  We consider a discrete-time dividend payout problem with risk sensitive
shareholders. It is assumed that they are equipped with a risk aversion
coefficient and construct their discounted payoff with the help of the
exponential premium principle. This leads to a non-expected recursive utility
of the dividends. Within such a framework not only the expected value of the
dividends is taken into account but also their variability. Our approach is
motivated by a remark in Gerber and Shiu (2004). We deal with the finite and
infinite time horizon problems and prove that, even in general setting, the
optimal dividend policy is a band policy. We also show that the policy
improvement algorithm can be used to obtain the optimal policy and the
corresponding value function. Next, an explicit example is provided, in which
the optimal policy of a barrier type is shown to exist. Finally, we present
some numerical studies and discuss the influence of the risk sensitive
parameter on the optimal dividend policy.


  Based on the recent proposal for the tetraquarks with the mixing scheme, we
investigate fall-apart decays of $a_0(980), a_0(1450)$ into two lowest-lying
mesons. This mixing scheme suggests that $a_0(980)$ and $a_0(1450)$ are the
tetraquarks with the mixtures of two spin configurations of diquark and
antidiquark. Due to the relative sign differences in the mixtures, the
couplings of fall-apart decays into two mesons are strongly enhanced for
$a_0(980)$ but suppressed for $a_0(1450)$. We report that this expectation is
supported by their experimental decays. In particular, the ratios of the
associated partial decay widths, which depend on some kinematical factors and
the couplings, are found to be around $\Gamma [a_0(980)\rightarrow \pi
\eta]/\Gamma [a_0(1450)\rightarrow \pi \eta] = 2.51-2.54$, $\Gamma
[a_0(980)\rightarrow K\bar{K}]/\Gamma [a_0(1450)\rightarrow K\bar{K}] =
0.52-0.89$, which seems to agree with the experimental ratios reasonably well.
This agreement can be interpreted as the tetraquark signatures for $a_0(980),
a_0(1450)$.


  This study exploits the advancements in information and communication
technology (ICT), connected and automated vehicles (CAVs), and sensing, to
develop proactive multi-objective eco-routing strategies. For a robust
application, several GHG costing approaches are examined. The predictive models
for the link level traffic and emission states are developed using long short
term memory deep network with exogenous predictors. It is found that proactive
routing strategies outperformed the myopic strategies, regardless of the
routing objective. Whether myopic or proactive, the multi-objective routing,
with travel time and GHG minimization as objectives, outperformed the single
objective routing strategies, causing a reduction in the average travel time
(TT), average vehicle kilometre travelled (VKT), total GHG and total NOx by
17%, 21%, 18%, and 20%, respectively. Finally, the additional TT and VKT
experienced by the vehicles in the network contributed adversely to the amount
of GHG and NOx produced in the network.


  We present an approach of calculating the group of braided autoequivalences
of the category of representations of the Drinfeld double of a finite
dimensional Hopf algebra $H$ and thus the Brauer-Picard group of
$H$-$\mathrm{mod}$. We consider two natural subgroups and a subset as
candidates for generators. In this article $H$ is the group algebra of a finite
group $G$. As our main result we prove that any element of the Brauer-Picard
group, fulfilling an additional cohomological condition, decomposes into an
ordered product of our candidates. For elementary abelian groups $G$ our
decomposition reduces to the Bruhat decomposition of the Brauer-Picard group,
which is in this case a Lie group over a finite field. Our results are
motivated by and have applications to symmetries and defects in $3d$-TQFT and
group extensions of fusion categories.


  The proliferation of mobile networks and their increasing importance to
modern life, combined with the emerging threat of quantum computing, present
new challenges and opportunities for cybersecurity. This paper addresses the
complexity of protecting these critical infrastructures against future quantum
attacks while considering operational sustainability. We begin with an overview
of the current landscape, identify the main vulnerabilities in mobile networks,
and evaluate existing security solutions with new post-quantum cryptography
(PQC) methods. We then present a quantum-secure architecture with PQC and
Quantum Key Distribution (QKD) tailored explicitly for sustainable mobile
networks and illustrate its applicability with several use cases that emphasize
the need for advanced protection measures in this new era. In addition, a
comprehensive analysis of PQC algorithm families is presented, focusing on
their suitability for integration in mobile environments, with particular
attention to the trade-offs between energy consumption and security
improvements. Finally, recommendations for strengthening mobile networks
against quantum threats are provided through a detailed examination of current
challenges and opportunities.


  We explicitly construct the four and two fermion zero modes for the
mixed-flux generalization of the Hofman-Maldacena giant magnon on two of the
AdS$_3$ backgrounds with maximal amount of supersymmetry, AdS$_3 \times$S$^3
\times$T$^4$ and AdS$_3 \times$S$^3 \times$S$^3 \times$S$^1$. We also show how
to get the $\mathfrak{psu}(1|1)^4$ and $\mathfrak{su}(1|1)^2$ superalgebras
from the semiclassically quantized fermion zero modes.


  Beside the minimization of the prediction error, two of the most desirable
properties of a regression scheme are stability and interpretability. Driven by
these principles, we propose continuous-domain formulations for one-dimensional
regression problems. In our first approach, we use the Lipschitz constant as a
regularizer, which results in an implicit tuning of the overall robustness of
the learned mapping. In our second approach, we control the Lipschitz constant
explicitly using a user-defined upper-bound and make use of a
sparsity-promoting regularizer to favor simpler (and, hence, more
interpretable) solutions. The theoretical study of the latter formulation is
motivated in part by its equivalence, which we prove, with the training of a
Lipschitz-constrained two-layer univariate neural network with rectified linear
unit (ReLU) activations and weight decay. By proving representer theorems, we
show that both problems admit global minimizers that are continuous and
piecewise-linear (CPWL) functions. Moreover, we propose efficient algorithms
that find the sparsest solution of each problem: the CPWL mapping with the
least number of linear regions. Finally, we illustrate numerically the outcome
of our formulations.


  Data augmentation uses artificially-created examples to support supervised
machine learning, adding robustness to the resulting models and helping to
account for limited availability of labelled data. We apply and evaluate a
synthetic data approach to relationship classification in digital libraries,
generating artificial books with relationships that are common in digital
libraries but not easier inferred from existing metadata. We find that for
classification on whole-part relationships between books, synthetic data
improves a deep neural network classifier by 91%. Further, we consider the
ability of synthetic data to learn a useful new text relationship class from
fully artificial training data.


  We evaluate the covariance matrix of the matter power spectrum using
perturbation theory up to dominant terms at 1-loop order and compare it to
numerical simulations. We decompose the covariance matrix into the disconnected
(Gaussian) part, trispectrum from the modes outside the survey (beat coupling
or super-sample variance), and trispectrum from the modes inside the survey,
and show how the different components contribute to the overall covariance
matrix. We find the agreement with the simulations is at a 10\% level up to $k
\sim 1 h {\rm Mpc^{-1}}$. We show that all the connected components are
dominated by the large-scale modes ($k<0.1 h {\rm Mpc^{-1}}$), regardless of
the value of the wavevectors $k,\, k'$ of the covariance matrix, suggesting
that one must be careful in applying the jackknife or bootstrap methods to the
covariance matrix. We perform an eigenmode decomposition of the connected part
of the covariance matrix, showing that at higher $k$ it is dominated by a
single eigenmode. The full covariance matrix can be approximated as the
disconnected part only, with the connected part being treated as an external
nuisance parameter with a known scale dependence, and a known prior on its
variance for a given survey volume. Finally, we provide a prescription for how
to evaluate the covariance matrix from small box simulations without the need
to simulate large volumes.


  While social media has become an empowering agent to individual voices and
freedom of expression, it also facilitates anti-social behaviors including
online harassment, cyberbullying, and hate speech. In this paper, we present
the first comparative study of hate speech instigators and target users on
Twitter. Through a multi-step classification process, we curate a comprehensive
hate speech dataset capturing various types of hate. We study the distinctive
characteristics of hate instigators and targets in terms of their profile
self-presentation, activities, and online visibility. We find that hate
instigators target more popular and high profile Twitter users, and that
participating in hate speech can result in greater online visibility. We
conduct a personality analysis of hate instigators and targets and show that
both groups have eccentric personality facets that differ from the general
Twitter population. Our results advance the state of the art of understanding
online hate speech engagement.


  Diagrammatic reducibility DR and its generalization vertex asphericity VA are
combinatorial tools developed for detecting asphericity of a 2-complex. Here we
present tests for a relative version of VA that apply to pairs of 2-complexes
$(L,K)$, where $K$ is a subcomplex of $L$. We show that a relative weight test
holds for injective labeled oriented trees, implying that they are VA and hence
aspherical. This strengthens a result obtained by the authors in 2017 and
simplifies the original proof.


  We introduce a combined molecular dynamics (MD) and quantum trajectories (QT)
code to simulate the effects of near-resonant optical fields on state-vector
evolution and particle motion in a collisional system. In contrast to
collisionless systems, in which the quantum dynamics of multi-level,
laser-driven particles with spontaneous emission can be described with the
optical Bloch equations (OBEs), particle velocities in sufficiently collisional
systems change on timescales comparable to those of the laser-induced,
quantum-state dynamics. These transient velocity changes can cause the
time-averaged velocity dependence of the quantum state to differ from the OBE
solution. We use this multiscale code to describe laser-cooling in a strontium
ultracold neutral plasma. Important phenomena described by the simulation
include suppression of electromagnetically induced transparencies through rapid
velocity changing collisions and thermalization between cooled and un-cooled
directions for anisotropic laser cooling.


  Factor importance measures the impact of each feature on output prediction
accuracy. Many existing works focus on the model-based importance, but an
important feature in one learning algorithm may hold little significance in
another model. Hence, a factor importance measure ought to characterize the
feature's predictive potential without relying on a specific prediction
algorithm. Such algorithm-agnostic importance is termed as intrinsic importance
in Williamson et al. (2023), but their estimator again requires model fitting.
To bypass the modeling step, we present the equivalence between predictiveness
potential and total Sobol' indices from global sensitivity analysis, and
introduce a novel consistent estimator that can be directly estimated from
noisy data. Integrating with forward selection and backward elimination gives
rise to FIRST, Factor Importance Ranking and Selection using Total (Sobol')
indices. Extensive simulations are provided to demonstrate the effectiveness of
FIRST on regression and binary classification problems, and a clear advantage
over the state-of-the-art methods.


