  Stochastic games are a well established model for multi-agent sequential
decision making under uncertainty. In practical applications, though, agents
often have only partial observability of their environment. Furthermore, agents
increasingly perceive their environment using data-driven approaches such as
neural networks trained on continuous data. We propose the model of
neuro-symbolic partially-observable stochastic games (NS-POSGs), a variant of
continuous-space concurrent stochastic games that explicitly incorporates
neural perception mechanisms. We focus on a one-sided setting with a
partially-informed agent using discrete, data-driven observations and another,
fully-informed agent. We present a new method, called one-sided NS-HSVI, for
approximate solution of one-sided NS-POSGs, which exploits the piecewise
constant structure of the model. Using neural network pre-image analysis to
construct finite polyhedral representations and particle-based representations
for beliefs, we implement our approach and illustrate its practical
applicability to the analysis of pedestrian-vehicle and pursuit-evasion
scenarios.


  We will show that group exactness is a von Neumann equivalence invariant.
This result generalizes the previously known fact stating that group exactness
is invariant under measure equivalence and W*-equivalence.


  We have used HRCam on the CFHT to obtain subarcsecond images of 26 galaxies
with z=0.1--0.7. The primary sample of 17 galaxies have enhanced star formation
indicated by [OII] equivalent widths greater than 20\AA, while the comparison
sample of 9 galaxies have equivalent widths less than 10\AA. By fitting disks
or bulges to BVI images we derive scalelengths for the blue and red stellar
populations and locate the star-formation in each galaxy. These
moderate-redshift galaxies generally have straightforward low-redshift
analogues, in that their colours, sizes and luminosities are consistent with
those of various types of z$\approx$0 galaxies. The star-forming objects have
structures consistent with the full range of present-day disk galaxies, and
absolute magnitudes M$^\ast$$-$1 to M$^\ast$+5. Some have star-formation
concentrated in their nuclei but in most it occurs across the entire disk. We
find companions at projected distances closer than 10 kpc for 30% of the
star-forming galaxies, whereas none of the comparison sample have such close
companions. This provides the first direct evidence linking interactions or
mergers to the increased fraction of star-forming field galaxies at moderate
redshifts.


  We present the WCCR10 data set of ten ligand dissociation energies of large
cationic transition metal complexes for the assessment of approximate
exchange--correlation functionals. We analyze nine popular functionals, namely
BP86, BP86-D3, B3LYP, B3LYP-D3, B97-D-D2, PBE, TPSS, PBE0, and TPSSh by mutual
comparison and by comparison to experimental gas-phase data measured with
well-known precision. The comparison of all calculated data reveals a large,
system-dependent scattering of results with nonnegligible consequences for
computational chemistry studies on transition metal compounds. Considering
further the comparison with experiment, the non-empirical functionals PBE and
TPSS turn out to be among the best functionals for our reference data set. The
deviation can be lowered further by including Hartree--Fock exchange.
Accordingly, PBE0 and TPSSh are the two most accurate functionals for our test
set, but also these functionals exhibit deviations from experiment by up to 50
kJ/mol for individual reactions. As an important result we found no functional
to be reliable for all reactions. Furthermore, for some of the ligand
dissociation energies studied in this work, dispersion corrections yield
results which increase the deviation from experiment. This deviation increases
further if structure optimization including dispersion corrections is
performed. Finally, we compare our results to other benchmark studies and
highlight that the performance assessed for different density functionals
depends significantly on the reference molecule set chosen.


  We propose a risk-averse statistical learning framework wherein the
performance of a learning algorithm is evaluated by the conditional
value-at-risk (CVaR) of losses rather than the expected loss. We devise
algorithms based on stochastic gradient descent for this framework. While
existing studies of CVaR optimization require direct access to the underlying
distribution, our algorithms make a weaker assumption that only i.i.d.\ samples
are given. For convex and Lipschitz loss functions, we show that our algorithm
has $O(1/\sqrt{n})$-convergence to the optimal CVaR, where $n$ is the number of
samples. For nonconvex and smooth loss functions, we show a generalization
bound on CVaR. By conducting numerical experiments on various machine learning
tasks, we demonstrate that our algorithms effectively minimize CVaR compared
with other baseline algorithms.


  We present a mechanism for the origin of the large-scale curvature
perturbation in our Universe by the late decay of a massive scalar field, the
curvaton. The curvaton is light during a period of cosmological inflation, when
it acquires a perturbation with an almost scale-invariant spectrum. This
corresponds initially to an isocurvature density perturbation, which generates
the curvature perturbation after inflation when the curvaton density becomes a
significant fraction of the total. The isocurvature density perturbation
disappears if the curvaton completely decays into thermalised radiation. Any
residual isocurvature perturbation is 100% correlated with the curvature. The
same mechanism can also generate the curvature perturbation in pre big
bang/ekpyrotic models, provided that the curvaton has a suitable non-canonical
kinetic term so as to generate a flat spectrum.


  Some attack scientific rationality, others defend it, but both miss the
point. What both parties take to be scientific rationality is actually a
species of irrationality masquerading as scientific rationality. The current
orthodox conception of science, taken for granted by scientists and
non-scientists alike, is irrational because it suppresses problematic
assumptions, inherent in the aims of science, having to do with metaphysics,
values, and political and social issues. We urgently need a more rigorous
conception of science to be adopted and implemented that honestly acknowledges
the problematic character of the aims of science, and seeks to improve them as
science proceeds.


  We have performed a 1.3 centimeter survey of 24 massive young stellar objects
(MYSOs) using the Expanded Very Large Array (EVLA). The sources in the sample
exhibit a broad range of massive star formation signposts including Infrared
Dark Clouds (IRDCs), UCHII regions, and extended 4.5 micron emission in the
form of Extended Green Objects (EGOs). In this work, we present results for
G35.03+0.35 which exhibits all of these phenomena. We simultaneously image the
1.3 cm ammonia (1,1) through (6,6) inversion lines, four methanol transitions,
two H recombination lines, plus continuum at 0.05 pc resolution. We find three
areas of thermal ammonia emission, two within the EGO (designated the NE and SW
cores) and one toward an adjacent IRDC. The NE core contains an UCHII region
(CM1) and a candidate HCHII region (CM2). A region of non-thermal, likely
masing ammonia (3,3) and (6,6) emission is coincident with an arc of 44 GHz
methanol masers. We also detect two new 25 GHz Class I methanol masers. A
complementary Submillimeter Array 1.3 mm continuum image shows that the
distribution of dust emission is similar to the lower-lying ammonia lines, all
peaking to the NW of CM2, indicating the likely presence of an additional MYSO
in this protocluster. By modeling the ammonia and 1.3 mm continuum data, we
obtain gas temperatures of 20-220 K and masses of 20-130 solar. The diversity
of continuum emission properties and gas temperatures suggest that objects in a
range of evolutionary states exist concurrently in this protocluster.


  We observe a multilinearity preserving property of conditional expectation
for infinite dimensional independent increment processes defined on some
abstract Banach space $B$. It is similar in nature to the polynomial preserving
property analysed greatly for finite dimensional stochastic processes and thus
offers an infinite dimensional generalisation. However, while polynomials are
defined using the multiplication operator and as such require a Banach algebra
structure, the multilinearity preserving property we prove here holds even for
processes defined on a Banach space which is not necessary a Banach algebra. In
the special case of $B$ being a commutative Banach algebra, we show that
independent increment processes are polynomial processes in a sense that
coincides with a canonical extension of polynomial processes from the finite
dimensional case. The assumption of commutativity is shown to be crucial and in
a non-commutative Banach algebra the multilinearity concept arises naturally.
Some of our results hold beyond independent increment processes and thus shed
light on infinite dimensional polynomial processes in general.


  In this paper we combine the Parareal parallel-in-time method together with
spatial parallelization and investigate this space-time parallel scheme by
means of solving the three-dimensional incompressible Navier-Stokes equations.
Parallelization of time stepping provides a new direction of parallelization
and allows to employ additional cores to further speed up simulations after
spatial parallelization has saturated. We report on numerical experiments
performed on a Cray XE6, simulating a driven cavity flow with and without
obstacles. Distributed memory parallelization is used in both space and time,
featuring up to 2,048 cores in total. It is confirmed that the
space-time-parallel method can provide speedup beyond the saturation of the
spatial parallelization.


  Pollard used contour integration to show that the Mittag-Leffler function is
the Laplace transform of a positive function, thereby proving that it is
completely monotone. He also cited personal communication by Feller of a
discovery of the result by "methods of probability theory". In his published
work, Feller used the two-dimensional Laplace transform of a bivariate
distribution to derive the Pollard result. But both approaches may be described
as analytic, despite the occurrence of the stable distribution in Feller's
starting point and in the Pollard result itself. We adopt a Bayesian
probabilistic approach that assigns a prior distribution to the scale parameter
of the stable distribution. We present Feller's method as a particular instance
of such assignment. The Bayesian framework enables generalisation of the
Pollard result. This leads to a novel integral representation of the
Mittag-Leffler function as well as a variant arising from polynomial tilting of
the stable density.


  Recent discoveries regarding current-induced spin-orbit torques produced by
heavy-metal/ferromagnet and topological-insulator/ferromagnet bilayers provide
the potential for dramatically-improved efficiency in the manipulation of
magnetic devices. However, in experiments performed to date, spin-orbit torques
have an important limitation -- the component of torque that can compensate
magnetic damping is required by symmetry to lie within the device plane. This
means that spin-orbit torques can drive the most current-efficient type of
magnetic reversal (antidamping switching) only for magnetic devices with
in-plane anisotropy, not the devices with perpendicular magnetic anisotropy
that are needed for high-density applications. Here we show experimentally that
this state of affairs is not fundamental, but rather one can change the allowed
symmetries of spin-orbit torques in spin-source/ferromagnet bilayer devices by
using a spin source material with low crystalline symmetry. We use WTe2, a
transition-metal dichalcogenide whose surface crystal structure has only one
mirror plane and no two-fold rotational invariance. Consistent with these
symmetries, we generate an out-of-plane antidamping torque when current is
applied along a low-symmetry axis of WTe2/Permalloy bilayers, but not when
current is applied along a high-symmetry axis. Controlling S-O torques by
crystal symmetries in multilayer samples provides a new strategy for optimizing
future magnetic technologies.


