  Massive stars are powerful cosmic engines. In the phases immediately
preceding core-collapse, massive stars in the Galaxy with $M_i \gtrsim 20$
$M_{\odot}$ may appear as classical Wolf-Rayet (WR) stars. As the final
contribution of a homogeneous RV survey, this work constrains the multiplicity
properties of northern Galactic late-type nitrogen-rich Wolf-Rayet (WNL) stars.
We compare their intrinsic binary fraction and orbital period distribution to
the carbon-rich (WC) and early-type nitrogen-rich (WNE) populations from
previous works. We obtained high-resolution spectra of the complete
magnitude-limited sample of 11 Galactic WNL stars with the Mercator telescope
on the island of La Palma. We used cross-correlation to measure relative RVs
and flagged binary candidates based on the peak-to-peak RV dispersion. By using
Monte Carlo sampling and a Bayesian framework, we computed the
three-dimensional likelihood and one-dimensional posteriors for the upper
period cut-off, power-law index, and intrinsic binary fraction. Adopting a
threshold $C$ of 50 km s$^{-1}$, our Bayesian analysis produced an intrinsic
fraction of $0.42\substack{+0.15 \\ -0.17}$ for the parent WNL population
alongside distributions for the power-law index and the orbital periods. The
observed period distribution of Galactic WN and WC binaries from the literature
is in agreement with what is found. The period distribution of Galactic WN
binaries peaks at $P{\sim}1$-$10$d and that of the WC population at
$P{\sim}5000\,$d. This shift cannot be reconciled by orbital evolution due to
mass loss or mass transfer. At long periods, the evolutionary sequence
O($\xrightarrow{}$LBV)$\xrightarrow{}$WN$\xrightarrow{}$WC seems feasible. The
high frequency of short-period WN binaries compared to WC binaries suggests
that they either tend to merge or that the WN components in these binaries
rarely evolve into WC stars in the Galaxy.


  3D object tracking is a critical task in autonomous driving systems. It plays
an essential role for the system's awareness about the surrounding environment.
At the same time there is an increasing interest in algorithms for autonomous
cars that solely rely on inexpensive sensors, such as cameras. In this paper we
investigate the use of triplet embeddings in combination with motion
representations for 3D object tracking. We start from an off-the-shelf 3D
object detector, and apply a tracking mechanism where objects are matched by an
affinity score computed on local object feature embeddings and motion
descriptors. The feature embeddings are trained to include information about
the visual appearance and monocular 3D object characteristics, while motion
descriptors provide a strong representation of object trajectories. We will
show that our approach effectively re-identifies objects, and also behaves
reliably and accurately in case of occlusions, missed detections and can detect
re-appearance across different field of views. Experimental evaluation shows
that our approach outperforms state-of-the-art on nuScenes by a large margin.
We also obtain competitive results on KITTI.


  Packaging software into containers is becoming a common practice when
deploying services in cloud and other environments. Docker images are one of
the most popular container technologies for building and deploying containers.
A container image usually includes a collection of software packages, that can
have bugs and security vulnerabilities that affect the container health. Our
goal is to support container deployers by analysing the relation between
outdated containers and vulnerable and buggy packages installed in them. We use
the concept of technical lag of a container as the difference between a given
container and the most up-to-date container that is possible with the most
recent releases of the same collection of packages. For 7,380 official and
community Docker images that are based on the Debian Linux distribution, we
identify which software packages are installed in them and measure their
technical lag in terms of version updates, security vulnerabilities and bugs.
We have found, among others, that no release is devoid of vulnerabilities, so
deployers cannot avoid vulnerabilities even if they deploy the most recent
packages. We offer some lessons learned for container developers in regard to
the strategies they can follow to minimize the number of vulnerabilities. We
argue that Docker container scan and security management tools should improve
their platforms by adding data about other kinds of bugs and include the
measurement of technical lag to offer deployers information of when to update.


  We demonstrate, by construction, that simple renormalizable matrix potentials
with S_N, as opposed to O(N), symmetry can exhibit an exponentially large
number of inequivalent deep local minima.


  We describe the design, implementation and performance of the RADICAL-Pilot
task overlay (RAPTOR). RAPTOR enables the execution of heterogeneous tasks --
i.e., functions and executables with arbitrary duration -- on HPC platforms,
providing high throughput and high resource utilization. RAPTOR supports the
high throughput virtual screening requirements of DOE's National Virtual
Biotechnology Laboratory effort to find therapeutic solutions for COVID-19.
RAPTOR has been used on $>8000$ compute nodes to sustain 144M/hour docking
hits, and to screen $\sim$10$^{11}$ ligands. To the best of our knowledge, both
the throughput rate and aggregated number of executed tasks are a factor of two
greater than previously reported in literature. RAPTOR represents important
progress towards improvement of computational drug discovery, in terms of size
of libraries screened, and for the possibility of generating training data fast
enough to serve the last generation of docking surrogate models.