  The LUX-ZEPLIN (LZ) experiment is a dark matter detector centered on a
dual-phase xenon time projection chamber. We report searches for new physics
appearing through few-keV-scale electron recoils, using the experiment's first
exposure of 60 live days and a fiducial mass of 5.5t. The data are found to be
consistent with a background-only hypothesis, and limits are set on models for
new physics including solar axion electron coupling, solar neutrino magnetic
moment and millicharge, and electron couplings to galactic axion-like particles
and hidden photons. Similar limits are set on weakly interacting massive
particle (WIMP) dark matter producing signals through ionized atomic states
from the Migdal effect.


  We compare and contrast two approaches to validating a trained classifier
while using all in-sample data for training. One is simultaneous validation
over an organized set of hypotheses (SVOOSH), the well-known method that began
with VC theory. The other is withhold and gap (WAG). WAG withholds a validation
set, trains a holdout classifier on the remaining data, uses the validation
data to validate that classifier, then adds the rate of disagreement between
the holdout classifier and one trained using all in-sample data, which is an
upper bound on the difference in error rates. We show that complex hypothesis
classes and limited training data can make WAG a favorable alternative.


  The Arizona Robotic Telescope Network (ARTN) project is a long term effort to
develop a system of telescopes to carry out a flexible program of PI observing,
survey projects, and time domain astrophysics including monitoring, rapid
response, and transient/target-of-opportunity followup. Steward Observatory
operates and shares in several 1-3m class telescopes with quality sites and
instrumentation, largely operated in classical modes. Science programs suited
to these telescopes are limited by scheduling flexibility and people-power of
available observers. Our goal is to adapt these facilities for multiple
co-existing queued programs, interrupt capability, remote/robotic operation,
and delivery of reduced data. In the long term, planning for the LSST era, we
envision an automated system coordinating across multiple telescopes and sites,
where alerts can trigger followup, classification, and triggering of further
observations if required, such as followup imaging that can trigger
spectroscopy. We are updating telescope control systems and software to
implement this system in stages, beginning with the Kuiper 61'' and Vatican
Observatory 1.8-m telescopes. The Kuiper 61'' and its Mont4K camera can now be
controlled and queue-scheduled by the RTS2 observatory control software, and
operated from a remote room at Steward. We discuss science and technical
requirements for ARTN, and some of the challenges in adapting heterogenous
legacy facilities, scheduling, data pipelines, and maintaining capabilities for
a diverse user base.


  Automated generation of high-quality topical hierarchies for a text
collection is a dream problem in knowledge engineering with many valuable
applications. In this paper a scalable and robust algorithm is proposed for
constructing a hierarchy of topics from a text collection. We divide and
conquer the problem using a top-down recursive framework, based on a tensor
orthogonal decomposition technique. We solve a critical challenge to perform
scalable inference for our newly designed hierarchical topic model. Experiments
with various real-world datasets illustrate its ability to generate robust,
high-quality hierarchies efficiently. Our method reduces the time of
construction by several orders of magnitude, and its robust feature renders it
possible for users to interactively revise the hierarchy.


  The nucleation field for infinite magnetic nanotubes, in the case of a
magnetic field applied parallel to the long axis of the tubes, is calculated as
a function of their geometric parameters and compared with those produced
inside the pores of anodic alumina membranes by atomic layer deposition. We
also extended this result to the case of an angular dependence. We observed a
transition from curling-mode rotation to coherent-mode rotation as a function
of the angle in which the external magnetic field is applied. Finally, we
observed that the internal radii of the tubes favors the magnetization curling
reversal.


  Despite the success of convolutional neural networks for 3D medical-image
segmentation, the architectures currently used are still not robust enough to
the protocols of different scanners, and the variety of image properties they
produce. Moreover, access to large-scale datasets with annotated regions of
interest is scarce, and obtaining good results is thus difficult. To overcome
these challenges, we introduce IB-U-Nets, a novel architecture with inductive
bias, inspired by the visual processing in vertebrates. With the 3D U-Net as
the base, we add two 3D residual components to the second encoder blocks. They
provide an inductive bias, helping U-Nets to segment anatomical structures from
3D images with increased robustness and accuracy. We compared IB-U-Nets with
state-of-the-art 3D U-Nets on multiple modalities and organs, such as the
prostate and spleen, using the same training and testing pipeline, including
data processing, augmentation and cross-validation. Our results demonstrate the
superior robustness and accuracy of IB-U-Nets, especially on small datasets, as
is typically the case in medical-image analysis. IB-U-Nets source code and
models are publicly available.


  We calculate the energy of threshold fluctuation $\delta F_{thr}$ which
triggers the transition of superconducting current-carrying bridge to resistive
state. We show that the dependence $\delta F_{thr}(I)\propto
I_{dep}\hbar(1-I/I_{dep})^{5/4}/e$, found by Langer and Ambegaokar for a long
bridge with length $L \gg \xi$, holds far below the critical temperature both
in dirty and clean limits (here $I_{dep}$ is the depairing current of the
bridge and $\xi$ is a coherence length). We also find that even 'weak' local
defect (leading to the small suppression of the critical current of the bridge
$I_c \lesssim I_{dep}$) provides $\delta F_{thr}\propto
I_c\hbar(1-I/I_c)^{3/2}/e$, typical for a short bridge with $L \ll \xi$ or a
Josephson junction.


  The Earth's ring current is highly dynamic and is strongly influenced by the
solar wind. The ring current alters the planet's magnetic field, defining
geomagnetic storms. In this study, we investigate the decay timescales of ring
current protons using observations from the Van Allen Probes. Since proton
fluxes typically exhibit exponential decay after big storms, the decay time
scales are calculated by performing linear regression on the logarithm of the
fluxes. We found that in the central region of the ring current, proton decay
timescales generally increase with increasing energies and increasing L-shells.
The ~10s keV proton decay timescales are about a few days, while the ~100 keV
proton decay time scale is about ~10 days, and protons of 269 keV have decay
timescales up to ~118 days. These findings provide valuable insights into the
ring current dynamics and can contribute to the development of more accurate
ring current models.


  Direct angular size measurements of the G0IV subgiant $\eta$ Boo from the
Palomar Testbed Interferometer are presented, with limb-darkened angular size
of $\theta_{LD}= 2.1894^{+0.0055}_{-0.0140} $ mas, which indicate a linear
radius of $R=2.672 \pm 0.028 R_\odot$. A bolometric flux estimate of $F_{BOL} =
22.1 \pm 0.28\times 10^{-7}$ erg cm$^{-2}$s$^{-1}$ is computed, which indicates
an effective temperature of $T_{EFF}=6100 \pm 28$ K and luminosity of $L = 8.89
\pm 0.16 L_\odot$ for this object. Similar data are established for a check
star, HD 121860. The $\eta$ Boo results are compared to, and confirm, similar
parameters established by the {\it MOST} asteroseismology satellite. In
conjunction with the mass estimate from the {\it MOST} investigation, a surface
gravity of $\log g=3.817 \pm 0.016$ [cm s$^{-2}$] is established for $\eta$
Boo.


  An integro-differential expression for the diffusion current of the
impurities diffusing by the mechanism of bound impurity-defect pairs has been
derived. The ensuing nonlocal diffusion equation generalizes the existing
theories of diffusion by the pair mechanism in unbounded systems with
homogeneous defect distributions on the systems with boundaries and variable
defect concentration. It has been established that the nonlocality manifests
itself only at short diffusion times while at large times, in particular, under
the stationary conditions, the predictions of the nonlocal theory would
coincide with existing approaches based on local diffusion currents.
Possibilities of experimental verification of the nonlocal theory have been
suggested. In particular, the explanation of the uphill diffusion in silicon by
the pair drag can be tested within slightly modified conventional experimental
setups. Also, it has been shown that in the nonlocal theory the impurity
segregation profile caused by the drag should differ at the initial stage of
evolution from the predictions of the local theories. Because the segregation
influences mechanical, corrosive, and electronic properties of materials, the
nonlocal character of the pair diffusion may have important implications for
nanostructured materials.


  The interference channel achievable rate region is presented when the
interference is treated as noise. The formulation starts with the 2-user
channel, and then extends the results to the n-user case. The rate region is
found to be the convex hull of the union of n power control rate regions, where
each power control rate region is upperbounded by a (n-1)-dimensional
hyper-surface characterized by having one of the transmitters transmitting at
full power. The convex hull operation lends itself to a time-sharing operation
depending on the convexity behavior of those hyper-surfaces. In order to know
when to use time-sharing rather than power control, the paper studies the
hyper-surfaces convexity behavior in details for the 2-user channel with
specific results pertaining to the symmetric channel. It is observed that most
of the achievable rate region can be covered by using simple On/Off binary
power control in conjunction with time-sharing. The binary power control
creates several corner points in the n-dimensional space. The crystallized rate
region, named after its resulting crystal shape, is hence presented as the
time-sharing convex hull imposed onto those corner points; thereby offering a
viable new perspective of looking at the achievable rate region of the
interference channel.


  I review the available empirical information on the equation of state of cold
strongly interacting matter, as well as the prospects for obtaining new
insights from the experimental study of gravitational waves emitted by neutron
stars.


  A search is presented for new massive resonances decaying to WW, WZ or ZZ
bosons in l nu quark anti-quark and quark anti-quark quark anti-quark final
states. Results are based on data corresponding to an integrated luminosity of
2.3-2.7 inverse femtobarns recorded in proton-proton collisions at sqrt(s) = 13
TeV with the CMS detector at the LHC. Decays of spin-1 and spin-2 resonances
into two vector bosons are sought in the mass range 0.6-4.0 TeV. No significant
excess over the standard model background is observed. Combining the results of
the l nu quark anti-quark and quark anti-quark quark anti-quark final states,
cross section and mass exclusion limits are set for models that predict heavy
spin-1 and spin-2 resonances. This is the first search for a narrow-width
spin-2 resonance at sqrt(s) = 13 TeV.


