  Reducing wall drag in turbulent pipe and channel flows is an issue of great
practical importance. In engineering applications, end-functionalized polymer
chains are often employed as agents to reduce drag. These are polymers which
are floating in the solvent and attach (either by adsorption or through
irreversible chemical binding) at one of their chain ends to the substrate
(wall). We propose a PDE model to study this setup in the simple setting where
the solvent is a viscous incompressible Navier-Stokes fluid occupying the bulk
of a smooth domain $\Omega\subset \mathbb{R}^d$, and the wall-grafted polymer
is in the so-called mushroom regime (inter-polymer spacing on the order of the
typical polymer length). The microscopic description of the polymer enters into
the macroscopic description of the fluid motion through a dynamical boundary
condition on the wall-tangential stress of the fluid, something akin to (but
distinct from) a history-dependent slip-length. We establish global
well-posedness of strong solutions in two-spatial dimensions and prove that the
inviscid limit to the strong Euler solution holds with a rate. Moreover, the
wall-friction factor $\langle f\rangle$ and the global energy dissipation
$\langle \varepsilon\rangle$ vanish inversely proportional to the Reynolds
number $Re$. This scaling corresponds to Poiseuille's law for the friction
factor $\langle f\rangle \sim1/ Re$ for laminar flow and thereby quantifies
drag reduction in our setting. These results are in stark contrast to those
available for physical boundaries without polymer additives modeled by, e.g.,
no-slip conditions, where no such results are generally known even in
two-dimensions.


  We propose a method to measure the ellipticities of dark matter halos using
the lens-shear-shear 3-point correlation function. This method is immune to
effects of galaxy-halo misalignments that can potentially limit 2-point
galaxy-galaxy lensing measurements of halo anisotropy. Using a simple model for
the projected mass distributions of dark matter halos, we construct an
ellipticity estimator that sums over all possible triangular configurations of
the 3-point function. By applying our estimator to halos from N-body
simulations, we find that systematic errors in the recovered ellipticity will
be at the <5% fractional level. We estimate that future imaging surveys like
LSST will have sufficient statistics to detect halo ellipticities using 3-point
lensing.


  Financial markets are difficult to predict due to its complex systems
dynamics. Although there have been some recent studies that use machine
learning techniques for financial markets prediction, they do not offer
satisfactory performance on financial returns. We propose a novel
one-dimensional convolutional neural networks (CNN) model to predict financial
market movement. The customized one-dimensional convolutional layers scan
financial trading data through time, while different types of data, such as
prices and volume, share parameters (kernels) with each other. Our model
automatically extracts features instead of using traditional technical
indicators and thus can avoid biases caused by selection of technical
indicators and pre-defined coefficients in technical indicators. We evaluate
the performance of our prediction model with strictly backtesting on historical
trading data of six futures from January 2010 to October 2017. The experiment
results show that our CNN model can effectively extract more generalized and
informative features than traditional technical indicators, and achieves more
robust and profitable financial performance than previous machine learning
approaches.


  The original position or veil of ignorance argument of John Rawls, perhaps
the most famous argument for egalitarianism, states that our concept of
fairness, justice, or welfare should be decided from behind a veil of
ignorance, and thus must consider everyone impartially (invariant to our
identity). This can be posed as a zero-sum game, where a Daemon constructs a
world, and an adversarial Angel then places the Daemon into the world. This
game incentivizes the Daemon to maximize the minimum utility over all people
(i.e., to maximize egalitarian welfare). In some sense, this is the most
extreme form of risk aversion or robustness, and we show that by weakening the
Angel, milder robust objectives arise, which we argue are effective robust
proxies for fair learning or allocation tasks. In particular, the utilitarian,
Gini, and power-mean welfare concepts arise from special cases of the
adversarial game, which has philosophical implications for the understanding of
each of these concepts. We also motivate a new fairness concept that
essentially fuses the nonlinearity of the power-mean with the piecewise nature
of the Gini class. Then, exploiting the relationship between fairness and
robustness, we show that these robust fairness concepts can all be efficiently
optimized under mild conditions via standard maximin optimization techniques.
Finally, we show that such methods apply in machine learning contexts, and
moreover we show generalization bounds for robust fair machine learning tasks.


  The change in Holographic entanglement entropy (HEE) for small fluctuations
about pure AdS is given by a perturbative expansion of the area functional in
terms of the change in the bulk metric and the embedded extremal surface.
However it is known that change in the embedding appear at second order or
higher. In this paper we show that these changes in the embedding can be
systematically calculated in the 2+1 dimensional case by accounting for the
deviation of the spacelike geodesics between a spacetime and perturbations over
it. Here we consider rotating BTZ as perturbation over $AdS_3$ and study
deviations of spacelike geodesics in them. We argue that these deviations arise
naturally as solutions of a "generalized geodesic deviation equation". Using
this we perturbatively calculate the changes in HEE upto second order, for
rotating BTZ. This expression matches with the small system size expansion of
the change in HEE obtained by HRT (Hubeny, Rangamani and Takayanagi) proposal
for rotating BTZ. We also write an alternative form of entanglement first law
for rotating BTZ. To do this one needs to go beyond the leading order in the
perturbation series discussed above. That's precisely the reason we consider
finding a systematic way to calculate it. To put our result on a firm footing
we further show that it is this alternative first law that approaches the
thermal first law in the large subsystem size limit.


  We initiate the investigation of the projective variety $E(r,g)$ of
elementary subalgebras of dimension $r$ of a ($p$-restricted) Lie algebra $g$
for some $r > 0$ and demonstrate that this variety encodes considerable
information about the representations of $g$. For various choices of $g$ and
$r$, we identify the geometric structure of $E(r,g)$. We show that special
classes of (restricted) representations of $g$ lead to algebraic vector bundles
on $E(r,g)$. For $g = Lie(G)$ the Lie algebra of an algebraic group $G$,
rational representations of $G$ enable us to realize familiar algebraic vector
bundles on $G$-orbits of $E(r, g)$.


  When thinking about dressing oneself, people often have a theme in mind
whether they're going to a tropical getaway or wish to appear attractive at a
cocktail party. A useful outfit generation system should come up with clothing
items that are compatible while matching a theme specified by the user.
Existing methods use item-wise compatibility between products but lack an
effective way to enforce a global constraint (e.g., style, occasion).
  We introduce a method that generates outfits whose items match a theme
described by a text query. Our method uses text and image embeddings to
represent fashion items. We learn a multimodal embedding where the image
representation for an item is close to its text representation, and use this
embedding to measure item-query coherence. We then use a discriminator to
compute compatibility between fashion items. This strategy yields a
compatibility prediction method that meets or exceeds the state of the art.
  Our method combines item-item compatibility and item-query coherence to
construct an outfit whose items are (a) close to the query and (b) compatible
with one another. Quantitative evaluation shows that the items in our outfits
are tightly clustered compared to standard outfits. Furthermore, outfits
produced by similar queries are close to one another, and outfits produced by
very different queries are far apart. Qualitative evaluation shows that our
method responds well to queries. A user study suggests that people understand
the match between the queries and the outfits produced by our method.


  In this note we prove the following good-$\lambda$ inequality, for $r>2$, all
$\lambda > 0$, $\delta \in \big(0, \frac{1}{2} \big)$ \[ \nu\big\{ V_r(f) > 3
\lambda ; \mathcal{M}(f) \leq \delta \lambda\big\} \leq 4 \nu\{s(f) > \delta
\lambda\} + {\delta^2 \left(1+\frac{16}{r-2}\right)^2} \cdot \nu\big\{ V_r(f) >
\lambda\big\}, \] where $\mathcal{M}(f)$ is the martingale maximal function,
$s(f)$ is the conditional martingale square function. This immediately proves
that $V_r(f)$ is bounded on $L^p$, $1 < p <\infty$ and moreover is integrable
when the maximal function is.


  Contrary to the see-saw models, extended Higgs sectors leading to
radiatively-induced neutrino masses do require the extra particles to be at the
TeV scale. However, these new states have often exotic decays, to which
experimental LHC searches performed so far, focused on scalars decaying into
pairs of same-sign leptons, are not sensitive. In this paper we show that their
experimental signatures can start to be tested with current LHC data if
dedicated multi-region analyses correlating different observables are used. We
also provide high-accuracy estimations of the complicated Standard Model
backgrounds involved. For the case of the Zee-Babu model, we show that regions
not yet constrained by neutrino data and low-energy experiments can be already
probed, while most of the parameter space could be excluded at the 95 % C.L. in
a high-luminosity phase of the LHC.


  In the studies of the Navier-Stokes (NS) regularity problem, it has become
increasingly clear that a more realistic path to improved a priori bounds is to
try to break away from the scaling of the energy-level estimates in the realm
of the blow-up-type arguments (the solution in view is regular/smooth up to the
possible blow-up time) rather than to try to improve regularity of arbitrary
Leray's weak solutions. The present article is a contribution in this
direction; more precisely, it is shown--in the context of an
algebraic/polynomial-type blow-up profile of arbitrary degree--that a very weak
condition on the vorticity direction field (membership in a local $bmo$ space
weighted with arbitrary many logarithms)--suffices to break the energy-level
scaling in the bounds on the vorticity. At the same time, the obtained bounds
transform a 3D NS criticality scenario depicted by the macro-scale long vortex
filaments into a no-singularity scenario.


