  We consider Wilson loop observables for Chern-Simons theory at large N and
its topological string dual and extend the previous checks for this duality to
the case of links. We find an interesting structure involving
representation/spin degeneracy of branes ending on branes which features in the
large N dual description of Chern-Simons theory. This leads to a refinement of
the integer invariants for links and knots. We illustrate our results with
explicit computations on the Chern-Simons side.


  We propose that the leptonic cosmic ray signals seen by PAMELA and ATIC
result from the annihilation or decay of dark matter particles via states of a
leptonic Higgs doublet to $\tau$ leptons, linking cosmic ray signals of dark
matter to LHC signals of the Higgs sector. The states of the leptonic Higgs
doublet are lighter than about 200 GeV, yielding large $\bar{\tau} \tau$ and
$\bar{\tau} \tau \bar{\tau} \tau$ event rates at the LHC. Simple models are
given for the dark matter particle and its interactions with the leptonic
Higgs, for cosmic ray signals arising from both annihilations and decays in the
galactic halo. For the case of annihilations, cosmic photon and neutrino
signals are on the verge of discovery.


  Can liquid-like and gas-like states be distinguished beyond the critical
point, where the liquid-gas phase transition no longer exists and
conventionally only a single supercritical fluid phase is defined? Recent
experiments and simulations report strong evidence of dynamical crossovers
above the critical temperature and pressure. Despite using different criteria,
many existing theoretical explanations consider a single crossover line
separating liquid-like and gas-like states in the supercritical fluid phase. We
argue that such a single-line scenario is inconsistent with the supercritical
behavior of the Ising model, which has two crossover lines due to its symmetry,
violating the universality principle of critical phenomena. To reconcile the
inconsistency, we define two thermodynamic crossover lines in supercritical
fluids as boundaries of liquid-like, indistinguishable, and gas-like states.
Near the critical point, the two crossover lines follow critical scalings with
exponents of the Ising universality class, supported by calculations of
theoretical models and analyses of experimental data from the standard
database. The upper line agrees with crossovers independently estimated from
the inelastic X-ray scattering data of supercritical argon, and from the
small-angle neutron scattering data of supercritical carbon dioxide. The lower
line is verified by the equation of states for the compressibility factor. This
work provides a fundamental framework for understanding supercritical physics
in general phase transitions.


  We introduce the notion of kinetic maximal $L^2$-regularity with temporal
weights for the (fractional) Kolmogorov equation. In particular, we determine
the function spaces for the inhomogeneity and the initial value which
characterize the regularity of solutions to the fractional Kolmogorov equation
in terms of fractional anisotropic Sobolev spaces. It is shown that solutions
of the homogeneous (fractional) Kolmogorov equation define a semi-flow in a
suitable function space and the property of instantaneous regularization is
investigated.


  Using Monte Carlo simulations, we study the hysteresis in unzipping of a
double stranded block copolymer DNA with $-A_n B_n-$ repeat units. Here $A$ and
$B$ represent two different types of base pairs having two- and three-bonds,
respectively, and $2n$ represents the number of such base pairs in a unit. The
end of the DNA are subjected to a time dependent periodic force with frequency
($\omega$) and amplitude ($g_0$) keeping the other end fixed. We find that the
equilibrium force-temperature phase diagram for the static force is independent
of the DNA sequence. For the periodic force case, the results are found to be
dependent on the block copolymer DNA sequence and also on the base pair type on
which the periodic force is acting. We observe hysteresis loops of various
shapes and sizes and obtain the scaling of loop area both at low and high
frequency regimes.


  We show that there is no uniform upper bound on |Out(Aut(A))| when A ranges
over all right-angled Artin groups. This is in contrast with the cases where A
is free or free abelian: for all n, Dyer-Formanek and Bridson-Vogtmann showed
that Out(Aut(F_n)) = 1, while Hua-Reiner showed |Out(Aut(Z^n)| = |Out(GL(n,Z))|
< 5. We also prove the analogous theorem for Out(Out(A)). We establish our
results by giving explicit examples; one useful tool is a new class of graphs
called austere graphs.


  Plasmonic nano-antennas have proven the outstanding ability of sensing
chemical and physical processes down to the nano-meter scale. Sensing is
usually achieved within the highly confined optical fields generated resonantly
by the nano-antennas, i.e. in contact to the nano-structures. In these paper,
We demonstrate the sensing capability of nano-antennas to their larger scale
environment, well beyond their plasmonic confinement volume, leading to the
concept of 'remote' (non contact) sensing on the nano-meter scale. On the basis
of a bowtie-aperture nano-antenna (BNA) integrated at the apex of a SNOM fiber
tip, we introduce an ultra-compact, move-able and background-free optical
nano-sensor for the remote sensing of a silicon surface (up to distance of 300
nm). Sensitivity of the BNA to its large scale environment is high enough to
expect the monitoring and control of the spacing between the nano-antenna and a
silicon surface with sub-nano-meter accuracy. This work paves the way towards a
new class of nano-positionning technique, based on nano-antenna resonance
monitoring, that are alternative to nano-mechanical and optical
interference-based devices.


  We prove that a model atom having one bound state will be fully ionized by a
time periodic potential of arbitrary strength r and frequency omega. Starting
with the system in the bound state, the survival probability is for small r
given by exp(-Gamma t) for times of order GAMMA^(-1)~r^(-2n) where n is the
minimum number of 'photons' required for ionization (with large modifications
at resonances). For late times the decay is as t^(-3) with the power law
modulated by oscillations. As r increases, the time over which there is
exponential decay becomes shorter and the power law behaviour starts earlier.
Results are for a parametrically excited one-dimensional system with zero-range
potential but comparison with analyticalworks and with experiments indicates
that many features are general.


  The design of new High Entropy Alloys that can achieve exceptional mechanical
properties is presently of great interest to the materials science community.
However, due to the difficulty of designing these alloys using traditional
methods, machine learning has recently emerged as an essential tool.
Particularly, the screening of candidate alloy compositions using surrogate
models has become a mainstay of materials design in recent years. Many of these
models use the atomic fractions of the alloying elements as inputs. However,
there are many possible representation schemes for encoding alloy compositions,
including both unstructured and structured variants. As the input features play
a critical role in determining surrogate model performance, we have
systematically compared these representation schemes on the basis of their
performance in single-task deep learning models and in transfer learning
scenarios. The results from these tests indicate that compared to the
unstructured and randomly ordered schemes, chemically meaningful arrangements
of elements within spatial representation schemes generally lead to better
models. However, we also observed that tree-based models using only the atomic
fractions as input were able to outperform these models in transfer learning.


  Earth-sized planets around nearby stars are being detected for the first time
by ground-based radial velocity and space-based transit surveys. This milestone
is opening the path towards the definition of missions able to directly detect
the light from these planets, with the identification of bio-signatures as one
of the main objectives. In that respect, both ESA and NASA have identified
nulling interferometry as one of the most promising techniques. The ability to
study distant planets will however depend on exozodiacal dust clouds
surrounding the target stars. In this paper, we assess the impact of
exozodiacal dust clouds on the performance of an infrared nulling
interferometer in the Emma X-array configuration. For the nominal mission
architecture with 2-m aperture telescopes, we found that point-symmetric
exozodiacal dust discs about 100 times denser than the solar zodiacal cloud can
be tolerated in order to survey at least 150 targets during the mission
lifetime. Considering modeled resonant structures created by an Earth-like
planet orbiting at 1 AU around a Sun-like star, we show that the tolerable dust
density for planet detection goes down to about 15 times the solar zodiacal
density for face-on systems and decreases with the disc inclination. The upper
limits on the tolerable exozodiacal dust density derived in this study must be
considered as rather pessimistic, but still give a realistic estimation of the
typical sensitivity that we will need to reach on exozodiacal discs in order to
prepare the scientific programme of future Earth-like planet characterisation
missions.


  We give a derivation of the Dirac operator on the noncommutative $2$-sphere
within the framework of the bosonic fuzzy sphere and define Connes' triple. It
turns out that there are two different types of spectra of the Dirac operator
and correspondingly there are two classes of quantized algebras. As a result we
obtain a new restriction on the Planck constant in Berezin's quantization. The
map to the local frame in noncommutative geometry is also discussed.


  We present detailed heat capacity measurements for Ba(Fe(1-x)Cox)2As2 and
Ba(Fe(1-x)Nix)2As2 single crystals in the vicinity of the superconducting
transitions. The specific heat jump at the superconducting transition
temperature (Tc), Delta Cp/Tc, changes by a factor ~ 10 across these series.
The Delta Cp/T$ vs. Tc data of this work (together with the literature data for
Ba(Fe0.939Co0.061)2As2, (Ba0.55K0.45)Fe2As2, and (Ba0.6K0.4)Fe2As2) scale well
to a single log-log plot over two orders of magnitude in Delta Cp/Tc and over
about an order of magnitude in Tc, giving Delta Cp/Tc ~ Tc^2.


  A classical measure of string comparison is given by the longest common
subsequence (LCS) problem on a pair of strings. We consider its generalisation,
called the semi-local LCS problem, which arises naturally in many
string-related problems. The semi-local LCS problem asks for the LCS scores for
each of the input strings against every substring of the other input string,
and for every prefix of each input string against every suffix of the other
input string. Such a comparison pattern provides a much more detailed picture
of string similarity than a single LCS score; it also arises naturally in many
string-related problems. In fact, the semi-local LCS problem turns out to be
fundamental for string comparison, providing a powerful and flexible
alternative to classical dynamic programming. It is especially useful when the
input to a string comparison problem may not be available all at once: for
example, comparison of dynamically changing strings; comparison of compressed
strings; parallel string comparison. The same approach can also be applied to
permutation strings, providing efficient solutions for local versions of the
longest increasing subsequence (LIS) problem, and for the problem of computing
a maximum clique in a circle graph. Furthermore, the semi-local LCS problem
turns out to have surprising connections in a few seemingly unrelated fields,
such as computational geometry and algebra of semigroups. This work is devoted
to exploring the structure of the semi-local LCS problem, its efficient
solutions, and its applications in string comparison and other related areas,
including computational molecular biology.