  Lattice QCD has the sign problem at real quark chemical potential. There are
some regions with no sign problem; one is the imaginary quark chemical
potential region and the others are the real and imaginary isospin chemical
potential regions. We show that the Polyakov-loop extended Nambu-Jona-Lasinio
(PNJL) model can reproduce LQCD data in the regions. We also determine the
model parameters from the data and predict the QCD phase diagram in the real
quark chemical potential region.


  The difficulty of describing excitons in semiconducting SWNTs analytically
lies with the fact that excitons can neither be considered strictly 1D nor 2D
objects. However, the situation changes in the case of metallic nanotubes
where, by virtue of screening from gapless metallic subbands, the radius of the
exciton becomes much larger than the radius of the nanotube $R_\text{ex}\gg R$.
Taking advantage of this, we develop the theory of excitons in metallic
nanotubes, determining that their binding energy is about $0.08v/R$, in
agreement with the existing experimental data. Additionally, because of the
presence of the gapless subbands, there are processes where bound excitons are
scattered into unbound electron-hole pairs belonging to the gapless subbands.
Such processes lead to a finite exciton lifetime and the broadening of its
spectral function. We calculate the corresponding decay rate of the excitons.


  The low energy fusion cross sections of charged-particle nuclear reactions
(and the respective reaction rates) in stellar plasmas are enhanced due to
plasma screening effects. We study the impact of those effects on predictive
stellar evolution simulations for detached double-lined eclipsing binaries. We
follow the evolution of binary systems (pre-main sequence or main sequence
stars) with precisely determined radii and masses from 1.1Mo to 23Mo (from
their birth until their present state). The results indicate that all the
discrepancies between the screened and unscreened models (in terms of
luminosity, stellar radius, and effective temperature) are within the
observational uncertainties. Moreover, no nucleosynthetic or compositional
variation was found due to screening corrections. Therefore all thermonuclear
screening effects on the charged-particle nuclear reactions that occur in the
binary stars considered in this work (from their birth until their present
state) can be totally disregarded. In other words, all relevant
charged-particle nuclear reactions can be safely assumed to take place in a
vacuum, thus simplifying and accelerating the simulation processes.


  We use a density matrix renormalization group method to study quantitatively
the phase diagram of a one-dimensional extended Hubbard model at half-filling
by investigating the correlation functions and structure factors. We confirm
the existence of a novel narrow region with long-rang bond-order-wave order
which is highly controversial recently between the charge-density-wave phase
and Mott insulator phase. We determined accurately the position of the
tricritical point $U_t\simeq 7.2t$, $V_t\simeq 3.746t$ which is quite different
from previous studies.


  Various MSr2YCu2Oz (M1212 with M = Fe, Co, Al and Ga) compounds have been
synthesized through a solid state reaction route. All these compounds are
derived from the basic CuBa2YCu2O7 structure. Rietveld structural refinement of
the room-temperature neutron diffraction data for Fe-1212 reveals that nearly
half of Fe remains at the M site, while the other half goes to the Cu site in
the CuO2 plane. For the M = Co, Al and Ga compounds, the basic unit cell gets
doubled due to two distinct M sites in the M-O layer and the space group Ima2,
without any cation intermixing is found more appropriate. For the M = Fe
sample, magnetic ordering of Fe spins is seen from magnetization data below 70
K. In the case of the M = Co compound, a shallow down-turn in magnetic
susceptibility is seen below 150 K, indicating low dimensional magnetic
characteristics of the CoO layer. The compounds with M = Al and Ga show
paramagnetic behaviour in their magnetization data down to 5 K. The oxygen
content, established for each compound based on oxygen occupancies, refined
from the neutron diffraction data comes close to 7.0 for each.


  Deep phenotyping study has become an emerging field to understand the gene
function and the structure of biological networks. For the living animal C.
elegans, recent advances in genome-editing tools, microfluidic devices and
phenotypic analyses allow for a deeper understanding of the
genotype-to-phenotype pathway. In this article, I reviewed the evolution of
deep phenotyping study in cell development, neuron activity, and the behaviors
of intact animals.


  In the paper the Goursat problem with non classical boundary conditions not
requiring the agreement conditions is considered for a sixth order
pseudoparabolic equation with the classical boundary condition is substantiated
in the case if the solution of the stated problem is sought in S.L.Sobolev
anisotropic space.