  Magnesium (Mg) and its alloys offer great potential for reducing vehicular
mass and energy consumption due to their inherently low densities.
Historically, widespread applicability has been limited by low strength
properties compared to other structural Al-, Ti- and Fe-based alloys. However,
recent studies have demonstrated high-specific-strength in a number of
nanocrystalline Mg-alloys. Even so, applications of these alloys would be
restricted to low-temperature automotive components due to microstructural
instability under high temperature creep loading. Hence, this work aims to gain
a better understanding of creep and associated deformation behavior of columnar
nanocrystalline Mg and Mg-yttrium (Y) (up to 3at.%Y(10wt.%Y)) with a grain size
of 5 nm and 10 nm. Using molecular dynamics (MD) simulations, nanocrystalline
magnesium with and without local concentrations of yttrium is subjected to
constant-stress loading ranging from 0 to 500 MPa at different initial
temperatures ranging from 473 to 723 K. In pure Mg, the analyses of the
diffusion coefficient and energy barrier reveal that at lower temperatures
(i.e., T < ~423K) the contribution of grain boundary diffusion to the overall
creep deformation is stronger that the contribution of lattice diffusion.
However, at higher temperatures (T > ~423K) lattice diffusion dominates the
overall creep behavior. Interestingly, for the first time, we have shown that
the(101-1),(101-2),(101-3) and (101-6) boundary sliding energy is reduced with
the addition of yttrium. This softening effect in the presence of yttrium
suggests that the experimentally observed high temperature beneficial effects
of yttrium addition is likely to be attributed to some combination of other
reported creep strengthening mechanisms or phenomena such as formation of
stable yttrium oxides at grain boundaries or increased forest dislocation-based
hardening.


  Unmanned aerial vehicles (UAVs) can provide wireless access to terrestrial
users, regardless of geographical constraints, and will be an important part of
future communication systems. In this paper, a multi-user downlink dual-UAVs
enabled covert communication system was investigated, in which a UAV transmits
secure information to ground users in the presence of multiple wardens as well
as a friendly jammer UAV transmits artificial jamming signals to fight with the
wardens. The scenario of wardens being outfitted with a single antenna is
considered, and the detection error probability (DEP) of wardens with finite
observations is researched. Then, considering the uncertainty of wardens'
location, a robust optimization problem with worst-case covertness constraint
is formulated to maximize the average covert rate by jointly optimizing power
allocation and trajectory. To cope with the optimization problem, an algorithm
based on successive convex approximation methods is proposed. Thereafter, the
results are extended to the case where all the wardens are equipped with
multiple antennas. After analyzing the DEP in this scenario, a tractable lower
bound of the DEP is obtained by utilizing Pinsker's inequality. Subsequently,
the non-convex optimization problem was established and efficiently coped by
utilizing a similar algorithm as in the single-antenna scenario. Numerical
results indicate the effectiveness of our proposed algorithm.


  Problems of particle dynamics involving unsteady Stokes flows in confined
geometries are typically harder to solve than their steady counterparts.
Approximation techniques are often the only resort. Felderhof (see e.g. 2005,
2009b) has developed a point-particle approximation framework to solve such
problems, especially in the context of Brownian motion. Despite excellent
agreement with past experiments, this framework has an inconsistency which we
address in this work. Upon implementing our modifications, the framework passes
consistency checks that it previously failed. Further, it is not obvious that
such an approximation should work for short time-scale motion. We investigate
its validity by deriving it from a general formalism based on integral
equations through a series of systematic approximations. We also compare
results from the point-particle framework against a calculation performed using
the method of reflections, for the specific case of a sphere near a full-slip
plane boundary. We find from our analysis that the reasons for the success of
the point-particle approximation are subtle and have to do with the nature of
the unsteady Oseen tensor. Finally, we provide numerical predictions for
Brownian motion near a full-slip and a no-slip plane wall based on the
point-particle approximation as used by Felderhof, our modified point-particle
approximation, and the method of reflections. We show that our modifications to
Felderhof's framework would become significant for systems of metallic
nanoparticles in liquids.


  We present a method for non-smooth convex minimization which is based on
subgradient directions and string-averaging techniques. In this approach, the
set of available data is split into sequences (strings) and a given iterate is
processed independently along each string, possibly in parallel, by an
incremental subgradient method (ISM). The end-points of all strings are
averaged to form the next iterate. The method is useful to solve sparse and
large-scale non-smooth convex optimization problems, such as those arising in
tomographic imaging. A convergence analysis is provided under realistic,
standard conditions. Numerical tests are performed in a tomographic image
reconstruction application, showing good performance for the convergence speed
when measured as the decrease ratio of the objective function, in comparison to
classical ISM.


  Free-response research-based assessments, like the Colorado Upper-division
Electrostatics Diagnostic (CUE), provide rich, fine-grained information about
students' reasoning. However, because of the difficulties inherent in scoring
these assessments, the majority of the large-scale conceptual assessments in
physics are multiple-choice. To increase the scalability and usability of the
CUE, we set out to create a new version of the assessment that preserves the
insights afforded by a free-response format while exploiting the logistical
advantages of a multiple-choice assessment. We used our extensive database of
responses to the free-response CUE to construct distractors for a new version
where students can select multiple responses and receive partial credit based
on the accuracy and consistency of their selections. Here, we describe the
development of this modified CUE format, which we call coupled
multiple-response (CMR), and present data from direct comparisons of both
versions. We find that the two formats have the same average score and perform
similarly on multiple measures of validity and reliability, suggesting that the
new version is a potentially viable alternative to the original CUE for the
purpose of large-scale research-based assessment. We also compare the details
of student responses on each of the two versions. While the CMR version does
not capture the full scope of potential student responses, nearly
three-quarters of our students' responses to the free-response version
contained one or more elements that matched options provided on the CMR
version.


  We make progress towards an analytical understanding of the regime of
validity of perturbation theory for large scale structures and the nature of
some non-perturbative corrections. We restrict ourselves to 1D gravitational
collapse, for which exact solutions before shell crossing are known. We review
the convergence of perturbation theory for the power spectrum, recently proven
by McQuinn and White, and extend it to non-Gaussian initial conditions and the
bispectrum. In contrast, we prove that perturbation theory diverges for the
real space two-point correlation function and for the probability density
function (PDF) of the density averaged in cells and all the cumulants derived
from it. We attribute these divergences to the statistical averaging intrinsic
to cosmological observables, which, even on very large and "perturbative"
scales, gives non-vanishing weight to all extreme fluctuations. Finally, we
discuss some general properties of non-perturbative effects in real space and
Fourier space.


  We introduce a family of noncommutative 4-spheres, such that the instanton
projector has its first Chern class trivial: $ch_1(e) = B \chi + b \xi$. We
construct for them a 4-dimensional cycle and calculate explicitly the
Chern-Connes paring for the instanton projector.


  This paper is concerned with the construction of high order schemes on
irregular grids for balance laws, including a discussion of an a-posteriori
error indicator based on the numerical entropy production. We also impose
well-balancing on non uniform grids for the shallow water equations, which can
be extended similarly to other cases, obtaining schemes up to fourth order of
accuracy with very weak assumptions on the regularity of the grid. Our results
show the expected convergence rates, the correct propagation of shocks across
grid discontinuities and demonstrate the improved resolution achieved with a
locally refined non-uniform grid.
  The error indicator based on the numerical entropy production, previously
introduced for the case of systems of conservation laws, is extended to balance
laws. Its decay rate and its ability to identify discontinuities is illustrated
on several tests. The schemes proposed in this work naturally can also be
applied to systems of conservation laws.


  Given a set $F$ of $n$ positive functions over a ground set $X$, we consider
the problem of computing $x^*$ that minimizes the expression $\sum_{f\in
F}f(x)$, over $x\in X$. A typical application is \emph{shape fitting}, where we
wish to approximate a set $P$ of $n$ elements (say, points) by a shape $x$ from
a (possibly infinite) family $X$ of shapes. Here, each point $p\in P$
corresponds to a function $f$ such that $f(x)$ is the distance from $p$ to $x$,
and we seek a shape $x$ that minimizes the sum of distances from each point in
$P$. In the $k$-clustering variant, each $x\in X$ is a tuple of $k$ shapes, and
$f(x)$ is the distance from $p$ to its closest shape in $x$.
  Our main result is a unified framework for constructing {\em coresets} and
{\em approximate clustering} for such general sets of functions. To achieve our
results, we forge a link between the classic and well defined notion of
$\varepsilon$-approximations from the theory of PAC Learning and VC dimension,
to the relatively new (and not so consistent) paradigm of coresets, which are
some kind of "compressed representation" of the input set $F$. Using
traditional techniques, a coreset usually implies an LTAS (linear time
approximation scheme) for the corresponding optimization problem, which can be
computed in parallel, via one pass over the data, and using only
polylogarithmic space (i.e, in the streaming model).
  We show how to generalize the results of our framework for squared distances
(as in $k$-mean), distances to the $q$th power, and deterministic
constructions.


  The bug triaging process, an essential process of assigning bug reports to
the most appropriate developers, is related closely to the quality and costs of
software development. As manual bug assignment is a labor-intensive task,
especially for large-scale software projects, many machine-learning-based
approaches have been proposed to automatically triage bug reports. Although
developer collaboration networks (DCNs) are dynamic and evolving in the
real-world, most automated bug triaging approaches focus on static tossing
graphs at a single time slice. Also, none of the previous studies consider
periodic interactions among developers. To address the problems mentioned
above, in this article, we propose a novel spatial-temporal dynamic graph
neural network (ST-DGNN) framework, including a joint random walk (JRWalk)
mechanism and a graph recurrent convolutional neural network (GRCNN) model. In
particular, JRWalk aims to sample local topological structures in a graph with
two sampling strategies by considering both node importance and edge
importance. GRCNN has three components with the same structure, i.e.,
hourly-periodic, daily-periodic, and weekly-periodic components, to learn the
spatial-temporal features of dynamic DCNs. We evaluated our approach's
effectiveness by comparing it with several state-of-the-art graph
representation learning methods in two domain-specific tasks that belong to
node classification. In the two tasks, experiments on two real-world,
large-scale developer collaboration networks collected from the Eclipse and
Mozilla projects indicate that the proposed approach outperforms all the
baseline methods.


  Parity-time (PT) symmetric dimers were introduced to highlight the unusual
properties of non-Hermitian systems that are invariant after a combined parity
and time reversal operation. They are also the building blocks of a variety of
symmetry and topologically protected structures, especially on integrated
photonic platforms. As the name suggests, it consists of two coupled
oscillators, which can be optical, mechanical, electronic, and so on in nature.
In this article, we show that its effective size, defined by the number of
lattice sites inversely proportional to the lattice momentum, is surprisingly
three instead of two from the perspective of energy quantization. More
specifically, we show analytically that the complex energy levels of a
one-dimensional concatenated chain with $N$ PT-dimers are determined by a
system size of $1+2N$, which reduces to three in the case of a single PT-dimer.
We note that while energy quantization conditions were established in various
non-Hermitian systems, exact and explicitly quantized complex energies as
reported here are still scarce. In connection, we also discuss the other
symmetries of a PT-dimer and concatenated PT-dimer chain, including
non-Hermitian particle-hole symmetry and chiral symmetry.