  In this paper, we prove that the global version of the ${\L}$ojasiewicz
gradient inequality holds for quadratic sphere constrained optimization problem
with exponent $\theta=\frac{3}{4}$. An example from Ting Kei Pong shows that
$\theta=\frac{3}{4}$ is tight. This is the first ${\L}$ojasiewicz gradient
inequality established for the sphere constrained optimization problem with a
linear term.


  In this Letter, we present the results of transient thermopower experiments,
performed at room temperature on yttrium iron garnet/platinum bilayers. Upon
application of a time-varying thermal gradient, we observe a characteristic
low-pass frequency response of the ensuing thermopower voltage with cutoff
frequencies of up to 37 MHz. We interpret our results in terms of the spin
Seebeck effect, and argue that small wavevector magnons are of minor importance
for the spin Seebeck effect in our thin film hybrid structures.


  In this paper we firstly demonstrate step by step that the factorization
hypothesis is valid at the next-to-leading order (NLO) for the exclusive
process $\rho \gamma^{\star} \to \rho$ by employing the collinear factorization
approach, and then extend this proof to the case of the $k_T$ factorization by
taking into account the transversal momentum of the light external quark
(anti-quark) lines in the $\rho$ meson. At the NLO level, we then show that the
soft divergences from different sub-diagrams will be canceled each other in the
quark level, while the remaining collinear divergences can be absorbed into the
NLO meson wave functions. The full NLO amplitudes can therefore be factorized
as the convolution of the NLO wave functions $ \Phi^{(1)}_{\rho}$ and the
infrared-finite leading order (LO) hard kernels $G^0_{X,IJ,kl}$ in the $k_T$
factorization. We also write down the polarized NLO $\rho$ meson wave functions
in the form of nonlocal hadron matrix elements with the gauge factor integral
path deviating from the light cone. These NLO $\rho$ meson wave functions can
be used to calculate the NLO hard corrections to some relevant exclusive
processes, such as $B \to \rho$ transition.


  For any ring $A$ and a small, preadditive, Hom-finite, and locally bounded
category $Q$ that has a Serre functor and satisfies the (strong) retraction
property, we show that the category of additive functors from $Q$ to the
category of (left) $A$-modules has a projective and an injective model
structure. These model structures have the same trivial objects and weak
equivalences, which in most cases can be naturally characterized in terms of
certain (co)homology functors introduced in this paper. The associated homotopy
category, which is triangulated, is called the $Q$-shaped derived category of
$A$. The usual derived category of $A$ is one example; more general examples
arise by taking $Q$ to be the mesh category of a suitably nice stable
translation quiver. This paper builds upon, and generalizes, works of Enochs,
Estrada, and Garcia-Rozas and of Dell'Ambrogio, Stevenson, and Stovicek.


  Luminosity upgrade at the LHC collider using longitudinally flat bunches in
combination with the large crossing angle (large Piwinski angle scheme) is
being studied with renewed interest in recent years. By design, the total
beam-beam tune shift at the LHC is less than 0.015 for two interaction points
together. But the 2010-11 3.5 TeV collider operation and dedicated studies
indicated that the beam-beam tune shift is >0.015 per interaction point. In
view of this development we have revisited the requirements for the Large
Piwinski Angle scheme at the LHC. In this paper we present a new set of
parameters and luminosity calculations for the desired upgrade by investigating
1) current performance of the LHC injectors, 2) e-cloud issues on nearly flat
bunches and 3) realistic beam particle distributions from longitudinal beam
dynamics simulations. We also make some remarks on the needed upgrades on the
LHC injector accelerators.


  The 4D compact U(1) lattice gauge theory (LGT) in the confinement phase is
studied with the multi-level algorithm. The static potential, force and
flux-tube profile between two static charges are precisely measured from
correlation functions involving the Polyakov loop. Universality of the
coefficient of the 1/r correction to the static potential, known as the
Luescher term, and the transversal width of the flux-tube profile are
investigated.


  Speech samples recorded in both indoor and outdoor environments are often
contaminated with secondary audio sources. Most end-to-end monaural speech
recognition systems either remove these background sounds using speech
enhancement or train noise-robust models. For better model interpretability and
holistic understanding, we aim to bring together the growing field of automated
audio captioning (AAC) and the thoroughly studied automatic speech recognition
(ASR). The goal of AAC is to generate natural language descriptions of contents
in audio samples. We propose several approaches for end-to-end joint modeling
of ASR and AAC tasks and demonstrate their advantages over traditional
approaches, which model these tasks independently. A major hurdle in evaluating
our proposed approach is the lack of labeled audio datasets with both speech
transcriptions and audio captions. Therefore we also create a multi-task
dataset by mixing the clean speech Wall Street Journal corpus with multiple
levels of background noises chosen from the AudioCaps dataset. We also perform
extensive experimental evaluation and show improvements of our proposed methods
as compared to existing state-of-the-art ASR and AAC methods.


  A thin liquid film coating a planar horizontal substrate may be unstable to
perturbations in the film thickness due to unfavourable intermolecular
interactions between the liquid and the substrate, which may lead to
finite-time rupture. The self-similar nature of the rupture has been studied
before by utilizing the standard lubrication approximation along with the
Derjaguin (or disjoining) pressure formalism used to account for the
intermolecular interactions, and a particular form of the disjoining pressure
with exponent $n=3$ has been used, namely, $\Pi(h)\propto -1/h^{3}$, where $h$
is the film thickness. In the present study, we use a numerical continuation
method to compute discrete solutions to self-similar rupture for a general
disjoining pressure exponent $n$. We focus on axisymmetric point-rupture
solutions and show that pairs of solution branches merge as $n$ decreases,
leading to a critical value $n_c \approx 1.485$ below which stable similarity
solutions do not appear to exist. We verify that this observation also holds
true for plane-symmetric line-rupture solutions for which the critical value
turns out to be slightly larger than for the axisymmetric case,
$n_c^{\mathrm{plane}}\approx 1.5$. Computation of the full time-dependent
problem also demonstrates the loss of stable similarity solutions and the
subsequent onset of cascading oscillatory structures.


  In principle, meta-reinforcement learning algorithms leverage experience
across many tasks to learn fast reinforcement learning (RL) strategies that
transfer to similar tasks. However, current meta-RL approaches rely on
manually-defined distributions of training tasks, and hand-crafting these task
distributions can be challenging and time-consuming. Can "useful" pre-training
tasks be discovered in an unsupervised manner? We develop an unsupervised
algorithm for inducing an adaptive meta-training task distribution, i.e. an
automatic curriculum, by modeling unsupervised interaction in a visual
environment. The task distribution is scaffolded by a parametric density model
of the meta-learner's trajectory distribution. We formulate unsupervised
meta-RL as information maximization between a latent task variable and the
meta-learner's data distribution, and describe a practical instantiation which
alternates between integration of recent experience into the task distribution
and meta-learning of the updated tasks. Repeating this procedure leads to
iterative reorganization such that the curriculum adapts as the meta-learner's
data distribution shifts. In particular, we show how discriminative clustering
for visual representation can support trajectory-level task acquisition and
exploration in domains with pixel observations, avoiding pitfalls of
alternatives. In experiments on vision-based navigation and manipulation
domains, we show that the algorithm allows for unsupervised meta-learning that
transfers to downstream tasks specified by hand-crafted reward functions and
serves as pre-training for more efficient supervised meta-learning of test task
distributions.


  Despite the recent progress in quantum computational algorithms for
chemistry, there is a dearth of quantum computational simulations focused on
material science applications, especially for the energy sector, where next
generation sorbing materials are urgently needed to battle climate change. To
drive their development, quantum computing is applied to the problem of CO$_2$
adsorption in Al-fumarate Metal-Organic Frameworks. Fragmentation strategies
based on Density Matrix Embedding Theory are applied, using a variational
quantum algorithm as a fragment solver, along with active space selection to
minimise qubit number. By investigating different fragmentation strategies and
solvers, we propose a methodology to apply quantum computing to Al-fumarate
interacting with a CO$_2$ molecule, demonstrating the feasibility of treating a
complex porous system as a concrete application of quantum computing. Our work
paves the way for the use of quantum computing techniques in the quest of
sorbents optimisation for more efficient carbon capture and conversion
applications.


  The mean-field study of the stripe phases of t-t'-U Hubbard model confirms
formation of the "impurity" subbands of states localized on domain walls. For
bond-aligned stripes it is shown that segments of the Fermi surfaces (FS) in
antinodal and nodal directions correspond to the "impurity" band or to that
delocalized over a whole antiferromagnet domain. This might explain the
dichotomy between corresponding quasiparticles in La_{2-x}Sr_xCuO_4. The Fermi
surface changes its topology at some doping depending on stripe width.
Additional ordering at the domain walls should be supposed to accord the
observed 1/8 anomaly and the stripe spacing. It is confirmed also that the
diagonal stripes can provide the dielectric state at nonzero doping.


  Optomechanical systems couple light to the motion of nanomechanical objects.
Intriguing new effects are observed in recent experiments that involve the
dynamics of more than one optical mode. There, mechanical motion can stimulate
strongly driven multi-mode photon dynamics that acts back on the mechanics via
radiation forces. We show that even for two optical modes
Landau-Zener-Stueckelberg oscillations of the light field drastically change
the nonlinear attractor diagram of the resulting phonon lasing oscillations.
Our findings illustrate the generic effects of Landau-Zener physics on
back-action induced self-oscillations.


  The optical flash accompanying GRB 990123 is believed to be powered by the
reverse shock of a thin shell. With the best fitted physical parameters for GRB
990123 (Panaitescu & Kumar 2001) and the assumption that the parameters in the
optical flash are the same as those in the afterglow, we show that: 1) the
shell is thick but not thin, and we have provided the light curve for the thick
shell case which coincides with the observation; 2) the theoretical peak flux
of the optical flash accounts for only 3\times10^{-4} of the observed. In order
to compensate this divergency, the physical parameters the electron energy
ration and the magnetic ratio $\epsilon_e$, $\epsilon_B$ should be 0.61, 0.39
respectively, which are much different from those in the late afterglow.