  We estimate the number of ends of smooth and singular Ricci shrinkers
focussing first on general ends and later on asymptotically conical ones. In
particular, we obtain a variety of applications to sequences of Ricci shrinkers
converging in a weak pointed sense to a possibly singular limit Ricci shrinker,
for instance no new conical end can form in the limit.


  We have made velocity observations of the star alpha Cen B from two sites,
allowing us to identify 37 oscillation modes with l=0-3. Fitting to these modes
gives the large and small frequency separations as a function of frequency. The
mode lifetime, as measured from the scatter of the oscillation frequencies
about a smooth trend, is similar to that in the Sun. Limited observations of
the star delta Pav show oscillations centred at 2.3 mHz with peak amplitudes
close to solar. We introduce a new method of measuring oscillation amplitudes
from heavily-smoothed power density spectra, from which we estimated amplitudes
for alpha Cen A and B, beta Hyi, delta Pav and the Sun. We point out that the
oscillation amplitudes may depend on which spectral lines are used for the
velocity measurements.


  The possibility of Y(2175) as a $2{^3D_1}$ $s\bar{s}$ meson is studied. We
study the decay of $2{^3D_1}$ $s\bar{s}$ from both the $^3P_0$ model and the
flux tube model, and the results are similar in the two models. We show that
the decay patterns of $1^{--}$ strangeonium hybrid and $2{^3D_1}$ $s\bar{s}$
are very different. The experimental search of the decay modes $KK$,
$K^{*}K^{*}$, $K(1460)K$, $h_1(1380)\eta$ is suggested to distinguish the two
pictures. Measuring the $K^{*}K^{*}$ partial width ratios is crucial to
discriminate the $2{^3D_1}$ from the $3{^3S_1}$ $s\bar{s}$ assignment.


  We study corrections to observables related to the muon in the context of
models of new physics which generate mass-enhanced corrections to the muon
dipole moments. Working in the Standard Model effective theory, we demonstrate
a correlation between the decay of the Higgs boson to muons, and the magnetic
and electric dipole moments of the muon generated by the dominant matching
corrections. This defines a novel way to classify predictions for a wide
variety of models of new physics based on the pattern of deviations of these
three observables. In particular, when applied to specific models we find that
this correlation has a potential to rule out whole models or set upper bounds
on the scale of new physics motivated by the muon anomalous magnetic moment.


  This work presents an efficient numerical method to evaluate the free energy
density and associated thermodynamic quantities of (quasi) one-dimensional
classical systems, by combining the transfer operator approach with a numerical
discretization of integral kernels using quadrature rules. For analytic
kernels, the technique exhibits exponential convergence in the number of
quadrature points. As demonstration, we apply the method to a classical
particle chain, to the semiclassical nonlinear Schr\"odinger equation and to a
classical system on a cylindrical lattice.


  We consider stochastic volatility dynamics driven by a general H\"older
continuous Volterra-type noise and with unbounded drift. For these so-called
SVV-models, we consider the explicit computation of quadratic hedging
strategies. While the theoretical hedge is well-known in terms of the
non-anticipating derivative for all square integrable claims, the fact that
these models are typically non-Markovian provides is a challenge in the direct
computation of conditional expectations at the core of the explicit hedging
strategy. To overcome this difficulty, we propose a Markovian approximation of
the model which stems from an adequate approximation of the kernel in the
Volterra noise. We study the approximation of the volatility, of the prices and
of the optimal mean-square hedge. We provide the corresponding error estimates.
The work is completed with numerical simulations.


  We present the Virtual Quantum Device (VQD) platform, a system based on the
QuEST quantum emulator. Through the use of VQDs, non-expert users can emulate
specific quantum computers with detailed error models, bespoke gate sets and
connectivities. The platform boasts an intuitive interface, powerful
visualisation, and compatibility with high-performance computation for
effective testing and optimisation of complex quantum algorithms or ideas
across a range of quantum computing hardware. We create and explore five
families of VQDs corresponding to trapped ions, nitrogen-vacancy-centres,
neutral atom arrays, silicon quantum dot spins, and superconducting devices.
Each is highly configurable through a set of tailored parameters. We showcase
the key characteristics of each virtual device, providing practical examples of
the tool's usefulness and highlighting each device's specific attributes. By
offering user-friendly encapsulated descriptions of diverse quantum hardware,
the VQD platform offers researchers the ability to rapidly explore algorithms
and protocols in a realistic setting; meanwhile hardware experts can create
their own VQDs to compare with their experiments.