  Today's wireless technologies are largely based on inflexible designs, which
makes them inefficient and prone to a variety of wireless attacks. To address
this key issue, wireless receivers will need to (i) infer on-the-fly the
physical-layer parameters currently used by transmitters; and if needed, (ii)
change their hardware and software structures to demodulate the incoming
waveform. In this paper, we introduce PolymoRF, a deep learning-based
polymorphic receiver able to reconfigure itself in real time based on the
inferred waveform parameters. Our key technical innovations are (i) a novel
embedded deep learning architecture, called RFNet, which enables the solution
of key waveform inference problems; (ii) a generalized hardware/software
architecture that integrates RFNet with radio components and signal processing.
We prototype PolymoRF on a custom software-defined radio platform, and show
through extensive over-the-air experiments that (i) RFNet achieves similar
accuracy to that of state-of-the-art yet with 52x and 8x latency and hardware
reduction; (ii) PolymoRF achieves throughput within 87% of a perfect-knowledge
Oracle system, thus demonstrating for the first time that polymorphic receivers
are feasible and effective.


  N\"orlund strong logarithmic means of double Fourier series acting from space
$% L\log L(\mathbb{T}^{2}) $ into space $L_{p}(\mathbb{T}% ^{2}), 0<p<1$ are
studied. The maximal Orlicz space such that the N\"o% rlund strong logarithmic
means of double Fourier series for the functions from this space converge in
two-dimensional measure is found.


  The dynamics of a quantum system following a sudden, highly non-adiabatic
change of its control parameter (quantum quench) is studied with quasiclassical
techniques. Recent works have shown, using exact quantum mechanical approach,
that equilibration after quantum quench exhibits specific features in the
presence of excited-state quantum phase transitions. In this paper, we
demonstrate that these features can be understood from the classical evolution
of the Wigner function in phase space.


  The equation for the wave-function localization length in terms of the
two-point correlation function of a weak random potential in 1D (F. M. Izrailev
and A. A. Krokhin, Phys. Rev. Lett. vol. 82, 4062 (1999)) is rederived using
the standard weak-localization theory. I propose a modified algorithm for
generation of arbitrary correlated random potentials, and show that numerical
calculation of the localization length in a specific correlated disorder in 1D
is then in accord with the analytic expression, removing the discrepancy noted
in the above Letter.


  The key transform of the REESSE1+ asymmetrical cryptosystem is Ci = (Ai * W ^
l(i)) ^ d (% M) with l(i) in Omega = {5, 7, ..., 2n + 3} for i = 1, ..., n,
where l(i) is called a lever function. In this paper, the authors give a
simplified key transform Ci = Ai * W ^ l(i) (% M) with a new lever function
l(i) from {1, ..., n} to Omega = {+/-5, +/-6, ..., +/-(n + 4)}, where "+/-"
means the selection of the "+" or "-" sign. Discuss the necessity of the new
l(i), namely that a simplified private key is insecure if the new l(i) is a
constant but not one-to-one function. Further, expound the sufficiency of the
new l(i) from four aspects: (1) indeterminacy of the new l(i), (2) insufficient
conditions for neutralizing the powers of W and W ^-1 even if Omega = {5, 6,
..., n + 4}, (3) verification by examples, and (4) running times of continued
fraction attack and W-parameter intersection attack which are the two most
efficient algorithms of the probabilistic polytime attacks so far. Last, the
authors detail the relation between a lever function and a random oracle.


  We study ballistic transport properties of graphene with a low concentration
of vacancies or adatoms. The conductance of graphene doped to the Dirac point
is found to depend on the relative distribution of impurities among different
sites of the honeycomb lattice labeled in general by six colors. The
conductivity is shown to be sensitive to the crystal orientation if adatom
sites have a preferred color. Our theory is confirmed by numerical simulations
using recursive Green's functions with no adjustable parameters.


  We obtain integral boundary decay estimates for solutions of fourth-order
elliptic equations on a bounded domain with regular boundary. We apply these
estimates to obtain stability bounds for the corresponding eigenvalues under
small perturbations of the boundary.


  Today, the number of IoT devices in all aspects of life is exponentially
increasing. The cities we are living in are getting smarter and informing us
about our surroundings in a contextual manner. However, there lay significant
challenges of deploying, managing and collecting data from these devices, in
addition to the problem of storing and mining that data for higher-quality IoT
services. Blockchain technology, even in today's nascent form, contains the
pillars to create a common, distributed, trustless and autonomous
infrastructure system. This paper describes a standardized IoT infrastructure;
where data is stored on a DDOS-resistant, fault-tolerant, distributed storage
service and data access is managed by a decentralized, trustless blockchain.
The illustrated system used LoRa as the emerging network technology, Swarm as
the distributed data storage and Ethereum as the blockchain platform. Such a
data backend will ensure high availability with minimal security risks while
replacing traditional backend systems with a single "smart contract".


  Three properties of matrices: the spark, the mutual incoherence and the
restricted isometry property have recently been introduced in the context of
compressed sensing. We study these properties for matrices that are Kronecker
products and show how these properties relate to those of the factors. For the
mutual incoherence we also discuss results for sums of Kronecker products.


  Recent work suggests a link between chiral asymmetry in the amino acid
iso-valine extracted from the Murchison meteorite and the extent of hydrous
alteration. We present the results of neutron scattering experiments on an
exchanged, 1-dimensionally ordered n-propyl ammonium vermiculite clay. The
vermiculite gel has a (001) d-spacing of order 5nm at the temperature and
concentration of the experiments and the d-spacing responds sensitively to
changes in concentration, temperature and electronic environment. The data show
that isothermal addition of D-histidine or L-histidine solutions produces
shifts in the d-spacing that are different for each enantiomer. This chiral
specificity is of interest for the question of whether clays could have played
an important role in the origin of biohomochirality.


  Like elementary particles carry energy and momentum in the Universe,
quasiparticles are the elementary carriers of energy and momentum quanta in
condensed matter. And, like elementary particles, under certain conditions
quasiparticles can be unstable and decay, emitting pairs of less energetic
ones. Pitaevskii proposed that such processes exist in superfluid helium, a
quantum fluid where the very concept of quasiparticles was borne, and which
provided the first spectacular triumph of that concept. Pitaevskii's decays
have important consequences, including possible breakdown of a quasiparticle.
Here, we present neutron scattering experiments, which provide evidence that
such decays explain the collapsing lifetime (strong damping) of higher-energy
phonon-roton sound-wave quasiparticles in superfluid helium. This damping
develops when helium is pressurized towards crystallization or warmed towards
approaching the superfluid transition. Our results resolve a number of puzzles
posed by previous experiments and reveal the ubiquity of quasiparticle decays
and their importance for understanding quantum matter.


  In this paper we present solutions of evolution equations for inclusive
distribution of gluons as produced by jet traversing quark-gluon plasma. We
reformulate the original equations in such a form that the virtual and
unresolved-real emissions as well as unresolved collisions with medium are
resummed in a Sudakov-type form factor. The resulting integral equations are
then solved most efficiently with use of newly developed Markov Chain Monte
Carlo algorithms implemented in a dedicated program called MINCAS. Their
results for a gluon energy density are compared with an analytical solution and
a differential numerical method. Some results for gluon transverse-momentum
distributions are also presented. They exhibit interesting patterns not
discussed so far in the literature, in particular a departure from the Gaussian
behaviour - which does not happen in approximate analytical solutions.


  A new finite-range electromagnetic (EM) theory containing both electric and
magnetic charges constructed using two vector potentials A^{} and Z^{} is
formulated in the spacetime algebra (STA) and in the algebra of the
three-dimensional physical space (APS) formalisms. Lorentz, local gauge and EM
duality invariances are discussed in detail in the APS formalism. Moreover,
considerations about signature and dimensionality of spacetime are discussed.
Finally, the two formulations are compared. STA and APS are equally powerful in
formulating our model, but the presence of a global commuting unit pseudoscalar
in the APS formulation and the consequent possibility of providing a geometric
interpretation for the imaginary unit employed throughout classical and quantum
physics lead us to prefer the APS approach.


  We introduce a Kauffman-Jones type polynomial $\mathcal{L}_{\gamma}(A)$ for a
curve $\gamma$ on an oriented surface, whose endpoints are on the boundary of
the surface. The polynomial $\mathcal{L}_{\gamma}(A)$ is a Laurent polynomial
in one variable $A$ and is an invariant of the homotopy class of $\gamma$. As
an application, we obtain an estimate in terms of the span of
$\mathcal{L}_{\gamma}(A)$ for the minimum self-intersection number of a curve
within its homotopy class. We then give a chord diagrammatic description of
$\mathcal{L}_{\gamma}(A)$ and show some computational results on the span of
$\mathcal{L}_{\gamma}(A)$.


  The coupling of a Nambu-Goto string to gravity allows for Schwarzschild black
holes whose entropy to area relation is $S=(A/4)(1-4\mu)$, where $\mu$ is the
string tension.


  Recently, the number of requests for multicast services through the wireless
networks has been increased. However, for successful deployment, security and
efficiency of content delivery must be provided at first. This paper presents a
new approach for secure multicast in wireless networks. This approach, CRAW
(Combination of Re-keying and Authentication in Wireless networks) combines
member authentication procedure with group key management protocol to provide
an efficient group re-keying process. One-time password is proposed for member
authentication and CKC (Code for Key Calculation) is suggested for group key
management in wireless networks. In fact, the combination of authentication
with group key management in wireless networks results in a simple and secure
mechanism both for authentication and group key management while mobile members
join/leave a group or move inter-area. Simulation results show that CRAW
reduces re-keying overhead at join from O(log2 n+1) to O(1) while security
requirements are saved. Also, CRAW introduces storing a main list to manage
mobile members' location while they move intra-group inter-area.