  Snapshot is a fundamental notion proposed for routing in mobile low earth
orbit (LEO) satellite networks which is characterized with predictable topology
dynamics. Its distribution has a great impact on the routing performance and
on-board storage. Originally, the snapshot distribution is invariable by using
the static snapshot partition method based on the mechanical steering antenna.
Utilizing nowadays advanced phased-array antenna technology, we proposed a
quasi-dynamic snapshot partition method based on the inter-satellite links
(ISLs) reassignment for the polar-orbit LEO satellite networks. By steering the
inter-satellite antennas when snapshot switches, we can reassign the ISLs and
add available ISLs for a better following snapshot. Results show that our
method can gain more stable routing performance by obtaining constant snapshot
duration, constant ISL number and lower end to end delay. Especially for
Iridium system, our method can gain not only longer merged snapshot duration,
half of the snapshot number, but also higher utilization ratio of inter-plane
ISLs. Potentially, our method could be very useful for the Iridium-next system.


  The polarization properties of extragalactic radio sources at frequencies
higher than 20 GHz are still poorly constrained. However, their
characterization would provide invaluable information about the physics of the
emission processes and is crucial to estimate their contamination as
foregrounds of the polarized cosmic microwave background (CMB) angular power
spectrum on scales < 30 arcmin. In this contribution, after summarizing the
state-of-the-art of polarimetric observations in the millimetric wavelength
bands, we present our observations of a complete sample of 53 sources with S >
200 mJy (at 20 GHz) carried out with the Australia Telescope Compact Array
between 5.5 and 38 GHz. The analysis clearly shows that polarization properties
cannot be simply inferred from total intensity ones, as the spectral behaviors
of the two signals are typically different.


  For many applications involving a sequence of linear systems with slowly
changing system matrices, subspace recycling, which exploits relationships
among systems and reuses search space information, can achieve huge gains in
iterations across the total number of linear system solves in the sequence.
However, for general (i.e., non-identity) shifted systems with the shift value
varying over a wide range, the properties of the linear systems vary widely as
well, which makes recycling less effective. If such a sequence of systems is
embedded in a nonlinear iteration, the problem is compounded, and special
approaches are needed to use recycling effectively.
  In this paper, we develop new, more efficient, Krylov subspace recycling
approaches for large-scale image reconstruction and restoration techniques that
employ a nonlinear iteration to compute a suitable regularization matrix. For
each new regularization matrix, we need to solve regularized linear systems,
${\bf A} + \gamma_\ell {\bf E}_k$, for a sequence of regularization parameters,
$\gamma_\ell$, to find the optimally regularized solution that, in turn, will
be used to update the regularization matrix.
  In this paper, we analyze system and solution characteristics to choose
appropriate techniques to solve each system rapidly. Specifically, we use an
inner-outer recycling approach with a larger, principal recycle space for each
nonlinear step and smaller recycle spaces for each shift. We propose an
efficient way to obtain good initial guesses from the principle recycle space
and smaller shift-specific recycle spaces that lead to fast convergence. Our
method is substantially reduces the total number of matrix-vector products that
would arise in a naive approach. Our approach is more generally applicable to
sequences of shifted systems where the matrices in the sum are positive
semi-definite.


  We have extracted 565 neutron spectroscopic factors of sd and fp shell nuclei
by systematically analyzing more than 2000 measured (d,p) angular
distributions. We are able to compare 125 of the extracted spectroscopic
factors to values predicted by large-basis shell-model calculations and
evaluate the accuracies of spectroscopic factors predicted by different
shell-model interactions in these regions. We find that the spectroscopic
factors predicted for most excited states of sd-shell nuclei using the latest
USDB or USDA interactions agree with the experimental values. For fp shell
nuclei, the inability of the current models to account for the core excitation
and fragmentation of the states leads to considerable discrepancies. In
particular, the agreement between data and shell-model predictions for Ni
isotopes is not better than a factor of two using either the GXPF1A or the XT
interaction.


  An automaton is monotonic if its states can be arranged in a linear order
that is preserved by the action of every letter. We prove that the problem of
deciding whether a given automaton is monotonic is NP-complete. The same result
is obtained for oriented automata, whose states can be arranged in a cyclic
order. Moreover, both problems remain hard under the restriction to binary
input alphabets.


  For the one-stage third-order gas-kinetic scheme (GKS), success applications
have been achieved for the three-dimensional compressible flow computations
[33]. The high-order accuracy of the scheme is obtained directly by integrating
a multidimensional time-accurate gas distribution function over the cell
interface within a time step without implementing Gaussian quadrature points
and Runge-Kutta time-stepping technique. However, for the further increasing
the order of the scheme, such as the fourth-order one, the formulation becomes
very complicated for the multidimensional flow. Recently, a two-stage
fourth-order GKS with high efficiency has been constructed for two-dimensional
inviscid and viscous flow computations [22,32], and the scheme uses the time
accurate flux function and its time derivatives. In this paper, a fourth-order
GKS is developed for the threedimensional flows under the two-stage framework.
Based on the three-dimensional WENO reconstruction and flux evaluation at
Gaussian quadrature points on a cell interface, the high-order accuracy in
space is achieved first. Then, the two-stage time stepping method provides the
high accuracy in time. In comparison with the formal third-order GKS [33], the
current fourth-order method not only improves the accuracy of the scheme, but
also reduces the complexity of the gas-kinetic solver greatly. More
importantly, the fourth-order GKS has the same robustness as the second-order
shock capturing scheme. This scheme is applied to both inviscid and viscous,
and low and high speed flow computations. Numerical results validate the
outstanding reliability and applicability of the scheme for three-dimensional
flows, such as turbulent one.


  We develop a general model of coherent synchrotron radiation (CSR) impedance
with shielding provided by two parallel conducting plates. This model allows us
to easily reproduce all previously known analytical CSR wakes and to expand the
analysis to the situations not explored before. It reduces calculations of the
impedance to taking integrals along the trajectory of the beam. New analytical
results are derived for the radiation impedance with shielding for the
following orbits: a kink, a bending magnet, a wiggler of finite length, and an
infinitely long wiggler. All our formulas are benchmarked agains numerical
simulations with the CSRZ computer code.


  We demonstrate the direct loading of a D$_2$ MOT of potassium-39 atoms from a
cryogenic buffer gas beam source. We load $10^8$ atoms in a 10 ms pulse, with
no degradation in performance up to a 10 Hz repetition rate. Observed densities
reach $\sim10^{11}$ atoms/cm$^3$ in a single pulse, achieved with no
sub-Doppler cooling or transverse compression. This system produces an ideal
starting point for ultracold atom experiments where high experimental
repetition rates are desirable and initial high densities are critical.
Extension to other atomic species (e.g., refractory metals) that present
technical challenges to high-yield production using oven-based sources is
straightforward.


  This paper introduces a new resource allocation problem in distributed
computing called distributed serving with mobile servers (DSMS). In DSMS, there
are $k$ identical mobile servers residing at the processors of a network. At
arbitrary points of time, any subset of processors can invoke one or more
requests. To serve a request, one of the servers must move to the processor
that invoked the request. Resource allocation is performed in a distributed
manner since only the processor that invoked the request initially knows about
it. All processors cooperate by passing messages to achieve correct resource
allocation. They do this with the goal to minimize the communication cost.
  Routing servers in large-scale distributed systems requires a scalable
location service. We introduce the distributed protocol GNN that solves the
DSMS problem on overlay trees. We prove that GNN is starvation-free and
correctly integrates locating the servers and synchronizing the concurrent
access to servers despite asynchrony, even when the requests are invoked over
time. Further, we analyze GNN for "one-shot" executions, i.e., all requests are
invoked simultaneously. We prove that when running GNN on top of a special
family of tree topologies---known as hierarchically well-separated trees
(HSTs)---we obtain a randomized distributed protocol with an expected
competitive ratio of $O(\log n)$ on general network topologies with $n$
processors. From a technical point of view, our main result is that GNN
optimally solves the DSMS problem on HSTs for one-shot executions, even if
communication is asynchronous. Further, we present a lower bound of
$\Omega(\max\{k, \log n/\log\log n\})$ on the competitive ratio for DSMS. The
lower bound even holds when communication is synchronous and requests are
invoked sequentially.


  This paper is focused on investigating the effects of a statistical
interaction for graphene-like systems, providing Haldane-like properties for
topologically trivial lattices. The associated self-energy correction yields an
effective next-nearest hopping, inducing the topological phase, whose specific
solutions are scrutinized. In the case of an external magnetic field, it leads
to a renormalized quasi-particle structure with generalized Landau levels and
explicit valley asymmetry. A suitable tool for implementing such achievements
is a judicious indefinite metric quantization, leading to advances in field
theory foundations. Since the topological behavior is encoded in the radiative
corrections, an unequivocal treatment using an integral representation is
carefully developed.


  The muon charge ratio of the lateral muon density distributions in single EAS
is studied by simulations, in context of recent proposals to measure this
observable in coincidence with EAS observations. While effects of the hadronic
interaction do not lead to significant differences of the total muon plus and
muon minus content, the differences of the azimuthal variation of the muon
densities of opposite charges and the azimuthal variation of the muon charge
ratio appear to be very much pronounced, dependent on the direction of EAS
incidence. This is due to the influence of the geomagnetic field which induces
related effects in radio emission from extended air showers.


  An abstract characterization of the representation category of the Woronowicz
twisted SU(d) group is given, generalizing analogous results known in the
classical case


  Human-in-the-loop (HiL) reinforcement learning is gaining traction in domains
with large action and state spaces, and sparse rewards by allowing the agent to
take advice from HiL. Beyond advice accommodation, a sequential decision-making
agent must be able to express the extent to which it was able to utilize the
human advice. Subsequently, the agent should provide a means for the HiL to
inspect parts of advice that it had to reject in favor of the overall
environment objective. We introduce the problem of Advice-Conformance
Verification which requires reinforcement learning (RL) agents to provide
assurances to the human in the loop regarding how much of their advice is being
conformed to. We then propose a Tree-based lingua-franca to support this
communication, called a Preference Tree. We study two cases of good and bad
advice scenarios in MuJoCo's Humanoid environment. Through our experiments, we
show that our method can provide an interpretable means of solving the
Advice-Conformance Verification problem by conveying whether or not the agent
is using the human's advice. Finally, we present a human-user study with 20
participants that validates our method.