  We prove existence of discrete solitons in infinite parity-time (PT-)
symmetric lattices by means of analytical continuation from the anticontinuum
limit. The energy balance between dissipation and gain implies that in the
anticontinuum limit the solitons are constructed from elementary PT-symmetric
blocks such as dimers, quadrimers, or more general oligomers. We consider in
detail a chain of coupled dimers, analyze bifurcations of discrete solitons
from the anticontinuum limit and show that the solitons are stable in a
sufficiently large region of the lattice parameters. The generalization of the
approach is illustrated on two examples of networks of quadrimers, for which
stable discrete solitons are also found.


  We consider partially hyperbolic \( C^{1+} \) diffeomorphisms of compact
Riemannian manifolds of arbitrary dimension which admit a partially hyperbolic
tangent bundle decomposition \( E^s\oplus E^{cu} \). Assuming the existence of
a set of positive Lebesgue measure on which \( f \) satisfies a weak nonuniform
expansivity assumption in the centre~unstable direction, we prove that there
exists at most a finite number of transitive attractors each of which supports
an SRB measure. As part of our argument, we prove that each attractor admits a
Gibbs-Markov-Young geometric structure with integrable return times. We also
characterize in this setting SRB measures which are liftable to
Gibbs-Markov-Young structures.


  In this paper we investigate the existence of singular endomorphisms of the
cuboidal Hamming graph $H(n_1,...,n_d,S)$ over the set $\left[ n_1\right]\times
\left[ n_2\right]\times \cdots \times \left[ n_d\right]$, where $\left[
n\right]=\{1,...,n\}$, which is a generalisation of the well-known (cubic)
Hamming graph over $\left[ n\right]^{d}$. Two vertices in $H$ are adjacent, if
their Hamming distance lies in the set $S$. In this paper $S=\{1,...,r\}$, for
some integer $1\leq r\leq d-1$, and we first show that the singular
endomorphisms of minimal rank ( which is the size of their image) of
$H(n,...,n,S)$ correspond to Latin hypercubes of class $r$ (those were
originally defined by Kishen (1950)). Then we generalise those hypercubes to
Latin hypercuboids of class $r$. We discuss the existence of these objects,
provide constructions and count Latin hypercuboids for small parameters. In the
last part, we extend the natural connection between Latin hypercubes of class
$r$ and MDS codes to Latin hypercuboids of class $r$ leading to the definition
of MDS codes for mixed codes (mixed MDS codes), that is for codes over
hypercuboids. Here, we demonstrate the interdependence between graph
endomorphisms, Latin hypercuboids and mixed MDS codes.


  The paper is devoted to the study of graph sequence G_n = (V_n, E_n) where
V_n is the set of all vectors v in R^n with coordinates from {-1, 0, 1} such
that |v| = sqrt(3), and E_n consists of all pairs of vertices with the scalar
product 1. We find exactly the independence number of G_n. As a corollary we
get some new lower bounds of chi(\R^n) and chi(\Q^n) for small values of n.


  We summarize the results of numerical simulations of colliding gas-rich disk
galaxies in which the impact velocity is set parallel to the spin axes of the
two galaxies. The effects of varying the impact speed are studied with
particular attention to the resulting gaseous structures and shockwave
patterns, and the time needed to produce these structures. The simulations
employ an N-body treatment of the stars and dark matter, together with an SPH
treatment of the gas, in which all components of the models are gravitationally
active. The results indicate that for such impact geometries, collisions can
lead to the very rapid formation of a central, rapidly rotating, dense gas
disk, and that in all cases extensive star formation is predicted by the very
high gas densities and prevalence of shocks, both in the nucleus and out in the
galactic disks. As the dense nucleus is forming, gas and stars are dispersed
over very large volumes, and only fall back towards the nucleus over long
times. In the case of low impact velocities, this takes an order of magnitude
more time than that needed for the formation of a dense nucleus.


  Example-guided image synthesis aims to synthesize an image from a semantic
label map and an exemplary image indicating style. We use the term "style" in
this problem to refer to implicit characteristics of images, for example: in
portraits "style" includes gender, racial identity, age, hairstyle; in full
body pictures it includes clothing; in street scenes, it refers to weather and
time of day and such like. A semantic label map in these cases indicates facial
expression, full body pose, or scene segmentation. We propose a solution to the
example-guided image synthesis problem using conditional generative adversarial
networks with style consistency. Our key contributions are (i) a novel style
consistency discriminator to determine whether a pair of images are consistent
in style; (ii) an adaptive semantic consistency loss; and (iii) a training data
sampling strategy, for synthesizing style-consistent results to the exemplar.