  We point out the necessity to consider $\beta$-decay observables in
resolutions of the $W$-boson anomaly in the Standard Model Effective Field
Theory that go beyond pure oblique corrections. We demonstrate that present
global analyses that explain the $W$-boson mass anomaly predict a large,
percent-level, violation of first-row CKM unitarity. We investigate what
solutions to the $W$-boson mass anomaly survive after including $\beta$-decay
constraints.


  A thorough analysis is performed to find traveling waves in a qualitative
reaction-diffusion system inspired by a predator-prey model. We provide
rigorous results coming from a standard local stability analysis, numerical
bifurcation analysis, and relevant computations of invariant manifolds to
exhibit homoclinic and heteroclinic connections, and periodic orbits in the
associated traveling wave system with four components. In so doing, we present
and describe a zoo of different traveling wave solutions. In addition,
homoclinic chaos is manifested via both saddle-focus and focus-focus
bifurcations as well as a Belyakov point. An actual computation of global
invariant manifolds near a focus-focus homoclinic bifurcation is also presented
to unravel a multiplicity of wave solutions in the model.


  We present a generic Markovian master equation inducing the gradual
classicalization of a bosonic quantum field. It leads to the decoherence of
quantum superpositions of field configurations, while leaving the Ehrenfest
equations for both the field and the mode-variables invariant. We characterize
the classicalization analytically and numerically, and show that the
semiclassical field dynamics is described by a linear Boltzmann equation in the
functional phase space of field configurations.


  This paper is about the flow of inference between communicative intentions,
discourse structure and the domain during discourse processing. We augment a
theory of discourse interpretation with a theory of distinct mental attitudes
and reasoning about them, in order to provide an account of how the attitudes
interact with reasoning about discourse structure.


  We study some dynamical aspects of the action of automorphisms in model
theory in particular in the presence of invariant measures. We give some
characterizations for NIP theories in terms of dynamics of automorphisms and
invariant measures for example in terms of compact systems, entropy and measure
algebras. Moreover, we study the concept of symbolic representation for models.
Amongst the results, we give some characterizations for dividing lines and
combinatorial configurations such as independence property, order property and
strictly order property in terms of symbolic representations.


  We investigate the structural and magnetic origins of the unusual ultrafast
second-harmonicgeneration (SHG) response of femtosecond-laser-excited nickel
oxide (NiO) previously attributed to oscillatory reorientation dynamics of the
magnetic structure induced by d-d excitations. Using time-resolved x-ray
diffraction from the (3/2 3/2 3/2) magnetic planes, we show that changes in the
magnitude of the magnetic structure factor following ultrafast optical
excitation are limited to $\Delta<F_m>/<F_m>$ = 1.5% in the first 30 ps. An
extended investigation of the ultrafast SHG response reveals a strong
dependence on wavelength as well as characteristic echoes, both of which give
evidence for an acoustic origin of the dynamics. We therefore propose an
alternative mechanism for the SHG response based on perturbations of the
nonlinear susceptibility via optically induced strain in a spatially confined
medium. In this model, the two observed oscillation periods can be understood
as the times required for an acoustic strain wave to traverse one coherence
length of the SHG process in either the collinear or anti-collinear geometries.


  The decay rates of P-wave heavy quarkonia to light hadrons are presented to
leading order in $v^2$ and next-to-leading order in $\alpha_s$. They include
contributions from both the color-singlet component and the color-octet
component of quarkonia. Applying these results to charmonium and using measured
decay rates for the $\chi_{c1}$ and $\chi_{c2}$ by E760, we determine the two
nonperturbative decay matrix elements, and then predict the hadronic decay
rates of $\chi_{c0}$ and $h_c$, and the electromagnetic decay rates of
$\chi_{c0}$ and $\chi_{c2}$. The obtained decay rates of $\chi_{c0}\to LH$ and
$\chi_{c0}\to\gamma\gamma$ are in agreement with the Crystal Ball result, and
also with the new measurement by BES. However, the results for
$\Gamma(\chi_{c0}\to LH)$ are dependent on the choice of renormalization scale.


  High-redshift galaxies are expected to be more turbulent than local galaxies
because of their smaller size and higher star formation and thus stronger
feedback from star formation, frequent mergers events, and gravitational
instabilities. However, this scenario has recently been questioned by the
observational evidence of a few galaxies at z~4-5 with a gas velocity
dispersion similar to what is observed in the local population. Our goal is to
determine whether galaxies in the first Gyrs of the Universe have already
formed a dynamically cold rotating disk similar to the local counterparts. We
studied the gas kinematic of 22 main-sequence star-forming galaxies at z > 5
and determined their dynamical state by estimating the ratio of the rotational
velocity and of the gas velocity dispersion. We mined the ALMA archive and
exploited the [CII] and [OIII] observations to perform a kinematic analysis of
the cold and warm gas of z>5 main-sequence galaxies. The gas kinematics of the
high-z galaxies is consistent within the errors with rotating but turbulent
disks. We infer a velocity dispersion that is systematically higher by 4 times
than the local galaxy population and the z~5 dust-obscured galaxies reported in
the literature. The difference between our results and those reported at
similar redshift can be ascribed to the systematic difference in the galaxy
properties in the two samples: the disks of massive dusty galaxies are
dynamically colder than the disks of dust-poor galaxies. The comparison with
the theoretical predictions suggests that the main driver of the velocity
dispersion in high-z galaxies is the gravitational energy that is released by
the transport of mass within the disk. Finally, we stress that future deeper
ALMA high-angular resolution observations are crucial to constrain the
kinematic properties of high-z galaxies and to distinguish rotating disks from
kpc-scale mergers.


  Non-probability survey samples are examples of data sources that have become
increasingly popular in recent years, also in official statistics. However,
statistical inference based on non-probability samples is much more difficult
because they are biased and are not representative of the target population
(Wu, 2022). In this paper we consider a method of joint calibration for totals
(Deville & S\"arndal, 1992) and quantiles (Harms & Duchesne, 2006) and use the
proposed approach to extend existing inference methods for non-probability
samples, such as inverse probability weighting, mass imputation and doubly
robust estimators. By including quantile information in the estimation process
non-linear relationships between the target and auxiliary variables can be
approximated the way it is done in step-wise (constant) regression. Our
simulation study has demonstrated that the estimators in question are more
robust against model mis-specification and, as a result, help to reduce bias
and improve estimation efficiency. Variance estimation for our proposed
approach is also discussed. We show that existing inference methods can be used
and that the resulting confidence intervals are at nominal levels. Finally, we
applied the proposed methods to estimate the share of vacancies aimed at
Ukrainian workers in Poland using an integrated set of administrative and
survey data about job vacancies. The proposed approaches have been implemented
in two R packages (nonprobsvy and jointCalib), which were used to conduct the
simulation and empirical study


  We study the degrees of freedom (DoF) of a $K$-user parallel MISO broadcast
channel with arbitrary levels of partial CSIT over each subchannel. We derive a
sum-DoF upperbound which depends on the average CSIT quality of each user. This
upperbound is shown to be tight under total order, i.e. when the order of users
with respect to their CSIT qualities is preserved over all subchannels. In this
case, it is shown that separate coding over each subchannel is optimum in a
sum-DoF sense.


  We present a theoretically consistent definition of the topological charge
operator based on renormalization group arguments. Results of the
  measurement of the topological susceptibility at zero and finite temperature
for SU(2) gauge theory are presented.


  This paper presents a self-supervised framework for learning to detect robust
keypoints for odometry estimation and metric localisation in radar. By
embedding a differentiable point-based motion estimator inside our
architecture, we learn keypoint locations, scores and descriptors from
localisation error alone. This approach avoids imposing any assumption on what
makes a robust keypoint and crucially allows them to be optimised for our
application. Furthermore the architecture is sensor agnostic and can be applied
to most modalities. We run experiments on 280km of real world driving from the
Oxford Radar RobotCar Dataset and improve on the state-of-the-art in
point-based radar odometry, reducing errors by up to 45% whilst running an
order of magnitude faster, simultaneously solving metric loop closures.
Combining these outputs, we provide a framework capable of full mapping and
localisation with radar in urban environments.


  Vector databases have emerged as key enablers for bridging intelligent
applications with unstructured data, providing generic search and management
support for embedding vectors extracted from the raw unstructured data. As
multiple data users can share the same database infrastructure, multi-tenancy
support for vector databases is increasingly desirable. This hinges on an
efficient filtered search operation, i.e., only querying the vectors accessible
to a particular tenant. Multi-tenancy in vector databases is currently achieved
by building either a single, shared index among all tenants, or a per-tenant
index. The former optimizes for memory efficiency at the expense of search
performance, while the latter does the opposite. Instead, this paper presents
Curator, an in-memory vector index design tailored for multi-tenant queries
that simultaneously achieves the two conflicting goals, low memory overhead and
high performance for queries, vector insertion, and deletion. Curator indexes
each tenant's vectors with a tenant-specific clustering tree and encodes these
trees compactly as sub-trees of a shared clustering tree. Each tenant's
clustering tree adapts dynamically to its unique vector distribution, while
maintaining a low per-tenant memory footprint. Our evaluation, based on two
widely used data sets, confirms that Curator delivers search performance on par
with per-tenant indexing, while maintaining memory consumption at the same
level as metadata filtering on a single, shared index.


  An experimental proposal to test the existence of a liquid-liquid critical
point in water, based on high pressure calorimetric measurements, is presented
on this paper. Considering the existence of an intramolecular correlation in
the water molecule we show how the response of the specific heat at high
pressure is different depending on the existence, or not, of the second
critical point. If the liquid-liquid critical point hypothesis is true there
must be a maximum in the specific heat at some temperature $T>T_{H}$ for any
pressure $P>P_{c}$ (being $T_{H}$ the homogeneous nucleation temperature and
$P_{c}$ the pressure of the second critical point). This maximum does not
appear for the singularity free scenario.


  We discuss how in supersymmetric models with D and F-flat directions, a
primordial monopole flux of order 10^{-16} - 10^{-18} cm^{-2} sec^{-1} sr^{-1}
can coexist with the observed baryon asymmetry. A modified Affleck-Dine
scenario yields the desired asymmetry if the monopoles are superheavy (~
10^{13}-10^{18} GeV). For lighter monopoles with masses ~ 10^{9}-10^{12} GeV,
the baryon asymmetry can arise via TeV scale leptogenesis.