  In the paper properties of the discrete analogue $D_m(h\beta)$ of the
differential operator $\frac{d^{2m}}{dx^{2m}}-\frac{d^{2m-2}}{dx^{2m-2}}$ are
studied. It is known, that zeros of differential operator
$\frac{d^{2m}}{dx^{2m}}-\frac{d^{2m-2}}{dx^{2m-2}}$ are functions $e^x$,
$e^{-x}$ and $P_{2m-3}(x)$. It is proved that discrete analogue $D_m(h\beta)$
of this differential operator also have similar properties.


  In applied image segmentation tasks, the ability to provide numerous and
precise labels for training is paramount to the accuracy of the model at
inference time. However, this overhead is often neglected, and recently
proposed segmentation architectures rely heavily on the availability and
fidelity of ground truth labels to achieve state-of-the-art accuracies. Failure
to acknowledge the difficulty in creating adequate ground truths can lead to an
over-reliance on pre-trained models or a lack of adoption in real-world
applications. We introduce Points2Polygons (P2P), a model which makes use of
contextual metric learning techniques that directly addresses this problem.
Points2Polygons performs well against existing fully-supervised segmentation
baselines with limited training data, despite using lightweight segmentation
models (U-Net with a ResNet18 backbone) and having access to only weak labels
in the form of object centroids and no pre-training. We demonstrate this on
several different small but non-trivial datasets. We show that metric learning
using contextual data provides key insights for self-supervised tasks in
general, and allow segmentation models to easily generalize across
traditionally label-intensive domains in computer vision.


  A single crystal of lead-zirconate-titanate (PZT), composition
Pb(Zr0.80Ti0.20)O3, was studied by polarized-Raman scattering as a function of
temperature. Raman spectra reveal that the local structure deviates from the
average structure in both ferroelectric and paraelectric phases. We show that
the crystal possesses several, inequivalent complex domain boundaries which
show no sign of instability even 200 K above the ferroelectric-to-paraelectric
phase transition temperature TC. Two types of boundaries are addressed. The
first boundary was formed between ferroelectric domains below TC. This boundary
remained stable up to the highest measurement temperatures, and stabilized the
domains so that they had the same orientation after repeated heating and
cooling cycles. These domains transformed normally to the cubic paraelectric
phase. Another type of boundary was formed at 673 K and exhibited no signs of
instability up to 923 K. The boundary formation was reversible: it formed and
vanished between 573 and 673 K during heating and cooling, respectively. A
model in which the crystal is divided into thin slices with different Zr/Ti
ratios is proposed. The physical mechanism behind the thermal-stress-induced
structural changes is related to the different thermal expansion of the slices,
which forces the domain to grow similarly after each heating and cooling cycle.
The results are interesting for non-volatile memory development, as it implies
that the original ferroelectric state can be restored after the material has
been transformed to the paraelectric phase. It also suggests that a
low-symmetry structure, stable up to high-temperatures, can be prepared through
controlled deposition of layers with desired compositions.


  We summarize the spectral characteristics of a sample of 22 bright gamma-ray
bursts detected with the gamma-ray burst sensors aboard the satellite Ginga.
This instrument employed a proportional and scintillation counter to provide
sensitivity to photons in the 2 - 400 keV range, providing a unique opportunity
to characterize the largely unexplored X-ray properties of gamma-ray bursts.
The photon spectra of the Ginga bursts are well described by a low energy
slope, a bend energy, and a high energy slope. In the energy range where they
can be compared, this result is consistent with burst spectral analyses
obtained from the BATSE experiment aboard the Compton Observatory. However,
below 20 keV we find evidence for a positive spectral number index in
approximately 40% of our burst sample, with some evidence for a strong rolloff
at lower energies in a few events. There is a correlation (Pearson's r = -0.62)
between the low energy slope and the bend energy. We find that the distribution
of spectral bend energies extends below 10 keV. The observed ratio of energy
emitted in the X-rays relative to the gamma-rays can be much larger than a few
percent and, in fact, is sometimes larger than unity. The average for our
sample is 24%.


  We show that the superconducting transition temperature Tc of FeSe1-xTex can
be computed to reasonable values in a modified McMillan approach in which the
electron-phonon coupling is amplified by the antiferromagnetism and the
out-of-plane phonons triggered by the tetrahedral lattice sites. This interplay
is not only effective at ambient pressure, but also under hydrostatic
compression. According to our model, the theoretical Tc of the compressed
FeSe0.5Te0.5 agrees with experiment results. More importantly, by taking into
account the interfacial effect between an FeSe monolayer and its SrTiO3
substrate as an additional gain factor, our calculated Tc value is up to 91 K
high, and provides evidence that the strong Tc enhancement recently observed in
such monolayers with Tc reaching 100 K may be due to an enhanced-electron
phonon coupling.


  We report the results from a study of the full sample of ~ 6.031 10^7 K+- ->
pi+- pi0 pi0 decays recorded by the NA48/2 experiment at the CERN SPS. As first
observed in this experiment, the pi0 pi0 invariant mass M00 distribution shows
a cusp-like anomaly in the region around M00 = 2m+, where m+ is the charged
pion mass. This anomaly has been interpreted as an effect due mainly to the
final state charge exchange scattering process pi+ pi- -> p0 p0 in K+- -> pi+-
pi+ pi- decay. Fits to the M00 distribution using two different theoretical
formulations provide the presently most precise determination of (a0 - a2), the
difference between the pi pi S-wave scattering lengths in the isospin I=0 and
I=2 states. Higher-order pi pi rescattering terms, included in the two
formulations, allow also an independent, though less precise, determination of
a2.


  In this paper, we would like to quantitatively measure the tumor volume
contained in the breast imaged by the Digital Breast Tomosynthesis (DBT), a
reconstructed 3D image. The estimated volume will add to the prognostic value
of risk classification of breast cancer. We develop an algorithm that offers an
alternative way of estimating the volume of the tumor in a breast. We segment
the region of interest by expressing the ratio of tumor region to normal region
as a function of the threshold value in the image. Next, we determine the
volume of the tumor region as a function of the threshold. We then find the
optimal threshold value that yields the volume of the tumor contained in the
breast with the rate of growth of the tumor volume function.


  In this paper, we construct the Brownian motion of Liouville Quantum Gravity
with central charge $c=1$ (more precisely we restrict to the corresponding free
field theory). Liouville quantum gravity with $c=1$ corresponds to
two-dimensional string theory and is the conjectural scaling limit of large
planar maps weighted with a $O(n=2)$ loop model or a $Q=4$-state Potts model
embedded in a two dimensional surface in a conformal manner.
  Following \cite{GRV1}, we start by constructing the critical LBM from one
fixed point $x\in\mathbb{R}^2$ (or $x\in\S^2$), which amounts to changing the
speed of a standard planar Brownian motion depending on the local behaviour of
the critical Liouville measure $M'(dx)=-X(x)e^{2X(x)}\,dx$ (where $X$ is a
Gaussian Free Field, say on $\mathbb{S}^2$). Extending this construction
simultaneously to all points in $\mathbb{R}^2$ requires a fine analysis of the
potential properties of the measure $M'$. This allows us to construct a strong
Markov process with continuous sample paths living on the support of $M'$,
namely a dense set of Hausdorff dimension $0$. We finally construct the
associated Liouville semigroup, resolvent, Green function, heat kernel and
Dirichlet form.
  In passing, we extend to quite a general setting the construction of the
critical Gaussian multiplicative chaos that was initiated in
\cite{Rnew7,Rnew12} and also establish new capacity estimates for the critical
Gaussian multiplicative chaos.


  We present equations of motion for charged particles using balanced
equations, and without introducing explicitly divergent quantities. This
derivation contains as particular cases some well known equations of motion, as
the Lorentz-Dirac equations. An study of our main equations in terms of order
of the interaction with the external field conduces us to the Landau-Lifshitz
equations. We find that the analysis in second order show a special behavior.
We give an explicit presentation up to third order of our main equations, and
expressions for the calculation of general orders.


  We present ISO (Infrared Space Observatory) observations of the planetary
nebula Lindsay 305 (L 305) in the Small Magellanic Cloud. L 305 is very
prominent in the ISOCAM frames at 6.75 and 11.5 $\mu$m, although it is under
the detection limit at 4.5 $\mu$m. The obtained spectral energy distribution
shows a strong mid-IR excess, which, depending on the amount of energy radiated
at wavelengths longer than 11.5 $\mu$m, may be as large as $\sim 1500
L_{\odot}$. However, since an accurate estimate of the total nebular luminosity
is not available up to date, the evolutionary status of L 305 can not yet be
constrained precisely.


  We consider the problem of discovering causal relations from independence
constraints selection bias in addition to confounding is present. While the
seminal FCI algorithm is sound and complete in this setup, no criterion for the
causal interpretation of its output under selection bias is presently known. We
focus instead on local patterns of independence relations, where we find no
sound method for only three variable that can include background knowledge.
Y-Structure patterns are shown to be sound in predicting causal relations from
data under selection bias, where cycles may be present. We introduce a
finite-sample scoring rule for Y-Structures that is shown to successfully
predict causal relations in simulation experiments that include selection
mechanisms. On real-world microarray data, we show that a Y-Structure variant
performs well across different datasets, potentially circumventing spurious
correlations due to selection bias.


  We study the deformed kinematics of point particles in the Horava theory of
gravity. This is achieved by considering particles as the optical limit of
fields with a generalized Klein-Gordon action. We derive the deformed geodesic
equation and study in detail the cases of flat and spherically symmetric
(Schwarzschild-like) spacetimes. As the theory is not invariant under local
Lorenz transformations, deviations from standard kinematics become evident even
for flat manifolds, supporting superluminal as well as massive luminal
particles. These deviations from standard behavior could be used for
experimental tests of this modified theory of gravity.


  We report a stoichiometric derivative of graphene with a fluorine atom
attached to each carbon. Raman, optical, structural, micromechanical and
transport studies show that the material is qualitatively different from the
known graphene-based nonstoichiometric derivatives. Fluorographene is a
high-quality insulator (resistivity >10^12 Ohm per square) with an optical gap
of 3 eV. It inherits the mechanical strength of graphene, exhibiting Young's
modulus of 100 N/m and sustaining strains of 15%. Fluorographene is inert and
stable up to 400C even in air, similar to Teflon.


