  Measurements of fiducial and total inclusive cross sections for W and Z boson
production are presented in proton-proton collisions at $\sqrt{s}$ = 5.02 and
13 TeV. Electron and muon decay modes ($ell$ = e or$\mu$) are studied in the
data collected with the CMS detector in 2017, in dedicated runs with reduced
instantaneous luminosity. The data sets correspond to integrated luminosities
of 298 $\pm$ 6 pb$^{-1}$ at 5.02 TeV and 206 $\pm$ 5 pb$^{-1}$ at 13 TeV.
Measured values of the products of the total inclusive cross sections and the
branching fractions at 5.02 TeV are $\sigma$(pp $\to$ W+X)$\mathcal{B}$(W $\to$
$\ell\nu$) = 7300 $\pm$ 10 (stat) $\pm$ 60 (syst) $\pm$ 140 (lumi) pb, and
$\sigma$(pp $\to$ Z+X)$\mathcal{B}$(Z $\to$ $\ell^+\ell^-$) = 669 $\pm$ 2
(stat) $\pm$ 6 (syst) $\pm$ 13 (lumi) pb for the dilepton invariant mass in the
range of 60-120 GeV. The corresponding results at 13 TeV are 20480 $\pm$ 10
(stat) $\pm$ 170 (syst) $\pm$ 470 (lumi) pb and 1952 $\pm$ 4 (stat) $\pm$ 18
(syst) $\pm$ 45 (lumi) pb. The measured values agree with cross section
calculations at next-to-next-to-leading-order in perturbative quantum
chromodynamics. Fiducial and total inclusive cross sections, ratios of cross
sections of W$^+$ and W$^-$ production as well as inclusive W and Z boson
production, and ratios of these measurements at 5.02 and 13 TeV are reported.


  Interstitial defects are inevitably present in doped semiconductors that
enable modern-day electronic, optoelectronic or thermoelectric technologies.
Understanding of stability of interstitials and their bonding mechanisms in the
silicon lattice was accomplished only recently with the advent of
first-principles modeling techniques, supported by powerful experimental
methods. However, much less attention has been paid to the effect of different
naturally occurring interstitials on the thermal and electrical properties of
silicon. In this work, we present a systematic study of the variability of heat
and charge transport properties of bulk silicon, in the presence of randomly
distributed interstitial defects (Si, Ge, C and Li). We find through atomistic
lattice dynamics and molecular dynamics modeling studies that, interstitial
defects scatter heat-carrying phonons to suppress thermal transport-1.56% of
randomly distributed Ge and Li interstitials reduce the thermal conductivity of
silicon by $\sim$ 30 and 34 times, respectively. Using first principles density
functional theory and semi-classical Boltzmann transport theory, we compute
electronic transport coefficients of bulk Si with 1.56% Ge, C, Si and Li
interstitials, in hexagonal, tetrahedral, split-interstitial and bond-centered
sites. We demonstrate that hexagonal-Si and hexagonal-Ge interstitials
minimally impact charge transport. To complete the study, we predict the
thermoelectric property of an experimentally realizable bulk Si sample that
contains Ge interstitials in different symmetry sites. Our research establishes
a direct relationship between the variability of structures dictated by
fabrication processes and heat and charge transport properties of silicon. The
relationship provides guidance to accurately estimate performance of Si-based
materials for various technological applications.


  Synthetic health data have the potential to mitigate privacy concerns when
sharing data to support biomedical research and the development of innovative
healthcare applications. Modern approaches for data generation based on machine
learning, generative adversarial networks (GAN) methods in particular, continue
to evolve and demonstrate remarkable potential. Yet there is a lack of a
systematic assessment framework to benchmark methods as they emerge and
determine which methods are most appropriate for which use cases. In this work,
we introduce a generalizable benchmarking framework to appraise key
characteristics of synthetic health data with respect to utility and privacy
metrics. We apply the framework to evaluate synthetic data generation methods
for electronic health records (EHRs) data from two large academic medical
centers with respect to several use cases. The results illustrate that there is
a utility-privacy tradeoff for sharing synthetic EHR data. The results further
indicate that no method is unequivocally the best on all criteria in each use
case, which makes it evident why synthetic data generation methods need to be
assessed in context.


  Building mathematical models of brains is difficult because of the sheer
complexity of the problem. One potential starting point is through basal
cognition, which give abstract representation of a range of organisms without
central nervous systems, including fungi, slime moulds and bacteria. We propose
one such model, demonstrating how a combination of oscillatory and
current-based reinforcement processes can be used to couple resources in an
efficient manner, mimicking the way these organisms function. A key ingredient
in our model, not found in previous basal cognition models, is that we
explicitly model oscillations in the number of particles (i.e. the nutrients,
chemical signals or similar, which make up the biological system) and the flow
of these particles within the modelled organisms. Using this approach, we find
that our model builds efficient solutions, provided the environmental
oscillations are sufficiently out of phase. We further demonstrate that
amplitude differences can promote efficient solutions and that the system is
robust to frequency differences. In the context of these findings, we discuss
connections between our model and basal cognition in biological systems and
slime moulds, in particular, how oscillations might contribute to
self-organised problem-solving by these organisms.


  We propose a novel approach to select IaaS cloud services for a long-term
period where the service providers offer limited QoS information. The proposed
approach leverages free short-term trials to obtain the previously undisclosed
QoS information. A new significance-based trial scheme is proposed using
frequency distribution analysis to test a consumer's long-term workloads in a
short trial. We introduce a novel IaaS signature technique to uniquely identify
the variability of a provider's QoS performance. A Signature-based QoS
Performance Discovery (SPD) algorithm is proposed which leverages the
combination of free trials and IaaS signatures. A set of exhaustive experiments
with real-world datasets is conducted to evaluate the proposed approach.


  IceCube is a cubic kilometer neutrino telescope under construction at the
South Pole. The primary goal is to discover astrophysical sources of high
energy neutrinos. We describe the detector and present results on atmospheric
muon neutrinos from 2006 data collected with nine detector strings.


  We shortly describe the leading twist formalism of spin and
transverse-momentum dependent fracture functions recently developed and present
results for the production of spinless hadrons in the target fragmentation
region (TFR) of SIDIS. In this case not all fracture functions can be accessed
and only a Sivers-like single spin azimuthal asymmetry shows up at LO
cross-section. Then, we demonstrate that the measurement of spin dependent
azimuthal asymmetries in double hadron production in polarized SIDIS -- with
one spinless hadron produced in the current fragmentation region (CFR) and
another in the TFR -- would provide access to all 16 leading twist fracture
functions.


  Contrary to topological insulators, topological semimetals possess a
nontrivial chiral anomaly that leads to negative magnetoresistance and are
hosts to both conductive bulk states and topological surface states with
intriguing transport properties for spintronics. Here, we fabricate
highly-ordered metallic Pt3Sn and Pt3SnxFe1-x thin films via sputtering
technology. Systematic angular dependence (both in-plane and out-of-plane)
study of magnetoresistance presents surprisingly robust quadratic and linear
negative longitudinal magnetoresistance features for Pt3Sn and Pt3SnxFe1-x,
respectively. We attribute the anomalous negative longitudinal
magnetoresistance to the type-II Dirac semimetal phase (pristine Pt3Sn) and/or
the formation of tunable Weyl semimetal phases through symmetry breaking
processes, such as magnetic-atom doping, as confirmed by first-principles
calculations. Furthermore, Pt3Sn and Pt3SnxFe1-x show the promising performance
for facilitating the development of advanced spin-orbit torque devices. These
results extend our understanding of chiral anomaly of topological semimetals
and can pave the way for exploring novel topological materials for spintronic
devices.


  We use a model where the cosmological term can be related to the chiral gauge
anomaly of a possible quantum scenario of the initial evolution of the
universe. We show that this term is compatible with the Friedmann behavior of
the present universe.


  Materials with strongly correlated electrons exhibit physical properties that
are often difficult to predict as they result from the interactions of large
numbers of electrons combined with several quantum degrees of freedom. The
layered oxide perovskite Sr$_2$RuO$_4$ is a strongly correlated electron
material that has been intensively investigated since its discovery due to its
unusual physical properties. Whilst recent experiments have reopened the debate
on the exact symmetry of the superconducting state in Sr$_2$RuO$_4$, a deeper
understanding of the Sr$_2$RuO$_4$ normal state appears crucial as this is the
background in which electron pairing occurs. Here, by using low-energy muon
spin spectroscopy we discover the existence of magnetism at the surface of
Sr$_2$RuO$_4$ in its normal state. We detect static weak dipolar fields yet
manifesting below a relatively high onset temperature larger than 50 K, which
reveals the unconventional nature of the observed magnetism. We relate the
origin of this phase breaking time reversal symmetry to electronic ordering in
the form of orbital loop currents that originate at the reconstructed
Sr$_2$RuO$_4$ surface. Our observations set a reference for the discovery of
the same magnetic phase in other materials and unveil an electronic ordering
mechanism that can influence unconventional electron pairing with broken time
reversal symmetry in those materials where the observed magnetic phase coexists
with superconductivity.


  The Silicon Photomultiplier (SiPM) is a mature photodetector concept that is
applied in a variety of applications ranging from medical imaging to automotive
LiDAR systems. Over the last few years, improvements of the sensor performance
are gradually approaching to a saturation. In this work we present our new
concept to overcome the intrinsic limitations of planar configurations of
electrodes. Our non-planar technology is based on focusing and enhancing the
electric fields by tip-like electrodes. The shape of the electric field and the
lack of typical micro-cell edges, allows us to exclude cell separation
boundaries and eliminate dead space around active cell areas. Our design
provides a high-density micro-cell layout with a high geometric efficiency. It
resolves the well-known trade-off between the detection efficiency and the
dynamic range. The first "Tip Avalanche Photodiode" (TAPD) prototypes show a
remarkable geometric efficiency above 80% for a micro-cell pitch of
15$\mathrm{\mu}$m. This directly translates into a photon detection efficiency
(PDE) record peak value of 73% at 600nm with respect to the state-of-the-art
SiPMs. Moreover, the PDE remains above a value of 45% up to a wavelength of
800nm with another record value of 22% at 905nm. The reduced micro-cell
capacity allows for a fast recovery time below 4ns, which improves the
operation at high photon rates. Overall, the TAPD is anticipated to be a very
promising SiPM generation for various wide-spectral and high-dynamic-range
applications in health science, biophysics, particle physics and LiDARs.