  We show that the spectral set conjecture by Fuglede holds in the setting of
cyclic groups of order $p^n q$, where $p$, $q$ are distinct primes and
$n\geq1$. This means that a subset $E$ of such a group $G$ tiles the group by
translation ($G$ can be partitioned into translates of $E$) if and only if
there exists an orthogonal basis of $L^2(E)$ consisting of group characters.
The main ingredient of the present proof is the structure of vanishing sums of
roots of unity of order $N$, where $N$ has at most two prime divisors; the
extension of this proof to the case of cyclic groups of order $p^n q^m$ seems
therefore feasible. The only previously known infinite family of cyclic groups,
for which Fuglede's conjecture is verified in both directions, is that of
cyclic $p$-groups, i.e. $\mathbb{Z}_{p^n}$.


  We study the holographic "complexity=action'" (CA) and "complexity=volume"
(CV) proposals in Einstein-dilaton gravity in all spacetime dimensions. We
analytically construct an infinite family of black hole solutions and use CA
and CV proposals to investigate the time evolution of the complexity. Using the
CA proposal, we find dimensional dependent violation of the Lloyd bound in
early as well as in late times. Moreover, depending on the parameters of the
theory, the bound violation relative to the conformal field theory result can
be tailored in the early times as well. In contrast to the CA proposal, the CV
proposal in our model yields results similar to those obtained in the
literature.


  A class of counting problems ask for the number of regions of a central
hyperplane arrangement. By duality, this is the same as counting the vertices
of a zonotope. We give several efficient algorithms, based on a linear
optimization oracle, that solve this and related enumeration problems. More
precisely, our algorithms compute the vertices of a zonotope from the set of
its generators and inversely, recover the generators of a zonotope from its
vertices. A variation of the latter algorithm also allows to decide whether a
polytope, given as its vertex set, is a zonotope and when it is not, to compute
its greatest zonotopal summand.


  Parallel tempering Monte Carlo simulations have been applied to a variety of
systems presenting rugged free-energy landscapes. Despite this, its efficiency
depends strongly on the temperature set. With this query in mind, we present a
comparative study among different temperature selection schemes in three
lattice-gas models. We focus our attention in the constant entropy method
(CEM), proposed by Sabo {\it et al}. In the CEM, the temperature is chosen by
the fixed difference of entropy between adjacent replicas. We consider a method
to determine the entropy which avoids numerical integrations of the specific
heat and other thermodynamic quantities. Different analyses for first- and
second-order phase transitions have been undertaken, revealing that the CEM may
be an useful criterion for selecting the temperatures in the parallel tempering


  Graphene, as a promising material of post-silicon electronics, opens a new
paradigm for the novel electronic properties and device applications. On the
other hand, the 2D feature of graphene makes it technically challenging to be
integrated into 3D transistors with a sufficient processor capacity. Although
there are many attempts to assemble 2D graphene into 3D structures, the
characteristics of massless Dirac fermions cannot be well preserved in these
materials for transistor applications. Here we report a high-performance
graphene transistor by utilizing 3D nanoporous graphene which is comprised of
an interconnected single graphene sheet and a commodious open porosity to
infuse an ionic liquid for a tunable electronic state by applying electric
fields. The 3D nanoporous graphene transistor, with high carrier mobility of
5000-7500 cm$^2$V$^{-1}$s$^{-1}$, exhibits two to three orders of magnitude
higher electric conductance and capacitance than those of 2D graphene devices,
along with preserved ambipolor electronic nature of Dirac cones. Moreover, the
3D graphene networks with Dirac fermions turn out to exhibit a unique nonlinear
Hall resistance in a wide range of the gate voltages. The high quality 3D
nanoporous graphene EDLT may open a new field for utilizing Dirac fermions in
3D network structures for various fundamental and practical applications.


  The hidden geometry of simplicial complexes can influence the collective
dynamics of nodes in different ways depending on the simplex-based interactions
of various orders and competition between local and global structural features.
We study a system of phase oscillators attached to nodes of 4-dimensional
simplicial complexes and interacting via positive/negative edges-based pairwise
$K_1$ and triangle-based triple $K_2\geq 0$ couplings. Three prototypal
simplicial complexes are grown by aggregation of 5-cliques, controlled by the
chemical affinity parameter $\nu$, resulting in sparse, mixed, and compact
architecture, all of which have 1-hyperbolic graphs but different spectral
dimensions. By changing the interaction strength $K_1\in[-4,2]$ along the
forward and backward sweeps, we numerically determine individual phases of each
oscillator and a global order parameter to measure the level of
synchronisation. Our results reveal how different architectures of simplicial
complexes, in conjunction with the interactions and internal-frequency
distributions, impact the shape of the hysteresis loop and lead to patterns of
locally synchronised groups that hinder global network synchronisation.


