  The slow solar wind exhibits strong variability on timescales from minutes to
days, likely related to magnetic reconnection processes in the extended solar
corona. Higginson2017b presented a numerical magnetohydrodynamic simulation
which showed interchange magnetic reconnection is ubiquitous and most likely
responsible for releasing much of the slow solar wind, in particular along
topological features known as the Separatrix-Web (S-Web). Here, we continue our
analysis, focusing on two specific aspects of structured slow solar wind
variability. The first type is present in the slow solar wind found near the
heliospheric current sheet, and the second we predict should be present
everywhere S-Web slow solar wind is observed. For the first type, we examine
the evolution of three-dimensional magnetic flux ropes formed at the top of the
helmet streamer belt by reconnection in the heliospheric current sheet (HCS).
For the second, we examine the simulated remote and in situ signatures of the
large-scale torsional Alfv\'en wave (TAW) which propagates along an S-Web arc
to high latitudes. We describe the similarities and differences between the
reconnection-generated flux ropes in the HCS, which resemble the well-known
"streamer blob'' observations, and the similarly structured TAW. We discuss the
implications of our results for the complexity of the HCS and surrounding
plasma sheet, and the potential for particle acceleration, as well as the
interchange reconnection scenarios which may generate TAWs in the solar corona.
We discuss predictions from our simulation results for the dynamic slow solar
wind in the extended corona and inner heliosphere.


  A concise explanation of studies on distortion of space-time dimension is
briefly introduced. Second we obtain the limits (i.e., bounded values) of the
dimensionless chemical potential $\mu$, the Sunyaev--Zeldovich (SZ) effect y
and distortion of the space-dimension $\varepsilon$ by Monte Carlo (MC)
analysis of the parameter set (T, $d=3+\varepsilon$, $\mu$, and $y$) in cosmic
microwave data assuming that the SZ effect is positive (y>0). In this analysis,
the magnitude of the space-dimension d with distortion of the space-dimension
$\varepsilon$ is defined by $d=3+\varepsilon$. The limits of $\mu$ and $y$ are
determined as $|\mu| < 9\times 10^{-5} (2\sigma$) ($\mu = (-3.9\pm 2.6)\times
10^{-5} (\sigma$)), $|y| < 5\times 10^{-6} (2\sigma$) ($y = (2.0\pm 1.4)\times
10^{-6} (\sigma$)), while the distortion of the space-dimension is
$|\varepsilon| < 6\times 10^{-5} (2\sigma$) ($\varepsilon = (-0.78\pm
2.50)\times 10^{-5} (\sigma$)). The magnitudes of these three estimated limits
are ordered as $|\mu| \ge |\varepsilon| > |y|$. The estimated limit of $|y| <
5\times 10^{-6}$ appears to be related to re-ionization processes occurring at
redshift $z_{ri}\sim 10$. We also present data analysis assuming a relativistic
SZ effect.


  The paper explores how the human natural language structure can be seen as a
product of evolution of inter-personal communication code, targeting
maximisation of such culture-agnostic and cross-lingual metrics such as
anti-entropy, compression factor and cross-split F1 score. The exploration is
done as part of a larger unsupervised language learning effort, the attempt is
made to perform meta-learning in a space of hyper-parameters maximising F1
score based on the "ground truth" language structure, by means of maximising
the metrics mentioned above. The paper presents preliminary results of
cross-lingual word-level segmentation tokenisation study for Russian, Chinese
and English as well as subword segmentation or morphological parsing study for
English. It is found that language structure form the word-level segmentation
or tokenisation can be found as driven by all of these metrics, anti-entropy
being more relevant to English and Russian while compression factor more
specific for Chinese. The study for subword segmentation or morphological
parsing on English lexicon has revealed straight connection between the
compression been found to be associated with compression factor, while,
surprising, the same connection with anti-entropy has turned to be the inverse.


  We develop practical coding schemes for the cognitive overlay radios as
modeled by the cognitive interference channel, a variation of the classical two
user interference channel where one of the transmitters has knowledge of both
messages. Inspired by information theoretical results, we develop a coding
strategy for each of the three parameter regimes where capacity is known. A key
feature of the capacity achieving schemes in these regimes is the joint
decoding of both users' codewords, which we accomplish by performing a
posteriori probability calculation over a combined trellis. The schemes are
shown to perform close to the capacity limit with low error rate.


  The simplest example of the $\lambda$-deformation connects the SU(2)
Wess-Zumino-Witten model with the non-Abelian T-dual (NATD) of the SU(2)
principal chiral model. We analyze spinning strings with one spin propagating
through the $\lambda$-deformation of the target space of the interpolation. We
show that the situation apart from the NATD limit parallels the undeformed
case. We demonstrate that regular spinning strings are either folded or
circular, and that nearly degenerate spinning strings are either nearly
point-like, fast, or slow. The effects of the $\lambda$-deformation are both
the overall increment of the energy of spinning strings and the enlargement of
the gap between the energies of folded and circular strings. In the NATD limit,
we prove that circular strings disappear and that fast strings realize the
dispersion relation of Gubser-Klebanov-Polyakov strings.


  Given a graph $G$ with $n$ vertices and an Abelian group $A$ of order $n$, an
$A$-distance antimagic labelling of $G$ is a bijection from $V(G)$ to $A$ such
that the vertices of $G$ have pairwise distinct weights, where the weight of a
vertex is the sum (under the operation of $A$) of the labels assigned to its
neighbours. An {$A$-distance magic labelling} of $G$ is a bijection from $V(G)$
to $A$ such that the weights of all vertices of $G$ are equal to the same
element of $A$. In this paper we study these new labellings under a general
setting with a focus on product graphs. We prove among other things several
general results on group antimagic or magic labellings for Cartesian, direct
and strong products of graphs. As applications we obtain several families of
graphs admitting group distance antimagic or magic labellings with respect to
elementary Abelian groups, cyclic groups or direct products of such groups.


  A disk graph is an intersection graph of disks in the Euclidean plane, where
the disks correspond to the vertices of the graph and a pair of vertices are
adjacent if and only if their corresponding disks intersect. The problem of
determining the time complexity of computing a maximum clique in a disk graph
is a long-standing open question. The problem is known to be open even when the
radii of all the disks are in the interval $[1,(1+\varepsilon)]$, where
$\varepsilon>0$. However, the maximum clique problem is known to be APX-hard
for the intersection graphs of many other convex objects such as intersection
graphs of ellipses, triangles, and a combination of unit disks and
axis-parallel rectangles. Furthermore, there exists an $O(n^3\log n)$-time
algorithm to compute a maximum clique for unit disks. Here we obtain the
following results.
  - We give an algorithm to compute a maximum clique in a unit disk graph in
$O(n^{2.5}\log n)$-time, which improves the previously best known running time
of $O(n^3\log n)$ [Eppstein '09].
  - We extend a widely used `co-2-subdivision approach' to prove that computing
a maximum clique in a combination of unit disks and axis-parallel rectangles is
NP-hard to approximate within $4448/4449 \approx 0.9997 $. The use of a
`co-2-subdivision approach' was previously thought to be unlikely in this
setting [Bonnet et al. '20]. Our result improves the previously known
inapproximability factor of $7633010347/7633010348\approx 0.9999$.
  - We show that the parameter minimum lens width of the disk arrangement may
be used to make progress in the case when disk radii are in
$[1,(1+\varepsilon)]$. For example, if the minimum lens width is at least
$0.265$ and $ \varepsilon\le 0.0001$, which still allows for non-Helly triples
in the arrangement, then one can find a maximum clique in polynomial time.


  A key obstacle to increasing renewable energy penetration in the power grid
is the lack of utility-scale storage capacity. Transportation electrification
has the potential to overcome this obstacle since Electric Vehicles (EVs) that
are not in transit can provide battery storage as a service to the grid. This
is referred to as EV-Power grid integration, and could potentially be a key
milestone in the pathway to decarbonize the electricity and the transportation
sectors. We first show that if EV-Power grid integration is not done carefully,
then contrary to improving the cost efficiency of operating the grid, it could
in fact be counterproductive to it. This fundamentally occurs due to two
phenomena operating in tandem - the randomness of EV usage patterns and the
possibility of strategic behavior by EV operators. We present a market-based
solution to address this issue. Specifically, we develop a mechanism for energy
storage markets using which the system operator can efficiently integrate a
fleet of strategic EVs with random usage patterns into the grid, utilize them
for storage, and satisfy the demand at minimum possible cost.


  The integer quantum Hall to insulator transition (IQHIT) is a paradigmatic
quantum critical point. Key aspects of this transition, however, remain
mysterious, due to the simultaneous effects of quenched disorder and strong
interactions. We study this transition using a composite fermion (CF)
representation, which incorporates some of the effects of interactions. As we
describe, the transition also marks a IQHIT of CFs: this suggests that the
transition may exhibit `self-duality'. We show the explicit equivalence of the
electron and CF Lagrangians at the critical point via the corresponding
non-linear sigma models, revealing the self-dual nature of the transition. We
show analytically that the resistivity tensor at the critical point is
$\rho^c_{xx} = \rho^c_{xy} = \frac{h}{e^2}$, which are consistent with the
expectations of self-duality, and in rough agreement with experiments.


  Quantum computers promise dramatic advantages over their classical
counterparts, but the answer to the most basic question "What is the source of
the power in quantum computing?" has remained elusive. Here we prove a
remarkable equivalence between the onset of contextuality and the possibility
of universal quantum computation via magic state distillation. This is a
conceptually satisfying link because contextuality provides one of the
fundamental characterizations of uniquely quantum phenomena and, moreover,
magic state distillation is the leading model for experimentally realizing
fault-tolerant quantum computation. Furthermore, this connection suggests a
unifying paradigm for the resources of quantum information: the nonlocality of
quantum theory is a particular kind of contextuality and nonlocality is already
known to be a critical resource for achieving advantages with quantum
communication. In addition to clarifying these fundamental issues, this work
advances the resource framework for quantum computation, which has a number of
practical applications, such as characterizing the efficiency and trade-offs
between distinct theoretical and experimental schemes for achieving robust
quantum computation and bounding the overhead cost for the classical simulation
of quantum algorithms.