  We consider thermodynamics of the excluded volume particles at finite
temperature and chemical potential, in the low density approximation. We assume
Boltzmann statistics and study the influence of the excluded volume on an ideal
gas thermodynamics at the same temperature, pressure and numbers of particles.
We show, that considering the change of the free enthalpy due to the excluded
volume, and using the Maxwell identities, one can derive relevant thermodynamic
functions and parameters of multi-component gases. The derivation is quite
general as particles may have different sizes and shapes which can also depend
on their momenta. Besides it's simplicity and generality, our approach has the
advantage of eliminating the transcendental equations occurring in earlier
studies. A representative example of the excluded volume thermodynamics is the
single-component gas of hard spheres. For this case, using the viral expansion,
the validity limits of the low-density approximation are also discussed.


  We present a practical and general scheme of remote preparation for pure and
mixed state, in which an auxiliary qubit and controlled-NOT gate are used. We
discuss the remote state preparation (RSP) in two important types of decoherent
channel (depolarizing and dephaseing). In our experiment, we realize RSP in the
dephaseing channel by using spontaneous parametric down conversion (SPDC),
linear optical elements and single photon detector.


  When point vortex equilibria of the 2D Euler equations are used as initial
conditions for the corre- sponding Navier-Stokes equations (viscous), typically
an interesting dynamical process unfolds at short and intermediate time scales,
before the long time single peaked, self-similar Oseen vortex state dom-
inates. In this paper, we describe the viscous evolution of a collinear three
vortex structure that cor- responds to an inviscid point vortex fixed
equilibrium. Using a multi-Gaussian 'core-growth' type of model, we show that
the system immediately begins to rotate unsteadily, a mechanism we attribute to
a 'viscously induced' instability. We then examine in detail the qualitative
and quantitative evolution of the system as it evolves toward the long-time
asymptotic Lamb-Oseen state, showing the sequence of topological bifurcations
that occur both in a fixed reference frame, and in an appropriately chosen
rotating reference frame. The evolution of passive particles in this viscously
evolving flow is shown and interpreted in relation to these evolving streamline
patterns.


  We describe a new method to compute general cubature formulae. The problem is
initially transformed into the computation of truncated Hankel operators with
flat extensions. We then analyse the algebraic properties associated to flat
extensions and show how to recover the cubature points and weights from the
truncated Hankel operator. We next present an algorithm to test the flat
extension property and to additionally compute the decomposition. To generate
cubature formulae with a minimal number of points, we propose a new relaxation
hierarchy of convex optimization problems minimizing the nuclear norm of the
Hankel operators. For a suitably high order of convex relaxation, the minimizer
of the optimization problem corresponds to a cubature formula. Furthermore
cubature formulae with a minimal number of points are associated to faces of
the convex sets. We illustrate our method on some examples, and for each we
obtain a new minimal cubature formula.


  In this lecture some mathematical tools necessary for a proper description of
the Heisenberg antiferromagnet are presented. We would like to point out
differences between ferro- and antiferromagnetic cases of Heisenberg
Hamiltonian for finite spin systems. The ground-state properties are discussed.


  For $0 \leq t \leq r$ let $m(t,r)$ be the maximum number $s$ such that every
$t$-edge-connected $r$-graph has $s$ pairwise disjoint perfect matchings. There
are only a few values of $m(t,r)$ known, for instance $m(3,3)=m(4,r)=1$, and
$m(t,r) \leq r-2$ for all $t \not = 5$, and $m(t,r) \leq r-3$ if $r$ is even.
We prove that $m(2l,r) \leq 3l - 6$ for every $l \geq 3$ and $r \geq 2 l$.


  Pulsars have proven instrumental in exploring a wide variety of physics.
Pulsars at low radio frequencies is crucial to further our understanding of
spectral properties and emission mechanisms.The Murchison Widefield Array
Voltage Capture System (MWA-VCS) has been routinely used to study and discover
pulsars at low frequencies, offering the unique opportunity of recording
complex voltages ,which can be off-line beamformed or imaged at millisecond
time resolution.Devising imaged-based methods for finding pulsar candidates,
which can be verified in beamformed data, can accelerate the complete process
and lead to more pulsar detections by reducing the number of tied-array beams
required, increasing compute resource efficiency.Despite a factor of ~4 loss in
sensitivity, searching for pulsar candidates in images from the MWA-VCS, we can
explore a larger parameter space, potentially leading to discoveries of pulsars
missed by high-frequency surveys such as pulsars obscured in high-time
resolution timeseries data by propagation effects.Image-based searches are also
essential to probing parts of parameter space inaccessible to traditional
beamformed searches with the MWA.In this paper we describe the innovative
approach and capability of dual-processing MWA VCS data, i.e. finding pulsar
candidates in these images, and verifying by forming tied-array beam.We
developed and tested image-based methods of finding pulsar candidates, based on
pulsar properties such as spectral index, polarisation and variability.The
efficiency of these methodologies has been verified on known pulsars, and the
main limitations explained in terms of sensitivity and low-frequency spectral
turnover of some pulsars.No candidates were confirmed to be a new pulsar.This
new capability will now be applied to multiple observations to accelerate
pulsar discoveries with MWA and speed up future searches with the SKA-Low.


  This paper is a follow-up to the recent paper "A note on isotropic
discrepancy and spectral test of lattice point sets" [J. Complexity, 58:101441,
2020]. We show that the isotropic discrepancy of a lattice point set is at most
$d \, 2^{2(d+1)}$ times its spectral test, thereby correcting the dependence on
the dimension $d$ and an inaccuracy in the proof of the upper bound in Theorem
2 of the mentioned paper. The major task is to bound the volume of the
neighbourhood of the boundary of a convex set contained in the unit cube.
  Further, we characterize averages of the distance to a lattice point set in
terms of the spectral test. As an application, we infer that the spectral test
-- and with it the isotropic discrepancy -- is crucial for the suitability of
the lattice point set for the approximation of Sobolev functions.


  The growth in data traffic and the increased demand for quality of service
had generated a large demand for network systems to be more efficient. The
introduction of improved routing systems to meet the increasing demand and
varied protocols to accommodate various scales of challenges in network
efficiency had further complicated the operations. This means that a better
mode of intelligence has to be infused into networking for smoother operations
and better autonomic features. Cognitive networks are defined and analyzed in
this angle. They are identified to have the potential to deal with the future
user related quality and efficiency of service at optimized levels. The
cognitive elements of a system like perception, learning, planning, reasoning
and decision forming can enable the systems to be more aware of their
environment and offer better services. These approaches are expected to
transform the mode of operation of future networks.


  Model-based Systems Engineering (MBSE) has been widely utilized to formalize
system artifacts and facilitate their development throughout the entire
lifecycle. During complex system development, MBSE models need to be frequently
exchanged across stakeholders. Concerns about data security and tampering using
traditional data exchange approaches obstruct the construction of a reliable
marketplace for digital assets. The emerging Distributed Ledger Technology
(DLT), represented by blockchain, provides a novel solution for this purpose
owing to its unique advantages such as tamper-resistant and decentralization.
In this paper, we integrate MBSE approaches with DLT aiming to create a
decentralized marketplace to facilitate the exchange of digital engineering
assets (DEAs). We first define DEAs from perspectives of digital engineering
objects, development processes and system architectures. Based on this
definition, the Graph-Object-Property-Point-Role-Relationship (GOPPRR) approach
is used to formalize the DEAs. Then we propose a framework of a decentralized
DEAs marketplace and specify the requirements, based on which we select a
Directed Acyclic Graph (DAG) structured DLT solution. As a proof-of-concept, a
prototype of the proposed DEAs marketplace is developed and a case study is
conducted to verify its feasibility. The experiment results demonstrate that
the proposed marketplace facilitates free DEAs exchange with a high level of
security, efficiency and decentralization.


  Laboratory astrophysics and complementary theoretical calculations are the
foundations of astronomy and astrophysics and will remain so into the
foreseeable future. The impact of laboratory astrophysics ranges from the
scientific conception stage for ground-based, airborne, and space-based
observatories, all the way through to the scientific return of these projects
and missions. It is our understanding of the under-lying physical processes and
the measurements of critical physical parameters that allows us to address
fundamental questions in astronomy and astrophysics. In this regard, laboratory
astrophysics is much like detector and instrument development at NASA, NSF, and
DOE. These efforts are necessary for the success of astronomical research being
funded by the agencies. Without concomitant efforts in all three directions
(observational facilities, detector/instrument development, and laboratory
astrophysics) the future progress of astronomy and astrophysics is imperiled.
In addition, new developments in experimental technologies have allowed
laboratory studies to take on a new role as some questions which previously
could only be studied theoretically can now be addressed directly in the lab.
With this in mind we, the members of the AAS Working Group on Laboratory
Astrophysics, have prepared this State of the Profession Position Paper on the
laboratory astrophysics infrastructure needed to ensure the advancement of
astronomy and astrophysics in the next decade.


  Bitcoin is an immutable permissionless blockchain system that has been
extensively used as a public bulletin board by many different applications that
heavily relies on its immutability. However, Bitcoin's immutability is not
without its fair share of demerits. Interpol exposed the existence of harmful
and potentially illegal documents, images and links in the Bitcoin blockchain,
and since then there have been several qualitative and quantitative analysis on
the types of data currently residing in the Bitcoin blockchain.
  Although there is a lot of attention on blockchains, surprisingly the
previous solutions proposed for data redaction in the permissionless setting
are far from feasible, and require additional trust assumptions. Hence, the
problem of harmful data still poses a huge challenge for law enforcement
agencies like Interpol (Tziakouris, IEEE S&P'18).
  We propose the first efficient redactable blockchain for the permissionless
setting that is easily integrable into Bitcoin, and that does not rely on heavy
cryptographic tools or trust assumptions. Our protocol uses a consensus-based
voting and is parameterised by a policy that dictates the requirements and
constraints for the redactions; if a redaction gathers enough votes the
operation is performed on the chain. As an extra feature, our protocol offers
public verifiability and accountability for the redacted chain. Moreover, we
provide formal security definitions and proofs showing that our protocol is
secure against redactions that were not agreed by consensus. Additionally, we
show the viability of our approach with a proof-of-concept implementation that
shows only a tiny overhead in the chain validation of our protocol when
compared to an immutable one.