  We present the first detailed study of the stellar populations of
star-forming galaxies at z~1.5, which are selected by their [O II] emission
line, detected in narrow-band surveys. We identified ~1,300 [O II] emitters at
z=1.47 and z=1.62 in the Subaru Deep Field with rest-frame EWs above 13\AA.
Optical and near-infrared spectroscopic observations for ~10% of our samples
show that our separation of [O II] from [O III] emission-line galaxies in
two-color space is 99% successful. We analyze the multi-wavelength properties
of a subset of ~1,200 galaxies with the best photometry. They have average
rest-frame EW of 45\AA, stellar mass of 3 x 10^9 M_sun, and stellar age of 100
Myr. In addition, our SED fitting and broad-band colors indicate that [O II]
emitters span the full range of galaxy populations at z~1.5. We also find that
80% of [O II] emitters are also photometrically classified as "BX/BM" (UV)
galaxies and/or the star-forming "BzK" (near-IR) galaxies. Our [O II] emission
line survey produces a far more complete, and somewhat deeper sample of z~1.5
galaxies than either the BX/BM or sBzK selection alone. We constructed average
SEDs and find that higher [O II] EW galaxies have somewhat bluer continua. SED
model-fitting shows that they have on average half the stellar mass of galaxies
with lower [O II] EW. The observed [O II] luminosity is well-correlated with
the far-UV continuum with a logarithmic slope slightly 0f 0.89\pm0.22. The
scatter of the [O II] luminosity against the far-UV continuum suggests that [O
II] can be used as a SFR indicator with a reliability of 0.23 dex.


  Being able to control the neutral excitonic flux is a mandatory step for the
development of future room-temperature two-dimensional excitonic devices.
Semiconducting Monolayer Transition Metal Dichalcogenides (TMD-ML) with
extremely robust and mobile excitons are highly attractive in this regard.
However, generating an efficient and controlled exciton transport over long
distances is a very challenging task. Here we demonstrate that an atomically
sharp TMD-ML lateral heterostructure (MoSe$_{2}$-WSe$_{2}$) transforms the
isotropic exciton diffusion into a unidirectional excitonic flow through the
junction. Using tip-enhanced photoluminescence spectroscopy (TEPL) and a
modified exciton transfer model, we show a discontinuity of the exciton density
distribution on each side of the interface. We introduce the concept of exciton
Kapitza resistance, by analogy with the interfacial thermal resistance referred
to as Kapitza resistance. By comparing different heterostructures with or
without top hexagonal boron nitride (hBN) layer, we deduce that the transport
properties can be controlled, over distances far greater than the junction
width, by the exciton density through near-field engineering and/or laser power
density. This work provides a new approach for controlling the neutral exciton
flow, which is key toward the conception of excitonic devices.


  We present the theoretical prediction of a gate tunable anomalous Hall effect
(AHE) in an oxide interface as a hallmark of spin-orbit coupling. The observed
AHE at low-temperatures in the presence of an external magnetic field emerges
from a complex structure of the Berry curvature of the electrons on the Fermi
surface and strongly depends on the orbital character of the occupied bands. A
detailed picture of the results comes from a multiband low-energy model with a
generalized Rashba interaction that supports characteristic out-of-plane spin
and orbital textures. We discuss strategies for optimizing the intrinsic AHE in
(111) SrTiO$_3$ heterostructure interfaces.


  Data from e+e- annihilation into hadrons collected by the JADE experiment at
centre-of-mass energies between 14 GeV and 44 GeV were used to study the
four-jet rate as a function of the Durham algorithm's resolution parameter
y_cut. The four-jet rate was compared to a QCD NLO order calculations including
NLLA resummation of large logarithms. The strong coupling constant measured
from the four-jet rate is alpha_S(M_Z) = 0.1169 +/- 0.0004 (stat) +/- 0.0012
(expt) +/- 0.0021 (had) +/- 0.0007 (theo), alpha_S(M_Z) = 0.1169 +/- 0.0026
(total error) in agreement with the world average.


  In this letter, we describe the modified decay rate and photonic Lamb
(frequency) shift of quantum emitters in terms of the resonant states of a
neighboring photonic resonator. This description illustrates a fundamental
distinction in the behaviors of closed (conservative) and open (dissipative)
systems: the Lamb shift is bounded by the emission linewidth in closed systems
while it overcomes this limit in open systems.


