context, we have developed qibojit: a new
module for the Qibo quantum computing framework, which uses a just-in-time
compilation approach through Python. We perform systematic performance
benchmarks between our JIT approach and a subset of relevant publicly available
libraries for quantum computing. We show that our novel approach simplifies the
complex aspects of the implementation without deteriorating performance.


  We adapt Fluorescence Correlation spectroscopy (FCS) formalism to the studies
of the dynamics of semi-flexible polymers and derive expressions relating FCS
correlation function to the longitudinal and transverse mean square
displacements of polymer segments. We use the derived expressions to measure
the dynamics of actin filaments in two experimental situations: filaments
labeled at distinct positions and homogeneously labeled filaments. Both
approaches give consistent results and allow to measure the temporal dependence
of the segmental mean-square displacement (MSD) over almost five decades in
time, from ~0.04ms to 2s. These noninvasive measurements allow for a detailed
quantitative comparison of the experimental data to the current theories of
semi-flexible polymer dynamics. Good quantitative agreement is found between
the experimental results and theories explicitly accounting for the
hydrodynamic interactions between polymer segments.


  Refined Algebraic Quantization and Group Averaging are powerful methods for
quantizing constrained systems. They give constructive algorithms for
generating observables and the physical inner product. This work outlines the
current status of these ideas with an eye toward quantum gravity. The main goal
is provide a description of outstanding problems and possible research topics
in the field.


  Quantum resource theories provide a mathematically rigorous way of
understanding the nature of various quantum resources. An important problem in
any quantum resource theory is to determine how quantum states can be converted
into each other within the physical constraints of the theory. The standard
approach to this problem is to study approximate or probabilistic
transformations. Very few results have been presented on the intermediate
regime between probabilistic and approximate transformations. Here, we
investigate this intermediate regime, providing limits on both, the fidelity
and the probability of state transitions. We derive limitations on the
transformations, which are valid in all quantum resource theories, by providing
bounds on the maximal transformation fidelity for a given transformation
probability. As an application, we show that these bounds imply an upper bound
on the asymptotic rates for various classes of states under probabilistic
transformations. We also show that the deterministic version of the single copy
bounds can be applied for drawing limitations on the manipulation of quantum
channels, which goes beyond the previously known bounds of channel
manipulations. Furthermore, we completely solve the question of
stochastic-approximate state conversion via local operations and classical
communication in the following two cases: (i) Both initial and target states
are pure bipartite entangled states of arbitrary dimensions. (ii) The target
state is a two-qubit entangled state and the initial state is a pure bipartite
state.


  Filament identification became a key step to tackling fundamental problems in
various fields of Astronomy. Nevertheless, existing filament identification
algorithms are critically user-dependent and require individual
parametrization. In this study, we aimed at adapting the neural networks
approach to elaborate the best model for filament identification that would not
require fine-tuning for a given astronomical map. First, we created training
samples based on the most commonly used maps of the interstellar medium
obtained by Planck and Herschel space telescopes and the atomic hydrogen
all-sky survey HI4PI. We used the Rolling Hough Transform, a widely used
algorithm for filament identification, to produce training outputs. In the next
step, we trained different neural network models and discovered that a
combination of the Mask R-CNN and U-Net architecture is most appropriate for
filament identification and determination of their orientation angles. We
showed that neural network training might be performed efficiently on a
relatively small training sample of only around 100 maps. Our approach
eliminates the parametrization bias and facilitates filament identification and
angle determination on large data sets.


  We study continuous mappings on the Heisenberg group that up to a time change
preserve horizontal Brownian motion. It is proved that only harmonic morphisms
possess this property.


  A simple hot electron thermocouple is realized in a two-dimensional electron
system (2DES) and used to measure the diffusion thermopower of the 2DES at zero
magnetic field. This hot electron technique, which requires no micron-scale
patterning of the 2DES, is much less sensitive than conventional methods to
phonon-drag effects. Our thermopower results are in good agreement with the
Mott formula for diffusion thermopower for temperatures up to T ~ 2 K.


  The problem of inferring an inductive invariant for verifying program safety
can be formulated in terms of binary classification. This is a standard problem
in machine learning: given a sample of good and bad points, one is asked to
find a classifier that generalizes from the sample and separates the two sets.
Here, the good points are the reachable states of the program, and the bad
points are those that reach a safety property violation. Thus, a learned
classifier is a candidate invariant. In this paper, we propose a new algorithm
that uses decision trees to learn candidate invariants in the form of arbitrary
Boolean combinations of numerical inequalities. We have used our algorithm to
verify C programs taken from the literature. The algorithm is able to infer
safe invariants for a range of challenging benchmarks and compares favorably to
other ML-based invariant inference techniques. In particular, it scales well to
large sample sets.


  We present a neurobiologically--inspired stochastic cellular automaton whose
state jumps with time between the attractors corresponding to a series of
stored patterns. The jumping varies from regular to chaotic as the model
parameters are modified. The resulting irregular behavior, which mimics the
state of attention in which a systems shows a great adaptability to changing
stimulus, is a consequence in the model of short--time presynaptic noise which
induces synaptic depression. We discuss results from both a mean--field
analysis and Monte Carlo simulations.


  We develop a solution theory in H\"older spaces for a quasilinear stochastic
PDE driven by an additive noise. The key ingredients are two deterministic PDE
Lemmas which establish a priori H\"older bounds for an equation with irregular
right hand side written in divergence form. We apply these deterministic bounds
to the case of a noise term which is white in time and trace class in space to
obtain stretched exponential bounds for the H\"older semi-norms of the solution
for the stochastic equation.


  The clockwork mechanism has recently been proposed as a natural way to
generate hierarchies among parameters in quantum field theories. The mechanism
is characterized by a very specific pattern of spontaneous and explicit
symmetry breaking, and the presence of new light states referred to as `gears'.
In this paper we begin by investigating the self-interactions of these gears in
a scalar clockwork model and find a parity-like selection rule at all orders in
the fields. We then proceed to investigate how the clockwork mechanism can be
realized in 5D linear dilaton models from the spontaneous symmetry breaking of
a complex bulk scalar field. We also discuss how the clockwork mechanism is
manifest in the scalar components of 5D gauge theories in the linear dilaton
model, and build their 4D deconstructed analogue. Finally we discuss attempts
at building both 4D and 5D realizations of a non-abelian scalar clockwork
mechanism, where in the latter we consider scenarios in which the Goldstone
bosons arise from 5D scalar and 5D gauge fields.


  An unexpected Minkowskian solution of the equation of General Relativity
(Einstein-1915) is trivial because it simply means that both members of the
equation are equal to zero. However, if alternatively, one considers the
complete equation with a non-zero (cosmological constant (Einstein-1917), a
Minkowskian solution is no longer trivial because it amounts to impose a
constraint on the right hand side of the equation (i.e. a non-null
stress-energy tensor). If furthermore one identifies (as usual) this tensor to
the one of a perfect fluid, one finds that this fluid has a positive energy
density and a negative pressure. We discover finally an expanding Universal
Minkowskian fluid (Flat Universe) that has not only dynamical properties
(acceleration) but also optical properties that are connected with Doppler
Redshift. Einstein special relativity in 1905 consisted in dissolving a ghost:
the old electromagnetic ether; our relativistic approach involves also the
dissolution of a ghost: the Dark Dnergy. This new cosmological ether becomes a
pure relativistic effect of Minkowskian solution with CC.


