  A particularly rich class of integrable systems arises from the AdS/CFT
duality. There, the two-dimensional quantum field theory living on the string
worldsheet may be understood in terms of a non-relativistic factorized S
matrix, and the energy spectrum may be derived by techniques such as the mirror
thermodynamic Bethe ansatz or the quantum spectral curve. In the case of
AdS$_3$/CFT$_2$ without Ramond-Ramond fluxes, the worldhseet theory is a
Wess-Zumino-Witten model with continous and discrete representations which, for
the lowest allowed level, is dual to the symmetric product orbifold of a free
theory. I will show how continuous representations may arise from
integrability, and that at lowest level the Bethe equations yield the symmetric
product orbifold partition function on the nose.


  The so called \emph{quantum game theory} has recently been proclaimed as one
of the new branches in the development of both quantum information theory and
game theory. However, the notion of a quantum game itself has never been
strictly defined, which has led to a lot of conceptual confusion among
different authors. In this paper we introduce a new conceptual framework of a
\emph{scenario} and an \emph{implementation} of a game. It is shown that the
procedures of "quantization" of games proposed in the literature lead in fact
to several different games which can be defined within the same scenario, but
apart from this they may have nothing in common with the original game. Within
the framework we put forward, a lot of conceptual misunderstandings that have
arisen around "quantum games" can be stated clearly and resolved uniquely. In
particular, the proclaimed essential role of entanglement in several static
"quantum games", and their connection with Bell inequalities, is disproved.


  Measurement-device-independent quantum key distribution (MDI-QKD) provides us
a powerful approach to resist all attacks at detection side. Besides the
unconditional security, people also seek for high key generation rate, but
MDI-QKD has relatively low key generation rate. In this paper, we provide an
efficient approach to increase the key generation rate of MDI-QKD by adopting
multiple degrees of freedom (DOFs) of single photons to generate keys. Compared
with other high-dimension MDI-QKD protocols encoding in one DOF, our protocol
is more flexible, for our protocol generating keys in independent subsystems
and the detection failure or error in a DOF not affecting the information
encoding in other DOFs. Based on above features, our MDI-QKD protocol may have
potential application in future quantum communication field.


  The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar
Information Of Nuclei) machine learning system provides an efficient and
accurate route to the prediction of NMR parameters from 3-dimensional chemical
structures. Here we demonstrate that machine learning predictions, trained on
quantum chemical computed values for NMR parameters, are essentially as
accurate but computationally much more efficient (tens of milliseconds per
molecule) than quantum chemical calculations (hours/days per molecule).
Training the machine learning systems on quantum chemical, rather than
experimental, data circumvents the need for existence of large, structurally
diverse, error-free experimental databases and makes IMPRESSION applicable to
solving 3-dimensional problems such as molecular conformation and isomerism


  Nous d\'ecrivons les singularit\'es de feuilletages holomorphes dicritiques
de petite multiplicit\'e en dimension $3$. En particulier nous relions
l'existence de d\'eformations et de d\'eploiements non triviaux \`a des
probl\`emes d'int\'egrabilit\'e liouvillienne.
  We describe the singularities of dicritical holomorphic foliations of small
multiplicity in dimension $3$. In particular we connect the existence of non
trivial deformations and deployments to problems of liouvillian integrability.


  In our previous work the nonlinearity parameter f_NL, which characterizes
nongaussianity in the cosmic microwave background, was estimated for a class of
inflationary models based on nonlocal field theory. These models include p-adic
inflation and generically have the remarkable property that slow roll inflation
can proceed even with an extremely steep potential. Previous calculations found
that large nongaussianity is possible; however, the technical complications
associated with studying perturbations in theories with infinitely many
derivatives forced us to provide only an order of magnitude estimate for f_NL.
We reconsider the problem of computing f_NL in nonlocal inflation models,
showing that a particular choice of field basis and recent progress in
cosmological perturbation theory makes an exact computation possible. We
provide the first quantitatively accurate computation of the bispectrum in
nonlocal inflation, confirming our previous claim that it can be observably
large. We show that the shape of the bispectrum in this class of models makes
it observationally distinguishable from Dirac-Born-Infeld inflation models.


  An atomistic method of calculating the spin-lattice relaxation times ($T_1$)
is presented for donors in silicon nanostructures comprising of millions of
atoms. The method takes into account the full band structure of silicon
including the spin-orbit interaction. The electron-phonon Hamiltonian, and
hence the deformation potential, is directly evaluated from the
strain-dependent tight-binding Hamiltonian. The technique is applied to single
donors and donor clusters in silicon, and explains the variation of $T_1$ with
the number of donors and electrons, as well as donor locations. Without any
adjustable parameters, the relaxation rates in a magnetic field for both
systems are found to vary as $B^5$ in excellent quantitative agreement with
experimental measurements. The results also show that by engineering electronic
wavefunctions in nanostructures, $T_1$ times can be varied by orders of
magnitude.


  There are several different common definitions of a property in topological
dynamics called "topological transitivity," and it is part of the folklore of
dynamical systems that under reasonable hypotheses, they are equivalent.
Various equivalences are proved in different places, but the full story is
difficult to find. This note provides a complete description of the
relationships among the different properties.


  In the statistical analysis of shape a goal beyond the analysis of static
shapes lies in the quantification of `same' deformation of different shapes.
Typically, shape spaces are modelled as Riemannian manifolds on which parallel
transport along geodesics naturally qualifies as a measure for the `similarity'
of deformation. Since these spaces are usually defined as combinations of
Riemannian immersions and submersions, only for few well featured spaces such
as spheres or complex projective spaces (which are Kendall's spaces for 2D
shapes), parallel transport along geodesics can be computed explicitly. In this
contribution a general numerical method to compute parallel transport along
geodesics when no explicit formula is available is provided. This method is
applied to the shape spaces of closed 2D contours based on angular direction
and to Kendall's spaces of shapes of arbitrary dimension. In application to the
temporal evolution of leaf shape over a growing period, one leaf's shape-growth
dynamics can be applied to another leaf. For a specific poplar tree
investigated it is found that leaves of initially and terminally different
shape evolve rather parallel, i.e. with comparable dynamics.


  Software Defined Networking (SDN) not only enables agility through the
realization of part of the network functionality in software but also
facilitates offering advanced features at the network layer. Hence, SDN can
support a wide range of middleware services; network performance monitoring is
an example of these services that are already deployed in practice. In this
paper, we exploit the use of SDNs to efficiently provide application monitoring
functionality. The recent rise of complex cloud applications has made
performance monitoring a major issue. We show that many performance indicators
can be inferred from messages exchanged among application components. By
analyzing these messages, we argue that the overhead of performance monitoring
could be effectively moved from the end hosts into the SDN middleware of the
cloud infrastructure which enables more flexible placement of logging
functionality. This paper explores several approaches for supporting
application monitoring through SDN. In particular, we combine selective
forwarding in SDN to enable message filtering and reformatting, and propose a
customized port sniffing technique. We describe the implementation of the
approach within the standard SDN software, namely OVS. We further provide a
comprehensive performance evaluation to analyze advantages and disadvantages of
our approach, and highlight the trade-offs.


  In this paper, we develop a general theory for adaptive nonparametric
estimation of the mean function of a non-stationary and nonlinear time series
model using deep neural networks (DNNs). We first consider two types of DNN
estimators, non-penalized and sparse-penalized DNN estimators, and establish
their generalization error bounds for general non-stationary time series. We
then derive minimax lower bounds for estimating mean functions belonging to a
wide class of nonlinear autoregressive (AR) models that include nonlinear
generalized additive AR, single index, and threshold AR models. Building upon
the results, we show that the sparse-penalized DNN estimator is adaptive and
attains the minimax optimal rates up to a poly-logarithmic factor for many
nonlinear AR models. Through numerical simulations, we demonstrate the
usefulness of the DNN methods for estimating nonlinear AR models with intrinsic
low-dimensional structures and discontinuous or rough mean functions, which is
consistent with our theory.


  The Near-Infrared Spectrograph (NIRSpec) is one the four focal plane
instruments on the James Webb Space Telescope (JWST) which was launched on
December 25, 2021. We present the in-flight status and performance of NIRSpec's
detector system as derived from the instrument commissioning data. The
instrument features two 2048 x 2048 HAWAII-2RG sensor chip assemblies (SCAs)
that are operated at a temperature of about 42.8 K and are read out via a pair
of SIDECAR ASICs. NIRSpec supports "Improved Reference Sampling and
Subtraction" (IRS2) readout mode that was designed to meet NIRSpec's stringent
noise requirements and to reduce 1/f and correlated noise. In addition, NIRSpec
features subarrays optimized for bright object time series observations, e.g.
for the observation of exoplanet transit around bright host stars. We focus on
the dark signal as well as the read and total noise performance of the
detectors.


  Crowdion defects are produced in body centred cubic metals under irradiation.
Their structure and diffusive dynamics play a governing role in microstructural
evolution, and hence the mechanical properties of nuclear materials. In this
paper we apply the analytical Frenkel-Kontorova model to crowdions and clusters
thereof (prismatic dislocation loops) and show that the Peierls potential in
which these defects diffuse is remarkably small (in the micro eV range as
compared to the eV range for other defects). We also develop a coarse-grained
statistical methodology for simulating these fast-diffusing objects in the
context of object kinetic Monte Carlo, which is less vulnerable to the low
barrier problem than na\"ive stochastic simulation.


  Interstitial content is online content which grays out, or otherwise obscures
the main page content. In this technical report, we discuss exploratory
research into detecting the presence of interstitial content in web pages. We
discuss the use of computer vision techniques to detect interstitials, and the
potential use of these techniques to provide a labelled dataset for machine
learning.


  In this paper, we introduce two novel methods to design outer polar codes for
two previously proposed concatenated polar code architectures: augmented polar
codes and local-global polar codes. These methods include a stopping set (SS)
construction and a nonstationary density evolution (NDE) construction.
Simulation results demonstrate the advantage of these methods over previously
proposed constructions based on density evolution (DE) and LLR evolution.