  Recent results of M.Junge and Q.Xu on the ergodic properties of the averages
of kernels in noncommutative L^p-spaces are applied to the analysis of the
almost uniform convergence of operators induced by the convolutions on compact
quantum groups.


  We study the relationship between solar wind helium to hydrogen abundance
ratio ($A_\mathrm{He}$), solar wind speed ($v_\mathrm{SW}$), and sunspot number
(SSN) over solar cycles 23 and 24. This is the first full 22-year Hale cycle
measured with the Wind spacecraft covering a full cycle of the solar dynamo
with two polarity reversals. While previous studies have established a strong
correlation between $A_\mathrm{He}$ and SSN, we show that the phase delay
between $A_\mathrm{He}$ and SSN is a monotonic increasing function of
$v_\mathrm{SW}$. Correcting for this lag, $A_\mathrm{He}$ returns to the same
value at a given SSN over all rising and falling phases and across solar wind
speeds. We infer that this speed-dependent lag is a consequence of the
mechanism that depletes slow wind $A_\mathrm{He}$ from its fast wind value
during solar wind formation.


  Adaptive optics (AO) is a technique to improve the resolution of ground-based
telescopes by correcting, in real-time, optical aberrations due to atmospheric
turbulence and the telescope itself. With the rise of Giant Segmented Mirror
Telescopes (GSMT), AO is needed more than ever to reach the full potential of
these future observatories. One of the main performance drivers of an AO system
is the wavefront sensing operation, consisting of measuring the shape of the
above mentioned optical aberrations. Aims. The non-modulated pyramid wavefront
sensor (nPWFS) is a wavefront sensor with high sensitivity, allowing the limits
of AO systems to be pushed. The high sensitivity comes at the expense of its
dynamic range, which makes it a highly non-linear sensor. We propose here a
novel way to invert nPWFS signals by using the principle of reciprocity of
light propagation and the Gerchberg-Saxton (GS) algorithm. We test the
performance of this reconstructor in two steps: the technique is first
implemented in simulations, where some of its basic properties are studied.
Then, the GS reconstructor is tested on the Santa Cruz Extreme Adaptive optics
Laboratory (SEAL) testbed located at the University of California Santa Cruz.
This new way to invert the nPWFS measurements allows us to drastically increase
the dynamic range of the reconstruction for the nPWFS, pushing the dynamics
close to a modulated PWFS. The reconstructor is an iterative algorithm
requiring heavy computational burden, which could be an issue for real-time
purposes in its current implementation. However, this new reconstructor could
still be helpful in the case of many wavefront control operations. This
reconstruction technique has also been successfully tested on the Santa Cruz
Extreme AO Laboratory (SEAL) bench where it is now used as the standard way to
invert nPWFS signal.


  The origin of allometric scaling patterns that are multiples of 1/4 has long
fascinated biologists. While not universal, scaling relationships with
exponents that are close to multiples of 1/4 are common and have been described
in all major clades. Foremost among these relationships is the 3/4 scaling of
metabolism with mass which underpins the 1/4 power dependence of biological
rates and times. Several models have been advanced to explain the underlying
mechanistic drivers of such patterns, but questions regarding a disconnect
between model structures and empirical data have limited their widespread
acceptance. Notable among these is a fractal branching model which predicts
power law scaling of both metabolism and physical dimensions. While a power law
is a useful first approximation to many datasets, non-linearity in some large
data compilations suggest the possibility of more complex or alternative
mechanisms. Here, we first show that quarter power scaling can be derived using
only the preservation of volume flow rate and velocity as model constraints.
Applying our model to the specific case of land plants, we show that
incorporating biomechanical principles and allowing different parts of plant
branching networks to be optimized to serve different functions predicts
non-linearity in allometric relationships, and helps explain why interspecific
scaling exponents covary along a fractal continuum. We also demonstrate that
while branching may be a stochastic process, due to the conservation of volume,
data may still be consistent with the expectations for a fractal network when
one examines subtrees within a tree. Data from numerous sources at the level of
plant shoots, stems, petioles, and leaves show strong agreement with our model
predictions. This novel theoretical framework provides an easily testable
alternative to current general models of plant metabolic allometry.


  We show that the backward orbit conjecture is true for powering map
$\phi(z)=z^d$ over a function field $K$ with a finite field of constants, and
when $d$ is relatively prime to the characteristic of $K$.


  We propose a semi-discrete scheme for 2D Keller-Segel equations based on a
symmetrization reformation, which is equivalent to the convex splitting method
and is free of any nonlinear solver. We show that, this new scheme is
unconditionally stable as long as the initial condition does not exceed certain
threshold, and it asymptotically preserves the quasi-static limit in the
transient regime. Furthermore, we prove that the fully discrete scheme is
conservative and positivity preserving, which makes it ideal for simulations.
The analogical schemes for the radial symmetric cases and the subcritical
degenerate cases are also presented and analyzed. With extensive numerical
tests, we verify the claimed properties of the methods and demonstrate their
superiority in various challenging applications.


  A fully self-consistent microscopic framework for evaluation of nuclear
weak-interaction rates at finite temperature is introduced, based on Skyrme
functionals. The single-nucleon basis and the corresponding thermal occupation
factors of the initial nuclear state are determined in the finite-temperature
Skyrme Hartree-Fock model, and charge-exchange transitions to excited states
are computed using the finite-temperature RPA. Effective interactions are
implemented self-consistently: both the finite-temperature single-nucleon
Hartree-Fock equations and the matrix equations of RPA are based on the same
Skyrme energy density functional. Using a representative set of Skyrme
functionals, the model is applied in the calculation of stellar
electron-capture cross sections for selected nuclei in the iron mass group and
for neutron-rich Ge isotopes.


  Direct observations of the first generation of luminous objects will likely
become feasible over the next decade. The advent of the Next Generation Space
Telescope (NGST) will allow imaging of numerous galaxies and mini-quasars at
redshifts z>5. We apply semi-analytic models of structure formation to estimate
the rate of multiple imaging of these sources by intervening gravitational
lenses. Popular CDM models for galaxy formation yield a lensing optical depth
of about 1% for sources at redshift 10. The expected slope of the luminosity
function of the early sources implies an additional magnification bias of about
5, bringing the fraction of lensed sources at z=10 to about 5%. We estimate the
angular size distribution of high-redshift disk galaxies and find that most of
them are more extended than the resolution limit of NGST, roughly 0.06
arcseconds. We also show that there is only a modest redshift evolution in the
mean surface brightness of galaxies at z>2. The expected increase by 1-2 orders
of magnitude in the number of resolved sources on the sky, due to observations
with NGST, will dramatically improve upon the statistical significance of
existing weak lensing measurements. We show that, despite this increase in the
density of sources, confusion noise from z>2 galaxies is expected to be small
for NGST observations.


  It has recently been observed that the SIR distributions of a variety of
cellular network models and transmission techniques look very similar in shape.
As a result, they are well approximated by a simple horizontal shift (or gain)
of the distribution of the most tractable model, the Poisson point process
(PPP). To study and explain this behavior, this paper focuses on general
single-tier network models with nearest-base station association and studies
the asymptotic gain both at 0 and at infinity.
  We show that the gain at 0 is determined by the so-called mean
interference-to-signal ratio (MISR) between the PPP and the network model under
consideration, while the gain at infinity is determined by the expected
fading-to-interference ratio (EFIR).
  The analysis of the MISR is based on a novel type of point process, the
so-called relative distance process, which is a one-dimensional point process
on the unit interval [0,1] that fully determines the SIR. A comparison of the
gains at 0 and infinity shows that the gain at 0 indeed provides an excellent
approximation for the entire SIR distribution. Moreover, the gain is mostly a
function of the network geometry and barely depends on the path loss exponent
and the fading. The results are illustrated using several examples of repulsive
point processes.


  Automated analysis of optical colonoscopy (OC) video frames (to assist
endoscopists during OC) is challenging due to variations in color, lighting,
texture, and specular reflections. Previous methods either remove some of these
variations via preprocessing (making pipelines cumbersome) or add diverse
training data with annotations (but expensive and time-consuming). We present
CLTS-GAN, a new deep learning model that gives fine control over color,
lighting, texture, and specular reflection synthesis for OC video frames. We
show that adding these colonoscopy-specific augmentations to the training data
can improve state-of-the-art polyp detection/segmentation methods as well as
drive next generation of OC simulators for training medical students. The code
and pre-trained models for CLTS-GAN are available on Computational Endoscopy
Platform GitHub (https://github.com/nadeemlab/CEP).


  Recent empirical results using quantum annealing hardware have shown that mid
anneal pausing has a surprisingly beneficial impact on the probability of
finding the ground state for of a variety of problems. A theoretical
explanation of this phenomenon has thus far been lacking. Here we provide an
analysis of pausing using a master equation framework, and derive conditions
for the strategy to result in a success probability enhancement. The
conditions, which we identify through numerical simulations and then prove to
be sufficient, require that relative to the pause duration the relaxation rate
is large and decreasing right after crossing the minimum gap, small and
decreasing at the end of the anneal, and is also cumulatively small over this
interval, in the sense that the system does not thermally equilibrate. This
establishes that the observed success probability enhancement can be attributed
to incomplete quantum relaxation, i.e., is a form of beneficial non-equilibrium
coupling to the environment.


  In this paper, we state and prove some congruence properties for the
trinomial coeficients, one of which is similar to the Wolstenholme's theorem.


  We propose and analyze a discontinuous least squares finite element method
for solving the indefinite time-harmonic Maxwell equations. The scheme is based
on the $L^2$ norm least squares functional with the weak imposition of the
continuity across the interior faces. We minimize the functional over the
piecewise polynomial spaces to seek numerical solutions. The method is shown to
be stable without any constraint on the mesh size. We prove the convergence
orders under both the energy norm and the $L^2$ norm. Numerical results in two
and three dimensions are presented to verify the error estimates.