  We present 307 Type Ia supernova (SN) light curves from the first four years
of the TESS mission. We use this sample to characterize the shapes of the early
time light curves, measure the rise times from first light to peak, and search
for companion star interactions. Using simulations, we show that light curves
must have noise $<$10% of the peak to avoid biases in the early time light
curve shape, restricting our quantitative analysis to 74 light curves. We find
that the mean power law index $t^{\beta_1}$ of the early time light curves is
1.93$\pm$ 0.57 and the mean rise time to peak is 15.7 $\pm$ 3.5 days. We also
estimate the underlying population distribution and find a Gaussian component
with mean $\beta_1 = 2.29$, width 0.34, and a tail extending to values less
than 1.0. We use model comparison techniques to test for the presence of
companion interactions. In contrast to recent results in the literature, we
find that the data can rarely distinguish between models with and without
companion interactions, and caution is needed when claiming detections of early
time flux excesses. Nevertheless, we find three high-quality SN light curves
that tentatively prefer the addition of a companion interaction model, but the
statistical evidence is not robust. We also find two SNe that disfavor the
addition of a companion interaction model to a curved power law model. Taking
the 74 SNe together, we calculate 3$\sigma$ upper limits on the presence of
companion signatures to control for orientation effects that can hide
companions in individual light curves. Our results rule out common progenitor
systems with companions having Roche lobe radii $>$ 31 R$_{\odot}$ (99.9%
confidence level) and disfavor companions having Roche lobe radii $>$ 10
R$_{\odot}$ (95% confidence level). Lastly, we discuss the implications of our
results for the intrinsic fraction of single degenerate progenitor systems.


  The origin of light is a unsolved mystery in nature. Recently, it was
suggested that light may originate from a new kind of order - quantum order. To
test this idea in experiments, we study systems of screened magnetic/electric
dipoles in 2D and 3D lattices. We show that our models contain an artificial
light -- a photon-like collective excitation. We discuss how to design
realistic devices that realize our models. We show that the ``speed of light''
and the ``fine structure constant'' of the artificial light can be tuned in our
models. The properties of artificial atoms (bound states of pairs of artificial
charges) are also discussed. The existence of artificial light (as well as
artificial electron) in condensed matter systems suggests that elementary
particles, such as light and electron, may not be elementary. They may be
collective excitations of quantum order in our vacuum. Our models further
suggest that a gauge theory is a string-net theory in disguise. Light is a
fluctuation of nets of large closed strings and charge is the end of open
strings.


  Gas giants orbiting their host star within the ice line are thought to have
migrated to their current locations from farther out. Here we consider the
origin and dynamical evolution of observed Jupiters, focusing on hot and warm
Jupiters with outer friends. We show that the majority of the observed Jupiter
pairs (twenty out of twenty-four) will be dynamically unstable if the inner
planet was placed at >~1AU distance from the stellar host. This finding is at
odds with formation theories that invoke the migration of such planets from
semi-major axes >~1AU due to secular dynamical processes (e.g., secular chaos,
Lidov-Kozai oscillations) coupled with tidal dissipation. In fact, the results
of N-body integrations show that the evolution of dynamically unstable systems
does not lead to tidal migration but rather to planet ejections and collisions
with the host star. This and other arguments lead us to suggest that most of
the observed planets with a companion could not have been transported from
further out through secular migration processes. More generally, by using a
combination of numerical and analytic techniques we show that the high-e
Lidov-Kozai migration scenario can only account for less than 10% of all gas
giants observed between 0.1-1 AU. Simulations of multi-planet systems support
this result. Our study indicates that rather than starting on highly eccentric
orbits with orbital periods above one year, these "warm" Jupiters are more
likely to have reached the region where they are observed today without having
experienced significant tidal dissipation.


  We report optical trapping of a polyatomic molecule, calcium monohydroxide
(CaOH). CaOH molecules from a magneto-optical trap are sub-Doppler laser cooled
to $20(3)~\mu\text{K}$ in free space and loaded into an optical dipole trap. We
attain an in-trap molecule number density of $3(1) \times 10^9~\text{cm}^{-3}$
at a temperature of $57(8)~\mu$K. Trapped CaOH molecules are optically pumped
into an excited vibrational bending mode, whose $\ell$-type parity doublet
structure is a potential resource for a wide range of proposed quantum science
applications with polyatomic molecules. We measure the spontaneous, radiative
lifetime of this bending mode state to be $\sim$$0.7~\text{s}$.


  We discuss simulations of a simple model for polymer blends in the framework
of the Rouse model. At odds with standard predictions, large dynamic asymmetry
between the two components induces strong non-exponentiality of the Rouse modes
for the fast component. Despite chains being much shorter than the entanglement
length, it also induces dynamic features resembling a crossover to
entangled-like chain dynamics. This unusual behavior is associated to strong
memory effects which break down the assumption of time uncorrelation of the
external forces acting on the tagged chain.


  We discuss how semidefinite programming can be used to determine the
second-order density matrix directly through a variational optimization. We
show how the problem of characterizing a physical or N -representable density
matrix leads to matrix-positivity constraints on the density matrix. We then
formulate this in a standard semidefinite programming form, after which two
interior point methods are discussed to solve the SDP. As an example we show
the results of an application of the method on the isoelectronic series of
Beryllium.


  Freiman's $3k-4$ Theorem states that if a subset $A$ of $k$ integers has a
Minkowski sum $A+A$ of size at most $3k-4$, then it must be contained in a
short arithmetic progression. We prove a function field analogue that is also a
generalisation: it states that if $K$ is a perfect field and if $S\supset K$ is
a vector space of dimension $k$ inside an extension $F/K$ in which~$K$ is
algebraically closed, and if the $K$-vector space generated by all products of
pairs of elements of $S$ has dimension at most $3k-4$, then $K(S)$ is a
function field of small genus, and $S$ is of small codimension inside a
Riemann-Roch space of $K(S)$.


  We determine the behaviour of entanglement between regions of space in a Bose
gas of fixed particle number around the critical temperature condensation.
Long-range correlations develop in the Bose-Einstein condensate (BEC) phase
transition and the aim here is to find out whether spatial coherence alone
implies entanglement. We use a purity measure of entanglement to derive an
entanglement witness that detects entanglement between two regions of space in
the BEC. It is shown that spatial coherence between the two regions is
necessary for entanglement with coherence and entanglement becoming equivalent
only when the regions occupy the entire confining volume of the gas. The
probabilities for Bosons to occupy the regions is the only other parameter that
influences the amount of entanglement. We calculate explicitly the amount of
entanglement between two regions for a cigar-shaped harmonic potential and find
that it increases with decreasing temperature. A second entanglement witness is
derived for entanglement between three regions of space, where again spatial
coherence between each pair of regions is precursor to entanglement. It is
shown that the state with the maximum amount of entanglement occurs when the
probabilities for the Bosons to occupy the three regions are equal.


  This letter presents a novel technique to calculate temperatures of building
rooftops and other impervious surfaces from high spatial resolution aerial
thermal images. In this study, we collected aerial radiance images of 30cm
spatial resolution using a FLIR Phoenix imager in long-wave and mid-wave
infrared wavelengths for the city of Cedar Falls, USA to estimate building roof
temperature loss. Simultaneous ground temperature measurements were made at
pre-selected ground targets and roofs using 9 Fluke 561r infrared thermometers.
Atmospheric correction of aerial images was performed by Empirical Line
Calibration (ELC) method. The resulting ground-leaving radiances were corrected
for the emissivity of different roof types and the true kinetic temperature of
the building roofs was calculated. The ELC model was observed to perform better
when only impervious surface targets were used for the regression. With an
R2=0.71 for ELC, the method produced a root mean squared error of 0.74{\deg}C
for asphalt roofs. Further, we observed that the microclimate plays a
significant role while synchronizing aerial and ground measurements.


  Heat waves resulting from prolonged extreme temperatures pose a significant
risk to human health globally. Given the limitations of observations of extreme
temperature, climate models are often used to characterize extreme temperature
globally, from which one can derive quantities like return values to summarize
the magnitude of a low probability event for an arbitrary geographic location.
However, while these derived quantities are useful on their own, it is also
often important to apply a spatial statistical model to such data in order to,
e.g., understand how the spatial dependence properties of the return values
vary over space and emulate the climate model for generating additional spatial
fields with corresponding statistical properties. For these objectives, when
modeling global data it is critical to use a nonstationary covariance function.
Furthermore, given that the output of modern global climate models can be on
the order of $\mathcal{O}(10^4)$, it is important to utilize approximate
Gaussian process methods to enable inference. In this paper, we demonstrate the
application of methodology introduced in Risser and Turek (2020) to conduct a
nonstationary and fully Bayesian analysis of a large data set of 20-year return
values derived from an ensemble of global climate model runs with over 50,000
spatial locations. This analysis uses the freely available BayesNSGP software
package for R.


  Kernel analog forecasting (KAF) is a powerful methodology for data-driven,
non-parametric forecasting of dynamically generated time series data. This
approach has a rigorous foundation in Koopman operator theory and it produces
good forecasts in practice, but it suffers from the heavy computational costs
common to kernel methods. This paper proposes a streaming algorithm for KAF
that only requires a single pass over the training data. This algorithm
dramatically reduces the costs of training and prediction without sacrificing
forecasting skill. Computational experiments demonstrate that the streaming KAF
method can successfully forecast several classes of dynamical systems
(periodic, quasi-periodic, and chaotic) in both data-scarce and data-rich
regimes. The overall methodology may have wider interest as a new template for
streaming kernel regression.


  As graph analytics often involves compute-intensive operations, GPUs have
been extensively used to accelerate the processing. However, in many
applications such as social networks, cyber security, and fraud detection,
their representative graphs evolve frequently and one has to perform a rebuild
of the graph structure on GPUs to incorporate the updates. Hence, rebuilding
the graphs becomes the bottleneck of processing high-speed graph streams. In
this paper, we propose a GPU-based dynamic graph storage scheme to support
existing graph algorithms easily. Furthermore, we propose parallel update
algorithms to support efficient stream updates so that the maintained graph is
immediately available for high-speed analytic processing on GPUs. Our extensive
experiments with three streaming applications on large-scale real and synthetic
datasets demonstrate the superior performance of our proposed approach.