  The effect of driving frequency in the range of 13.56 MHz to 73 MHz on
electron energy distribution and electron heating modes in a 50 mTorr
capacitively coupled argon plasma discharge is studied using 1D-3V
particle-in-cell simulations. Calculated electron energy probability functions
exhibit three distinct ``temperatures'' for low-, mid-, and high-energy
electrons. When compared to published experimental data, the calculated
probability functions show a reasonable agreement for the energy range resolved
in the measurements (about 2 eV to 10 eV). Discrepancies outside this range
lead to differences between computational and experimental values of the
electron number density determined from the distribution functions, but the
predicted effective electron temperature is within 25\% of experimental values.
The impedance of the discharge is interpreted in terms of a homogeneous
equivalent circuit model and the driving frequency dependence of the inferred
combined sheath thickness is found to obey a known, theoretically-derived,
power law. The average power transferred from the field to the electrons
(electron heating) is computed, and a region of negative heating near the
sheath edge, particularly at higher driving frequencies, is identified.
Analysis of the electron momentum equation shows that electron inertia, which
would average to zero in a linear regime, is responsible for negative values of
power deposition near the sheath edge at high driving frequencies due to the
highly nonlinear behavior of the discharge.


  We study star formation in a sample of 1204 galaxies in minor (| \Delta m_z |
\geq 2) pairs and compact groups, drawn from the Sloan Digital Sky Survey Data
Release 5 (SDSS DR5). We analyze an analogous sample of 2409 galaxies in major
(| \Delta m_z | < 2$) pairs and compact groups to ensure that our selection
reproduces previous results, and we use a ``field'' sample of 65,570 galaxies
for comparison. Our major and minor pairs samples include only galaxies in
spectroscopically confirmed pairs, where the recessional velocity separation
$\Delta V < 500$ km/s and the projected spatial separation $\Delta D < 50$
kpc/h. The relative magnitude (a proxy for the mass ratio) of the pair is an
important parameter in the effectiveness of the tidally triggered star
formation in minor interactions. As expected, the secondary galaxies in minor
pairs show evidence for tidally triggered star formation, whereas the primary
galaxies in the minor pairs do not. The galaxy color is also an important
parameter in the effectiveness of triggered star formation in the major galaxy
pairs. In the major pairs sample, there is a correlation between the specific
H$\alpha$ star formation rate (SSFR) and $\Delta D$ in the blue primary and
blue secondary galaxies; for the red primary and red secondary galaxies, there
is none. Galaxies in pairs have a higher mean SSFR at every absolute magnitude
compared to matched sets of field galaxies, and the relative increase in mean
SSFR becomes larger with decreasing intrinsic luminosity. We also detect a
significantly increased AGN fraction in the pair galaxies compared to matched
sets of field galaxies.


  The Cylindrical Algebraic Decomposition (CAD) algorithm is a comprehensive
tool to perform quantifier elimination over real closed fields. CAD has doubly
exponential running time, making it infeasible for practical purposes. We
propose to use the notions of clause normal forms and virtual substitutions to
develop a preprocessor for CAD, that will enable an input-level parallelism. We
study the performance of CAD in the presence of the preprocessor by extensive
experimentation. Since parallelizability of CAD depends on the structure of
given prenex formula, we introduce some structural notions to study the
performance of CAD with the proposed preprocessor.


  We estimate the Cronin effect in pA collisions at the CERN LHC and at RHIC,
using a Glauber-Eikonal model of initial state multiparton interactions. For a
correct determination of the initial parton flux, we upgrade the model cross
section, taking carefully into account all kinematical constraints of each
multi-parton interaction process. As compared with previous results, derived
with approximate kinematics, we obtain a softer spectrum of produced partons,
while improving the agreement of the model with the recent measurements of
neutral pions production in d+Au collisions at sqrt(s)=200 AGeV.


  Many studies have been performed to characterize the dynamics and stability
of the microbiome across a range of environmental contexts [Costello et al.,
2012, Faust et al., 2015]. For example, it is often of interest to identify
time intervals within which certain subsets of taxa have an interesting pattern
of behavior. Viewed abstractly, these problems often have a flavor not just of
time series modeling but also of regime detection, a problem with a rich
history across a variety of applications, including speech recognition [Fox et
al., 2011], finance [Lee, 2009], EEG analysis [Camilleri et al., 2014], and
geophysics [Weatherley and Mora, 2002]. In spite of the parallels, regime
detection methods are rarely used in microbiome analysis, most likely due to
the fact that references for these methods are scattered across several
literatures, descriptions are inaccessible outside limited research
communities, and implementations are difficult to come across.
  We distill the core ideas of different regime detection methods, provide
example applications, and share reproducible code, making these techniques more
accessible to microbiome researchers. We re-analyze data of Dethlefsen and
Relman [2011], a study of the effects of antibiotics on the microbiome, using
Classification and Regression Trees (CART) [Breiman et al., 1984], Hidden
Markov Models (HMMs) [Rabiner and Juang, 1986], Bayesian nonparametric HMMs
[Teh and Jordan, 2010, Fox et al., 2008], mixtures of Gaussian Processes (GPs)
[Rasmussen and Ghahramani, 2002], switching dynamical systems [Linderman et
al., 2016], and multiple changepoint detection [Fan and Mackey, 2015]. Along
the way, we summarize each method, their relevance to the microbiome, and
tradeoffs associated with using them. Ultimately, our goal is to describe types
of temporal or regime switching structure that can be incorporated into studies
of microbiome dynamics.


  Space-based gravitational-wave detectors consist of a triangle of three
spacecraft, which makes it possible to detect polarization modes of
gravitational waves due to the motion of the detectors in space. In this paper
we explore the ability of Taiji to detect the polarization modes in the
parametrized post-Einsteinian framework. Assuming massive black hole binaries
with the total mass of $M=4\times10^5\,M_{\odot}$ at redshift of $z=1$, we find
that Taiji can measure the dipole and quadruple emission
($\Delta\alpha_D/\alpha_D$ and $\Delta\alpha_Q/\alpha_Q$) with the accuracy of
up to $\sim 0.04\%$, with the fiducial value of $\alpha_D=0.001$, the scalar
transverse and longitudinal modes ($\Delta\alpha_B$ and $\Delta\alpha_L$) up to
$\sim 0.01$, and the vector modes ($\Delta\alpha_V$) up to $\sim 0.0005$.


  Wettability is a pore-scale property that has an important impact on
capillarity, residual trapping, and hysteresis in porous media systems. In many
applications, the wettability of the rock surface is assumed to be constant in
time and uniform in space. However, many fluids are capable of altering the
wettability of rock surfaces permanently and dynamically in time. Experiments
have shown wettability alteration can significantly decrease capillarity in
CO$_2$ storage applications. For these systems, the standard capillary-pressure
model that assumes static wettability is insufficient to describe the physics.
In this paper, we develop a new dynamic capillary-pressure model that takes
into account changes in wettability at the pore-level by adding a dynamic term
to the standard capillary pressure function. We simulate the dynamic system
using a bundle-of-tubes (BoT) approach, where a mechanistic model for
time-dependent contact angle change is introduced at the pore scale. The
resulting capillary pressure curves are then used to quantify the dynamic
component of the capillary pressure function. This study shows the importance
of time-dependent wettability for determining capillary pressure over
timescales of months to years. The impact of wettability has implications for
experimental methodology as well as macroscale simulation of
wettability-altering fluids.


  Source detection in graphs has demonstrated robust efficacy in the domain of
rumor source identification. Although recent solutions have enhanced
performance by leveraging deep neural networks, they often require complete
user data. In this paper, we address a more challenging task, rumor source
detection with incomplete user data, and propose a novel framework, i.e.,
Source Detection in Graphs with Incomplete Nodes via Positional Encoding and
Attentive Fusion (GIN-SD), to tackle this challenge. Specifically, our approach
utilizes a positional embedding module to distinguish nodes that are incomplete
and employs a self-attention mechanism to focus on nodes with greater
information transmission capacity. To mitigate the prediction bias caused by
the significant disparity between the numbers of source and non-source nodes,
we also introduce a class-balancing mechanism. Extensive experiments validate
the effectiveness of GIN-SD and its superiority to state-of-the-art methods.


  We discuss the so called gauge invariant quantization of anomalous gauge
field theory, originally due to Faddeev and Shatashvili. It is pointed out that
the further non invariance of relevant path integral measures poses a problem
when one tries to translate it to BRST formalism. The method by which we
propose to get around of this problem introduces certain arbitrariness in the
model. We speculate on the possibility of using such an arbitrariness to build
series of non equivalent models of two dimensional induced gravity.


  We show how implementing invariance under divergence-free gauge
transformations leads to a remarkably simple Lagrangian description of massless
bosons of any spin. Our construction covers both flat and (A)dS backgrounds and
extends to tensors of arbitrary mixed-symmetry type. Irreducible and traceless
fields produce single-particle actions, while whenever trace constraints can be
dispensed with the resulting Lagrangians display the same reducible,
multi-particle spectra as those emerging from the tensionless limit of free
open-string field theory. For all explored options the corresponding kinetic
operators take essentially the same form as in the spin-one, Maxwell case.


  We show that the radial profiles of the temperature and density of the
electrons as well as the magnetic field strength around the massive black hole
at the Galactic center, SgrA*, may be constrained directly from existing radio
data without any need to make prior assumptions about the dynamics of the
emitting gas. The observed spectrum and wavelength-dependent angular size of
SgrA* indicate that the synchrotron emission originates from an optically-thick
plasma of quasi-thermal electrons. We find that the electron temperature rises
above the virial temperature within tens of Schwarzschild radii from the black
hole, suggesting that the emitting plasma may be outflowing. Constraints on the
electron density profile are derived from polarization measurements. Our
best-fit results differ from expectations based on existing theoretical models.
However, these models cannot be ruled out as of yet due to uncertainties in the
source size measurements. Our constraints could tighten considerably with
future improvements in the size determination and simultaneous polarization
measurements at multiple wavelengths.