  As part of the NASA Kepler Guest Observer program, we requested and obtained
long-cadence data on about 2700 faint (magnitude 14-16) Kepler stars with
effective temperatures and surface gravities that lie near or within the
pulsation instability region for main-sequence gamma Doradus and delta Scuti
pulsating variables. These variables are of spectral type A-F with masses of
1.4 to 2.5 solar masses. The delta Scuti stars pulsate in radial and non-radial
acoustic modes, with periods of a few hours (frequencies around 10 cycles/day),
while gamma Doradus variables pulsate in nonradial gravity modes with periods
0.3 to 3 days (frequencies around 1 cycle/day). Here we consider the light
curves and Fourier transforms of 633 stars in an unbiased sample observed by
Kepler in Quarters 6-13 (June 2010-June 2012). We show the location of these
stars in the log surface gravity--effective temperature diagram compared to the
instability region limits established from ground-based observations, and
taking into account uncertainties and biases in the Kepler Input Catalog T_eff
values. While hundreds of variables have been discovered in the Kepler data,
about 60% of the stars in our sample do not show any frequencies between 0.2
and 24.4 cycles per day with amplitude above 20 parts per million. We find that
six of these apparently constant stars lie within the pulsation instability
region. We discuss some possible reasons that these stars do not show
photometric variability in the Kepler data. We also comment on the non-constant
stars, and on 26 variable-star candidates, many of which also do not lie within
the expected instability regions.


  We report on the degree of order of the vortex solid in YBa2Cu3O7 single
crystals observed in ac susceptibility measurements. We show that when vortices
are "shaken" by a temporarily symmetric ac field they are driven into an
easy-to-move, ordered structure but, on the contrary, when the ac field is
temporarily asymmetric, they are driven into a more pinned disordered state.
This is characteristic of tearing of the vortex lattice and shows that ordering
due to symmetric ac fields is essentially different from an equilibration
process or a dynamical crystallization that is expected to occur at high
driving currents.


  We compute the orbital magnetization in real materials by evaluating a
recently discovered formula for periodic systems, within density functional
theory. We obtain improved values of the orbital magnetization in the
ferromagnetic metals Fe, Co, and Ni, by taking into account the contribution of
the interstitial regions neglected so far in literature. We also use the
orbital magnetization to compute the EPR $g$-tensor in molecules and solids.
The present approach reproduces the $g$-tensor obtained by linear response
(LR), when the spin-orbit can be treated as a perturbation. However, it can
also be applied to radicals and defects with an orbital-degenerate ground-state
or containing heavy atoms, that can not be properly described by LR.


  We analyze quantum network primitives which are entanglement-breaking. We
show superadditivity of quantum and classical capacity regions for quantum
multiple-access channel and quantum butterfly network. Since the effects are
especially visible at high noise they suggest that quantum information effects
may be particularly helpful in the case of the networks with occasional high
noise rates. To our knowledge the present effects provide the first qualitative
borderline between superadditivities of bipartite and multipartite systems.


  We investigate the properties of interference fragmentation functions
measurable from the distribution of two hadrons produced in the same jet in the
current fragmentation region of a hard process. We discuss the azimuthal
angular dependences in the leading order cross section of two-hadron inclusive
lepton-nucleon scattering as an example how these interference fragmentation
functions can be addressed separately.


  We investigate the task of inserting new concepts extracted from texts into
an ontology using language models. We explore an approach with three steps:
edge search which is to find a set of candidate locations to insert (i.e.,
subsumptions between concepts), edge formation and enrichment which leverages
the ontological structure to produce and enhance the edge candidates, and edge
selection which eventually locates the edge to be placed into. In all steps, we
propose to leverage neural methods, where we apply embedding-based methods and
contrastive learning with Pre-trained Language Models (PLMs) such as BERT for
edge search, and adapt a BERT fine-tuning-based multi-label Edge-Cross-encoder,
and Large Language Models (LLMs) such as GPT series, FLAN-T5, and Llama 2, for
edge selection. We evaluate the methods on recent datasets created using the
SNOMED CT ontology and the MedMentions entity linking benchmark. The best
settings in our framework use fine-tuned PLM for search and a multi-label
Cross-encoder for selection. Zero-shot prompting of LLMs is still not adequate
for the task, and we propose explainable instruction tuning of LLMs for
improved performance. Our study shows the advantages of PLMs and highlights the
encouraging performance of LLMs that motivates future studies.


  In this paper we determine the metric dimension of Andrasfai graphs and some
related graphs.


  In this paper, an Empirical Mode Decomposition-based method is proposed for
the detection of transformer faults from Dissolve gas analysis (DGA) data.
Ratio-based DGA parameters are ranked using their skewness. Optimal sets of
intrinsic mode function coefficients are obtained from the ranked DGA
parameters. A Hierarchical classification scheme employing XGBoost is presented
for classifying the features to identify six different categories of
transformer faults. Performance of the Proposed Method is studied for publicly
available DGA data of 377 transformers. It is shown that the proposed method
can yield more than 90% sensitivity and accuracy in the detection of
transformer faults, a superior performance as compared to conventional methods
as well as several existing machine learning-based techniques.


  In this work, $\pi$, $\sigma^+$ and $\sigma^-$ transitions between magnetic
sublevels of the $D_1$ line of all alkali atoms are considered analytically.
General block Hamiltonian matrices in presence of a magnetic field for the
ground and excited states are built in order to describe all the transitions.
Eigenvalues and eigenkets describing ground and excited levels are calculated,
"modified" and unperturbed transfer coefficients as a function of the nuclear
spin $I$, the magnetic quantum number $m$ and the magnetic field magnitude $B$
are defined. Transition cancellations are observed only for some $\pi$
transitions of each isotope. The main result is that we obtain one single
formula which expresses the magnetic field values cancelling these transitions.
These values also correspond to the case when some of other transitions
intensity have their maximum. In addition, we examine the derivative of $\pi$
transition "modified" transfer coefficients in order to find the magnetic field
values which correspond to the intensities maximum. The accuracy of the
magnetic field $B$ values is only limited by the uncertainty of the involved
physical quantities.


  Results are presented for the dynamics of an almost strong edge mode which is
the quasi-stable Majorana edge mode occurring in non-integrable spin chains.
The dynamics of the edge mode is studied using exact diagonalization, and
compared with time-evolution with respect to an effective semi-infinite model
in Krylov space obtained from the recursion method. The effective Krylov
Hamiltonian is found to resemble a spatially inhomogeneous SSH model where the
hopping amplitude increases linearly with distance into the bulk, typical of
thermalizing systems, but also has a staggered or dimerized structure
superimposed on it. The non-perturbatively long lifetime of the edge mode is
shown to be due to this staggered structure which diminishes the effectiveness
of the linearly growing hopping amplitude. On taking the continuum limit of the
Krylov Hamiltonian, the edge mode is found to be equivalent to the quasi-stable
mode of a Dirac Hamiltonian on a half line, with a mass which is non-zero over
a finite distance, before terminating into a gapless metallic bulk. The
analytic estimates are found to be in good agreement with the numerically
obtained lifetimes of the edge mode.