  We generalize the cohomological mirror duality of Borcea and Voisin in any
dimension and for any number of factors. Our proof applies to all examples
which can be constructed through Berglund-H\"{u}bsch duality. Our method is a
variant of the so-called Landau-Ginzburg/Calabi-Yau correspondence of
Calabi-Yau orbifolds with an involution that does not preserve the volume form.
We deduce a version of mirror duality for the fixed loci of the involution,
which are beyond the Calabi-Yau category and feature hypersurfaces of general
type.


  A system of soft ellipsoid molecules confined between two planar walls is
studied using classical Density Functional Theory (DFT). Both the isotropic and
nematic phases are considered. The excess free energy is evaluated using two
different Ansaetze and the intermolecular interaction is incorporated using two
different direct correlation functions (DCF). The first is a numerical DCF
obtained from simulations of bulk soft ellipsoid fluids and the second is taken
from Parsons-Lee theory. In both the isotropic and nematic phases the numerical
DCF gives density and order parameter profiles in reasonable agreement with
simulation. The Parsons-Lee DCF also gives reasonable agreement in the
isotropic phase but poor agreement in the nematic phase.


  We introduce a condition on the monodromy of a free-by-cyclic group,
$G_\phi$, called the chain flare condition, that implies that the
$L^2$-torsion, $\rho^{(2)}(G_\phi)$, is non-zero. We conjecture that this
condition holds whenever the monodromy is exponentially growing.


  We recall a classical theory of torsion gravity with an asymmetric metric,
sourced by a Nambu-Goto + Kalb-Ramond string . We explain why this is a
significant gravitational theory, and in what sense classical general
relativity is an approximation to it. We propose that a non-commutative
generalisation of this theory (in the sense of Connes' non-commutative geometry
and Adler's Trace Dynamics) is a `quantum theory of gravity'. The theory is in
fact a classical matrix dynamics with only two fundamental constants -- the
square of the Planck length and the speed of light, along with the two string
tensions as parameters. The guiding symmetry principle is that the theory
should be covariant under general coordinate transformations of non-commuting
coordinates.The action for this non-commutative torsion gravity can be
elegantly expressed as an invariant area integral, and represents an atom of
space-time-matter. The statistical thermodynamics of a large number of such
atoms yields the laws of quantum gravity and quantum field theory, at
thermodynamic equilibrium. Spontaneous localisation caused by large
fluctuations away from equilibrium is responsible for the emergence of
classical space-time and the field equations of classical general relativity.
The resolution of the quantum measurement problem by spontaneous collapse is an
inevitable consequence of this process. Quantum theory and general relativity,
are both seen as emergent phenomena, resulting from coarse-graining of the
underlying non-commutative geometry. We explain the profound role played by
entanglement in this theory: entanglement describes interaction between the
atoms of space-time-matter, and indeed entanglement appears to be more
fundamental than quantum theory or space-time. We also comment on possible
implications for black hole entropy and evaporation, and for cosmology.


  We study the stationary properties of the Ising model that, while evolving
towards its equilibrium state at temperature $T$ according to the Glauber
dynamics, is stochastically reset to its fixed initial configuration with
magnetisation $m_0$ at a constant rate $r$. Resetting breaks detailed balance
and drives the system to a non-equilibrium stationary state where the
magnetisation acquires a nontrivial distribution, leading to a rich phase
diagram in the $(T,r)$ plane. We establish these results exactly in
one-dimension and present scaling arguments supported by numerical simulations
in two-dimensions. We show that resetting gives rise to a novel "pseudo-ferro"
phase in the $(T,r)$ plane for $r > r^*(T)$ and $T>T_c$ where $r^*(T)$ is a
crossover line separating the pseudo-ferro phase from a paramagnetic phase.
This pseudo-ferro phase is characterised by a non-zero typical magnetisation
and a vanishing gap near $m=0$ of the magnetisation distribution.


  Mixture models provide a flexible representation of heterogeneity in a finite
number of latent classes. From the Bayesian point of view, Markov Chain Monte
Carlo methods provide a way to draw inferences from these models. In
particular, when the number of subpopulations is considered unknown, more
sophisticated methods are required to perform Bayesian analysis. The Reversible
Jump Markov Chain Monte Carlo is an alternative method for computing the
posterior distribution by simulation in this case. Some problems associated
with the Bayesian analysis of these class of models are frequent, such as the
so-called "label-switching" problem. However, as the level of heterogeneity in
the population increases, these problems are expected to become less frequent
and the model's performance to improve. Thus, the aim of this work is to
evaluate the normal mixture model fit using simulated data under different
settings of heterogeneity and prior information about the mixture proportions.
A simulation study is also presented to evaluate the model's performance
considering the number of components known and estimating it. Finally, the
model is applied to a censored real dataset containing antibody levels of
Cytomegalovirus in individuals.


  This note compares two approaches both alternatively used when establishing
normality theorems in univariate Extreme Value Theory. When the underlying
distribution function ($df$) is the extremal domain of attraction, it is
possible to use representations for the quantile function and regularity
conditions (RC), based on these representations, under which strong and weak
convergence are valid. It is also possible to use the now fashion second order
condition (SOC), whenever it holds, to do the same. Some authors usually favor
the first approach (the SOC one) while others are fond of the second approach
that we denote as the representational one. This note aims at comparing the two
approaches and show how to get from one to the other. The auxiliary functions
used in each approach are computed and compared. Statistical applications using
simultaneously both approaches are provided. A final comparison is provided.


  In this paper, we consider the partial gathering problem of mobile agents in
asynchronous unidirectional rings equipped with whiteboards on nodes. The
partial gathering problem is a new generalization of the total gathering
problem. The partial gathering problem requires, for a given integer $g$, that
each agent should move to a node and terminate so that at least $g$ agents
should meet at the same node. The requirement for the partial gathering problem
is weaker than that for the (well-investigated) total gathering problem, and
thus, we have interests in clarifying the difference on the move complexity
between them. We propose three algorithms to solve the partial gathering
problem. The first algorithm is deterministic but requires unique ID of each
agent. This algorithm achieves the partial gathering in $O(gn)$ total moves,
where $n$ is the number of nodes. The second algorithm is randomized and
requires no unique ID of each agent (i.e., anonymous). This algorithm achieves
the partial gathering in expected $O(gn)$ total moves. The third algorithm is
deterministic and requires no unique ID of each agent. For this case, we show
that there exist initial configurations in which no algorithm can solve the
problem and agents can achieve the partial gathering in $O(kn)$ total moves for
solvable initial configurations, where $k$ is the number of agents. Note that
the total gathering problem requires $\Omega (kn)$ total moves, while the
partial gathering problem requires $\Omega (gn)$ total moves in each model.
Hence, we show that the move complexity of the first and second algorithms is
asymptotically optimal.


  In most electricity theft detection schemes, consumers' power consumption
data is directly input into the detection center. Although it is valid in
detecting the theft of consumers, the privacy of all consumers is at risk
unless the detection center is assumed to be trusted. In fact, it is
impractical. Moreover, existing schemes may result in some security problems,
such as the collusion attack due to the presence of a trusted third party, and
malicious data tampering caused by the system operator (SO) being attacked.
Aiming at the problems above, we propose a blockchain-based privacy-preserving
electricity theft detection scheme without a third party. Specifically, the
proposed scheme uses an improved functional encryption scheme to enable
electricity theft detection and load monitoring while preserving consumers'
privacy; distributed storage of consumers' data with blockchain to resolve
security problems such as data tampering, etc. Meanwhile, we build a long
short-term memory network (LSTM) model to perform higher accuracy for
electricity theft detection. The proposed scheme is evaluated in a real
environment, and the results show that it is more accurate in electricity theft
detection within acceptable communication and computational overhead. Our
system analysis demonstrates that the proposed scheme can resist various
security attacks and preserve consumers' privacy.


  Sign Language is used to facilitate the communication between Deaf and
non-Deaf people. It uses signs-words with basic structural elements such as
handshape, parts of face, body or space, and the orientation of the
fingers-palm. Sign Languages vary from people to people and from country to
country and evolve as spoken languages. In the current study, an application
which aims at Greek Sign Language and English Sign Language learning by hard of
hearing people and talking people, has been developed. The application includes
grouped signs in alphabetical order. The user can find Greek Sign Language
signs, English sign language signs and translate from Greek sign language to
English sign language. The written word of each sign, and the corresponding
meaning are displayed. In addition, the sound is activated in order to enable
users with partial hearing loss to hear the pronunciation of each word. The
user is also provided with various tasks in order to enable an interaction of
the knowledge acquired by the user. This interaction is offered mainly by
multiplechoice tasks, incorporating text or video. The current application is
not a simple sign language dictionary as it provides the interactive
participation of users. It is a platform for Greek and English sign language
active learning.


  This paper presents a recently developed particle simulation code package
PIFE-PIC, which is a novel three-dimensional (3-D) Parallel
Immersed-Finite-Element (IFE) Particle-in-Cell (PIC) simulation model for
particle simulations of plasma-material interactions. This framework is based
on the recently developed non-homogeneous electrostatic IFE-PIC algorithm,
which is designed to handle complex plasma-material interface conditions
associated with irregular geometries using a Cartesian-mesh-based PIC.
Three-dimensional domain decomposition is utilized for both the electrostatic
field solver with IFE and the particle operations in PIC to distribute the
computation among multiple processors. A simulation of the
orbital-motion-limited (OML) sheath of a dielectric sphere immersed in a
stationary plasma is carried out to validate PIFE-PIC and profile the parallel
performance of the code package. Furthermore, a large-scale simulation of
plasma charging at a lunar crater containing 2 million PIC cells (10 million
FE/IFE cells) and about 520 million particles, running for 20,000 PIC steps in
about 109 wall-clock hours, is presented to demonstrate the high-performance
computing capability of PIFE-PIC.


  By the financial support of Vietnam Atomic Energy Commission and kind
cooperation of Frank Laboratory, in the year of 2005 a measure system based on
summation of amplitude pulses was established on the tangential channel of
Dalat Research Reactor. After a serial of testing, the measure system was
explored. In this, we would like to show the initial results were gotten with
36-Cl isotope.