  Magneto-optical imaging is a powerful technique for studying qualitative
features of magnetic flux distributions in superconductors and other magnetic
samples. However, magneto-optical imaging does not automatically return
two-dimensional maps with the actual values of the magnetic field, due to the
non-linear response functions of magneto-optical indicator films and in
practice also non-uniform illumination. A quantitative treatment is needed in
order to achieve such a calibration. After calibration, one can proceed to
deduce the corresponding two-dimensional maps of the current density
distribution from inversion of the Biot-Savart law. While there has been
published a large amount of work on quantitative magneto-optical imaging, the
material is scattered and notation differs significantly between different
authors. These notes originate from parts of the Ph. D. thesis of the author
and collect the most important materials into a unified and updated treatment.
Basic aspects of magnetic flux penetration in superconductors including the
Bean model are reviewed briefly in chapter 1. In chapter 2, the method of
magneto-optical imaging using the Faraday effect is introduced, together with
practical aspects of magneto-optical experiments at low temperatures and the
semi-qualitative technique of RGB addition for checking the reproducibility of
flux patterns. The physics of magneto-optical indicator films and calibration
of magneto-optical images into maps of magnetic field values are discussed in
chapter 3. Next, in chapter 4 the Biot-Savart law and fast Fourier
transformations are discussed before proceeding to inversion of magnetic field
maps into current density maps. Finally, magnetometry with Faraday
magneto-optical imaging is treated. MATLAB codes for several of the discussed
concepts can be found at https://github.com/atlejq/Magneto-optics


  We compare time-dependent solutions of different phase-field models for
dendritic solidification in two dimensions, including a thermodynamically
consistent model and several ad hoc models. The results are identical when the
phase-field equations are operating in their appropriate sharp interface limit.
The long time steady state results are all in agreement with solvability
theory. No computational advantage accrues from using a thermodynamically
consistent phase-field model.


  In this work, we consider the optimization formulation of personalized
federated learning recently introduced by Hanzely and Richt\'arik (2020) which
was shown to give an alternative explanation to the workings of local {\tt SGD}
methods. Our first contribution is establishing the first lower bounds for this
formulation, for both the communication complexity and the local oracle
complexity. Our second contribution is the design of several optimal methods
matching these lower bounds in almost all regimes. These are the first provably
optimal methods for personalized federated learning. Our optimal methods
include an accelerated variant of {\tt FedProx}, and an accelerated
variance-reduced version of {\tt FedAvg}/Local {\tt SGD}. We demonstrate the
practical superiority of our methods through extensive numerical experiments.


  In this note, we show that for each minimal norm $N(\cdot)$ on the algebra
$M_n$ of all $n \times n$ complex matrices, there exist norms $\|\cdot\|_1$ and
$\|\cdot\|_2$ on ${\mathbb C}^n$ such that $$N(A)=\max\{\|Ax\|_2: \|x\|_1=1,
x\in {\mathbb C}^n\}$$ for all $A \in M_n$. This may be regarded as an
extension of a known result on characterization of minimal algebra norms.


  We provide an in-depth study of the knowledge-theoretic aspects of
communication in so-called gossip protocols. Pairs of agents communicate by
means of calls in order to spread information---so-called secrets---within the
group. Depending on the nature of such calls knowledge spreads in different
ways within the group. Systematizing existing literature, we identify 18
different types of communication, and model their epistemic effects through
corresponding indistinguishability relations. We then provide a classification
of these relations and show its usefulness for an epistemic analysis in
presence of different communication types. Finally, we explain how to formalise
the assumption that the agents have common knowledge of a distributed epistemic
gossip protocol.


  This article shows yet another proof of NP=CoNP$. In a previous article, we
proved that NP=PSPACE and from it we can conclude that NP=CoNP immediately. The
former proof shows how to obtain polynomial and, polynomial in time checkable
Dag-like proofs for all purely implicational Minimal logic tautologies. From
the fact that Minimal implicational logic is PSPACE-complete we get the proof
that NP=PSPACE.
  This first proof of NP=CoNP uses Hudelmaier linear upper-bound on the height
of Sequent Calculus minimal implicational logic proofs. In an addendum to the
proof of NP=PSPACE, we observe that we do not need to use Hudelmaier
upper-bound since any proof of non-hamiltonicity for any graph is linear
upper-bounded. By the CoNP-completeness of non-hamiltonicity, we obtain NP=CoNP
as a corollary of the first proof. In this article we show the third proof of
CoNP=NP, also providing polynomial size and polynomial verifiable certificates
that are Dags. They are generated from normal Natural Deduction proofs, linear
height upper-bounded too, by removing redundancy, i.e., repeated parts. The
existence of repeated parts is a consequence of the redundancy theorem for a
family of super-polynomial proofs in the purely implicational Minimal logic. It
is mandatory to read at least two previous articles to get the details of the
proof presented here. The article that proves the redundancy theorem and the
article that shows how to remove the repeated parts of a normal Natural
Deduction proof to have a polynomial Dag certificate for minimal implicational
logic tautologies.


