  Tracing dust in small dense molecular cores is a powerful tool to study the
conditions required for ices to form during the pre-stellar phase. To study
these environments, five molecular cores were observed: three with ongoing
low-mass star formation (B59, B335, and L483) and two starless collapsing cores
(L63 and L694-2). Deep images were taken in the infrared JHK bands with the
United Kingdom Infrared Telescope (UKIRT) WFCAM (Wide Field Camera) instrument
and IRAC channels 1 and 2 on the Spitzer Space Telescope. These five
photometric bands were used to calculate extinction along the line of sight
toward background stars. After smoothing the data, we produced high spatial
resolution extinction maps ($\sim$13-29") . The maps were then projected into
the third dimension using the AVIATOR algorithm implementing the inverse Abel
transform. The volume densities of the total hydrogen were measured along lines
of sight where ices (H$_2$O, CO, and CH$_3$OH) have previously been detected.
We find that lines of sight with pure CH$_3$OH or a mixture of CH$_3$OH with CO
have maximum volume densities above 1.0$\times$10$^5$ cm$^{-3}$. These
densities are only reached within a small fraction of each of the cores
($\sim$0.3-2.1%). CH$_3$OH presence may indicate the onset of complex organic
molecule formation within dense cores and thus we can constrain the region
where this onset can begin. The maximum volume densities toward star-forming
cores in our sample ($\sim$1.2-1.7$\times$10$^6$ cm$^{-3}$) are higher than
those toward starless cores ($\sim$3.5-9.5$\times$10$^5$ cm$^{-3}$).


  Three-body recombination rates for cold $^4$He are calculated with a new
method which exploits the simple relationship between the imaginary part of the
atom-dimer elastic scattering phase shift and the $S$-matrix for recombination.
The elastic phase shifts are computed above breakup threshold by solving a
three-body Faddeev equation in momentum space with inputs based on a variety of
modern atom-atom potentials. Recombination coefficients for the HFD-B3-FCII
potential agree very well with the only previously published results. Since the
elastic scattering and recombination processes for $^4$He are governed by
"Efimov physics", they depend on universal functions of a scaling variable. The
newly computed recombination coefficients for potentials other than HFD-B3-FCII
make it possible to determine these universal functions for the first time.


  Influence of surrounding matter on the properties of clusters is considered
by an approach combining the methods of statistical and quantum mechanics. A
cluster is treated as a bound N-particle system and surrounding matter as
thermostat. It is shown that, despite arbitrary strong interactions between
particles, cluster energy can be calculated by using the controlled
perturbation theory. The accuracy of the latter is found to be much higher than
that of the quasiclassical approximation. Spectral distribution is obtained by
minimizing conditional entropy. Increasing the thermostat temperature leads to
the depletion of bound states. The characteristic temperature when bound states
become essentially depleated defines the temperature of cluster evaporation.
The inverse process of lowering the thermostate temperature, yielding the
filling of bound states, corresponds to cluster condensation.


  Big bang nucleosynthesis has long provided the primary determination of the
cosmic baryon density $\omb h^2$, or equivalently the baryon-to-photon ratio,
\eta. Recently, data on CMB anisotropies have become increasingly sensitive to
\eta. The comparison of these two independent measures provides a key test for
big bang cosmology. The first release of results from the Wilkinson Microwave
Anisotropy Probe (WMAP) marks a milestone in this test. With the precision of
WMAP, the CMB now offers a significantly stronger constraint on \eta. We
discuss the current state of BBN theory and light element observations
(including their possible lingering systematic errors). The resulting BBN
baryon density prediction is in overall agreement with the WMAP prediction, an
important and non-trivial confirmation of hot big bang cosmology. Going beyond
this, the powerful CMB baryometer can be used as an input to BBN and one can
accurately predict the primordial light element abundances. By comparing these
with observations one can obtain new insight into post-BBN nucleosynthesis
processes and associated astrophysics. Finally, one can test the possibility of
nonstandard physics at the time of BBN, now with all light elements available
as probes. Indeed, with the WMAP precision \eta, deuterium is already beginning
to rival \he4's sensitivity to nonstandard physics, and additional D/H
measurements can improve this further.


  I review some recent work where ideas and methods from Quantum Field Theory
have proved useful in probability and vice versa. The topics discussed include
the use of Renormalization Group theory in Stochastic Partial Differential
Equations driven by space-time white noise and the use of the theory of
Gaussian Multiplicative Chaos in the study of two dimensional Liouville
Conformal Field theory.


  Charge transfer in a DNA duplex chain is studied by constructing a system
with virtual electrodes connected at the ends of each DNA strand. The systeym
is described by the tight-binding model and its transport is analyzed by the
transfer matrix method. The very weak distance dependence in long
(G:C)(T:A)_M(G:C)_3 DNA chain observed in experiment [B. Giese, et al., Nature
412, 318 (2001)] is explained by a unistep two-strand superexchange bridge
without the need for the multi-step thermally-induced hopping mechanism or the
dephasing effect. The crossover number M_c of (T:A) base pairs, where crossover
between strong and weak distance dependence occurs, reflects the ratio of
intra- and inter-strand neighboring base-base couplings.


  We suggest a new multimodal imaging technique for quantitatively measuring
the integral (thickness-average) refractive index of the nuclei of live
biological cells in suspension. For this aim, we combined quantitative phase
microscopy with simultaneous 2-D fluorescence microscopy. We used 2-D
fluorescence microscopy to localize the nucleus inside the quantitative phase
map of the cell, as well as for measuring the nucleus radii. As verified
offline by both 3-D confocal fluorescence microscopy and by 2-D fluorescence
microscopy while rotating the cells during flow, the nucleus of cells in
suspension that are not during division can be assumed to be an ellipsoid. The
entire shape of a cell in suspension can be assumed to be a sphere. Then, the
cell and nucleus 3-D shapes can be evaluated based on their in-plain radii
available from the 2-D phase and fluorescent measurements, respectively.
Finally, the nucleus integral refractive index profile is calculated. We
demonstrate the new technique on cancer cells, obtaining nucleus refractive
index values that are lower than those of the cytoplasm, coinciding with recent
findings. We believe that the proposed technique has the potential to be used
for flow cytometry, where full 3-D refractive index tomography is too slow to
be implemented during flow.


  We pursue, in this contribution, an investigation of the contribution of the
electron's magnetic dipole moment (MDM) to the electron's electric dipole
moment (EDM) (or the charged leptons, more generally) in the framework of the
Born-Infeld Electrodynamics and in a gravity background with a non-vanishing
cosmological constant, where CP-symmetry is broken down. Our point of view
consist in associating a non-trivial EDM to a non-symmetry of the charge
distribution of the elementary particle under consideration. The bound on the
electron's EDM published in 2014 is used to constrain the coupling parameter of
the electromagnetic sector to the scalar curvature of the space-time in the
case we consider gravity to be responsible for the asymmetry in the
distribution.


  Recent neural news recommenders (NNRs) extend content-based recommendation
(1) by aligning additional aspects (e.g., topic, sentiment) between candidate
news and user history or (2) by diversifying recommendations w.r.t. these
aspects. This customization is achieved by "hardcoding" additional constraints
into the NNR's architecture and/or training objectives: any change in the
desired recommendation behavior thus requires retraining the model with a
modified objective. This impedes widespread adoption of multi-aspect news
recommenders. In this work, we introduce MANNeR, a modular framework for
multi-aspect neural news recommendation that supports on-the-fly customization
over individual aspects at inference time. With metric-based learning as its
backbone, MANNeR learns aspect-specialized news encoders and then flexibly and
linearly combines the resulting aspect-specific similarity scores into
different ranking functions, alleviating the need for ranking function-specific
retraining of the model. Extensive experimental results show that MANNeR
consistently outperforms state-of-the-art NNRs on both standard content-based
recommendation and single- and multi-aspect customization. Lastly, we validate
that MANNeR's aspect-customization module is robust to language and domain
transfer.


  Urban environments develop complex, non-obvious structures that are often
hard to represent in the form of maps or guides. Finding the right place to go
often requires intimate familiarity with the location in question and cannot
easily be deduced by visitors. In this work, we exploit large-scale samples of
usage information, in the form of mobile phone traces and geo-tagged Twitter
messages in order to automatically explore and annotate city maps via kernel
density estimation. Our experiments are based on one year's worth of mobile
phone activity collected by Nokia's Mobile Data Challenge (MDC). We show that
usage information can be a strong predictor of semantic place categories,
allowing us to automatically annotate maps based on the behavior of the local
user base.


  The DeepMind Control Suite is a set of continuous control tasks with a
standardised structure and interpretable rewards, intended to serve as
performance benchmarks for reinforcement learning agents. The tasks are written
in Python and powered by the MuJoCo physics engine, making them easy to use and
modify. We include benchmarks for several learning algorithms. The Control
Suite is publicly available at https://www.github.com/deepmind/dm_control . A
video summary of all tasks is available at http://youtu.be/rAai4QzcYbs .


  We present the results of a muon-spin relaxation (muSR) investigation into
magnetic ordering in several families of layered quasi-two-dimensional
molecular antiferromagnets based on transition metal ions such as S=1/2 Cu2+
bridged with organic ligands such as pyrazine. In many of these materials
magnetic ordering is difficult to detect with conventional magnetic probes. In
contrast, muSR allows us to identify ordering temperatures and study the
critical behavior close to T_N . Combining this with measurements of in-plane
magnetic exchange J and predictions from quantum Monte Carlo simulations we may
assess the degree of isolation of the 2D layers through estimates of the
effective inter-layer exchange coupling and in-layer correlation lengths at T_N
. We also identify the likely metal-ion moment sizes and muon stopping sites in
these materials, based on probabilistic analysis of the magnetic structures and
of muon-fluorine dipole-dipole coupling in fluorinated materials.


  Safe and efficient co-planning of multiple robots in pedestrian participation
environments is promising for applications. In this work, a novel multi-robot
social-aware efficient cooperative planner that on the basis of off-policy
multi-agent reinforcement learning (MARL) under partial dimension-varying
observation and imperfect perception conditions is proposed. We adopt
temporal-spatial graph (TSG)-based social encoder to better extract the
importance of social relation between each robot and the pedestrians in its
field of view (FOV). Also, we introduce K-step lookahead reward setting in
multi-robot RL framework to avoid aggressive, intrusive, short-sighted, and
unnatural motion decisions generated by robots. Moreover, we improve the
traditional centralized critic network with multi-head global attention module
to better aggregates local observation information among different robots to
guide the process of individual policy update. Finally, multi-group
experimental results verify the effectiveness of the proposed cooperative
motion planner.