  We further develop Weber's notion of elementary 2-topos by proposing certain
new axioms. We show that in a 2-category C satisfying these axioms, the
"discrete opfibration (DOF) classifier" S is always an internal elementary
1-topos, in an appropriate sense. The axioms introduced for this purpose are
closure conditions on the DOFs having "S-small fibres". Among these closure
conditions, the most interesting one asserts that a certain DOF, analogous to
the "subset fibration" over Set, has small fibres.
  The remaining new axioms concern "groupoidal" objects in a 2-category, which
are seen to play a significant role in the general theory. We prove two results
to the effect that a 2-category C satisfying these axioms is "determined by"
its groupoidal objects: the first shows that C is equivalent to a 2-category of
internal categories built out of groupoidal objects, and the second shows that
the groupoidal objects are dense in C.


  In this paper we present an approach to estimate Free Space from a Stereo
image pair using stochastic occupancy grids. We do this in the domain of
autonomous driving on the famous benchmark dataset KITTI. Later based on the
generated occupancy grid we match 2 image sequences to compute the top view
representation of the map. We do this to map the environment. We compute a
transformation between the occupancy grids of two successive images and use it
to compute the top view map. Two issues need to be addressed for mapping are
discussed - computing a map and dealing with dynamic objects for computing the
map. Dynamic Objects are detected in successive images based on an idea similar
to tracking of foreground objects from the background objects based on motion
flow. A novel RANSAC based segmentation approach has been proposed here to
address this issue.


  We introduce PQ-NET, a deep neural network which represents and generates 3D
shapes via sequential part assembly. The input to our network is a 3D shape
segmented into parts, where each part is first encoded into a feature
representation using a part autoencoder. The core component of PQ-NET is a
sequence-to-sequence or Seq2Seq autoencoder which encodes a sequence of part
features into a latent vector of fixed size, and the decoder reconstructs the
3D shape, one part at a time, resulting in a sequential assembly. The latent
space formed by the Seq2Seq encoder encodes both part structure and fine part
geometry. The decoder can be adapted to perform several generative tasks
including shape autoencoding, interpolation, novel shape generation, and
single-view 3D reconstruction, where the generated shapes are all composed of
meaningful parts.


  We relate certain abelian invariants of a knot, namely the Alexander
polynomial, the Blanchfield form, and the Arf invariant, to intersection data
of a Whitney tower in the 4-ball bounded by the knot. We also give a new
3-dimensional algorithm for computing these invariants.


  Purpose: Diffusion-weighted steady-state free precession (DW-SSFP) is shown
to provide a means to probe non-Gaussian diffusion through manipulation of the
flip angle. A framework is presented to define an effective b-value in DW-SSFP.
Theory: The DW-SSFP signal is a summation of coherence pathways with different
b-values. The relative contribution of each pathway is dictated by the flip
angle. This leads to an apparent diffusion coefficient (ADC) estimate that
depends on the flip angle in non-Gaussian diffusion regimes. By acquiring
DW-SSFP data at multiple flip angles and modelling the variation in ADC for a
given form of non-Gaussianity, the ADC can be estimated at a well-defined
effective b-value. Methods: A gamma distribution is used to model non-Gaussian
diffusion, embedded in the Buxton signal model for DW-SSFP. Monte-Carlo
simulations of non-Gaussian diffusion in DW-SSFP and diffusion-weighted
spin-echo (DW-SE) sequences are used to verify the proposed framework.
Dependence of ADC on flip angle in DW-SSFP is verified with experimental
measurements in a whole, human post-mortem brain. Results: Monte-Carlo
simulations reveal excellent agreement between ADCs estimated with DW-SE and
the proposed framework. Experimental ADC estimates vary as a function of flip
angle over the corpus callosum of the postmortem brain, estimating the mean and
standard deviation of the gamma distribution as $1.50\cdot 10^{-4} mm^2/s$ and
$2.10\cdot 10^{-4} mm^2/s$. Conclusion: DW-SSFP can be used to investigate
non-Gaussian diffusion by varying the flip angle. By fitting a model of
non-Gaussian diffusion, the ADC in DW-SSFP can be estimated at an effective
b-value, comparable to more conventional diffusion sequences.


  Flow-guided localization using in-body nanodevices in the bloodstream is
expected to be beneficial for early disease detection, continuous monitoring of
biological conditions, and targeted treatment. The nanodevices face size and
power constraints that produce erroneous raw data for localization purposes.
On-body anchors receive this data, and use it to derive the locations of
diagnostic events of interest. Different Machine Learning (ML) approaches have
been recently proposed for this task, yet they are currently restricted to a
reference bloodstream of a resting patient. As such, they are unable to deal
with the physical diversity of patients' bloodstreams and cannot provide
continuous monitoring due to changes in individual patient's activities. Toward
addressing these issues for the current State-of-the-Art (SotA) flow-guided
localization approach based on Graph Neural Networks (GNNs), we propose a
pipeline for GNN adaptation based on individual physiological indicators
including height, weight, and heart rate. Our results indicate that the
proposed adaptions are beneficial in reconciling the individual differences
between bloodstreams and activities.


  We introduce and study the class ${\bf\mathcal{G}}(\alpha,\beta)$ comprising
analytic functions associated with a sector domain, where
$\alpha,\beta\in(0,1]$. Using the extended version of Jack's lemma, we deduce
Open-Door lemma type sufficient conditions for functions to be in
${\bf\mathcal{G}}(\alpha,\beta)$. Furthermore, we point out special cases of
our results that align with known results.


  Vehicle control algorithms exploiting connectivity and automation, such as
Connected and Automated Vehicles (CAVs) or Advanced Driver Assistance Systems
(ADAS), have the opportunity to improve energy savings. However, lower levels
of automation involve a human-machine interaction stage, where the presence of
a human driver affects the performance of the control algorithm in closed loop.
This occurs for instance in the case of Eco-Driving control algorithms
implemented as a velocity advisory system, where the driver is displayed an
optimal speed trajectory to follow to reduce energy consumption. Achieving the
control objectives relies on the human driver perfectly following the
recommended speed. If the driver is unable to follow the recommended speed, a
decline in energy savings and poor vehicle performance may occur. This warrants
the creation of methods to model and forecast the response of a human driver
when operating in the loop with a speed advisory system.
  This work focuses on developing a sequence to sequence long-short term memory
(LSTM)-based driver behavior model that models the interaction of the human
driver to a suggested desired vehicle speed trajectory in real-world
conditions. A driving simulator is used for data collection and training the
driver model, which is then compared against the driving data and a
deterministic model. Results show close proximity of the LSTM-based model with
the driving data, demonstrating that the model can be adopted as a tool to
design human-centered speed advisory systems.


  Currently, adaptive voice applications supported by voice assistants (VA) are
very popular (i.e., Alexa skills and Google Home Actions). Under this
circumstance, how to design and evaluate these voice interactions well is very
important. In our study, we developed a voice crawler to collect responses from
100 most popular Alexa skills under 10 different categories and evaluated these
responses to find out how they comply with 8 selected design guidelines
published by Amazon. Our findings show that basic commands support are the most
followed ones while those related to personalised interaction are relatively
less. There also exists variation in design guidelines compliance across
different skill categories. Based on our findings and real skill examples, we
offer suggestions for new guidelines to complement the existing ones and
propose agendas for future HCI research to improve voice applications' user
experiences.


  We report on the first dedicated monitoring campaign of spectroscopic
variability in young brown dwarfs. High-resolution optical spectra of six
targets in nearby star-forming regions were obtained over 11 nights between
2005 January-March on the Magellan 6.5m telescope. We find significant
variability in Halpha and a number of other emission lines related to accretion
and outflow processes on a variety of timescales ranging from hours to weeks to
years. The most dramatic changes are seen for 2M1207, 2M1101 and ChaI-ISO217.
We observe possible accretion rate changes by about an order of magnitude in
two of these objects, over timescales of weeks (2M1207) or hours (2M1101). The
accretion 'burst' seen in 2M1101 could be due to a 'clumpy' flow. We also see
indications for changes in the outflow rate in at least three objects. In one
case (ISO217), there appears to be a ~1-hour time lag between outflow and
accretion variations, consistent with a scenario in which the wind originates
from the inner disk edge. Our variability study supports a close to edge-on
inclination for the brown dwarf LS-RCrA 1. The fact that all targets in our
sample show variations in accretion and/or outflow indicators suggests that
studies of young brown dwarf properties should be based either on large samples
or time series. As an example, we demonstrate that the large scatter in the
recently found accretion rate vs. mass relationship can be explained primarily
with variability. The observed profile variations imply asymmetric accretion
flows in brown dwarfs, which, in turn, is evidence for magnetic funneling by
large-scale fields. We show that accreting sub-stellar objects may harbor
magnetic fields with ~kG strength (abridged).


  Nonconvex penalty methods for sparse modeling in linear regression have been
a topic of fervent interest in recent years. Herein, we study a family of
nonconvex penalty functions that we call the trimmed Lasso and that offers
exact control over the desired level of sparsity of estimators. We analyze its
structural properties and in doing so show the following:
  1) Drawing parallels between robust statistics and robust optimization, we
show that the trimmed-Lasso-regularized least squares problem can be viewed as
a generalized form of total least squares under a specific model of
uncertainty. In contrast, this same model of uncertainty, viewed instead
through a robust optimization lens, leads to the convex SLOPE (or OWL) penalty.
  2) Further, in relating the trimmed Lasso to commonly used sparsity-inducing
penalty functions, we provide a succinct characterization of the connection
between trimmed-Lasso- like approaches and penalty functions that are
coordinate-wise separable, showing that the trimmed penalties subsume existing
coordinate-wise separable penalties, with strict containment in general.
  3) Finally, we describe a variety of exact and heuristic algorithms, both
existing and new, for trimmed Lasso regularized estimation problems. We include
a comparison between the different approaches and an accompanying
implementation of the algorithms.


  We consider Quantum Electrodynamics with an even number $N_f$ of bosonic or
fermionic flavors, allowing for interactions respecting at least $U(N_f/2)^2$
global symmetry. Both in the bosonic and in the fermionic case, we find four
interacting fixed points: two with $U(N_f/2)^2$ symmetry, two with $U(N_f)$
symmetry. Large $N_f$ arguments suggest that, lowering $N_f$, all these fixed
points merge pairwise and become complex CFT's. In the bosonic QED's the
merging happens around $N_f\sim 9{-}11$ and does not break the global symmetry.
In the fermionic QED's the merging happens around $N_f\sim3{-}7$ and breaks
$U(N_f)$ to $U(N_f/2)^2$. When $N_f=2$, we show that all four bosonic fixed
points are one-to-one dual to the fermionic fixed points. The merging pattern
suggested at large $N_f$ is consistent with the four $N_f=2$ boson $\lra$
fermion dualities, providing support to the validity of the scenario.