  This paper introduces a solution to the problem of selecting dynamically
(online) the ``optimal'' p-norm to combat outliers in linear adaptive filtering
without any knowledge on the probability density function of the outliers. The
proposed online and data-driven framework is built on kernel-based
reinforcement learning (KBRL). To this end, novel Bellman mappings on
reproducing kernel Hilbert spaces (RKHSs) are introduced. These mappings do not
require any knowledge on transition probabilities of Markov decision processes,
and are nonexpansive with respect to the underlying Hilbertian norm. The
fixed-point sets of the proposed Bellman mappings are utilized to build an
approximate policy-iteration (API) framework for the problem at hand. To
address the ``curse of dimensionality'' in RKHSs, random Fourier features are
utilized to bound the computational complexity of the API. Numerical tests on
synthetic data for several outlier scenarios demonstrate the superior
performance of the proposed API framework over several non-RL and KBRL schemes.


  The introduction of deep learning and transfer learning techniques in fields
such as computer vision allowed a leap forward in the accuracy of image
classification tasks. Currently there is only limited use of such techniques in
neuroscience. The challenge of using deep learning methods to successfully
train models in neuroscience, lies in the complexity of the information that is
processed, the availability of data and the cost of producing sufficient high
quality annotations. Inspired by its application in computer vision, we
introduce transfer learning on electrophysiological data to enable training a
model with limited amounts of data. Our method was tested on the dataset of the
BCI competition IV 2a and compared to the top results that were obtained using
traditional machine learning techniques. Using our DL model we outperform the
top result of the competition by 33%. We also explore transferability of
knowledge between trained models over different experiments, called
inter-experimental transfer learning. This reduces the amount of required data
even further and is especially useful when few subjects are available. This
method is able to outperform the standard deep learning methods used in the BCI
competition IV 2b approaches by 18%. In this project we propose a method that
can produce reliable electroencephalography (EEG) signal classification, based
on modest amounts of training data through the use of transfer learning.


  We show that quantum mechanics can be represented as an asymptotic projection
of statistical mechanics of classical fields. Thus our approach does not
contradict to a rather common opinion that quantum mechanics could not be
reduced to statistical mechanics of classical particles. Notions of a system
and causality can be reestablished on the prequantum level, but the price is
sufficiently high -- the infinite dimension of the phase space. In our approach
quantum observables, symmetric operators in the Hilbert space, are obtained as
derivatives of the second order of functionals of classical fields. Statistical
states are given by Gaussian ensembles of classical fields with zero mean value
(so these are vacuum fluctuations) and dispersion $\alpha$ which plays the role
of a small parameter of the model (so these are small vacuum fluctuations). Our
approach might be called {\it Prequantum Classical Statistical Field Theory} -
PCSFT. Our model is well established on the mathematical level. However, to
obtain concrete experimental predictions -- deviations of real experimental
averages from averages given by the von Neumann trace formula - we should find
the energy scale $\alpha$ of prequantum classical fields.


  Strangeness photoproduction in the elementary reactions $\gamma N\rightarrow
KY$ offers the possibility to study nucleon resonances in the mass region above
1.7 GeV, where the number of states and their properties are still under
debate. These reactions could allow the first 'complete' experiment in
pseudoscalar meson photoproduction due to the easier access to recoil
polarization observables in experiments. In addition to measurements on proton
targets, a full isospin decomposition of the $I=1/2$ electromagnetic amplitudes
requires also data obtained from quasifree neutron targets. This contribution
shows first preliminary results on feasibility studies for such measurements at
the A2 experiment at MAMI.


  In differential algebra, a proper scheme $X$ defined over an algebraically
closed field $K$ with a derivation $\partial$ on it descends to the field of
constants $K^\partial$ if $X$ itself lifts the derivation $\partial$. This is a
result by A. Buium. Now in the arithmetic case, the notion of a derivation is
replaced by the notion of a $\pi$-derivation $\delta$ or equivalently in the
flat case, a lift of Frobenius $\phi$. We will show an analogous result in the
arithmetic case of equal characteristic. We show our results using the
arithmetic analogue of Taylor expansion using Witt vectors.


  Photon echo schemes are excellent candidates for high efficiency coherent
optical memory. They are capable of high-bandwidth multi-pulse storage, pulse
resequencing and have been shown theoretically to be compatible with quantum
information applications. One particular photon echo scheme is the gradient
echo memory (GEM). In this system, an atomic frequency gradient is induced in
the direction of light propagation leading to a Fourier decomposition of the
optical spectrum along the length of the storage medium. This Fourier encoding
allows precision spectral manipulation of the stored light. In this letter, we
show frequency shifting, spectral compression, spectral splitting, and fine
dispersion control of optical pulses using GEM.


  In this paper we have introduced a new symmetry property of spacetime which
is named as semiconformal curvature collineation, and its relationship with
other known symmetry properties has been established. This new symmetry
property of the spacetime has also been studied for non-null and null
electromagnetic fields.


  We study the boundary behavior of any limit-interface arising from a sequence
of general critical points of the Allen-Cahn energy functionals on a smooth
bounded domain. Given any such sequence with uniform energy bounds, we prove
that the limit-interface is a free boundary varifold which is integer
rectifiable up to the boundary. This extends earlier work of Hutchinson and
Tonegawa on the interior regularity of such limit-interface. A key novelty in
our result is that no convexity assumption of the boundary is required and it
is valid even when the limit-interface clusters near the boundary. Moreover,
our arguments are local and thus works in the Riemannian setting. This work
provides the first step towards the regularity theory for the Allen-Cahn
min-max theory for free boundary minimal hypersurfaces, which was developed in
the Almgren-Pitts setting by the first-named author and Zhou.


  We introduce an algorithmic framework to investigate spherical and geodesic
growth series of braid groups relatively to the Artin's or Birman-Ko-Lee's
generators. We present our experimentations in the case of three and four
strands and conjecture rational expressions for the spherical growth series
with respect to the Birman-Ko-Lee's generators.


  The solar activity in the current, that is, the 24-th, sunspot cycle is
analyzed. Cyclic variations in the sunspot number (SSN) and radiation fluxes in
various spectral ranges have been estimated in comparison with the general
level of the solar radiation, which is traditionally determined by the radio
emission flux $F_ {10.7} $ at a wavelength of 10.7 cm (2.8 GHz). The
comparative analysis of the variations in the solar constant and solar indices
in the UV range, which are important for modeling the state of the Earth's
atmosphere, in the weak 24th cycle and strong 22nd and 23rd cycles showed
relative differences in the amplitudes of variations from the minimum to the
maximum of the cycle. The influence of the hysteresis effect between the
activity indices and $F_ {10.7} $ in the 24-th cycle, which is considered here,
makes it possible to refine the forecast of the UV indices and solar constant
depending on the quadratic regression coefficients that associate the solar
indices with $F_ {10.7} $ depending on the phase of the cycle.