  We study the clustering properties of galaxy clusters expected to be observed
by various forthcoming surveys both in the X-ray and sub-mm regimes by the
thermal Sunyaev-Zel'dovich effect. Several different background cosmological
models are assumed, including the concordance $\Lambda$CDM and various
cosmologies with dynamical evolution of the dark energy. Particular attention
is paid to models with a significant contribution of dark energy at early times
which affects the process of structure formation. Past light cone and selection
effects in cluster catalogs are carefully modeled by realistic scaling
relations between cluster mass and observables and by properly taking into
account the selection functions of the different instruments. The results show
that early dark-energy models are expected to produce significantly lower
values of effective bias and both spatial and angular correlation amplitudes
with respect to the standard $\Lambda$CDM model. Among the cluster catalogues
studied in this work, it turns out that those based on \emph{eRosita},
\emph{Planck}, and South Pole Telescope observations are the most promising for
distinguishing between various dark-energy models.


  The software of track reconstruction of the vertex chamber of BESII-VCJULI
was studied when it was transplanted from the HP-Unix platform to PC-Linux. The
problems of distinct dictionary storage and precision treatment in these two
different platforms were found and settled in the modified software. Then the
obvious differences of the candidate track number in an event and some track
parameters caused by them were reduced from 74% to 0.02% and from 5% to 0.5%,
respectively. As a result, the quality of the track finding was greatly
improved and the CPU time saved.


  New spatially scanned spectropolarimetry sunspot observations are made of
photospheric atomic and molecular absorption lines near 4135nm. The relative
splittings among several atomic lines are measured and shown to agree with
values calculated with configuration interaction and intermediate coupling.
Large splitting is seen in a line identified with Fe I at 4137nm, showing
multiple Stokes V components and an unusual linear polarization. This line will
be a sensitive probe of quiet Sun magnetic fields, with a magnetic sensitivity
of 2.5 times larger than that of the well-known 1565nm Fe I line.


  We show that the $\operatorname{Sine}_{\beta}$ point process, defined as the
scaling limit of the Circular Beta Ensemble when the dimension goes to
infinity, and generalizing the determinantal sine-kernel process, is rigid in
the sense of Ghosh and Peres: the number of points in a given bounded Borel set
$B$ is almost surely equal to a measurable function of the position of the
points outside $B$.


  The search for intermediate-mass black holes (IMBHs) in the centre of
globular clusters is often based on the observation of a central cusp in the
surface brightness profile and a rise towards the centre in the velocity
dispersion profiles. Similar signatures, however, could result from other
effects, that need to be taken into account in order to determine the presence
(or the absence) of an IMBH in these stellar systems. Following our previous
exploration of the role of radial anisotropy in shaping these observational
signatures, we analyse here the effects produced by the presence of a
population of centrally concentrated stellar-mass black holes. We fit dynamical
models to omega Cen data, and we show that models with ~5% of their mass in
black holes (consistent with ~100% retention fraction after natal kicks) can
reproduce the data. When simultaneously considering both radial anisotropy and
mass segregation, the best-fit model includes a smaller population of remnants,
and a less extreme degree of anisotropy with respect to the models that include
only one of these features. These results underline that before conclusions
about putative IMBHs can be made, the effects of stellar-mass black holes and
radial anisotropy need to be properly accounted for.


  We generalize the construction of a moduli space of semistable pairs
parametrizing isomorphism classes of morphisms from a fixed coherent sheaf to
any sheaf with fixed Hilbert polynomial under a notion of stability to the case
of projective Deligne-Mumford stacks. We study the deformation and obstruction
theories of stable pairs, and then prove the existence of virtual fundamental
classes for some cases of dimension two and three. This leads to a definition
of Pandharipande-Thomas invariants on three-dimensional smooth projective
Deligne-Mumford stacks.


  Motivated by excellent comparisons of spin wave theory (SWT) with Quantum
Monte Carlo (QMC) calculations of non-interacting hardore bosons (Bernardet et
al.) and superlattices (Hen et al.), we develop an SWT for interacting hardcore
bosons on a d-dimensional cubic lattice at zero temperature and compare
thermodynamical properties with results of Stochastic Series Expansions (SSE):
there is good comparison between SWT and SSE results for the simple cubic
lattice at small repulsive strengths but differences arise between the results
of the two approaches for the square lattice at strong repulsions. The
boundaries of the three phases - Mott insulating (MI), superfluid (SF) and
N\'{e}el solid (NS) - can be readily estimated from calculations of particle
density, signalling either a first order (SF <--> NS) or second order
transition (MI <--> SF). Comparison is made with results of the Bethe ansatz in
one dimension and QMC in two dimensions.


  We present the QCD predictions for the azimuthal $\cos 2\varphi$ asymmetry in
charm leptoproduction for the kinematics of the COMPASS experiment at CERN. The
asymmetry is predicted to be large, about 15%. The radiative corrections to the
QCD predictions for the $\cos 2\varphi$ distribution are estimated to be small,
less than 10%. Our calculations show that the azimuthal asymmetry in charm
production is well defined in pQCD: it is stable both perturbatively and
parametrically, and practically insensitive to theoretical uncertainties in the
input parameters. We analyze the nonperturbative contributions to the $\cos
2\varphi$ distribution due to the gluon transverse motion in the target and the
$c$-quark fragmentation. Because of the $c$-quark low mass, the nonperturbative
contributions are expected to be sizable, about (30--40)%. We conclude that
extraction of the azimuthal asymmetries from available COMPASS data will
provide valuable information about the transverse momentum dependent
distribution of the gluon in the proton and the $c$-quark hadronization
mechanism. Finally, we discuss the $\cos 2\varphi$ asymmetry as a probe of the
gluonic analogue of the Boer-Mulders function, $h_{1}^{\perp g}$, describing
the linear polarization of gluons inside unpolarized proton.


  Calculations are presented for the time evolution of $^{240}$Pu from the
proximity of the outer saddle point until the fission fragments are well
separated, using the time-dependent density functional theory extended to
superfluid systems. We have tested three families of nuclear energy density
functionals and found that all functionals exhibit a similar dynamics: the
collective motion is highly dissipative and with little trace of inertial
dynamics, due to the one-body dissipation mechanism alone. This finding
justifies the validity of using the overdamped collective motion approach and
to some extent the main assumptions in statistical models of fission. This
conclusion is robust with respect to the nuclear energy density functional
used. The configurations and interactions left out of the present theory
framework only increase the role of the dissipative couplings. An unexpected
finding is varying the pairing strength within a quite large range has only
minor effects on the dynamics. We find notable differences in the excitation
energy sharing between the fission fragments in the cases of spontaneous and
induced fission. With increasing initial excitation energy of the fissioning
nucleus more excitation energy is deposited in the heavy fragment, in agreement
with experimental data on average neutron multiplicities.


  Activation functions play a pivotal role in determining the training dynamics
and neural network performance. The widely adopted activation function ReLU
despite being simple and effective has few disadvantages including the Dying
ReLU problem. In order to tackle such problems, we propose a novel activation
function called Serf which is self-regularized and nonmonotonic in nature. Like
Mish, Serf also belongs to the Swish family of functions. Based on several
experiments on computer vision (image classification and object detection) and
natural language processing (machine translation, sentiment classification and
multimodal entailment) tasks with different state-of-the-art architectures, it
is observed that Serf vastly outperforms ReLU (baseline) and other activation
functions including both Swish and Mish, with a markedly bigger margin on
deeper architectures. Ablation studies further demonstrate that Serf based
architectures perform better than those of Swish and Mish in varying scenarios,
validating the effectiveness and compatibility of Serf with varying depth,
complexity, optimizers, learning rates, batch sizes, initializers and dropout
rates. Finally, we investigate the mathematical relation between Swish and
Serf, thereby showing the impact of preconditioner function ingrained in the
first derivative of Serf which provides a regularization effect making
gradients smoother and optimization faster.


  Anisotropic flow is an important observable in the study of the Quark-Gluon
Plasma that is expected to be formed in heavy-ion collisions. With a multiphase
transport (AMPT) model we investigate the elliptic($\emph{v}_{2}$),
triangular($\emph{v}_{3}$), and quadrangular($\emph{v}_{4}$) flow of charged
particles in Pb+Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV. Then We
compare our flow results with the published ALICE flow results. We found our
AMPT simulated results are consistent with ALICE experimental data.


  Predicting clinical outcomes to anti-cancer drugs on a personalized basis is
challenging in cancer treatment due to the heterogeneity of tumors. Traditional
computational efforts have been made to model the effect of drug response on
individual samples depicted by their molecular profile, yet overfitting occurs
because of the high dimension for omics data, hindering models from clinical
application. Recent research shows that deep learning is a promising approach
to build drug response models by learning alignment patterns between drugs and
samples. However, existing studies employed the simple feature fusion strategy
and only considered the drug features as a whole representation while ignoring
the substructure information that may play a vital role when aligning drugs and
genes. Hereby in this paper, we propose TCR (Transformer based network for
Cancer drug Response) to predict anti-cancer drug response. By utilizing an
attention mechanism, TCR is able to learn the interactions between drug
atom/sub-structure and molecular signatures efficiently in our study.
Furthermore, a dual loss function and cross sampling strategy were designed to
improve the prediction power of TCR. We show that TCR outperformed all other
methods under various data splitting strategies on all evaluation matrices
(some with significant improvement). Extensive experiments demonstrate that TCR
shows significantly improved generalization ability on independent in-vitro
experiments and in-vivo real patient data. Our study highlights the prediction
power of TCR and its potential value for cancer drug repurpose and precision
oncology treatment.


  It is well documented, and has been the topic of much research, that Computer
Science courses tend to have higher than average drop out rates at third level.
This is a problem that needs to be addressed with urgency but also caution. The
required number of Computer Science graduates is growing every year but the
number of graduates is not meeting this demand and one way that this problem
can be alleviated is to encourage students at an early age towards studying
Computer Science courses.
  This paper presents a systematic literature review on the role of visual and
textual programming languages when learning to program, particularly as a first
programming language. The approach is systematic, in that a structured search
of electronic resources has been conducted, and the results are presented and
quantitatively analysed. This study will give insight into whether or not the
current approaches to teaching young learners programming are viable, and
examines what we can do to increase the interest and retention of these
students as they progress through their education.