  We study the finite-temperature properties of the half-filled two-band
Hubbard model in the presence of Hund's rule coupling and orbital anisotropy.
We use the mean-field treatment of the $Z_2$ slave-spin theory with a
finite-temperature extension of the zero-temperature gauge variable previously
developed by Hassan and de' Medici
http://dx.doi.org/10.1103/PhysRevB.81.035106}{Phys. Rev. B {\bf 81}, 035106
(2010)}]. We consider the instability of the Fermi liquid phases and how it is
enhanced by the Hund's rule. We identify paramagnetic solutions that have zero
quasi-particle weight with bad metallic phases, and the first-order transition
temperature between it and the Fermi liquid phase as a coherence temperature
that signals the crossover to the bad metallic state. When orbital anisotropy
is present, we found an intermediate transition to an orbital-selective bad
metal (OSBM), where the narrow band becomes a bad metal while the wide band
remains a renormalised Fermi liquid. The temperatures $T_\textrm{coh}$ and
$T_\textrm{OSBM}$ at which the system transitions to the bad metal phases can
be orders of magnitude less than the Fermi temperature associated with the
non-interacting band. The parameter dependence of the temperature at which the
OSBM is destroyed can be understood in terms of a ferromagnetic Kondo-Hubbard
lattice model. In general, Hund's rule coupling enhances the bad metallic
phases, reduce interorbital charge fluctuations and increase spin fluctuations.
The qualitative difference found in the ground state whether the Hund's rule is
present or not, related to the degeneracy of the low energy manifold, is also
maintained for finite temperatures.


  Due to advancements in nanomagnetism and latest nanomagnetic materials and
devices, a new potential field has been opened up for research and applications
which was not possible before. We herein propose a new research field and
application for nanomagnetism for the visualization of dynamic magnetic fields
in real-time. In short, Nano Magnetic Vision. A new methodology, technique and
apparatus were invented and prototyped in order to demonstrate and test this
new application. As an application example the visualization of the dynamic
magnetic field on a transmitting antenna was chosen. Never seen before
high-resolution, photos and real-time color video revealing the actual dynamic
magnetic field inside a transmitting radio antenna rod has been captured for
the first time. The antenna rod is fed with six hundred volts, orthogonal
pulses. This unipolar signal is in the very low frequency (i.e. VLF) range. The
signal combined with an extremely short electrical length of the rod, ensures
the generation of a relatively strong fluctuating magnetic field, analogue to
the signal transmitted, along and inside the antenna. This field is induced
into a ferrolens and becomes visible in real-time within the normal human eyes
frequency spectrum. The name we have given to the new observation apparatus is,
SPIONs Superparamagnetic Ferrolens Microscope (SSFM), a powerful passive
scientific observation tool with many other potential applications in the near
future.


  Randomized smoothing has shown promising certified robustness against
adversaries in classification tasks. Despite such success with only
zeroth-order access to base models, randomized smoothing has not been extended
to a general form of regression. By defining robustness in regression tasks
flexibly through probabilities, we demonstrate how to establish upper bounds on
input data point perturbation (using the $\ell_2$ norm) for a user-specified
probability of observing valid outputs. Furthermore, we showcase the asymptotic
property of a basic averaging function in scenarios where the regression model
operates without any constraint. We then derive a certified upper bound of the
input perturbations when dealing with a family of regression models where the
outputs are bounded. Our simulations verify the validity of the theoretical
results and reveal the advantages and limitations of simple smoothing
functions, i.e., averaging, in regression tasks. The code is publicly available
at \url{https://github.com/arekavandi/Certified_Robust_Regression}.


  In numerous industrial processes involving fluids, viscosity is a determinant
factor for reaction rates, flows, drying, mixing, etc. Its importance is even
more determinant for phenomena observed are at the micro- and nano- scales as
in nanopores or in micro and nanochannels for instance. However, despite
notable progresses of the techniques used in microrheology in recent years, the
quantification, mapping and study of viscosity at small scales remains
challenging. Fluorescent molecular rotors are molecules whose fluorescence
properties are sensitive to local viscosity: they thus allow to obtain
viscosity maps by using fluorescence microscopes. While they are well-known as
contrast agents in bioimaging, their use for quantitative measurements remains
scarce. This paper is devoted to the use of such molecules to perform
quantitative, \textit{in situ} and local measurements of viscosity in
heterogeneous microfluidic flows. The technique is first validated in the
well-controlled situation of a microfluidic co-flow, where two streams mix
through transverse diffusion. Then, a more complex situation of mixing in
passive micromixers is considered and mixing efficiency is characterized and
quantified. The methodology developed in this study thus opens a new path for
flow characterization in confined, heterogeneous and complex systems.he
methodology developed in this study thus opens a new path for flow
characterization in confined, heterogeneous micro- and nano- systems.


  Let $F_n :(\Sigma, h_n) \to \mathbb C^2$ be a sequence of conformally
immersed Lagrangian self-shrinkers with a uniform area upper bound to the mean
curvature flow, and suppose that the sequence of metrics $\{h_n\}$ converges
smoothly to a Riemannian metric $h$. We show that a subsequence of $\{F_n\}$
converges smoothly to a branched conformally immersed Lagrangian self-shrinker
$F_\infty : (\Sigma, h)\to \mathbb C^2$. When the area bound is less than
$16\pi$, the limit $F_\infty$ is an embedded torus. When the genus of $\Sigma$
is one, we can drop the assumption on convergence $h_n\to h$. When the genus of
$\Sigma$ is zero, we show that there is no branched immersion of $\Sigma$ as a
Lagrangian shrinker, generalizing the rigidity result of Smoczyk in dimension
two by allowing branch points.


  There is a plethora of dark energy parameterizations that can fit current
supernovae Ia data. However, this data is only sensitive to redshifts up to
order one. In fact, many of these parameterizations break down at higher
redshifts. In this paper we study the effect of dark energy models on the
formation of dark halos. We select a couple of dark energy parameterizations
which are sensible at high redshifts and compute their effect on the evolution
of density perturbations in the linear and non-linear regimes. Using the
Press-Schechter formalism we show that they produce distinguishable signatures
in the number counts of dark halos. Therefore, future observations of galaxy
clusters can provide complementary constraints on the behavior of dark energy.


  We present the mass and X-ray temperature functions derived from a sample of
more than 15,000 galaxy clusters of the MareNostrum Universe cosmological SPH
simulations. In these simulations, we follow structure formation in a cubic
volume of 500/h Mpc on a side assuming cosmological parameters consistent with
either the first or third year WMAP data and gaussian initial conditions. We
compare our numerical predictions with the most recent observational estimates
of the cluster X-ray temperature functions and find that the low normalization
cosmological model inferred from the 3 year WMAP data results is barely
compatible with the present epoch X-ray cluster abundances. We can only
reconcile the simulations with the observational data if we assume a
normalization of the Mass-Temperature relation which is a factor of 2.5--3
smaller than our non-radiative simulations predict. This deviation seems to be
too large to be accounted by the effects of star formation or cooling in the
ICM, not taken into account in these simulations.


  Recent progress on the understanding of the Random Conductance Model is
reviewed and commented. A particular emphasis is on the results on the scaling
limit of the random walk among random conductances for almost every realization
of the environment, observations on the behavior of the effective resistance as
well as the scaling limit of certain models of gradient fields with non-convex
interactions. The text is an expanded version of the lecture notes for a course
delivered at the 2011 Cornell Summer School on Probability.


  The formation of kink wave fronts (KWFs) in a quasi-two-dimensional granular
system is investigated numerically with a focus on the role of cohesive
interactions between individual particles. The cohesive particle-particle
interaction is achieved through tuning the velocity-dependent coefficient of
restitution, based on an analytical model introduced recently. A comparison
with experimental results indicates that the threshold for the emergence of
traveling KWFs matches the regime in which the center of mass height of the
granular layers fluctuates with a period that triples the vibration period.
Further comparisons between dry and wet granular dynamics reveal that KWFs are
more pronounced in wet granular layers because of enhanced collective motion
induced by cohesion.


  We study the removability of compact sets for continuous Sobolev functions.
In particular, we focus on sets with infinitely many complementary components,
called "detour sets", which resemble the Sierpi\'nski gasket. The main theorem
is that if $K\subset \mathbb R^n$ is a detour set and its complementary
components are sufficiently regular, then $K$ is $W^{1,p}$-removable for $p>n$.
Several examples and constructions of sets where the theorem applies are given,
including the Sierpi\'nski gasket, Apollonian gaskets, and Julia sets.


  The Narrow Line Seyfert 1 galaxy REJ1034+396 is so far unique amongst AGN in
showing a Quasi-periodic oscillation (QPO) in its variability power spectrum.
There are multiple types of QPO seen in black hole binary (BHB) systems, so we
need to identify which BHB QPO corresponds to the one seen in the AGN. A key
hint is the `hot disc dominated' energy spectrum of REJ1034+396 which is
sufficiently unusual that it suggests a mildly super-Eddington flow, also
favoured by the most recent mass estimates for the AGN. This suggests the 67Hz
QPO seen occasionally in the mildly super-Eddington BHB GRS 1915+105 as the
most likely counterpart, assuming mass scaling of the QPO frequency. This is
supported by the fact that these data from GRS 1915+105 have an energy spectrum
which is also dominated by a `hot disc' component. Here we show that the
underlying broad band power spectral shape and normalisation are also similar,
providing further consistency checks for this identification. Thus the AGN QPO
adds to the growing evidence for a simple mass scaling of the accretion flow
properties between stellar and supermassive black holes.


  In this paper, we consider an incompressible viscous fluid in an infinitely
deep ocean, being bounded above by a free moving boundary. The governing
equations are the gravity-driven incompressible Navier-Stokes equations with
variable density and no surface tension is taken into account on the free
surface. After using the Lagrangian transformation, we write the main equations
in a perturbed form in a fixed domain. In the first part, we describe a
spectral analysis of the linearized equations around a hydrostatic equilibrium
$(\rho_0(x_3), 0, P_0(x_3))$ for a smooth increasing density profile $\rho_0$.
Precisely, we prove that there exist multiple normal modes to the linearized
equations by following the operator method initiated by Lafitte and Nguyen
\cite{LN20}. In the second part, we study the nonlinear Rayleigh-Taylor
instability around the above profile by constructing a wide class of initial
data for the nonlinear perturbation problem departing from the equilibrium,
based on the finding of multiple normal modes. Our nonlinear result follows the
previous framework of Guo and Strauss \cite{GS95} and also of Grenier
\cite{Gre00} with a refinement.