  We discuss the similarities and differences for the theories of Rapp, Wambach
and collaborators (called R/W in short) and those based on Brown-Rho scaling
(called B/R), as applied to reproduce the dileptons measured by the CERES
collaboration in the CERN experiments. In both theories the large number of
dileptons at invariant masses $\sim$~$m_\rho/2$ are shown to be chiefly
produced by a density-dependent $\rho$-meson mass. In R/W the medium dependence
is dynamically calculated using hadronic variables defined in the matter-free
vacuum. In B/R scaling it follows from movement towards chiral symmetry
restoration due to medium-induced vacuum change, and is described in terms of
constituent (or quasiparticle) quarks. We argue that the R/W description should
be reliable up to densities somewhat beyond nuclear density, where hadrons are
the effective variables. At higher density there should be a crossover to
constituent quarks as effective variables scaling according to B/R. In the
crossover region, the two descriptions must be ``dual''.


  We analyze the f_0(1810) state recently observed by the BES collaboration via
radiative J/\psi decay to a resonant \phi\omega spectrum and confront it with
DM2 data and glueball theory. The DM2 group only measured \omega\omega decays
and reported a pseudoscalar but no scalar resonance in this mass region. A
rescattering mechanism from the open flavored KKbar decay channel is considered
to explain why the resonance is only seen in the flavor asymmetric \omega\phi
branch along with a discussion of positive C parity charmonia decays to
strengthen the case for preferred open flavor glueball decays. We also
calculate the total glueball decay width to be roughly 100 MeV, in agreement
with the narrow, newly found f_0, and smaller than the expected estimate of
200-400 MeV. We conclude that this discovered scalar hadron is a solid glueball
candidate and deserves further experimental investigation, especially in the
K-Kbar channel. Finally we comment on other, but less likely, possible
assignments for this state.


  We study and characterize stability, NIP and NSOP in terms of topological and
measure theoretical properties of classes of functions. We study a measure
theoretic property, `Talagrand's stability', and explain the relationship
between this property and NIP in continuous logic. Using a result of Bourgain,
Fremlin and Talagrand, we prove the `almost definability' and `Baire~1
definability' of coheirs assuming NIP. We show that a formula $\phi(x,y)$ has
the strict order property if and only if there is a convergent sequence of
continuous functions on the space of $\phi$-types such that its limit is not
continuous. We deduce from this a theorem of Shelah and point out the
correspondence between this theorem and the Eberlein-\v{S}mulian theorem.


  In this paper we consider the class of lambda-nondeterministic linear
automata as a model of the class of linear languages. As usual in other
automata models, lambda-moves do not increase the acceptance power. The main
contribution of this paper is to introduce the deterministic linear automata
and even linear automata, i.e. the natural restriction of nondeterministic
linear automata for the deterministic and even linear language classes,
respectively. In particular, there are different, but not equivalents,
proposals for the class of "deterministic" linear languages. We proved here
that the class of languages accepted by the deterministic linear automata are
not contained in any of the these classes and in fact they properly contain
these classes. Another, contribution is the generation of an infinite hierarchy
of formal languages, going from the class of languages accepted by
deterministic linear automata and achieved, in the limit, the class of linear
languages.


  We give a generalization of the Shestakov-Umirbaev inequality which plays an
important role in their solution of the tame generators conjecture.


  We obtain non-asymptotic Gaussian concentration bounds for the difference
between the invariant measure $\nu$ of an ergodic Brownian diffusion process
and the empirical distribution of an approximating scheme with decreasing time
step along a suitable class of (smooth enough) test functions f such that f --
$\nu$(f) is a coboundary of the infinitesimal generator. We show that these
bounds can still be improved when the (squared) Fr{\"o}benius norm of the
diffusion coefficient lies in this class. We apply these bounds to design
computable non-asymptotic confidence intervals for the approximating scheme. As
a theoretical application, we finally derive non-asymptotic deviation bounds
for the almost sure Central Limit Theorem.


  Liquid-liquid extraction based on surface nanodroplets, namely
nanoextraction, can continuously extract and enrich target analytes from the
flow of a sample solution. This sample preconcentration technique is easy to
operate in a continuous flow system with low organic solvent consumption and a
high enrichment factor. In this review, the evolution from single drop
microextraction to advanced nanoextraction will be briefly introduced. Also,
the formation principle and key features of surface nanodroplets will be
summarized. Further, the major findings of nanoextraction combined with
in-droplet chemistry toward sensitive and quantitative detection will be
discussed. Finally, we will give our perspectives on the future trend of
nanoextraction.


  Shape-programmable soft materials that exhibit integrated multifunctional
shape manipulations, including reprogrammable, untethered, fast, and reversible
shape transformation and locking, are highly desirable for a plethora of
applications, including soft robotics, morphing structures, and biomedical
devices. Despite recent progress, it remains challenging to achieve multiple
shape manipulations in one material system. Here, we report a novel magnetic
shape memory polymer composite to achieve this. The composite consists of two
types of magnetic particles in an amorphous shape memory polymer matrix. The
matrix softens via magnetic inductive heating of low-coercivity particles, and
high-remanence particles with reprogrammable magnetization profiles drive the
rapid and reversible shape change under actuation magnetic fields. Once cooled,
the actuated shape can be locked. Additionally, varying the particle loadings
for heating enables sequential actuation. The integrated multifunctional shape
manipulations are further exploited for applications including soft magnetic
grippers with large grabbing force, sequential logic for computing, and
reconfigurable antennas.
  Keyword: shape memory polymers, soft active materials, magnetic soft
material, soft robotics, soft material computing


  I will discuss hadron-hadron collisions where the final state is
kinematically of the kind associated with double-pomeron-exchange (DPE) and has
large transverse momentum jets. I will show that in addition to the
conventional factorized (FDPE) contribution, there is a non-factorized (NDPE)
contribution which has no pomeron beam jet. Results of calculations from a
simple model of DPE for two-jet total and differential cross sections at
Tevatron energy scales are presented. The NDPE contribution is dominant.


  Broadening the variety of two-dimensional (2D) materials and improving the
synthesis of ultrathin films are crucial to the development of the
semiconductor industry. As a state-of-the-art 2D material, Ga2Se2 has
attractive optoelectronic properties when it reaches the atomically-thin
regime. However, its van der Waals epitaxial growth, especially for the
atomically-thin films, has seldom been studied. In this paper, we used
molecular beam epitaxy to synthesize Ga2Se2 single-crystal films with a surface
roughness down to 1.82 nm on c-plane sapphire substrates by optimizing
substrate temperature, Se:Ga flux ratio, and growth rate. Then we used a 3-step
mode to grow Ga2Se2 films with a thickness as low as 3 tetralayers and a
surface roughness as low as 0.61 nm, far exceeding the performance of direct
growth. Finally, we found that the surface morphology strongly depends on the
Se:Ga flux ratio, and higher growth rates widened the suitable flux ratio
window for growing Ga2Se2. Overall, this work advances the understanding of the
vdW epitaxy growth mechanism for post-transition metal monochalcogenides on
sapphire substrates.