  In a `shortcut-to-adiabaticity' (STA) protocol, the counter-diabatic
Hamiltonian, which suppresses the non-adiabatic transition of a reference
`adiabatic' trajectory, induces a quantum uncertainty of the work cost in the
framework of quantum thermodynamics. Following a theory derived recently [Funo
et al 2017 Phys. Rev. Lett. 118 100602], we perform an experimental measurement
of the STA work statistics in a high-quality superconducting Xmon qubit.
Through the frozen-Hamiltonian and frozen-population techniques, we
experimentally realize the two-point measurement of the work distribution for
given initial eigenstates. Our experimental statistics verify (i) the
conservation of the average STA work and (ii) the equality between the STA
excess of work fluctuations and the quantum geometric tensor.


  We investigate the magnetocaloric properties of the two-dimensional
frustrated J1-J2 model on a square lattice. This model describes well the
magnetic behavior of two classes of quasi-two-dimensional S = 1/2 vanadates,
namely the Li2VOXO4 (X = Si, Ge) and AA'VO(PO4)2 (A, A' = Pb, Zn, Sr, Ba)
compounds. The magnetocaloric effect (MCE) consists in the adiabatic
temperature change upon changing the external magnetic field. In frustrated
systems, the MCE can be enhanced close to the saturation field because of
massive degeneracies among low lying excitations. We discuss results for the
MCE in the two distinct antiferromagnetic regimes of the phase diagram.
Numerical finite temperature Lanczos as well as analytical methods based on the
spin wave expansion are employed and results are compared. We give explicit
values for the saturation fields of the vanadium compounds. We predict that at
subcritical fields there is first a (positive) maximum followed by sign change
of the MCE, characteristic of all magnetically ordered phases.


  It has been a long-standing question whether momentum space integral
equations of the Faddeev type are applicable to reactions of three charged
particles, in particular above the three-body threshold. For, the presence of
long-range Coulomb forces has been thought to give rise to such severe
singularities in their kernels that the latter may lack the compactness
property known to exist in the case of purely short-range interactions.
Employing the rigorously equivalent formulation in terms of an
effective-two-body theory we have proved in a preceding paper [Phys. Rev. C
{\bf 61}, 064006 (2000)] that, for all energies, the nondiagonal kernels
occurring in the integral equations which determine the transition amplitudes
for all binary collision processes, possess on and off the energy shell only
integrable singularities, provided all three particles have charges of the same
sign, i.e., all Coulomb interactions are repulsive. In the present paper we
prove that, for particles with charges of equal sign, the diagonal kernels, in
contrast, possess one, but only one, nonintegrable singularity. The latter can,
however, be isolated explicitly and dealt with in a well-defined manner. Taken
together these results imply that modified integral equations can be
formulated, with kernels that become compact after a few iterations. This
concludes the proof that standard solution methods can be used for the
calculation of all binary (i.e., (in-)elastic and rearrangement) amplitudes by
means of momentum space integral equations of the effective-two-body type.


  Understanding predictions made by deep neural networks is notoriously
difficult, but also crucial to their dissemination. As all machine learning
based methods, they are as good as their training data, and can also capture
unwanted biases. While there are tools that can help understand whether such
biases exist, they do not distinguish between correlation and causation, and
might be ill-suited for text-based models and for reasoning about high level
language concepts. A key problem of estimating the causal effect of a concept
of interest on a given model is that this estimation requires the generation of
counterfactual examples, which is challenging with existing generation
technology. To bridge that gap, we propose CausaLM, a framework for producing
causal model explanations using counterfactual language representation models.
Our approach is based on fine-tuning of deep contextualized embedding models
with auxiliary adversarial tasks derived from the causal graph of the problem.
Concretely, we show that by carefully choosing auxiliary adversarial
pre-training tasks, language representation models such as BERT can effectively
learn a counterfactual representation for a given concept of interest, and be
used to estimate its true causal effect on model performance. A byproduct of
our method is a language representation model that is unaffected by the tested
concept, which can be useful in mitigating unwanted bias ingrained in the data.


  Using the gamma-ray bursts simultaneously detected by Swift/BAT and Fermi/GBM
we performed a joint spectral and temporal analysis of the prompt emission data
and confirm the rough correlation between the BAT-band photon index Gamma_BAT
and the peak spectral energy Epeak. With the redshift known sub-sample, we
derived the isotropic gamma-ray energy E_gamma,iso and also confirm the
E_gamma,iso - Epeak,rest relation, with a larger scatter than the Amati sample
but consistent with GBM team analyses. We also compare the T_90 values derived
in the GBM band with those derived in the BAT band and find that for long GRBs
the BAT T_90 is usually longer than the GBM T_90, while for short GRBs the
trend reverses. This is consistent with the soft/hard nature of long/short GRBs
and suggests the importance of an energy-dependent temporal analysis of GRBs.


  Define the incremental fractional Brownian field $B_{H}(s+\tau)-B_{H}(s),
H\in (0,1)$, where $B_{H}(s)$ is a standard fractional Brownian motion with
Hurst index $H\in(0,1)$. In this paper we derive the exact asymptotic behaviour
of the maximum $\max_{(\tau,s)\in[0,1]\times[0,T]} (B_{H}(s+\tau)-B_{H}(s)) $
for any $H\in (0,1/2)$ complimenting thus the result of Zholud (2008) for the
Brownian motion.


  This paper seeks whether Alfv\'en waves (AW) can be produced in
laboratory-scale liquid metal experiments, \emph{i.e.} at low-magnetic Reynolds
Number ($R\!m$). AW are incompressible waves propagating along magnetic fields
typically found geo and astrophysical systems. Until now, only faint linear
waves have been experimentally produced in liquid metals because of the large
magnetic dissipation they undergo when $R\!m\ll1$. Yet, controlling laboratory
AW could emulate such far remote processes as anomalous heating in the solar
corona, oscillations of the Earth inner core or turbulence in the solar wind.
To answer this question, we force AW with an AC electric current in a liquid
metal channel in a transverse magnetic field. We derive a wave-bearing
extension of the usual low$-R\!m$ MHD approximation to identify two linear
regimes: The purely diffusive regime exists when $N_\omega$, the ratio of the
oscillation period to the timescale of diffusive two-dimensionalisation by the
Lorentz force, is small. The propagative regime is governed by the ratio of the
forcing period to the AW propagation timescale which, we call the Jameson
number $J\!a$ after Jameson (1964), JFM. In this regime, AW are dissipative and
dispersive as they propagate more slowly where velocity gradients are higher.
Both regimes are recovered in the FLOWCUBE experiment, in excellent agreement
with the model up to $J\!a \lesssim 0.85$ but near the $J\!a=1$ resonance, high
amplitude waves become clearly nonlinear. Hence, in electrically driving AW, we
were able to produce some of the propagative, diffusive and nonlinear processes
of astro and geophysical AW.


  We introduce a novel parsing concept called local lexing. It integrates the
classically separated stages of lexing and parsing by allowing lexing to be
dependent upon the parsing progress and by providing a simple mechanism for
constraining lexical ambiguity. This makes it possible for language design to
be composable not only at the level of context-free grammars, but also at the
lexical level. It also makes it possible to include lightweight error-handling
directly as part of the language specification instead of leaving it up to the
implementation.
  We present a high-level algorithm for local lexing, which is an extension of
Earley's algorithm. We have formally verified the correctness of our algorithm
with respect to its local lexing semantics in Isabelle/HOL.


  Noisy-Intermediate-Scale-Quantum (NISQ) devices are nowadays starting to
become available to the final user, hence potentially allowing to show the
quantum speedups predicted by the quantum information theory. However, before
implementing any quantum algorithm, it is crucial to have at least a partial or
possibly full knowledge on the type and amount of noise affecting the quantum
machine. Here, by generalizing quantum generative adversarial learning from
quantum states (Q-GANs) to quantum operations/superoperators/channels (here
named as SuperQGANs), we propose a very promising framework to characterize
noise in a realistic quantum device, even in the case of spatially and
temporally correlated noise (memory channels) affecting quantum circuits. The
key idea is to learn about the noise by mimicking it in a way that one cannot
distinguish between the real (to be sensed) and the fake (generated) one. We
find that, when applied to the benchmarking case of Pauli channels, the
SuperQGAN protocol is able to learn the associated error rates even in the case
of spatially and temporally correlated noise. Moreover, we also show how to
employ it for quantum metrology applications. We believe our SuperQGANs pave
the way for new hybrid quantum-classical machine learning protocols for a
better characterization and control of the current and future unavoidably noisy
quantum devices.


  We present an unsupervised deep learning model for 3D object classification.
Conventional Hebbian learning, a well-known unsupervised model, suffers from
loss of local features leading to reduced performance for tasks with complex
geometric objects. We present a deep network with a novel Neuron Activity Aware
(NeAW) Hebbian learning rule that dynamically switches the neurons to be
governed by Hebbian learning or anti-Hebbian learning, depending on its
activity. We analytically show that NeAW Hebbian learning relieves the bias in
neuron activity, allowing more neurons to attend to the representation of the
3D objects. Empirical results show that the NeAW Hebbian learning outperforms
other variants of Hebbian learning and shows higher accuracy over fully
supervised models when training data is limited.


  We analyse the long-distance contribution to $B\to X_s\ell^+\ell^-$
differential decay rate when the momentum dependence of $\psi$ and
$\psi'$-$\gamma$ conversion strength is taken into account. The results
indicate that the resonance to nonresonance interference in the dilepton
invariant mass distribution is substantially reduced.


  The models of peer to peer protocols are presented with the help of one-step
processes. On the basis of this presentation and the method of randomization of
one-step processes described method for constructing models of peer to peer
protocols. As specific implementations of proposed method the models of
FastTrack and Bittorrent protocols are studied.


  In this paper, we present a novel universal coupled theory for metamaterial
Bound states in the continuum (BIC) or quasi-Bound states in the continuum
(quasi-BIC) which provides ultra-high Q resonance for metamaterial devices. Our
theory analytically calculates the coupling of two bright modes with phase. Our
method has much more accuracy for ultra-strong coupling comparing with the
previous theory (the coupling of one bright mode and one dark mode). Therefore,
our theory is much more suitable for BIC or quasi-BIC and we can accurately
predict the transmission spectrum of metamaterial BIC or quasi-BIC for the
first time.