  Our previous theorems on exponential sums often did not apply or did not give
sharp results when certain powers of a variable appearing in the polynomial
were divisible by p. We remedy that defect in this paper by systematically
applying "p-power reduction," making it possible to strengthen and extend our
earlier results.


  A large number of novel two-dimensional (2D) materials are constantly
discovered and deposed into the databases. Consolidate implementation of
machine learning algorithms and density functional theory (DFT) based
predictions have allowed creating several databases containing an unimaginable
amount of 2D samples. The next step in this chain, the investigation leads to a
comprehensive study of the functionality of the invented materials. In this
work, a family of transition metal dichlorides has been screened out for
systematical investigation of their structural stability, fundamental
properties, structural defects, and environmental stability via DFT based
calculations. The work highlights the importance of using the potential of the
invented materials and proposes a comprehensive characterization of a new
family of 2D materials.


  We investigate the geometric characterization of pure state bipartite
entanglement of $(2\times{D})$- and $(3\times{D})$-dimensional composite
quantum systems. To this aim, we analyze the relationship between states and
their images under the action of particular classes of local unitary
operations. We find that invariance of states under the action of single-qubit
and single-qutrit transformations is a necessary and sufficient condition for
separability. We demonstrate that in the $(2\times{D})$-dimensional case the
von Neumann entropy of entanglement is a monotonic function of the minimum
squared Euclidean distance between states and their images over the set of
single qubit unitary transformations. Moreover, both in the $(2\times{D})$- and
in the $(3\times{D})$-dimensional cases the minimum squared Euclidean distance
exactly coincides with the linear entropy (and thus as well with the tangle
measure of entanglement in the $(2\times{D})$-dimensional case). These results
provide a geometric characterization of entanglement measures originally
established in informational frameworks. Consequences and applications of the
formalism to quantum critical phenomena in spin systems are discussed.


  We study extension of scalars for sheaves of vector spaces, assembling
results that follow from well-known statements about vector spaces, but also
developing some complements. In particular, we formulate Galois descent in this
context, and we also discuss the case of derived categories and perverse
sheaves. Most of the results are not new, but our aim is to give an accessible
introduction to this subject relying only on techniques from basic sheaf
theory. Our proofs also illustrate some applications of results about the
structure of constructible and perverse sheaves.


  We develop several tools for the determination of sample size and design for
MediCal audits. This audit setting involves a population of claims for
reimbursement by a healthcare provider which need to be reviewed by an auditor
to determine the correct amount for each claim. The existing literature
regarding sample planning for audits is incomplete and often includes
restrictive assumptions. To fill these gaps, we exploit the special
relationship between the known claim amounts and the unknown post-audit
amounts. We propose a hypergeometric generative process for audit populations
which we use to derive estimators of variances needed for sample size
determination. We further develop a criterion for choosing between simple
expansion and ratio estimation and an efficient method for determining exact
optimal strata breakpoints in populations with repeated values. We also derive
a variance estimator under a more general "partial error" model than previous
researchers have used. These tools apply more generally to audits where an
overstated book/claim amount is the primary concern and estimation of the total
dollar value of the claim errors is the goal. The sample design methods we
develop are illustrated on two simulated audit populations.


  In this article we consider affine generalizations of the Merton jump
diffusion model [Merton, J. Fin. Econ., 1976] and the respective pricing of
European options. On the one hand, the Brownian motion part in the Merton model
may be generalized to a log-Heston model, and on the other hand, the jump part
may be generalized to an affine process with possibly state dependent jumps.
While the characteristic function of the log-Heston component is known in
closed form, the characteristic function of the second component may be unknown
explicitly. For the latter component we propose an approximation procedure
based on the method introduced in [Belomestny et al., J. Func. Anal., 2009]. We
conclude with some numerical examples.


  We investigate angular momentum acquisition in Milky Way-sized galaxies by
comparing five high resolution zoom-in simulations, each implementing identical
cosmological initial conditions, but utilizing different hydrodynamic codes:
Enzo, Art, Ramses, Arepo, and Gizmo-PSPH. Each code implements a distinct set
of feedback and star formation prescriptions. We find that while many galaxy
and halo properties vary between the different codes (and feedback
prescriptions), there is qualitative agreement on the process of angular
momentum acquisition in the galaxy's halo. In all simulations, cold filamentary
gas accretion to the halo results in ~4 times more specific angular momentum in
cold halo gas ($\lambda_{cold} \gtrsim 0.1$) than in the dark matter halo. At
z>1, this inflow takes the form of inspiraling cold streams that are
co-directional in the halo of the galaxy and are fueled, aligned, and
kinematically connected to filamentary gas infall along the cosmic web. Due to
the qualitative agreement among disparate simulations, we conclude that the
buildup of high angular momentum halo gas and the presence of these inspiraling
cold streams are robust predictions of Lambda Cold Dark Matter galaxy
formation, though the detailed morphology of these streams is significantly
less certain. A growing body of observational evidence suggests that this
process is borne out in the real universe.


  Based upon our previous works (Eur.Phys.J.D 6, 319(1999); Chin.Phys.Lett. 18,
236(2001)), we develop a classical approach to calculate the high-order
harmonic generation of the laser driven atoms and molecules. The Coulomb
singularities in the system have been removed by a regularization procedure.
Action-angle variables have been used to generate the initial microcanonical
distribution which satisfies the inversion symmetry of the system. The
numerical simulation show, within a proper laser intensity, a harmonic plateau
with only odd harmonics appears. At higher intensities, the spectra become
noisier because of the existence of chaos. With further increase in laser
intensity, ionization takes place, and the high-order harmonics disappear. Thus
chaos introduces noise in the spectra, and ionization suppresses the harmonic
generation, with the onset of the ionization follows the onset of chaos.


  In order to estimate the conditional risk of a portfolio's return, two
strategies can be advocated. A multivariate strategy requires estimating a
dynamic model for the vector of risk factors, which is often challenging, when
at all possible, for large portfolios. A univariate approach based on a dynamic
model for the portfolio's return seems more attractive. However, when the
combination of the individual returns is time varying, the portfolio's return
series is typically non stationary which may invalidate statistical inference.
An alternative approach consists in reconstituting a "virtual portfolio", whose
returns are built using the current composition of the portfolio and for which
a stationary dynamic model can be estimated.
  This paper establishes the asymptotic properties of this method, that we call
Virtual Historical Simulation. Numerical illustrations on simulated and real
data are provided.


  In spite of the advances in pattern recognition technology, Handwritten
Bangla Character Recognition (HBCR) (such as alpha-numeric and special
characters) remains largely unsolved due to the presence of many perplexing
characters and excessive cursive in Bangla handwriting. Even the best existing
recognizers do not lead to satisfactory performance for practical applications.
To improve the performance of Handwritten Bangla Digit Recognition (HBDR), we
herein present a new approach based on deep neural networks which have recently
shown excellent performance in many pattern recognition and machine learning
applications, but has not been throughly attempted for HBDR. We introduce
Bangla digit recognition techniques based on Deep Belief Network (DBN),
Convolutional Neural Networks (CNN), CNN with dropout, CNN with dropout and
Gaussian filters, and CNN with dropout and Gabor filters. These networks have
the advantage of extracting and using feature information, improving the
recognition of two dimensional shapes with a high degree of invariance to
translation, scaling and other pattern distortions. We systematically evaluated
the performance of our method on publicly available Bangla numeral image
database named CMATERdb 3.1.1. From experiments, we achieved 98.78% recognition
rate using the proposed method: CNN with Gabor features and dropout, which
outperforms the state-of-the-art algorithms for HDBR.


  The noncommutative Gurarij space $\mathbb{\mathbb{\mathbb{NG}}}$, initially
defined by Oikhberg, is a canonical object in the theory of operator spaces. As
the Fra\"{\i}ss\'{e} limit of the class of finite-dimensional nuclear operator
spaces, it can be seen as the noncommutative analogue of the classical Gurarij
Banach space. In this paper, we prove that the automorphism group of
$\mathbb{\mathbb{NG}}$ is extremely amenable, i.e.\ any of its actions on
compact spaces has a fixed point. The proof relies on the Dual Ramsey Theorem,
and a version of the Kechris--Pestov--Todorcevic correspondence in the setting
of operator spaces.
  Recent work of Davidson and Kennedy, building on previous work of Arveson,
Effros, Farenick, Webster, and Winkler, among others, shows that nuclear
operator systems can be seen as the noncommutative analogue of Choquet
simplices. The analogue of the Poulsen simplex in this context is the matrix
state space $\mathbb{NP}$ of the Fra\"{\i}ss\'{e} limit $A(\mathbb{NP})$ of the
class of finite-dimensional nuclear operator systems. We show that the
canonical action of the automorphism group of $\mathbb{NP}$ on the compact set
$\mathbb{NP}_1$ of unital linear functionals on $A(\mathbb{NP})$ is minimal and
it factors onto any minimal action, whence providing a description of the
universal minimal flow of \textrm{Aut}$\left( \mathbb{NP}% \right) $.


  Fast multipole methods (FMM) were originally developed for accelerating
$N$-body problems for particle-based methods. FMM is more than an $N$-body
solver, however. Recent efforts to view the FMM as an elliptic Partial
Differential Equation (PDE) solver have opened the possibility to use it as a
preconditioner for a broader range of applications. FMM can solve Helmholtz
problems with optimal $\mathcal{O}(N \log N)$ complexity, has compute-bound
inner kernels, and highly asynchronous communication patterns. The combination
of these features makes FMM an interesting candidate as a preconditioner for
sparse solvers on architectures of the future. The use of FMM as a
preconditioner allows us to use lower order multipole expansions than would be
required as a solver because individual solves need not be accurate. This
reduces the amount of computation and communication significantly and makes the
time-to-solution competitive with state-of-the-art preconditioners.
Furthermore, the high asynchronicity of FMM allows it to scale to much larger
core counts than factorization-based and multilevel methods. We describe our
tests in reproducible details with freely available codes.