  Starting from the definition of an $n\times n$ $g$-Toeplitz matrix,
$T_{n,g}(u)=\left[\widehat{u}_{r-gs}\right]_{r,s=0}^{n-1},$ where $g$ is a
given nonnegative parameter, $\{\widehat{u}_{k}\}$ is the sequence of Fourier
coefficients of the Lebesgue integrable function $u$ defined over the domain
$\mathbb{T}=(-\pi,\pi]$, we consider the product of $g$-Toeplitz sequences of
matrices, $\{T_{n,g}(f_{1})T_{n,g}(f_{2})\},$ which extends the product of
Toeplitz structures, $\{T_{n}(f_{1})T_{n}(f_{2})\},$ in the case where the
symbols $f_{1},f_{2}\in L^{\infty}(\mathbb{T}).$ Under suitable assumptions,
the spectral distribution in the eigenvalues sequence is completely
characterized for the products of $g$-Toeplitz structures. Specifically, for
$g\geq2$ our result shows that the sequences $\{T_{n,g}(f_{1})T_{n,g}(f_{2})\}$
are clustered to zero. This extends the well-known result, which concerns the
classical case (that is, $g=1$) of products of Toeplitz matrices. Finally, a
large set of numerical examples confirming the theoretic analysis is presented
and discussed.


  Fractional Brownian motion (fBm) is a ubiquitous diffusion process in which
the memory effects of the stochastic transport result in the mean squared
particle displacement following a power law, $\langle {\Delta r}^2 \rangle \sim
t^{\alpha}$, where the diffusion exponent $\alpha$ characterizes whether the
transport is subdiffusive, ($\alpha<1$), diffusive ($\alpha = 1$), or
superdiffusive, ($\alpha >1$). Due to the abundance of fBm processes in nature,
significant efforts have been devoted to the identification and
characterization of fBm sources in various phenomena. In practice, the
identification of the fBm sources often relies on solving a complex and
ill-posed inverse problem based on limited observed data. In the general case,
the detected signals are formed by an unknown number of release sources,
located at different locations and with different strengths, that act
simultaneously. This means that the observed data is composed of mixtures of
releases from an unknown number of sources, which makes the traditional inverse
modeling approaches unreliable. Here, we report an unsupervised learning
method, based on Nonnegative Matrix Factorization, that enables the
identification of the unknown number of release sources as well the anomalous
diffusion characteristics based on limited observed data and the general form
of the corresponding fBm Green's function. We show that our method performs
accurately for different types of sources and configurations with a
predetermined number of sources with specific characteristics and introduced
noise.


  The measurement of Higgs pair production will be a cornerstone of the LHC
program in the coming years. Double Higgs production provides a crucial window
upon the mechanism of electroweak symmetry breaking and has a unique
sensitivity to the Higgs trilinear coupling. We study the feasibility of a
measurement of Higgs pair production in the $b\bar{b}b\bar{b}$ final state at
the LHC. Our analysis is based on a combination of traditional cut-based
methods with state-of-the-art multivariate techniques. We account for all
relevant backgrounds, including the contributions from light and charm jet
mis-identification, which are ultimately comparable in size to the irreducible
$4b$ QCD background. We demonstrate the robustness of our analysis strategy in
a high pileup environment. For an integrated luminosity of $\mathcal{L}=3$
ab$^{-1}$, a signal significance of $S/\sqrt{B}\simeq 3$ is obtained,
indicating that the $b\bar{b}b\bar{b}$ final state alone could allow for the
observation of double Higgs production at the High-Luminosity LHC.


  Rare kaon decays provide unique opportunity to test the Standard Model and
probe its possible extensions. The final result on the lepton universality test
by measuring the ratio R_K = G(K+- to e+- nu)/G(K+- to mu+- nu) is presented as
well as the status of the study of the rare decays K+- to pi+- gamma gamma and
K+ to e+ nu gamma. The primary goal of the NA62 experiment is the measurement
of the Br(K+ to pi+ nu anti-nu) decay with a precision of 10% in two years of
data taking. The detector setup together with the analysis technique is
described.


  We propose to characterize the shapes of flat pebbles in terms of the
statistical distribution of curvatures measured along the pebble contour. This
is demonstrated for the erosion of clay pebbles in a controlled laboratory
apparatus. Photographs at various stages of erosion are analyzed, and compared
with two models. We find that the curvature distribution complements the usual
measurement of aspect ratio, and connects naturally to erosion processes that
are typically faster at protruding regions of high curvature.


  The thawing quintessence model with a nearly flat potential provides a
natural mechanism to produce an equation of state parameter, w, close to -1
today. We examine the behavior of such models for the case in which the
potential satisfies the slow roll conditions: [(1/V)(dV/dphi)]^2 << 1 and
(1/V)(d^2 V/dphi^2) << 1, and we derive the analog of the slow-roll
approximation for the case in which both matter and a scalar field contribute
to the density. We show that in this limit, all such models converge to a
unique relation between 1+w, Omega_phi, and the initial value of
(1/V)(dV/dphi). We derive this relation, and use it to determine the
corresponding expression for w(a), which depends only on the present-day values
for w and Omega_phi. For a variety of potentials, our limiting expression for
w(a) is typically accurate to within delta w < 0.005 for w<-0.9. For redshift z
< 1, w(a) is well-fit by the Chevallier-Polarski-Linder parametrization, in
which w(a) is a linear function of a.


  Finite N effects on the time evolution of fuzzy 2-spheres moving in flat
spacetime are studied using the non-Abelian DBI action for N D0-branes.
Constancy of the speed of light leads to a definition of the physical radius in
terms of symmetrised traces of large powers of Lie algebra generators. These
traces, which determine the dynamics at finite N, have a surprisingly simple
form. The energy function is given by a quotient of a free multi-particle
system, where the dynamics of the individual particles are related by a simple
scaling of space and time. We show that exotic bounces of the kind seen in the
1/N expansion do not exist at finite N. The dependence of the time of collapse
on N is not monotonic. The time-dependent brane acts as a source for gravity
which, in a region of parameter space, violates the dominant energy condition.
We find regimes, involving both slowly collapsing and rapidly collapsing
branes, where higher derivative corrections to the DBI action can be neglected.
We propose some generalised symmetrised trace formulae for higher dimensional
fuzzy spheres and observe an application to D-brane charge calculations.


  We present an estimate of the behaviour of the shear and bulk viscosity
coefficients when the QCD critical point is approached from the hadronic side,
describing hadronic matter within the statistical bootstrap model (SBM) of
strong interactions. The boostrap model shows critical behavior near the
quark-hadron transition temperature if the parameter characterizing the
degeneracy of Hagedorn states is properly chosen. We calculate the critical
exponents and amplitudes of relevant thermodynamic quantities near the QCD
critical point and combine them with an Ansatz for the shear and bulk viscosity
coefficients to derive the behavior of these coefficients near the critical
point. The shear viscosity to entropy density ratio is found to decrease when
the temperature is increased, and to approach the Kovtun-Son-Starinets bound
1/(4{\pi}) faster near the critical point, while the bulk viscosity coefficient
is found to rise very rapidly.


  In order to find the selection rules that can be applied to the
electromagnetic transitions when the chiral geometry is achieved, a model for a
special configuration in triaxial odd-odd nuclei is constructed which exhibits
degenerate chiral bands with a sizable rotation. A quantum number obtained from
the invariance of the Hamiltonian is given and the selection rule for
electromagnetic transition probabilities in chiral bands is derived in terms of
this quantum number. Among the available candidates for chiral bands of odd-odd
nuclei, in which the near degeneracy of two $\Delta I = 1$ bands is observed,
the measured electromagnetic properties of the two bands in
$^{128}_{55}$Cs$_{73}$ and $^{126}_{55}$Cs$_{71}$ are consistent with the
rules, while those of $^{134}_{59}$Pr$_{75}$ and $^{132}_{57}$La$_{75}$ are
not.


  Arm technology is becoming increasingly important in HPC. Recently, Fugaku,
an \arm-based system, was awarded the number one place in the Top500 list.
Raspberry Pis provide an inexpensive platform to become familiar with this
architecture. However, Pis can also be useful on their own. Here we describe
our efforts to configure and benchmark the use of a Raspberry Pi cluster with
the HPX/Phylanx platform (normally intended for use with HPC applications) and
document the lessons we learned. First, we highlight the required changes in
the configuration of the Pi to gain performance. Second, we explore how limited
memory bandwidth limits the use of all cores in our shared memory benchmarks.
Third, we evaluate whether low network bandwidth affects distributed
performance. Fourth, we discuss the power consumption and the resulting
trade-off in cost of operation and performance.


  In this paper, we use a new hybrid method to compute the thermodynamic
behavior of the spin-1/2 Kagome antiferromagnet under the influence of a large
external magnetic field. We find a T^2 low-temperature behavior and a very low
sensitivity of the specific heat to a strong external magnetic field. We
display clear evidence that this low temperature magneto-thermal effect is
associated to the existence of low-lying fluctuating singlets, but also that
the whole picture (T^2 behavior of Cv and thermally activated spin
susceptibility) implies contribution of both non magnetic and magnetic
excitations. Comparison with experiments is made.


  We consider the entanglement entropy for the 2D Ising model at the conformal
fixed point in the presence of interfaces. More precisely, we investigate the
situation where the two subsystems are separated by a defect line that
preserves conformal invariance. Using the replica trick, we compute the
entanglement entropy between the two subsystems. We observe that the entropy,
just like in the case without defects, shows a logarithmic scaling behavior
with respect to the size of the system. Here, the prefactor of the logarithm
depends on the strength of the defect encoded in the transmission coefficient.
We also comment on the supersymmetric case.


  Electrophoresis is a motion of charged dispersed particles relative to a
fluid in a uniform electric field. The effect is widely used to separate
macromolecules, to assemble colloidal structures, to transport particles in
nano- and micro-fluidic devices and displays. Typically, the fluid is isotropic
(for example, water) and the electrophoretic velocity is linearly proportional
to the electric field. In linear electrophoresis, only a direct current (DC)
field can drive the particles. An alternate current (AC) field is more
desirable because it allows one to overcome problems such as electrolysis and
absence of steady flows. Here we show that when the electrophoresis is
performed in a nematic fluid, the effect becomes strongly non-linear with a
velocity component that is quadratic in the applied voltage and has a direction
that generally differs from the direction of linear velocity. The new
phenomenon is caused by distortions of the LC orientation around the particle
that break the fore-aft (or left-right) symmetry. The effect allows one to
transport both charged and neutral particles, even when the particles
themselves are perfectly symmetric (spherical), thus enabling new approaches in
display technologies, colloidal assembly, separation, microfluidic and
micromotor applications.