  We review the hypothetical interactions predicted beyond the Standard Model
which could be constrained by using the results of tabletop laboratory
experiments. These interactions are described by the power-type potentials with
different powers, Yukawa potential, other spin-independent potentials, and by
the spin-dependent potentials of different kinds. In all these cases the
current constraints on respective hypothetical interactions are considered
which follow from the Casimir effect and some other tabletop physics. The
exotic particles and constraints on them are discussed in the context of
problems of the quantum vacuum, dark energy, and the cosmological constant.


  In this paper, the deep learning (DL) approach is applied to a joint training
scheme for asynchronous motor imagery-based Brain-Computer Interface (BCI). The
proposed DL approach is a cascade of one-dimensional convolutional neural
networks and fully-connected neural networks (CNN-FC). The focus is mainly on
three types of brain responses: non-imagery EEG (\textit{background EEG}),
(\textit{pure imagery}) EEG, and EEG during the transitional period between
background EEG and pure imagery (\textit{transitional imagery}). The study of
transitional imagery signals should provide greater insight into real-world
scenarios. It may be inferred that pure imagery and transitional EEG are high
and low power EEG imagery, respectively. Moreover, the results from the CNN-FC
are compared to the conventional approach for motor imagery-BCI, namely the
common spatial pattern (CSP) for feature extraction and support vector machine
(SVM) for classification (CSP-SVM). Under a joint training scheme, pure and
transitional imagery are treated as the same class, while background EEG is
another class. Ten-fold cross-validation is used to evaluate whether the joint
training scheme significantly improves the performance task of classifying pure
and transitional imagery signals from background EEG. Using sparse of just a
few electrode channels ($C_{z}$, $C_{3}$ and $C_{4}$), mean accuracy reaches
71.52 % and 70.27 % for CNN-FC and CSP-SVM, respectively. On the other hand,
mean accuracy without the joint training scheme achieve only 62.68 % and 52.41
% for CNN-FC and CSP-SVM, respectively.


  Alice has made a decision in her mind. While she does not want to reveal it
to Bob at this moment, she would like to convince Bob that she is committed to
this particular decision and that she cannot change it at a later time. Is
there a way for Alice to get Bob's trust? Until recently, researchers had
believed that the above task can be performed with the help of quantum
mechanics. And the security of the quantum scheme lies on the uncertainty
principle. Nevertheless, such optimism was recently shattered by Mayers and by
us, who found that Alice can always change her mind if she has a quantum
computer. Here, we survey this dramatic development and its implications on the
security of other quantum cryptographic schemes.


  This paper proposes a new reinforcement learning with hyperbolic discounting.
Combining a new temporal difference error with the hyperbolic discounting in
recursive manner and reward-punishment framework, a new scheme to learn the
optimal policy is derived. In simulations, it is found that the proposal
outperforms the standard reinforcement learning, although the performance
depends on the design of reward and punishment. In addition, the averages of
discount factors w.r.t. reward and punishment are different from each other,
like a sign effect in animal behaviors.


  In this paper, we discuss the dissociative electron attachment process in
Formic Acid and Propyl Amine. These are molecules containing more than one
functional group and have low symmetry (Cs group). We measured the kinetic
energy and angular distributions of fragment H^{-} ions from the resonances
observed in these molecules and compared with that in the precursor molecules,
namely - Water, Ammonia and Methane. Measurements suggest that the dissociation
dynamics in bigger molecules are independent of overall symmetry of the
molecule, rather depend only on the local symmetry of functional group and bond
orientation factors.


  A radiative transfer code is used to model the spectral energy distributions
of 57 mass-losing Asymptotic Giant Branch (AGB) stars and red supergiants
(RSGs) in the Large Magellanic Cloud (LMC) for which ISO spectroscopic and
photometric data are available. As a result we derive mass-loss rates and
bolometric luminosities.
  A gap in the luminosity distribution around M_bol = -7.5 mag separates AGB
stars from RSGs. The luminosity distributions of optically bright carbon stars,
dust-enshrouded carbon stars and dust-enshrouded M-type stars have only little
overlap, suggesting that the dust-enshrouded AGB stars are at the very tip of
the AGB and will not evolve significantly in luminosity before mass loss ends
their AGB evolution.
  Derived mass-loss rates span a range from Mdot about 10^-7 to 10^-3 M_sun/yr.
More luminous and cooler stars are found to reach higher mass-loss rates. The
highest mass-loss rates exceed the classical limit set by the momentum of the
stellar radiation field, L/c, by a factor of a few due to multiple scattering
of photons in the circumstellar dust envelope. Mass-loss rates are lower than
the mass consumption rate by nuclear burning, Mdot_nuc, for most of the RSGs.
Two RSGs have Mdot >> Mdot_nuc, however, suggesting that RSGs shed most of
their stellar mantles in short phases of intense mass loss. Stars on the
thermal pulsing AGB may also experience episodes of intensified mass loss, but
their quiescent mass-loss rates are usually already higher than Mdot_nuc.


  We study the dynamics of bond-disordered Ising spin systems on random graphs
with finite connectivity, using generating functional analysis. Rather than
disorder-averaged correlation and response functions (as for fully connected
systems), the dynamic order parameter is here a measure which represents the
disorder averaged single-spin path probabilities, given external perturbation
field paths. In the limit of completely asymmetric graphs our macroscopic laws
close already in terms of the single-spin path probabilities at zero external
field. For the general case of arbitrary graph symmetry we calculate the first
few time steps of the dynamics exactly, and we work out (numerical and
analytical) procedures for constructing approximate stationary solutions of our
equations. Simulation results support our theoretical predictions.


  We consider the problem of attitude tracking for small-scale aerobatic
helicopters. A small scale helicopter has two subsystems: the fuselage, modeled
as a rigid body; and the rotor, modeled as a first order system. Due to the
coupling between rotor and fuselage, the complete system does not inherit the
structure of a simple mechanical system. The coupled rotor fuselage dynamics is
first transformed to rigid body attitude tracking problem with a first order
actuator dynamics. The proposed controller is developed using geometric and
backstepping control technique. The controller is globally defined on $SO(3)$
and is shown to be locally exponentially stable. The controller is validated in
simulation and experiment for a 10 kg class small scale flybarless helicopter
by demonstrating aggressive roll attitude tracking.


  A rational function belongs to the Hardy space, $H^2$, of square-summable
power series if and only if it is bounded in the complex unit disk. Any such
rational function is necessarily analytic in a disk of radius greater than one.
The inner-outer factorization of a rational function, $\mathfrak{r} \in H^2$ is
particularly simple: The inner factor of $\mathfrak{r}$ is a (finite) Blaschke
product and (hence) both the inner and outer factors are again rational.
  We extend these and other basic facts on rational functions in $H^2$ to the
full Fock space over $\mathbb{C}^d$, identified as the \emph{non-commutative
(NC) Hardy space} of square-summable power series in several NC variables. In
particular, we characterize when an NC rational function belongs to the Fock
space, we prove analogues of classical results for inner-outer factorizations
of NC rational functions and NC polynomials, and we obtain spectral results for
NC rational multipliers.


  The problem of the motion of a charged particle in an electric dipole field
is used to illustrate that the Hamilton-Jacobi method does not necessarily give
all solutions to the equations of motion of a mechanical system. The
mathematical reason for this phenomenon is discussed. In the particular case
under consideration, it is shown how to circumvent the difficulty and find the
missing solutions by means of a very special limiting procedure.


  Modeling magnitude Magnetic Resonance Images (MRI) rician denoising in a
Bayesian or generalized Tikhonov framework using Total Variation (TV) leads
naturally to the consideration of nonlinear elliptic equations. These involve
the so called $1$-Laplacian operator and special care is needed to properly
formulate the problem. The rician statistics of the data are introduced through
a singular equation with a reaction term defined in terms of modified first
order Bessel functions. An existence theory is provided here together with
other qualitative properties of the solutions. Remarkably, each positive global
minimum of the associated functional is one of such solutions. Moreover, we
directly solve this non--smooth non--convex minimization problem using a
convergent Proximal Point Algorithm. Numerical results based on synthetic and
real MRI demonstrate a better performance of the proposed method when compared
to previous TV based models for rician denoising which regularize or convexify
the problem. Finally, an application on real Diffusion Tensor Images, a
strongly affected by rician noise MRI modality, is presented and discussed.


  Automatic exploit generation is a relatively new area of research. Work in
this area aims to automate the manual and labor intensive task of finding
exploits in software. In this paper we present a novel program logic to support
automatic exploit generation. We develop a program logic called Reachability
Logic, which formally defines the relation between reachability of an assertion
and the preconditions which allow them to occur. This relation is then used to
calculate the search space of preconditions. We show that Reachability Logic is
a powerful tool in automatically finding evidence that an assertion is
reachable. We verify that the system works for small litmus tests, as well as
real-world algorithms. An implementation has been developed, and the entire
system is proven to be sound and complete in a theorem prover. This work
represents an important step towards formally verified automatic exploit
generation.


  The group of direct isometries of the real n-dimensional hyperbolic space is
G=SOo(n,1). This isometric action admits many differentiable compactifications
into an action on the closed ball. We prove that all such compactifications are
topologically conjugate but not necessarily differentiably conjugate. We give
the classifications of real analytic and smooth compactifications.


  The optical conductivity of graphene, bilayer graphene, and graphite in
quantizing magnetic fields is studied. Both dynamical conductivities,
longitudinal and Hall's, are analytically evaluated. The conductivity peaks are
explained in terms of electron transitions. We have shown that trigonal warping
can be considered within the perturbation theory for strong magnetic fields
larger than 1 T and in the semiclassical approach for weak fields when the
Fermi energy is much larger than the cyclotron frequency. The main optical
transitions obey the selection rule with \Deltan = 1 for the Landau number n,
however the \Deltan = 2 transitions due to the trigonal warping are also
possible. The Faraday/Kerr rotation and light transmission/reflection in the
quantizing magnetic fields are calculated. Parameters of the
Slonczewski-Weiss-McClure model are used in the fit taking into account the
previous dHvA measurements and correcting some of them for the case of strong
magnetic fields.