  Adaptation to external and internal changes is major for robotic systems in
uncertain environments. Here we present a novel multisensory active inference
torque controller for industrial arms that shows how prediction can be used to
resolve adaptation. Our controller, inspired by the predictive brain
hypothesis, improves the capabilities of current active inference approaches by
incorporating learning and multimodal integration of low and high-dimensional
sensor inputs (e.g., raw images) while simplifying the architecture. We
performed a systematic evaluation of our model on a 7DoF Franka Emika Panda
robot arm by comparing its behavior with previous active inference baselines
and classic controllers, analyzing both qualitatively and quantitatively
adaptation capabilities and control accuracy. Results showed improved control
accuracy in goal-directed reaching with high noise rejection due to multimodal
filtering, and adaptability to dynamical inertial changes, elasticity
constraints and human disturbances without the need to relearn the model nor
parameter retuning.


  The space of subvarieties of P^n with a fixed Hilbert polynomial is not
complete. Grothendieck defined a completion by relaxing "variety" to "scheme",
giving the complete_Hilbert scheme_ of subschemes of P^n with fixed Hilbert
polynomial.
  We instead relax "sub" to "branch", where a_branchvariety of_ P^n is defined
to be a_reduced_ (though possibly reducible) scheme_with a finite morphism to_
P^n. Our main theorems are that the moduli stack of branchvarieties of P^n with
fixed Hilbert polynomial and total degrees of i-dimensional components is a
proper (complete and separated) Artin stack with finite stabilizer, and has a
coarse moduli space which is a proper algebraic space.
  Families of branchvarieties have many more locally constant invariants than
families of subschemes; for example, the number of connected components is a
new invariant. In characteristic 0, one can extend this count to associate a
Z-labeled rooted forest to any branchvariety.


  We examine zero-temperature one-particle spectral functions for the
one-dimensional two-band spinless fermions with different velocities and
general forward-scattering interactions. By using the bosonization technique
and diagonalizing the model to two Tomonaga-Luttinger-liquid Hamiltonians, we
obtain general expressions for the spectral functions which are given in terms
of the Appell hypergeometric functions. For the case of identical two-band
fermions, corresponding to the SU(2) symmetric spin-1/2 fermions with repulsive
interactions,the spectral functions can be expressed in terms of the Gauss
hypergeometric functions and are shown to recover the double-peak structure
suggesting the well-known "spin-charge" separation. By tuning the difference in
velocities for the two-band fermions, we clarify the crossover in spectral
functions from the "spin-charge" separation to the decoupled fermions. We
discuss the relevance of our results to the spin-1/2 Hubbard model under a
magnetic field which can be mapped onto two-band spinless fermions.


  Inspired by natural cellular materials such as trabecular bone, lattice
structures have been developed as a new type of lightweight material. In this
paper we present a novel method to design lattice structures that conform with
both the principal stress directions and the boundary of the optimized shape.
Our method consists of two major steps: the first optimizes concurrently the
shape (including its topology) and the distribution of orthotropic lattice
materials inside the shape to maximize stiffness under application-specific
external loads; the second takes the optimized configuration (i.e.
locally-defined orientation, porosity, and anisotropy) of lattice materials
from the previous step, and extracts a globally consistent lattice structure by
field-aligned parameterization. Our approach is robust and works for both 2D
planar and 3D volumetric domains. Numerical results and physical verifications
demonstrate remarkable structural properties of conforming lattice structures
generated by our method.


  Over the past decades, direct imaging has confirmed the existence of
substellar companions (exoplanets or brown dwarfs) on wide orbits (>10 au) from
their host stars. To understand their formation and evolution mechanisms, we
have initiated in 2015 the SPHERE infrared survey for exoplanets (SHINE), a
systematic direct imaging survey of young, nearby stars to explore their
demographics.} {We aim to detect and characterize the population of giant
planets and brown dwarfs beyond the snow line around young, nearby stars.
Combined with the survey completeness, our observations offer the opportunity
to constrain the statistical properties (occurrence, mass and orbital
distributions, dependency on the stellar mass) of these young giant planets.}
{In this study, we present the observing and data analysis strategy, the
ranking process of the detected candidates, and the survey performances for a
subsample of 150 stars, which are representative of the full SHINE sample. The
observations were conducted in an homogeneous way from February 2015 to
February 2017 with the dedicated ground-based VLT/SPHERE instrument equipped
with the IFS integral field spectrograph and the IRDIS dual-band imager
covering a spectral range between 0.9 and 2.3 $\mu$m. We used coronographic,
angular and spectral differential imaging techniques to reach the best
detection performances for this study down to the planetary mass regime.}


  A theorem of McCann shows that for any two absolutely continuous probability
measures on R^d there exists a monotone transformation sending one probability
measure to the other. A consequence of this theorem, relevant to statistics, is
that density estimation can be recast in terms of transformations. In
particular, one can fix any absolutely continuous probability measure, call it
P, and then reparameterize the whole class of absolutely continuous probability
measures as monotone transformations from P. In this paper we utilize this
reparameterization of densities, as monotone transformations from some P, to
construct semiparametric and nonparametric density estimates. We focus our
attention on classes of transformations, developed in the image processing and
computational anatomy literature, which are smooth, invertible and which have
attractive computational properties. The techniques developed for this class of
transformations allow us to show that a penalized maximum likelihood estimate
(PMLE) of a smooth transformation from P exists and has a finite dimensional
characterization, similar to those results found in the spline literature.
These results are derived utilizing an Euler-Lagrange characterization of the
PMLE which also establishes a surprising connection to a generalization of
Stein's lemma for characterizing the normal distribution.


  The study of outer disc radius variations in close binary systems is
important for understanding the structure and evolution of accretion discs.
These variations are predicted by models of both quasi steady and
time-dependent discs, and these predictions can be confronted with
observations. We consider theoretical and observational consequences of such
variations in cataclysmic variables and low mass X-ray binaries. We find that
the action of tidal torques, that determine the outer radius at which the disc
is truncated, must be important also well inside the tidal radius. We conclude
that it is doubtful that the tidal-thermal instability is responsible for the
superoutburst/superhump phenomena in dwarf novae, and confirm that it cannot be
the reason for the outbursts of soft X-ray transients. It is likely that tidal
torques play a role during superoutbursts of very-low mass-ratio systems but
they cannot be the main and only cause of superhumps.


  We review some of the geometry of the quantum projective plane with emphasis
on the construction of a differential calculus and of the Dirac operator (of a
spin^c-structure). We also report on anti-self-dual connections on line
bundles, the spectrum of associated (gauged) Laplacian operators, and on
classical and quantum characteristic classes.


  We present new white dwarf evolutionary sequences for low-metallicity
progenitors. White dwarf sequences have been derived from full evolutionary
calculations that take into account the entire history of progenitor stars,
including the thermally-pulsing and the post-asymptotic giant branch phases. We
show that for progenitor metallicities in the range 0.00003--0.001, and in the
absence of carbon enrichment due to the occurrence of a third dredge-up
episode, the resulting H envelope of the low-mass white dwarfs is thick enough
to make stable H burning the most important energy source even at low
luminosities. This has a significant impact on white dwarf cooling times. This
result is independent of the adopted mass-loss rate during the
thermally-pulsing and post-AGB phases, and the planetary nebulae stage. We
conclude that in the absence of third dredge-up episodes, a significant part of
the evolution of low-mass white dwarfs resulting from low-metallicity
progenitors is dominated by stable H burning. Our study opens the possibility
of using the observed white dwarf luminosity function of low-metallicity
globular clusters to constrain the efficiency of third dredge up episodes
during the thermally-pulsing AGB phase of low-metallicity progenitors.


  Given a countable transitive model of set theory and a partial order
contained in it, there is a natural countable Borel equivalence relation on
generic filters over the model; two are equivalent if they yield the same
generic extension. We examine the complexity of this equivalence relation for
various partial orders, with particular focus on Cohen and random forcing. We
prove, amongst other results, that the former is an increasing union of
countably many hyperfinite Borel equivalence relations, while the latter is
neither amenable nor treeable.


  3D Multi-object tracking (MOT) ensures consistency during continuous dynamic
detection, conducive to subsequent motion planning and navigation tasks in
autonomous driving. However, camera-based methods suffer in the case of
occlusions and it can be challenging to accurately track the irregular motion
of objects for LiDAR-based methods. Some fusion methods work well but do not
consider the untrustworthy issue of appearance features under occlusion. At the
same time, the false detection problem also significantly affects tracking. As
such, we propose a novel camera-LiDAR fusion 3D MOT framework based on the
Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and
LiDAR data and significantly reduces tracking failures caused by occlusion and
false detection. For occlusion problems, we are the first to propose an
occlusion head to select the best object appearance features multiple times
effectively, reducing the influence of occlusions. To decrease the impact of
false detection in tracking, we design a motion cost matrix based on confidence
scores which improve the positioning and object prediction accuracy in 3D
space. As existing multi-object tracking methods only consider a single
category, we also propose to build a multi-category loss to implement
multi-object tracking in multi-category scenes. A series of validation
experiments are conducted on the KITTI and nuScenes tracking benchmarks. Our
proposed method achieves state-of-the-art performance and the lowest identity
switches (IDS) value (23 for Car and 137 for Pedestrian) among all multi-modal
MOT methods on the KITTI test dataset. And our proposed method achieves
state-of-the-art performance among all algorithms on the nuScenes test dataset
with 75.3% AMOTA.


  In this paper we develop a lattice-based computational model focused on bone
resorption by osteoclasts in a single cortical basic multicellular unit (BMU).
Our model takes into account the interaction of osteoclasts with the bone
matrix, the interaction of osteoclasts with each other, the generation of
osteoclasts from a growing blood vessel, and the renewal of osteoclast nuclei
by cell fusion. All these features are shown to strongly influence the
geometrical properties of the developing resorption cavity including its size,
shape and progression rate, and are also shown to influence the distribution,
resorption pattern and trajectories of individual osteoclasts within the BMU.
We demonstrate that for certain parameter combinations, resorption cavity
shapes can be recovered from the computational model that closely resemble
resorption cavity shapes observed from microCT imaging of human cortical bone.