  We present an ultraviolet spectrum and light curve of the short orbital
period cataclysmic variable EZ Lyn obtained with the Cosmic Origins
Spectrograph on the Hubble Space Telescope 14 months after its dwarf nova
outburst, along with ground-based optical photometry. The UV spectrum can be
fit with a 13,100K, log g=8 white dwarf using 0.5 solar composition, while fits
to the individual lines are consistent with solar abundance for Si and Al, but
only 0.3 solar for C. The Discrete Fourier Transforms of the UV and optical
light curves at 14 months following outburst show a prominent period at 256
sec. This is the same period reported by Pavlenko in optical data obtained 7
months and one year after outburst, indicating its long term stability over
several months, but this period is not evident in the pre-outburst data and is
much shorter than the 12.6 min period that was seen in observations obtained
during an interval from 8 months to 2.5 years after the 2006 outburst. In some
respects, the long and short periods are similar to the behavior seen in GW Lib
after its outburst but the detailed explanation for the appearance and
disappearance of these periods and their relation to non-radial pulsation modes
remain to be explored with theoretical models.


  Gossip learning (GL), as a decentralized alternative to federated learning
(FL), is more suitable for resource-constrained wireless networks, such as
Flying Ad-Hoc Networks (FANETs) that are formed by unmanned aerial vehicles
(UAVs). GL can significantly enhance the efficiency and extend the battery life
of UAV networks. Despite the advantages, the performance of GL is strongly
affected by data distribution, communication speed, and network connectivity.
However, how these factors influence the GL convergence is still unclear.
Existing work studied the convergence of GL based on a virtual quantity for the
sake of convenience, which failed to reflect the real state of the network when
some nodes are inaccessible. In this paper, we formulate and investigate the
impact of inaccessible nodes to GL under a dynamic network topology. We first
decompose the weight divergence by whether the node is accessible or not. Then,
we investigate the GL convergence under the dynamic of node accessibility and
theoretically provide how the number of inaccessible nodes, data
non-i.i.d.-ness, and duration of inaccessibility affect the convergence.
Extensive experiments are carried out in practical settings to comprehensively
verify the correctness of our theoretical findings.


  In this article, we study the family of probability measures (indexed by a
positive real number t), obtained by penalization of the Brownian motion by a
given functional of its local times at time t. We prove that this family tends
to a limit measure when t goes to infinity if the functional satisfies some
conditions of domination, and we check these conditions in several particular
cases.


  Generation of an (arbitrarily) long string of bits unique to a given
finite-length numerical seed is of great value in the field of random number
generation, computer simulations, and other areas of computer science.
Extending this idea such that the bitstring cannot be reverse-engineered to
recover the original seed value extends the value of such a system to the field
of cryptography, as the string can be used directly as an encryption mask, or
as the input to some other cryptographic function. The longer the string that
can be generated, the closer the system would come to the ideal cryptographic
case of the One Time Pad.
  In this paper we propose a scheme for taking an initial seed (nominally a
128-bit integer, but not restricted to such), and expanding this into a unique
bitstring of a length determined by a limit cycle that makes it useful in
practical applications. We utilize novel mathematical concepts such as
combinatorial decomposition to turn the seed value into unique rotations of a
pre-defined table, which are operated on via destructive functions such as
exclusive-OR (XOR) and add-with-carry (ADC) to eventually produce the unique
bitstring.
  We assert that the process of iterating the XOR or ADC operation conforms to
the known NP-complete problem known as Subset-Sum, meaning that the reversal of
the process that produced the bitstring is tantamount to solving the
NP-Complete Subset-Sum problem, which in turn is less efficient than the
brute-force method of testing every seed value until the corresponding
bitstring is found, making the system highly relevant in a cryptographic
context.


  We study nonparametric contextual bandits where Lipschitz mean reward
functions may change over time. We first establish the minimax dynamic regret
rate in this less understood setting in terms of number of changes $L$ and
total-variation $V$, both capturing all changes in distribution over context
space, and argue that state-of-the-art procedures are suboptimal in this
setting.
  Next, we tend to the question of an adaptivity for this setting, i.e.
achieving the minimax rate without knowledge of $L$ or $V$. Quite importantly,
we posit that the bandit problem, viewed locally at a given context $X_t$,
should not be affected by reward changes in other parts of context space $\cal
X$. We therefore propose a notion of change, which we term experienced
significant shifts, that better accounts for locality, and thus counts
considerably less changes than $L$ and $V$. Furthermore, similar to recent work
on non-stationary MAB (Suk & Kpotufe, 2022), experienced significant shifts
only count the most significant changes in mean rewards, e.g., severe best-arm
changes relevant to observed contexts.
  Our main result is to show that this more tolerant notion of change can in
fact be adapted to.


  We study ballistic hole transport through Ge/Si core/shell nanowires at low
temperatures. We observe Fabry-P$\acute{e}$rot interference patterns as well as
conductance plateaus at integer multiples of 2e$^2$/h at zero magnetic field.
Magnetic field evolution of these plateaus reveals large effective
Land$\acute{e}$ g-factors. Ballistic effects are observed in nanowires with
silicon shell thicknesses of 1 - 3 nm, but not in bare germanium wires. These
findings inform the future development of spin and topological quantum devices
which rely on ballistic subband-resolved transport.


  Diffusion generative models have recently greatly improved the power of
text-conditioned image generation. Existing image generation models mainly
include text conditional diffusion model and cross-modal guided diffusion
model, which are good at small scene image generation and complex scene image
generation respectively. In this work, we propose a simple yet effective
approach, namely UPainting, to unify simple and complex scene image generation,
as shown in Figure 1. Based on architecture improvements and diverse guidance
schedules, UPainting effectively integrates cross-modal guidance from a
pretrained image-text matching model into a text conditional diffusion model
that utilizes a pretrained Transformer language model as the text encoder. Our
key findings is that combining the power of large-scale Transformer language
model in understanding language and image-text matching model in capturing
cross-modal semantics and style, is effective to improve sample fidelity and
image-text alignment of image generation. In this way, UPainting has a more
general image generation capability, which can generate images of both simple
and complex scenes more effectively. To comprehensively compare text-to-image
models, we further create a more general benchmark, UniBench, with well-written
Chinese and English prompts in both simple and complex scenes. We compare
UPainting with recent models and find that UPainting greatly outperforms other
models in terms of caption similarity and image fidelity in both simple and
complex scenes. UPainting project page \url{https://upainting.github.io/}.


  The application of deep learning techniques to medical problems has garnered
widespread research interest in recent years, such as applying convolutional
neural networks to medical image classification tasks. However, data in the
medical field is often highly private, preventing different hospitals from
sharing data to train an accurate model. Federated learning, as a
privacy-preserving machine learning architecture, has shown promising
performance in balancing data privacy and model utility by keeping private data
on the client's side and using a central server to coordinate a set of clients
for model training through aggregating their uploaded model parameters. Yet,
this architecture heavily relies on a trusted third-party server, which is
challenging to achieve in real life. Swarm learning, as a specialized
decentralized federated learning architecture that does not require a central
server, utilizes blockchain technology to enable direct parameter exchanges
between clients. However, the mining of blocks requires significant
computational resources, limiting its scalability. To address this issue, this
paper integrates the brain storm optimization algorithm into the swarm learning
framework, named BSO-SL. This approach clusters similar clients into different
groups based on their model distributions. Additionally, leveraging the
architecture of BSO, clients are given the probability to engage in
collaborative learning both within their cluster and with clients outside their
cluster, preventing the model from converging to local optima. The proposed
method has been validated on a real-world diabetic retinopathy image
classification dataset, and the experimental results demonstrate the
effectiveness of the proposed approach.


  Solving Sudoku puzzles is one of the most popular pastimes in the world.
Puzzles range in difficulty from easy to very challenging; the hardest puzzles
tend to have the most empty cells. The current paper explains and compares
three algorithms for solving Sudoku puzzles. Backtracking, simulated annealing,
and alternating projections are generic methods for attacking combinatorial
optimization problems. Our results favor backtracking. It infallibly solves a
Sudoku puzzle or deduces that a unique solution does not exist. However,
backtracking does not scale well in high-dimensional combinatorial
optimization. Hence, it is useful to expose students in the mathematical
sciences to the other two solution techniques in a concrete setting. Simulated
annealing shares a common structure with MCMC (Markov chain Monte Carlo) and
enjoys wide applicability. The method of alternating projections solves the
feasibility problem in convex programming. Converting a discrete optimization
problem into a continuous optimization problem opens up the possibility of
handling combinatorial problems of much higher dimensionality.


  Index Modulations, in the form of Spatial Modulation or Polarized Modulation,
are gaining traction for both satellite and terrestrial next generation
communication systems. Adaptive Index Modulation based links are needed to
fully exploit the transmission capacity of time-variant channels. The
adaptation of code and/or modulation requires a real-time evaluation of the
channel achievable rates. Some existing results in the literature present a
computational complexity which scales quadratically with the number of transmit
antennas and the constellation order. Moreover, the accuracy of these
approximations is low and it can lead to wrong Modulation and Coding Scheme
selection. In this work we apply a Multilayer Feedforward Neural Network to
compute the achievable rate of a generic Index Modulation link. The case of two
antennas/polarizations is analyzed throughly showing the neural network not
only a one-hundred fold decrement of the Mean Square Error in the estimation of
the capacity compared with existing analytical approximations, but it also
reduces fifty times the computational complexity. Moreover, the extension to an
arbitrary number of antennas is explained and supported with simulations. More
generally, neural networks can be considered as promising candidates for the
practical estimation of complex metrics in communication related settings.