  For $0<\alpha<\frac{1}{3}$ we construct unique solutions to the fractal
Burgers equation $\partial_t u + u\partial_xu + (-\Delta)^\alpha u = 0$ which
develop a first shock in finite time, starting from smooth generic initial
data. This first singularity is an asymptotically self-similar, stable $H^6$
perturbation of a stable, self-similar Burgers shock profile. Furthermore, we
are able to compute the spatio-temporal location and H\"older regularity for
the first singularity. There are many results showing that gradient blowup
occurs in finite time for the supercritical range, but the present result is
the first example where singular solutions have been explicitly constructed and
so precisely characterized.


  This paper studies the optimal power allocation for outage minimization in
point-to-point fading channels with the energy-harvesting constraints and
channel distribution information (CDI) at the transmitter. Both the cases with
non-causal and causal energy state information (ESI) are considered, which
correspond to the energy harvesting rates being known and unknown prior to the
transmissions, respectively. For the non-causal ESI case, the average outage
probability minimization problem over a finite horizon is shown to be
non-convex for a large class of practical fading channels. However, the
globally optimal "offline" power allocation is obtained by a forward search
algorithm with at most $N$ one-dimensional searches, and the optimal power
profile is shown to be non-decreasing over time and have an interesting
"save-then-transmit" structure. In particular, for the special case of N=1, our
result revisits the classic outage capacity for fading channels with uniform
power allocation. Moreover, for the case with causal ESI, we propose both the
optimal and suboptimal "online" power allocation algorithms, by applying the
technique of dynamic programming and exploring the structure of optimal offline
solutions, respectively.


  While in string theory the subject of sources in imaginary time has received
some attention, we demonstrate the power of imaginary sources by proving that
they constitute in several field theories a complete basis for all smooth and
time dependent source free solutions. These proofs promote the study of
imaginary sources to a new and crucial viewpoint for understanding time
dependent backgrounds. From our field theory examples we further propose a
completeness conjecture that every regular solution to a field equation of
motion has a corresponding imaginary source configuration. We define charges
for spacelike sources and show their compatibility with the usual charge
definition for timelike sources. Many new non-singular time dependent field
theory solutions are discussed, including Wick rotations of abelian instantons
which demonstrate a close relationship between electric-magnetic duals and
analytic continuation.


  The advance of Machine Learning (ML) has led to a strong interest in this
technology to support decision making. While complex ML models provide
predictions that are often more accurate than those of traditional tools, such
models often hide the reasoning behind the prediction from their users, which
can lead to lower adoption and lack of insight. Motivated by this tension,
research has put forth Explainable Artificial Intelligence (XAI) techniques
that uncover patterns discovered by ML. Despite the high hopes in both ML and
XAI, there is little empirical evidence of the benefits to traditional
businesses. To this end, we analyze data on 220,185 customers of an energy
retailer, predict cross-purchases with up to 86% correctness (AUC), and show
that the XAI method SHAP provides explanations that hold for actual buyers. We
further outline implications for research in information systems, XAI, and
relationship marketing.


  In this paper, we study topological properties of the right action by
translation of the Weyl Chamber flow on the space of Weyl chambers. We obtain a
necessary and sufficient condition for topological mixing. (1)


  We present a novel way to compare the statistical cost of privacy mechanisms
using their Dobrushin coefficient. Specifically, we provide upper and lower
bounds for the Dobrushin coefficient of a privacy mechanism in terms of its
maximal leakage and local differential privacy guarantees. Given the geometric
nature of the Dobrushin coefficient, this approach provides some insights into
the general statistical cost of these privacy guarantees. We highlight the
strength of this method by applying our results to develop new bounds on the
$\ell_2$-minimax risk in a distribution estimation setting under maximal
leakage constraints. Specifically, we find its order with respect to the sample
size and privacy level, allowing a quantitative comparison with the
corresponding local differential privacy $\ell_2$-minimax risk.


  The variant graph is a new, hybrid algorithm that combines the strengths of
established global grain graph and local neighbor level voting approaches,
while alleviating their shortcomings, to reconstruct parent grains from
orientation maps of partially or fully phase-transformed microstructures. The
variant graph algorithm is versatile and is capable of reconstructing
transformation microstructures from any parent-child combination by clustering
together child grains based on a common parent orientation variant. The main
advantage of the variant graph over the grain graph is its inherent ability to
more accurately detect prior austenite grain boundaries.
  A critical examination of Markovian clustering and neighbor level voting as
methods to reconstruct prior austenite orientations is first conducted.
Following this, the performance of the variant graph algorithm is showcased by
reconstructing the prior austenite grains and boundaries from an example
low-carbon lath martensite steel microstructure. Programmatic extensions to the
variant graph algorithm for specific morphological conditions and the merging
of variants with small mutual disorientation angles are also proposed. The
accuracy of the reconstruction and the computational performance of the variant
graph algorithm is either on-par or outperforms alternate methods for parent
grain reconstruction.
  The variant graph algorithm is implemented as a new addition to the
functionalities for phase transformation analysis in MTEX 5.8 and is freely
available for download by the community.


  We present the final results from our survey of luminous $z \sim $ 5.5
quasars. This is the first systematic quasar survey focusing on quasars at $z
\sim$ 5.5, during the post-reionization epoch. It has been challenging to
select quasars at $5.3 < z < 5.7$ using conventional color selections, due to
their similar optical colors to those of late-type stars, especially M dwarfs.
We developed a new selection technique for $z \sim$ 5.5 quasars based on
optical, near-IR, and mid-IR photometry, using data from the Sloan Digital Sky
Survey (SDSS), PanSTARR1 (PS1), the UKIRT Infrared Deep Sky Surveys - Large
Area Survey, the UKIRT Hemisphere Survey, the VISTA Hemisphere Survey, and the
Wide Field Infrared Survey Explorer ($WISE$), covering $\sim$ 11000 deg$^2$ of
high galactic latitude sky. In this paper, we present the discovery of 15 new
quasars at $z\sim 5.5$. Together with results from Yang et al. (2017), our
survey provides a complete, flux-limited sample of 31 quasars at $5.3 \le z \le
5.7$. We measure the quasar spatial density at $z \sim 5.5$ and $M_{1450} <
-26.2$. Our result is consistent with the rapid decline of quasar spatial
density from $z = 5$ to 6, with $k=-0.66 \pm 0.05$ ($\rho(z) \propto 10^{kz}$).
In addition, we present a new survey using optical colors only from the full
PS1 area for luminous quasars at $z = 5.0 - 5.5$, beyond the SDSS footprint,
and report the preliminary results from this survey, including 51 new quasars
discovered at $4.61\le z \le5.71$.


  We propose the another, in principe nonperturbative, method of the evaluation
of the Wiener functional integral for $\phi^4$ term in the action. All infinite
summations in the results are proven to be convergent. We finf the
"generalized" Gelfand -- Yaglom differential equation implying the functional
integral in the continuum limit.


  Brane inflation is a specific realization of the inflationary universe
scenario in the early universe within the brane world framework in string
theory. The naturalness and robustness of this realistic scenario is explained.
Its predictions on the cosmological observables in the cosmic microwave
background radiation, especially possible distinct stringy features, such as
large non-Gaussianity or large tensor mode that deviates from that predicted in
the slow roll scenario, are discussed. Stringy KK modes as hidden dark matter
is also a possibility. Another generic consequence of brane inflation is the
production of cosmic strings towards the end of inflation. These cosmic strings
are nothing but superstrings stretched to cosmological sizes. The properties of
these cosmic superstrings and their subsequent cosmological evolution into a
scaling network open up their possible detections in the near future, via
cosmological, astronomical and/or gravitational wave measurements. At the
moment, cosmological data is already imposing strong constraints on the details
of the scenario. Finding distinctive stringy signatures in cosmological
observations will go a long way in revealing the specific brane inflationary
scenario and validating string theory as well as the brane world picture.
Precision measurements may even reveal the structures of the flux
compactification. Irrespective of the final outcome, we see that string theory
is confronting data and making predictions.


  We study the statistical distribution of the ergotropy and of the efficiency
of a single-qubit battery ad of a single-qubit Otto engine, respectively
fuelled by random collisions. The single qubit, our working fluid, is assumed
to exchange energy with two reservoirs, a non-equilibrium "hot" reservoir and a
zero temperature cold reservoir. The interactions between the qubit and the
reservoirs is described in terms of a collision model of open system dynamics.
The qubit interacts with the non-equilibrium reservoir (a large ensemble of
qudits all prepared in the same pure state) via random unitary collisions and
with the cold reservoir (a large ensemble of qubits in their ground state) via
a partial swap. Due to the random nature of the interaction with the hot
reservoir, fluctuations in ergotropy, heat, and work are present, shrinking
with the size of the qudits in the hot reservoir. While the mean, "macroscopic"
efficiency of the Otto engine is the same as in the case in which the hot
reservoir is a thermal one, the distribution of efficiencies does not support
finite moments, so that the mean of efficiencies does not coincide with the
macroscopic efficiency.


  Electron spins in solids constitute remarkable quantum sensors. Individual
defect centers in diamond were used to detect individual nuclear spins with
nanometer scale resolution, and ensemble magnetometers rival SQUID and vapor
cell magnetometers when taking into account room temperature operation and
size. NV center spins can also detect electric field vectors, despite their
weak coupling to electric fields. %even that of an isolated fundamental charge,
despite their weak coupling to electric fields. Here, we employ ensembles of NV
center spins to measure macroscopic AC electric vector fields with high
precision. We utilize low strain, $^{12}$C enriched diamond to achieve maximum
sensitivity and tailor the spin Hamiltonian via proper magnetic field
adjustment to map out the AC electric field strength and polarization and
arrive at refined electric field coupling constants. For high precision
measurements we combine classical lock-in detection with aspects from quantum
phase estimation for effective suppression of technical noise. Eventually, this
enables $t^{-1/2}$ uncertainty scaling of the electric field strength over
extended averaging periods, enabling us to reach a sensitivity down to
$10^{-7}$ V/$\mu$m.


  In this article, we present the least squares estimator for the drift
parameter in a linear regression model driven by the increment of a fractional
Brownian motion sampled at random times. For two different random times,
Jittered and renewal process sampling, consistency of the estimator is proven.
A simulation study is provided to illustrate the performance of the estimator
under different values of the Hurst parameter H.