  We study the differential-geometric properties of the loci of fixed points of
the elliptic isometries of the manifold of definite positive real matrices with
the trace metric. We also give an explicit description of such loci and in
particular we find their De Rham decomposition.


  We present a mass measurement for the secondary lens along the line of sight
(LoS) in the multi-plane strong lens modeling of the group-scale lens CASSOWARY
31 (CSWA 31). The secondary lens at redshift $z = 1.49$ is a spiral galaxy well
aligned along the LoS with the main lens at $z = 0.683$. Using the MUSE
integral-field spectroscopy of this spiral galaxy, we measure its rotation
velocities and determine the mass from the gas kinematics. We compare the mass
estimation of the secondary lens from the lensing models to the mass
measurement from kinematics, finding that the predictions from strong lensing
tend to be higher. By introducing an additional lens plane at $z = 1.36$ for an
overdensity known to be present, we find a mass of $\simeq 10^{10}$ M$_\odot$
enclosed within 3.3 kpc from the centroid of the spiral galaxy, approaching the
estimate from kinematics. This shows that secondary-lens mass measurements from
multiple-plane modeling are affected by systematic uncertainties from the
degeneracies between lens planes and the complex LoS structure. Conducting a
detailed analysis of the LoS structures is therefore essential to improve the
mass measurement of the secondary lens.


  A complexification of the twisted $\N=2$ theory allows one to determine the
N=4 Yang--Mills theory in its third twist formulation. The imaginary part of
the gauge symmetry is used to eliminate two scalars fields and create gauge
covariant longitudinal components for the imaginary part of the gauge field.
The latter becomes the vector field of the thirdly twisted $\N=4$ theory.
Eventually, one gets a one to one correspondence between the fields of both
theories. Analogous complexifications can be done for topological 2d-gravity
and topological sigma models.


  We revisit the studies of the isotopic shift in the charge radii of {\it
even-even} isotopes of Sn and Pb nuclei at $N$ = 82, and 126, respectively,
within the relativistic mean-field and Relativistic-Hartree-Bogoliubov
approach. The shell model is also used to estimate isotopic shift in these
nuclei, for the first time, to the best of our knowledge. The ground state
single-particle energies ($spe$) are calculated for non-linear NL3 \& NL3$^*$
and density-dependent DD-ME2 parameter sets compared with the experimental
data, wherever available. We establish a correlation between the filling of
single-particle levels and the isotopic shift in occupation probabilities. The
obtained $spe$ from the relativistic mean-field and
Relativistic-Hartree-Bogoliubov approaches are in line with those used in the
shell model and experimental data for both the Sn and Pb isotopic chains. The
shell model calculated isotopic shift agrees with relativistic mean-field and
Relativistic-Hartree-Bogoliubov approaches that explain the experimental data
quite well.


  We propose a geometric multiparty extension of Clauser-Horne (CH) inequality.
The standard CH inequality can be shown to be an implication of the fact that
statistical separation between two events, $A$ and $B$, defined as $P(A\oplus
B)$, where $A\oplus B=(A-B)\cup(B-A)$, satisfies the axioms of a distance. Our
extension for tripartite case is based on triangle inequalities for the
statistical separations of three probabilistic events $P(A\oplus B \oplus C)$.
We show that Mermin inequality can be retrieved from our extended CH inequality
for three subsystems. With our tripartite CH inequality, we investigate quantum
violations by GHZ-type and W-type states. Our inequalities are compared to
another type, so-called $N$-site CH inequality. In addition we argue how to
generalize our method for more subsystems and measurement settings. Our method
can be used to write down several Bell-type inequalities in a systematic
manner.


  We prove some general Sobolev-Orlicz, Nash and Faber-Krahn inequalities for
positive operators of given ultracontractive norms of the spectral projectors
on ]0, lambda]. For invariant operators on coverings of finite simplicial
complexes this "ultracontractive spectral decay" is equivalent to von-Neumann's
spectral density function. This allows in the polynomial decay case to relate
the Novikov-Shubin numbers of such coverings to Sobolev inequalities on exact
$\ell^2$-cochains, and to the vanishing of the torsion of the
$\ell^{p,2}$-cohomology for some $p \geq 2$.


  This article briefly reviews our current understanding (or lack thereof) of
the evolution of magnetic fields in neutron stars, with an emphasis on the
binary systems. In particular, the significance of the newly emerging
population of accreting millisecond pulsars (AMXP) is discussed.


  We analyze the constraints imposed by experimental upper limits on electric
dipole moments (EDMs) within the Maximally CP- and Minimally Flavour-Violating
(MCPMFV) version of the MSSM. Since the MCPMFV scenario has 6 non-standard
CP-violating phases, in addition to the CP-odd QCD vacuum phase \theta_QCD,
cancellations may occur among the CP-violating contributions to the three
measured EDMs, those of the Thallium, neutron and Mercury, leaving open the
possibility of relatively large values of the other CP-violating observables.
We develop a novel geometric method that uses the small-phase approximation as
a starting point, takes the existing EDM constraints into account, and enables
us to find maximal values of other CP-violating observables, such as the EDMs
of the Deuteron and muon, the CP-violating asymmetry in b --> s \gamma decay,
and the B_s mixing phase. We apply this geometric method to provide upper
limits on these observables within specific benchmark supersymmetric scenarios,
including extensions that allow for a non-zero \theta_QCD.


  We suggest a modification of the Barrett-Crane spin foam model of
4-dimensional Lorentzian general relativity motivated by the canonical
quantization. The starting point is Lorentz covariant loop quantum gravity. Its
kinematical Hilbert space is found as a space of the so-called projected spin
networks. These spin networks are identified with the boundary states of a spin
foam model and provide a generalization of the unique Barrette-Crane
intertwiner. We propose a way to modify the Barrett-Crane quantization
procedure to arrive at this generalization: the B field (bi-vectors) should be
promoted not to generators of the gauge algebra, but to their certain
projection. The modification is also justified by the canonical analysis of
Plebanski formulation. Finally, we compare our construction with other
proposals to modify the Barret-Crane model.


  Let R be an integral domain and I a nonzero ideal of R. A sub-ideal J of I is
a t-reduction of I if (JI^{n})_{t}=(I^{n+1})_{t} for some positive integer n.
An element x in R is t-integral over I if there is an equation x^{n} +
a_{1}x^{n-1} +...+ a_{n-1}x + a_{n} = 0 with a_{i} in (I^{i})_{t} for I =
1,...,n. The set of all elements that are t-integral over I is called the
t-integral closure of I. This paper investigates the t-reductions and
t-integral closure of ideals. Our objective is to establish satisfactory
t-analogues of well-known results, in the literature, on the integral closure
of ideals and its correlation with reductions. Namely, Section 2 identifies
basic properties of t-reductions of ideals and features explicit examples
discriminating between the notions of reduction and t-reduction. Section 3
investigates the concept of t-integral closure of ideals, including its
correlation with t-reductions. Section 4 studies the persistence and
contraction of t-integral closure of ideals under ring homomorphisms. All along
the paper, the main results are illustrated with original examples.


  We study the dynamics of dark-bright solitons in binary mixtures of Bose
gases at finite temperature using a system of two coupled dissipative
Gross-Pitaevskii equations. We develop a perturbation theory for the
two-component system to derive an equation of motion for the soliton centers
and identify different temperature-dependent damping regimes. We show that the
effect of the bright ("filling") soliton component is to partially stabilize
"bare" dark solitons against temperature-induced dissipation, thus providing
longer lifetimes. We also study analytically thermal effects on dark-bright
soliton "molecules" (i.e., two in- and out-of-phase dark-bright solitons),
showing that they undergo expanding oscillations while interacting. Our
analytical findings are in good agreement with results obtained via a
Bogoliubov-de Gennes analysis and direct numerical simulations.


  Temporal language grounding in videos aims to localize the temporal span
relevant to the given query sentence. Previous methods treat it either as a
boundary regression task or a span extraction task. This paper will formulate
temporal language grounding into video reading comprehension and propose a
Relation-aware Network (RaNet) to address it. This framework aims to select a
video moment choice from the predefined answer set with the aid of
coarse-and-fine choice-query interaction and choice-choice relation
construction. A choice-query interactor is proposed to match the visual and
textual information simultaneously in sentence-moment and token-moment levels,
leading to a coarse-and-fine cross-modal interaction. Moreover, a novel
multi-choice relation constructor is introduced by leveraging graph convolution
to capture the dependencies among video moment choices for the best choice
selection. Extensive experiments on ActivityNet-Captions, TACoS, and
Charades-STA demonstrate the effectiveness of our solution. Codes have been
available.


  We present quantitative predictions of the performance of nanotubes and
nanowires as antennas, including the radiation resistance, the input reactance
and resistance, and antenna efficiency, as a function of frequency and nanotube
length. Particular attention is paid to the quantum capacitance and kinetic
inductance. In so doing, we also develop a circuit model for a transmission
line made of two parallel nanotubes, which has applications for
nano-interconnect technology.


  The Fano-Kondo effect in zero-bias conductance is investigated based on a
theoretical model for the T-shaped quantum dot. The conductance as a function
of the gate voltage is generally characterized by a Fano asymmetric parameter
q. With varying temperature the conductance shows a crossover between the high
and low temperature regions compared with the Kondo temperature T_K: two Fano
asymmetric peaks at high temperatures and the Fano-Kondo plateau inside a Fano
peak at low temperatures. Temperature dependence of conductance is calculated
numerically by the Finite temperature density matrix renormalization group
method (FT-DMRG).


  We compare the colimit and 2-colimit of strict 2-functors in the 2-category
of groupoids, over a certain type of posets. These posets are of special
importance, as they correspond to coverings of a topological space. The main
result of this paper gives conditions on the 2-functor $\mathfrak{F}$, for
which $\mathsf{colim}\mathfrak{F}\simeq2\mathsf{colim}\mathfrak{F}$. One can
easily see that any 2-functor $\mathfrak{F}$ can be deformed to a 2-functor
$\mathfrak{F}'$, which satisfied the conditions of the theorem.
  At last, we also optimise our conditions, reducing from exponential to
polynomial complexity.