  We investigate the various time-dependent bubble spacetimes that can be
obtained from double analytic continuation of asymptotically locally flat/AdS
spacetimes with NUT charge. We find different time-dependent explicit solutions
of general relativity from double analytic continuations of Taub-Nut(-AdS) and
Kerr-Nut(-AdS) spacetimes. One solution in particular has Milne-like evolution
throughout, and another is a NUT-charged generalization of the AdS soliton.
These solutions are all four dimensional. In certain situations the NUT charge
induces an ergoregion into the bubble spacetime and in other situations it
quantitatively modifies the evolution of the bubble, as when rotation is
present. In dimensions greater than four, no consistent bubble solutions are
found that have only one timelike direction.


  Transit timing variations (TTVs) have proven to be a powerful technique for
confirming Kepler planet candidates, for detecting non-transiting planets, and
for constraining the masses and orbital elements of multi-planet systems. These
TTV applications often require the numerical integration of orbits for
computation of transit times (as well as impact parameters and durations);
frequently tens of millions to billions of simulations are required when
running statistical analyses of the planetary system properties. We have
created a fast code for transit timing computation, TTVFast, which uses a
symplectic integrator with a Keplerian interpolator for the calculation of
transit times (Nesvorny et al. 2013). The speed comes at the expense of
accuracy in the calculated times, but the accuracy lost is largely unnecessary,
as transit times do not need to be calculated to accuracies significantly
smaller than the measurement uncertainties on the times. The time step can be
tuned to give sufficient precision for any particular system. We find a
speed-up of at least an order of magnitude relative to dynamical integrations
with high precision using a Bulirsch-Stoer integrator.


  The 2D gravity described by the action which is an arbitrary function of the
scalar curvature $f(R)$ is considered. The classical vacuum solutions are
analyzed. The one-loop renormalizability is studied. For the function $f=R \ln
R$ the model coupled with scalar (conformal) matter is exactly integrated and
is shown to describe a black hole of the 2D dilaton gravity type. The influence
of one-loop quantum corrections on a classical black hole configuration is
studied by including the Liouville-Polyakov term. The resulting model turns out
to be exactly solvable. The general solution is analyzed and shown to be free
from the space-time singularities for a certain number of scalar fields.


  In this paper, we study virtual full-duplex (FD) buffer-aided relaying to
recover the loss of multiplexing gain caused by half-duplex (HD) relaying in a
multiple relay network, where each relay is equipped with a buffer and multiple
antennas, through joint opportunistic relay selection (RS) and beamforming (BF)
design. The main idea of virtual FD buffer-aided relaying is that the source
and one of the relays simultaneously transmit their own information to another
relay and the destination, respectively. In such networks, inter-relay
interference (IRI) is a crucial problem which has to be resolved like
self-interference in the FD relaying. In contrast to previous work that
neglected IRI, we propose joint RS and BF schemes taking IRI into consideration
by using multiple antennas at the relays. In order to maximize average
end-to-end rate, we propose a weighted sum-rate maximization strategy assuming
that adaptive rate transmission is employed in both the source to relay and
relay to destination links. Then, we propose several BF schemes cancelling or
suppressing IRI in order to maximize the weighted sum-rate. Numerical results
show that our proposed optimal, zero forcing, and minimum mean square error
BF-based RS schemes asymptotically approach the ideal FD relaying upper bound
when increasing the number of antennas and/or the number of relays.


  Multiplicity is a key statistic for understanding the formation of very low
mass (VLM) stars and brown dwarfs. Currently, the separation distribution of
VLM binaries remains poorly constrained at small separations (< 1 AU), leading
to uncertainty in the overall binary fraction. We approach this problem by
searching for spectral binaries whose identification is independent of
separation. The combined spectra of these systems exhibit traces of methane
imposed on an earlier-type spectra. When the primary of such a system is a
late-type M or early-type L dwarf, however, the relative faintness of the T
dwarf secondary (up to 5 magnitudes at K-band) renders these features extremely
subtle. We present a set of spectral indices newly designed to identify these
systems, and a spectral fitting method to confirm and characterize them. We
apply this method to a library of over 750 spectra from the SpeX Prism Spectral
Libraries. We present new spectral binary candidates, compare them to recent
discoveries, and describe ongoing followup to search for resolved companions
and/or radial velocity variability.


  We propose a scheme to detect the diffusive cloak proposed by Schittny et al
[Science 345, 427 (2014)]. We exploit the fact that diffusion of light is an
approximation that disregards wave interference. The long-range contribution to
intensity correlation is sensitive to locations of paths crossings and the
interference inside the medium, allowing one to detect the size and position,
including the depth, of the diffusive cloak. Our results also suggest that it
is possible to separately manipulate the first- and the second-order statistics
of wave propagation in turbid media.


  For any polynomial map with a single critical point, we prove that its lower
Lyapunov exponent at the critical value is negative if and only if the map has
an attracting cycle. Similar statement holds for the exponential maps and some
other complex dynamical systems. We prove further that for the unicritical
polynomials with positive area Julia sets almost every point of the Julia set
has zero Lyapunov exponent. Part of this statement generalizes as follows:
every point with positive upper Lyapunov exponent in the Julia set of an
arbitrary polynomial is not a Lebegue density point.


  Face recognition remains a challenging task in unconstrained scenarios,
especially when faces are partially occluded. To improve the robustness against
occlusion, augmenting the training images with artificial occlusions has been
proved as a useful approach. However, these artificial occlusions are commonly
generated by adding a black rectangle or several object templates including
sunglasses, scarfs and phones, which cannot well simulate the realistic
occlusions. In this paper, based on the argument that the occlusion essentially
damages a group of neurons, we propose a novel and elegant occlusion-simulation
method via dropping the activations of a group of neurons in some elaborately
selected channel. Specifically, we first employ a spatial regularization to
encourage each feature channel to respond to local and different face regions.
In this way, the activations affected by an occlusion in a local region are
more likely to be located in a single feature channel. Then, the locality-aware
channel-wise dropout (LCD) is designed to simulate the occlusion by dropping
out the entire feature channel. Furthermore, by randomly dropping out several
feature channels, our method can well simulate the occlusion of larger area.
The proposed LCD can encourage its succeeding layers to minimize the
intra-class feature variance caused by occlusions, thus leading to improved
robustness against occlusion. In addition, we design an auxiliary spatial
attention module by learning a channel-wise attention vector to reweight the
feature channels, which improves the contributions of non-occluded regions.
Extensive experiments on various benchmarks show that the proposed method
outperforms state-of-the-art methods with a remarkable improvement.


  The doubly charmed baryon $\Xi_{cc}^{++}$ has been observed by LHCb through
the non-leptonic decay modes of
$\Xi_{cc}^{++}\to\Lambda_{c}^{+}K^{-}\pi^{+}\pi^{+}$ and $\Xi_{c}^{+}\pi^{+}$
in 2017. After that, the experimentalists turn their attention to finding other
doubly charmed baryons $\Xi_{cc}^{+}$ and $\Omega_{cc}^{+}$. In this work, we
investigate the nonleptonic weak decays of doubly charmed baryons ${\cal
B}_{cc}\to{\cal B}_{c}P$, where ${\cal B}_{cc}$ denotes the doubly charmed
baryons $(\Xi_{cc}^{++},\Xi_{cc}^{+},\Omega_{cc}^{+})$, ${\cal B}_{c}$
represents the singly charmed baryons $({\cal B}_{\bar{3}},{\cal B}_{6})$ and
$P$ is the light pseudoscalar. For these non-leptonic decay modes, their
short-distance contributions can be accurately estimated in theoretical
calculations. However, dealing with the long-distance contributions for
final-state-interaction effects is challenging. To address this, we use the
rescattering mechanism to calculate the long-distance contributions and first
derive the whole hadronic loop contributions for these two-body nonleptonic
decays of doubly charmed baryons. Then the decay widths and branching ratios of
the 45 nonleptonic decays of doubly charmed baryon are predicted. Among that,
the ratio of the branching ratios ${\cal RB}=\frac{{\cal
B}(\Xi_{cc}^{++}\to\Xi_{c}^{\prime+}\pi^{+})}{{\cal
B}(\Xi_{cc}^{++}\to\Xi_{c}^{+}\pi^{+})}=1.15\pm0.45$ is consistent with the
experimental results within statistical errors.


  Two coupled two-level systems placed under external time-dependent magnetic
fields are modeled by a general Hamiltonian endowed with a symmetry that
enables us to reduce the total dynamics into two independent two-dimensional
sub-dynamics. Each of the sub-dynamics is shown to be brought into an exactly
solvable form by appropriately engineering the magnetic fields and thus we
obtain an exact time evolution of the compound system. Several physically
relevant and interesting quantities are evaluated exactly to disclose
intriguing phenomena in such a system.


  We extend the numerical renormalization-group method to treat Bose-Fermi
Kondo models (BFKMs) describing a local moment coupled both to a conduction
band and to a dissipative bosonic bath representing, e.g., lattice or spin
collective excitations of the environment. We apply the method to the
Ising-symmetry BFKM with a structureless band and a bath spectral function
\eta(\omega)\propto \omega^s. The method is valid for all bath exponents s and
all temperatures T. For 0<s<1, the range of interest in the context of
heavy-fermion quantum criticality, an interacting critical point, characterized
by hyperscaling of exponents and \omega/T-scaling, describes a continuous
quantum phase transition between Kondo-screened and localized phases. For Ohmic
dissipation s=1, where the model is relevant to certain dissipative mesoscopic
qubit devices, the transition is found to be Kosterlitz-Thouless-like. In both
regimes the impurity spectral function for the corresponding Anderson model
shows clearly the collapse of the Kondo resonance at the transition. Connection
is made to other recent results for the BFKM and the spin-boson model.


  The muon intensity and angular distribution in the shallow-underground
laboratory Felsenkeller in Dresden, Germany have been studied using a portable
muon detector based on the close cathode chamber design. Data has been taken at
four positions in Felsenkeller tunnels VIII and IX, where a new 5 MV
underground ion accelerator is being installed, and in addition at four
positions in Felsenkeller tunnel IV, which hosts a low-radioactivity counting
facility. At each of the eight positions studied, seven different orientations
of the detector were used to compile a map of the upper hemisphere with
0.85{\deg} angular resolution. The muon intensity is found to be suppressed by
a factor of 40 due to the 45 m thick rock overburden, corresponding to 140
meters water equivalent. The angular data are matched by two different
simulations taking into account the known geodetic features of the terrain:
First, simply by determining the cutoff energy using the projected slant depth
in rock and the known muon energy spectrum, and second, in a Geant4 simulation
propagating the muons through a column of rock equal to the known slant depth.
The present data are instrumental for studying muon-induced effects at these
depths and also in the planning of an active veto for accelerator-based
underground nuclear astrophysics experiments.