  To enhance Large Language Models' (LLMs) reliability, calibration is
essential -- the model's assessed confidence scores should align with the
actual likelihood of its responses being correct. However, current confidence
elicitation methods and calibration metrics typically rely on a binary
true/false assessment of response correctness. This approach does not apply to
long-form generation, where an answer can be partially correct. Addressing this
gap, we introduce a unified calibration framework, in which both the
correctness of the LLMs' responses and their associated confidence levels are
treated as distributions across a range of scores. Within this framework, we
develop three metrics to precisely evaluate LLM calibration and further propose
two confidence elicitation methods based on self-consistency and
self-evaluation. Our experiments, which include long-form QA and summarization
tasks, demonstrate that larger models don't necessarily guarantee better
calibration, that calibration performance is found to be metric-dependent, and
that self-consistency methods excel in factoid datasets. We also find that
calibration can be enhanced through techniques such as fine-tuning, integrating
relevant source documents, scaling the temperature, and combining
self-consistency with self-evaluation. Lastly, we showcase a practical
application of our system: selecting and cascading open-source models and
ChatGPT to optimize correctness given a limited API budget. This research not
only challenges existing notions of LLM calibration but also offers practical
methodologies for improving trustworthiness in long-form generation.


  Transitional pulsars provide us with a unique laboratory to study the physics
of accretion onto a magnetic neutron star. PSR J1023+0038 (J1023) is the best
studied of this class. We investigate the X-ray spectral properties of J1023 in
the framework of a working radio pulsar during the active state. We modelled
the X-ray spectra in three modes (low, high, and flare) as well as in
quiescence, to constrain the emission mechanism and source parameters. The
emission model, formed by an assumed pulsar emission (thermal and
magnetospheric) plus a shock component, can account for the data only adding a
hot dense absorber covering ~30% of the emitting source in high mode. The
covering fraction is similar in flaring mode, thus excluding total enshrouding,
and decreases in the low mode despite large uncertainties. This provides
support to the recently advanced idea of a mini-pulsar wind nebula (PWN), where
X-ray and optical pulsations arise via synchrotron shock emission in a very
close (~100 km, comparable to a light cylinder), PWN-like region that is
associated with this hot absorber. In low mode, this region may expand,
pulsations become undetectable, and the covering fraction decreases.


  The estimation of EEG generating sources constitutes an Inverse Problem (IP)
in Neuroscience. This is an ill-posed problem, due to the non-uniqueness of the
solution, and many kinds of prior information have been used to constrain it. A
combination of smoothness (L2 norm-based) and sparseness (L1 norm-based)
constraints is a flexible approach that have been pursued by important examples
such as the Elastic Net (ENET) and mixed-norm (MXN) models. The former is used
to find solutions with a small number of smooth non-zero patches, while the
latter imposes sparseness and smoothness simultaneously along different
dimensions of the spatio-temporal matrix solutions. Both models have been
addressed within the penalized regression approach, where the regularization
parameters are selected heuristically, leading usually to non-optimal
solutions. The existing Bayesian formulation of ENET allows hyperparameter
learning, but using computationally intensive Monte Carlo/Expectation
Maximization methods. In this work we attempt to solve the EEG IP using a
Bayesian framework for models based on mixtures of L1/L2 norms penalization
functions (Laplace/Normal priors) such as ENET and MXN. We propose a Sparse
Bayesian Learning algorithm based on combining the Empirical Bayes and the
iterative coordinate descent procedures to estimate both the parameters and
hyperparameters. Using simple but realistic simulations we found that our
methods are able to recover complicated source setups more accurately and with
a more robust variable selection than the ENET and LASSO solutions using
classical algorithms. We also solve the EEG IP using data coming from a visual
attention experiment, finding more interpretable neurophysiological patterns
with our methods, as compared with other known methods such as LORETA, ENET and
LASSO FUSION using the classical regularization approach.


  We probe, using the Surface Forces Apparatus, the thermal response of
poly(N-isopropylacrylamide) (PNIPAM) brushes of various grafting densities,
grown from plasma-activated mica by means of surface-initiated polymerization.
We thus show that dense thermoresponsive brushes collapse gradually as
temperature is increased, and that grafting density greatly affects their
ability to swell: the swelling ratio of the brushes, which characterizes the
thickness variation between the swollen and the collapsed state, is found to
decrease from $\sim 7$ to $\sim 3$ as the number of grafted chains per unit
area increases. Such a result, obtained with an unprecedent resolution in
grafting density, provides qualitative support to calculations by Mendez {\it
et al.} [{\it Macromolecules} {\bf 2005} {\it 38}, 174]. We further show that,
in contrast to swelling, adhesion between two PNIPAM brushes appears to be
rather insensitive to their molecular structure.


  We present U, B, V, I broad-band, 6200A TiO medium-band and Halpha photometry
of the Orion Nebula Cluster obtained with the WFI imager at the ESO/MPI 2.2
telescope. The nearly-simultaneous observations cover the entire ONC in a field
of about 34x34 arcmin. They enable us to determine stellar colors avoiding the
additional scatter in the photometry induced by stellar variability typical of
pre-main sequence stars. We identify 2,612 point-like sources in I band, 58%,
43% and 17% of them detected also in V, B and U, respectively. 1040 sources are
identified in Halpha band. In this paper we present the observations, the
calibration techniques, and the resulting catalog. We show the derived CMD of
the population and discuss the completeness of our photometry. We define a
spectro-photometric TiO index from the fluxes in V, I, and TiO-band. We find a
correlation between the index and the spectral type valid for M-type stars,
that is accurate to better than 1 spectral sub-class for M3-M6 types and better
than 2 spectral subclasses for M0-M2 types. This allows us to newly classify
217 stars. We subtract from our Halpha photometry the photospheric continuum at
its wavelength, deriving calibrated line excess for the full sample. This
represents the largest Halpha star catalog obtained to date on the ONC. This
data set enables a full re-analysis of the properties of the Pre-Main Sequence
population in the Orion Nebula Cluster to be presented, in an accompanying
paper.


  A next step in development of the KrkNLO method of including complete NLO QCD
corrections to hard processes in a LO parton-shower Monte Carlo (PSMC) is
presented. It consists of generalisation of the method, previously used for the
Drell-Yan process, to Higgs-boson production. This extension is accompanied
with the complete description of parton distribution functions (PDFs) in a
dedicated, Monte Carlo (MC) factorisation scheme, applicable to any process of
production of one or more colour-neutral particles in hadron-hadron collisions.


  The annihilation of dark matter particles in the galactic halo of the Milky
Way may lead to cosmic ray signatures that can be probed by the AMS-02
experiment, which has measured the composition and fluxes of charged cosmic
rays with unprecedented precision. Given the absence of characteristic spectral
features in the electron and positron fluxes measured by AMS-02, we derive
upper limits on the dark matter annihilation cross section for leptophilic dark
matter models. Our limits are based on a new background model that describes
all recent measurements of the energy spectra of cosmic ray positrons and
electrons. For thermal dark matter relics, we can exclude dark matter masses
below about 100 GeV. We include the radiation of electroweak gauge bosons in
the dark matter annihilation process and compute the antiproton signal that can
be expected within leptophilic dark matter models.