  We consider spherically symmetric static solutions of the Einstein equations
with a positive cosmological constant $\Lambda,$ which are regular at the
centre, and we investigate the influence of $\Lambda$ on the bound of M/R,
where M is the ADM mass and R is the area radius of the boundary of the static
object. We find that for any solution which satisfies the energy condition
$p+2p_{\perp}\leq\rho,$ where $p\geq 0$ and $p_{\perp}$ are the radial and
tangential pressures respectively, and $\rho\geq 0$ is the energy density, and
for which $0\leq \Lambda R^2\leq 1,$ the inequality
  \frac{M}{R}\leq\frac29-\frac{\Lambda R^2}{3}+\frac29 \sqrt{1+3\Lambda R^2},
holds. If $\Lambda=0$ it is known that infinitely thin shell solutions uniquely
saturate the inequality, i.e. the inequality is sharp in that case. The
situation is quite different if $\Lambda>0.$ Indeed, we show that infinitely
thin shell solutions do not generally saturate the inequality except in the two
degenerate situations $\Lambda R^2=0$ and $\Lambda R^2=1$. In the latter
situation there is also a constant density solution, where the exterior
spacetime is the Nariai solution, which saturates the inequality, hence, the
saturating solution is non-unique. In this case the cosmological horizon and
the black hole horizon coincide. This is analogous to the charged situation
where there is numerical evidence that uniqueness of the saturating solution is
lost when the inner and outer horizons of the Reissner-Nordstr\"{o}m solution
coincide.


  Refractory metals that can withstand at high temperatures and harsh
conditions are of utmost importance for solar-thermal and energy storage
applications. Thin films of TiN have been deposited using cathodic vacuum arc
deposition (CVA) at relatively low temperatures ~ 300 oC using the substrate
bias ~ -60V. The nanomechanical properties of these films were investigated
using nanoindentation and the spatial fluctuations were observed. The
nanoindentation results were simulated using finite element method (FEM)
through Johnson-Cook model. We have found the local nitridation plays an
important role on nanomechanical properties of TiN thin films and confirms that
the nitrogen deficient regions are ductile with low yield stress and hardening
modulus. This study further opens the opportunities of modelling the nanoscale
system using FEM analysis.


  The space mission PLATO will usher in a new era of exoplanetary science by
expanding our current inventory of transiting systems and constraining host
star ages, which are currently highly uncertain. This capability might allow
PLATO to detect changes in planetary system architecture with time,
particularly because planetary scattering due to Lagrange instability may be
triggered long after the system was formed. Here, we utilize previously
published instability timescale prescriptions to determine PLATO's capability
to detect a trend of decreasing planet frequency with age for systems with
equal-mass planets. For two-planet systems, our results demonstrate that PLATO
may detect a trend for planet masses which are at least as massive as
super-Earths. For systems with three or more planets, we link their initial
compactness to potentially detectable frequency trends in order to aid future
investigations when these populations will be better characterized.


  A detailed comparison of Faddeev and variational wave functions for $^3$H,
calculated with realistic nuclear forces, has been made to study the form of
three-body correlations in few-body nuclei. Three new three-body correlations
for use in variational wave functions have been identified, which substantially
reduce the difference with the Faddeev wave function. The difference between
the variational upper bound and the Faddeev binding energy is reduced by half,
to typically $<2\%$. These three-body correlations also produce a significant
lowering of the variational binding energy for $^4$He and larger nuclei.


  We study metal enrichment originating from stellar wind and supernovae in
low-metallicity clouds by performing three-dimensional radiation hydrodynamics
simulations. We find that metals ejected from stellar wind are accumulated,
leading to subsequent star formation in the nitrogen-enriched gas. During this
early phase, the N/O ratios are similar to observed nitrogen-enriched galaxies
(${\rm [N/O]}\gtrsim0.5$). Then, once supernovae occur, the N/O ratios decrease
significantly. If the duration of star formation is comparable to the timescale
of SNe, the mass fraction of nitrogen-enriched stars reaches half the mass of
star clusters. We suggest that the mass of the star cluster needs to exceed
$\sim 10^6~M_{\odot}$ to have multiple populations due to stellar wind,
considering the condition for massive star cluster formation and the timescales
of stellar evolution.


  The fractional diffusion equation is rigorously derived as a scaling limit
from a deterministic Rayleigh gas, where particles interact via short range
potentials with support of size $\varepsilon$ and the background is distributed
in space $R^3$ according to a Poisson process with intensity $N$ and in
velocity according some fat-tailed distribution. As an intermediate step a
linear Boltzmann equation is obtained in the Boltzmann-Grad limit as
$\varepsilon$ tends to zero and $N$ tends to infinity with $N \varepsilon^2
=c$. The convergence of the empiric particle dynamics to the Boltzmann-type
dynamics is shown using semigroup methods to describe probability measures on
collision trees associated to physical trajectories in the case of a Rayleigh
gas. The fractional diffusion equation is a hydrodynamic limit for times $t \in
[0,T]$, where $T$ and inverse mean free path $c$ can both be chosen as some
negative rational power $\varepsilon^{-k}$.


  Hybrid RL is the setting where an RL agent has access to both offline data
and online data by interacting with the real-world environment. In this work,
we propose a new hybrid RL algorithm that combines an on-policy actor-critic
method with offline data. On-policy methods such as policy gradient and natural
policy gradient (NPG) have shown to be more robust to model misspecification,
though sometimes it may not be as sample efficient as methods that rely on
off-policy learning. On the other hand, offline methods that depend on
off-policy training often require strong assumptions in theory and are less
stable to train in practice. Our new approach integrates a procedure of
off-policy training on the offline data into an on-policy NPG framework. We
show that our approach, in theory, can obtain a best-of-both-worlds type of
result -- it achieves the state-of-art theoretical guarantees of offline RL
when offline RL-specific assumptions hold, while at the same time maintaining
the theoretical guarantees of on-policy NPG regardless of the offline RL
assumptions' validity. Experimentally, in challenging rich-observation
environments, we show that our approach outperforms a state-of-the-art hybrid
RL baseline which only relies on off-policy policy optimization, demonstrating
the empirical benefit of combining on-policy and off-policy learning. Our code
is publicly available at https://github.com/YifeiZhou02/HNPG.


  We propose a new single photon source based on the principle of active
multiplexing of heralded single photons which, unlike previously reported
architecture, requires a limited amount of physical resources. We discuss both
its feasibility and the purity and indistinguishability of single photons as
function of the key parameters of a possible implementation.


  We provide the estimates for the constant in the weighted Poincar\'e
inequality for a special class of planar domains and weights. Based on this, we
prove the lower bounds for the first non-zero eigenvalue $\mu_\rho$ of the
Neumann Laplacian with density $\rho$. These estimates depend on the density
function and the geometry of the domain. In particular, it is shown, that
$\mu_\rho$ can be made arbitrarily large by changing the mass density of the
domain.


  This paper proposes a simple voting protocol based on quantum blockchain.
Besides being simple, our voting protocol is anonymous, binding, non-reusable,
verifiable, eligible, fair and self-tallying. Our protocol is also realizable
by the current technology.


  Counterfactual quantum key distribution provides natural advantage against
the eavesdropping on the actual signal particles. It can prevent the
photon-number-splitting attack when a weak coherent light source is used for
the practical implementation. We realized the counterfactual quantum key
distribution in an unbalanced Mach-Zehnder interferometer of 12.5-km-long
quantum channel with a high-fringe visibility of 96:4%. As a result, we
obtained secure keys against the noise-induced attack (eg. the vacuum attack)
and passive photon-number-splitting attack.


  With the remarkable recent progress on learning deep generative models, it
becomes increasingly interesting to develop models for controllable image
synthesis from reconfigurable inputs. This paper focuses on a recent emerged
task, layout-to-image, to learn generative models that are capable of
synthesizing photo-realistic images from spatial layout (i.e., object bounding
boxes configured in an image lattice) and style (i.e., structural and
appearance variations encoded by latent vectors). This paper first proposes an
intuitive paradigm for the task, layout-to-mask-to-image, to learn to unfold
object masks of given bounding boxes in an input layout to bridge the gap
between the input layout and synthesized images. Then, this paper presents a
method built on Generative Adversarial Networks for the proposed
layout-to-mask-to-image with style control at both image and mask levels.
Object masks are learned from the input layout and iteratively refined along
stages in the generator network. Style control at the image level is the same
as in vanilla GANs, while style control at the object mask level is realized by
a proposed novel feature normalization scheme, Instance-Sensitive and
Layout-Aware Normalization. In experiments, the proposed method is tested in
the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art
performance obtained.


  General arguments as well as different approximations for the in-medium quark
propagator in a quark-gluon plasma lead to quark dispersion relations that
exhibit a minimum in one branch (plasmino). This minimum causes Van Hove
singularities in the dilepton production rate and mesonic correlators, which
might have observable consequences.


  For every positive regular Borel measure, possibly infinite valued, vanishing
on all sets of $p$-capacity zero, we characterize the compactness of the
embedding $W^{1,p}({\bf R}^N)\cap L^p ({\bf R}^N,\mu)\hr L^q({\bf R}^N)$ in
terms of the qualitative behavior of some characteristic PDE. This question is
related to the well posedness of a class of geometric inequalities involving
the torsional rigidity and the spectrum of the Dirichlet Laplacian introduced
by Polya and Szeg\"o in 1951. In particular, we prove that finite torsional
rigidity of an arbitrary domain (possibly with infinite measure), implies the
compactness of the resolvent of the Laplacian.


  We prove that certain asymptotically flat initial data sets with nontrivial
topology and/or differentiable structure collapse to form singularities. The
class of such initial data sets is characterized by a new smooth invariant, the
maximal Yamabe invariant, defined through smooth compactification of the
asymptotically flat manifold. Our singularity theorem applies to spacetimes
admitting a Cauchy surface of nonpositive maximal Yamabe invariant with initial
data that satisfies the dominant energy condition. This class of spacetimes
includes simply connected spacetimes with a single asymptotic region, a class
not covered by prior singularity theorems for topological structures. The
maximal Yamabe invariant can be related to other invariants including, in 4
dimensions, the A-genus and the Seiberg-Witten invariants. In particular,
5-dimensional spacetimes with asymptotically flat Cauchy surfaces with
non-trivial Seiberg-Witten invariants are singular. This singularity is due to
the differentiable structure of the manifold.