  We report the discovery of 31.18 ms pulsations from the INTEGRAL source IGR
J14003-6326 using the Rossi X-ray Timing Explorer (RXTE). This pulsar is most
likely associated with the bright Chandra X-ray point source lying at the
center of G310.6-1.6, a previously unrecognised Galactic composite supernova
remnant with a bright central non-thermal radio and X-ray nebula, taken to be
the pulsar wind nebula (PWN). PSR J1400-6325 is amongst the most energetic
rotation-powered pulsars in the Galaxy, with a spin-down luminosity of Edot =
5.1E+37 erg.s-1. In the rotating dipole model, the surface dipole magnetic
field strength is B_s = 1.1E+12 G and the characteristic age tau_c = P/2Pdot =
12.7 kyr. The high spin-down power is consistent with the hard spectral indices
of the pulsar and the nebula of 1.22 +/- 0.15 and 1.83 +/- 0.08, respectively,
and a 2-10 keV flux ratio F_PWN/F_PSR ~ 8. Follow-up Parkes observations
resulted in the detection of radio emission at 10 and 20 cm from PSR J1400-6325
at a dispersion measure of ~ 560 cm-3 pc, which implies a relatively large
distance of 10 +/- 3 kpc. However, the resulting location off the Galactic
Plane of ~ 280 pc would be much larger than the typical thickness of the
molecular disk, and we argue that G310.6-1.6 lies at a distance of ~ 7 kpc.
There is no gamma-ray counterpart to the nebula or pulsar in the Fermi data
published so far. A multi-wavelength study of this new composite supernova
remnant, from radio to very-high energy gamma-rays, suggests a young (< 1000
yr) system, formed by a sub-energetic (~ 1E+50 ergs), low ejecta mass (M_ej ~ 3
Msun) SN explosion that occurred in a low-density environment (n_0 ~ 0.01
cm-3).


  In Templeton (2010), the Approximate Bayesian Computation (ABC) algorithm
(see, e.g., Pritchard et al., 1999, Beaumont et al., 2002, Marjoram et al.,
2003, Ratmann et al., 2009) is criticised on mathematical and logical grounds:
"the [Bayesian] inference is mathematically incorrect and formally illogical".
Since those criticisms turn out to be bearing on Bayesian foundations rather
than on the computational methodology they are primarily directed at, we
endeavour to point out in this note the statistical errors and inconsistencies
in Templeton (2010), refering to Beaumont et al. (2010) for a reply that is
broader in scope since it also covers the phylogenetic aspects of nested clade
versus a model-based approach.


  We give an explicit description of smoothly bounded Reinhardt domains with
noncompact automorphism groups. In particular, this description confirms a
special case of a conjecture of Greene/Krantz.


  Previous studies have shown that Twitter users have biases to tweet from
certain locations, locational bias, and during certain hours, temporal bias. We
used three years of geolocated Twitter Data to quantify these biases and test
our central hypothesis that Twitter users biases are consistent across US
cities. Our results suggest that temporal and locational bias of Twitter users
are inconsistent between three US metropolitan cities. We derive conclusions
about the role of the complexity of the underlying data producing process on
its consistency and argue for the potential research avenue for Geospatial Data
Science to test and quantify these inconsistencies in the class of organically
evolved Big Data.


  Subgap structure in the surface bound states spectrum of Superfluid $^3$He-B
with rough surface is discussed. The subgap is formed by the level repulsion
between the surface bound state and the continuum states in the course of
multiple scattering by the surface roughness. We show that the level repulsion
is originated from the nature of the wave function of the surface bound state
that is now recognized as Majorana Fermion. We study the superfluid $^3$He-B
with a rough surface and under magnetic field perpendicular to the surface
using the quasi-classical Green function together with random S-matrix model.
We calculate the self-consistent order parameters, the spin polarization
density and the surface density of states. It is shown that the subgap is found
also under the magnetic field perpendicular to the surface. Magnetic field
dependence of the transverse acoustic impedance is also discussed.


  Tasking machine learning to predict segments of a time series requires
estimating the parameters of a ML model with input/output pairs from the time
series. Using the equivalence between statistical data assimilation and
supervised machine learning, we revisit this task. The training method for the
machine utilizes a precision annealing approach to identifying the global
minimum of the action (-log[P]). In this way we are able to identify the number
of training pairs required to produce good generalizations (predictions) for
the time series. We proceed from a scalar time series $s(t_n); t_n = t_0 + n
\Delta t$ and using methods of nonlinear time series analysis show how to
produce a $D_E > 1$ dimensional time delay embedding space in which the time
series has no false neighbors as does the observed $s(t_n)$ time series. In
that $D_E$-dimensional space we explore the use of feed forward multi-layer
perceptrons as network models operating on $D_E$-dimensional input and
producing $D_E$-dimensional outputs.


  A microscopic analysis of the optical potentials (OPs) and cross sections of
quasielastic scattering of $^{12,14}$Be on $^{12}$C at 56 MeV/nucleon and on
protons at energy near 700 MeV is carried out. For lower energy scattering the
real part of the OP is calculated by using of double-folding procedure
accounting for the anti-symmetrization effects, while the imaginary part is
obtained on the base of the high-energy approximation (HEA). The HEA is also
applied to the calculations of both real and imaginary OPs when solving the
relativistic equation for the high-energy proton-nucleus elastic scattering.
The neutron and proton density distributions computed in different microscopic
models for $^{12}$Be and $^{14}$Be are used. In the present hybrid model of the
optical potential the only free parameters are the depths of the real and
imaginary parts of OP obtained by fitting the experimental data. The role of
the inelastic scattering channel to the first excited $2^{+}$ and $3^{-}$
states in $^{12}$C when calculating the quasielastic cross sections, as well as
the modified density of the $^{12}$C target accounting for the surface effects
are studied. In addition, the cluster model, in which $^{14}$Be consists of a
$2n$-halo and the $^{12}$Be core, is applied to calculate the cross sections of
diffraction breakup and stripping reactions in $^{14}$Be+$^{12}$C scattering
and longitudinal momentum distributions of $^{12}$Be fragments at energy of 56
MeV/nucleon. A good agreement of the theoretical results with the available
experimental data of both quasielstic scattering and breakup processes is
obtained.


  Tidal heating is the major source of heat in the outer Solar System. Because
of its strong tidal interaction with Jupiter and the other Galilean Satellites,
Io is incredibly volcanically active. We use the directly measured volcanic
activity level of Io's volcanoes as a proxy for surface heat flow and compare
to tidal heating model predictions. Volcanic activity is a better proxy for
heat flow than simply the locations of volcanic constructs. We determine the
volcanic activity level using three data sets: Galileo PPR, Galileo NIMS, and
New Horizons LEISA. We also present a systematic reanalysis of the Galileo NIMS
observations to determine the 3.5 micron brightness of 51 active volcanoes. We
find that potential differences in volcanic style between high and low
latitudes make high latitude observations unreliable in distinguishing between
tidal heating models. Observations of Io's polar areas, such as those by JUNO,
are necessary to unambiguously understand Io's heat flow. However, all three of
the data sets examined show a relative dearth of volcanic brightness near 180 W
(anti-Jovian point) and the equator and the only data set with good
observations of the sub-Jovian point (LEISA) also shows a lack of volcanic
brightness in that region. These observations are more consistent with the
mantle-heating model than the asthenospheric-heating model. Furthermore, all
three of the data sets are consistent with four-fold symmetry in longitude and
peak heat flow at mid-latitudes, which best matches with the combined heating
case of Tackley et al. (2001).


  It has been conjectured that higher-dimensional rotating black holes become
unstable at a sufficiently large value of the rotation, and that new black
holes with pinched horizons appear at the threshold of the instability. We
search numerically, and find, the stationary axisymmetric perturbations of
Myers-Perry black holes with a single spin that mark the onset of the
instability and the appearance of the new black hole phases. We also find new
ultraspinning Gregory-Laflamme instabilities of rotating black strings and
branes.


  Replacing 4D Minkowski space by 5D canonical space leads to a clearer
derivation of the main features of wave mechanics, including the wave function
and the velocity of de Broglie waves. Recent tests of wave-particle duality
could be adapted to investigate whether de Broglie waves are basically 4D or 5D
in nature.


  Massive quantum matter of prescribed spin permits infinitely many
possibilities of covariantization in terms of spinorial (undotted/dotted)
pointlike fields, whereas massless finite helicity representations lead to
large gap in this spinorial spectrum which for s=1 excludes vectorpotentials.
Since the nonexistence of such pointlike generators is the result of a deep
structural clash between modular localization and the Hilbert space setting of
QT, there are two ways out: gauge theory which sacrifies the Hilbert space and
keeps the pointlike formalism and the use of stringlike potentials which allows
to preserve the Hilbert space. The latter setting contains also
string-localized charge-carrying operators whereas the gauge theoretic
formulation is limited to point-like generated observables. This description
also gives a much better insight into the Higgs mechanism which leads to a
revival of the more physical "Schwinger-Higgs" screening idea. The new
formalism is not limited to m=0, s=1, it leads to renormalizable interactions
in the sense of power-counting for all s in massless representations. The
existence of stringlike vectorpotentials is preempted by the Aharonov-Bohm
effect in QFT; it is well-known that the use of pointlike vectorpotentials in
Stokes theorem would with lead to wrong results. Their use in Maxwell's
equations is known to lead to zero Maxwell charge. The role of
string-localization in the problem behind the observed invisibility and
confinement of gluons and quarks leads to new questions and problems. PACS:
11.10.-z, 11.15-q, 11.10Gh, 12.20.-m, 12.38.-t


  The colour dipole multiplicity distribution is analysed for the wave function
of a heavy onium state at small $x$. Numerical results for the average
multiplicity and the effect of cutoffs on its power growth are presented. Then,
the full multiplicity distribution is analysed: the second multiplicity moment
is derived and the tail of the distribution is shown to behave as $\exp(-\log^2
n)$. These results are confirmed by a Monte Carlo simulation which also gives
the fluctuations in the spatial density of dipoles.


  We derive a distance of $15.8\pm0.4$ Mpc to the archetypical Seyfert 1 galaxy
NGC 4151 based on the near-infrared Cepheid Period-Luminosity relation and new
Hubble Space Telescope multiband imaging. This distance determination, based on
measurements of 35 long-period ($P > 25$d) Cepheids, will support the absolute
calibration of the supermassive black hole mass in this system, as well as
studies of the dynamics of the feedback or feeding of its active galactic
nucleus.