  With the rise of Large Language Models (LLMs), AI assistants' ability to
utilize tools, especially through API calls, has advanced notably. This
progress has necessitated more accurate evaluation methods. Many existing
studies adopt static evaluation, where they assess AI assistants' API call
based on pre-defined dialogue histories. However, such evaluation method can be
misleading, as an AI assistant might fail in generating API calls from
preceding human interaction in real cases. Instead of the resource-intensive
method of direct human-machine interactions, we propose Automated Dynamic
Evaluation (AutoDE) to assess an assistant's API call capability without human
involvement. In our framework, we endeavor to closely mirror genuine human
conversation patterns in human-machine interactions, using a LLM-based user
agent, equipped with a user script to ensure human alignment. Experimental
results highlight that AutoDE uncovers errors overlooked by static evaluations,
aligning more closely with human assessment. Testing four AI assistants using
our crafted benchmark, our method further mirrored human evaluation compared to
conventional static evaluations.


  BACKGROUND. Signal recognition and information processing is a fundamental
cellular function, which in part involves comprehensive transcriptional
regulatory (TR) mechanisms carried out in response to complex environmental
signals in the context of the cell's own internal state. However, the network
topological basis of developing such integrated responses remains poorly
understood.
  RESULTS. By studying the TR network of the yeast Saccharomyces cerevisiae we
show that an intermediate layer of transcription factors naturally segregates
into distinct subnetworks. In these topological units transcription factors are
densely interlinked in a largely hierarchical manner and respond to external
signals by utilizing a fraction of these subnets.
  CONCLUSIONS. As transcriptional regulation represents the "slow" component of
overall information processing, the identified topology suggests a model in
which successive waves of transcriptional regulation originating from distinct
fractions of the TR network control robust integrated responses to complex
stimuli.


  Constrained model predictive control (MPC) is a widely used control strategy,
which employs moving horizon-based on-line optimisation to compute the optimum
path of the manipulated variables. Nonlinear MPC can utilize detailed models
but it is computationally expensive; on the other hand linear MPC may not be
adequate. Piecewise affine (PWA) models can describe the underlying nonlinear
dynamics more accurately, therefore they can provide a viable trade-off through
their use in multi-model linear MPC configurations, which avoid integer
programming. However, such schemes may introduce uncertainty affecting the
closed loop stability. In this work, we propose an input to output stability
analysis for closed loop systems, consisting of PWA models, where an observer
and multi-model linear MPC are applied together, under unstructured
uncertainty. Integral quadratic constraints (IQCs) are employed to assess the
robustness of MPC under uncertainty. We create a model pool, by performing
linearisation on selected transient points. All the possible uncertainties and
nonlinearities (including the controller) can be introduced in the framework,
assuming that they admit the appropriate IQCs, whilst the dissipation
inequality can provide necessary conditions incorporating IQCs. We demonstrate
the existence of static multipliers, which can reduce the conservatism of the
stability analysis significantly. The proposed methodology is demonstrated
through two engineering case studies.


  We compute the resonance energy transfer (RET) in a system composed of two
quantum emitters near a host dielectric matrix in which metallic inclusions are
inserted until the medium undergoes a dielectric-metal transition at
percolation. We show that there is no peak in the RET rate at percolation, in
contrast to what happens with the spontaneous emission rate of an emitter near
the same critical medium. This result suggests that RET does not strongly
correlate with the local density of states.


  We propose a novel convolutional neural network for lesion detection from
weak labels. Only a single, global label per image - the lesion count - is
needed for training. We train a regression network with a fully convolutional
architecture combined with a global pooling layer to aggregate the 3D output
into a scalar indicating the lesion count. When testing on unseen images, we
first run the network to estimate the number of lesions. Then we remove the
global pooling layer to compute localization maps of the size of the input
image. We evaluate the proposed network on the detection of enlarged
perivascular spaces in the basal ganglia in MRI. Our method achieves a
sensitivity of 62% with on average 1.5 false positives per image. Compared with
four other approaches based on intensity thresholding, saliency and class maps,
our method has a 20% higher sensitivity.


  We investigate whether the identification between Connes' spectral distance
in noncommutative geometry and the Monge-Kantorovich distance of order 1 in the
theory of optimal transport - that has been pointed out by Rieffel in the
commutative case - still makes sense in a noncommutative framework. To this
aim, given a spectral triple (A, H, D) with noncommutative A, we introduce a
"Monge-Kantorovich"-like distance W_D on the space of states of A, taking as a
cost function the spectral distance d_D between pure states. We show in full
generality that d_D is never greater than W_D, and exhibit several examples
where the equality actually holds true, in particular on the unit two-ball
viewed as the state space of the algebra of complex 2-by-2 matrices. We also
discuss W_D in a two-sheet model (product of a manifold by C^2), pointing
towards a possible interpretation of the Higgs field as a cost function that
does not vanish on the diagonal.


  We characterise all the quasi-stationary distributions and the Q-process
associated with a continuous state branching process that explodes in finite
time. We also provide a rescaling for the continuous state branching process
conditioned on non-explosion when the branching mechanism is regularly varying
at 0.


  We investigate the generalized second-order Arnoldi (GSOAR) method, a
generalization of the SOAR method proposed by Bai and Su [{\em SIAM J. Matrix
Anal. Appl.}, 26 (2005): 640--659.], and the Refined GSOAR (RGSOAR) method for
the quadratic eigenvalue problem (QEP). The two methods use the GSOAR procedure
to generate an orthonormal basis of a given generalized second-order Krylov
subspace, and with such basis they project the QEP onto the subspace and
compute the Ritz pairs and the refined Ritz pairs, respectively. We develop
implicitly restarted GSOAR and RGSOAR algorithms, in which we propose certain
exact and refined shifts for respective use within the two algorithms.
Numerical experiments on real-world problems illustrate the efficiency of the
restarted algorithms and the superiority of the restarted RGSOAR to the
restarted GSOAR. The experiments also demonstrate that both IGSOAR and IRGSOAR
generally perform much better than the implicitly restarted Arnoldi method
applied to the corresponding linearization problems, in terms of the accuracy
and the computational efficiency.


  The diffusive motion of a colloidal particle trapped inside a small cavity
filled with fluid is reduced by hydrodynamic interactions with the confining
walls. In this work, we study these wall effects on a spherical particle
entrapped in a closed cylinder. We calculate the diffusion coefficient along
the radial, azimuthal and axial direction for different particle positions. At
all locations the diffusion is smaller than in a bulk fluid and it becomes
anisotropic near the container's walls. We present a simple model which
reasonably well desribes the simulation results for the given dimensions of the
cylinder, which are taken from recent experimental work.


  We introduce splitter networks, which abstract the behavior of conveyor belts
found in the video game Factorio. Based on this definition, we show how to
compute the steady-state of a splitter network. Then, leveraging insights from
the players community, we provide multiple designs of splitter networks capable
of load-balancing among several conveyor belts, and prove that any
load-balancing network on $n$ belts must have $\Omega(n \log n)$ nodes.
Incidentally, we establish connections between splitter networks and various
concepts including flow algorithms, flows with equality constraints, Markov
chains and the Knuth-Yao theorem about sampling over rational distributions
using a fair coin.


  A completely nonparametric method for the estimation of mixture cure models
is proposed. A nonparametric estimator of the incidence is extensively studied
and a nonparametric estimator of the latency is presented. These estimators,
which are based on the Beran estimator of the conditional survival function,
are proved to be the local maximum likelihood estimators. An i.i.d.
representation is obtained for the nonparametric incidence estimator. As a
consequence, an asymptotically optimal bandwidth is found. Moreover, a
bootstrap bandwidth selection method for the nonparametric incidence estimator
is proposed. The introduced nonparametric estimators are compared with existing
semiparametric approaches in a simulation study, in which the performance of
the bootstrap bandwidth selector is also assessed. Finally, the method is
applied to a database of colorectal cancer from the University Hospital of A
Coru\~na (CHUAC).


  It was recently observed that boundary correlators of the elementary scalar
field of the Liouville theory on AdS$_2$ background are the same (up to a
non-trivial proportionality coefficient) as the correlators of the chiral
stress tensor of the Liouville CFT on the complex plane restricted to the real
line. The same relation generalizes to the conformal abelian Toda theory:
boundary correlators of Toda scalars on AdS$_2$ are directly related to the
correlation functions of the chiral $\mathcal W$-symmetry generators in the
Toda CFT and thus are essentially controlled by the underlying
infinite-dimensional symmetry. These may be viewed as examples of
AdS$_2$/CFT$_1$ duality where the CFT$_1$ is the chiral half of a 2d CFT; we
shall refer to this as AdS$_{2}$/CFT$^{1/2}_{2}$. In this paper we demonstrate
that this duality applies also to the non-abelian Toda theory containing a
Liouville scalar coupled to a 2d $\sigma$-model originating from the $SL(2,
\mathbb R)/U(1)$ gauged WZW model. Here the Liouville scalar is again dual to
the chiral stress tensor $T$ while the other two scalars are dual to the
parafermionic operators $V^\pm$ of the non-abelian Toda CFT. We explicitly
check the duality at the next-to-leading order in the large central charge
expansion by matching the chiral CFT correlators of $(T, V^+,V^-)$ (computed
using a free field representation) with the boundary correlators of the three
Toda scalars given by the tree-level and one-loop Witten diagrams in AdS$_{2}$.


  We discuss two semi-independent calibration techniques used to determine the
in-flight radiometric calibration for the New Horizons' Multi-spectral Visible
Imaging Camera (MVIC). The first calibration technique compares the observed
stellar flux to modeled values. The difference between the two provides a
calibration factor that allows the observed flux to be adjusted to the expected
levels for all observations, for each detector. The second calibration
technique is a channel-wise relative radiometric calibration for MVIC's blue,
near-infrared and methane color channels using observations of Charon and
scaling from the red channel stellar calibration. Both calibration techniques
produce very similar results (better than 7% agreement), providing strong
validation for the techniques used. Since the stellar calibration can be
performed without a color target in the field of view and covers all of MVIC's
detectors, this calibration was used to provide the radiometric keywords
delivered by the New Horizons project to the Planetary Data System (PDS). These
keywords allow each observation to be converted from counts to physical units;
a description of how these keywords were generated is included. Finally,
mitigation techniques adopted for the gain drift observed in the near-infrared
detector and one of the panchromatic framing cameras is also discussed.