  An overview of recent results on reaction dynamics in the energy region 20 -
50 A.MeV is given. The results of the study of projectile multifragmentation
using the detector array FAUST are presented. Reaction mechanism is determined
and thermodynamical properties of the hot quasiprojectile are investigated.
Preliminary results on fragment isospin asymmetry obtained using the 4$\pi$
detector array NIMROD are given. Procedure for selecting centrality in
two-dimensional multiplicity histograms is described. Possibility to extract
thermodynamical temperature from systematics of isotope ratios is investigated.
Reaction mechanism leading to production of hot sources is discussed.
Furthermore, the possibilities for production of rare isotopes are discussed
and recent experimental results obtained using recoil separator MARS are
presented.


  We study the molecular gas properties in the central kiloparsec of 29 barred
and 15 unbarred spirals from the BIMA Survey of Nearby Galaxies (SONG). We find
that the mean nuclear molecular gas surface density of barred spirals
(<Sigma_{nuc}>=309 +/- 71 Msun pc^{-2}) is three times higher than that of
unbarred spirals (<Sigma_{nuc}>=107 +/- 29 Msun pc^{-2}). Nine out of the
eleven bars with Sigma_{nuc} > 300 Msun pc^{-2} are early types. Comparison
with estimates of the star formation threshold density (Sigma_{crit}) indicates
that enhanced star formation in bars may be due to a larger fraction having
Sigma_{nuc} > typical Sigma_{crit}. We also find that barred spirals are more
centrally concentrated than unbarred spirals. The median value of the
concentration parameter for barred spirals
(f_{con}=Sigma_{nuc}/Sigma_{disk}=27.7) is a factor of four higher than that
for the unbarred spirals (f_{con}=6.2). Finally we investigate the dependence
of the central concentration on bar properties and find it may be weakly
correlated with the bulge size, but is not correlated with the bar length. This
suggests that inner Lindblad resonances, bar ellipticity and circumnuclear star
formation play important roles in determining the gas accretion in the central
kiloparsec.
  See http://www.astro.caltech.edu/~kartik/thesis.html for more detailed
description of these thesis results. This research was made possible with
funding from NSF grants AST-9981308, AST-9981289 and AST-9981546.


  In this paper we analyse some questions concerning trees on $\kappa$, both
for the countable and the uncountable case, and the connections with Cohen
reals. In particular, we provide a proof for one of the implications left open
in \cite[Question 5.2]{FKK16} about the diagram for regularity properties.


  Cs2CuCl4 is known to possess a quantum spin liquid phase with
antiferromagnetic interaction below 2.8 K. We report the observation of a new
metastable magnetic phase of the triangular frustrated quantum spin system
Cs2CuCl4 induced by the application of hydrostatic pressure. We measured the
magnetic properties of Cs2CuCl4 following the application and release of
pressure after 3 days. We observed a previously unknown ordered magnetic phase
with a transition temperature of 9 K. Furthermore, the recovered sample with
new magnetic ground state possesses an equivalent crystal structure to the
uncompressed one with antiferromagnetic quantum spin liquid phase.


  Recently, chiral structured illumination microscopy has been proposed to
image fluorescent chiral domains at sub-wavelength resolution. Chiral
structured illumination microscopy is based on the combination of structured
illumination microscopy, fluorescence-detected circular dichroism, and optical
chirality engineering. Since circular dichroism of natural chiral molecules is
typically weak, the differential fluorescence is also weak and can be easily
buried by the noise, hampering the fidelity of the reconstructed images. In
this work, we systematically study the impact of the noise on the quality and
resolution of chiral domain images obtained by chiral SIM. We analytically
describe the signal-to-noise ratio of the reconstructed chiral SIM image in the
Fourier domain and verify our theoretical calculations with numerical
demonstrations. Accordingly, we discuss the feasibility of chiral SIM in
different experimental scenarios and propose possible strategies to enhance the
signal-to-noise ratio for samples with weak circular dichroism.


  Many machine learning models are vulnerable to adversarial examples: inputs
that are specially crafted to cause a machine learning model to produce an
incorrect output. Adversarial examples that affect one model often affect
another model, even if the two models have different architectures or were
trained on different training sets, so long as both models were trained to
perform the same task. An attacker may therefore train their own substitute
model, craft adversarial examples against the substitute, and transfer them to
a victim model, with very little information about the victim. Recent work has
further developed a technique that uses the victim model as an oracle to label
a synthetic training set for the substitute, so the attacker need not even
collect a training set to mount the attack. We extend these recent techniques
using reservoir sampling to greatly enhance the efficiency of the training
procedure for the substitute model. We introduce new transferability attacks
between previously unexplored (substitute, victim) pairs of machine learning
model classes, most notably SVMs and decision trees. We demonstrate our attacks
on two commercial machine learning classification systems from Amazon (96.19%
misclassification rate) and Google (88.94%) using only 800 queries of the
victim model, thereby showing that existing machine learning approaches are in
general vulnerable to systematic black-box attacks regardless of their
structure.


  Modeling variability in software architectures is a fundamental part of
software product line development. ?-MontiArc allows describing architectural
variability in a modular way by a designated core architecture and a set of
architectural delta models modifying the core architecture to realize other
architecture variants. Delta models have to satisfy a set of applicability
conditions for the definedness of the architectural variants. The applicability
conditions can in principle be checked by generating all possible architecture
variants, which requires considering the same intermediate architectures
repeatedly. In order to reuse previously computed architecture variants, we
propose a family-based analysis of the applicability conditions using the
concept of inverse deltas.


  Dynamic properties of spreading infection through a heterogeneous population
are studied numerically and analytically using a dynamic variant of Watts and
Strogatz Small World Network-based stochastic
Susceptible-Exposed-Infectious-Removed (SEIR) epidemic model. This model
includes the main realistic parameters usually characterizing transmissible
diseases, such as the force of infection, latency and infection times. As far
as the latency time remains smaller than that of infection, the proportion of
infected individuals increases exponentially with time, otherwise an
oscillatory behavior appears. This may explain the periodic behaviors in time
observed by the health prevention services. It is also shown that periodic
epidemiological surveys overestimate or underestimate the dynamics of infection
if the survey periods do not exactly correspond to the characteristic times of
the infection. Further discussion is provided on the diffusion and relaxation
processes involved in this model, and their relation to the infection
characteristic time.


  Multifractal processes are a relatively new tool of stock market analysis.
Their power lies in the ability to take multiple orders of autocorrelations
into account explicitly. In the first part of the paper we discuss the
framework of the Lux model and refine the underlying phenomenological picture.
We also give a procedure of fitting all parameters to empirical data. We
present a new approach to account for the effective length of power-law memory
in volatility. The second part of the paper deals with the consequences of
asymmetry in returns. We incorporate two related stylized facts, skewness and
leverage autocorrelations into the model. Then from Monte Carlo measurements we
show, that this asymmetry significantly increases the mean squared error of
volatility forecasts. Based on a filtering method we give evidence on similar
behavior in empirical data.


  Recently, Odrzywolek and Rafelski (arXiv:1612.03556) have found three
distinct categories of exoplanets, when they are classified based on density.
We first carry out a similar classification of exoplanets according to their
density using the Gaussian Mixture Model, followed by information theoretic
criterion (AIC and BIC) to determine the optimum number of components. Such a
one-dimensional classification favors two components using AIC and three using
BIC, but the statistical significance from both the tests is not significant
enough to decisively pick the best model between two and three components. We
then extend this GMM-based classification to two dimensions by using both the
density and the Earth similarity index (arXiv:1702.03678), which is a measure
of how similar each planet is compared to the Earth. For this two-dimensional
classification, both AIC and BIC provide decisive evidence in favor of three
components.


  We present a simple model of driven matter in a 1D medium with pinning
impurities, applicable to magnetic domains walls, confined colloids, and other
systems. We find rich dynamics, including hysteresis, reentrance,
quasiperiodicity, and two distinct routes to chaos. In contrast to other
minimal models of driven matter, the model is solvable: we derive the full
phase diagram for small $N$, and for large $N$, derive expressions for order
parameters and several bifurcation curves. The model is also realistic. Its
collective states match those seen in the experiments of magnetic domain walls,
and its force-velocity curve imitates those of superconductor vortices.


  Philosophers of science have long postulated how collective scientific
knowledge grows. Empirical validation has been challenging due to limitations
in collecting and systematizing large historical records. Here, we capitalize
on the largest online encyclopedia to formulate knowledge as growing networks
of articles and their hyperlinked inter-relations. We demonstrate that concept
networks grow not by expanding from their core but rather by creating and
filling knowledge gaps, a process which produces discoveries that are more
frequently awarded Nobel prizes than others. Moreover, we operationalize
paradigms as network modules to reveal a temporal signature in structural
stability across scientific subjects. In a network formulation of scientific
discovery, data-driven conditions underlying breakthroughs depend just as much
on identifying uncharted gaps as on advancing solutions within scientific
communities.


  We analyze structure-preserving model order reduction methods for
Ornstein-Uhlenbeck processes and linear S(P)DEs with multiplicative noise based
on balanced truncation. For the first time, we include in this study the
analysis of non-zero initial conditions. We moreover allow for
feedback-controlled dynamics for solving stochastic optimal control problems
with reduced-order models and prove novel error bounds for a class of linear
quadratic regulator problems. We provide numerical evidence for the bounds and
discuss the application of our approach to enhanced sampling methods from
non-equilibrium statistical mechanics.


  Undoped diamond, a remarkable bulk electrical insulator, exhibits a high
surface conductivity in air when the surface is hydrogen-terminated. Although
theoretical models have claimed that a two-dimensional hole gas is established
as a result of surface energy band bending, no definitive experimental
demonstration has been reported so far. Here, we prove the two-dimensional
character of the surface conductivity by low temperature characterization of
diamond in-plane gated field-effect transistors that enable the lateral
confinement of the transistor's drain-source channel to nanometer dimensions.
In these devices, we observe Coulomb blockade effects of multiple quantum
islands varying in size with the gate voltage. The charging energy and thus the
size of these zero-dimensional islands exhibits a gate voltage dependence which
is the direct result of the two-dimensional character of the conductive channel
formed at hydrogen-terminated diamond surfaces.