  We study the quantum radiation of particle production by vacuum from an
ultra-relativistic moving mirror (dynamical Casimir effect) solution that
allows (possibly for the first time) analytically calculable time evolution of
particle creation and an Airy particle spectral distribution. The reality of
the beta Bogoliubov coefficients is responsible for the simplicity, and the
mirror is asymptotically inertial at the speed of light, with finite energy
production. We also discuss general relations regarding negative energy flux,
the transformation to the 1-D Schr{\"o}dinger equation, and the incompleteness
of entanglement entropy.


  We show that fluctuations in the ratio of non-core to core funding in the
banking systems of advanced economies are driven by a handful of global factors
of both real and financial natures, with country-specific factors playing no
significant roles. Exchange rate flexibility helps insulate the non-core to
core ratio from such global factors but only significantly so outside periods
of major global financial disruptions, as in 2008-2009.


  We present WidthFormer, a novel transformer-based module to compute
Bird's-Eye-View (BEV) representations from multi-view cameras for real-time
autonomous-driving applications. WidthFormer is computationally efficient,
robust and does not require any special engineering effort to deploy. We first
introduce a novel 3D positional encoding mechanism capable of accurately
encapsulating 3D geometric information, which enables our model to compute
high-quality BEV representations with only a single transformer decoder layer.
This mechanism is also beneficial for existing sparse 3D object detectors.
Inspired by the recently proposed works, we further improve our model's
efficiency by vertically compressing the image features when serving as
attention keys and values, and then we develop two modules to compensate for
potential information loss due to feature compression. Experimental evaluation
on the widely-used nuScenes 3D object detection benchmark demonstrates that our
method outperforms previous approaches across different 3D detection
architectures. More importantly, our model is highly efficient. For example,
when using $256\times 704$ input images, it achieves 1.5 ms and 2.8 ms latency
on NVIDIA 3090 GPU and Horizon Journey-5 computation solutions. Furthermore,
WidthFormer also exhibits strong robustness to different degrees of camera
perturbations. Our study offers valuable insights into the deployment of BEV
transformation methods in real-world, complex road environments. Code is
available at https://github.com/ChenhongyiYang/WidthFormer .


  Hyper-parameter optimization is a crucial problem in machine learning as it
aims to achieve the state-of-the-art performance in any model. Great efforts
have been made in this field, such as random search, grid search, Bayesian
optimization. In this paper, we model hyper-parameter optimization process as a
Markov decision process, and tackle it with reinforcement learning. A novel
hyper-parameter optimization method based on soft actor critic and hierarchical
mixture regularization has been proposed. Experiments show that the proposed
method can obtain better hyper-parameters in a shorter time.


  The communication complexity of achieving secret key (SK) capacity in the
multiterminal source model of Csisz$\'a$r and Narayan is the minimum rate of
public communication required to generate a maximal-rate SK. It is well known
that the minimum rate of communication for omniscience, denoted by
$R_{\text{CO}}$, is an upper bound on the communication complexity, denoted by
$R_{\text{SK}}$. A source model for which this upper bound is tight is called
$R_{\text{SK}}$-maximal. In this paper, we establish a sufficient condition for
$R_{\text{SK}}$-maximality within the class of pairwise independent network
(PIN) models defined on hypergraphs. This allows us to compute $R_{\text{SK}}$
exactly within the class of PIN models satisfying this condition. On the other
hand, we also provide a counterexample that shows that our condition does not
in general guarantee $R_{\text{SK}}$-maximality for sources beyond PIN models.


  The non-interacting band structure of spinless fermions in a two-dimensional
($d=2$) $p$-band honeycomb lattice exhibits two quadratic band touching points
(QBTPs), which lie at the Fermi levels of filling $\nu=1/4$ and its
particle-hole conjugated filling $\nu=3/4$. A weak Hubbard interaction $U$
spontaneously breaks the time-reversal symmetry and removes the QBTP, rendering
the system into a quantum anomalous Hall insulator (QAHI). The first-order
topological nature of QAHI is characterized by a nontrivial Chern number and
supports ($d-1$)-dimensional chiral edge modes. With increasing the interaction
$U$, the system is driven into a Dirac semimetal by breaking the crystal
symmetry through a discontinuous quantum phase transition. The emergent Dirac
points each with Berry flux $\pi$ are generated in pairs, originating from the
$2\pi$ Berry flux of QBTP. A sufficiently large $U$ ultimately drives the
system into a dimerized insulator (DI) by simultaneously annihilating the Dirac
points at the Brillouin zone boundary. The second-order topological nature of
DI is characterized by the quantized polarizations and supports
($d-2$)-dimensional corner states. Our study provides a unique setting for
exploring the topological switch between the first-order and second-order
topological insulators.


  Two-dimensional (2D) van der Waals (vdW) magnetic $3d$-transition metal
trihalides are a new class of functional materials showing exotic physical
properties useful for spintronic and memory storage applications. In this
article, we report the synthesis and electromagnetic characterization of
single-crystalline vanadium trichloride, VCl$_3$, a novel 2D layered vdW Mott
insulator, which has a rhombohedral structure (R$\overline{3}$, No. 148) at
room temperature. VCl$_3$ undergoes a structural phase transition at 103 K and
a subsequent antiferromagnetic transition at 21.8 K. Combining core levels and
valence bands x-ray photoemission spectroscopy (XPS) with first-principles
density functional theory (DFT) calculations, we demonstrate the Mott Hubbard
insulating nature of VCl$_3$ and the existence of electron small 2D magnetic
polarons localized on V atom sites by V-Cl bond relaxation. The polarons
strongly affect the electromagnetic properties of VCl$_3$ promoting the
occupation of dispersion-less spin-polarized V-3d $a_{1g}$ states and band
inversion with $e^{'}_{g}$ states. Within the polaronic scenario, it is
possible to reconcile different experimental evidences on vanadium trihalides,
suggesting that also VI$_3$ hosts polarons. Our results highlight the complex
physical behavior of this class of crystals determined by charge trapping,
lattice distortions, correlation effects, mixed valence states, and magnetic
states.


  We present a proof of the Riemannian Penrose inequality with charge in the
context of asymptotically flat initial data sets for the Einstein-Maxwell
equations, having possibly multiple black holes with no charged matter outside
the horizon, and satisfying the relevant dominant energy condition. The proof
is based on a generalization of Hubert Bray's conformal flow of metrics adapted
to this setting.


  We use a noisy signal with finite correlation time to drive a spin
(dissipative impurity) in the quantum XY spin chain and calculate the dynamics
of entanglement entropy of a bipartition of spins, for a stochastic quantum
trajectory. We compute the noise averaged entanglement entropy of a bipartition
of spins and observe that its speed of spreading decreases at strong
dissipation, as a result of the Zeno effect. We recover the Zeno crossover and
show that noise averaged entanglement entropy can be used as a proxy for the
heating and Zeno regimes. Upon increasing the correlation time of the noise,
the location of the Zeno crossover shifts at stronger dissipation, extending
the heating regime.


  Frequentist and Bayesian methods differ in many aspects, but share some basic
optimal properties. In real-life classification and regression problems,
situations exist in which a model based on one of the methods is preferable
based on some subjective criterion. Nonparametric classification and regression
techniques, such as decision trees and neural networks, have frequentist
(classification and regression trees (CART) and artificial neural networks) as
well as Bayesian (Bayesian CART and Bayesian neural networks) approaches to
learning from data. In this work, we present two hybrid models combining the
Bayesian and frequentist versions of CART and neural networks, which we call
the Bayesian neural tree (BNT) models. Both models exploit the architecture of
decision trees and have lesser number of parameters to tune than advanced
neural networks. Such models can simultaneously perform feature selection and
prediction, are highly flexible, and generalize well in settings with a limited
number of training observations. We study the consistency of the proposed
models, and derive the optimal value of an important model parameter. We also
provide illustrative examples using a wide variety of real-life regression data
sets.


  We study the expected completion time of some recently proposed algorithms
for distributed computing which redundantly assign computing tasks to multiple
machines in order to tolerate a certain number of machine failures. We
analytically show that not only the amount of redundancy but also the
task-to-machine assignments affect the latency in a distributed system. We
study systems with a fixed number of computing tasks that are split in possibly
overlapping batches, and independent exponentially distributed machine service
times. We show that, for such systems, the uniform replication of non-
overlapping (disjoint) batches of computing tasks achieves the minimum expected
computing time.


  We review some interrelated aspects of thick brane worlds constructed within
the framework of 5D gravity coupled to a scalar field depending on the extra
dimension. It turns out that when analyzing localization of 4D gravity in this
smooth version of the Randall-Sundrum model, a kind of dichotomy emerges. In
the first case the geometry is completely smooth and the spectrum of metric
fluctuations shows a single massless bound state, corresponding to the 4D
graviton, and a tower of massive states described by a continuous spectrum of
Kaluza-Klein excitations starting from zero mass, indicating the lack of a mass
gap. In the second case, there are two bound states, a massless 4D graviton and
a massive excitation, separated by a mass gap from a continuous spectrum of
massive modes; nevertheless, the presence of a mass gap in the graviton
spectrum of the theory is responsible for a naked singularity at the boundaries
(or spatial infinity) of the Riemannian manifold. However, the imposition of
unitary boundary conditions, which is equivalent to eliminating the continuous
spectrum of gravitational massive modes, renders these singularities harmless
from the physical point of view, providing the viability of the model.


  Let $f=a_0+ a_{1}x+\cdots+a_m x^m\in \Bbb{Z}[x]$ be a primitive polynomial.
Suppose that there exists a positive real number $\alpha$ such that $|a_m|
\alpha^m>|a_0|+|a_1|\alpha+\cdots+|a_{m-1}|\alpha^{m-1}$. We prove that if
there exist natural numbers $n$ and $d$ satisfying $n\geq \alpha+ d$ for which
either $|f(n)|/d$ is a prime, or $|f(n)|/d$ is a prime-power coprime to
$|f'(n)|$, then $f$ is irreducible in $\mathbb{Z}[x]$.