  Within the perturbative QCD approach, we investigated the
Cabibbo-Kobayashi-Maskawa-favored $B \to \overline{D} S$ ("$S$" denoting the
scalar meson) decays on the basis of the two-quark picture. Supposing the
scalar mesons are the ground states or the first excited states, we calculated
the the branching ratios of 72 decay modes. Most of the branching ratios are in
the range $10^{-4}$ to $10^{-7}$, which can be tested in the ongoing LHCb
experiment and the forthcoming Belle-II experiment. Some decays, such as $B^+
\to \overline{D}^{(*)0} a_0^+(980/1450)$ and $B^+ \to D^{(*)-}
a_0^+(980/1450)$, could be used to probe the inner structure and the character
of the scalar mesons, if the experiments are available. In addition, the ratios
between the $Br(B^0\to \overline{D}^{(*)0}\sigma)$ and $Br(B^0\to
\overline{D}^{(*)0}f_0(980))$ provide a potential way to determine the mixing
angle between $\sigma$ and $f_0(980)$. Moreover, since in the standard model
these decays occur only through tree operators and have no $CP$ asymmetries,
any deviation will be signal of the new physics beyond the standard model.


  Offline reinforcement learning (RL), which refers to decision-making from a
previously-collected dataset of interactions, has received significant
attention over the past years. Much effort has focused on improving offline RL
practicality by addressing the prevalent issue of partial data coverage through
various forms of conservative policy learning. While the majority of algorithms
do not have finite-sample guarantees, several provable conservative offline RL
algorithms are designed and analyzed within the single-policy concentrability
framework that handles partial coverage. Yet, in the nonlinear function
approximation setting where confidence intervals are difficult to obtain,
existing provable algorithms suffer from computational intractability,
prohibitively strong assumptions, and suboptimal statistical rates. In this
paper, we leverage the marginalized importance sampling (MIS) formulation of RL
and present the first set of offline RL algorithms that are statistically
optimal and practical under general function approximation and single-policy
concentrability, bypassing the need for uncertainty quantification. We identify
that the key to successfully solving the sample-based approximation of the MIS
problem is ensuring that certain occupancy validity constraints are nearly
satisfied. We enforce these constraints by a novel application of the augmented
Lagrangian method and prove the following result: with the MIS formulation,
augmented Lagrangian is enough for statistically optimal offline RL. In stark
contrast to prior algorithms that induce additional conservatism through
methods such as behavior regularization, our approach provably eliminates this
need and reinterprets regularizers as "enforcers of occupancy validity" than
"promoters of conservatism."


  This model assumes the baryons, radiation, three families of massless
neutrinos, and cold dark matter were mutually thermalized before the baryon
number was fixed, primeval curvature fluctuations were subdominant, and
homogeneity was broken by scale-invariant fluctuations in a new dark matter
component that behaves like a relativistic ideal fluid. The fluid behavior
could follow if this new component were a single scalar field that interacts
only with gravity and with itself by a pure quartic potential. The initial
energy distribution could follow if this component were gravitationally
produced by inflation. The power spectra of the present distributions of mass
and radiation in this model are not inconsistent with the measurements but are
sufficiently different from the adiabatic cold dark matter model to allow a
sharp test in the near future.


  In this paper we make a partial progress on the following conjecture: for
every $\mu>0$ and large enough $n$, every Steiner triple system $S$ on at least
$(1+\mu)n$ vertices contains every hypertree $T$ on $n$ vertices. We prove that
the conjecture holds if $T$ is a perfect $d$-ary hypertree.


  Recent work on the gas dynamics in the Galactic Center has improved our
understanding of the accretion processes in galactic nuclei, particularly with
regard to properties such as the specific angular momentum distribution,
density, and temperature of the inflowing plasma. This information can be
valuable in trying to determine the origin of the Broad Line Region (BLR) in
Active Galactic Nuclei (AGNs). In this paper, we explore various scenarios for
the cloud formation based on the underlying principle that the source of plasma
is ultimately that portion of the gas trapped by the central black hole from
the interstellar medium. Based on what we know about the Galactic Center, it is
likely that in highly dynamic environments such as this, the supply of matter
is due mostly to stellar winds from the central cluster. Winds accreting onto a
central black hole are subjected to several disturbances capable of producing
shocks, including a Bondi-Hoyle flow, stellar wind-wind collisions, and
turbulence. Shocked gas is initially compressed and heated out of thermal
equilibrium with the ambient radiation field; a cooling instability sets in as
the gas is cooled via inverse-Compton and bremsstrahlung processes. If the
cooling time is less than the dynamical flow time through the shock region, the
gas may clump to form the clouds responsible for broad line emission seen in
many AGN spectra. Clouds produced by this process display the correct range of
densities and velocity fields seen in broad emission lines. Very importantly,
the cloud distribution agrees with the results of reverberation studies, in
which it is seen that the central line peak responds slower to continuum
changes than the line wings.


  I.A.B. Strachan introduced the notion of a natural Frobenius submanifold of a
Frobenius manifold and gave a sufficient but not necessary condition for a
submanifold to be a natural Frobenius submanifold. This paper will give a
necessary and sufficient condition and classify the natural Frobenius
hypersurfaces.


  Working together on complex collaborative tasks requires agents to coordinate
their actions. Doing this explicitly or completely prior to the actual
interaction is not always possible nor sufficient. Agents also need to
continuously understand the current actions of others and quickly adapt their
own behavior appropriately. Here we investigate how efficient, automatic
coordination processes at the level of mental states (intentions, goals), which
we call belief resonance, can lead to collaborative situated problem-solving.
We present a model of hierarchical active inference for collaborative agents
(HAICA). It combines efficient Bayesian Theory of Mind processes with a
perception-action system based on predictive processing and active inference.
Belief resonance is realized by letting the inferred mental states of one agent
influence another agent's predictive beliefs about its own goals and
intentions. This way, the inferred mental states influence the agent's own task
behavior without explicit collaborative reasoning. We implement and evaluate
this model in the Overcooked domain, in which two agents with varying degrees
of belief resonance team up to fulfill meal orders. Our results demonstrate
that agents based on HAICA achieve a team performance comparable to recent
state of the art approaches, while incurring much lower computational costs. We
also show that belief resonance is especially beneficial in settings were the
agents have asymmetric knowledge about the environment. The results indicate
that belief resonance and active inference allow for quick and efficient agent
coordination, and thus can serve as a building block for collaborative
cognitive agents.


  This is a short technical report introducing the solution of the Team
TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC)
Workshop and Challenge at CVPR 2021. In this paper, we introduce a strong
backbone which is cross-window based Shuffle Transformer for presenting
accurate face parsing representation. To further obtain the finer segmentation
results, especially on the edges, we introduce a Feature Alignment Aggregation
(FAA) module. It can effectively relieve the feature misalignment issue caused
by multi-resolution feature aggregation. Benefiting from the stronger backbone
and better feature aggregation, the proposed method achieves 86.9519% score in
the Short-video Face Parsing track of the 3rd Person in Context (PIC) Workshop
and Challenge, ranked the first place.


  We study homogeneous cosmological models in formulations of general
relativity with cosmological constant based on a (complexified) connection
rather than a spacetime metric, in particular in a first order theory obtained
by integrating out the self-dual two-forms in the chiral Plebanski formulation.
Classical dynamics for the Bianchi IX model are studied in the Lagrangian and
Hamiltonian formalism, where we emphasise the reality conditions needed to
obtain real Lorentzian solutions. The solutions to these reality conditions
fall into different branches, which in turn lead to different real Hamiltonian
theories, only one of which is the usual Lorentzian Bianchi IX model. We also
show the simpler case of the flat Bianchi I model, for which both the reality
conditions and dynamical equations simplify considerably. We discuss the
relation of a real Euclidean version of the same theory to this complex theory.
Finally, we study the quantum theory of homogeneous and isotropic models, for
which the pure connection action for general relativity reduces to a pure
boundary term and the path integral is evaluated immediately, reproducing known
results in quantum cosmology. An intriguing aspect of these theories is that
the signature of the effective spacetime metric, and hence the interpretation
of the cosmological constant, are intrinsically ambiguous.


  In this article, we study properties of multilinear Fourier integral
operators on weighted modulation spaces. In particular, using the theory of
Gabor frames, we study boundedness of multilinear Fourier integral operators on
products of weighted modulation spaces. Further, we investigate the periodic
multilinear Fourier integral operator. Finally, we study continuity of bilinear
pseudo-differential operators on modulation spaces for certain symbol classes,
namely $\textbf{SG}$-class.


  Beyond 2025 we will enter the High-Luminosity era of the LHC, right after the
upgrades of the third Long Shutdown of the Large Hadron Collider (LHC). The
ongoing state-of-the-art experimental instrument upgrades require
high-performance simulation support in the background, that is modern, robust,
and comes with long term support. The original FORTRAN based HIJING (Heavy Ion
Jet INteraction Generator) has been used intensively since almost three decades
by the high energy physics and heavy-ion community. However, it is getting
overly challenging to conform to these new requirements. Our novel Monte Carlo
event generator, the HIJING++ is the successor of the old FORTRAN version
containing all the physics that its predecessor have. Moreover, among others a
flexible module handling layer and an analysis interface is also introduced.
This latter supports the most popular event container formats such as simple
ascii, ROOT and the HepMC format, together with RIVET support. In this paper we
compare the pre-release results of HIJING++ with proton-proton experimental
data and Pythia8 calculations.


  Mechanisms of pattern formation---of which the Turing instability is an
archetype---constitute an important class of dynamical processes occurring in
biological, ecological and chemical systems. Recently, it has been shown that
the Turing instability can induce pattern formation in discrete media such as
complex networks, opening up the intriguing possibility of exploring it as a
generative mechanism in a plethora of socioeconomic contexts. Yet, much remains
to be understood in terms of the precise connection between network topology
and its role in inducing the patterns. Here, we present a general mathematical
description of a two-species reaction-diffusion process occurring on different
flavors of network topology. The dynamical equations are of the predator-prey
class, that while traditionally used to model species population, has also been
used to model competition between antagonistic ideas in social systems. We
demonstrate that the Turing instability can be induced in any network topology,
by tuning the diffusion of the competing species, or by altering network
connectivity. The extent to which the emergent patterns reflect topological
properties is determined by a complex interplay between the diffusion
coefficients and the localization properties of the eigenvectors of the graph
Laplacian. We find that networks with large degree fluctuations tend to have
stable patterns over the space of initial perturbations, whereas patterns in
more homogenous networks are purely stochastic.