  The whole computer hardware industry embraced multicores. For these machines,
the extreme optimisation of sequential algorithms is no longer sufficient to
squeeze the real machine power, which can be only exploited via thread-level
parallelism. Decision tree algorithms exhibit natural concurrency that makes
them suitable to be parallelised. This paper presents an approach for
easy-yet-efficient porting of an implementation of the C4.5 algorithm on
multicores. The parallel porting requires minimal changes to the original
sequential code, and it is able to exploit up to 7X speedup on an Intel
dual-quad core machine.


  Local symmetries are spatial symmetries present in a subdomain of a complex
system. By using and extending a framework of so-called non-local currents that
has been established recently, we show that one can gain knowledge about the
structure of eigenstates in locally symmetric setups through a Kirchhoff-type
law for the non-local currents. The framework is applicable to all discrete
planar Schr\"odinger setups, including those with non-uniform connectivity.
Conditions for spatially constant non-local currents are derived and we explore
two types of locally symmetric subsystems in detail, closed-loops and
one-dimensional open ended chains. We find these systems to support locally
similar or even locally symmetric eigenstates.


  We propose a comprehensive theoretical description of hysteresis in capillary
condensation of gases in mesoporous disordered materials. Applying mean-field
density functional theory to a coarse-grained lattice-gas model, we show that
the morphology of the hysteresis loops is influenced by out-of-equilibrium
transitions that are different on filling and on draining. In particular,
desorption may be associated to a depinning process and be percolation-like
without explicit pore-blocking effects.


  Current techniques for formally verifying circuits implemented in Galois
field (GF) arithmetic are limited to those with a known irreducible polynomial
P(x). This paper presents a computer algebra based technique that extracts the
irreducible polynomial P(x) used in the implementation of a multiplier in
GF(2^m). The method is based on first extracting a unique polynomial in Galois
field of each output bit independently. P(x) is then obtained by analyzing the
algebraic expression in GF(2^m) of each output bit. We demonstrate that this
method is able to reverse engineer the irreducible polynomial of an n-bit GF
multiplier in n threads. Experiments were performed on Mastrovito and
Montgomery multipliers with different P (x), including NIST-recommended
polynomials and optimal polynomials for different microprocessor architectures.


  This is an exciting era for exo-planetary exploration. The recently launched
JWST, and other upcoming space missions such as Ariel, Twinkle and ELTs are set
to bring fresh insights to the convoluted processes of planetary formation and
evolution and its connections to atmospheric compositions. However, with new
opportunities come new challenges. The field of exoplanet atmospheres is
already struggling with the incoming volume and quality of data, and machine
learning (ML) techniques lands itself as a promising alternative. Developing
techniques of this kind is an inter-disciplinary task, one that requires domain
knowledge of the field, access to relevant tools and expert insights on the
capability and limitations of current ML models. These stringent requirements
have so far limited the developments of ML in the field to a few isolated
initiatives. In this paper, We present the Atmospheric Big Challenge Database
(ABC Database), a carefully designed, organised and publicly available database
dedicated to the study of the inverse problem in the context of exoplanetary
studies. We have generated 105,887 forward models and 26,109 complementary
posterior distributions generated with Nested Sampling algorithm. Alongside
with the database, this paper provides a jargon-free introduction to non-field
experts interested to dive into the intricacy of atmospheric studies. This
database forms the basis for a multitude of research directions, including, but
not limited to, developing rapid inference techniques, benchmarking model
performance and mitigating data drifts. A successful application of this
database is demonstrated in the NeurIPS Ariel ML Data Challenge 2022.


  Stimulated emission of photons from a large number of atoms into the mode of
a strong light field is the principle mechanism for lasing in "classical"
lasers. The onset of lasing is marked by a threshold which can be characterised
by a sharp increase in photon flux as a function of external pumping strength.
The same is not necessarily true for the fundamental building block of a laser:
a single trapped atom interacting with a single optical radiation mode. It has
been shown that such a "quantum" laser can exhibit thresholdless lasing in the
regime of strong coupling between atom and radiation field. However, although
theoretically predicted, a threshold at the single-atom level could not be
experimentally observed so far. Here, we demonstrate and characterise a
single-atom laser with and without threshold behaviour by changing the strength
of atom-light field coupling. We observe the establishment of a laser threshold
through the accumulation of photons in the optical mode even for a mean photon
number substantially lower than for the classical case. Furthermore,
self-quenching occurs for very strong external pumping and constitutes an
intrinsic limitation of single-atom lasers. Moreover, we find that the
statistical properties of the emitted light can be adjusted for weak external
pumping, from the quantum to the classical domain. Our observations mark an
important step towards fundamental understanding of laser operation in the
few-atom limit including systems based on semiconductor quantum dots or
molecules.


  This is the second of a series of four articles studying various
generalisations of Khovanov's diagram algebra. In this article we develop the
general theory of Khovanov's diagrammatically defined "projective functors" in
our setting. As an application, we give a direct proof of the fact that the
quasi-hereditary covers of generalised Khovanov algebras are Koszul.


  We propose TRADE for robust tracking and 3D localization of a moving target
in cluttered environments, from UAVs equipped with a single camera. Ultimately
TRADE enables 3d-aware target following.
  Tracking-by-detection approaches are vulnerable to target switching,
especially between similar objects. Thus, TRADE predicts and incorporates the
target 3D trajectory to select the right target from the tracker's response
map. Unlike static environments, depth estimation of a moving target from a
single camera is a ill-posed problem. Therefore we propose a novel 3D
localization method for ground targets on complex terrain. It reasons about
scene geometry by combining ground plane segmentation, depth-from-motion and
single-image depth estimation. The benefits of using TRADE are demonstrated as
tracking robustness and depth accuracy on several dynamic scenes simulated in
this work. Additionally, we demonstrate autonomous target following using a
thermal camera by running TRADE on a quadcopter's board computer.


  Multiparticle production in high energy proton -- proton collisions has been
analysed in the frame of Strongly Correlated Quark Model (SCQM) of the hadron
structure elaborated by the author. It is shown that inelasticity decreases at
high energies and this effect together with the total cross section growth and
the increasing with collision energy the masses of intermediate clusters result
in the violation of KNO -- scaling.


  We apply the theory of linear recurrence sequences to find an expression for
the ultimate ruin probability in a discrete-time risk process. We assume the
claims follow an arbitrary distribution with support $\{0,1,\ldots,m\}$, for
some integer $m\ge2$. The method requires to find the zeroes of an $m$ degree
polynomial and to solve a system of $m$ linear equations. An approximation is
derived from the exact ruin formula and several numerical results and plots are
provided as examples.


  The enhanced superconductivity in monolayer FeSe on titanates opens a
fascinating pathway towards the rational design of high-temperature
superconductors. Utilizing the state-of-the-art oxide plus chalcogenide
molecular beam epitaxy systems in situ connected to a synchrotron
angle-resolved photoemission spectroscope, epitaxial LaTiO3 layers with varied
atomic thicknesses are inserted between monolayer FeSe and SrTiO3, for
systematic modulation of interfacial chemical potential.With the dramatic
increase of electron accumulation at the LaTiO3-SrTiO3 surface, providing a
substantial surge of work function mismatch across the FeSe-oxide interface,
the charge transfer and the superconducting gap in the monolayer FeSe are found
to remain markedly robust. This unexpected finding indicates the existence of
an intrinsically anchored magic doping within the monolayer FeSe systems.


  We report on the first measurement of the charged hadron composition in
charged jets from pp collisions. The ALICE detector at the LHC was used to
study charged pion, kaon and (anti-)proton production in jets. The results were
extracted from $2 \times 10^8$ minimum bias events at a centre-of-mass energy
of $\sqrt{s} = 7$ TeV.
  We present the transverse momentum ($p_{\mathrm{T}}$) spectra and reduced
momentum spectra ($z^{\mathrm{ch}} \equiv p_{\mathrm{T}}^{\mathrm{track}} /
p_{\mathrm{T, jet}}^{\mathrm{ch}}$) of $\pi$, K and p in charged jets with
$p_{\mathrm{T, jet}}^{\mathrm{ch}}$ between 5 and 20 GeV/$c$. The measurements
are compared to Monte Carlo calculations.


  In this communication, we present a new approach towards RT-TDDFT through
time-dependent KS equations based on an \emph{adiabatic eigenstate subspace}
(AES) procedure. It introduces a second-order split operator technique in
energy representation to implement the approximate TD propagator in AES. Most
of the elements in TDKS matrix are directly computed in Cartesian coordinate
grid (CCG). To demonstrate the internal consistency of our proposed scheme, we
computed the TD dipole moment and high harmonic generation spectra using an
adiabatic local density approximation. The comparison with available
theoretical results ensures the feasibility of this proposed route.


  We propose a novel method that renders point clouds as if they are surfaces.
The proposed method is differentiable and requires no scene-specific
optimization. This unique capability enables, out-of-the-box, surface normal
estimation, rendering room-scale point clouds, inverse rendering, and ray
tracing with global illumination. Unlike existing work that focuses on
converting point clouds to other representations--e.g., surfaces or implicit
functions--our key idea is to directly infer the intersection of a light ray
with the underlying surface represented by the given point cloud. Specifically,
we train a set transformer that, given a small number of local neighbor points
along a light ray, provides the intersection point, the surface normal, and the
material blending weights, which are used to render the outcome of this light
ray. Localizing the problem into small neighborhoods enables us to train a
model with only 48 meshes and apply it to unseen point clouds. Our model
achieves higher estimation accuracy than state-of-the-art surface
reconstruction and point-cloud rendering methods on three test sets. When
applied to room-scale point clouds, without any scene-specific optimization,
the model achieves competitive quality with the state-of-the-art novel-view
rendering methods. Moreover, we demonstrate ability to render and manipulate
Lidar-scanned point clouds such as lighting control and object insertion.