  Black hole X-ray binaries (BHXBs) show rich phenomenology in the spectral and
timing properties. We collected the spectral data of 20 BHXBs from the
literature across different spectral states. The spectral properties are
studied in the forms of the inner disc temperature ($T_{\rm in}$), photon index
($\Gamma$), hot electron temperature ($kT_{\rm e}$), X-ray flux ($F_{\rm X}$)
and luminosity ($L_{\rm X}$). We studied various correlations among different
spectral parameters to understand the accretion process on a global scale. In
the thermal soft states (TSS), we find most of the sources followed $F_{\rm
disc} \propto T_{\rm in}^4$ relation. A `V'-shaped correlation is found between
$\Gamma$ and total luminosity ($L_{\rm tot}$) in the hard Comptonized state
(HCS). The Comptonized luminosity is observed to be correlated with the disc
luminosity in the HCS and TSS. No notable correlation is observed in the
intermediate state (IMS). The evolution of the inner disc radius ($R_{\rm in}$)
is unclear in the HCS and IMS. We also discuss how the hot electron temperature
changes with other spectral parameters. We observe that the iron line flux
correlates with disc and Comptonized fluxes. The strength of the reprocessed
emission is found to vary across spectral states.


  Gibbs energy representations for ice III, V and VI are reported. These were
constructed using new measurements of volumes at high pressure over a range of
low temperatures combined with calculated vibrational energies grounded in
statistical physics. The collection of representations including ice Ih and
water (released as the open source SeaFreeze framework) allow accurate
determinations of thermodynamics properties (phase boundaries, density, heat
capacity, bulk modulus, thermal expansivity, chemical potentials) and seismic
wave velocities over the entire range of conditions encountered in hydrospheres
in our solar system (220 - 500K to 2300 MPa). These comprehensive
representations allow exploration of the rich spectrum of thermodynamic
behavior in the H2O system. Although the results are broadly applicable in
science and engineering, their use in habitability analysis in water-rich
planetary bodies of our solar system and beyond is particularly relevant.


  Long distance wind dispersal requires small spores with low terminal
velocities, which can be held aloft by turbulent air currents until they are
deposited in suitable habitats for colonization. The inherent difficulty in
dispersing spores by wind is that spores easily carried by wind are also
rapidly decelerated when moving through still air. Thus the height of spore
release is critical in determining their range of dispersal. Vascular plants
with wind dispersed spores use the height of the plant to lift spores into
sufficient wind currents for dispersal, however non-vascular plants such as
Sphagnum cannot grow sufficiently tall. These fluid dynamics videos show how
exploding capsules of {\em Sphagnum} moss generate vortex rings to carry spores
to heights above 10 cm with an initial velocity of 16 m s$^{-1}$. In contrast
spores launched ballistically at these speeds through still air would travel
only 2-7 mm.


  We define sink marks for branched complexes and find conditions for them to
determine a branched surface structure. These will be used to construct
branched surfaces in knot and tangle complements. We will extend Delman's
theorem and prove that a Montesinos knot $K$ of length at least 3 has a
persistently laminar branched surface unless it is equivalent to $K(1/2q_1,\,
1/q_2,\, 1/q_3,\, -1)$ for some positive integers $q_i$. In most cases these
branched surfaces are genuine, in which case $K$ admits no atoroidal Seifert
fibered surgery. It will also be shown that there are many persistently laminar
tangles.


  The growing interest in axion-like particles (ALPs) stems from the fact that
they provide successful theoretical explanations of physics phenomena, from the
anomaly of the CP-symmetry conservation in strong interactions to the
observation of an unexpectedly large TeV photon flux from astrophysical
sources, at distances where the strong absorption by the intergalactic medium
should make the signal very dim. In this latter condition, which is the focus
of this review, a possible explanation is that TeV photons convert to ALPs in
the presence of strong and/or extended magnetic fields, such as those in the
core of galaxy clusters or around compact objects, or even those in the
intergalactic space. This mixing affects the observed ${\gamma}$-ray spectrum
of distant sources, either by signal recovery or the production of
irregularities in the spectrum, called "wiggles", according to the specific
microscopic realization of the ALP and the ambient magnetic field at the
source, and in the Milky Way, where ALPs may be converted back to ${\gamma}$
rays. ALPs are also proposed as candidate particles for the Dark Matter.
Imaging Atmospheric Cherenkov telescopes (IACTs) have the potential to detect
the imprint of ALPs in the TeV spectrum from several classes of sources. In
this contribution, we present the ALP case and review the past decade of
searches for ALPs with this class of instruments.


  Protein phosphorylation cycles are important mechanisms of the post
translational modification of a protein and as such an integral part of
intracellular signaling and control. We consider the sequential phosphorylation
and dephosphorylation of a protein at two binding sites. While it is known that
proteins where phosphorylation is processive and dephosphorylation is
distributive admit oscillations (for some value of the rate constants and total
concentrations) it is not known whether or not this is the case if both
phosphorylation and dephosphorylation are distributive. We study four
simplified mass action models of sequential and distributive phosphorylation
and show that for each of those there do not exist rate constants and total
concentrations where a Hopf bifurcation occurs. To arrive at this result we use
convex parameters to parameterize the steady state and Hurwitz matrices.


  Constraints over finite sequences of variables are ubiquitous in sequencing
and timetabling. Moreover, the wide variety of such constraints in practical
applications led to general modelling techniques and generic propagation
algorithms, often based on deterministic finite automata (DFA) and their
extensions. We consider counter-DFAs (cDFA), which provide concise models for
regular counting constraints, that is constraints over the number of times a
regular-language pattern occurs in a sequence. We show how to enforce domain
consistency in polynomial time for atmost and atleast regular counting
constraints based on the frequent case of a cDFA with only accepting states and
a single counter that can be incremented by transitions. We also prove that the
satisfaction of exact regular counting constraints is NP-hard and indicate that
an incomplete algorithm for exact regular counting constraints is faster and
provides more pruning than the existing propagator from [3]. Regular counting
constraints are closely related to the CostRegular constraint but contribute
both a natural abstraction and some computational advantages.


  We introduce approximate, functional renormalization group based schemes to
obtain correlation functions in pure excited eigenstates of large fermionic
many-body systems at arbitrary energies. The algorithms are thouroughly
benchmarked and their strengths and shortcomings are documented using a
one-dimensional interacting tight-binding chain as a prototypical testbed. We
study two `toy applications' from the world of Luttinger liquid physics: the
survival of power laws in lowly-excited states as well as the spectral function
of high-energy `block' excitations which feature several single-particle Fermi
edges.


  Pervasive healthcare is a promising assisted-living solution for chronic
patients. However, current cutting-edge communication technologies are not able
to strictly meet the requirements of these applications, especially in the case
of life-threatening events. To bridge this gap, this paper proposes a new
architecture to support indoor healthcare monitoring, with a focus on epileptic
patients. Several novel elements are introduced. The first element is the
cascading of a WLAN and a cellular network, where IEEE 802.11ax is used for the
wireless local area network to collect physiological and environmental data
in-home and 5G-enabled Fixed Wireless Access links transfer them to a remote
hospital. The second element is the extension of the network slicing concept to
the WLAN, and the introduction of two new slice types to support both regular
monitoring and emergency handling. Moreover, the inclusion of local computing
capabilities at the WLAN router, together with a mobile edge computing
resource, represents a further architectural enhancement. Local computation is
required to trigger not only health-related alarms, but also the network
slicing change in case of emergency: in fact, proper radio resource scheduling
is necessary for the cascaded networks to handle healthcare traffic together
with other promiscuous everyday communication services. Numerical results
demonstrate the effectiveness of the proposed approach while highlighting the
performance gain achieved with respect to baseline solutions.


  OO Aql is a rare W UMa-type eclipsing binary in which the two solar-type
stars may have only recently evolved into contact. The binary has an unusually
high mass ratio (0.84), and a relatively long orbital period (0.506 d) for it
spectral type (mid-G). Twelve ultraviolet spectra of OO Aql were obtained in
1988 with the IUE satellite, including a series of consecutive observations
that cover nearly a complete orbital cycle. Chromospheric activity is studied
by means of the Mg II h+k emission at 2800 A. The Mg II emission is found to
vary, even when the emission is normalized to the adjacent continuum flux. This
variation may be correlated with orbital phase in the 1988 observations. It
also appears that the normalized Mg II emission varies with time, as seen in
spectra obtained at two different epochs in 1988 and when compared with two
spectra obtained several years earlier. The level of chromospheric activity in
OO Aql is less than that of other W UMa-type binaries of similar colors, but
this is attributed to its early stage of contact binary evolution. Ultraviolet
light curves were composed from measurements of the ultraviolet continuum in
the spectra. These were analyzed along with visible light curves of OO Aql to
determine the system parameters. The large wavelength range in the light curves
enabled a well-constrained fit to a cool spot in the system.


  A potential thermoelectric material CuAlO$_2$ is theoretically studied. We
first construct a model Hamiltonian of CuAlO$_2$ based on the first principles
band calculation, and calculate the Seebeck coefficient. Then, we compare the
model with that of a well-known thermoelectric material Na$_x$CoO$_2$, and
discuss the similarities and the differences. It is found that the two
materials are similar from an electronic structure viewpoint in that they have
a peculiar pudding-mold type band shape, which is advantageous as
thermoelectric materials. There are however some differences, and we analyze
the origin of the difference from a microscopic viewpoint. The band shape of
CuAlO$_2$ is found to be even more ideal than that of Na$_x$CoO$_2$, and we
predict that once a significant amount of holes is doped in CuAlO$_2$,
thermoelectric properties (especially the power factor) even better than that
of Na$_x$CoO$_2$ can be expected.


  Video prediction is a challenging task. The quality of video frames from
current state-of-the-art (SOTA) generative models tends to be poor and
generalization beyond the training data is difficult. Furthermore, existing
prediction frameworks are typically not capable of simultaneously handling
other video-related tasks such as unconditional generation or interpolation. In
this work, we devise a general-purpose framework called Masked Conditional
Video Diffusion (MCVD) for all of these video synthesis tasks using a
probabilistic conditional score-based denoising diffusion model, conditioned on
past and/or future frames. We train the model in a manner where we randomly and
independently mask all the past frames or all the future frames. This novel but
straightforward setup allows us to train a single model that is capable of
executing a broad range of video tasks, specifically: future/past prediction --
when only future/past frames are masked; unconditional generation -- when both
past and future frames are masked; and interpolation -- when neither past nor
future frames are masked. Our experiments show that this approach can generate
high-quality frames for diverse types of videos. Our MCVD models are built from
simple non-recurrent 2D-convolutional architectures, conditioning on blocks of
frames and generating blocks of frames. We generate videos of arbitrary lengths
autoregressively in a block-wise manner. Our approach yields SOTA results
across standard video prediction and interpolation benchmarks, with computation
times for training models measured in 1-12 days using $\le$ 4 GPUs. Project
page: https://mask-cond-video-diffusion.github.io ; Code :
https://github.com/voletiv/mcvd-pytorch


  Quantum fluctuations of a complex, baryonic charged scalar field caused by
inflation can generate large domains, which convert later into antimatter
regions. As a result the Universe can become globally matter-dominated, with
minor contribution of antimatter regions. The distribution and evolution of
such antimatter regions could cause every galaxy to be a harbour of an
anti-star globular cluster. At the same time, the scenario does not lead to
large-scale isocuvature perturbations, which would disturb observable CMB
anisotropy. The existence of one of such antistar globular cluster in our
Galaxy does not contradict the observed $\gamma$-ray background, but the
expected fluxes of $\bar{\rm ^4He}$ and $\bar{\rm ^3He}$ from such an
antimatter object are definitely accessible to the sensitivity of the coming
AMS--02 experiment.