  In 1958 E. Lehmer found an explicit description of those primes p for which a
given prime q is a cubic residue. In this paper we demonstrate that a similar
result may be obtained for cubic nonresidues, yielding a cubic character for
fixed p that provides an effective means for ascertaining whether or not an
arbitrary integer c is a cubic residue modulo p. As an illustration of this
technique, we determine whether 1982 is a cubic residue modulo the 131-digit
prime p=(3^19+5^82)/4, a question which is essentially impossible to answer
with Lehmer's original criterion.


  Penalized methods are applied to quasi likelihood analysis for stochastic
differential equation models. In this paper, we treat the quasi likelihood
function and the associated statistical random field for which a polynomial
type large deviation inequality holds. Then penalty terms do not disturb a
polynomial type large deviation inequality. This property ensures the
convergence of moments of the associated estimator which plays an important
role to evaluate the upper bound of the probability that model selection is
incorrect.


  CalcHEP is a package for computation of Feynman diagrams and integration over
multi-particle phase space. The main idea prescribed into CalcHEP is to make
available passing on from Lagrangians to the final distributions effectively
with a high level of automation. This article presents new options of CalcHEP
available in version 2.3. They are a) MSSM model with different SUGRA
scenarios, b) interface with PDFLIB and implementation of new MRST/CTEQ
structure functions, c) realization of approach to structure functions for
models with diagonal CKM, d) generation of events and interface with PYTHIA, e)
calculations in non-interactive (batch) regime, f)generation of code of
different matrix elements for other programs, g) many new interface facilities.


  This article illustrates a novel Quantum Secure Aggregation (QSA) scheme that
is designed to provide highly secure and efficient aggregation of local model
parameters for federated learning. The scheme is secure in protecting private
model parameters from being disclosed to semi-honest attackers by utilizing
quantum bits i.e. qubits to represent model parameters. The proposed security
mechanism ensures that any attempts to eavesdrop private model parameters can
be immediately detected and stopped. The scheme is also efficient in terms of
the low computational complexity of transmitting and aggregating model
parameters through entangled qubits. Benefits of the proposed QSA scheme are
showcased in a horizontal federated learning setting in which both a
centralized and decentralized architectures are taken into account. It was
empirically demonstrated that the proposed QSA can be readily applied to
aggregate different types of local models including logistic regression (LR),
convolutional neural networks (CNN) as well as quantum neural network (QNN),
indicating the versatility of the QSA scheme. Performances of global models are
improved to various extents with respect to local models obtained by individual
participants, while no private model parameters are disclosed to semi-honest
adversaries.


  Recently, deep learning, which uses Deep Neural Networks (DNN), plays an
important role in many fields. A secure neural network model with a secure
training/inference scheme is indispensable to many applications. To accomplish
such a task usually needs one of the entities (the customer or the service
provider) to provide private information (customer's data or the model) to the
other. Without a secure scheme and the mutual trust between the service
providers and their customers, it will be an impossible mission. In this paper,
we propose a novel privacy-preserving deep learning model and a secure
training/inference scheme to protect the input, the output, and the model in
the application of the neural network. We utilize the innate properties of a
deep neural network to design a secure mechanism without using any complicated
cryptography component. The security analysis shows our proposed scheme is
secure and the experimental results also demonstrate that our method is very
efficient and suitable for real applications.


  We study the production of bound cascade hypernuclei via the (K-,K+) reaction
on 12C and 28Si targets within a covariant effective Lagrangian model,
employing the cascade bound state spinors derived from the latest quark-meson
coupling model as well as Dirac single particle wave functions. The K+-cascade
production vertex is described by excitation, propagation and decay of Lambda
and Sigma resonance states in the initial collision of a K- meson with a target
proton in the incident channel. The parameters of the resonance vertices are
fixed by describing the available data on total and differential cross sections
for the cascade production in elementary (K-,K+) reaction. We find that both
the elementary and hypernuclear production cross sections are dominated by the
contributions from the Lambda(1520) intermediate resonant state. The 0 degree
differential cross sections for the formation of simple s-state cascade
particle-hole states peak at a beam momentum around 1.0 GeV/c, with a value in
excess of 1 mub.


  This note corrects conditions in Proposition 3.4 and Theorem 5.2(ii) and
comments on imprecisions in Propositions 4.2 and 4.4 in Fissler and Ziegel
(2016).


  A phenomenological analysis of radiative $V\to P\gamma$ and $P\to V\gamma$
decays is performed with the purpose of determining the gluonic content of the
$\eta$ and $\eta^\prime$ wave functions. Our results show that within our model
there is no evidence for a gluonium contribution in the $\eta$,
$Z_\eta^2=0.00\pm 0.12$, or the $\eta^\prime$, $Z_{\eta^\prime}^2=0.04\pm
0.09$. In terms of a mixing angle description this corresponds to
$\phi_P=(41.4\pm 1.3)^\circ$ and $|\phi_{\eta^\prime G}|=(12\pm 13)^\circ$. In
addition, the $\eta$-$\eta^\prime$ mixing angle is found to be $\phi_P=(41.5\pm
1.2)^\circ$ if we don't allow for a gluonium component.


  In a recent Letter [PRL, 113, 050401 (2014)], it is shown that the quantum
violation of a three-time Leggett-Garg inequality (LGI) for a dichotomic qutrit
system can exceed the L$\ddot{u}$ders bound. This is obtained by using a
degeneracy breaking projective measurement rule which the authors termed as von
Neumann rule. Such violation can even approach the algebraic maximum in the
asymptotic limit of system size. In this paper, we question the implication of
such violation of L$\ddot{u}$ders bound and its conceptual relevance in LG
scenario. We note an important fact that the basis for implementing the
proposed von Neumann rule for a degenerate observable is non-unique and show
that the violation of L$\ddot{u}$ders bound is crucially dependent on the
choice of basis. Further, we demonstrate the violation of L$\ddot{u}$ders bound
of the simplest non-contextual inequality (NCI) which is in contrast to the
reasoning provided in the aforementioned Letter. This result further raises the
doubts regarding the validity of the proposed rule as a viable projective
measurement. We discuss the relevance of such results with respect to the usual
quantum violation of LGI and NCI.


  As one of the leading causes of mortality and disability worldwide, Acute
Ischemic Stroke (AIS) occurs when the blood supply to the brain is suddenly
interrupted because of a blocked artery. Within seconds of AIS onset, the brain
cells surrounding the blocked artery die, which leads to the progression of the
lesion. The automated and precise prediction of the existing lesion plays a
vital role in the AIS treatment planning and prevention of further injuries.
The current standard AIS assessment method, which thresholds the 3D measurement
maps extracted from Computed Tomography Perfusion (CTP) images, is not accurate
enough. Due to this fact, in this article, we propose the imbalanced Temporal
Deep Gaussian Process (iTDGP), a probabilistic model that can improve AIS
lesions prediction by using baseline CTP time series. Our proposed model can
effectively extract temporal information from the CTP time series and map it to
the class labels of the brain's voxels. In addition, by using batch training
and voxel-level analysis iTDGP can learn from a few patients and it is robust
against imbalanced classes. Moreover, our model incorporates a post-processor
capable of improving prediction accuracy using spatial information. Our
comprehensive experiments, on the ISLES 2018 and the University of Alberta
Hospital (UAH) datasets, show that iTDGP performs better than state-of-the-art
AIS lesion predictors, obtaining the (cross-validation) Dice score of 71.42%
and 65.37% with a significant p<0.05, respectively.


  We review and extend the Alexandrov-Kontsevich-Schwarz-Zaboronsky
construction of solutions of the Batalin-Vilkovisky classical master equation.
In particular, we study the case of sigma models on manifolds with boundary. We
show that a special case of this construction yields the Batalin-Vilkovisky
action functional of the Poisson sigma model on a disk. As we have shown in a
previous paper, the perturbative quantization of this model is related to
Kontsevich's deformation quantization of Poisson manifolds and to his formality
theorem. We also discuss the action of diffeomorphisms of the target manifolds.


  Periodic-finite-type shifts (PFT's) form a class of sofic shifts that
strictly contains the class of shifts of finite type (SFT's). In this paper, we
investigate how the notion of "period" inherent in the definition of a PFT
causes it to differ from an SFT, and how the period influences the properties
of a PFT.


  We consider a linear symmetric operator in a Hilbert space that is neither
bounded from above nor from below, admits a block decomposition corresponding
to an orthogonal splitting of the Hilbert space and has a variational gap
property associated with the block decomposition. A typical example is the
Dirac-Coulomb operator defined on $C^\infty_c(\mathbb R^3\setminus\{0\},
\mathbb C^4)$. In this paper we define a distinguished self-adjoint extension
with a spectral gap and characterize its eigenvalues in that gap by a min-max
principle. This has been done in the past under technical conditions. Here we
use a different, geometric strategy, to achieve that goal by making only
minimal assumptions. Our result applied to the Dirac-Coulomb-like Hamitonians
covers sign-changing potentials as well as molecules with an arbitrary number
of nuclei having atomic numbers less than or equal to 137.


  We study the derived categories of coherent sheaves of weighted projective
spaces and their noncommutative deformations, and the derived categories of
Lagrangian vanishing cycles of their mirror Landau-Ginzburg models. In
particular, we show that the derived category of coherent sheaves (B-branes) on
the weighted projective plane $\CP^2(a,b,c)$ is equivalent to the derived
category of vanishing cycles (A-branes) on the affine hypersurface
$X=\{x^ay^bz^c=1\}\subset (\C^*)^3$ equipped with an exact symplectic form and
the superpotential $W=x+y+z$. Hence, the homological mirror symmetry conjecture
holds for weighted projective planes. Moreover, we also show that this mirror
correspondence between derived categories can be extended to toric
noncommutative deformations of $\CP^2(a,b,c)$ where B-branes are concerned, and
their mirror counterparts, non-exact deformations of the symplectic structure
of $X$ where A-branes are concerned. We also obtain similar results for other
examples such as weighted projective lines or Hirzebruch surfaces.


  We classify certain algebras of matrix-valued cross-sections over an annulus
up to complete isometric isomorphism, based on topological bundle invariants.
In particular, we study sections of matrix bundles which are continuous on the
closure of the annulus and holomorphic on its interior. Our strategy includes
exploiting the relationship between concomitants and modulus automorphic
functions, as well as the classification of $n$-homogeneous $C^*$-algebras by
Fell and Tomiyama-Takesaki. Furthermore, we describe a partial extension of our
results over the annulus to larger classes of finitely and smoothly bordered
planar domains.