  We present Hubble Space Telescope (HST) imaging of the site of SN 2015bh in
the nearby spiral galaxy NGC 2770 taken between 2017 and 2019, nearly four
years after the peak of the explosion. In 2017-2018, the transient fades
steadily in optical filters before declining more slowly to $F814W = -7.1$ mag
in 2019, $\approx$4 mag below the level of its eruptive luminous blue variable
(LBV) progenitor observed with HST in 2008-2009. The source fades at a constant
color of $F555W - F814W = 0.4$ mag until 2018, similar to SN 2009ip and
consistent with a spectrum dominated by interaction of the ejecta with
circumstellar material (CSM). A deep optical spectrum obtained in 2021 lacks
signatures of ongoing interaction ($L_{\mathrm{H}\alpha} \lesssim 10^{38}$ erg
s$^{-1}$ for broadened emission $\lesssim$2000 km s$^{-1}$), but indicates the
presence of a nearby H II region ($\lesssim$300 pc). The color evolution of the
fading source makes it unlikely that emission from a scattered-light echo or
binary OB companion of the progenitor contributes significantly to the
flattening of the late-time light curve. The remaining emission in 2019 may
plausibly be attributed an evolved/inflated companion or an unresolved
($\lesssim$3 pc), young stellar cluster. Importantly, the color evolution of SN
2015bh rules out scenarios in which the surviving progenitor is obscured by
nascent dust and does not clearly indicate a transition to a hotter, optically
faint state. The simplest explanation is that the massive progenitor did not
survive. SN 2015bh likely represents a remarkable example of the terminal
explosion of a massive star preceded by decades of end-stage eruptive
variability.


  Predicting the timing and occurrence of events is a major focus of data
science applications, especially in the context of biomedical research.
Performance for models estimating these outcomes, often referred to as
time-to-event or survival outcomes, is frequently summarized using measures of
discrimination, in particular time-dependent AUC and concordance. Many
estimators for these quantities have been proposed which can be broadly
categorized as either semi-parametric estimators or non-parametric estimators.
In this paper, we review various estimators' mathematical construction and
compare the behavior of the two classes of estimators. Importantly, we identify
a previously unknown feature of the class of semi-parametric estimators that
can result in vastly over-optimistic out-of-sample estimation of discriminative
performance in common applied tasks. Although these semi-parametric estimators
are popular in practice, the phenomenon we identify here suggests this class of
estimators may be inappropriate for use in model assessment and selection based
on out-of-sample evaluation criteria. This is due to the semi-parametric
estimators' bias in favor of models that are overfit when using out-of-sample
prediction criteria (e.g., cross validation). Non-parametric estimators, which
do not exhibit this behavior, are highly variable for local discrimination. We
propose to address the high variability problem through penalized regression
splines smoothing. The behavior of various estimators of time-dependent AUC and
concordance are illustrated via a simulation study using two different
mechanisms that produce over-optimistic out-of-sample estimates using
semi-parametric estimators. Estimators are further compared using a case study
using data from the National Health and Nutrition Examination Survey (NHANES)
2011-2014.


  This paper reframes approachability theory within the context of population
games. Thus, whilst one player aims at driving her average payoff to a
predefined set, her opponent is not malevolent but rather extracted randomly
from a population of individuals with given distribution on actions. First,
convergence conditions are revisited based on the common prior on the
population distribution, and we define the notion of \emph{1st-moment
approachability}. Second, we develop a model of two coupled partial
differential equations (PDEs) in the spirit of mean-field game theory: one
describing the best-response of every player given the population distribution
(this is a \emph{Hamilton-Jacobi-Bellman equation}), the other capturing the
macroscopic evolution of average payoffs if every player plays its best
response (this is an \emph{advection equation}). Third, we provide a detailed
analysis of existence, nonuniqueness, and stability of equilibria (fixed points
of the two PDEs). Fourth, we apply the model to regret-based dynamics, and use
it to establish convergence to Bayesian equilibrium under incomplete
information.


  The recent observation of a ferroelectric-like structural transition in
metallic LiOsO$_3$ has generated a flurry of interest in the properties of
polar metals. Such materials are thought to be rare because free electrons
screen out the long-range electrostatic forces that favor a polar structure
with a dipole moment in every unit cell. In this work, we question whether
long-range electrostatic forces are always the most important ingredient in
driving polar distortions. We use crystal chemical models, in combination with
first-principles Density Functional Theory calculations, to explore the
mechanisms of inversion-symmetry breaking in LiOsO$_3$ and both insulating and
electron-doped ATiO$_3$ perovskites, A = Ba, Sr, Ca. Although electrostatic
forces do play a significant role in driving the polar instability of BaTiO$_3$
(which is suppressed under electron doping), the polar phases of CaTiO$_3$ and
LiOsO$_3$ emerge through a mechanism driven by local bonding preferences and
this mechanism is `resistant' to the presence of charge carriers. Hence, our
results suggest that there is no fundamental incompatibility between
metallicity and polar distortions. We use the insights gained from our
calculations to suggest design principles for new polar metals and promising
avenues for further research.


  A general scheme for increasing the difference in magnetic flux density
between a high and a low magnetic field region by removing unnecessary magnet
material is presented. This is important in, e.g., magnetic refrigeration where
magnet arrays has to deliver high field regions in close proximity to low field
regions. Also, a general way to replace magnet material with a high
permeability soft magnetic material where appropriate is discussed. As an
example these schemes are applied to a two dimensional concentric Halbach
cylinder design resulting in a reduction of the amount of magnet material used
by 42% while increasing the difference in flux density between a high and a low
field region by 45%.


  Antimonene is a novel two-dimensional (2D) semiconducting material of group V
elements proposed in a recent literature [Zhang et al., Angew. Chem. Int. Ed.
54, 1-5 (2015)]. Using first-principles calculations, we demonstrated that the
buckled configuration of antimonene enables it sustain large tensile strain up
to 20%. Band inversion takes place in the vicinity of the Gamma point as the
tensile strain is larger than 14.5%, leading to six tilted Dirac cones in the
Brillouin zone. Spin-orbital coupling (SOC) effect opens up a topologically
nontrivial bulk band gap at the Dirac points, exhibiting the features of 2D
topological insulators characterized by a nonzero Z2 topological invariant. The
tunable bulk band gap, 101-560 meV, make the antimonene a promising candidate
material for achieving quantum spin Hall effect (QSH) at high temperatures
which meet the requirement of future electronic devices with low power
consumption.


  Many safety-critical applications, especially in autonomous driving, require
reliable object detectors. They can be very effectively assisted by a method to
search for and identify potential failures and systematic errors before these
detectors are deployed. Systematic errors are characterized by combinations of
attributes such as object location, scale, orientation, and color, as well as
the composition of their respective backgrounds. To identify them, one must
rely on something other than real images from a test set because they do not
account for very rare but possible combinations of attributes. To overcome this
limitation, we propose a pipeline for generating realistic synthetic scenes
with fine-grained control, allowing the creation of complex scenes with
multiple objects. Our approach, BEV2EGO, allows for a realistic generation of
the complete scene with road-contingent control that maps 2D bird's-eye view
(BEV) scene configurations to a first-person view (EGO). In addition, we
propose a benchmark for controlled scene generation to select the most
appropriate generative outpainting model for BEV2EGO. We further use it to
perform a systematic analysis of multiple state-of-the-art object detection
models and discover differences between them.


  Blockchains lie at the heart of Bitcoin and other cryptocurrencies that have
shown great promise to revolutionize finance and commerce. Although they are
gaining increasing popularity, they face technical challenges when it comes to
scaling to support greater demand while maintaining their desirable security
properties. In an exciting line of recent work, many researchers have proposed
various scalable blockchain protocols that demonstrate the potential to solve
these challenges. However, many of these protocols come with the assumptions of
honest majority and symmetric network access which may not accurately reflect
the real world where the participants may be self-interested or rational.
Secondly, these works show that their protocol works in an ideal environment
where each party has equal access to the network whereas different parties have
varying latencies and network speeds. These assumptions may render the
protocols susceptible to security threats in the real world, as highlighted by
the literature focused on exploring game-theoretic attacks on these protocols.
  We propose a scalable blockchain protocol, Interlude, which comes with the
typical security guarantees while focusing on game-theoretic soundness and
network fairness. The novelty of Interlude is that it has a relatively simple
design consisting of a sequence of parallel blocks containing disjoint
transaction sets that can be mined quickly followed by a series block that is
slow to mine and gives the honest parties in the network time to synchronize.
Thus, between the chaos of parallel blocks, our blockchain protocol masquerades
an interlude moment of harmony in series blocks that synchronize the network.


  We study a class of 4-dimensional $SU(N)$ chiral gauge theories with fermions
in the 2-index symmetric and antisymmetric representations and classify their
infrared phases. The choice $N=4\mathbb{Z}$ corresponds to gauging the fermion
number and makes the theory purely bosonic. We examine the most general
background fields of the centers of the gauge, non-abelian flavor, and
$U(1)$-axial groups that can be consistently activated, thereby determine the
faithful global continuous and discrete symmetries of the theory. This allows
us to identify new mixed 0-form/1-form `t Hooft anomalies on both spin and
nonspin manifolds. If the theory confines, the absence of composite fermions
implies that continuous symmetries must be broken down to anomaly-free
subgroups. Anomalies associated with discrete symmetries can be saturated
either by breaking the symmetry or by a symmetry-preserving topological quantum
field theory (TQFT). The latter, however, is obstructed on spin manifold. The
interplay between these features greatly restricts the possible infrared
physics. We present two examples that demonstrate our approach. We argue that
if the theory confines, the zoo of anomalies and TQFT obstruction greatly
restrict the viable infrared condensates. We also discuss the possibility that
some theories flow to a conformal fixed point.


  In this paper, we present the use of Reinforcement Learning (RL) based on
Robust Model Predictive Control (RMPC) for the control of an Autonomous Surface
Vehicle (ASV). The RL-MPC strategy is utilized for obstacle avoidance and
target (set-point) tracking. A scenario-tree robust MPC is used to handle
potential failures of the ship thrusters. Besides, the wind and ocean current
are considered as unknown stochastic disturbances in the real system, which are
handled via constraints tightening. The tightening and other cost parameters
are adjusted by RL, using a Q-learning technique. An economic cost is
considered, minimizing the time and energy required to achieve the ship
missions. The method is illustrated in simulation on a nonlinear 3-DOF model of
a scaled version of the Cybership 2.


  Dries Sels and Allan Gr{\o}nlund have demonstrated numerical algorithms that
outperform the players of our Quantum Moves game and they call the conclusions
of our article Exploring the Quantum Speed Limit with Computer Games, Nature,
532, 210 (2016)] 'untenable'. Sels and Gr{\o}nlund ignore that our article
already emphasizes that the players do not solve the quantum optimization
challenge to any desired accuracy. Our key finding is that player solutions can
be employed either as seeds for numerical optimization algorithms or as
guidance towards low-dimensional representations of the solutions.