  In computer vision, unsupervised domain adaptation (UDA) is an approach to
transferring knowledge from a label-rich source domain to a fully-unlabeled
target domain. Conventional UDA approaches have two problems. The first problem
is that a class classifier can be biased to the source domain because it is
trained using only source samples. The second is that previous approaches align
image-level features regardless of foreground and background, although the
classifier requires foreground features. To solve these problems, we introduce
Weight-based Mask Network (WEMNet) composed of Domain Ignore Module (DIM) and
Semantic Enhancement Module (SEM). DIM obtains domain-agnostic feature
representations via the weight of the domain discriminator and predicts
categories. In addition, SEM obtains class-related feature representations
using the classifier weight and focuses on the foreground features for domain
adaptation. Extensive experimental results reveal that the proposed WEMNet
outperforms the competitive accuracy on representative UDA datasets.


  We present a novel notion of complexity that interpolates between and
generalizes some classic existing complexity notions in learning theory: for
estimators like empirical risk minimization (ERM) with arbitrary bounded
losses, it is upper bounded in terms of data-independent Rademacher complexity;
for generalized Bayesian estimators, it is upper bounded by the data-dependent
information complexity (also known as stochastic or PAC-Bayesian,
$\mathrm{KL}(\text{posterior} \operatorname{\|} \text{prior})$ complexity. For
(penalized) ERM, the new complexity reduces to (generalized) normalized maximum
likelihood (NML) complexity, i.e. a minimax log-loss individual-sequence
regret. Our first main result bounds excess risk in terms of the new
complexity. Our second main result links the new complexity via Rademacher
complexity to $L_2(P)$ entropy, thereby generalizing earlier results of Opper,
Haussler, Lugosi, and Cesa-Bianchi who did the log-loss case with $L_\infty$.
Together, these results recover optimal bounds for VC- and large (polynomial
entropy) classes, replacing localized Rademacher complexity by a simpler
analysis which almost completely separates the two aspects that determine the
achievable rates: 'easiness' (Bernstein) conditions and model complexity.


  We investigate the homogeneous Dirichlet problem in uniformly convex domains
for a large class of degenerate elliptic equations with singular zero order
term. In particular we establish sharp existence and uniqueness results of
positive viscosity solutions.


  We demonstrate how the presence of continuous weak symmetry can be used to
analytically diagonalize the Liouvillian of a class Markovian dissipative
systems with arbitrary strong interactions or nonlinearity. This enables an
exact description of the full dynamics and dissipative spectrum. Our method can
be viewed as implementing an exact, sector-dependent mean-field decoupling, or
alternatively, as a kind of quantum-to-classical mapping. We focus on two
canonical examples: a nonlinear bosonic mode subject to incoherent loss and
pumping, and an inhomogeneous quantum Ising model with arbitrary connectivity
and local dissipation. In both cases, we calculate and analyze the full
dissipation spectrum. Our method is applicable to a variety of other systems,
and could provide a powerful new tool for the study of complex
driven-dissipative quantum systems.


  The fundamental resonances of near-extremal Kerr black holes due to massive
scalar perturbations are derived {\it analytically}. We show that there exists
a critical mass parameter, $\mu_c$, below which increasing the mass $\mu$ of
the field increases the oscillation frequency $\Re(\omega)$ of the resonance.
On the other hand, above the critical field mass increasing the mass $\mu$
increases the damping rate $\Im(\omega)$ of the mode. We confirm our analytical
results by numerical computations.


  The class-agnostic counting (CAC) task has recently been proposed to solve
the problem of counting all objects of an arbitrary class with several
exemplars given in the input image. To address this challenging task, existing
leading methods all resort to density map regression, which renders them
impractical for downstream tasks that require object locations and restricts
their ability to well explore the scale information of exemplars for
supervision. To address the limitations, we propose a novel localization-based
CAC approach, termed Scale-modulated Query and Localization Network (SQLNet).
It fully explores the scales of exemplars in both the query and localization
stages and achieves effective counting by accurately locating each object and
predicting its approximate size. Specifically, during the query stage, rich
discriminative representations of the target class are acquired by the
Hierarchical Exemplars Collaborative Enhancement (HECE) module from the few
exemplars through multi-scale exemplar cooperation with equifrequent size
prompt embedding. These representations are then fed into the Exemplars-Unified
Query Correlation (EUQC) module to interact with the query features in a
unified manner and produce the correlated query tensor. In the localization
stage, the Scale-aware Multi-head Localization (SAML) module utilizes the query
tensor to predict the confidence, location, and size of each potential object.
Moreover, a scale-aware localization loss is introduced, which exploits
flexible location associations and exemplar scales for supervision to optimize
the model performance. Extensive experiments demonstrate that SQLNet
outperforms state-of-the-art methods on popular CAC benchmarks, achieving
excellent performance not only in counting accuracy but also in localization
and bounding box generation. Our codes will be available at
https://github.com/HCPLab-SYSU/SQLNet


  It is known that there is a class of semilinear parabolic equations for which
interior gradient blow-up (in finite time) occurs for some solutions. We
construct a continuation of such solutions after gradient blow-up. This
continuation is global in time and we give an example when it never becomes a
classical solution again.


  We formulate analytically the reflection of a one dimensional, expanding free
wave-packet (wp) from an infinite barrier. Three types of wp's are considered,
representing an electron, a molecule and a classical object. We derive a
threshold criterion for the values of the dynamic parameters so that reciprocal
(Kramers-Kronig) relations hold {\it in the time domain} between the
log-modulus of the wp and the (analytic part of its) phase acquired during the
reflection. For an electron, in a typical case, the relations are shown to be
satisfied. For a molecule the modulus-phase relations take a more complicated
form, including the so called Blaschke term. For a classical particle
characterized by a large mean momentum ($\hbar K >> \frac{\hbar trajectory
length} {(size of wave-packet)^2} >>> \frac{\hbar}{size of wave-packet}$) the
rate of acquisition of the relative phase between different wp components is
enormous (for a bullet it is typically $10^{14}$ GHertz) with also a very large
value for the phase maximum.


  We characterize different cell states, related to cancer and ageing
phenotypes, by a measure of entropy of network ensembles, integrating gene
expression values and protein interaction networks. The entropy measure
estimates the parameter space available to the network ensemble, that can be
interpreted as the level of plasticity of the system for high entropy values
(the ability to change its internal parameters, e.g. in response to
environmental stimuli), or as a fine tuning of the parameters (that restricts
the range of possible parameter values) in the opposite case. This approach can
be applied at different scales, from whole cell to single biological functions,
by defining appropriate subnetworks based on a priori biological knowledge,
thus allowing a deeper understanding of the cell processes involved. In our
analysis we used specific network features (degree sequence, subnetwork
structure and distance between gene profiles) to obtain informations at
different biological scales, providing a novel point of view for the
integration of experimental transcriptomic data and a priori biological
knowledge, but the entropy measure can also highlight other aspects of the
biological systems studied depending on the constraints introduced in the model
(e.g. community structures).


  Despite increasing instances of machine translation (MT) systems including
contextual information, the evidence for translation quality improvement is
sparse, especially for discourse phenomena. Popular metrics like BLEU are not
expressive or sensitive enough to capture quality improvements or drops that
are minor in size but significant in perception. We introduce the first of
their kind MT benchmark datasets that aim to track and hail improvements across
four main discourse phenomena: anaphora, lexical consistency, coherence and
readability, and discourse connective translation. We also introduce evaluation
methods for these tasks, and evaluate several baseline MT systems on the
curated datasets. Surprisingly, we find that existing context-aware models do
not improve discourse-related translations consistently across languages and
phenomena.


  A measurement is presented for the energy flow of minimum bias events in the
forward region (3.15 <|\eta |< 4.9, where \eta is the pseudorapidity) of the
CMS detector at the LHC for center-of-mass energies sqrt(s) = 0.9 TeV, 2.36 TeV
and 7 TeV. The measurement is compared to Monte Carlo simulations, which use a
model of multiparton interactions for the underlying event. In addition,
production of forward jets was studied for the very first pp collisions at
sqrt(s) = 0.9 TeV at LHC.


  We use a recently discovered constrained de Finetti reduction (aka
"Post-Selection Lemma") to study the parallel repetition of multi-player
non-local games under no-signalling strategies. Since the technique allows us
to reduce general strategies to independent plays, we obtain parallel
repetition (corresponding to winning all rounds) in the same way as exponential
concentration of the probability to win a fraction larger than the value of the
game.
  Our proof technique leads us naturally to a relaxation of no-signalling (NS)
strategies, which we dub sub-no-signalling (SNOS). While for two players the
two concepts coincide, they differ for three or more players. Our results are
most complete and satisfying for arbitrary number of sub-no-signalling players,
where we get universal parallel repetition and concentration for any game,
while the no-signalling case is obtained as a corollary, but only for games
with "full support".


  Gallium metallic photonic crystals with 100% filling factor have been
fabricated via infiltration of liquid gallium into opals of 300-nm silica
spheres using a novel high pressure-high temperature technique. The electrical
resistance of the Ga-opal crystals was measured at temperatures from 10 to 280
K. The data obtained show that Ga-opal crystals are metallic network with
slightly smaller temperature coefficient of resistivity than that for bulk
gallium. Optical reflectivity of bulk gallium, plain opal and several Ga-opal
crystals were measured at photon energies from 0.3 to 6 eV. A pronounced
photonic stop band in the visible spectral range was found in both the plain
and Ga infiltrated opals. The reflectivity spectra also show increase in
reflectivity below 0.6 eV; which we interpret as a significantly lower
effective plasma frequency of the metallic mesh in the infiltrated opal compare
to the plasma frequency in the pure metal.