  We present an algorithm to compute a primary decomposition of an ideal in a
polynomial ring over the integers. For this purpose we use algorithms for
primary decomposition in polynomial rings over the rationals resp. over finite
fields, and the idea of Shimoyama-Yokoyama resp. Eisenbud-Hunecke-Vasconcelos
to extract primary ideals from pseudo-primary ideals. A parallelized version of
the algorithm is implemented in SINGULAR. Examples and timings are given at the
end of the article.


  Lack of vivid sensory imagery has recently become an active subject of
research, under the name of aphantasia. Extremely vivid imagery, or
hyperphantasia, is at the other end of the spectrum of individual differences.
While most research has focused on visual imagery in this paper I argue that
from a neuropsychological perspective this phenomenon is much more widespread,
and should be categorized as imagery sensory agnosia. After over twenty years
of learning to play music phenomenology of auditory imagery agnosia is
described from the first-person perspective. Reflections on other forms of
imagery agnosia and deficits of autobiographical memories are presented and a
hypothesis about putative brain processes that can account for such phenomena
is discussed. Extreme individual differences in imagery and in autobiographical
memory have implications for many fields of study, from consciousness research
to education.


  A search is presented for decays beyond the standard model of the 125 GeV
Higgs bosons to a pair of light bosons, based on models with extended scalar
sectors. Light boson masses between 5 and 62.5 GeV are probed in final states
containing four tau leptons, two muons and two b quarks, or two muons and two
tau leptons. The results are from data in proton-proton collisions
corresponding to an integrated luminosity of 19.7 inverse femtobarns,
accumulated by the CMS experiment at the LHC at a center-of-mass energy of 8
TeV. No evidence for such exotic decays is found in the data. Upper limits are
set on the product of the cross section and branching fraction for several
signal processes. The results are also compared to predictions of
two-Higgs-doublet models, including those with an additional scalar singlet.


  Objective: Functional coupling between the motor cortex and muscle activity
is commonly detected and quantified by cortico-muscular coherence (CMC) or
Granger causality (GC) analysis, which are applicable only to linear couplings
and are not sufficiently sensitive: some healthy subjects show no significant
CMC and GC, and yet have good motor skills. The objective of this work is to
develop measures of functional cortico-muscular coupling that have improved
sensitivity and are capable of detecting both linear and non-linear
interactions. Methods: A multiscale wavelet transfer entropy (TE) methodology
is proposed. The methodology relies on a dyadic stationary wavelet transform to
decompose electroencephalogram (EEG) and electromyogram (EMG) signals into
functional bands of neural oscillations. Then, it applies TE analysis based on
a range of embedding delay vectors to detect and quantify intra- and
cross-frequency band cortico-muscular coupling at different time scales.
Results: Our experiments with neurophysiological signals substantiate the
potential of the developed methodologies for detecting and quantifying
information flow between EEG and EMG signals for subjects with and without
significant CMC or GC, including non-linear cross-frequency interactions, and
interactions across different temporal scales. The obtained results are in
agreement with the underlying sensorimotor neurophysiology. Conclusion: These
findings suggest that the concept of multiscale wavelet TE provides a
comprehensive framework for analysing cortex-muscle interactions. Significance:
The proposed methodologies will enable developing novel insights into movement
control and neurophysiological processes more generally.


  We study self avoiding random walks in an environment where sites are
excluded randomly, in two and three dimensions. For a single polymer chain, we
study the statistics of the time averaged monomer density and show that these
are well described by multifractal statistics. This is true even far from the
percolation transition of the disordered medium. We investigate solutions of
chains in a disordered environment and show that the statistics cease to be
multifractal beyond the screening length of the solution.


  Vehicles are becoming connected entities. As a result, a likely scenario is
that such entities might be literally bombarded with information from a
multitude of devices. In this context, a key challenging requirement for both
connected and autonomous vehicles is that they will need to become cognitive
bodies, able to parse information and use only the pieces of information that
are relevant to the vehicle in the context of a given journey. In order to
address this fundamental requirement, a decision engine is presented in this
paper. The engine makes it possible for the vehicle to understand which pieces
of information are really relevant, and subsequently to process only those
pieces of information. In order to illustrate the key features of our system,
we show that it is possible to build upon the engine to develop a distributed
traffic management system, and then we validate such a system via both
conventional (numerical and SUMO-based) simulations and a Hardware-in-the-Loop
(HIL) platform. Both the conventional simulations and the HIL validation showed
that the engine can be effectively used to design a distributed traffic
management system.


  It is well-known that parallel manipulators are prone to singularities.
However, there is still a lack of distance evaluation functions, referred to as
metrics, for computing the distance between two 3-RPR configurations. The
proposed extrinsic metrics take the combinatorial structure of the manipulator
into account as well as different design options. Utilizing these extrinsic
metrics, we formulate constrained optimization problems. These problems are
aimed at identifying the closest singular configurations for a given
non-singular configuration. The solution to the associated system of polynomial
equations relies on algorithms from numerical algebraic geometry implemented in
the software package \texttt{Bertini}. Furthermore, we have developed a
computational pipeline for determining the distance to singularity during a
one-parametric motion of the manipulator. To facilitate these computations for
the user, an open-source interface is developed between software packages
\texttt{Maple}, \texttt{Bertini}, and \texttt{Paramotopy}. The effectiveness of
the presented approach is demonstrated based on numerical examples and compared
with existing indices evaluating the singularity closeness.


  Understanding of the precise mechanisms of high-temperature superconductivity
is elusive. In particular, in order to solve the puzzle of the pairing
mechanism, it is important to understand the detailed nature of the excitations
at energies around the superconducting gap. While measurements of the dynamics
of excited electronic populations have been able to give some insight, they
have largely neglected the intricate dynamics of quantum coherence. Here, we
apply multidimensional coherent spectroscopy for the first time to a
prototypical cuprate and report unprecedented coherent dynamics persisting for
~500 fs, originating directly from the quantum superposition of optically
excited states separated by 20 - 60 meV. These results reveal the correlation
between high and low energy excitations, and indicate that the interplay
between many-body states on different energy scales conserves phase coherence.
In revealing these dynamics we demonstrate that multidimensional coherent
spectroscopy can address electronic correlations and interrogate many-body
quantum systems in unprecedented ways.


  In this paper, we propose a model to investigate the electron thermionic
emission from a single-layer graphene (ignoring the effects of substrate) and
to explore its application as the emitter of thermionic energy convertor (TIC).
An analytical formula has been derived, which is a function of temperature,
work function and Fermi energy level. The formula is significantly different
from the traditional Richardson-Dushman (RD) law for which it is independent of
mass to account for the supply function of the electrons in the graphene
behaving like massless Fermion quasiparticles. By comparing with a recent
experiment [Kaili Jiang et al., Nano Research 7, 553 (2014)] measuring electron
thermionic emission from a suspended single layer graphene, our model predicts
that the intrinsic work function of a single-layer graphene is about 4.514 eV
with a Fermi energy level of 0.083 eV. For a given work function, a new scaling
of $T^{3}$ is predicted, which is different from the traditional RD scaling of
$T^2$. If the work function of the graphene is lowered to 2.5 to 3 eV, and the
Fermi energy level is increased to 0.8 to 0.9 eV, it is possible to design a
graphene cathode based TIC operating at around 900 K or lower, as compared with
the metal-based cathode TIC (operating at about 1500 K). With a graphene based
cathode (work function = 4.514 eV) at 900 K, and a metallic based anode (work
function = 2.5 eV) like LaB$_6$ at 425 K, the efficiency of our proposed-TIC is
about 45$\%$


  Different aspects of particle dynamics on AdS_2 x S^2 background with
two-form flux are discussed. These include solution of equations of motion, a
canonical transformation to conformal mechanics and an N=4 supersymmetric
extension.


  The quantum capacity of thermal noise channel is studied. The extremal input
state is obtained at the postulation that the coherent information is convex or
concave at its vicinity. When the input energy tends to infinitive, it is
verified by perturbation theory that the coherent information reaches its
maximum at the product of identical thermal state input. The quantum capacity
is obtained for lower noise channel and it is equal the one shot capacity.


  With the help of some remarkable examples, it is shown that conditional
measurements performed on two-level atoms just after they have interacted with
a resonant cavity field mode are able to recover the coherence of number-state
superpositions, which is lost due to dissipation.


  Approximate distance estimation can be used to determine fundamental
landscape properties including complexity and openness. We show that variations
in the skyline of landscape photos can be used to estimate distances to trees
on the horizon. A methodology based on the variations of the skyline has been
developed and used to investigate potential relationships with the distance to
skyline objects. The skyline signal, defined by the skyline height expressed in
pixels, was extracted for several Land Use/Cover Area frame Survey (LUCAS)
landscape photos. Photos were semantically segmented with DeepLabV3+ trained
with the Common Objects in Context (COCO) dataset. This provided pixel-level
classification of the objects forming the skyline. A Conditional Random Fields
(CRF) algorithm was also applied to increase the details of the skyline signal.
Three metrics, able to capture the skyline signal variations, were then
considered for the analysis. These metrics shows a functional relationship with
distance for the class of trees, whose contours have a fractal nature. In
particular, regression analysis was performed against 475 ortho-photo based
distance measurements, and, in the best case, a R2 score equal to 0.47 was
achieved. This is an encouraging result which shows the potential of skyline
variation metrics for inferring distance related information.


  Ensemble learning is a very prevalent method employed in machine learning.
The relative success of ensemble methods is attributed to their ability to
tackle a wide range of instances and complex problems that require different
low-level approaches. However, ensemble methods are relatively less popular in
reinforcement learning owing to the high sample complexity and computational
expense involved in obtaining a diverse ensemble. We present a novel training
and model selection framework for model-free reinforcement algorithms that use
ensembles of policies obtained from a single training run. These policies are
diverse in nature and are learned through directed perturbation of the model
parameters at regular intervals. We show that learning and selecting an
adequately diverse set of policies is required for a good ensemble while
extreme diversity can prove detrimental to overall performance. Selection of an
adequately diverse set of policies is done through our novel policy selection
framework. We evaluate our approach on challenging discrete and continuous
control tasks and also discuss various ensembling strategies. Our framework is
substantially sample efficient, computationally inexpensive and is seen to
outperform state-of-the-art (SOTA) scores in Atari 2600 and Mujoco.