  In this paper, we first generalize a new energy approach, developed by Y. Guo
and Y. Wang \cite{GW12}, in the framework of homogeneous Besov spaces for
proving the optimal temporal decay rates of solutions to the fractional power
dissipative equation, then we apply this approach to the supercritical and
critical quasi-geostrophic equation and the critical Keller-Segel system. We
show that the negative Besov norm of solutions is preserved along time
evolution, and obtain the optimal temporal decay rates of the spatial
derivatives of solutions by the Fourier splitting approach and the
interpolation techniques.


  It has been recently shown that, in the first order (Palatini) formalism,
there is universality of Einstein equations and Komar energy-momentum complex,
in the sense that for a generic nonlinear Lagrangian depending only on the
scalar curvature of a metric and a torsionless connection one always gets
Einstein equations and Komar's expression for the energy-momentum complex. In
this paper a similar analysis (also in the framework of the first order
formalism) is performed for all nonlinear Lagrangians depending on the
(symmetrized) Ricci square invariant. The main result is that the universality
of Einstein equations and Komar energy-momentum complex also extends to this
case (modulo a conformal transformation of the metric).


  In this paper we compute and analyse the transition rates and duration of
reactive trajectories of the stochastic 1-D Allen-Cahn equations for both the
Freidlin-Wentzell regime (weak noise or temperature limit) and finite-amplitude
white noise, as well as for small and large domain. We demonstrate that
extremely rare reactive trajectories corresponding to direct transitions
between two metastable states are efficiently computed using an algorithm
called adaptive multilevel splitting. This algorithm is dedicated to the
computation of rare events and is able to provide ensembles of reactive
trajectories in a very efficient way. In the small noise limit, our numerical
results are in agreement with large-deviation predictions such as
instanton-like solutions, mean first passages and escape probabilities. We show
that the duration of reactive trajectories follows a Gumbel distribution like
for one degree of freedom systems. Moreover, the mean duration growths
logarithmically with the inverse temperature. The prefactor given by the
potential curvature grows exponentially with size. The main novelty of our work
is that we also perform an analysis of reactive trajectories for large noises
and large domains. In this case, we show that the position of the reactive
front is essentially a random walk. This time, the mean duration grows linearly
with the inverse temperature and quadratically with the size. Using a
phenomenological description of the system, we are able to calculate the
transition rate, although the dynamics is described by neither
Freidlin--Wentzell or Eyring--Kramers type of results. Numerical results
confirm our analysis.


  Let $X$ be a locally compact Abelian group, $Y$ be its character group.
Following A. Kagan and G. Sz\'ekely we introduce a notion of $Q$-independence
for random variables with values in $X$. We prove group analogues of the
Cram\'er, Kac-Bernstein, Skitovich-Darmois and Heyde theorems for
$Q$-independent random variables with values in $X$. The proofs of these
theorems are reduced to solving some functional equations on the group $Y$.


  The problem of description of superintegrable systems (i.e., systems with
closed trajectories in a certain domain) in the class of rotationally symmetric
natural mechanical systems goes back to Bertrand and Darboux. We describe all
superintegrable (in a domain of slow motions) systems in the class of
rotationally symmetric magnetic geodesic flows. We show that all sufficiently
slow motions in a central magnetic field on a two-dimensional manifold of
revolution are periodic if and only if the metric has a constant scalar
curvature and the magnetic field is homogeneous, i.e. proportional to the area
form.


  A theory for anomalous enhancement of NMR relaxation rate $1/T_{1}T$ of
$^{125}$Te toward zero temperature observed in Pb$_{1-x}$Tl$_{x}$Te ($x$=0.01)
is presented on the idea of the charge Kondo effect of valence skipping element
Tl. It is found that such enhancement in $1/T_{1}T$ is caused through
enhancement of the pair-hopping and inter-orbital interactions between 6s
electrons localized on Tl site and conduction electrons doped in the hole band
the semiconductor PbTe, which is the heart of the charge Kondo effect. It is
also found that the Knight shift is not influenced in the temperature region
where the relaxation rate is enhanced which is consistent with the experimental
observation showing that the Korringa relation is apparently broken.


  Robotic mobility in microgravity is necessary to expand human utilization and
exploration of outer space. Bio-inspired multi-legged robots are a possible
solution for safe and precise locomotion. However, a dynamic motion of a robot
in microgravity can lead to failures due to gripper detachment caused by
excessive motion reactions. We propose a novel Reaction-Aware Motion Planning
(RAMP) to improve locomotion safety in microgravity, decreasing the risk of
losing contact with the terrain surface by reducing the robot's momentum
change. RAMP minimizes the swing momentum with a Low-Reaction Swing Trajectory
(LRST) while distributing this momentum to the whole body, ensuring zero
velocity for the supporting grippers and minimizing motion reactions. We verify
the proposed approach with dynamic simulations indicating the capability of
RAMP to generate a safe motion without detachment of the supporting grippers,
resulting in the robot reaching its specified location. We further validate
RAMP in experiments with an air-floating system, demonstrating a significant
reduction in reaction forces and improved mobility in microgravity.


  The evolution equations for the generalized microscopic phase densities are
introduced. The evolution equations of average values of microscopic phase
densities are derived and a solution of the initial-value problem of the
obtained hydrodynamic type hierarchy is constructed.


  An algorithm to calculate the density of states, based on the well-known
Wang-Landau method, is introduced. Independent random walks are performed in
different restricted ranges of energy, and the resultant density of states is
modified by a function of time, F(t)=1/t, for large time. As a consequence, the
calculated density of state, gm(E,t), approaches asymptotically the exact value
gex(E) as 1/sqrt(t), avoiding the saturation of the error. It is also shown
that the growth of the interface of the energy histogram belongs to the random
deposition universality class.


  The generalized Stirling numbers of the second kind together with the
Stirling numbers of the first kind are used to present a novel method to
approximate the Riemann zeta function at integer values by rationals. We show
that the error committed in such approximation decay exponentially to zero


  The comparative study of the substitution of zinc and lithium for iron in the
"114" ferrites, YBaFe4O7 and CaBaFe4O7, shows that these diamagnetic cations
play a major role in tuning the competition between ferrimagnetism and magnetic
frustration in these oxides. The substitution of Li or Zn for Fe in the cubic
phase YBaFe4O7 leads to a structural transition to a hexagonal phase
YBaFe4-xMxO7, for M = Li (0.30 < x < 0.75) and for M = Zn (0.40 < x < 1.50). It
is seen that for low doping values i.e. x = 0.30 (for Li) and x = 0.40 (for
Zn), these diamagnetic cations induce a strong ferrimagnetic component in the
samples, in contrast to the spin glass behaviour of the cubic phase. In all the
hexagonal phases, YBaFe4-xMxO7 and CaBaFe4-xMxO7 with M = Li and Zn, it is seen
that in the low doping regime (x ~ 0.3 to 0.5), the competition between
ferrimagnetism and 2 D magnetic frustration is dominated by the average valency
of iron. In contrast, in the high doping regime (x ~ 1.5), the emergence of a
spin glass is controlled by the high degree of cationic disorder, irrespective
of the iron valency.


  We study the early-stage kinetics of thermodynamically unstable systems with
quenched disorder. We show analytically that the growth of initial fluctuations
is amplified by the presence of disorder. This is confirmed by numerical
simulations of morphological phase separation (MPS) in thin liquid films and
spinodal decomposition (SD) in binary mixtures. We also discuss the
experimental implications of our results.


  Semi-supervised learning (SSL) is a widely used technique in scenarios where
labeled data is scarce and unlabeled data is abundant. While SSL is popular for
image and text classification, it is relatively underexplored for the task of
extractive text summarization. Standard SSL methods follow a teacher-student
paradigm to first train a classification model and then use the classifier's
confidence values to select pseudo-labels for the subsequent training cycle;
however, such classifiers are not suitable to measure the accuracy of
pseudo-labels as they lack specific tuning for evaluation, which leads to
confidence values that fail to capture the semantics and correctness of the
generated summary. To address this problem, we propose a prompt-based
pseudo-labeling strategy with LLMs that picks unlabeled examples with more
accurate pseudo-labels than using just the classifier's probability outputs.
Our approach also includes a relabeling mechanism that improves the quality of
pseudo-labels. We evaluate our method on three text summarization datasets:
TweetSumm, WikiHow, and ArXiv/PubMed. We empirically show that a
prompting-based LLM that scores and generates pseudo-labels outperforms
existing SSL methods on ROUGE-1, ROUGE-2, and ROUGE-L scores on all the
datasets. Furthermore, our method achieves competitive L-Eval scores
(evaluation with LLaMa-3) as a fully supervised method in a data-scarce setting
and outperforms fully supervised method in a data-abundant setting.


  Randomized experiments have been the gold standard for assessing the
effectiveness of a treatment or policy. The classical complete randomization
approach assigns treatments based on a prespecified probability and may lead to
inefficient use of data. Adaptive experiments improve upon complete
randomization by sequentially learning and updating treatment assignment
probabilities. However, their application can also raise fairness and equity
concerns, as assignment probabilities may vary drastically across groups of
participants. Furthermore, when treatment is expected to be extremely
beneficial to certain groups of participants, it is more appropriate to expose
many of these participants to favorable treatment. In response to these
challenges, we propose a fair adaptive experiment strategy that simultaneously
enhances data use efficiency, achieves an envy-free treatment assignment
guarantee, and improves the overall welfare of participants. An important
feature of our proposed strategy is that we do not impose parametric modeling
assumptions on the outcome variables, making it more versatile and applicable
to a wider array of applications. Through our theoretical investigation, we
characterize the convergence rate of the estimated treatment effects and the
associated standard deviations at the group level and further prove that our
adaptive treatment assignment algorithm, despite not having a closed-form
expression, approaches the optimal allocation rule asymptotically. Our proof
strategy takes into account the fact that the allocation decisions in our
design depend on sequentially accumulated data, which poses a significant
challenge in characterizing the properties and conducting statistical inference
of our method. We further provide simulation evidence to showcase the
performance of our fair adaptive experiment strategy.


  The current status of observational studies of lithium depletion in open
clusters is reviewed, concentrating mainly on G and K type stars. I attempt to
answer the following questions: Can the lithium depletion patterns seen in open
clusters be explained in terms of standard stellar evolution models? What is
the observational evidence for non-standard mixing processes and on what
timescales do they operate? Does metallicity play a significant role in lithium
depletion? Can lithium still be used as a means of dating young stars? What
future observations might yield better answers to these questions?