  In this work the development of a machine learning-based Reduced Order Model
(ROM) for the investigation of hemodynamics in a patient-specific configuration
of Coronary Artery Bypass Graft (CABG) is proposed. The computational domain is
referred to left branches of coronary arteries when a stenosis of the Left Main
Coronary Artery (LMCA) occurs. The method extracts a reduced basis space from a
collection of high-fidelity solutions via a Proper Orthogonal Decomposition
(POD) algorithm and employs Artificial Neural Networks (ANNs) for the
computation of the modal coefficients. The Full Order Model (FOM) is
represented by the incompressible Navier-Stokes equations discretized using a
Finite Volume (FV) technique. Both physical and geometrical parametrization are
taken into account, the former one related to the inlet flow rate and the
latter one related to the stenosis severity. With respect to the previous works
focused on the development of a ROM framework for the evaluation of coronary
artery disease, the novelties of our study include the use of the FV method in
a patient-specific configuration, the use of a data-driven ROM technique and
the mesh deformation strategy based on a Free Form Deformation (FFD) technique.
The performance of our ROM approach is analyzed in terms of the error between
full order and reduced order solutions as well as the speedup achieved at the
online stage.


  In this paper, we prove that for some Generalized Takagi Classes, in
particular for the Takagi-Van der Waerden Class, the functions are nowhere
differentiable if, and only if, the sequence of weights does not belong to
$c_0$.


  Mobile machines typically working in a closed site, have a high potential to
utilize autonomous driving technology. However, vigorously thriving development
and innovation are happening mostly in the area of passenger cars. In contrast,
although there are also many research pieces about autonomous driving or
working in mobile machines, a consensus about the SOTA solution is still not
achieved. We believe that the most urgent problem that should be solved is the
absence of a public and challenging visual dataset, which makes the results
from different researches comparable. To address the problem, we publish the
KIT MOMA dataset, including eight classes of commonly used mobile machines,
which can be used as a benchmark to evaluate the SOTA algorithms to detect
mobile construction machines. The view of the gathered images is outside of the
mobile machines since we believe fixed cameras on the ground are more suitable
if all the interesting machines are working in a closed site. Most of the
images in KIT MOMA are in a real scene, whereas some of the images are from the
official website of top construction machine companies. Also, we have evaluated
the performance of YOLO v3 on our dataset, indicating that the SOTA computer
vision algorithms already show an excellent performance for detecting the
mobile machines in a specific working site. Together with the dataset, we also
upload the trained weights, which can be directly used by engineers from the
construction machine industry. The dataset, trained weights, and updates can be
found on our Github. Moreover, the demo can be found on our Youtube.


  For the generalized Dedekind sums s_{ij}(p,q) defined in association with the
x^{i}y^{j}-coefficient of the Todd power series of the lattice cone in R^2
generated by (1,0) and (q,p), we associate an exponential sum. We obtain this
exponential sum using the cocycle property of the Todd series of 2d cones and
the nonsingular cone decomposition along with the continued fraction of q/p.
Its Weil bound is given for the modulus q, applying the purity theorem of the
cohomology of the related l-adic sheaf due to Denef and Loeser. The Weil type
bound of Denef and Loeser fulfills the Weyl's equidistribution criterion for
R(i,j)q^{i+j-2} s_{ij}(p,q). As a special case, we recover the equidistribution
result of the classical Dedekind sums multiplied by 12 not using the modular
weight of the Dedekind's \eta(\tau).


  We have used the Very Long Baseline Array at 5 GHz to image the nucleus of
NGC 1068 at two epochs separated by 2.92 yr. No relative motion was detected
between the high brightness-temperature knots within components NE and C
relative to the nuclear component S1, placing an upper limit of 0.075 c on the
relative component speeds at distances of 21 pc and 43 pc from the AGN. The low
speed is consistent with the low bulk flow speed previously inferred from
indirect arguments based on ram pressure at the bow shock and on line emission
from the jet-cloud collision at cloud C. The components are probably shocks in
the jet, and the bulk flow speed could conceivably be higher than the limit
reported here.


  Most existing multi-object tracking methods typically learn visual tracking
features via maximizing dis-similarities of different instances and minimizing
similarities of the same instance. While such a feature learning scheme
achieves promising performance, learning discriminative features solely based
on visual information is challenging especially in case of environmental
interference such as occlusion, blur and domain variance. In this work, we
argue that multi-modal language-driven features provide complementary
information to classical visual features, thereby aiding in improving the
robustness to such environmental interference. To this end, we propose a new
multi-object tracking framework, named LG-MOT, that explicitly leverages
language information at different levels of granularity (scene-and
instance-level) and combines it with standard visual features to obtain
discriminative representations. To develop LG-MOT, we annotate existing MOT
datasets with scene-and instance-level language descriptions. We then encode
both instance-and scene-level language information into high-dimensional
embeddings, which are utilized to guide the visual features during training. At
inference, our LG-MOT uses the standard visual features without relying on
annotated language descriptions. Extensive experiments on three benchmarks,
MOT17, DanceTrack and SportsMOT, reveal the merits of the proposed
contributions leading to state-of-the-art performance. On the DanceTrack test
set, our LG-MOT achieves an absolute gain of 2.2\% in terms of target object
association (IDF1 score), compared to the baseline using only visual features.
Further, our LG-MOT exhibits strong cross-domain generalizability. The dataset
and code will be available at ~\url{https://github.com/WesLee88524/LG-MOT}.


  Let $D$ be a strong balanced digraph on $2a$ vertices. Adamus et al. have
proved that $D$ is hamiltonian if $d(u)+d(v)\ge 3a$ whenever $uv\notin A(D)$
and $vu\notin A(D)$. The lower bound $3a$ is tight. In this paper, we shall
show that the extremal digraph on this condition is two classes of digraphs
that can be clearly characterized. Moreover, we also show that if
$d(u)+d(v)\geq 3a-1$ whenever $uv\notin A(D)$ and $vu\notin A(D)$, then $D$ is
traceable. The lower bound $3a-1$ is tight.


  The degree of mass loss, i.e. the fraction of stars lost by globular
clusters, and specifically by their different populations, is still poorly
understood. Many scenarios of the formation of multiple stellar populations,
especially the ones involving self-enrichment, assume that the first generation
(FG) was more massive at birth than now to reproduce the current mass of the
second generation (SG). This assumption implies that, during their long-term
evolution, clusters lose around $90\%$ of the FG. We have tested whether such
strong mass loss could take place in a massive globular cluster orbiting the
Milky Way at $4\ {\rm kpc}$ from the centre and composed of two generations. We
perform a series of $N$-body simulations for ${12\ \rm Gyr}$ to probe the
parameter space of internal cluster properties. We have derived that, for an
extended FG and a low-mass second one, the cluster loses almost $98\%$ of its
initial FG mass and the cluster mass can be as much as 20 times lower after a
Hubble time. Furthermore, under these conditions, the derived fraction of SG
stars, $f_{\rm enriched}$, falls in the range occupied by observed clusters of
similar mass ($\sim 0.6-0.8$). In general, the parameters that affect the most
the degree of mass loss are the presence or not of primordial segregation, the
depth of the central potential, $W_{0,FG}$, the initial mass of the SG,
$M^{ini}_{SG}$, and the initial half-mass radius of the SG, $r_{h,SG}$. Higher
$M^{ini}_{SG}$ have not been found to imply higher final $f_{\rm enriched}$ due
to the deeper cluster potential well which slows down mass loss.


  I provide a calculation at full two-loop order of the complex pole squared
mass of the W boson in the Standard Model in the pure MS-bar renormalization
scheme, with Goldstone boson mass effects resummed. This approach is an
alternative to earlier ones that use on-shell or hybrid renormalization
schemes. The renormalization scale dependence of the real and imaginary parts
of the resulting pole mass are studied. Both deviate by about $\pm 4$ MeV from
their median values as the renormalization scale is varied from 50 GeV to 200
GeV, but the theory error is likely larger. A surprising feature of this scheme
is that the 2-loop QCD correction has a larger scale-dependence, but a smaller
magnitude, than the 2-loop non-QCD correction, unless the renormalization scale
is chosen very far from the top-quark mass.


  The bremsstrahlung of accelerated electrons passing through a converter is
used to study multiparticle photo-nuclear reactions. The results of
calculations, numerical modeling, design, and testing of a special magnetic
cleaning system to obtain a "pure" beam of bremsstrahlung quanta when studying
the cross-sections of such reactions at the LUE-40 linac are presented. The
system is based on commercially available permanent magnets of rectangular
cross-sections. The maximum on-axis field is 0.9 T, which provides sufficient
separation of the electron beam and gamma rays at a distance of more than 90 mm
from the magnet.


  Using a holographic proposal for the geometric entropy we study its behavior
in the geometry of Schwarzschild black holes in global $AdS_p$ for $p=3,4,5$.
Holographically, the entropy is determined by a minimal surface. On the gravity
side, due to the presence of a horizon on the background, generically there are
two solutions to the surfaces determining the entanglement entropy. In the case
of $AdS_3$, the calculation reproduces precisely the geometric entropy of an
interval of length $l$ in a two dimensional conformal field theory with
periodic boundary conditions. We demonstrate that in the cases of $AdS_{4}$ and
$AdS_{5}$ the sign of the difference of the geometric entropies changes,
signaling a transition. Euclideanization implies that various embedding of the
holographic surface are possible. We study some of them and find that the
transitions are ubiquitous. In particular, our analysis renders a very
intricate phase space, showing, for some ranges of the temperature, up to three
branches. We observe a remarkable universality in the type of results we obtain
from $AdS_4$ and $AdS_5$.


  We theoretically investigate possible photoinduced topological phase
transitions in the organic salt $\alpha$-(BEDT-TTF)$_2$I$_3$, which possesses a
pair of inclined massless Dirac-cone bands between the conduction and valence
bands under uniaxial pressure. The Floquet analyses of a driven tight-binding
model for this material reveal rich photoinduced variations of band structures,
Chern numbers, and Hall conductivities under irradiation with elliptically
polarized light. The obtained phase diagrams contain a variety of
nonequilibrium steady phases, e.g., the Floquet Chern insulator, Floquet
semimetal, and Floquet normal insulator phases. This work widens a scope of
target materials for research on photoinduced topological phase transitions and
contributes to development of research on the optical manipulations of
electronic states in matters.