  The two-point correlation function of chaotic systems with spin 1/2 is
evaluated using periodic orbits. The spectral form factor for all times thus
becomes accessible. Equivalence with the predictions of random matrix theory
for the Gaussian symplectic ensemble is demonstrated. A duality between the
underlying generating functions of the orthogonal and symplectic symmetry
classes is semiclassically established.


  2D materials are expected to be favorable channel materials for field-effect
transistor (FET) with extremely short channel length because of their superior
immunity to short-channel effects (SCE). Graphene, which is the most famous 2D
material, has no bandgap without additional techniques and this property is
major hindrance in reducing the drain leakage. Therefore, 2D materials with
finite band gap, such as transition metal dichalcogenides (TMDs, e.g. MoS2
WSe2) or phosphorene, are required for the low power consumption FETs. Hafnium
disulfide (HfS2) is a novel TMD, which has not been investigated as channel
material. We focused on its potential for well-balanced mobility and bandgap
properties. The higher electron affinity of Hf dichalcogenides compared with Mo
or W chalcogenides facilitates the formation of low resistance contact and
staggered heterojunction with other 2D materials. Here we demonstrate the first
few layer HfS2 FET with robust current saturation and high current on/off ratio
of more than 10^4.


  In nuclear collisions at RHIC energies, an excess of $\Omega$ hyperons over
$\bar{\Omega}$ is observed, indicating that $\Omega$ carries a net baryon
number despite $s$ and $\bar{s}$ quarks being produced in pairs. The baryon
number in $\Omega$ could have been transported from the incident nuclei and/or
produced in baryon-pair production of $\Omega$ with other types of
anti-hyperons, such as $\bar{\Xi}$. To investigate these two scenarios, we
propose to measure correlations between $\Omega$ and $K$, as well as between
$\Omega$ and anti-hyperons. We will use two versions, the default and
string-melting, of a multiphase transport (AMPT) model to illustrate the method
to measure the correlation and to demonstrate the general shape of the
correlation. We will present the $\Omega$-hadron correlations from simulated
$\mathrm{Au}$+$\mathrm{Au}$ collisions at $\sqrt{s_{NN}} = 7.7$ and $14.6 \
\mathrm{GeV}$, and discuss the dependence on collision energy and on the
hadronization scheme in these two AMPT versions. These correlations can be used
to explore the mechanism of baryon number transport and the effects of baryon
number and strangeness conservation in nuclear collisions.


  We present a novel high-definition (HD) snapshot diffractive spectral imaging
system utilizing a diffractive filter array (DFA) to capture a single image
that encodes both spatial and spectral information. This single diffractogram
can be computationally reconstructed into a spectral image cube, providing a
high-resolution representation of the scene across 25 spectral channels in the
440-800 nm range at 1304x744 spatial pixels (~1 MP). This unique approach
offers numerous advantages including snapshot capture, a form of optical
compression, flexible offline reconstruction, the ability to select the
spectral basis after capture, and high light throughput due to the absence of
lossy filters. We demonstrate a 30-50 nm spectral resolution and compared our
reconstructed spectra against ground truth obtained by conventional
spectrometers. Proof-of-concept experiments in diverse applications including
biological tissue classification, food quality assessment, and simulated
stellar photometry validate our system's capability to perform robust and
accurate inference. These results establish the DFA-based imaging system as a
versatile and powerful tool for advancing scientific and industrial imaging
applications.


  We highlight a physical effect that is often not considered that impacts the
calculation of model spectra of planets at secondary eclipse, affecting both
emission and reflection spectra. The radius of the emitting surface of the
planet is not merely one value measured from a transit light curve, but is
itself a function of wavelength, yet it is not directly measurable. At high
precision, a similar effect is well-known in transit "transmission
spectroscopy" but this related effect also impacts emission and reflection. As
is well-appreciated, the photospheric radius can vary across $\sim$4-8
atmospheric scale heights, depending on atmospheric opacity and spectral
resolution. This effect leads to a decreased weighting in model calculations at
wavelengths where atmospheric opacity is low, and one sees more deeply into the
atmosphere, to a smaller radius. The overall effect serves to mute emission
spectra features for atmospheres with no thermal inversion but to enhance
features for atmospheres with a thermal inversion. While this effect can be
ignored for current \emph{Hubble} observations, it can lead to
wavelength-dependent 10-20\% changes in planet-to-star flux ratios in the
infrared at $R\sim~200-1000$ (readily achievable for JWST) for low-gravity hot
Jupiters, although values of 5\% are more typical for the population. The
effect is mostly controlled by the ratio of the atmospheric scale height to the
planet radius, and can be important at any planetary temperature. Of known
planets, the effect is largest for the cool "super-puffs" at very low surface
gravity, where it can alter calculated flux ratios by over 100\%. We discuss
complexities of including this photospheric radius effect in 1D and 3D
atmosphere models.


  We determine the distributions of lengths of runs in random sequences of
elements from a totally ordered set (total order) or partially ordered set
(partial order). In particular, we produce novel formulae for the expected
value, variance, and probability generating function (PGF) of such lengths in
the case of an arbitrary total order. Our focus is on the case of distributions
with both atoms and diffuse (absolutely or singularly continuous) mass which
has not been addressed in this generality before. We also provide a method of
calculating the PGF of run lengths for countably series-parallel partial
orders. Additionally, we prove a strong law of large numbers for the
distribution of run lengths in a particular realization of an infinite
sequence.


  Distribution-to-Distribution (D2D) point cloud registration algorithms are
fast, interpretable, and perform well in unstructured environments.
Unfortunately, existing strategies for predicting solution error for these
methods are overly optimistic, particularly in regions containing large or
extended physical objects. In this paper we introduce the Iterative Closest
Ellipsoidal Transform (ICET), a novel 3D LIDAR scan-matching algorithm that
re-envisions NDT in order to provide robust accuracy prediction from first
principles. Like NDT, ICET subdivides a LIDAR scan into voxels in order to
analyze complex scenes by considering many smaller local point distributions,
however, ICET assesses the voxel distribution to distinguish random noise from
deterministic structure. ICET then uses a weighted least-squares formulation to
incorporate this noise/structure distinction into computing a localization
solution and predicting the solution-error covariance. In order to demonstrate
the reasonableness of our accuracy predictions, we verify 3D ICET in three
LIDAR tests involving real-world automotive data, high-fidelity simulated
trajectories, and simulated corner-case scenes. For each test, ICET
consistently performs scan matching with sub-centimeter accuracy. This level of
accuracy, combined with the fact that the algorithm is fully interpretable,
make it well suited for safety-critical transportation applications. Code is
available at https://github.com/mcdermatt/ICET


  The present article is a review of recent developments concerning the notion
of F{\o}lner sequences both in operator theory and operator algebras. We also
give a new direct proof that any essentially normal operator has an increasing
F{\o}lner sequence $\{P_n\}$ of non-zero finite rank projections that strongly
converges to 1. The proof is based on Brown-Douglas-Fillmore theory. We use
F{\o}lner sequences to analyze the class of finite operators introduced by
Williams in 1970. In the second part of this article we examine a procedure of
approximating any amenable trace on a unital and separable C*-algebra by
tracial states $\mathrm{Tr}(\cdot P_n)/\mathrm{Tr}(P_n)$ corresponding to a
F{\o}lner sequence and apply this method to improve spectral approximation
results due to Arveson and B\'edos. The article concludes with the analysis of
C*-algebras admitting a non-degenerate representation which has a F{\o}lner
sequence or, equivalently, an amenable trace. We give an abstract
characterization of these algebras in terms of unital completely positive maps
and define F{\o}lner C*-algebras as those unital separable C*-algebras that
satisfy these equivalent conditions. This is analogous to Voiculescu's abstract
characterization of quasidiagonal C*-algebras.


  In this paper, we propose a Spatial Robust Mixture Regression model to
investigate the relationship between a response variable and a set of
explanatory variables over the spatial domain, assuming that the relationships
may exhibit complex spatially dynamic patterns that cannot be captured by
constant regression coefficients. Our method integrates the robust finite
mixture Gaussian regression model with spatial constraints, to simultaneously
handle the spatial nonstationarity, local homogeneity, and outlier
contaminations. Compared with existing spatial regression models, our proposed
model assumes the existence a few distinct regression models that are estimated
based on observations that exhibit similar response-predictor relationships. As
such, the proposed model not only accounts for nonstationarity in the spatial
trend, but also clusters observations into a few distinct and homogenous
groups. This provides an advantage on interpretation with a few stationary
sub-processes identified that capture the predominant relationships between
response and predictor variables. Moreover, the proposed method incorporates
robust procedures to handle contaminations from both regression outliers and
spatial outliers. By doing so, we robustly segment the spatial domain into
distinct local regions with similar regression coefficients, and sporadic
locations that are purely outliers. Rigorous statistical hypothesis testing
procedure has been designed to test the significance of such segmentation.
Experimental results on many synthetic and real-world datasets demonstrate the
robustness, accuracy, and effectiveness of our proposed method, compared with
other robust finite mixture regression, spatial regression and spatial
segmentation methods.


  We present a robust sample of very high-redshift galaxy candidates from the
first epoch of {\it JWST}/NIRCam imaging from the Next Generation Extragalactic
Exploratory Deep (NGDEEP) Survey. The NGDEEP NIRCam imaging in the Hubble Ultra
Deep Field Parallel Field 2 (HUDF-Par2) reaches $m=30.4$ (5$\sigma$,
point-source) in F277W, making it the deepest public {\it JWST} GO imaging
dataset to date. We describe our detailed data reduction process of the
six-filter broad-band {\it JWST}/NIRCam imaging, incorporating custom
corrections for systematic effects to produce high-quality calibrated images.
Using robust photometric redshift selection criteria, we identify a sample of
38 $z \gtrsim 9$ galaxy candidates. These objects span a redshift range of
$z=8.5-15.8$, and apparent magnitudes of $m_\mathrm{F277W} = 27-30.5$ AB mag,
reaching $\sim 1.5$ mag deeper than previous public {\it JWST} imaging surveys.
We calculate the rest-frame ultraviolet (UV) luminosity function at $z \sim$ 9
and 11, and present a new measurement of the luminosity function faint-end
slope at $z \sim 11$. There is no significant evolution in the faint-end slope
and number density from $z=9$ to 11. Comparing our results with theoretical
predictions, we find that some models produce better agreement at the faint end
than the bright end. These results will help to constrain how stellar feedback
impacts star formation at these early epochs.