  We present a new application of affine Lie algebras to massive quantum field
theory in 2 dimensions, by investigating the $q\to 1$ limit of the q-deformed
affine $\hat{sl(2)}$ symmetry of the sine-Gordon theory, this limit occurring
at the free fermion point. Working in radial quantization leads to a
quasi-chiral factorization of the space of fields. The conserved charges which
generate the affine Lie algebra split into two independent affine algebras on
this factorized space, each with level 1 in the anti-periodic sector, and level
$0$ in the periodic sector. The space of fields in the anti-periodic sector can
be organized using level-$1$ highest weight representations, if one supplements
the $\slh$ algebra with the usual local integrals of motion. Introducing a
particle-field duality leads to a new way of computing form-factors in radial
quantization. Using the integrals of motion, a momentum space bosonization
involving vertex operators is formulated. Form-factors are computed as vacuum
expectation values in momentum space. (Based on talks given at the Berkeley
Strings 93 conference, May 1993, and the III International Conference on
Mathematical Physics, String Theory, and Quantum Gravity, Alushta, Ukraine,
June 1993.)


  In this article we obtain estimates on the approximate eigenstructure of
channels with a spreading function supported only on a set of finite measure
$|U|$.Because in typical application like wireless communication the spreading
function is a random process corresponding to a random Hilbert--Schmidt channel
operator $\BH$ we measure this approximation in terms of the ratio of the
$p$--norm of the deviation from variants of the Weyl symbol calculus to the
$a$--norm of the spreading function itself. This generalizes recent results
obtained for the case $p=2$ and $a=1$. We provide a general approach to this
topic and consider then operators with $|U|<\infty$ in more detail. We show the
relation to pulse shaping and weighted norms of ambiguity functions. Finally we
derive several necessary conditions on $|U|$, such that the approximation error
is below certain levels.


  In this paper, we present GaNI, a Global and Near-field Illumination-aware
neural inverse rendering technique that can reconstruct geometry, albedo, and
roughness parameters from images of a scene captured with co-located light and
camera. Existing inverse rendering techniques with co-located light-camera
focus on single objects only, without modeling global illumination and
near-field lighting more prominent in scenes with multiple objects. We
introduce a system that solves this problem in two stages; we first reconstruct
the geometry powered by neural volumetric rendering NeuS, followed by inverse
neural radiosity that uses the previously predicted geometry to estimate albedo
and roughness. However, such a naive combination fails and we propose multiple
technical contributions that enable this two-stage approach. We observe that
NeuS fails to handle near-field illumination and strong specular reflections
from the flashlight in a scene. We propose to implicitly model the effects of
near-field illumination and introduce a surface angle loss function to handle
specular reflections. Similarly, we observe that invNeRad assumes constant
illumination throughout the capture and cannot handle moving flashlights during
capture. We propose a light position-aware radiance cache network and
additional smoothness priors on roughness to reconstruct reflectance.
Experimental evaluation on synthetic and real data shows that our method
outperforms the existing co-located light-camera-based inverse rendering
techniques. Our approach produces significantly better reflectance and slightly
better geometry than capture strategies that do not require a dark room.


  Intracranial EEG (iEEG) is the gold standard technique for epileptogenic zone
(EZ) localisation, but requires a hypothesis of which tissue is epileptogenic,
guided by qualitative analysis of seizure semiology and other imaging
modalities such as magnetoencephalography (MEG). We hypothesised that if
quantifiable MEG band power abnormalities were sampled by iEEG, then patients'
post-resection seizure outcome were better.
  Thirty-two individuals with neocortical epilepsy underwent MEG and iEEG
recordings as part of pre-surgical evaluation. Interictal MEG band power
abnormalities were derived using 70 healthy controls as a normative baseline.
MEG abnormality maps were compared to electrode implantation, with the spatial
overlap of iEEG electrodes and MEG abnormalities recorded. Finally, we assessed
if the implantation of electrodes in abnormal tissue, and resection of the
strongest abnormalities determined by MEG and iEEG explained surgical outcome.
  Intracranial electrodes were implanted in brain tissue with the most abnormal
MEG findings in individuals that were seizure-free post-resection (T=3.9,
p=0.003). The overlap between MEG abnormalities and iEEG electrodes
distinguished outcome groups moderately well (AUC=0.68). In isolation, the
resection of the strongest MEG and iEEG abnormalities separated surgical
outcome groups well (AUC=0.71, AUC=0.74 respectively). A model incorporating
all three features separated outcome groups best (AUC=0.80).
  Intracranial EEG is a key tool to delineate the EZ and help render patients
seizure-free after resection. We showed that data-driven abnormalities derived
from interictal MEG recordings have clinical value and may help guide electrode
placement in individuals with neocortical epilepsy. Finally, our predictive
model of post-operative seizure-freedom, which leverages both MEG and iEEG
recordings, may aid patient counselling of expected outcome.


  We address the problem of accelerating column generation for set cover
problems in which we relax the state space of the columns to do efficient
pricing. We achieve this by adapting the recently introduced smooth and
flexible dual optimal inequalities (DOI) for use with relaxed columns. Smooth
DOI exploit the observation that similar items are nearly fungible, and hence
should be associated with similarly valued dual variables. Flexible DOI exploit
the observation that the change in cost of a column induced by removing an item
can be bounded. We adapt these DOI to the problem of capacitated vehicle
routing in the context of ng-route relaxations. We demonstrate significant
speed ups on a benchmark data set, while provably not weakening the relaxation.


  Attribute reduction is one of the most important topics in rough set theory.
Heuristic attribute reduction algorithms have been presented to solve the
attribute reduction problem. It is generally known that fitness functions play
a key role in developing heuristic attribute reduction algorithms. The
monotonicity of fitness functions can guarantee the validity of heuristic
attribute reduction algorithms. In probabilistic rough set model, distribution
reducts can ensure the decision rules derived from the reducts are compatible
with those derived from the original decision table. However, there are few
studies on developing heuristic attribute reduction algorithms for finding
distribution reducts. This is partly due to the fact that there are no
monotonic fitness functions that are used to design heuristic attribute
reduction algorithms in probabilistic rough set model. The main objective of
this paper is to develop heuristic attribute reduction algorithms for finding
distribution reducts in probabilistic rough set model. For one thing, two
monotonic fitness functions are constructed, from which equivalence definitions
of distribution reducts can be obtained. For another, two modified monotonic
fitness functions are proposed to evaluate the significance of attributes more
effectively. On this basis, two heuristic attribute reduction algorithms for
finding distribution reducts are developed based on addition-deletion method
and deletion method. In particular, the monotonicity of fitness functions
guarantees the rationality of the proposed heuristic attribute reduction
algorithms. Results of experimental analysis are included to quantify the
effectiveness of the proposed fitness functions and distribution reducts.


  First essential m-dissipativity of an infinite-dimensional Ornstein-Uhlenbeck
operator $N$, perturbed by the gradient of a potential, on a domain
$\mathcal{F}C_b^{\infty}$ of finitely based, smooth and bounded functions, is
shown. Our considerations allow unbounded diffusion operators as coefficients.
We derive corresponding second order regularity estimates for solutions $f$ of
the Kolmogorov equation $\alpha f-Nf=g$, $\alpha \in (0,\infty)$, generalizing
some results of Da Prato and Lunardi. Second we prove essential m-dissipativity
for generators $(L_{\Phi},\mathcal{F}C_b^{\infty})$ of infinite-dimensional
non-linear degenerate diffusion processes. We emphasize that the essential
m-dissipativity of $(L_{\Phi},\mathcal{F}C_b^{\infty})$ is useful to apply
general resolvent methods developed by Beznea, Boboc and R\"ockner, in order to
construct martingale/weak solutions to infinite-dimensional non-linear
degenerate diffusion equations. Furthermore, the essential m-dissipativity of
$(L_{\Phi},\mathcal{F}C_b^{\infty})$ and $(N,\mathcal{F}C_b^{\infty})$, as well
as the regularity estimates are essential to apply the general abstract Hilbert
space hypocoercivity method from Dolbeault, Mouhot, Schmeiser and Grothaus,
Stilgenbauer, respectively, to the corresponding diffusions.


  We describe NIMBLE, a system for programming statistical algorithms for
general model structures within R. NIMBLE is designed to meet three challenges:
flexible model specification, a language for programming algorithms that can
use different models, and a balance between high-level programmability and
execution efficiency. For model specification, NIMBLE extends the BUGS language
and creates model objects, which can manipulate variables, calculate log
probability values, generate simulations, and query the relationships among
variables. For algorithm programming, NIMBLE provides functions that operate
with model objects using two stages of evaluation. The first stage allows
specialization of a function to a particular model and/or nodes, such as
creating a Metropolis-Hastings sampler for a particular block of nodes. The
second stage allows repeated execution of computations using the results of the
first stage. To achieve efficient second-stage computation, NIMBLE compiles
models and functions via C++, using the Eigen library for linear algebra, and
provides the user with an interface to compiled objects. The NIMBLE language
represents a compilable domain-specific language (DSL) embedded within R. This
paper provides an overview of the design and rationale for NIMBLE along with
illustrative examples including importance sampling, Markov chain Monte Carlo
(MCMC) and Monte Carlo expectation maximization (MCEM).


  As an alternative approach to ionic data transmission with hydrogel as
substrate, this work explores the possible applications of liquid electrolyte
filling cavity of a stretchable, flexible elastomeric tubing, which is the
primary ingredient used in redox flow battery systems. While hydrogel-based
ionic impedance characterization and its data communication capability have
been well studied, the multifunctional use of redox flow battery electrolyte
for data communication, in addition to powering, is novel, especially in the
context of soft robotics. This work also describes a simple signal conditioning
technique that addresses the signal decaying problem due to long-distance ionic
data transmission. Finally, we describe the prototypical design of a
decentralized data communication between two, or possibly more, systems of
power and control. This work presents the concept of a decentralized control of
a robot's hydraulic actuation system, that will allow for the system's
functional robustness in the event of one of the two, or possibly more, modules
lose power or become severed from the robot's body.