  This paper deals with the minimization of large sum of convex functions by
Inexact Newton (IN) methods employing subsampled functions, gradients and
Hessian approximations. The Conjugate Gradient method is used to compute the
inexact Newton step and global convergence is enforced by a nonmonotone line
search procedure. The aim is to obtain methods with affordable costs and fast
convergence. Assuming strongly convex functions, R-linear convergence and
worst-case iteration complexity of the procedure are investigated when
functions and gradients are approximated with increasing accuracy. A set of
rules for the forcing parameters and subsample Hessian sizes are derived that
ensure local q-linear/superlinear convergence of the proposed method.
  The random choice of the Hessian subsample is also considered and convergence
in the mean square, both for finite and infinite sums of functions, is proved.
Finally, global convergence with asymptotic R-linear rate of IN methods is
extended to the case of sum of convex function and strongly convex objective
function. Numerical results on well known binary classification problems are
also given. Adaptive strategies for selecting forcing terms and Hessian
subsample size, streaming out of the theoretical analysis, are employed and the
numerical results showed that they yield effective IN methods.


  To preserve client privacy in the data mining process, a variety of
techniques based on random perturbation of data records have been proposed
recently. In this paper, we present a generalized matrix-theoretic model of
random perturbation, which facilitates a systematic approach to the design of
perturbation mechanisms for privacy-preserving mining. Specifically, we
demonstrate that (a) the prior techniques differ only in their settings for the
model parameters, and (b) through appropriate choice of parameter settings, we
can derive new perturbation techniques that provide highly accurate mining
results even under strict privacy guarantees. We also propose a novel
perturbation mechanism wherein the model parameters are themselves
characterized as random variables, and demonstrate that this feature provides
significant improvements in privacy at a very marginal cost in accuracy.
  While our model is valid for random-perturbation-based privacy-preserving
mining in general, we specifically evaluate its utility here with regard to
frequent-itemset mining on a variety of real datasets. The experimental results
indicate that our mechanisms incur substantially lower identity and support
errors as compared to the prior techniques.


  A theoretical approach with a microscopic model is proposed for the observed
"high temperature superconductivity" in the Iron-based compounds. The above
scheme takes into account two important aspects viz. (i) superconducting
transition close to magnetic ordering and (ii) the layered structure. From the
calculation of the superconducting transition temperature, it is shown that in
the Fe-based superconductors the magnetic mechanism for superconductivity,
operating through the effective attractive Coulomb interaction within the
framework of the Fermi Liquid theory, is highly plausible.


  We use integral-field and long-slit spectroscopy to study the bright extended
nebulosity discovered in the isolated lenticular galaxy NGC 4460 during a
recent H-alpha survey of nearby galaxies. An analysis of archival SDSS, GALEX,
and HST images indicates that current star formation is entirely concentrated
in the central kiloparsec of the galaxy disc. The observed ionized gas
parameters (morphology, kinematics and ionization state) can be explained by a
gas outflow above the plane of the galaxy caused by a star formation in the
circumnuclear region. Galactic wind parameters in NGC 4460: outflow velocity,
total kinetic energy - are several times smaller comparing with the known
galactic wind in NGC 253, which is explained substantially lower total star
formation rate. We discuss the cause of the star formation processes in NGC
4460 and in two other known isolated S0 and E galaxies of the Local volume: NGC
404 and NGC 855. We provide evidence suggesting that feeding of isolated
galaxies by intergalactic gas on a cosmological time scale is a steady process
without significant variations.


  The ground state of QCD with two flavors at a finite baryon chemical
potential under rapid rotation is a chiral soliton lattice (CSL) of the $\eta$
meson, consisting of a stack of sine-Gordon solitons carrying a baryon number,
due to the anomalous coupling of the $\eta$ meson to the rotation. In a large
parameter region, the ground state becomes a non-Abelian CSL, in which due to
the neutral pion condensation each $\eta$ soliton decays into a pair of
non-Abelian sine-Gordon solitons carrying $S^2$ moduli originated from
Nambu-Goldstone (NG) modes localized around it, corresponding to the
spontaneously broken vector symmetry SU$(2)_{\rm V}$. There, the $S^2$ modes of
neighboring solitons are anti-aligned, and these modes should propagate in the
transverse direction of the lattice due to the interaction between the $S^2$
modes of neighboring solitons. In this paper, we calculate excitations
including gapless NG modes and excited modes around non-Abelian and Abelian
($\eta$) CSLs, and find three gapless NG modes with linear dispersion relations
(type-A NG modes): two isospinons ($S^2$ modes) and a phonon corresponding to
the spontaneously broken vector SU$(2)_{\rm V}$ and translational symmetries
around the non-Abelian CSL, respectively, and only a phonon for the Abelian CSL
because of the recovering SU$(2)_{\rm V}$. We also find in the deconfined phase
that the dispersion relation of the isospinons becomes of the Dirac type, {\it
i.~e.~} linear even at large momentum.


  The purpose of this article is twofold. First, we highlight a simple
connection between the real part of quasinormal modes (QNMs) in the eikonal
limit and shadow radius of BHs and then explore the effect of dark matter on
the QNMs of massless scalar field and electromagnetic field perturbations in a
black hole (BH) spacetime surrounded by perfect fluid dark matter (BHPFDM).
Using the WKB approximation we show that the quasinormal mode spectra of BHPFDM
deviate from those of Schwarzschild black hole due to the presence of the PFDM
encoded by the parameter $k$. Moreover it is shown that for any $k>0$, the real
part and the absolute value of the imaginary part of QNM frequencies increases
and this means that the field perturbations in the presence of PFDM decays more
rapidly compared to Schwarzschild vacuum BH. We point out that there exists a
reflecting point $k_0$ corresponding to maximal values for the real part of QNM
frequencies. Namely, as the PFDM parameter $k$ increases in the interval
$k<k_0$, the QNM frequencies increases and reach their maximum values at
$k=k_0$. Finally we show that $k_0$ is also a reflecting point for the shadow
radius while this conclusion can be deduced directly from the inverse relation
between the real part of QNMs and the shadow radius.


  We present here a methodology, using holographic interferometry, enabling to
measure the pure surface reaction rate constant of the dissolution of a mineral
in water, unambiguously free from the influence of mass transport. We use that
technique to access to this value for gypsum and we demonstrate that it was
never measured before but could be deduced a posteriori from the literature
results if hydrodynamics is taken into account with accuracy. It is found to be
much smaller than expected. This method enables to provide reliable rate
constants for the test of dissolution models and the interpretation of in situ
measurements, and gives clues to explain the inconsistency between dissolution
rates of calcite and aragonite, for instance, in the literature.


  The thermopower of mesoscopic normal metal/superconductor structures has been
measured at low temperatures. Effect of supercurrent present in normal part of
the structure was studied in two cases: when it was created by applied external
magnetic field and when it was applied directly using extra superconducting
electrodes. Temperature and magnetic field dependencies of thermopower are
compared to the numerical simulations based on the quasiclassical theory of the
superconducting proximity effect.


  In this paper, we study the Painlev\'{e} VI equation with parameter $(\frac
{9}{8},\frac{-1}{8},\frac{1}{8},\frac{3}{8})$. We prove
  (i) An explicit formula to count the number of poles of an algebraic solution
with the monodromy group $D_{N}$, where $D_{N}$ is the dihedral group of order
$2N$.
  (ii) There are only four solutions without poles in $\mathbb{C}\backslash
\left \{ 0,1\right \} $.
  (iii) If the monodromy group of the associated linear ODE of a solution
$\lambda \left( t\right) $ is unitary, then $\lambda ( t) $ has no poles in
$\mathbb{R}% \backslash \{ 0,1\} $.


  Drones provide a versatile platform for remote sensing and atmospheric
studies. However, strict payload mass limits and intense vibrations have proven
obstacles to adoption for astronomy. We present a concept for system-level
testing of a long-baseline CubeSat space interferometer using drones, taking
advantage of their cm-level xyz station-keeping, 6-dof freedom of movement,
large operational environment, access to guide stars for end-to-end testing of
optical train and control algorithms, and comparable mass and power
requirements. We have purchased two different drone platforms (Aurelia X6 Pro,
Freefly Alta X) and present characterization studies of vibrations, flight
stability, gps positioning precision, and more. We also describe our progress
in sub-system development, including inter-drone laser metrology, realtime
gimbal control, and LED beacon tracking. Lastly, we explore whether
custom-built drone-borne telescopes could be used for interferometry of bright
objects over km-level baselines using vibration-isolation platforms and a small
fast delay for fringe-tracking.


  In this paper we introduce a method for visually analyzing contextualized
embeddings produced by deep neural network-based language models. Our approach
is inspired by linguistic probes for natural language processing, where tasks
are designed to probe language models for linguistic structure, such as
parts-of-speech and named entities. These approaches are largely confirmatory,
however, only enabling a user to test for information known a priori. In this
work, we eschew supervised probing tasks, and advocate for unsupervised probes,
coupled with visual exploration techniques, to assess what is learned by
language models. Specifically, we cluster contextualized embeddings produced
from a large text corpus, and introduce a visualization design based on this
clustering and textual structure - cluster co-occurrences, cluster spans, and
cluster-word membership - to help elicit the functionality of, and relationship
between, individual clusters. User feedback highlights the benefits of our
design in discovering different types of linguistic structures.


  Standardisation of healthcare has been the focus of hospital management and
clinicians since the 1990's. Electronic health records were already intended to
provide clinicians with real-time access to clinical knowledge and care plans
while also recording and storing vast amounts of patient data. It took more
than three decades for electronic health records to start to become ubiquitous
in all aspects of healthcare. Learning health systems are the next stage in
health information systems whose potential benefits have been promoted for more
than a decade - yet few are seen in clinical practice. Clinical care process
specifications are a primary form of clinical documentation used in all aspects
of healthcare, but they lack standardisation. This thesis contends that this
lack of standardisation was inherited by electronic health records and that
this is a significant issue holding back the development and adoption of
learning health systems. Standardisation of clinical documents is used to
mitigate issues in electronic health records as a basis for enabling learning
health systems. One type of clinical document, the caremap, is standardised in
order to achieve an effective approach to containing resources and ensuring
consistency and quality. This led not only to improved clinicians'
comprehension and acceptance of the clinical document, but also to reduced time
expended in developing complicated learning health systems built using the
input of clinical experts.