  We propose a novel protocol for the creation of macroscopic quantum
superposition (MQS) states based on a measurement of a non-monotonous function
of a quantum collective variable. The main advantage of this protocol is that
it does not require switching on and off nonlinear interactions in the system.
We predict this protocol to allow the creation of multiatom MQS by measuring
the number of atoms coherently outcoupled from a two-component (spinor)
Bose-Einstein condensate.


  We investigate fluorescence detection using a standing wave of blue-detuned
light of one or more atoms held in a deep, microscopic dipole trap. The
blue-detuned standing wave realizes a Sisyphus laser cooling mechanism so that
an atom can scatter many photons while remaining trapped. When imaging more
than one atom, the blue detuning limits loss due to inelastic light-assisted
collisions. Using this standing wave probe beam, we demonstrate that we can
count from one to the order of 100 atoms in the microtrap with sub-poissonian
precision.


  Diffusion-based generative models have significantly advanced text-to-image
generation but encounter challenges when processing lengthy and intricate text
prompts describing complex scenes with multiple objects. While excelling in
generating images from short, single-object descriptions, these models often
struggle to faithfully capture all the nuanced details within longer and more
elaborate textual inputs. In response, we present a novel approach leveraging
Large Language Models (LLMs) to extract critical components from text prompts,
including bounding box coordinates for foreground objects, detailed textual
descriptions for individual objects, and a succinct background context. These
components form the foundation of our layout-to-image generation model, which
operates in two phases. The initial Global Scene Generation utilizes object
layouts and background context to create an initial scene but often falls short
in faithfully representing object characteristics as specified in the prompts.
To address this limitation, we introduce an Iterative Refinement Scheme that
iteratively evaluates and refines box-level content to align them with their
textual descriptions, recomposing objects as needed to ensure consistency. Our
evaluation on complex prompts featuring multiple objects demonstrates a
substantial improvement in recall compared to baseline diffusion models. This
is further validated by a user study, underscoring the efficacy of our approach
in generating coherent and detailed scenes from intricate textual inputs.


  Phase shifts induced by the Kerr effect are usually very small at the
single-photon level. We propose two circuits for enhancing the cross-Kerr phase
shift by applying one- and two-mode quadrature squeezing operators. Our results
are based on the vector coherent state theory and can be implemented by
physical operations satisfying the commutation relations for generators of the
generalized special unitary group SU(1,1). While the proposed methods could be
useful for the realization of quantum optical entangling gates based on Kerr
nonlinear media at the single-photon level, they also indicate a general
alternative approach to enhance higher-order nonlinearities by applying
lower-order nonlinear effects.


  The interplay between the nematic order and magnetism in FeSe is not yet well
understood. There is a controversy concerning the relationship between orbital
and spin degrees of freedom in FeSe and their relevance for superconductivity.
Here we investigate the effect of S substitution on the nematic transition
temperature ($T_{\rm n}$) and the low-energy spin fluctuations (SF) in FeSe
single crystals. We show that the low-energy SF emerge below the nematic
transition. The difference between the onset temperature for the critical SF
($T_{\rm SF}$) and $T_{\rm n}$ is small for FeSe but significantly increases
with S substitution. Below $T_{\rm SF}$ the Korringa relation is violated and
the effective muon hyperfine coupling constant changes a sign. Our results
exclude a direct coupling of the low-energy SF to the electronic nematic order
indicating a presence of multiple spin degrees of freedom in FeSe$_{\rm
1-x}$S$_{\rm x}$.


  We prove a representation for the average wave function of the Schr\"odinger
equation with a white noise potential in $d=1,2$, in terms of the renormalized
self-intersection local time of a Brownian motion.


  Yellow luminescence (YL) is probably the longest and most studied
defect-related luminescence band in GaN, yet its electronic structure or
chemical identity remain unclear. Most of the theoretical work so far has
attributed the feature to bulk defects, whereas spectroscopic studies have
suggested a surface origin. Here, we apply deep level spectroscopy using
sub-bandgap surface photovoltage that provides the energy distribution of the
surface charge density. Comparison of surface charge spectra obtained under
identical conditions before and after various surface treatments reveals the
dynamics of the surface charge density. Further comparison with spectra of the
entire state obtained using photoluminescence shows how the charge density
stored in YL-related defects is eliminated upon a mild anneal in vacuum. This
suggests that the YL-related defect involves a certain molecule adsorbed on the
GaN surface, possibly in a complex with an intrinsic surface defect. The
observed interaction with air strongly indicates that the YL-related deep level
is a surface state.


  This paper's objective is to improve the existing proof of the derivation of
the Rayleigh--Boltzmann equation from the nonideal Rayleigh gas [6], yielding a
far faster convergence rate. This equation is a linear version of the Boltzmann
equation, describing the behavior of a small fraction of tagged particles
having been perturbed from thermodynamic equilibrium. This linear equation,
derived from the microscopic Newton laws as suggested by the Hilbert's sixth
problem, is much better understood than the quadratic Boltzmann equation, and
even enable results on long time scales for the kinetic description of gas
dynamics.The present paper improves the physically poor convergence rate that
had been previously proved, into a much more satisfactory rate which is more
than exponentially better.


  We present a quantitative study of most prominent incompressible quantum Hall
states in the partially filled first excited Landau level (LL1) which have been
recently studied experimentally by Choi et al. The pseudopotential describing
the electron - electron interaction in LL1 is harmonic at short range. It
produces a series of incompressible states which is different from its LL0
counterpart. The numerical data indicate that the most prominent states
$\nu={5/2}$, 7/3, and 8/3 are not produced by Laughlin correlated electrons,
but result from a tendency of electrons to form pairs or larger clusters which
eventually become Laughlin correlated. States with smaller gaps at filling
factors 14/5, 16/7, 11/5, 19/7 are Laughlin correlated electron or hole states
and fit Jain's sequence of filled $\rm{CF}^4$ levels.


  We obtain a polynomial-time 17/12-approximation algorithm for the
minimum-cost 2-vertex-connected spanning subgraph problem, restricted to graphs
of minimum degree at least 3. Our algorithm uses the framework of
ear-decompositions for approximating connectivity problems, which was
previously used in algorithms for finding the smallest 2-edge-connected
spanning subgraph by Cheriyan, Seb\H{o} and Szigeti (SIAM J.Discrete Math.
2001) who gave a 17/12-approximation algorithm for this problem, and by
Seb\H{o} and Vygen (Combinatorica 2014), who improved the approximation ratio
to 4/3.


  Recent developments in the determination of the planar S-matrix of N=4 Super
Yang-Mills are closely related to its Yangian symmetry. Here we provide
evidence for a yet unobserved additional symmetry: the Yangian level-one
helicity operator.


  We aim to improve upon contemporary methods to estimate host-galaxy reddening
of stripped-envelope (SE) supernovae (SNe). To this end the Carnegie Supernova
Project (CSP-I) SE SNe photometry data release, consisting of nearly three
dozen objects, is used to identify a minimally reddened sub-sample for each
traditionally defined spectroscopic sub-types (i.e, SNe~IIb, SNe~Ib, SNe~Ic).
Inspection of the optical and near-infrared (NIR) colors and color evolution of
the minimally reddened sub-samples reveals a high degree of homogeneity,
particularly between 0d to +20d relative to B-band maximum. This motivated the
construction of intrinsic color-curve templates, which when compared to the
colors of reddened SE SNe, yields an entire suite of optical and NIR color
excess measurements. Comparison of optical/optical vs. optical/NIR color excess
measurements indicates the majority of the CSP-I SE SNe suffer relatively low
amounts of reddening and we find evidence for different R_(V)^(host) values
among different SE SN. Fitting the color excess measurements of the seven most
reddened objects with the Fitzpatrick (1999) reddening law model provides
robust estimates of the host visual-extinction A_(V)^(host) and R_(V)^(host).
In the case of the SE SNe with relatively low amounts of reddening, a preferred
value of R_(V)^(host) is adopted for each sub-type, resulting in estimates of
A_(V)^(host) through Fitzpatrick (1999) reddening law model fits to the
observed color excess measurements. Our analysis suggests SE SNe reside in
galaxies characterized by a range of dust properties. We also find evidence SNe
Ic are more likely to occur in regions characterized by larger R_(V)^(host)
values compared to SNe IIb/Ib and they also tend to suffer more extinction.
These findings are consistent with work in the literature suggesting SNe Ic
tend to occur in regions of on-going star formation.


  We discuss the possibilities of the simultaneous determination of the
neutrino masses and the evolution of dark energy from future cosmological
observations such as cosmic microwave background (CMB), large scale structure
(LSS) and the cross correlation between them. Recently it has been discussed
that there is a degeneracy between the neutrino masses and the equation of
state for dark energy. It is also known that there are some degeneracies among
the parameters describing the dark energy evolutions. We discuss the
implications of these on the cross correlation of CMB with LSS in some details.
Then we consider to what extent we can determine the neutrino masses and the
dark energy evolution using the expected data from CMB, LSS and their cross
correlation.


  Electric Autonomous Vehicles (EAVs) promise to be an effective way to solve
transportation issues such as accidents, emissions and congestion, and aim at
establishing the foundation of Machine-to-Machine (M2M) economy. For this to be
possible, the market should be able to offer appropriate charging services
without involving humans. The state-of-the-art mechanisms of charging and
billing do not meet this requirement, and often impose service fees for value
transactions that may also endanger users and their location privacy. This
paper aims at filling this gap and envisions a new charging architecture and a
billing framework for EAV which would enable M2M transactions via the use of
Distributed Ledger Technology (DLT).


  Recently a general growth curve including the well known growth equations,
such as Malthus, logistic, Bertallanfy, Gompertz, has been studied. We now
propose two stochastic formulations of this growth equation. They are obtained
starting from a suitable parametrization of the deterministic model, by adding
an additive and multiplicative noise respectively. For these processes we focus
attention on the First Passage Time from a barrier and on the First Exit Time
from a region delimited by two barriers. We consider thresholds, generally time
dependent, for which there exist closed-forms of the probability densities of
the first passage time and of the first exit time.