  The microscopic mechanism of thermal transport in liquids and amorphous
solids has been an outstanding problem for a long time. There have been several
different approaches to explain the thermal conductivities for these systems,
for example, the Bridgman's formula for simple liquids, the concept of the
minimum thermal conductivity for amorphous solids, and the thermal resistance
network model for amorphous polymers. Here, we present a ubiquitous formula to
explain the thermal conductivities of liquids and amorphous solids in a unified
way. The calculated thermal conductivities using this formula without fitting
parameters are in excellent agreement with the experimental data for these
systems. Our formula is not only providing detailed implications on microscopic
mechanisms of heat transfer in these systems, but also solves the discrepancies
between existing formulae and experimental data.


  We discuss the distribution of transmission eigenvalues in the strongly
localized regime in the presence of both a magnetic field and spin--orbit
scattering. We show that, under suitable conditions, this distribution can be
described by a new universality class labelled not only by the index $\beta$
but also by a new index $\eta$. This result is obtained by mapping the problem
into that of a suitable Calogero-Sutherland model.


  The formulations and approximations of the branch flow model for mesh power
networks (Mesh-BranchFlow) are given in this paper. Using different sets of the
power flow equations, six formats of the exact Mesh-BranchFlow model are
listed. These six formats are mathematically equivalent with each other. Linear
approximation and second-order cone programming (SOCP) are then used to derive
the six formats of the convex Mesh-BranchFlow model. The branch ampacity
constraints considering the shunt conductance and capacitance of the
transmission line $\Pi$-model are derived. The key foundation of deriving the
ampacity constraints is the correct interpretation of the physical meaning of
the transmission line $\Pi$-model. An exact linear expression of the ampacity
constraints of the power loss variable is derived. The applications of the
Mesh-BranchFlow model in deriving twelve formats of the exact optimal power
flow (OPF) model and twelve formats of the approximate OPF model are formulated
and analyzed. Using the Julia programming language, the extensive numerical
investigations of all formats of the OPF models show the accuracy and
computational efficiency of the Mesh-BranchFlow model. A penalty function based
approximation gap reduction method is finally proposed and numerically
validated to improve the AC-feasibility of the approximate Mesh-BranchFlow
model.


  We have developed a scheme to generate, control, transmit and measure
entangled photonic qutrits (two photons each of dimension d = 3). A Bell test
of this source has previously been reported elsewhere [1], therefore, here we
focus on how the control of the system is realized. Motivated by these results,
we outline how the scheme can be used for two specific quantum protocols,
namely key distribution and coin tossing and discuss some of their advantages
and disadvantages.


  Classifier chains is a key technique in multi-label classification, since it
allows to consider label dependencies effectively. However, the classifiers are
aligned according to a static order of the labels. In the concept of dynamic
classifier chains (DCC) the label ordering is chosen for each prediction
dynamically depending on the respective instance at hand. We combine this
concept with the boosting of extreme gradient boosted trees (XGBoost), an
effective and scalable state-of-the-art technique, and incorporate DCC in a
fast multi-label extension of XGBoost which we make publicly available. As only
positive labels have to be predicted and these are usually only few, the
training costs can be further substantially reduced. Moreover, as experiments
on eleven datasets show, the length of the chain allows for a more control over
the usage of previous predictions and hence over the measure one want to
optimize.


  Ballistic transport through nanoscale devices with time-dependent Rashba-type
spin-orbit interaction (SOI) can lead to spin-polarized wave packets that
appear even for completely unpolarized input. The SOI that oscillates in a
finite domain generates density and spin polarization fluctuations that leave
the region as propagating waves. Particularly, spin polarization has space and
time dependence even in regions without SOI. Our results are based on an
analytic solution of the time-dependent Schr\"odinger equation. The relevant
Floquet quasi-energies that are obtained appear in the energy spectrum of both
the transmitted and reflected waves.


  We present phi-meson transverse momentum distribution as well as its elliptic
flow (v_{2}) measurements in Au + Au collisions at center-of-mass energy per
nucleon pair \sqrt{s_{NN}} = 7.7, 11.5 and 39 GeV with the data taken from STAR
experiment at RHIC in the year 2010. We discuss the energy dependence of
phi-meson elliptic flow (v_{2}) and central-to-peripheral nuclear modification
factors (R_{CP}). The v_{2} of phi-mesons are compared to those from other
hadron species. The implications on partonic-hadronic phase transition are
discussed.


  $C_5^A(Q^2)$ axial form factor is extracted from the ANL neutrino-deuteron
scattering data with deuteron structure effects taken into consideration. The
best fit of the $C_5^A(Q^2)$ axial form factor is obtained assuming dipole
parametrization with $C_5^A(0)=1.13\pm0.15$ and $M_A=0.94\pm 0.08$ GeV.


  Results on products of the distributions $x_{\pm}^{a}$ and $\de^{(p)}(x)$
with coinciding singularities are derived when the products are balanced so
that they exist in the distribution space. These results follow the idea of a
known result on distributional products published by Jan Mikusi\'nski in 1966.
They are obtained in Colombeau algebra $\EuScript{G}(\mathbb{R})$ of
generalized functions that contains the distribution space and the notion of
'association' in it allows obtaining results in terms of distributions.


  This paper studies weakly mixing (singular) and mixing masas in type
$\rm{II}_{1}$ factors from a bimodule point of view. Several necessary and
sufficient conditions to characterize the normalizing algebra of a masa are
presented. We also study the structure of mixing inclusions, with special
attention paid to masas of product class. A recent result of Jolissaint and
Stalder concerning mixing masas arising out of inclusions of groups is
revisited. One consequence of our structural results rules out the existence of
certain Koopman-realizable measures, arising from semidirect products, which
are absolutely continuous but not Lebesgue. We also show that there exist
uncountably many pairwise non--conjugate mixing masas in the free group factors
each with Puk\'{a}nszky invariant $\{1,\infty\}$.


  It is becoming increasingly important in the software industry, especially
with the growing complexity of software ecosystems and the emphasis on security
and compliance for manufacturers to inventory software used on their systems. A
Software-Bill-of-Materials (SBOM) is a comprehensive inventory detailing a
software application's components and dependencies. Current approaches rely on
case-based reasoning to inconsistently identify the software components
embedded in binary files. We propose a different route, an automated method for
generating SBOMs to prevent disastrous supply-chain attacks. Remaining on the
topic of static code analysis, we interpret this problem as a semantic
similarity task wherein a transformer model can be trained to relate a product
name to corresponding version strings. Our test results are compelling,
demonstrating the model's strong performance in the zero-shot classification
task, further demonstrating the potential for use in a real-world cybersecurity
context.


  We study the problem of generating an approximately i.i.d. string at the
output of a discrete memoryless channel using a limited amount of randomness at
its input in presence of causal noiseless feedback. Feedback does not decrease
the channel resolution, the minimum entropy rate required to achieve an
accurate approximation of an i.i.d. output string. However, we show that, at
least over a binary symmetric channel, a significantly larger resolvability
exponent (the exponential decay rate of the divergence between the output
distribution and product measure), compared to the best known achievable
resolvability exponent in a system without feedback, is possible. We show that
by employing a variable-length resolvability scheme and using an average number
of coin-flips per channel use, the average divergence between the distribution
of the output sequence and product measure decays exponentially fast in the
average length of output sequence with an exponent equal to $[R-I(U;V)]^+$
where $I(U;V)$ is the mutual information developed across the channel.


  CLOVER is an experiment which aims to detect the signature of gravitational
waves from inflation by measuring the B-mode polarization of the cosmic
microwave background. CLOVER consists of three telescopes operating at 97, 150,
and 220 GHz. The 97-GHz telescope has 160 horns in its focal plane while the
150 and 220-GHz telescopes have 256 horns each. The horns are arranged in a
hexagonal array and feed a polarimeter which uses finline-coupled TES
bolometers as detectors. To detect the two polarizations the 97-GHz telescope
has 320 detectors while the 150 and 220-GHz telescopes have 512 detectors each.
To achieve the required NEPs the detectors are cooled to 100 mK for the 97 and
150-GHz polarimeters and 230 mK for the 220-GHz polarimeter. Each detector is
fabricated as a single chip to guarantee fully functioning focal planes. The
detectors are contained in linear modules made of copper which form split-block
waveguides. The detector modules contain 16 or 20 detectors each for
compatibility with the hexagonal arrays of horns in the telescopes' focal
planes. Each detector module contains a time-division SQUID multiplexer to read
out the detectors. Further amplification of the multiplexed signals is provided
by SQUID series arrays. The first prototype detectors for CLOVER operate with a
bath temperature of 230 mK and are used to validate the detector design as well
as the polarimeter technology. We describe the design of the CLOVER detectors,
detector blocks, and readout, and give an update on the detector development.


  Noise plagues many numerical datasets, where the recorded values in the data
may fail to match the true underlying values due to reasons including:
erroneous sensors, data entry/processing mistakes, or imperfect human
estimates. We consider general regression settings with covariates and a
potentially corrupted response whose observed values may contain errors. By
accounting for various uncertainties, we introduced veracity scores that
distinguish between genuine errors and natural data fluctuations, conditioned
on the available covariate information in the dataset. We propose a simple yet
efficient filtering procedure for eliminating potential errors, and establish
theoretical guarantees for our method. We also contribute a new error detection
benchmark involving 5 regression datasets with real-world numerical errors (for
which the true values are also known). In this benchmark and additional
simulation studies, our method identifies incorrect values with better
precision/recall than other approaches.


  Given the clinical notes written in electronic health records (EHRs), it is
challenging to predict the diagnostic codes which is formulated as a
multi-label classification task. The large set of labels, the hierarchical
dependency, and the imbalanced data make this prediction task extremely hard.
Most existing work built a binary prediction for each label independently,
ignoring the dependencies between labels. To address this problem, we propose a
two-stage framework to improve automatic ICD coding by capturing the label
correlation. Specifically, we train a label set distribution estimator to
rescore the probability of each label set candidate generated by a base
predictor. This paper is the first attempt at learning the label set
distribution as a reranking module for medical code prediction. In the
experiments, our proposed framework is able to improve upon best-performing
predictors on the benchmark MIMIC datasets. The source code of this project is
available at https://github.com/MiuLab/ICD-Correlation.