  In this answer to the Comment by Hansen and Engelen it is shown, that if
there is any violation of the baryon number conservation law in H(0) nuclear
reactions, it is not at all of the form that the authors believe. Their belief
is disproved by cited well-known scientific results from other groups. It is
further shown that quantum mechanics in H(0) molecules is different than these
authors believe, not formulated in kinetic energy terms but defined by angular
momentum quantization. Repetition of experiments is required, not pondering by
non-specialists.


  High-resolution optical spectra were obtained in 2017-2019 with The Southern
African Large Telescope of fifteen stars in the direction of the Vela supernova
remnant. Interstellar Ca ii H and K and Na i D lines are discussed in this
paper. In particular, the line profiles are compared with profiles at a
comparable spectral resolution obtained in 1993-1996 by Cha & Sembach. Ten of
the lines of sight show changes to one or more of the components in that line
of sight. Changes include small changes (1-2 km/s) in radial velocity and/or
increases/decreases in equivalent width over the two decades between the
periods of observation. Changes are more obvious in the Ca K line than in the
Na D lines. These changes are attributed to gas disturbed by interactions
between the supernova ejecta and the surrounding interstellar medium. A
representative timescale may be 20-50 years. Small-scale variations in line
profiles across the face of the remnant suggest, as previously remarked, that a
linear scale for interactions is a small fraction of the 40 pc size of the
present remnant.


  We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018)
coreference system on Dutch datasets of two domains: literary novels and
news/Wikipedia text. The results provide insight into the relative strengths of
data-driven and knowledge-driven systems, as well as the influence of domain,
document length, and annotation schemes. The neural system performs best on
news/Wikipedia text, while the rule-based system performs best on literature.
The neural system shows weaknesses with limited training data and long
documents, while the rule-based system is affected by annotation differences.
The code and models used in this paper are available at
https://github.com/andreasvc/crac2020


  Designing a robust affinity model is the key issue in multiple target
tracking (MTT). This paper proposes a novel affinity model by learning feature
representation and distance metric jointly in a unified deep architecture.
Specifically, we design a CNN network to obtain appearance cue tailored towards
person Re-ID, and an LSTM network for motion cue to predict target position,
respectively. Both cues are combined with a triplet loss function, which
performs end-to-end learning of the fused features in a desired embedding
space. Experiments in the challenging MOT benchmark demonstrate, that even by a
simple Linear Assignment strategy fed with affinity scores of our method, very
competitive results are achieved when compared with the most recent
state-of-theart approaches.


  We present an efficient, robust computational method for modeling the
Newtonian dynamics for rotation curve analysis of thin-disk galaxies. For a
disk galaxy with a typical flat rotation curve, our modeling results show that
the surface mass density monotonically decreases from the galactic center
toward periphery, according to Newtonian dynamics. In a large portion of the
galaxy, the surface mass density follows an approximately exponential law of
decay with respect to the galactic radial coordinate. Yet the radial scale
length for the surface mass density seems to be generally larger than that of
the measured brightness distribution, suggesting an increasing mass-to-light
ratio with the radial distance in a disk galaxy. In a nondimensionalized form,
our mathematical system contains a dimensionless parameter which we call the
"galactic rotation number" that represents the gross ratio of centrifugal force
and gravitational force. The value of this galactic rotation number is
determined as part of the numerical solution. Through a systematic
computational analysis, we have illustrated that this galactic rotation number
remains within $\pm 10%$ of 1.70 for a wide variety of rotation curves. This
implies that the total mass in a disk galaxy is proportional to $V_0^2\,R_g$,
with $V_0$ denoting the characteristic rotation velocity and $R_g$ the radius
of the galactic disk. The predicted total galactic mass of the Milky Way is in
good agreement with the star-count data.


  The COVID-19 outbreak has stimulated the interest in the proposal of novel
epidemiological models to predict the course of the epidemic so as to help
planning effective control strategies. In particular, in order to properly
interpret the available data, it has become clear that one must go beyond most
classic epidemiological models and consider models that, like the recently
proposed SIDARTHE, offer a richer description of the stages of infection. The
problem of learning the parameters of these models is of crucial importance
especially when assuming that they are time-variant, which further enriches
their effectiveness. In this paper we propose a general approach for learning
time-variant parameters of dynamic compartmental models from epidemic data. We
formulate the problem in terms of a functional risk that depends on the
learning variables through the solutions of a dynamic system. The resulting
variational problem is then solved by using a gradient flow on a suitable,
regularized functional. We forecast the epidemic evolution in Italy and France.
Results indicate that the model provides reliable and challenging predictions
over all available data as well as the fundamental role of the chosen strategy
on the time-variant parameters.


  This paper proposes Capstan: a scalable, parallel-patterns-based,
reconfigurable dataflow accelerator (RDA) for sparse and dense tensor
applications. Instead of designing for one application, we start with common
sparse data formats, each of which supports multiple applications. Using a
declarative programming model, Capstan supports application-independent sparse
iteration and memory primitives that can be mapped to vectorized,
high-performance hardware. We optimize random-access sparse memories with
configurable out-of-order execution to increase SRAM random-access throughput
from 32% to 80%.
  For a variety of sparse applications, Capstan with DDR4 memory is 18x faster
than a multi-core CPU baseline, while Capstan with HBM2 memory is 16x faster
than an Nvidia V100 GPU. For sparse applications that can be mapped to
Plasticine, a recent dense RDA, Capstan is 7.6x to 365x faster and only 16%
larger.


  In order to mitigate the security threat of quantum computers, NIST is
undertaking a process to standardize post-quantum cryptosystems, aiming to
assess their security and speed up their adoption in production scenarios.
Several hardware and software implementations have been proposed for each
candidate, while only a few target heterogeneous platforms featuring CPUs and
FPGAs. This work presents a HW/SW co-design of BIKE for embedded platforms
featuring both CPUs and small FPGAs and employs high-level synthesis (HLS) to
timely deliver the hardware accelerators. In contrast to state-of-the-art
solutions targeting performance-optimized HLS accelerators, the proposed
solution targets the small FPGAs implemented in the heterogeneous platforms for
embedded systems. Compared to the software-only execution of BIKE, the
experimental results collected on the systems-on-chip of the entire Xilinx
Zynq-7000 family highlight a performance speedup ranging from 1.37x, on Z-7010,
to 2.78x, on Z-7020.


  We introduce a pipeline to easily generate collections of web accessible UCSC
genome browsers interrelated by an alignment. Using the alignment, all
annotations and the alignment itself can be efficiently viewed with reference
to any genome in the collection, symmetrically. A new, intelligently scaled
alignment display makes it simple to view all changes between the genomes at
all levels of resolution, from substitutions to complex structural
rearrangements, including duplications.


  Resource identification and quantification is an essential element of both
classical and quantum information theory. Entanglement is one of these
resources, arising when quantum communication and nonlocal operations are
expensive to perform. In the first part of this thesis we quantify the
effective entanglement when operations are additionally restricted. For an
important class of errors we find a linear relationship between the usual and
effective higher dimensional generalization of concurrence, a measure of
entanglement.
  In the second chapter we focus on nonlocality in the presence of
superselection rules, where we propose a scheme that may be used to activate
nongenuinely multipartite nonlocality with multiple copies of the state. We
show that whenever the number of particles is insufficient, the genuinely
multipartite nonlocality is degraded to nongenuinely multipartite.
  While in the first few chapters we focus on understanding the resources
present in quantum states, in the final part we turn the picture around and
instead treat operations themselves as a resource. We provide our observers
with free access to classical operations - ie. those that cannot detect or
generate quantum coherence. We show that the operation of interest can then be
used to either generate or detect quantum coherence if and only if it violates
a particular commutation relation. Using the relative entropy, the commutation
relation provides us with a measure of nonclassicality of operations. We show
that the measure is a sum of two contributions, the generating power and the
distinguishing power, each of which is separately an essential ingredient in
quantum communication and information processing. The measure also sheds light
on the operational meaning of quantum discord, which we show can be interpreted
as the difference in superdense coding capacity between a quantum state and a
classical state.


  Fractons and other subdimensional particles are an exotic class of emergent
quasi-particle excitations with severely restricted mobility. A wide class of
models featuring these quasi-particles have a natural description in the
language of symmetric tensor gauge theories, which feature conservation laws
restricting the motion of particles to lower-dimensional sub-spaces, such as
lines or points. In this work, we investigate the fate of symmetric tensor
gauge theories in the presence of spatial curvature. We find that weak
curvature can induce small (exponentially suppressed) violations on the
mobility restrictions of charges, leaving a sense of asymptotic
fractonic/sub-dimensional behavior on generic manifolds. Nevertheless, we show
that certain symmetric tensor gauge theories maintain sharp mobility
restrictions and gauge invariance on certain special curved spaces, such as
Einstein manifolds or spaces of constant curvature.


  We check the validity of the widely used classical apsidal motion formula as
a function of orbital parameters, stellar structure, and stellar rotation rate
by comparing dynamical calculations of the periastron advance with the static
tidal formula. We find that the classical formula gives very accurate results
when the periods of the low order quadrupole g, f and p modes are smaller than
the periastron passage time by a factor of about 7 or more. However, when this
condition is not satisfied, the difference between the classical formula and
the exact result can be quite large, and even periastron recession can result.
The largest difference arises when one of the low order modes of the star is
nearly resonant with an integer multiple of the orbital frequency minus twice
the rotation rate of the star. The resonance of higher order g-modes (number of
radial nodes $\gta 4$) with the orbit is very unlikely to cause significant
deviation from the classical result because of their weak coupling to the tidal
force and thus their small contribution to the apsidal motion. Resonances
involving rotational modes of the star are also unlikely to make much
contribution to the apsidal motion because of their small overlap with the
tidal force, even though they have periods comparable to the periastron passage
time.
  We apply our work to two famous binary systems (AS Cam and DI Her) which show
abnormally small apsidal motion, and conclude that dynamical effects are
unimportant for these systems, i.e. the static tide assumption is an excellent
approximation.