  Automatic vertebra localization and identification in CT scans is important
for numerous clinical applications. Much progress has been made on this topic,
but it mostly targets positional localization of vertebrae, ignoring their
orientation. Additionally, most methods employ heuristics in their pipeline
that can be sensitive in real clinical images which tend to contain
abnormalities. We introduce a simple pipeline that employs a standard
prediction with a U-Net, followed by a single graph neural network to associate
and classify vertebrae with full orientation. To test our method, we introduce
a new vertebra dataset that also contains pedicle detections that are
associated with vertebra bodies, creating a more challenging landmark
prediction, association and classification task. Our method is able to
accurately associate the correct body and pedicle landmarks, ignore false
positives and classify vertebrae in a simple, fully trainable pipeline avoiding
application-specific heuristics. We show our method outperforms traditional
approaches such as Hungarian Matching and Hidden Markov Models. We also show
competitive performance on the standard VerSe challenge body identification
task.


  The transformation stretch tensor plays an essential role in the evaluation
of conditions of compatibility between phases and the use of the Cauchy-Born
rule. This tensor is difficult to measure directly from experiment. We give an
algorithm for the determination of the transformation stretch tensor from x-ray
measurements of structure and lattice parameters. When evaluated on some
traditional and emerging phase transformations the algorithm gives unexpected
results.


  The performance of reinforcement learning depends upon designing an
appropriate action space, where the effect of each action is measurable, yet,
granular enough to permit flexible behavior. So far, this process involved
non-trivial user choices in terms of the available actions and their execution
frequency. We propose a novel framework for reinforcement learning that
effectively lifts such constraints. Within our framework, agents learn
effective behavior over a routine space: a new, higher-level action space,
where each routine represents a set of 'equivalent' sequences of granular
actions with arbitrary length. Our routine space is learned end-to-end to
facilitate the accomplishment of underlying off-policy reinforcement learning
objectives. We apply our framework to two state-of-the-art off-policy
algorithms and show that the resulting agents obtain relevant performance
improvements while requiring fewer interactions with the environment per
episode, improving computational efficiency.


  With many large science equipment constructing and putting into use,
astronomy has stepped into the big data era. The new method and infrastructure
of big data processing has become a new requirement of many astronomers. Cloud
computing, Map/Reduce, Hadoop, Spark, etc. many new technology has sprung up in
recent years. Comparing to the high performance computing(HPC), Data is the
center of these new technology. So, a new computing architecture infrastructure
is necessary, which can be shared by both HPC and big data processing. Based on
Astronomy Cloud project of Chinese Virtual Observatory (China-VO), we have made
much efforts to optimize the designation of the hybrid computing platform.
which include the hardware architecture, cluster management, Job and Resource
scheduling.


  Fuzzy modeling has many advantages over the non-fuzzy methods, such as
robustness against uncertainties and less sensitivity to the varying dynamics
of nonlinear systems. Data-driven fuzzy modeling needs to extract fuzzy rules
from the input/output data, and train the fuzzy parameters. This paper takes
advantages from deep learning, probability theory, fuzzy modeling, and extreme
learning machines. We use the restricted Boltzmann machine (RBM) and
probability theory to overcome some common problems in data based modeling
methods. The RBM is modified such that it can be trained with continuous
values. A probability based clustering method is proposed to partition the
hidden features from the RBM, and extract fuzzy rules with probability
measurement. An extreme learning machine and an optimization method are applied
to train the consequent part of the fuzzy rules and the probability parameters.
The proposed method is validated with two benchmark problems.


  In this paper we apply the time-dependent quantum Monte Carlo (TDQMC) method
to explore a midified single- and double-slit diffraction of matter waves. By
using a simplified model of two electrons prepared in the ground state of an
atom (molecule) and then suddenly released we are able to calculate the
diffraction patterns in one spatial dimension in close correspondence with the
numerically exact results. Through the Coulomb repulsion the one electron
serves as an environment for the other thus introducing decoherence in the
quantum state which is easily quantified. It is demonstrated that the set of
single particle wave functions yield by TDQMC can be used to directly construct
density matrix for that particle without tracing out the other particles from
the density matrix of the whole system. In this way it is possible to build
explicitly time-dependent density matrices for different components of a
complex quantum system straight within the TDQMC algorithm which may widen our
understanding of quantum dynamics in many-body systems.


  By numerically solving the relativistic Boltzmann equations, we compute the
time scale for relaxation to thermal equilibrium for an optically thick
electron-positron plasma with baryon loading. We focus on the time scales of
electromagnetic interactions. The collisional integrals are obtained directly
from the corresponding QED matrix elements. Thermalization time scales are
computed for a wide range of values of both the total energy density (over 10
orders of magnitude) and of the baryonic loading parameter (over 6 orders of
magnitude). This also allows us to study such interesting limiting cases as the
almost purely electron-positron plasma or electron-proton plasma as well as
intermediate cases. These results appear to be important both for laboratory
experiments aimed at generating optically thick pair plasmas as well as for
astrophysical models in which electron-positron pair plasmas play a relevant
role.


  The following paper is on the emergence of observable complexity in urban
networks visualized as product of essentially non-observable social processes.
The methodology unfolded here draws on recent insight of econophysics in the
strict sense under a top-down perspective of laying the foundations for a
modern view to the evolution of dynamical structures in nature. The conception
presented here deals with a section of ongoing cooperative research work being
undertaken by the authors in collaboration with Giorgio Colacchio (now U
Lecce). A first perspective as to the basic aspects of approach has been given
in a joint paper in order to lay down the main ideas in some detail:
R.E.Zimmermann, A.Soci, G.Colacchio (2001): Reconstructing Bologna. The City as
an Emergent Computational System. An Interdisciplinary Study in the Complexity
of Urban Structures. Part I: Basic Ideas & Fundamental Concepts.
http://www.arXiv.org/pdf/nlin.AO/0109025 v2.


  In this work we study the Lie group analysis of a generalized invicid
Burgers' equations with damping. Seven inequivalent classes of this generalized
equation were classified and many exact and transformed solutions were obtained
for each class.


  In this work, we focus on separable convex optimization problems with box
constraints and a set of triangular linear constraints. The solution is given
in closed-form as a function of some Lagrange multipliers that can be computed
through an iterative procedure in a finite number of steps. Graphical
interpretations are given casting valuable insights into the proposed algorithm
and allowing to retain some of the intuition spelled out by the water-filling
policy. It turns out that it is not only general enough to compute the solution
to different instances of the problem at hand but also remarkably simple in the
way it operates. We also show how some power allocation problems in signal
processing and communications can be solved with the proposed algorithm.


  The 2d CP(N-1) models share a number of features with QCD, like asymptotic
freedom, a dynamically generated mass gap and topological sectors. They have
been formulated and analysed successfully in the framework of the so-called
D-theory, which provides a smooth access to the continuum limit. In that
framework, we propose an experimental set-up for the quantum simulation of the
CP(2) model. It is based on ultra-cold Alkaline-Earth Atoms (AEAs) located on
the sites of an optical lattice, where the nuclear spins represent the relevant
degrees of freedom. We present numerical results for the correlation length and
for the real time decay of a false vacuum, to be compared with such a future
experiment. The latter could also enable the exploration of theta-vacua and of
the phase diagram at finite chemical potentials, since it does not suffer from
any sign problem.


  We introduce the dispersion-minimized mass for isogeometric analysis to
approximate the structural vibration which we model as a second-order
differential eigenvalue problem. The dispersion-minimized mass reduces the
eigenvalue error significantly, from the optimum order of $2p$ to the
superconvergence order of $2p+2$ for the $p$-th order isogeometric elements
with maximum continuity, which in return leads to more robust of the
isogeomectric analysis. We first establish the dispersion error for arbitrary
polynomial order isogeometric elements. We derive the dispersion-minimized mass
in one dimension by solving a $p$-dimensional local matrix problem for the
$p$-th order approximation and then extend it to multiple dimensions on
tensor-product grids. We show that the dispersion-minimized mass can also be
obtained by approximating the mass matrix using optimally blended quadratures.
We generalize the results of optimally blended quadratures from polynomial
orders $p=1,\cdots, 7$ that were studied in \cite{calo2017dispersion} to
arbitrary polynomial order isogeometric approximations. Various numerical
examples validate the eigenvalue and eigenfunction error estimates we derive.


  We study full counting statistics of electron transport through a Majorana
single-charge transistor. At low bias voltage, transport is dominated by the
so-called Josephson-Majorana cycle, a sequence of normal and anomalous
single-charge and Josephson tunneling. Factorial cumulants characterizing the
full counting statistics elucidate the correlated nature of the charge
transfers in this cycle. Moreover, we predict a topological transition in the
full counting statistics from a perfect Poissonian transfer of Cooper pairs to
a correlated switching between two distinct fermion parity states with
increasing Josephson coupling.


  As an important part of the field programmable gate array (FPGA) market,
Intel FPGA has also great potential for implementation of time-to-digital
convertor (TDC). In this paper, the basic tapped delay line (TDL) TDC structure
is adapted in Intel Arria 10 FPGA, which is manufactured with 20 nm process
technology. Because of the serious bubble problem for FPGA made by state-of-art
process, the ones counter encoding scheme is employed to maintain the delay
elements in TDL resolvable for achieving high TDC time precision. The test of
TDC bin width reveals that the characteristics of the delay chain are highly
consistent with the fundamental structure of logic resource in the FPGA. To
improve TDC time precision, four TDLs are combined parallel for final TDC
implementation. Using two identical TDC channels, the average RMS precision for
measurements of time-intervals in the range from 0 to 50 ns reaches 5.45 ps.
The test results demonstrate that high performance TDC can be implemented in
current Intel main-stream FPGAs as well.