  Results have been moved to a published article, see
arXiv:0812.2669v4[math.PR]


  The deconfinement phase transition from hadronic matter to quark matter can
continuously occur during spins down of neutron stars. It will lead to the
release of latent heat if the transition is the first-order one. We have
investigated the energy release of such deconfinement phase transition for
rotating hybrid stars model which include mixed phase of hadronic matter and
quark matter. The release of latent heat per baryon is calculated through
studying a randomly process of infinitesimal compressing. Finally, we can
self-consistently get the heating luminosity of deconfinement phase transition
by imputing the EOS of mixed phase, and based on the equation of rotation
structure of stars.


  The cost involved in training deep neural networks (DNNs) on von-Neumann
architectures has motivated the development of novel solutions for efficient
DNN training accelerators. We propose a hybrid in-memory computing (HIC)
architecture for the training of DNNs on hardware accelerators that results in
memory-efficient inference and outperforms baseline software accuracy in
benchmark tasks. We introduce a weight representation technique that exploits
both binary and multi-level phase-change memory (PCM) devices, and this leads
to a memory-efficient inference accelerator. Unlike previous in-memory
computing-based implementations, we use a low precision weight update
accumulator that results in more memory savings. We trained the ResNet-32
network to classify CIFAR-10 images using HIC. For a comparable model size,
HIC-based training outperforms baseline network, trained in floating-point
32-bit (FP32) precision, by leveraging appropriate network width multiplier.
Furthermore, we observe that HIC-based training results in about 50% less
inference model size to achieve baseline comparable accuracy. We also show that
the temporal drift in PCM devices has a negligible effect on post-training
inference accuracy for extended periods (year). Finally, our simulations
indicate HIC-based training naturally ensures that the number of write-erase
cycles seen by the devices is a small fraction of the endurance limit of PCM,
demonstrating the feasibility of this architecture for achieving hardware
platforms that can learn in the field.


  Results of Bohr-Neugebauer type are obtained for recurrent functions : If $y$
is a bounded uniformly continuous solution of a linear neutral
difference-differential system with recurrent right-hand side, then $y$ is
recurrent if $c_0 \not \subset X$ ; also analogues and extensions to half lines
are given. For this, various subclasses "$rec$" are introduced which are linear
(the set REC of all recurrent functions is not), invariant, closed etc. Also,
analogues of the Bohl-Bohr-Amerio-Kadets and Esclangon- Landau results for REC
are obtained.


  State estimation is usually analyzed in the situation when copies are in a
product state, either mixed or pure. We investigate here the concept of state
estimation on correlated copies. We analyze state estimation on correlated N
qubit states, which are permutationally invariant. Using a correlated state we
try to estimate as good as possible the direction of the Bloch vector of a
single particle reduced density matrix. We derive the optimal fidelity for all
permutation invariant states. We find the optimal state, which yields the
highest estimation fidelity among the states with the same reduced density
matrix. Interestingly this state is not a product state. We also point out that
states produced by optimal universal cloning machines are the worst form the
point of view of estimating the reduced density matrix.


  For many biological systems that involve elastic structures immersed in
fluid, small length scales mean that inertial effects are also small, and the
fluid obeys the Stokes equations. One way to solve the model equations
representing such systems is through the Stokeslet, the fundamental solution to
the Stokes equations, and its regularized counterpart, which treats the
singularity of the velocity at points where force is applied. In two
dimensions, an additional complication arises from Stokes' paradox, whereby the
velocity from the Stokeslet is unbounded at infinity when the net hydrodynamic
force within the domain is nonzero, invalidating the solutions. A
straightforward computationally inexpensive method is presented for obtaining
valid solutions to the Stokes equations for net nonzero forcing. The approach
is based on imposing a mean zero velocity condition on a large curve that
surrounds the domain of interest. The condition is shown to be equivalent to a
net-zero force condition, where the opposite forces are applied on the large
curve. The numerical method is applied to models of cellular motility and
blebbing.


  A simple $n$-gon is a polygon with $n$ edges with each vertex belonging to
exactly two edges and every other point belonging to at most one edge. Brass
asked the following question: For $n \geq 5$ odd, what is the maximum perimeter
of a simple $n$-gon contained in a Euclidean unit disk?
  In 2009, Audet, Hansen and Messine answered this question, and showed that
the optimal configuration is an isosceles triangle with a multiple edge,
inscribed in the disk. In this note we give a shorter and simpler proof of
their result, which we generalize also for hyperbolic disks, and for spherical
disks of sufficiently small radii.


  eBay partners with external vendors, which allows customers to freely select
a vendor to complete their eBay experiences. However, vendor outages can hinder
customer experiences. Consequently, eBay can disable a problematic vendor to
prevent customer loss. Disabling the vendor too late risks losing customers
willing to switch to other vendors, while disabling it too early risks losing
those unwilling to switch. In this paper, we propose a data-driven solution to
answer whether eBay should disable a problematic vendor and when to disable it.
Our solution involves forecasting customer behavior. First, we use a
multiplicative seasonality model to represent behavior if all vendors are fully
functioning. Next, we use a Monte Carlo simulation to represent behavior if the
problematic vendor remains enabled. Finally, we use a linear model to represent
behavior if the vendor is disabled. By comparing these forecasts, we determine
the optimal time for eBay to disable the problematic vendor.


  This paper introduces variational design methods that are novel to
Geophysics, and discusses their benefits and limitations in the context of
geophysical applications and more established design methods. Variational
methods rely on functional approximations to probability distributions and
model-data relationships. They can be used to design experiments that best
resolve either all model parameters, or the answer to specific questions about
the system to be interrogated. The methods are tested in three schematic
geophysical applications: (i) estimating a source location given arrival times
at sensor locations, and (ii) estimating the contrast in seismic wavefield
velocity across a stratal interface given measurements of the amplitudes of
seismic wavefield reflections from that interface, and (iii) designing a survey
to best constrain CO2 saturation in a subsurface storage scenario. Variational
methods allow the value of an experiment to be calculated and optimised
simultaneously, which results in substantial savings in computational cost. In
the context of designing a survey to best constrain CO2 in a subsurface storage
scenario, we show that optimal designs may change substantially depending on
the questions of interest. Overall, this work indicates that optimal design
methods should be used more widely in Geophysics, as they are in other
scientifically advanced fields.


  We construct a generalized Smarr formula which could provide a thermodynamic
route to derive the covariant field equation of general theories of gravity in
dynamic spacetimes. Combining some thermodynamic variables and a new chemical
potential conjugated to the number of degree of freedom on the holographic
screen, we find a universal Cardy-Verlinde formula and give its braneworld
interpretation. We demonstrate that the associated AdS-Bekenstein bound is
tighten than the previous expression for multi-charge black holes in the gauged
supergravities. The Cardy-Verlinde formula and the AdS-Bekenstein bound are
derived from the thermodynamics of bulk trapping horizons, which strongly
suggests the underlying holographic duality between dynamical bulk spacetime
and boundary field theory.


  We examine the evolution of a closed, homogeneous and anisotropic cosmology
subject to a variation of the fine structure 'constant', \alpha, within the
context of the theory introduced by Bekenstein, Sandvik, Barrow and Magueijo,
which generalises Maxwell's equations and general relativity. The variation of
\alpha permits an effective ghost scalar field, whose negative energy density
becomes dominant at small length scales, leading to a bouncing cosmology. A
thermodynamically motivated coupling which describes energy exchange between
the effective ghost field and the radiation field leads to an expanding,
isotropizing sequence of bounces. In the absence of entropy production we also
find solutions with stable anisotropic oscillations around a static universe.


  A spanning tree without a vertex of degree two is called a Hist which is an
abbreviation for homeomorphically irreducible spanning tree. We provide a
necessary condition for the existence of a Hist in a cubic graph. As one
consequence, we answer affirmatively an open question on Hists by Albertson,
Berman, Hutchinson and Thomassen.


  To meet the growing local and distributed computing needs, the cloud is now
descending to the network edge and sometimes to user equipments. This approach
aims at distributing computing, data processing, and networking services closer
to the end users. Instead of concentrating data and computation in a small
number of large clouds, many edge systems are envisioned to be deployed close
to the end users or where computing and intelligent networking can best meet
user needs. In this paper, we go further converging such massively distributed
computing systems with multiple radio accesses. We propose an architecture
called MUREN (Multi-Radio Edge Node) for managing traffic in future mobile edge
networks. Our solution is based on the Mobile Edge Cloud (MEC) architecture and
its close interaction with Software Defined Networking (SDN), the whole jointly
interacting with Software-Defined Radios (SDR). We have implemented our
architecture in a proof of concept and tested it with two edge scenarios. Our
experiments show that centralizing the intelligence in the MEC allows to
guarantee the requirements of the edge services either by adapting the waveform
parameters, or through changing the radio interface or even by reconfiguring
the applications. More generally, the best decision can be seen as the optimal
reaction to the wireless links variations


  In previous work we introduced a Khovanov multicurve invariant
$\operatorname{\widetilde{Kh}}$ associated with Conway tangles. Applying ideas
from homological mirror symmetry we show that $\operatorname{\widetilde{Kh}}$
is subject to strong geography restrictions: Every component of the invariant
is linear, in the sense that it admits a lift to a curve homotopic to a
straight line in an appropriate planar cover of the tangle boundary.


  Recent advances in reinforcement learning with social agents have allowed
such models to achieve human-level performance on specific interaction tasks.
However, most interactive scenarios do not have a version alone as an end goal;
instead, the social impact of these agents when interacting with humans is as
important and largely unexplored. In this regard, this work proposes a novel
reinforcement learning mechanism based on the social impact of rivalry
behavior. Our proposed model aggregates objective and social perception
mechanisms to derive a rivalry score that is used to modulate the learning of
artificial agents. To investigate our proposed model, we design an interactive
game scenario, using the Chef's Hat Card Game, and examine how the rivalry
modulation changes the agent's playing style, and how this impacts the
experience of human players in the game. Our results show that humans can
detect specific social characteristics when playing against rival agents when
compared to common agents, which directly affects the performance of the human
players in subsequent games. We conclude our work by discussing how the
different social and objective features that compose the artificial rivalry
score contribute to our results.