  We map out the ground state phase diagram of a one-dimensional SU($N$)
Kondo-Heisenberg lattice model at half filling and in the fully antisymmetric
self-adjoint representation as a function of $\tfrac {1}{N}$ and Kondo coupling
$J_k/t$. On the basis of auxiliary field quantum Monte Carlo (QMC) simulations
with even $N$ up to 8, we show that the enlarged SU($N\ge 4$) symmetry realizes
a quantum phase transition separating a valence bond solid (VBS) phase
occupying a weak coupling part of the phase diagram and the Kondo insulator
(KI) state dominating in the strong $J_k/t$ limit. Along the phase boundary, we
always observe critical exponents that belong to a two-dimensional classical
Ising universality class. We next trace the evolution of the composite fermion
and spin spectral functions across the phase boundary and conclude that VBS
order triggers a bond order wave state of conduction electrons, both coexisting
with Kondo screening. Upon further reducing $J_k/t$ we observe that composite
quasiparticles lose their spectral weight indicating that VBS order gradually
liberates localized $f$ spins from forming Kondo singlets with conduction
electron spins. We contrast the QMC results with a static large-$N$
approximation, and we show that in the limit of infinite degeneracy
$N\to\infty$ the order-disorder transition becomes of first order and is
accompanied by a full decoupling of conduction electrons and localized $f$
spins. We complete our analysis by considering the limit of a vanishing
Heisenberg coupling $J_h=0$, and we provide evidence that the enlarged SU($N$)
symmetry and low dimensionality of the RKKY exchange interaction are not
sufficient to induce VBS order in the conventional SU($N$) Kondo chain which
hosts solely a KI phase.


  We reconsider the problem of U(1) flux and D0-charge for D-branes in the WZW
model and investigate the relationship between the different definitions that
have been proposed recently. We identify the D0-charge as a particular
reduction of a class in the relative cohomology of the group modulo the
D-submanifold. We investigate under which conditions this class is equivalent
to the first Chern class of a line bundle on the D-submanifold and we find that
in general there is an obstruction given by the cohomology class of the NS
3-form. Therefore we conclude that for topologically nontrivial B-fields, there
is strictly speaking no U(1) gauge field on the D-submanifold. Nevertheless the
ambiguity in the flux is not detected by the D0-charge. This has a natural
interpretation in terms of gerbes.


  After presenting various concepts and results concerning the classical
Steklov eigenproblem, we focus on analogous problems for time-harmonic
Maxwell's equations in a cavity. In this direction, we discuss recent rigorous
results concerning natural Steklov boundary value problems for the curlcurl
operator. Moreover, we explicitly compute eigenvalues and eigenfunctions in the
unit ball of the three-dimensional Euclidean space by using classical vector
spherical harmonics.


  Data augmentation is a widely used technique in many machine learning tasks,
such as image classification, to virtually enlarge the training dataset size
and avoid overfitting. Traditional data augmentation techniques for image
classification tasks create new samples from the original training data by, for
example, flipping, distorting, adding a small amount of noise to, or cropping a
patch from an original image. In this paper, we introduce a simple but
surprisingly effective data augmentation technique for image classification
tasks. With our technique, named SamplePairing, we synthesize a new sample from
one image by overlaying another image randomly chosen from the training data
(i.e., taking an average of two images for each pixel). By using two images
randomly selected from the training set, we can generate $N^2$ new samples from
$N$ training samples. This simple data augmentation technique significantly
improved classification accuracy for all the tested datasets; for example, the
top-1 error rate was reduced from 33.5% to 29.0% for the ILSVRC 2012 dataset
with GoogLeNet and from 8.22% to 6.93% in the CIFAR-10 dataset. We also show
that our SamplePairing technique largely improved accuracy when the number of
samples in the training set was very small. Therefore, our technique is more
valuable for tasks with a limited amount of training data, such as medical
imaging tasks.


  By constructing the recovery operations of the protocol of remote
implementation of partially unknown quantum operation of two qubits [An Min
Wang: PRA, \textbf{74}, 032317(2006)], we present a scheme to implement it in
cavity QED. Long-lived Rydberg atoms are used as qubits, and the interaction
between the atoms and the field of cavity is a nonresonant one. Finally, we
analyze the experimental feasibility of this scheme.


  Holography encodes the three dimensional (3D) information of a sample in the
form of an intensity-only recording. However, to decode the original sample
image from its hologram(s), auto-focusing and phase-recovery are needed, which
are in general cumbersome and time-consuming to digitally perform. Here we
demonstrate a convolutional neural network (CNN) based approach that
simultaneously performs auto-focusing and phase-recovery to significantly
extend the depth-of-field (DOF) in holographic image reconstruction. For this,
a CNN is trained by using pairs of randomly de-focused back-propagated
holograms and their corresponding in-focus phase-recovered images. After this
training phase, the CNN takes a single back-propagated hologram of a 3D sample
as input to rapidly achieve phase-recovery and reconstruct an in focus image of
the sample over a significantly extended DOF. This deep learning based DOF
extension method is non-iterative, and significantly improves the algorithm
time-complexity of holographic image reconstruction from O(nm) to O(1), where n
refers to the number of individual object points or particles within the sample
volume, and m represents the focusing search space within which each object
point or particle needs to be individually focused. These results highlight
some of the unique opportunities created by data-enabled statistical image
reconstruction methods powered by machine learning, and we believe that the
presented approach can be broadly applicable to computationally extend the DOF
of other imaging modalities.


  Metric localization plays a critical role in vision-based navigation. For
overcoming the degradation of matching photometry under appearance changes,
recent research resorted to introducing geometry constraints of the prior scene
structure. In this paper, we present a metric localization method for the
monocular camera, using the Signed Distance Field (SDF) as a global map
representation. Leveraging the volumetric distance information from SDFs, we
aim to relax the assumption of an accurate structure from the local Bundle
Adjustment (BA) in previous methods. By tightly coupling the distance factor
with temporal visual constraints, our system corrects the odometry drift and
jointly optimizes global camera poses with the local structure. We validate the
proposed approach on both indoor and outdoor public datasets. Compared to the
state-of-the-art methods, it achieves a comparable performance with a minimal
sensor configuration.


  We present a simple mechanical model for dynamic wetting phenomena. Metallic
balls spread along a periodically corrugated surface simulating molecules of
liquid advancing along a solid substrate. A vertical stack of balls mimics a
liquid droplet. Stochastic motion of the balls, driven by mechanical vibration
of the corrugated surface, induces diffusional motion. Simple theoretical
estimates are introduced and agree with the results of the analog experiments,
with numerical simulation, and with experimental data for microscopic spreading
dynamics.


  The time variation of contacts in a networked system may fundamentally alter
the properties of spreading processes and affect the condition for large-scale
propagation, as encoded in the epidemic threshold. Despite the great interest
in the problem for the physics, applied mathematics, computer science and
epidemiology communities, a full theoretical understanding is still missing and
currently limited to the cases where the time-scale separation holds between
spreading and network dynamics or to specific temporal network models. We
consider a Markov chain description of the Susceptible-Infectious-Susceptible
process on an arbitrary temporal network. By adopting a multilayer perspective,
we develop a general analytical derivation of the epidemic threshold in terms
of the spectral radius of a matrix that encodes both network structure and
disease dynamics. The accuracy of the approach is confirmed on a set of
temporal models and empirical networks and against numerical results. In
addition, we explore how the threshold changes when varying the overall time of
observation of the temporal network, so as to provide insights on the optimal
time window for data collection of empirical temporal networked systems. Our
framework is both of fundamental and practical interest, as it offers novel
understanding of the interplay between temporal networks and spreading
dynamics.


  We study subelliptic biharmonic maps, i.e. smooth maps from a compact
strictly pseudoconvex CR manifold M into a Riemannian manifold N which are
critical points of a certain bienergy functional. We show that a map is
subelliptic biharmonic if and only if its vertical lift to the (total space of
the) canonical circle bundle is a biharmonic map with respect to the Fefferman
metric.


  We report the measurement of the inhomogeneous linewidth, homogeneous
linewidth and spin state lifetime of Pr3+ ions in a novel waveguide
architecture. The TeO2 slab waveguide deposited on a bulk Pr3+:Y2SiO5 crystal
allows the 3H4 - 1D2 transition of Pr3+ ions to be probed by the optical
evanescent field that extends into the substrate. The 2 GHz inhomogeneous
linewidth, the optical coherence time of 70 +- 5 us, and the spin state
lifetime of 9.8 +- 0.3 s indicate that the properties of ions interacting with
the waveguide mode are consistent with those of bulk ions. This result
establishes the foundation for large, integrated and high performance
rare-earth-ion quantum systems based on a waveguide platform.


  In this paper, we use a topological quantum field theory (TQFT) to define
families of new homology theories of a $2$-dimensional CW complex of a smooth
closed surface. The dimensions of these homology groups can be used to count
the number of ways that each face of the CW complex can be colored with one of
$n$ colors so that no two adjacent faces have the same color. We use these
homologies to define new invariants of graphs, give new characterizations of
well-known polynomial invariants of graphs, and rephrase and offer new
approaches to famous conjectures about graph coloring. In particular, we show
that the TQFT has the potential to generate $4$-face colorings of a bridgeless
planar graph, leading to a constructive approach to the four color theorem. The
TQFT has ramifications for the study of smooth surfaces and provides examples
of new types of Frobenius algebras.


  Suppose we are given finitely generated groups $\Gamma_1,...,\Gamma_m$
equipped with irreducible random walks. Thereby we assume that the expansions
of the corresponding Green functions at their radii of convergence contain only
logarithmic or algebraic terms as singular terms up to sufficiently large order
(except for some degenerate cases). We consider transient random walks on the
free product {$\Gamma_1 \ast ... \ast\Gamma_m$} and give a complete
classification of the possible asymptotic behaviour of the corresponding
$n$-step return probabilities. They either inherit a law of the form
$\varrho^{n\delta} n^{-\lambda_i} \log^{\kappa_i}n$ from one of the free
factors $\Gamma_i$ or obey a $\varrho^{n\delta} n^{-3/2}$-law, where
$\varrho<1$ is the corresponding spectral radius and $\delta$ is the period of
the random walk. In addition, we determine the full range of the asymptotic
behaviour in the case of nearest neighbour random walks on free products of the
form $\Z^{d_1}\ast ... \ast \Z^{d_m}$. Moreover, we characterize the possible
phase transitions of the non-exponential types $n^{-\lambda_i}\log^{\kappa_i}n$
in the case $\Gamma_1\ast\Gamma_2$.