  Large Language Models (LLMs) have showcased remarkable impacts across a wide
spectrum of natural language processing tasks. Fine-tuning these pre-trained
models on downstream datasets provides further significant performance gains,
but this process has been challenging due to its extraordinary resource
requirements. To this end, existing efforts focus on parameter-efficient
fine-tuning, which, unfortunately, fail to capitalize on the powerful potential
of full-parameter fine-tuning. In this work, we propose QFT, a novel Quantized
Full-parameter Tuning framework for LLMs that enables memory-efficient
fine-tuning without harming performance. Our framework incorporates two novel
ideas: (i) we adopt the efficient Lion optimizer, which only keeps track of the
momentum and has consistent update magnitudes for each parameter, an inherent
advantage for robust quantization; and (ii) we quantize all model states and
store them as integer values, and present a gradient flow and parameter update
scheme for the quantized weights. As a result, QFT reduces the model state
memory to 21% of the standard solution while achieving comparable performance,
e.g., tuning a LLaMA-7B model requires only <30GB of memory, satisfied by a
single A6000 GPU.


  We propose that stress can decrease the internal friction of amorphous
solids, either by increasing the potential barriers of defects, thus reducing
their tunneling and thermal activation that produce loss, or by decreasing the
coupling between defects and phonons. This stress can be from impurities,
atomic bonding constraints, or externally applied stress. Externally applied
stress also reduces mechanical loss through dissipation dilution. Our results
are consistent with the experiments, and predict that stress could
substantially reduce dielectric loss and increase the thermal conductivity.


  The integration of large language models into public transit systems
represents a significant advancement in urban transportation management and
passenger experience. This study examines the impact of LLMs within San
Antonio's public transit system, leveraging their capabilities in natural
language processing, data analysis, and real time communication. By utilizing
GTFS and other public transportation information, the research highlights the
transformative potential of LLMs in enhancing route planning, reducing wait
times, and providing personalized travel assistance. Our case study is the city
of San Antonio as part of a project aiming to demonstrate how LLMs can optimize
resource allocation, improve passenger satisfaction, and support decision
making processes in transit management. We evaluated LLM responses to questions
related to both information retrieval and also understanding. Ultimately, we
believe that the adoption of LLMs in public transit systems can lead to more
efficient, responsive, and user-friendly transportation networks, providing a
model for other cities to follow.


  Very important research, with both theoretical and experimental results in
the advancement of physical models that explain the MR effect, try to keep the
MRF formulation as simple as possible, usually with only two components: a
magnetic dispersed phase and a carrier liquid. Many patents of MRF include
three or four components, such as some surfactant and thixotropic additives. In
order to formulate a good and reliable MRF for different applications such as
MR shock absorbers, clutches, brakes, the MRF redispersibility is a challenge,
but mandatory, key property for out of lab real world applications. This
chapter, we shows how to measure and evaluate the MRF redispersibility.


  This work addresses the optimal control of multibody systems being actuated
with control forces in order to find a dynamically feasible minimum-energy
trajectory of the system. The optimal control problem and its constraints are
integrated in a discrete version of the equation of motion allowing the
minimization of system energy with respect to a discrete state and control
trajectory. The work is centred on a specific type of open-chain multibody
system, with strong local propensity, where the overall system kinematics is
described essentially by the torsion around the links that connect rigid
bodies. The coupling between the rigid body motion, and the optimal
conformation is described as an elastic band of replicas of the original system
with different conformations. The band forces are used to control system's
motion directly, reflecting the influence of the system energy field on its
conformation, using for that the Nudged-Elastic Band method. Here the equation
of motion of the multibody grid are solved by using the augmented Lagrangean
method. In this context, if a feasible minimum-energy trajectory of the
original system exists it is a stationary state of the extended system. This
approach is applied to the folding of a single chain protein.


  In the present work, the triply heavy tetraquarks states $QQ\bar{Q}\bar{q}$
with $Q=(c, b)$ and $q=(u, d, s)$ with all possible quantum numbers are
systematically investigated in the framework of the chiral quark model with the
resonance ground method. Two kinds of structures, including the meson-meson
configuration (the color-singlet channels and the hidden-color channels) and
the diquark-antidiquark configuration (the color sextet-antisextet and the
color triplet-antitriplet), are considered. In the considered system, several
bound states are obtained for the $cc\bar{c}\bar{q^{'}}$,
$bb\bar{c}\bar{q^{'}}$ and $bc\bar{c}\bar{q}$ tetraquarks. From the present
estimations, we find that the coupled channel effect is of great significance
for forming the below thresholds tetraquark states, which are stable for strong
decays.


  GPU (graphics processing unit) has been used for many data-intensive
applications. Among them, deep learning systems are one of the most important
consumer systems for GPU nowadays. As deep learning applications impose deeper
and larger models in order to achieve higher accuracy, memory management
becomes an important research topic for deep learning systems, given that GPU
has limited memory size. Many approaches have been proposed towards this issue,
e.g., model compression and memory swapping. However, they either degrade the
model accuracy or require a lot of manual intervention. In this paper, we
propose two orthogonal approaches to reduce the memory cost from the system
perspective. Our approaches are transparent to the models, and thus do not
affect the model accuracy. They are achieved by exploiting the iterative nature
of the training algorithm of deep learning to derive the lifetime and
read/write order of all variables. With the lifetime semantics, we are able to
implement a memory pool with minimal fragments. However, the optimization
problem is NP-complete. We propose a heuristic algorithm that reduces up to
13.3% of memory compared with Nvidia's default memory pool with equal time
complexity. With the read/write semantics, the variables that are not in use
can be swapped out from GPU to CPU to reduce the memory footprint. We propose
multiple swapping strategies to automatically decide which variable to swap and
when to swap out (in), which reduces the memory cost by up to 34.2% without
communication overhead.


  Seas of videos are uploaded daily with the popularity of social channels;
thus, retrieving the most related video contents with user textual queries
plays a more crucial role. Most methods consider only one joint embedding space
between global visual and textual features without considering the local
structures of each modality. Some other approaches consider multiple embedding
spaces consisting of global and local features separately, ignoring rich
inter-modality correlations.
  We propose a novel mixture-of-expert transformer RoME that disentangles the
text and the video into three levels; the roles of spatial contexts, temporal
contexts, and object contexts. We utilize a transformer-based attention
mechanism to fully exploit visual and text embeddings at both global and local
levels with mixture-of-experts for considering inter-modalities and structures'
correlations. The results indicate that our method outperforms the
state-of-the-art methods on the YouCook2 and MSR-VTT datasets, given the same
visual backbone without pre-training. Finally, we conducted extensive ablation
studies to elucidate our design choices.


  Administering examinations both in public and private academic institutions
can be tedious and unmanageable. The multiplicity of problems affecting the
conduct of departmental and periodical examination can be greatly reduced by
automating the examination process. The purpose of this action research is to
provide an alternative technical solution in administering test through the use
of Examination System. This software application can facilitate a plenitude of
examinees for different subjects that implements a random questioning technique
and can generate item analysis and test results. The Departmental and
Periodical Examination System was developed using Visual Basic language. The
software modules were tested using the functional testing method. Using the
criteria and metrics of ISO 9126 software quality model, the system was
evaluated by a group of students, teachers, school administrators and
information technology professionals and has received an overall weighted mean
of 4.56585 with an excellent descriptive rating. Therefore, the performance of
the application software provides solution that can surmount the gargantuan
problems of test administration and post-examination issues and performs all
the operations specified in the objectives.


  Machine learning (ML) practitioners are increasingly tasked with developing
models that are aligned with non-technical experts' values and goals. However,
there has been insufficient consideration on how practitioners should translate
domain expertise into ML updates. In this paper, we consider how to capture
interactions between practitioners and experts systematically. We devise a
taxonomy to match expert feedback types with practitioner updates. A
practitioner may receive feedback from an expert at the observation- or
domain-level, and convert this feedback into updates to the dataset, loss
function, or parameter space. We review existing work from ML and
human-computer interaction to describe this feedback-update taxonomy, and
highlight the insufficient consideration given to incorporating feedback from
non-technical experts. We end with a set of open questions that naturally arise
from our proposed taxonomy and subsequent survey.


  We employ three-dimensional state of the art magnetohydrodynamic models of
the early solar wind and heliosphere and a two-dimensional model for cosmic ray
transport to investigate the cosmic ray spectrum and flux near the Archean
Earth. We assess how sensitive the cosmic ray spectrum is to changes in the
sunspot placement and magnetic field strength, the large scale dipole magnetic
field strength, the wind ram pressure, and the Sun's rotation period. Overall,
our results confirm earlier work that suggested the Archean Earth would have
experienced a greatly reduced cosmic ray flux than is the case today. The
cosmic ray reduction for the early Sun is mainly due to the shorter solar
rotation period and tighter winding of the Parker spiral, and to the different
surface distribution of the more active solar magnetic field. These effects
lead to a global reduction of the cosmic ray flux at 1AU by up to two orders of
magnitude or more. Variations in the sunspot magnetic field have more effect on
the flux than variations in the dipole field component. The wind ram pressure
affects the cosmic ray flux through its influence on the size of the
heliosphere via the pressure balance with the ambient interstellar medium.
Variations in the interstellar medium pressure experienced by the solar system
in orbit through Galaxy could lead to order of magnitude changes in the cosmic
ray flux at Earth on timescales of a few million years.


  We present the results of photometric surveys for stellar rotation in the
Hyades and in Praesepe, using data obtained as part of the SuperWASP
exoplanetary transit-search programme.We determined accurate rotation periods
for more than 120 sources whose cluster membership was confirmed by common
proper motion and colour-magnitude fits to the clusters' isochrones. This
allowed us to determine the effect of magnetic braking on a wide range of
spectral types for expected ages of ~600Myr for the Hyades and Praesepe. Both
clusters show a tight and nearly linear relation between J - Ks colour and
rotation period in the F,G and K spectral range. This confirms that loss of
angular momentum was significant enough that stars with strongly different
initial rotation rates have converged to the same rotation period for a given
mass, by the age of Hyades and Praesepe. In the case of the Hyades our
colour-period sequence extends well into the M dwarf regime and shows a steep
increase in the scatter of the colour- period relation, with identification of
numerous rapid rotators from 0.5SM down to the lowest masses probed by our
survey (0.25SM). This provides crucial constraints on the rotational braking
timescales and further clears the way to use gyrochronology as an accurate age
measurement tool for main-sequence stars.