  In industrial facilities and household equipments scale formation leads to
reduced efficiency and damages. Therefore various devices for anti-scale have
been designed for a long time. Recently one of them, an aggregation of ceramic
spheres, was proposed to prevent scale formation and its efficiency shown
experimentally. The purpose of this paper is to clarify the function of this
device by proposing several mathematical models and by pursuing their
mathematical and numerical analysis. The first model measures the effect on
nucleation of calcite of the electric potential near the surface of a ceramic
sphere in natural water. The second model is based on a crystal lattice model
and argues that the surface tension energy of the calcite particles is reduced
by the polarization energy brought by the ceramic balls. The third model is
macroscopic and numerical and studies the effect of two ceramic balls
arrangements in a flow of water containing calcite particles.


  The recent Quantum Hall experiments in graphene have confirmed the
theoretically well-understood picture of the quantum Hall (QH) conductance in
fermion systems with continuum Dirac spectrum. In this paper we take into
account the lattice, and perform an exact diagonalization of the Landau problem
on the hexagonal lattice. At very large magnetic fields the Dirac argument
fails completely and the Hall conductance, given by the number of edge states
present in the gaps of the spectrum, is dominated by lattice effects. As the
field is lowered, the experimentally observed situation is recovered through a
phenomenon which we call band collapse. As a corollary, for low magnetic field,
graphene will exhibit two qualitatively different QHE's: at low filling, the
QHE will be dominated by the "relativistic" Dirac spectrum and the Hall
conductance will be odd-integer; above a certain filling, the QHE will be
dominated by a non-relativistic spectrum, and the Hall conductance will span
all integers, even and odd.


  We present a numerical approach for accurately evolving a dust grain-size
distribution undergoing number-conserving (such as sputtering) and/or
mass-conserving (such as shattering) processes. As typically observed
interstellar dust distributions follow a power-law, our method adopts a
power-law discretisation and uses both the grain mass and number densities in
each bin to determine the power-law parameters. This power-law method is
complementary to piecewise-constant and linear methods in the literature. We
find that the power-law method surpasses the other two approaches, especially
for small bin numbers. In the sputtering tests the relative error in the total
grain mass remains below 0.01% independent of the number of bins N, while the
other methods only achieve this for N > 50 or higher. Likewise, the shattering
test shows that the method also produces small relative errors in the total
grain numbers while conserving mass. Not only does the power-law method
conserve the global distribution properties, it also preserves the inter-bin
characteristics so that the shape of the distribution is recovered to a high
degree. This does not always happen for the constant and linear methods,
especially not for small bin numbers. Implementing the power-law method in a
hydrodynamical code thus minimises the numerical cost whilst maintaining high
accuracy. The method is not limited to dust grain distributions, but can also
be applied to the evolution of any distribution function, such as a cosmic-ray
distribution affected by synchrotron radiation or inverse-Compton scattering.


  We show how two-dimensional waveguide arrays can be used to probe the effect
of on-site interaction on Anderson localization of two interacting bosons in
one dimension. It is shown that classical light and linear elements are
sufficient to experimentally probe the interplay between interaction and
disorder in this setting. For experimental relevance, we evaluate the
participation ratio and the intensity correlation function as measures of
localization for two types of disorder (diagonal and off-diagonal), for two
types of interaction (repulsive and attractive), and for a variety of initial
input states. Employing a commonly used set of initial states, we show that the
effect of interaction on Anderson localization is strongly dependent on the
type of disorder and initial conditions, but is independent of whether the
interaction is repulsive or attractive. We then analyze a certain type of
entangled input state where the type of interaction is relevant and discuss how
it can be naturally implemented in waveguide arrays. We conclude by laying out
the details of the two-dimensional photonic lattice implementation including
the required parameter regime.


  We have investigated Andreev reflection at interfaces between superconducting
indium (T_c = 3.4 K) and several normal conducting non-magnetic metals
(palladium, platinum, and silver) down to T = 0.1 K as well as zinc (T_c = 0.87
K) in its normal state at T = 2.5 K. We analysed the point-contact spectra with
the modified one-dimensional BTK theory valid for ballistic transport. It
includes Dynes' quasi-particle lifetime as fitting parameter Gamma in addition
to superconducting energy gap 2Delta and strength Z of the interface barrier.
For contact areas from less than 1 nm^2 to 10000 nm^2 the BTK Z-parameter was
close to 0.5, corresponding to transmission coefficients of about 80%,
independent of the normal metal. The Z-parameter varies by less than +/-0.1
around its average value, indicating that the interfaces have a negligible
dielectric tunneling barrier. Also Fermi surface mismatch does not account for
the observed Z. The extracted value Z approx 0.5 can be explained by assuming
that practically all of our point contacts are in the diffusive regime.


  We give two applications of the the duality between the complex Homogeneous
Monge-Amp\`ere Equation (HMAE) and the Hele-Shaw flow. First, we prove
existence of smooth boundary data for which the weak solution to the Dirichlet
problem for the HMAE over $\mathbb P^1\times \overline{\mathbb D}$ is not twice
differentiable at a given collection of points, and also examples that are not
twice differentiable along a set of codimension one in $\mathbb{P}^1\times
\partial \mathbb{D}$. Second, we produce explicit families of smooth geodesic
rays in the space of K\"ahler metrics on $\mathbb P^1$ and on the unit disc
$\mathbb D$ that are constructed from an exhausting family of increasing
smoothly varying simply connected domains.


  A theory of a non-relativistic, complex scalar field with derivatively
coupled interaction terms is investigated. This toy model is considered as a
prototype of a classicalizing theory and in particular of general relativity,
for which the black hole constitutes a prominent example of a classicalon.
Accordingly, the theory allows for a non-trivial solution of the stationary
Gross-Pitaevskii equation corresponding to a black hole in the case of GR.
Quantum fluctuations on this classical background are investigated within the
Bogoliubov approximation. It turns out that the perturbative approach is
invalidated by a high occupation of the Bogoliubov modes. Recently, it was
proposed that a black hole is a Bose-Einstein condensate of gravitons that
dynamically ensures to stay at the verge of a quantum phase transition. Our
result is understood as an indication for that claim. Furthermore, it motivates
a non-linear numerical analysis of the model.


  Swimming microorganisms often self propel in fluids with complex rheology.
While past theoretical work indicates that fluid viscoelasticity should hinder
their locomotion, recent experiments on waving swimmers suggest a possible
non-Newtonian enhancement of locomotion. We suggest a physical mechanism, based
on fluid-structure interaction, leading to swimming in a viscoelastic fluid at
a higher speed than in a Newtonian one. Using Taylor's two-dimensional swimming
sheet model, we solve for the shape of an active swimmer as a balance between
the external fluid stresses, the internal driving moments, and the passive
elastic resistance. We show that this dynamic balance leads to a generic
transition from hindered rigid swimming to enhanced flexible locomotion. The
results are physically interpreted as due to a viscoelastic suction increasing
the swimming amplitude in a non-Newtonian fluid and overcoming viscoelastic
damping.


  Procedural text understanding is a challenging language reasoning task that
requires models to track entity states across the development of a narrative. A
complete procedural understanding solution should combine three core aspects:
local and global views of the inputs, and global view of outputs. Prior methods
considered a subset of these aspects, resulting in either low precision or low
recall. In this paper, we propose Coalescing Global and Local Information
(CGLI), a new model that builds entity- and timestep-aware input
representations (local input) considering the whole context (global input), and
we jointly model the entity states with a structured prediction objective
(global output). Thus, CGLI simultaneously optimizes for both precision and
recall. We extend CGLI with additional output layers and integrate it into a
story reasoning framework. Extensive experiments on a popular procedural text
understanding dataset show that our model achieves state-of-the-art results;
experiments on a story reasoning benchmark show the positive impact of our
model on downstream reasoning.


  Pedestrian classifiers decide which image windows contain a pedestrian. In
practice, such classifiers provide a relatively high response at neighbor
windows overlapping a pedestrian, while the responses around potential false
positives are expected to be lower. An analogous reasoning applies for image
sequences. If there is a pedestrian located within a frame, the same pedestrian
is expected to appear close to the same location in neighbor frames. Therefore,
such a location has chances of receiving high classification scores during
several frames, while false positives are expected to be more spurious. In this
paper we propose to exploit such correlations for improving the accuracy of
base pedestrian classifiers. In particular, we propose to use two-stage
classifiers which not only rely on the image descriptors required by the base
classifiers but also on the response of such base classifiers in a given
spatiotemporal neighborhood. More specifically, we train pedestrian classifiers
using a stacked sequential learning (SSL) paradigm. We use a new pedestrian
dataset we have acquired from a car to evaluate our proposal at different frame
rates. We also test on a well known dataset: Caltech. The obtained results show
that our SSL proposal boosts detection accuracy significantly with a minimal
impact on the computational cost. Interestingly, SSL improves more the accuracy
at the most dangerous situations, i.e. when a pedestrian is close to the
camera.


  Using the power of superspace formalism, we investigate the decoupling
effects of heavy states in N=1 supersymmetric field theory. We find that
"mixed" couplings in the superpotential between the heavy and light fields
contribute to the effective superpotential at the leading order, and also
contribute to the effective K\"{a}hler potential (in the next to leading
order). Mixed couplings in the K\"{a}hler potential always contribute to the
effective K\"{a}hler potential at the leading order. Several examples are
presented which illustrate the effects explicitly.


  In the s = 1/2 antiferromagnetic spin chain material TiPO4 the formation of a
spin gap takes place in a two step process with two characteristic
temperatures, T*=111 K and TSP=74 K. We observe an unusual lattice dynamics
over a large temperature regime as well as evidence for an orbital instability
preceding the spin-Peierls transition. We relate different intrachain exchange
interactions of the high temperature compared to the spin-Peierls phase to a
modification of the orbital ordering pattern. In particular, our observation of
a high energy excitation of mixed electronic and lattice origin suggests an
exotic dimerization process different from other spin-Peierls materials.