  Point forecasts can be interpreted as functionals (i.e., point summaries) of
predictive distributions. We consider the situation where forecasters'
directives are hidden and develop methodology for the identification of the
unknown functional based on time series data of point forecasts and associated
realizations. Focusing on the natural cases of state-dependent quantiles and
expectiles, we provide a generalized method of moments estimator for the
functional, along with tests of optimality relative to information sets that
are specified by instrumental variables. Using simulation, we demonstrate that
our optimality test is better calibrated and more powerful than existing
solutions. In empirical examples, Greenbook gross domestic product (GDP)
forecasts of the US Federal Reserve and model output for precipitation from the
European Centre for Medium-Range Weather Forecasts (ECMWF) are indicative of
overstatement in anticipation of extreme events.


  For the equations of a planar magnetohydrodynamic (MHD) compressible flow
with the viscosity depending on the specific volume of the gas and the heat
conductivity being proportional to a positive power of the temperature, we
obtain global existence of the unique strong solutions to the Cauchy problem or
the initial-boundary-value one under natural conditions on the initial data in
one-dimensional unbounded domains. Our result generalizes the classical one of
the compressible Navier-Stokes system with constant viscosity and heat
conductivity ([Kazhikhov. Siberian Math. J. (1982)]) to the planar MHD
compressible flow with nonlinear viscosity and degenerate heat-conductivity,
which means no shock wave, vacuum, or mass or heat concentration will be
developed in finite time, although the interaction between the magnetodynamic
effects and hydrodynamic is complex and the motion of the flow has large
oscillations.


  We study the relation between critical exponents and Hausdorff dimensions of
limit sets for projective Anosov representations. We prove that the Hausdorff
dimension of the symmetric limit set in $\mathbf{P}(\mathbb{R}^{n}) \times
\mathbf{P}({\mathbb{R}^{n}}^*)$ is bounded between two critical exponents
associated respectively to a highest weight and a simple root.


  The growth dynamics of rigid biopolymers, consisting of $N$ parallel
protofilaments, is investigated theoretically using simple approximate models.
In our approach, the structure of a polymer's growing end and lateral
interactions between protofilaments are explicitly taken into account, and it
is argued that only few conformations are important for biopolymer's growth. As
a result, exact analytic expressions for growth velocity and dispersion are
obtained for {\it any} number of protofilaments and arbitrary geometry of the
growing end of the biopolymer. Our theoretical predictions are compared with a
full description of biopolymer growth dynamics for the simplest N=2 model. It
is found that the results from the approximate theory are approaching the exact
ones for large lateral interactions between the protofilaments. Our theory is
also applied to analyze the experimental data on the growth of microtubules.


  Let P=\circ(P_{1},P_{2},...,P_{n}) and Q=\circ(Q_{1},Q_{2},...,Q_{n}) be
n\timesn circulant matrices where P_{n} and Q_{n} are nth Pell and Pell-Lucas
numbers, respectively. The determinants of the matrices P and Q were expressed
by the Pell and Pell-Lucas numbers. After, we prove that the matrices P and Q
are the invertible for n\geq3 and then the inverses of the matrices P and Q are
derived.


  Our brain can almost effortlessly decompose visual data streams into
background and salient objects. Moreover, it can anticipate object motion and
interactions, which are crucial abilities for conceptual planning and
reasoning. Recent object reasoning datasets, such as CATER, have revealed
fundamental shortcomings of current vision-based AI systems, particularly when
targeting explicit object representations, object permanence, and object
reasoning. Here we introduce a self-supervised LOCation and Identity tracking
system (Loci), which excels on the CATER tracking challenge. Inspired by the
dorsal and ventral pathways in the brain, Loci tackles the binding problem by
processing separate, slot-wise encodings of `what' and `where'. Loci's
predictive coding-like processing encourages active error minimization, such
that individual slots tend to encode individual objects. Interactions between
objects and object dynamics are processed in the disentangled latent space.
Truncated backpropagation through time combined with forward eligibility
accumulation significantly speeds up learning and improves memory efficiency.
Besides exhibiting superior performance in current benchmarks, Loci effectively
extracts objects from video streams and separates them into location and
Gestalt components. We believe that this separation offers a representation
that will facilitate effective planning and reasoning on conceptual levels.


  We consider the multifractal analysis of the pointwise dimension for Gibbs
measures on countable Markov shifts. Our paper analyses the set of non-analytic
points or phase transitions of the multifractal spectrum. By Sarig's
thermodynamic formalism for countable Markov shifts and Iommi's expression of
the multifractal spectrum, we apply analyticity arguments on the pressure
function for the countable shift. Finally, we apply our results about the phase
transitions of the multifractal spectrum to the Gauss map.


  We investigate the topological regularity and stability of noncollapsed Ricci
limit spaces $(M_i^n,g_i,p_i)\to (X^n,d)$. We confirm a conjecture proposed by
Colding and Naber in dimension $n=4$, showing that the cross-sections of
tangent cones at a given point $x\in X^4$ are all homeomorphic to a fixed
spherical space form $S^3/\Gamma_x$, and $\Gamma_x$ is trivial away from a
$0$-dimensional set. In dimensions $n>4$, we show an analogous statement at
points where all tangent cones are $(n-4)$-symmetric. Furthermore, we prove
that $(n-3)$-symmetric noncollapsed Ricci limits are topological manifolds,
thus confirming a particular case of a conjecture due to Cheeger, Colding, and
Tian. Our analysis relies on two key results, whose importance goes beyond
their applications in the study of cross-sections of noncollapsed Ricci limit
spaces: (i) A new manifold recognition theorem for noncollapsed ${\rm
RCD}(-2,3)$ spaces. (ii) A cone rigidity result ruling out noncollapsed Ricci
limit spaces of the form $\mathbb{R}^{n-3}\times C(\mathbb{RP}^2)$.


  Bound states of massive fermions in the Aharonov-Bohm like fields have
analytically been studied. The Hamiltonians with the Aharonov--Bohm like
potentials are essentially singular and therefore require specification of a
one-parameter self-adjoint extension. We construct self-adjoint Dirac
Hamiltonians with the Aharonov-Bohm (AB) potential in 2+1 dimensions that are
specified by boundary conditions at the origin. It is of interest that for some
range of extension parameter the AB potential can bind relativistic charged
massive fermions. The bound-state energy is determined by the AB magnetic flux
and depends upon fermion spin and extension parameter; it is a periodical
function of the magnetic flux. We also construct self-adjoint Hamiltonians for
the so-called Aharonov-Casher (AC) problem, show that nonrelativistic neutral
massive fermions can be bound by the Aharonov-Casher background, determine the
range of extension parameter in which fermion bound states exist and find their
energies as well as wave functions.


  This paper is concerned with the process of risk allocation for a generic
multivariate model when the risk measure is chosen as the Value-at-Risk (VaR).
We recast the traditional Euler contributions from an expectation conditional
on an event of zero probability to a ratio involving conditional expectations
whose conditioning events have strictly positive probability. We derive an
analytical form of the proposed representation of VaR contributions for various
parametric models. Our numerical experiments show that the estimator using this
novel representation outperforms the standard Monte Carlo estimator in terms of
bias and variance. Moreover, unlike the existing estimators, the proposed
estimator is free from hyperparameters under a parametric setting.


  A search for exotic Higgs boson decays to light pseudoscalars in the final
state of two muons and two $\tau$ leptons is performed using proton-proton
collision data recorded by the CMS experiment at the LHC at a center-of-mass
energy of 13 TeV in 2016, corresponding to an integrated luminosity of 35.9
fb$^{-1}$. Masses of the pseudoscalar boson between 15.0 and 62.5 GeV are
probed, and no significant excess of data is observed above the prediction of
the standard model. Upper limits are set on the branching fraction of the Higgs
boson to two light pseudoscalar bosons in different types of two-Higgs-doublet
models extended with a complex scalar singlet.


  We present the Bright SHARC (Serendipitous High-Redshift Archival ROSAT
Cluster) Survey, which is an objective search for serendipitously detected
extended X-ray sources in 460 deep ROSAT PSPC pointings. The Bright SHARC
Survey covers an area of 178.6 sq.deg and has yielded 374 extended sources. We
discuss the X-ray data reduction, the candidate selection and present results
from our on-going optical follow-up campaign. The optical follow-up
concentrates on the brightest 94 of the 374 extended sources and is now 97%
complete. We have identified thirty-seven clusters of galaxies, for which we
present redshifts and luminosities. The clusters span a redshift range of
0.0696<z<0.83 and a luminosity range of 0.065<Lx<8.3e44 erg/s [0.5-2.0 keV]
(assuming Ho = 50 km/s/Mpc and qo=0.5). Twelve of the clusters have redshifts
greater than z=0.3, eight of which are at luminosities brighter than Lx=3e44
erg/s. Seventeen of the 37 optically confirmed Bright SHARC clusters have not
been listed in any previously published catalog. We also report the discovery
of three candidate ``fossil groups'' of the kind proposed by Ponman et al.
(1994).


  We present light transmission measurements and frequency-delay reflectometry
maps for GaAs photonic crystal membranes, which show the transition from
propagation with a well defined group velocity to a regime completely dominated
by disorder-induced coherent scattering. Employing a self-consistent optical
scattering theory, with only statistical functions to describe the structural
disorder, we obtain an excellent agreement with the experiments using no
fitting parameters. Our experiments and theory together provide clear physical
insight into naturally occurring light localization and multiple
coherent-scattering phenomena in slow-light waveguides.


  Chaotic systems, that have a small Lyapunov exponent, do not obey the common
random matrix theory predictions within a wide "weak quantum chaos" regime.
This leads to a novel prediction for the rate of heating for cold atoms in
optical billiards with vibrating walls. The Hamiltonian matrix of the driven
system does not look like one from a Gaussian ensemble, but rather it is very
sparse. This sparsity can be characterized by parameters $s$ and $g$ that
reflect the percentage of large elements, and their connectivity respectively.
For $g$ we use a resistor network calculation that has direct relation to the
semi-linear response characteristics of the system.


  We here present a model of the dynamics of extremism based on opinion
dynamics in order to understand the circumstances which favour its emergence
and development in large fractions of the general public. Our model is based on
the bounded confidence hypothesis and on the evolution of initially
anti-conformist agents to extreme positions.
  Numerical analyses demonstrate that a few anti-conformists are able to drag a
large fraction of conformists agents to their position provided that they
express their views more often than the conformists. The most influential
parameter controlling the outcome of the dynamics is the uncertainty of the
conformist agents; the higher their uncertainty, the higher is the influence of
anti-conformists. Systematic scans of the parameter space show the existence of
two regime transitions, one following the conformists uncertainty parameter and
the other one following the anti-conformism strength.