  We use pseudospectral direct numerical simulations (DNSs) to solve the
three-dimensional (3D) Hall-Vinen-Bekharevich-Khalatnikov (HVBK) model of
superfluid Helium. We then explore the statistical properties of inertial
particles, in both coflow and counterflow superfluid turbulence (ST) in the 3D
HVBK system; particle motion is governed by a generalization of the
Maxey-Riley-Gatignol equations. We first characterize the anisotropy of
counterflow ST by showing that there exist large vortical columns. The light
particles show confined motion as they are attracted towards these columns and
they form large clusters; by contrast, heavy particles are expelled from these
vortical regions. We characterise the statistics of such inertial particles in
3D HVBK ST: (1) The mean angle $\Theta(\tau)$, between particle positions,
separated by the time lag $\tau$, exhibits two different scaling regions in (a)
dissipation and (b) inertial ranges, for different values of the parameters in
our model; in particular, the value of $\Theta(\tau)$, at large $\tau$, depends
on the magnitude of ${\bf U}_{ns}$. (2) The irreversibility of 3D HVBK
turbulence is quantified by computing the statistics of energy increments for
inertial particles. (3) The probability distribution function (PDF) of energy
increments is of direct relevance to recent experimental studies of
irreversibility in superfluid turbulence; we find, in agreement with these
experiments, that, for counterflow ST, the skewness of this PDF is less
pronounced than its counterparts for coflow ST or for classical-fluid
turbulence.


  Pionless effective field theory in a finite volume (FVEFT$_{\pi\!/}$) is
investigated as a framework for the analysis of multi-nucleon spectra and
matrix elements calculated in lattice QCD (LQCD). By combining FVEFT$_{\pi\!/}$
with the stochastic variational method, the spectra of nuclei with atomic
number $A\in\{2,3\}$ are matched to existing finite-volume LQCD calculations at
heavier-than-physical quark masses corresponding to a pion mass $m_\pi=806$
MeV, thereby enabling infinite-volume binding energies to be determined using
infinite-volume variational calculations. Based on the variational
wavefunctions that are constructed in this approach, the finite-volume matrix
elements of various local operators are computed in FVEFT$_{\pi\!/}$ and
matched to LQCD calculations of the corresponding QCD operators in the same
volume, thereby determining the relevant one and two-body EFT counterterms and
enabling an extrapolation of the LQCD matrix elements to infinite volume. As
examples, the scalar, tensor, and axial matrix elements are considered, as well
as the magnetic moments and the isovector longitudinal momentum fraction.


  Non-adiabatic pumping of discrete charges, realized by a dynamical quantum
dot in an AlGaAs/GaAs heterostructure, is studied under influence of a
perpendicular magnetic field. Application of an oscillating voltage in the
GHz-range to one of two top gates, crossing a narrow wire and confining a
quantum dot, leads to quantized pumped current plateaus in the gate
characteristics. The regime of pumping one single electron is traced back to
the diverse tunneling processes into and out-of the dot. Extending the theory
to multiple electrons allows to investigate conveniently the pumping
characteristics in an applied magnetic field. In this way, a qualitatively
different behavior between pumping even or odd numbers of electrons is
extracted.


  Quantum computing is a nascent technology, which is advancing rapidly. There
is a long history of research into using computers for music. Nowadays
computers are absolutely essential for the music economy. Thus, it is very
likely that quantum computers will impact the music industry in time to come.
This chapter lays the foundations of the new field of 'Quantum Computer Music'.
It begins with an introduction to algorithmic computer music and methods to
program computers to generate music, such as Markov chains and random walks.
Then, it presents quantum computing versions of those methods. The discussions
are supported by detailed explanations of quantum computing concepts and
walk-through examples. A bespoke generative music algorithm is presented, the
Basak-Miranda algorithm, which leverages a property of quantum mechanics known
as constructive and destructive interference to operate a musical Markov chain.
An Appendix introducing the fundamentals of quantum computing deemed necessary
to understand the chapter and a link to access Jupyter Notebooks with examples
are also provided.


  An S-adic expansion of an infinite word is a way of writing it as the limit
of an infinite product of substitutions (i.e., morphisms of a free monoid).
Such a description is related to continued fraction expansions of numbers and
vectors. A fundamental example of this relation is between Sturmian sequences
and regular continued fractions. We study S-adic words from different
perspectives, namely word combinatorics, ergodic theory, and Diophantine
approximation, by stressing the parallel with continued fraction expansions.


  We have reduced the data taken with the Spectral and Photometric Imaging
Receiver (SPIRE) photometer on board the Herschel Space Observatory in the
Science Demonstration Phase (SDP) of the Herschel Astrophysical Terahertz Large
Area Survey (H-ATLAS). We describe the data reduction, which poses specific
challenges, both because of the sheer size of the data, and because only two
scans are made for each region. We implement effective solutions to process the
bolometric timelines into maps, and show that correlations among detectors are
negligible, and that the photometer is stable on time scales up to 250 s. This
is longer than the time the telescope takes to cross the observed sky region,
and it allows us to use naive binning methods for an optimal reconstruction of
the sky emission. The maps have equal contribution of confusion and white
instrumental noise, and the latter is estimated to 5.3, 6.4, and 6.7 mJy/beam
(1-{\sigma}), at 250, 350, and 500 \mu{m}, respectively. This pipeline is used
to reduce other H-ATLAS observations, as they became available, and we discuss
how it can be used with the optimal map maker implemented in the Herschel
Interactive Processing Environment (HIPE), to improve computational efficiency
and stability. The SDP dataset is available from http://www.h-atlas.org/.


  We introduce the concept of monogamy deficit for quantum correlation by
combining together two types of monogamy inequalities depending on different
measurement sides. For tripartite pure state, we demonstrate a relation which
connects two types of monogamy inequalities for quantum discord and provide the
difference between them. By using this relation, we obtain an unified physical
interpretation for these two monogamy deficit. In addition, we find an
interesting fact that there is a general monogamy condition for several quantum
correlations for tripartite pure states. We then provide a necessary and
sufficient condition for the establishment of one kind of monogamy inequality
for tripartite mixed state and generalize it to multipartite quantum state.


  Probabilistic shaping of quadrature amplitude modulation (QAM) is used to
enhance the sensitivity of an optical communication system. Sensitivity gains
of 0.43 dB and 0.8 dB are demonstrated in back-to-back experiments by shaping
of 16QAM and 64QAM, respectively. Further, numerical simulations are used to
prove the robustness of probabilistic shaping to a mismatch between the
constellation used and the signal-to-noise ratio (SNR) of the channel. It is
found that, accepting a 0.1 dB SNR penalty, only four shaping distributions are
required to support these gains for 64QAM.


  In this paper, we shall first establish the theory of bivariate Revuz
correspondence of positive additive functionals under a semi-Dirichlet form,
which is associated with a right Markov process $X$ satisfying the sector
condition but without duality. We extend most of the classical results about
the bivariate Revuz measures under the duality assumptions to the case of
semi-Dirichlet forms. As the main results of this paper, we prove that for any
exact multiplicative functional $M$ of $X$, the subprocess $X^M$ of $X$ killed
by $M$ also satisfies the sector condition and we then characterize the
semi-Dirichlet form associated with $X^M$ by using the bivariate Revuz measure,
which extends the classical Feynman-Kac formula.


  In this paper, we propose a new framework to construct confidence sets for a
$d$-dimensional unknown sparse parameter $\theta$ under the normal mean model
$X\sim N(\theta,\sigma^2I)$. A key feature of the proposed confidence set is
its capability to account for the sparsity of $\theta$, thus named as {\em
sparse} confidence set. This is in sharp contrast with the classical methods,
such as Bonferroni confidence intervals and other resampling based procedures,
where the sparsity of $\theta$ is often ignored. Specifically, we require the
desired sparse confidence set to satisfy the following two conditions: (i)
uniformly over the parameter space, the coverage probability for $\theta$ is
above a pre-specified level; (ii) there exists a random subset $S$ of
$\{1,...,d\}$ such that $S$ guarantees the pre-specified true negative rate
(TNR) for detecting nonzero $\theta_j$'s. To exploit the sparsity of $\theta$,
we define that the confidence interval for $\theta_j$ degenerates to a single
point 0 for any $j\notin S$. Under this new framework, we first consider
whether there exist sparse confidence sets that satisfy the above two
conditions. To address this question, we establish a non-asymptotic minimax
lower bound for the non-coverage probability over a suitable class of sparse
confidence sets. The lower bound deciphers the role of sparsity and minimum
signal-to-noise ratio (SNR) in the construction of sparse confidence sets.
Furthermore, under suitable conditions on the SNR, a two-stage procedure is
proposed to construct a sparse confidence set. To evaluate the optimality, the
proposed sparse confidence set is shown to attain a minimax lower bound of some
properly defined risk function up to a constant factor. Finally, we develop an
adaptive procedure to the unknown sparsity and SNR. Numerical studies are
conducted to verify the theoretical results.


  The recently developed quantum mechanical string+${}^3P_0$ model of polarized
quark fragmentation with pseudoscalar and vector meson production has been
implemented in a stand alone Monte Carlo program. The simulation enables the
detailed study of the quark spin dependent effects in the fragmentation process
such as the transverse spin asymmetries. In particular, it allows to
investigate the effects of the production and decay of vector mesons on the
Collins and dihadron asymmetries of the observed hadrons, and to study the
still unknown Collins effect for vector mesons. The main results of the Monte
Carlo simulations obtained so far are summarized and the comparison with the
existing experimental data on SIDIS and $e^+e^-$ annihilation to hadrons
processes are presented.