  This paper presents a novel scheme for the annotation of hate speech in
corpora of Web 2.0 commentary. The proposed scheme is motivated by the critical
analysis of posts made in reaction to news reports on the Mediterranean
migration crisis and LGBTIQ+ matters in Malta, which was conducted under the
auspices of the EU-funded C.O.N.T.A.C.T. project. Based on the realization that
hate speech is not a clear-cut category to begin with, appears to belong to a
continuum of discriminatory discourse and is often realized through the use of
indirect linguistic means, it is argued that annotation schemes for its
detection should refrain from directly including the label 'hate speech,' as
different annotators might have different thresholds as to what constitutes
hate speech and what not. In view of this, we suggest a multi-layer annotation
scheme, which is pilot-tested against a binary +/- hate speech classification
and appears to yield higher inter-annotator agreement. Motivating the
postulation of our scheme, we then present the MaNeCo corpus on which it will
eventually be used; a substantial corpus of on-line newspaper comments spanning
10 years.


  Generative models and inferential autoencoders mostly make use of $\ell_2$
norm in their optimization objectives. In order to generate perceptually better
images, this short paper theoretically discusses how to use Structural
Similarity Index (SSIM) in generative models and inferential autoencoders. We
first review SSIM, SSIM distance metrics, and SSIM kernel. We show that the
SSIM kernel is a universal kernel and thus can be used in unconditional and
conditional generated moment matching networks. Then, we explain how to use
SSIM distance in variational and adversarial autoencoders and unconditional and
conditional Generative Adversarial Networks (GANs). Finally, we propose to use
SSIM distance rather than $\ell_2$ norm in least squares GAN.


  Cataclysmic variable SDSS J143317.78+101123.3 with P=0.054241 d (78.1-min)
was suspected to be a possible dwarf nova of WZ Sge type with the sub-stellar
donor, but without detected outbursts so far. Checking the newly available data
from ATLAS survey has revealed the outburst by at least 6 magnitudes in
September 2020, thus confirming the dwarf nova nature of this object with the
brown dwarf secondary. Other projects and individual observers have stopped
their monitoring of this target several days before the outburst. This finding
strengthens the value of observing the twilight zone by the professional
surveys and amateurs.


  Efficient optimization of operating room (OR) activity poses a significant
challenge for hospital managers due to the complex and risky nature of the
environment. The traditional "one size fits all" approach to OR scheduling is
no longer practical, and personalized medicine is required to meet the diverse
needs of patients, care providers, medical procedures, and system constraints
within limited resources. This paper aims to introduce a scientific and
practical tool for predicting surgery durations and improving OR performance
for maximum benefit to patients and the hospital. Previous works used
machine-learning models for surgery duration prediction based on preoperative
data. The models consider covariates known to the medical staff at the time of
scheduling the surgery. Given a large number of covariates, model selection
becomes crucial, and the number of covariates used for prediction depends on
the available sample size. Our proposed approach utilizes multi-task regression
to select a common subset of predicting covariates for all tasks with the same
sample size while allowing the model's coefficients to vary between them. A
regression task can refer to a single surgeon or operation type or the
interaction between them. By considering these diverse factors, our method
provides an overall more accurate estimation of the surgery durations, and the
selected covariates that enter the model may help to identify the resources
required for a specific surgery. We found that when the regression tasks were
surgeon-based or based on the pair of operation type and surgeon, our suggested
approach outperformed the compared baseline suggested in a previous study.
However, our approach failed to reach the baseline for an operation-type-based
task.


  A new iterative algorithm for solving initial data inverse problems from
partial observations has been recently proposed in Ramdani, Tucsnak and Weiss
[15]. Based on the concept of observers (also called Luenberger observers),
this algorithm covers a large class of abstract evolution PDE's. In this paper,
we are concerned with the convergence analysis of this algorithm. More
precisely, we provide a complete numerical analysis for semi-discrete (in
space) and fully discrete approximations derived using finite elements in space
and finite differences in time. The analysis is carried out for abstract
Schr\"odinger and wave conservative systems with bounded observation (locally
distributed).


  We present up-to-date constraints on the value of the fine-structure constant
at the epoch of decoupling from the recent observations made by the Wilkinson
Microwave Anisotropy Probe (WMAP) satellite. In the framework of models we
considered, a positive (negative) variation of the value of $\alpha$ at
decoupling with respect to the present-day value is now bounded to be smaller
than 2% (6%) at 95% confidence level. We point out that the existence of an
early reionization epoch as suggested by the above measurements will, when more
accurate cosmic microwave background polarization data is available, lead to
considerably tighter constraints. The so-called `reionization bump', in
particular, will be extremely useful for this purpose. We find that constraints
on $\alpha$ can in principle only be as small as $0.1 %$ using CMB data
alone--tighter constraints will require further (non-CMB) priors.


  Much recent work in cardinal characteristics has focused on generalizing
results about $\omega$ to uncountable cardinals by studying analogues of
classical cardinal characteristics on the generalized Baire and Cantor spaces
$\kappa^\kappa$ and $2^\kappa$. In this note I look at generalizations to other
function spaces, focusing particularly on the space of functions
$f:\omega^\omega \to \omega^\omega$. By considering classical cardinal
invariants on Baire space in this setting I derive a number of "higher
dimensional" analogues of such cardinals, ultimately introducing 18 new
cardinal invariants, alongside a framework that allows for numerous others.
These 18 form two separate diagrams consisting of 6 and 12 cardinals
respectively, each resembling versions of the Cicho\'n diagram. These
ZFC-inequalities are the first main result of the paper. I then consider other
relations between these cardinals, as well as the cardinal $\mathfrak{c}^+$ and
show that these results rely on additional assumptions about cardinal
characteristics on $\omega$. Finally, using variations of Cohen, Hechler, and
localization forcing I prove a number of consistency results for possible
values of the new cardinals.


  Expanding maps with indifferent fixed points, a.k.a. intermittent maps, are
popular models in nonlinear dynamics and infinite ergodic theory. We present a
simple proof of the exactness of a wide class of expanding maps of [0,1], with
countably many surjective branches and a strongly neutral fixed point in 0.


  Gas rich galaxies are typically star-forming. We make use of HI-MaNGA, a
program of HI follow-up for the Mapping Nearby Galaxies at Apache Point
Observatory (MaNGA) survey of the Sloan Digital Sky Surveys to construct a
sample of unusual neutral hydrogen (HI, 21cm) rich galaxies which have low Star
Formation Rates (SFRs); using infra-red color from the Wide-field Infrared
Survey Explorer (WISE) as a proxy for specific SFR. Out of a set of 1575 MaNGA
galaxies with HI-MaNGA detections, we find 83 (5%) meet our selection criteria
to be HI rich with low SFR. We construct two stellar mass-matched control
samples: HI rich galaxies with typical SFR (High SF Control) and HI poor
galaxies with low SFR (Low HI Control). We investigate the properties of each
of these samples, comparing physical parameters such as ionization state maps,
stellar and ionized gas velocity and dispersion, environment measures,
metallicity, and morphology to search for the reasons why these unusual HI rich
galaxies are not forming stars. We find evidence for recent external accretion
of gas in some galaxies (via high counter-rotating fractions), along with some
evidence for AGN feedback (from a high cLIER and/or red geyser fraction), and
bar quenching (via an enhanced strong bar fraction). Some galaxies in the
sample are consistent with simply having their HI in a high angular momentum,
large radius, low density disc. We conclude that no single physical process can
explain all HI rich, low SFR galaxies.


  Positron scattering and annihilation on noble gas atoms below the positronium
formation threshold is studied ab initio using many-body theory methods. The
many-body theory provides a near-complete understanding of the
positron-noble-gas-atom system at these energies and yields accurate numerical
results. It accounts for positron-atom and electron-positron correlations,
e.g., polarization of the atom by the incident positron and the
non-perturbative process of virtual positronium formation. These correlations
have a large effect on the scattering dynamics and result in a strong
enhancement of the annihilation rates compared to the independent-particle
mean-field description. Computed elastic scattering cross sections are found to
be in good agreement with recent experimental results and Kohn variational and
convergent close-coupling calculations. The calculated values of the
annihilation rate parameter $Z_{\rm eff}$ (effective number of electrons
participating in annihilation) rise steeply along the sequence of noble gas
atoms due to the increasing strength of the correlation effects, and agree well
with experimental data.


  The dispute on whether the three-dimensional (3D) incompressible Euler
equations develop an infinitely large vorticity in a finite time (blowup) keeps
increasing due to ambiguous results from state-of-the-art direct numerical
simulations (DNS), while the available simplified models fail to explain the
intrinsic complexity and variety of observed structures. Here, we propose a new
model formally identical to the Euler equations, by imitating the calculus on a
3D logarithmic lattice. This model clarifies the present controversy at the
scales of existing DNS and provides the unambiguous evidence of the following
transition to the blowup, explained as a chaotic attractor in a renormalized
system. The chaotic attractor spans over the anomalously large six-decade
interval of spatial scales. For the original Euler system, our results suggest
that the existing DNS strategies at the resolution accessible now (and
presumably rather long into the future) are unsuitable, by far, for the blowup
analysis, and establish new fundamental requirements for the approach to this
long-standing problem.


  Starshade in formation flight with a space telescope is a rapidly maturing
technology that would enable imaging and spectral characterization of small
planets orbiting nearby stars in the not-too-distant future. While performance
models of the starshade-assisted exoplanet imaging have been developed and used
to design future missions, their results have not been verified from the
analyses of synthetic images. Following a rich history of using community data
challenges to evaluate image-processing capabilities in astronomy and exoplanet
fields, the Starshade Technology Development to TRL5 (S5), a focused technology
development activity managed by the NASA Exoplanet Exploration Program, is
organizing and implementing a starshade exoplanet data challenge. The purpose
of the data challenge is to validate the flow down of requirements from science
to key instrument performance parameters and to quantify the required accuracy
of noisy background calibration with synthetic images. This data challenge
distinguishes itself from past efforts in the exoplanet field in that (1) it
focuses on the detection and spectral characterization of small planets in the
habitable zones of nearby stars, and (2) it develops synthetic images that
simultaneously include multiple background noise terms -- some specific to
starshade observations -- including residual starlight, solar glint,
exozodiacal light, detector noise, as well as variability resulting from
starshade's motion and telescope jitter. In this paper, we provide an overview
of the design and rationale of the data challenge. Working with data challenge
participants, we expect to achieve improved understanding of the noise budget
and background calibration in starshade-assisted exoplanet observations in the
context of both Starshade Rendezvous with Roman and HabEx.