  We study the effects of a magnetic flux and an inhomogeneous gap on the
energy spectrum of graphene magnetic quantum dots (GMQDs). By considering the
Dirac equation in the infinite mass framework, we can analytically obtain
eigenspinor expressions. By applying boundary conditions, we obtain an energy
spectrum equation in terms of system parameters such as radius, magnetic field,
energy, flux, and gap. In the infinite limit, we recover Landau levels for
graphene in a magnetic field. We show that the energy spectrum increases
significantly in the presence of flux and a gap inside the GMQDs, which
prolongs the lifetime of the trapped electron states. We show that higher flux
also produces new Landau levels of negative angular momentum. Meanwhile, we
find that the gap increases the separation between the electron and hole energy
bands. As shown in the radial probability analysis, flux and gap emerge as
influential factors in controlling electron mobility, affecting confinement,
and prolonging the presence of quasi-bound states.


  Black hole space times evaporate in discrete steps due to remarkably slow
Hawking radiation. We here identify evaporation with essentially extremal
states at the limit of quantum computation, performing $2.7\times 10^{79}$ bit
calculations per photon emission in a one solar mass black hole. During
evaporation, particles in free fall co-evolve satisfying $EM=$constant, where
$E$ and $M$ denote the total mass energy-at-infinity of the particle and,
respectively, black hole. Particles are hereby increasingly entangled with the
black hole space-time over the course of its evaporation.


  An optimization of the CERN SPL beam line has been performed guided by the
sensitivities to the \theta_{13} mixing angle and to the \delta_{CP} Dirac CP
violating phase. A UNO-like 440 ktons water Cerenkov detector located at 130 km
from the target in a new foreseen Frejus laboratory has been used as a generic
detector. Concerning the \delta_{CP} independent \theta_{13} sensitivity, a
gain of about 20% may be reached using a 3.5 GeV proton beam with a 40 m long,
2 m radius decay tunnel compared to the up to now considered 2.2 GeV beam
energy and 20 m long, 1 m radius decay tunnel. This may motivate new machine
developments to upgrade the nominal SPL proton beam energy.


  We extend state-to-state chemistry to a realm where besides vibrational,
rotational and hyperfine quantum states magnetic quantum numbers are also
resolved. For this, we make use of the Zeeman effect which energetically splits
levels of different magnetic quantum numbers. The chemical reaction which we
choose to study is three-body recombination in an ultracold quantum gas of
$^{87}$Rb atoms forming weakly-bound Rb$_2$ molecules. Here, we find the
propensity rule that the total $m_F$ quantum number of the two atoms forming
the molecule is conserved. Our method can be employed for many other reactions
and inelastic collisions and will allow for novel insights into few-body
processes.


  In this paper we inquire inflationary scenarios built on a simplified version
of the polynomial affine model of gravity. Given the absence of a metric tensor
in the formulation of the model, we build a \emph{kinetic term} contracting the
derivatives of scalar field with the most general $(2,0)$-tensor density build
using the affine connection, and introduce a self-interacting potential via a
scaling of the volume form. We analyse the cosmological solutions derived from
this setup.


  We prove the SBV regularity of the characteristic speed of the scalar
hyperbolic conservation law and SBV-like regularity of the eigenvalue functions
of the Jacobian matrix of flux function for general systems of conservation
laws.
  More precisely, for the equation u_t + f(u)_x = 0, \quad u : \R^+ \times \R
\to \Omega \subset \R^N, we only assume the flux $f$ is $C^2$ function in the
scalar case (N=1) and Jacobian matrix $Df$ has distinct real eigenvalues in the
system case $(N\geq 2)$. Using the modification of the main decay estimate in
Lau and localization method applied in \cite{R}, we show that for the scalar
equation $f'(u)$ belongs to SBV, and for system of conservation laws the scalar
measure \[\big(D_u \lambda_i(u) \cdot r_i(u) \big) \big(l_i(u) \cdot u_x \big)]
has no Cantor part, where $\lambda_i$, $r_i$, $l_i$ are the $i$-th eigenvalue,
$i$-th right eigenvector and $i$-th left eigenvector of the matrix $Df$.


  We have mapped the radio emission in the error ellipse of GeV J1417-6100
(2EGS J1418-6049) at 13cm and 20cm using the Australia Telescope Compact Array.
We find a large shell with extended wings, at the edge of which is a
non-thermal, polarized structure with a center filled morphology (the
`Rabbit'), coincident with an extended, hard X-ray source. We discuss the
various sources seen within the ellipse as potential counterparts of the
gamma-ray source. We conclude that the most likely scenario is that the Rabbit
is a wind nebula surrounding a radio-quiet gamma-ray pulsar.


  Non-canonical scalar fields with the Lagrangian ${\cal L} = X^\alpha -
V(\phi)$, possess the attractive property that the speed of sound, $c_s^{2} =
(2\,\alpha - 1)^{-1}$, can be exceedingly small for large values of $\alpha$.
This allows a non-canonical field to cluster and behave like warm/cold dark
matter on small scales. We demonstrate that simple potentials including $V =
V_0\coth^2{\phi}$ and a Starobinsky-type potential can unify dark matter and
dark energy. Cascading dark energy, in which the potential cascades to lower
values in a series of discrete steps, can also work as a unified model. In all
of these models the kinetic term $X^\alpha$ plays the role of dark matter,
while the potential term $V(\phi)$ plays the role of dark energy.


  In this work, we present a novel matrix-encoding method that is particularly
convenient for neural networks to make predictions in a privacy-preserving
manner using homomorphic encryption. Based on this encoding method, we
implement a convolutional neural network for handwritten image classification
over encryption. For two matrices $A$ and $B$ to perform homomorphic
multiplication, the main idea behind it, in a simple version, is to encrypt
matrix $A$ and the transpose of matrix $B$ into two ciphertexts respectively.
With additional operations, the homomorphic matrix multiplication can be
calculated over encrypted matrices efficiently. For the convolution operation,
we in advance span each convolution kernel to a matrix space of the same size
as the input image so as to generate several ciphertexts, each of which is
later used together with the ciphertext encrypting input images for calculating
some of the final convolution results. We accumulate all these intermediate
results and thus complete the convolution operation.
  In a public cloud with 40 vCPUs, our convolutional neural network
implementation on the MNIST testing dataset takes $\sim$ 287 seconds to compute
ten likelihoods of 32 encrypted images of size $28 \times 28$ simultaneously.
The data owner only needs to upload one ciphertext ($\sim 19.8$ MB) encrypting
these 32 images to the public cloud.


  This paper introduces a $K$-function for assessing second-order properties of
inhomogeneous random measures generated by marked point processes. The marks
can be geometric objects like fibers or sets of positive volume, and the
presented $K$-function takes into account geometric features of the marks, such
as tangent directions of fibers. The $K$-function requires an estimate of the
inhomogeneous density function of the random measure. We introduce parametric
estimates for the density function based on parametric models that represent
large scale features of the inhomogeneous random measure. The proposed
methodology is applied to simulated fiber patterns as well as a
three-dimensional data set of steel fibers in concrete.


  The push-forward operation enables one to redistribute a probability measure
through a deterministic map. It plays a key role in statistics and
optimization: many learning problems (notably from optimal transport,
generative modeling, and algorithmic fairness) include constraints or penalties
framed as push-forward conditions on the model. However, the literature lacks
general theoretical insights on the (non)convexity of such constraints and its
consequences on the associated learning problems. This paper aims at filling
this gap. In a first part, we provide a range of sufficient and necessary
conditions for the (non)convexity of two sets of functions: the maps
transporting one probability measure to another; the maps inducing equal output
distributions across distinct probability measures. This highlights that for
most probability measures, these push-forward constraints are not convex. In a
second time, we show how this result implies critical limitations on the design
of convex optimization problems for learning generative models or group-fair
predictors. This work will hopefully help researchers and practitioners have a
better understanding of the critical impact of push-forward conditions onto
convexity.


  We study standard quantum teleportation of one-qubit state for the situation
in which the channel is subject to decoherence, and where the evolution of the
channel state is ruled by a master equation in the Lindblad form. A detailed
calculation reveals that the quality of teleportation is determined by both the
entanglement and the purity of the channel state, and only the optimal matching
of them ensures the highest fidelity of standard quantum teleportation. Also
our results demonstrated that the decoherence induces distortion of the Bloch
sphere for the output state with different rates in different directions, which
implies that different input states will be teleported with different
fidelities.


  We develop a hybrid type of quantum annealing in which we control temperature
and quantum field simultaneously. We study the efficiency of proposed quantum
annealing and find a good schedule of changing thermal fluctuation and quantum
fluctuation. In this paper, we focus on clustering problems which are important
topics in information science and engineering. We obtain the better solution of
the clustering problem than the standard simulated annealing by proposed
quantum annealing.


  We present a systematic analysis of the complete set of observations of the
black hole (BH) binary XTE J1550-564 obtained by the Rossi X-ray Timing
Explorer. We study the fast time variability properties of the source and
determine the spin of the black hole through the relativistic precession model.
Similarly to what is observed in the BH binary GRO J1655-40, the frequencies of
the QPOs and broad band noise components match the general relativistic
frequencies of particle motion close to the compact object predicted by the
relativistic precession model. The combination of two simultaneously observed
quasi-periodic oscillation (QPO) frequencies together with the dynamical BH
mass from optical/infrared observations yields a spin equal to a = 0.34 +/-
0.01, consistent with previous determinations from X-ray spectroscopy. Based on
the derived BH parameters, the low frequency QPO emission radii vary from about
30 gravitational radii to the innermost stable orbit for this spin (about 5
gravitational radii), where they sharply disappear as observed for the case of
GRO J1655-40.


  We define generalized Hamming weights for almost affine codes. We show how
various aspects and applications of generalized Hamming weights for linear
codes, such as Wei duality, generalized Kung's bound, profiles, connection to
wire-tap channels of type II, apply to the larger class of almost affine codes
in general. In addition we discuss duality of almost affine codes,and of the
smaller class of multilinear codes. We also give results about weight
distributions of infinite series of almost affine codes, each series obtained
from a fixed code by extending the code alphabet.