  We propose a simple model of influence in a network, based on edge density.
In the model vertices (people) follow the opinion of the group they belong to.
The opinion percolates down from an active vertex, the influencer, at the head
of the group. Groups can merge, based on interactions between influencers
(i.e., interactions along `active edges' of the network), so that the number of
opinions is reduced. Eventually no active edges remain, and the groups and
their opinions become static.


  As of August 2022, blockchain-based assets boast a combined market
capitalisation exceeding one trillion USD, among which the most prominent are
the decentralised autonomous organisation (DAO) tokens associated with
decentralised finance (DeFi) protocols. In this work, we seek to value DeFi
tokens using the canonical multiples and Discount Cash Flow (DCF) approaches.
We examine a subset of DeFi services including decentralised exchanges (DEXs),
protocol for loanable funds (PLFs), and yield aggregators. We apply the same
analysis to some publicly traded firms and compare them with DeFi tokens of the
analogous category. Interestingly, despite the crypto bear market lasting for
more than one year as of August 2022, both approaches evidence overvaluation in
DeFi.


  In the field of functional genomics, the analysis of gene expression profiles
through Machine and Deep Learning is increasingly providing meaningful insight
into a number of diseases. The paper proposes a novel algorithm to perform
Feature Selection on genomic-scale data, which exploits the reconstruction
capabilities of autoencoders and an ad-hoc defined Explainable Artificial
Intelligence-based score in order to select the most informative genes for
diagnosis, prognosis, and precision medicine. Results of the application on a
Chronic Lymphocytic Leukemia dataset evidence the effectiveness of the
algorithm, by identifying and suggesting a set of meaningful genes for further
medical investigation.


  For any unital separable simple infinite-dimensional nuclear C*-algebra with
finitely many extremal traces, we prove that Z-absorption, strict comparison,
and property (SI) are equivalent. We also show that any unital separable simple
nuclear C*-algebra with tracial rank zero is approximately divisible, and hence
is Z-absorbing.


  A many-body theory approach developed by the authors [Phys. Rev. A 70, 032720
(2004)] is applied to positron bound states and annihilation rates in atomic
systems. Within the formalism, full account of virtual positronium (Ps)
formation is made by summing the electron-positron ladder diagram series, thus
enabling the theory to include all important many-body correlation effects in
the positron problem. Numerical calculations have been performed for positron
bound states with the hydrogen and halogen negative ions, also known as Ps
hydride and Ps halides. The Ps binding energies of 1.118, 2.718, 2.245, 1.873
and 1.393 eV and annihilation rates of 2.544, 2.482, 1.984, 1.913 and 1.809
ns$^{-1}$, have been obtained for PsH, PsF, PsCl, PsBr and PsI, respectively.


  We present results from galaxy evolution simulations with a mutiphase
Interstellar medium (ISM), a mass resolution of $4$ M$_{\odot}$ and a spatial
resolution of 0.5 pc. These simulations include a stellar feedback model that
includes the resolved feedback from individual massive stars and accounts for
heating from the far UV-field, non-equilibrium cooling and chemistry and
photoionization. In the default setting, individual supernova (SN) remnants are
realized as thermal injections of $10^{51}$ erg; this is our reference
simulation WLM-fid. Among the remaining seven simulations, there are two runs
where we vary this number by fixing the energy at $10^{50}$ erg and $10^{52}$
erg (WLM-1e50 and WLM-1e52, respectively). We carry out three variations with
variable SN-energy based on the data of Sukhbold et al. (2016) (WLM-variable,
WLM-variable-lin, and WLM-variable-stoch). We run two simulations where only 10
or 60 percent of stars explode as SNe with $10^{51}$ erg, while the remaining
stars do not explode (WLM-60prob and WLM-10prob). We find that the variation in
the SN-energy, based on the tables of Sukhbold et al. (2016), has only minor
effects: the star formation rate changes by roughly a factor of two compared to
the fiducial run, and the strength of the galactic outflows in mass and energy
only decreases by roughly 30 percent, with typical values of $\eta_m \sim 0.1$
and $\eta_e \sim 0.05$ (measured at a height of 3 kpc after the hot wind is
fully decoupled from the galactic ISM). In contrast, the increase and decrease
in the canonical SN-energy has a clear impact on the phase structure, with
loading factors that are at least 10 times lower/higher and a clear change in
the phase structure. We conclude that these slight modulations are driven not
by the minor change in SN-energy but rather by the stochasticity of whether or
not an event occurs when variable SN-energies are applied.


  In 1974, M. B. Nathanson proved that every irrational number $\alpha$
represented by a simple continued fraction with infinitely many elements
greater than or equal to $k$ is approximable by an infinite number of rational
numbers $p/q$ satisfying $|\alpha-p/q|<1/(\sqrt{k^2+4}q^2)$. In this paper we
refine this result.


  We develop and apply a fully covariant 1+3 electromagnetic analogy for
gravity. The free gravitational field is covariantly characterized by the Weyl
gravito-electric and gravito-magnetic spatial tensor fields, whose dynamical
equations are the Bianchi identities. Using a covariant generalization of
spatial vector algebra and calculus to spatial tensor fields, we exhibit the
covariant analogy between the tensor Bianchi equations and the vector Maxwell
equations. We identify gravitational source terms, couplings and potentials
with and without electromagnetic analogues. The nonlinear vacuum Bianchi
equations are shown to be invariant under covariant spatial duality rotation of
the gravito-electric and gravito-magnetic tensor fields. We construct the
super-energy density and super-Poynting vector of the gravitational field as
natural U(1) group invariants, and derive their super-energy conservation
equation. A covariant approach to gravito-electric/magnetic monopoles is also
presented.


  In this paper I show how the statistics of the gravitational field is changed
when the system is characterized by a non-uniform distribution of particles. I
show how the distribution functions W(dF/dt) giving the joint probability that
a test particle is subject to a force F and an associated rate of change of F
given by dF/dt, are modified by inhomogeneity. Then I calculate the first
moment of dF/dt to study the effects of inhomogenity on dynamical friction.
Finally I test, by N-Body simulations, that the theoretical W(F) and dF/dt
describes correctly the experimental data and I find that the stochastic force
distribution obtained for the evolved system is in good agreement with theory.
Moreover, I find that in an inhomogeneous background the friction force is
actually enhanced relative to the homogeneous case.


  We advocate the study of external-field quantum electrodynamics with $N$
charged particle flavors. Our main focus is on the Heisenberg-Euler effective
action for this theory in the large $N$ limit which receives contributions from
all loop orders. The contributions beyond one loop stem from one-particle
reducible diagrams. We show that specifically in constant electromagnetic
fields the latter are generated by the one-loop Heisenberg-Euler effective
Lagrangian. Hence, in this case the large $N$ Heisenberg-Euler effective action
can be determined explicitly at any desired loop order. We demonstrate that
further analytical insights are possible for electric-and magnetic-like field
configurations characterized by the vanishing of one of the secular invariants
of the electromagnetic field and work out the all-orders strong field limit of
the theory.