  Semi-supervised learning (SSL) has achieved great success in leveraging a
large amount of unlabeled data to learn a promising classifier. A popular
approach is pseudo-labeling that generates pseudo labels only for those
unlabeled data with high-confidence predictions. As for the low-confidence
ones, existing methods often simply discard them because these unreliable
pseudo labels may mislead the model. Nevertheless, we highlight that these data
with low-confidence pseudo labels can be still beneficial to the training
process. Specifically, although the class with the highest probability in the
prediction is unreliable, we can assume that this sample is very unlikely to
belong to the classes with the lowest probabilities. In this way, these data
can be also very informative if we can effectively exploit these complementary
labels, i.e., the classes that a sample does not belong to. Inspired by this,
we propose a novel Contrastive Complementary Labeling (CCL) method that
constructs a large number of reliable negative pairs based on the complementary
labels and adopts contrastive learning to make use of all the unlabeled data.
Extensive experiments demonstrate that CCL significantly improves the
performance on top of existing methods. More critically, our CCL is
particularly effective under the label-scarce settings. For example, we yield
an improvement of 2.43% over FixMatch on CIFAR-10 only with 40 labeled data.


  This comprehensive survey serves as an indispensable resource for researchers
embarking on the journey of fake news detection. By highlighting the pivotal
role of dataset quality and diversity, it underscores the significance of these
elements in the effectiveness and robustness of detection models. The survey
meticulously outlines the key features of datasets, various labeling systems
employed, and prevalent biases that can impact model performance. Additionally,
it addresses critical ethical issues and best practices, offering a thorough
overview of the current state of available datasets. Our contribution to this
field is further enriched by the provision of GitHub repository, which
consolidates publicly accessible datasets into a single, user-friendly portal.
This repository is designed to facilitate and stimulate further research and
development efforts aimed at combating the pervasive issue of fake news.


  For electron optics in graphene, the propagation effect has so far been the
only physical mechanism available. The resulting electron-optics-based
components are large in size and operate at low temperatures to avoid violating
the ballistic transport limits. In this paper, Dirac fermion metasurfaces,
electronic counterparts of optical metasurfaces, are introduced for graphene
electronics. By a metasurface, formally a linear array of gate-bias-controlled
circular quantum dots, the wavefront of electron beams can be shaped within a
one-quantum-dot-diameter distance, far below the ballistic limits at room
temperature. This provides opportunities to create electron-optics-based
devices that operate under ambient conditions. Moreover, unlike optical
metasurfaces, Dirac fermion metasurfaces have near-perfect operating
efficiencies and their high tunability allows for free and fast switching among
functionalities. The concept of metasurface electron optics might open up a
promising avenue for improving the performance of quantum devices in Dirac
fermion materials.


  In this paper I will review my work on disordered systems -spin glass model
with two body and $p>2$ body interactions- with long but finite interaction
range $R$. I will describe the relation of these model with Mean Field Theory
in the Kac limit and some attempts to go beyond mean field.


  Lutefisk is a traditional Norwegian Christmas dish, made of dry cod soaked in
a lye solution before re-hydrated. We report measurements of the tissue
softness of cooked Lutefisk. Surprisingly, we find that the elastic modulus
does not seem to depend heavily on cooking time, cooking temperature and the
amount of salt, but depends instead mainly on the size of the fish fillets and
the period of fishing. Although the salting and cooking of the Lutefisk affects
strongly the visual aspect of the fish fillets, these changes are not
measurable with a rheometer.


  We deal with the representation theory of quantum groups and Hecke algebras
at roots of unity. We relate the philosophy of Andersen, Jantzen and Soergel on
graded translated functors to the Lascoux, Leclerc and Thibon-algorithm. This
goes via the Murphy standard basis theory and the idempotents coming from the
Murphy-Jucys operators. Our results lead to a guess on a tilting algorithm
outside the lowest $p^2$ alcove, which at least in the $SL_2$-case coincides
with Erdmann's results.


  Stochastic epidemic models which incorporate interactions between space and
human mobility are a key tool to inform prioritisation of outbreak control to
appropriate locations. However, methods for fitting such models to
national-level population data are currently unfit for purpose due to the
difficulty of marginalising over high-dimensional, highly-correlated censored
epidemiological event data. Here we propose a new Bayesian MCMC approach to
inference on a spatially-explicit stochastic SEIR meta-population model, using
a suite of novel model-informed Metropolis-Hastings samplers. We apply this
method to UK COVID-19 case data, showing real-time spatial results that were
used to inform UK policy during the pandemic.


  We have confirmed numerically that a subcritical laminar-turbulence
transition that belongs to directed percolation (DP) universality class occurs
in a purely two-dimensional (2D) simple Navier-Stokes (NS) flow without any
walls. The flow is called (extended) Kolmogorov flow which is governed by 2D NS
equation in a doubly periodic box with a linear drag and a finite flow rate in
the direction in which Galilean invariance is broken. We examine the mechanism
of DP class transition focusing on the role of the additional control
parameters: the drag coefficient and the flow rate. The drag kills coherent
active structures of the system size. The flow rate interferes the growth of
weak disturbances. These two effects control two essential and intrinsic
elements of an absorbing state phase transition, i.e., the existence of an
absorbing state and the locality of active dynamical structures. We also
discuss what physically corresponds to the additional parameters in general
flow.


  For any graph G with n edges, the spanning subgraphs and the orientations of
G are both counted by the evaluation T_G(2,2)=2^n of its Tutte polynomial. We
define a bijection $\Phi$ between spanning subgraphs and orientations and
explore its enumerative consequences regarding the Tutte polynomial. The
bijection $\Phi$ is closely related to a recent characterization of the Tutte
polynomial relying on a combinatorial embedding of the graph G, that is, on a
choice of cyclic order of the edges around each vertex. Among other results, we
obtain a combinatorial interpretation for each of the evaluations T_G(i,j),
0\leq i,j \leq 2 of the Tutte polynomial in terms of orientations. The strength
of our approach is to derive all these interpretations by specializing the
bijection $\Phi$ in various ways. For instance, we obtain a bijection between
the connected subgraphs of G (counted by T_G(1,2)) and the root-connected
orientations. We also obtain a bijection between the forests (counted by
T_G(2,1)) and outdegree sequences which specializes into a bijection between
spanning trees (counted by T_G(1,1)) and root-connected outdegree sequences. We
also define a bijection between spanning trees and recurrent configurations of
the sandpile model. Combining our results we obtain a bijection between
recurrent configurations and root-connected outdegree sequences which leaves
the configurations at level 0 unchanged.


  Despite considerable progress in maternal healthcare, maternal and perinatal
deaths remain high in low-to-middle income countries. Fetal ultrasound is an
important component of antenatal care, but shortage of adequately trained
healthcare workers has limited its adoption. We developed and validated an
artificial intelligence (AI) system that uses novice-acquired "blind sweep"
ultrasound videos to estimate gestational age (GA) and fetal malpresentation.
We further addressed obstacles that may be encountered in low-resourced
settings. Using a simplified sweep protocol with real-time AI feedback on sweep
quality, we have demonstrated the generalization of model performance to
minimally trained novice ultrasound operators using low cost ultrasound devices
with on-device AI integration. The GA model was non-inferior to standard fetal
biometry estimates with as few as two sweeps, and the fetal malpresentation
model had high AUC-ROCs across operators and devices. Our AI models have the
potential to assist in upleveling the capabilities of lightly trained
ultrasound operators in low resource settings.


  In density matrix theory, entanglement is monogamous. However, we show that
qubits can be arbitrarily entangled in a different, recently constructed model
of qubit entanglement [arXiv:1907.11805]. We illustrate the differences between
these two models, analyse how the density matrix property of monogamy of
entanglement originates in assumptions of classical correlations in the
construction of that model, and explain the counterexample to monogamy in the
alternative model. We conclude that monogamy of entanglement is a theoretical
assumption, not necessarily a physical property, and discuss how contemporary
theory relies on that assumption. The properties of entanglement entropy are
very different in the two models; a priori, the entropy in the alternative
model is classical.


  We present a rigorous and relatively fast method for the computation of the
"complexity" of a natural number (sequence A005245), and answer some "old and
new" questions related to the question in the title of this note. We also
extend the known terms of the related sequence A005520.
  We put this paper in the arXiv only for possible reference. This note was
written in 2008. It is exactly the copy we have sent to Martin N. Fuller in
February 11, 2009 proposing to him to join us as co-author. The ensuing
collaboration resulted in a better program and many results about the
complexity. We have waited four years in the hope of contacting him again. In
the mean time Iraids et al. published arXiv:1203.6462 containing some of our
results here. Our program is similar to the one posted in the OEIS by Martin N.
Fuller, but its correctness is proved here for the first time.


  There has been significant progress in sensing, perception, and localization
for automated driving, However, due to the wide spectrum of traffic/road
structure scenarios and the long tail distribution of human driver behavior, it
has remained an open challenge for an intelligent vehicle to always know how to
make and execute the best decision on road given available sensing / perception
/ localization information. In this chapter, we talk about how artificial
intelligence and more specifically, reinforcement learning, can take advantage
of operational knowledge and safety reflex to make strategical and tactical
decisions. We discuss some challenging problems related to the robustness of
reinforcement learning solutions and their implications to the practical design
of driving strategies for autonomous vehicles. We focus on automated driving on
highway and the integration of reinforcement learning, vehicle motion control,
and control barrier function, leading to a robust AI driving strategy that can
learn and adapt safely.


  In recent years, it has become possible to measure not only the magnitude of
the electric ($G_E$) and magnetic ($G_M$) form factors of spin $\frac{1}{2}$
baryons, but also to measure the relative phases of those quantities in the
timelike kinematic region. Aiming to interpret present $|G_E/G_M|$ data on
hyperons of the baryon octet, as well as to predict future data, we present
model calculations of that ratio for large invariant 4-momentum square $q^2$ in
the timelike region ($q^2>0$). Without any further parameter fitting, we extend
to the timelike region a covariant quark model previously developed to describe
the kinematic spacelike region ($q^2 \le 0$) of the baryon octet form factors.
The model takes into account both the effects of valence quarks and the
excitations of the meson cloud which dresses the baryons. We report here that
our results are in good agreement with very recent data from BESIII, within the
uncertainty limits of our model. We also make new predictions which may be
tested in the near future, when the range of the present measurements is
expanded to the 20--50 GeV$^2$ region.