  Significant progress has been made in recent studies of thermal and
thermoelectric transport phenomena in nanostructures and low-dimensional
systems. This article reviews several intriguing quantum and classical size
effects on thermal and thermoelectric properties that have been predicted by
theoretical calculations or observed in experiments. Attention is focused on
the Casimir limit in phonon boundary scattering and the effect of phonon
confinement on the lattice thermal conductivity of semiconductor nanowires
(NWs) and nanomeshes; the effects of thickness, lateral size, and interface
interaction on the lattice thermal conductivity of carbon nanotubes (CNTs) and
graphene; and the phonon-drag thermopower and quantum size effects on the
thermoelectric power factor in semiconductor NWs. Further experimental and
theoretical investigations are suggested for better understanding of some of
these nanoscale transport phenomena.


  Aspect-based opinion mining is the task of identifying sentiment at the
aspect level in opinionated text, which consists of two subtasks: aspect
category extraction and sentiment polarity classification. While aspect
category extraction aims to detect and categorize opinion targets such as
product features, sentiment polarity classification assigns a sentiment label,
i.e. positive, negative, or neutral, to each identified aspect. Supervised
learning methods have been shown to deliver better accuracy for this task but
they require labeled data, which is costly to obtain, especially for
resource-poor languages like Vietnamese. To address this problem, we present a
supervised aspect-based opinion mining method that utilizes labeled data from a
foreign language (English in this case), which is translated to Vietnamese by
an automated translation tool (Google Translate). Because aspects and opinions
in different languages may be expressed by different words, we propose using
word embeddings, in addition to other features, to reduce the vocabulary
difference between the original and translated texts, thus improving the
effectiveness of aspect category extraction and sentiment polarity
classification processes. We also introduce an annotated corpus of aspect
categories and sentiment polarities extracted from restaurant reviews in
Vietnamese, and conduct a series of experiments on the corpus. Experimental
results demonstrate the effectiveness of the proposed approach.


  A comprehensive theory of the Weyl-Wigner formalism for the canonical pair
angle-angular momentum is presented. Special attention is paid to the problems
linked to rotational periodicity and angular-momentum discreteness.


  A generalized magnetothermoelasticity, in the context of Lord-Shulman theory,
is employed to investigate the interaction of a homogeneous and isotropic
perfect conducting half space with rotation. The Laplace transform for time
variable is used to formulate a vector-matrix differential equation which is
then solved by eigenvalue method. The continuous solution of displacement
component while the discontinuous solutions of stress components, temperature
distribution, induced magnetic and electric field have been analyzed in an
approximate manner using assymptotic expansion for small time. The graphical
representations also prove this continuity and discontinuity of the solutions.


  The law of action-reaction is thoroughly used in textbooks to derive the
conservation laws of linear and angular momentum, and it was considered by
Ernst Mach the the cornerstone of physics. We give here a background survey of
several questions raised by the action-reaction law, and in particular, the
role of the physical vacuum is shown to provide an appropriate framework to
clarify the occurrence of possible violations of the action-reaction law. It is
also obtained an expression for the general linear momentum of a body-particle
in the context of statistical mechanics. It is shown that Newton's third law is
not verified in systems out of equilibrium due to an additional entropic
gradient term present in the particle's momentum.


  A quantum-enhanced experiment, in which information is transduced from a
system of interest and processed on a quantum computer, has the possibility of
exponential advantage in sampling tasks over a traditional experiment, where
only the measurement outcomes of projective or weak measurements are stored on
a classical computer. In this work, we demonstrate that, similar to the
measurement induced phase transition~(MIPT) occurring in traditional
experiments, quantum-enhanced experiments can also show entanglement phase
transitions. We introduce a noisy transduction operation which replaces
projective measurements and acts independently on two qubits, recording the
quantum state of one qubit in the measurement apparatus, while erasing the
quantum state of the other qubit with the environment. We then construct a
random brickwork circuit which shows an entanglement transition tuned by the
rate of noisy transduction operations. Below the critical rate of noisy
transduction, an extensive amount of entanglement is generated between regions
of the system and apparatus, while above the critical rate, entanglement
satisfies area law scaling. Our work delineates the prerequisites for
quantum-enhanced experiments to manifest entanglement transitions, thereby
establishing a foundational framework that connects quantum-enhanced sensing
with emergent entanglement phenomena.


  We propose a multi-step Richardson-Romberg extrapolation method for the
computation of expectations $E f(X_{_T})$ of a diffusion $(X_t)_{t\in [0,T]}$
when the weak time discretization error induced by the Euler scheme admits an
expansion at an order $R\ge 2$. The complexity of the estimator grows as $R^2$
(instead of $2^R$) and its variance is asymptotically controlled by considering
some consistent Brownian increments in the underlying Euler schemes. Some Monte
carlo simulations carried with path-dependent options (lookback, barriers)
which support the conjecture that their weak time discretization error also
admits an expansion (in a different scale). Then an appropriate
Richardson-Romberg extrapolation seems to outperform the Euler scheme with
Brownian bridge.


  This paper introduces multiuser multiple-input multiple-output (MU-MIMO)
architectures based on non-linear precoding and stream combining techniques
using rate-splitting (RS), where the transmitter often has only partial
knowledge of the channel state information (CSI). In contrast to existing
works, we consider deployments where the receivers may be equipped with
multiple antennas. This allows us to employ linear combining techniques based
on the Min-Max, the maximum ratio and the minimum mean-square error criteria
along with Tomlinson-Harashima precoders (THP) for RS-based MU-MIMO systems to
enhance the sum-rate performance. Moreover, we incorporate the Multi-Branch
(MB) concept into the RS architecture to further improve the sum-rate
performance. Closed-form expressions for the signal-to-interference-plus-noise
ratio and the sum-rate at the receiver end are devised through statistical
analysis. Simulation results show that the proposed RS-THP schemes achieve
better performance than conventional linear and THP precoders.


  By coupling the EUropean Heliospheric FORcasting Information Asset (EUHFORIA)
and the improved Particle Acceleration and Transport in the Heliosphere (iPATH)
model, two energetic storm particle (ESP) events, originating from the same
active region (AR 13088) and observed by Solar Orbiter (SolO) on August 31 2022
and September 05 2022, are modelled. While both events originated from the same
active region, they exhibited notable differences, including: 1) the August ESP
event lasted for 7 hours, while the September event persisted for 16 hours; 2)
The time intensity profiles for the September event showed a clear cross-over
upstream of the shock where the intensity of higher energy protons exceeds
those of lower energy protons, leading to positive (``reverse'') spectral
indices prior to the shock passage. For both events, our simulations replicate
the observed duration of the shock sheath, depending on the deceleration
history of the CME. Imposing different choices of escaping length scale, which
is related to the decay of upstream turbulence, the modelled time intensity
profiles prior to the shock arrival also agree with observations. In
particular, the cross-over of this time profile in the September event is well
reproduced. We show that a ``reverse'' upstream spectrum is the result of the
interplay between two length scales. One characterizes the decay of upstream
shock accelerated particles, which are controlled by the energy-dependent
diffusion coefficient, and the other characterizes the decay of upstream
turbulence power, which is related to the process of how streaming protons
upstream of the shock excite Alfv\'{e}n waves. Simulations taking into account
real-time background solar wind, the dynamics of the CME propagation, and
upstream turbulence at the shock front are necessary to thoroughly understand
the ESP phase of large SEP events.


  The success of the mean field composite Fermion (MFCF) picture in predicting
the lowest energy band of angular momentum multiplets in fractional quantum
Hall systems cannot be found in a cancellation between the Coulomb and
Chern--Simons interactions beyond the mean field, due to their totally
different energy scales. We show that the MFCF approximation can be regarded as
a kind of semi-empirical Hund's rule for monopole harmonics. The plausibility
of the rule is easily established, but rigorous proof relies on comparison with
detailed numerical calculations.


  I present here a simple proof that, under general regularity conditions, the
standard parametrization of generalized linear mixed model is identifiable. The
proof is based on the assumptions of generalized linear mixed models on the
first and second order moments and some general mild regularity conditions,
and, therefore, is extensible to quasi-likelihood based generalized linear
models. In particular, binomial and Poisson mixed models with dispersion
parameter are identifiable when equipped with the standard parametrization.


  Safety alignment of large language models (LLMs) has been gaining increasing
attention. However, current safety-aligned LLMs suffer from the fragile and
imbalanced safety mechanisms, which can still be induced to generate unsafe
responses, exhibit over-safety by rejecting safe user inputs, and fail to
preserve general utility after safety alignment. To this end, we propose a
novel post safety alignment (PSA) method to address these inherent and emerging
safety challenges, including safety enhancement, over-safety mitigation, and
utility preservation. In specific, we introduce \textsc{SafePatching}, a novel
framework for comprehensive and efficient PSA, where two distinct safety
patches are developed on the harmful data to enhance safety and mitigate
over-safety concerns, and then seamlessly integrated into the target LLM
backbone without compromising its utility. Extensive experiments show that
\textsc{SafePatching} achieves a more comprehensive and efficient PSA than
baseline methods. It even enhances the utility of the backbone, further
optimizing the balance between being helpful and harmless in current aligned
LLMs. Also, \textsc{SafePatching} demonstrates its superiority in continual PSA
scenarios.


  This short summary reviews the recent progress in the computation of
higher-order corrections to W+b W-antib production. In addition, new
phenomenological studies reveal potential problems that may affect precision
measurements at the LHC.


  We study the order parameter symmetry in bi-layer cuprates such as YBaCuO,
where interesting $\pi$ phase shifts have been observed in Josephson junctions.
Taking models which represent the measured spin fluctuation spectra of this
cuprate, as well as more general models of Coulomb correlation effects, we
classify the allowed symmetries and determine their associated physical
properties. $\pi $ phase shifts are shown to be a general consequence of
repulsive interactions, independent of whether a magnetic mechanism is
operative. While it is known to occur in d-states, this behavior can also be
associated with (orthorhombic) s-symmetry when the two sub-band gaps have
opposite phase. Implications for the magnitude of $T_c$ are discussed.