  We study 3d theories containing $\mathcal{N}=3$ Chern-Simons vector
multiplets coupled to the $\mathrm{SU}(N)^3$ flavour symmetry of 3d $T_N$
theories with Chern-Simons level $k_1$, $k_2$ and $k_3$. It was formerly
pointed out that these theories flow to infrared SCFTs with enhanced
$\mathcal{N}=4$ supersymmetry when $1/k_1+1/k_2+1/k_3=0$. We examine
superconformal indices of these theories which reveal that supersymmetry of the
infrared SCFTs may get enhanced to $4 \leq \mathcal{N} \leq 6$ if such a
condition is satisfied. Moreover, even if the Chern-Simons levels do not obey
the aforementioned condition, we find that there is still an infinite family of
theories that flows to infrared SCFTs with $\mathcal{N}=4$ supersymmetry. The
't Hooft anomalies of the one-form symmetries of these theories are analysed.
As a by-product, we observe that there is generally a decoupled topological
sector in the infrared. When the infrared SCFTs have $\mathcal{N} \geq 4$
supersymmetry, we also study the Higgs and Coulomb branch limits of the indices
which provide geometric information of the moduli space of the theories in
question in terms of the Hilbert series.


  Human pose assessment and correction play a crucial role in applications
across various fields, including computer vision, robotics, sports analysis,
healthcare, and entertainment. In this paper, we propose a Spatial-Temporal
Transformer based Framework (STTF) for human pose assessment and correction in
education scenarios such as physical exercises and science experiment. The
framework comprising skeletal tracking, pose estimation, posture assessment,
and posture correction modules to educate students with professional,
quick-to-fix feedback. We also create a pose correction method to provide
corrective feedback in the form of visual aids. We test the framework with our
own dataset. It comprises (a) new recordings of five exercises, (b) existing
recordings found on the internet of the same exercises, and (c) corrective
feedback on the recordings by professional athletes and teachers. Results show
that our model can effectively measure and comment on the quality of students'
actions. The STTF leverages the power of transformer models to capture spatial
and temporal dependencies in human poses, enabling accurate assessment and
effective correction of students' movements.


  We summarize the extent to which one can use Ward identities to
non-perturbatively improve flavor singlet and non-singlet bilinears with three
flavors of non-degenerate dynamical Wilson-like fermions.


  We will investigate the influence of the inhomogeneity of the universe,
especially that of the Lema{\^i}tre-Tolman-Bondi (LTB) model, on a
gravitationally bound local system such as the solar system. We concentrate on
the dynamical perturbation to the planetary motion and derive the leading order
effect generated from the LTB model. It will be shown that there appear not
only a well-known cosmological effect arisen from the homogeneous and isotropic
model, such as the Robertson-Walker (RW) model, but also the additional terms
due to the radial inhomogeneity of the LTB model. We will also apply the
obtained results to the problem of secular increase in the astronomical unit,
reported by Krasinsky and Brumberg (2004), and imply that the inhomogeneity of
the universe cannot have a significant effect for explaining the observed
$d{\rm AU}/dt = 15 \pm 4 ~{\rm [m/century]}$.


  Although recent studies have shown that electricity systems with shares of
wind and solar above 80% can be affordable, economists have raised concerns
about market integration. Correlated generation from variable renewable sources
depresses market prices, which can cause wind and solar to cannibalise their
own revenues and prevent them from covering their costs from the market. This
cannibalisation appears to set limits on the integration of wind and solar, and
thus to contradict studies that show that high shares are cost effective. Here
we show from theory and with simulation examples how market incentives interact
with prices, revenue and costs for renewable electricity systems. The decline
in average revenue seen in some recent literature is due to an implicit policy
assumption that technologies are forced into the system, whether it be with
subsidies or quotas. This decline is mathematically guaranteed regardless of
whether the subsidised technology is variable or not. If instead the driving
policy is a carbon dioxide cap or tax, wind and solar shares can rise without
cannibalising their own market revenue, even at penetrations of wind and solar
above 80%. The strong dependence of market value on the policy regime means
that market value needs to be used with caution as a measure of market
integration. Declining market value is not necessarily a sign of integration
problems, but rather a result of policy choices.


  Accurate 3D kinematics estimation of human body is crucial in various
applications for human health and mobility, such as rehabilitation, injury
prevention, and diagnosis, as it helps to understand the biomechanical loading
experienced during movement. Conventional marker-based motion capture is
expensive in terms of financial investment, time, and the expertise required.
Moreover, due to the scarcity of datasets with accurate annotations, existing
markerless motion capture methods suffer from challenges including unreliable
2D keypoint detection, limited anatomic accuracy, and low generalization
capability. In this work, we propose a novel biomechanics-aware network that
directly outputs 3D kinematics from two input views with consideration of
biomechanical prior and spatio-temporal information. To train the model, we
create synthetic dataset ODAH with accurate kinematics annotations generated by
aligning the body mesh from the SMPL-X model and a full-body OpenSim skeletal
model. Our extensive experiments demonstrate that the proposed approach, only
trained on synthetic data, outperforms previous state-of-the-art methods when
evaluated across multiple datasets, revealing a promising direction for
enhancing video-based human motion capture


  We address the asymptotic and approximate distributions of a large class of
test statistics with quadratic forms used in association studies. The
statistics of interest do not necessarily follow a chi-square distribution and
take the general form $D=X^T A X$, where $X$ follows the multivariate normal
distribution, and $A$ is a general similarity matrix which may or may not be
positive semi-definite. We show that $D$ can be written as a linear combination
of independent chi-square random variables, whose distribution can be
approximated by a chi-square or the difference of two chi-square distributions.
In the setting of association testing, our methods are especially useful in two
situations. First, for a genome screen, the required significance level is much
smaller than 0.05 due to multiple comparisons, and estimation of p-values using
permutation procedures is particularly challenging. An efficient and accurate
estimation procedure would therefore be useful. Second, in a candidate gene
study based on haplotypes when phase is unknown a computationally expensive
method-the EM algorithm-is usually required to infer haplotype frequencies.
Because the EM algorithm is needed for each permutation, this results in a
substantial computational burden, which can be eliminated with our mathematical
solution. We assess the practical utility of our method using extensive
simulation studies based on two example statistics and apply it to find the
sample size needed for a typical candidate gene association study when phase
information is not available. Our method can be applied to any quadratic form
statistic and therefore should be of general interest.


  Multiple-choice multiple-response (MCMR) items (i.e., multiple-choice
questions for which more than one response may be selected) can be a valuable
tool for assessment. Like traditional multiple-choice single-response
questions, they are easy to score, but MCMR items can provide more information
about student thinking by probing multiple reasoning facets in a single problem
context. In this paper, we discuss differences in performance on MCMR items
that result from differences in administration method (paper vs. online). In
particular, we find a tendency for ``clickiness'' in online administration:
students choose more responses to MCMR items when taking the electronic version
of the assessment. Based on these findings, we recommend that online
administration be compared only to other online administrations and not to
paper administrations when comparing student results on MCMR items. These
results also suggest that MCMR items provide a unique opportunity to probe
differences in online and on-paper administration of low-stakes assessments.


  A redundant generating function is a generating function having terms which
are not part of the solution of the original problem. We use redundant
generating functions to study two path problems. In the first application we
explain a surprising occurrence of Catalan numbers in counting paths that stay
below the line y = 2x. In the second application we prove a conjecture of
Niederhausen and Sullivan.