  The paper focuses on the automatic generating of the witnesses for the word
equation satisfiability problem by means of specializing an interpreter which
tests whether a composition of variable substitutions of a given word equation
system produces its solution. We specialize such an interpreter w.r.t. the
equation system, while the substitutions are unknown. We show that several
variants of such interpreters, when specialized using the basic unfold/fold
specialization methods, are able to construct the whole solution sets for some
classes of the word equations whose left- and right-hand sides share variables.
We prove that the specialization process wrt the constructed interpreters gives
a simple syntactic criterion of the satisfiability of the equations considered,
and show that the suggested approach can solve some equations not solvable by
Z3str3 and CVC4, the widely-used SMT-solvers.


  The dwarf spheroidal galaxies (dSphs) in the Milky Way are the primary
targets in the indirect searches for particle dark matter. To set robust
constraints on candidate of dark matter particles, understanding the dark halo
structure of these systems is of substantial importance. In this paper, we
first evaluate the astrophysical factors for dark matter annihilation and decay
for 24 dSphs, with taking into account a non-spherical dark halo, using
generalized axisymmetric mass models based on axisymmetric Jeans equations.
First, from a fitting analysis of the most recent kinematic data available, our
axisymmetric mass models are a much better fit than previous spherical ones,
thus, our work should be the most realistic and reliable estimator for
astrophysical factors. Secondly, we find that among analysed dSphs, the
ultra-faint dwarf galaxies Triangulum II and Ursa Major II are the most
promising but large uncertain targets for dark matter annihilation while the
classical dSph Draco is the most robust and detectable target for dark matter
decay. It is also found that the non-sphericity of luminous and dark components
influences the estimate of astrophysical factors, even though these factors
largely depend on the sample size, the prior range of parameters and the
spatial extent of the dark halo. Moreover, owing to these effects, the
constraints on the dark matter annihilation cross-section are more conservative
than those of previous spherical works. These results are important for
optimizing and designing dark matter searches in current and future
multi-messenger observations by space and ground-based telescopes.


  The ability of an agent to comprehend a sentence is tightly connected to the
agent's prior experiences and background knowledge. The paper suggests to
interpret comprehension as a modality and proposes a complete bimodal logical
system that describes an interplay between comprehension and knowledge
modalities.


  The Gel'fand-Yaglom theorem has been used to calculate the one-loop effective
action in quantum field theory by means of the "partial-wave-cutoff method".
This method works well for a wide class of background fields and is essentially
exact. However, its implementation has been semi-analytical so far since it
involves solving a non-linear ordinary differential equation for which
solutions are in general unknown. Within the context of quantum electrodynamics
(QED) and $O(2)\times O(3)$ symmetric backgrounds, we present two complementary
asymptotic methods that provide approximate analytical solutions to this
equation. We test these approximations for different background field
configurations and mass regimes and demonstrate that the effective action can
indeed be calculated with good accuracy using these asymptotic expressions. To
further probe these methods, we analyze the massless limit of the effective
action and obtain its divergence structure with respect to the radial
suppression parameter of the background field, comparing our findings with
previously reported results.


  The onset of condensation of hard spheres in a gravitational field is studied
using density functional theory. In particular, we find that the local density
approximation yields results identical to those obtained previously using the
kinetic theory [Physica A 271, 192, (1999)], and a weighted density functional
theory gives qualitatively similar results, namely, that the temperature at
which condensation begins at the bottom scales linearly with weight, diameter,
and number of layers of particles.


  Hybrid codes are widely used to model ion-scale phenomena in space plasmas.
Hybrid codes differ from full particle (PIC) codes in that the electrons are
modeled as a fluid that is usually assumed to be massless, while the electric
field is not advanced in time, but instead calculated at the new time level
from the advanced ion quantities and the magnetic field. In this chapter we
concentrate on such hybrid models with massless electrons, beginning with a
discussion of the basics of a simple hybrid code algorithm. We then show
examples of recent use of hybrid codes for large-scale space plasma simulations
of structures formed at planetary bow shock--foreshock systems, magnetic
reconnection at the magnetopause, and complex phenomena in the magnetosheath
due to the interaction of kinetic processes associated with the bow shock,
magnetic reconnection, and turbulence. A discussion then follows of a number of
other hybrid codes based on different algorithms that are presently in active
use to investigate a variety of plasma processes in space as well as some
recent work on the development of new models. We conclude with a few brief
comments concerning the future development and use of hybrid codes.


  We perform a coupled--channel calculation of the H dibaryon within a chiral
constituent quark model. The problem is solved within a quark model constrained
by the experimental data of strangeness --1 and --2 two--baryon systems. We
examine in detail the role played by the different contributions of the
interacting potential as well as the number of coupled channels considered.
Special attention has been payed to the parameter dependence, flavor symmetry
breaking and spatial configurations. The value extracted for the binding energy
of the H dibaryon, being compatible with the restrictions imposed by the Nagara
event, falls within a plausible extrapolation of recent lattice QCD results.


  The excess of cosmic positrons observed by the HEAT experiment may be the
result of Kaluza-Klein dark matter annihilating in the galactic halo.
Kaluza-Klein dark matter annihilates dominantly into charged leptons that yield
a large number and hard spectrum of positrons per annihilation. Given a
Kaluza-Klein dark matter particle with a mass in the range of 300-400 GeV, no
exceptional substructure or clumping is needed in the local distribution of
dark matter to generate a positron flux that explains the HEAT observations.
This is in contrast to supersymmetric dark matter that requires unnaturally
large amounts of dark substructure to produce the observed positron excess.
Future astrophysical and collider tests are outlined that will confirm or rule
out this explanation of the HEAT data.


  We introduce a new notion of commutator which depends on a choice of
subvariety in any variety of omega-groups. We prove that this notion
encompasses Higgins's commutator, Froehlich's central extensions and the
Peiffer commutator of precrossed modules.


  Cooperative perception, or collective perception (CP) is an emerging and
promising technology for intelligent transportation systems (ITS). It enables
an ITS station (ITS-S) to share its local perception information with others by
means of vehicle-to-X (V2X) communication, thereby achieving improved
efficiency and safety in road transportation. In this paper, we present our
recent progress on the development of a connected and automated vehicle (CAV)
and intelligent roadside unit (IRSU). We present three different experiments to
demonstrate the use of CP service within intelligent infrastructure to improve
awareness of vulnerable road users (VRU) and thus safety for CAVs in various
traffic scenarios. We demonstrate in the experiments that a connected vehicle
(CV) can "see" a pedestrian around the corners. More importantly, we
demonstrate how CAVs can autonomously and safely interact with walking and
running pedestrians, relying only on the CP information from the IRSU through
vehicle-to-infrastructure (V2I) communication. This is one of the first
demonstrations of urban vehicle automation using only CP information. We also
address in the paper the handling of collective perception messages (CPMs)
received from the IRSU, and passing them through a pipeline of CP information
coordinate transformation with uncertainty, multiple road user tracking, and
eventually path planning/decision making within the CAV. The experimental
results were obtained with manually driven CV, fully autonomous CAV, and an
IRSU retrofitted with vision and laser sensors and a road user tracking system.


  Deep learning has revolutionized biomedical research by providing
sophisticated methods to handle complex, high-dimensional data. Multimodal deep
learning (MDL) further enhances this capability by integrating diverse data
types such as imaging, textual data, and genetic information, leading to more
robust and accurate predictive models. In MDL, differently from early and late
fusion methods, intermediate fusion stands out for its ability to effectively
combine modality-specific features during the learning process. This systematic
review aims to comprehensively analyze and formalize current intermediate
fusion methods in biomedical applications. We investigate the techniques
employed, the challenges faced, and potential future directions for advancing
intermediate fusion methods. Additionally, we introduce a structured notation
to enhance the understanding and application of these methods beyond the
biomedical domain. Our findings are intended to support researchers, healthcare
professionals, and the broader deep learning community in developing more
sophisticated and insightful multimodal models. Through this review, we aim to
provide a foundational framework for future research and practical applications
in the dynamic field of MDL.


  We study the limiting behaviour of solutions to abelian vortex equations when
the volume of the underlying Riemann surface grows to infinity. We prove that
the solutions converge smoothly away from finitely many points. The proof
relies on a priori estimates for functions satisfying generalised Kazdan-Warner
equations. We relate our results to the work of Hong, Jost, and Struwe on
classical vortices, and that of Haydys and Walpuski on the Seiberg-Witten
equations with multiple spinors.


  We determine the tensor rank of all semifields of order 16 over
$\mathbb{F}_2$ and of all semifields of order 81 over $\mathbb{F}_3$. Our
results imply that some semifields of order 81 have lower multiplicative
complexity than the finite field $\mathbb{F}_{81}$ over $\mathbb{F}_3$. We
prove new results on the correspondence between linear codes and tensor rank,
including a generalisation of a theorem of Brockett and Dobkin to arbitrary
tensors, which makes the problem computationally feasible.


  The magnetic properties of iron-based superconductors $A$Fe$_2$As$_2$ ($A=$K,
Cs, and Rb), which are characterized by the V-shaped dependence of the critical
temperature ($T_{\rm c}$) on pressure ($P$) were studied by means of the muon
spin rotation/relaxation technique. In all three systems studied the magnetism
was found to appear for pressures slightly below the critical one ($P_{\rm
c}$), i.e. at pressure where $T_{\rm c}(P)$ changes the slope. Rather than
competing, magnetism and superconductivity in $A$Fe$_2$As$_2$ are coexisting at
$P\gtrsim P_{\rm c}$ pressure region. Our results support the scenario of a
transition from one pairing state to another, with different symmetries on
either side of $P_{\rm c}$.


  The CLEO Collaboration has observed the first hadronic transition among
bottomonium (b bbar) states other than the dipion transitions among vector
states, Upsilon(nS) -> pi pi Upsilon(mS). In our study of Upsilon(3S) decays,
we find a significant signal for Upsilon(3S) -> gamma omega Upsilon(1S) that is
consistent with radiative decays Upsilon(3S) -> gamma chi_{b 1,2}(2P), followed
by chi_{b 1,2} -> omega Upsilon(1S). The branching ratios we obtain are
Br(chi_{b1} -> omega Upsilon(1S) = 1.63 (+0.35 -0.31) (+0.16 -0.15) % and
Br(chi_{b2} -> omega Upsilon(1S) = 1.10 (+0.32 -0.28) (+0.11 - 0.10)%, in which
the first error is statistical and the second is systematic.