  Coinduction refers to both a technique for the definition of infinite
streams, so-called codata, and a technique for proving the equality of
coinductively specified codata. This article first reviews coinduction in
declarative programming. Second, it reviews and slightly extends the formalism
commonly used for specifying codata. Third, it generalizes the coinduction
proof principle, which has been originally specified for the equality predicate
only, to other predicates. This generalization makes the coinduction proof
principle more intuitive and stresses its closeness with structural induction.
The article finally suggests in its conclusion extensions of functional and
logic programming with limited and decidable forms of the generalized
coinduction proof principle.


  The aim of this brief note for the volume in honor of S. S. Chern is to give
a construction for $p$-torsion line bundles in characteristic $p>0$ which plays
a similar r\^ole as the standard connection on an $n$-torsion line bundle in
characteristic 0.


  The metazoan body plan is established during early embryogenesis via
collective cell rearrangements and evolutionarily conserved gene networks, as
part of a process commonly referred to as gastrulation. While substantial
progress has been achieved in terms of characterizing the embryonic development
of several model organisms, underlying principles of many early patterning
processes nevertheless remain enigmatic. Despite the diversity of
(pre-)gastrulating embryo and adult body shapes across the animal kingdom, the
body axes, which are arguably the most fundamental features, generally remain
identical between phyla. Recently there has been a renewed appreciation of ex
vivo and in vitro embryo-like systems to model early embryonic patterning
events. Here, we briefly review key examples and propose that similarities in
morphogenesis as well as associated gene expression dynamics may reveal an
evolutionarily conserved developmental mode as well as provide further insights
into the role of external or extraembryonic cues in shaping the early embryo.
In summary, we argue that embryo-like systems can be employed to inform
previously uncharted aspects of animal body plan evolution as well as
associated patterning rules.


  This paper studies n-player games where players beliefs about their opponents
behaviour are capacities (fuzzy measures, non-additive probabilities). The
concept of an equilibrium under uncertainty was introduced by J.Dow and
S.Werlang (1994) for two players and was extended to n-player games by
J.Eichberger and D.Kelsey (2000). Expected utility (payoff function) was
expressed by Choquet integral. The concept of an equilibrium under uncertainty
with expected utility expressed by Sugeno integral were considered by T.Radul
(2018). We consider in this paper an equilibrium with expected utility
expressed by fuzzy integral generated by a continuous t-norm which is a natural
generalization of Sugeno integral.


  Van der Waals (vdW) forces play a fundamental role in the structure and
behavior of diverse systems. Thanks to development of functionals that include
non-local correlation, it is possible to study the effects of vdW interactions
in systems of industrial and tribological interest. Here we simulated within
the framework of density functional theory (DFT) the adsorption of isooctane
(2,2,4-trimethylpentane) and ethanol on a Fe(100) surface, employing various
exchange-correlation functionals to take vdW forces into account. In
particular, this paper discusses the effect of vdW forces on the magnitude of
adsorption energies, equilibrium geometries and their role in the binding
mechanism. According to our calculations, vdW interactions increase the
adsorption energies and reduce the equilibrium distances. Nevertheless, they do
not influence the spatial configuration of the adsorbed molecules. Their effect
on the electronic density is a non-isotropic, delocalized accumulation of
charge between the molecule and the slab. In conclusion, vdW forces are
essential for the adsorption of isooctane and ethanol on a bcc Fe(100) surface.


  Cross-lingual named entity recognition (NER) aims to train an NER system that
generalizes well to a target language by leveraging labeled data in a given
source language. Previous work alleviates the data scarcity problem by
translating source-language labeled data or performing knowledge distillation
on target-language unlabeled data. However, these methods may suffer from label
noise due to the automatic labeling process. In this paper, we propose CoLaDa,
a Collaborative Label Denoising Framework, to address this problem.
Specifically, we first explore a model-collaboration-based denoising scheme
that enables models trained on different data sources to collaboratively
denoise pseudo labels used by each other. We then present an
instance-collaboration-based strategy that considers the label consistency of
each token's neighborhood in the representation space for denoising.
Experiments on different benchmark datasets show that the proposed CoLaDa
achieves superior results compared to previous methods, especially when
generalizing to distant languages.


  The recently discovered massive binary system Apep is the most powerful
synchrotron emitter among the known Galactic colliding-wind binaries. This
makes this particular system of great interest to investigate stellar winds and
the non-thermal processes associated with their shocks. This source was
detected at various radio bands, and in addition the wind-collision region was
resolved by means of very-long baseline interferometric observations. We use a
non-thermal emission model for colliding-wind binaries to derive physical
properties of this system. The observed morphology in the resolved maps allows
us to estimate the system projection angle on the sky to be $\psi \approx
85^\circ$. The observed radio flux densities also allow us to characterise both
the intrinsic synchrotron spectrum of the source and its modifications due to
free--free absorption in the stellar winds at low frequencies; from this we
derive mass-loss rates of the stars of $\dot{M}_\mathrm{WN} \approx
4\times10^{-5}$ $\mathrm{M}_\odot$ yr$^{-1}$ and $\dot{M}_\mathrm{WC} \approx
2.9\times10^{-5}$ $\mathrm{M}_\odot$ yr$^{-1}$. Finally, the broadband spectral
energy distribution is calculated for different combinations of the remaining
free parameters, namely the intensity of the magnetic field and the injected
power in non-thermal particles. We show that the degeneracy of these two
parameters can be solved with observations in the high-energy domain, most
likely in the hard X-rays but also possibly in $\gamma$-rays under favourable
conditions.


  A novel form of Ramsey narrowing is identified and characterized. For
long-lived coherent atomic states coupled by laser fields, the diffusion of
atoms in-and-out of the laser beam induces a spectral narrowing of the atomic
resonance lineshape. Illustrative experiments and an intuitive analytical model
are presented for this diffusion-induced Ramsey narrowing, which occurs
commonly in optically-interrogated systems.


  Machine learning techniques for more efficient video compression and video
enhancement have been developed thanks to breakthroughs in deep learning. The
new techniques, considered as an advanced form of Artificial Intelligence (AI),
bring previously unforeseen capabilities. However, they typically come in the
form of resource-hungry black-boxes (overly complex with little transparency
regarding the inner workings). Their application can therefore be unpredictable
and generally unreliable for large-scale use (e.g. in live broadcast). The aim
of this work is to understand and optimise learned models in video processing
applications so systems that incorporate them can be used in a more trustworthy
manner. In this context, the presented work introduces principles for
simplification of learned models targeting improved transparency in
implementing machine learning for video production and distribution
applications. These principles are demonstrated on video compression examples,
showing how bitrate savings and reduced complexity can be achieved by
simplifying relevant deep learning models.


  Let k be a local field and G the set of k-points of a connected semisimple
algebraic k-group of rank one. We describe all torsion-free discrete subgroups
of G\times G acting properly discontinuously on G by left and right
multiplication. To this end, we prove a general result on the Cartan projection
of discrete groups acting properly discontinuously on corank-one reductive
homogeneous spaces over k.