  This paper uses and recycles data from a third-party digital marketing firm,
to explore how targeted ads contribute to larger systems of racial
discrimination. Focusing on a case study of targeted ads for educational
searches in New York City, it discusses data visualizations and mappings of
trends in the advertisements' targeted populations alongside U.S census data
corresponding to these target zipcodes. We summarize and reflect on the results
to consider how internet platforms systemically and differentially target
advertising messages to users based on race; the tangible harms and risks that
result from an internet traffic system designed to discriminate; and finally,
novel approaches and frameworks for further auditing systems amid opaque,
black-boxed processes forestalling transparency and accountability.


  Electromagnetic responses in superconductors provide valuable information on
the pairing symmetry as well as physical quantities such as the superfluid
density. However, at the superconducting gap energy scale, optical excitations
of the Bogoliugov quasiparticles are forbidden in conventional
Bardeen-Cooper-Schrieffer superconductors when momentum is conserved.
Accordingly, far-infrared optical responses have been understood in the
framework of a dirty-limit theory by Mattis and Bardeen for over 60 years. Here
we show, by investigating the selection rules imposed by particle-hole symmetry
and unitary symmetries, that intrinsic momentum-conserving optical excitations
can occur in clean multi-band superconductors when one of the following three
conditions is satisfied: (i) inversion symmetry breaking, (ii) symmetry
protection of the Bogoliubov Fermi surfaces, or (iii) simply finite spin-orbit
coupling with unbroken time reversal and inversion symmetries. This result
indicates that clean-limit optical responses are common beyond the
straightforward case of broken inversion symmetry. We apply our theory to
optical responses in FeSe, a clean multi-band superconductor with inversion
symmetry and significant spin-orbit coupling. This result paves the way for
studying clean-limit superconductors through optical measurements.


  One of the great challenges of modern science is to faithfully model, and
understand, matter at a wide range of scales. Starting with atoms, the vastness
of the space of possible configurations poses a formidable challenge to any
simulation of complex atomic and molecular systems. We introduce a
computational method to reduce the complexity of atomic configuration space by
systematically recognising hierarchical levels of atomic structure, and
identifying the individual components. Given a list of atomic coordinates, a
network is generated based on the distances between the atoms. Using the
technique of modularity optimisation, the network is decomposed into modules.
This procedure can be performed at different resolution levels, leading to a
decomposition of the system at different scales, from which hierarchical
structure can be identified. By considering the amount of information required
to represent a given modular decomposition we can furthermore find the most
succinct descriptions of a given atomic ensemble. Our straightforward,
automatic and general approach is applied to complex crystal structures. We
show that modular decomposition of these structures considerably simplifies
configuration space, which in turn can be used in discovery of novel crystal
structures, and opens up a pathway towards accelerated molecular dynamics of
complex atomic ensembles. The power of this approach is demonstrated by the
identification of a possible allotrope of boron containing 56 atoms in the
primitive unit cell, which we uncover using an accelerated structure search,
based on a modular decomposition of a known dense phase of boron,
$\gamma$-B$_{28}$.


  In this paper, we propose mathematical models for reconstructing the optical
flow in time-harmonic elastography. In this image acquisition technique, the
object undergoes a special time-harmonic oscillation with known frequency so
that only the spatially varying amplitude of the velocity field has to be
determined. This allows for a simpler multi-frame optical flow analysis using
Fourier analytic tools in time. We propose three variational optical flow
models and show how their minimization can be tackled via Fourier transform in
time. Numerical examples with synthetic as well as real-world data demonstrate
the benefits of our approach.
  Keywords: optical flow, elastography, Fourier transform, iteratively
reweighted least squares, Horn--Schunck method


  We propose the use of ponderomotive forces to entangle the motions of
different atoms. Two situations are analyzed: one where the atoms belong to the
same optical cavity and interact with the same radiation field mode; the other
where each atom is placed in own optical cavity and the output field of one
cavity enters the other.


  Among $\R^3$-valued triples of random vectors $(X,Y,Z)$ having fixed marginal
probability laws, what is the best way to jointly draw $(X,Y,Z)$ in such a way
that the simplex generated by $(X,Y,Z)$ has maximal average volume? Motivated
by this simple question, we study optimal transportation problems with several
marginals when the objective function is the determinant or its absolute value.


  We give a combinatorial intepretation of cluster variables of a specific
cluster algebra under a mutation sequence of period 6, in terms of perfect
matchings of subgraphs of the brane tiling dual to the quiver associated with
the cluster algebra.


  We find a 4+1 dimensional stationary vacuum hyper-cylindrical solution
solution which is spherically symmetric in $3-$dimensions and invariant under
the translation along the fifth coordinate. The solution is characterized by
three parameters, mass, tension, and conserved momentum along the fifth
coordinate. The metric is locally equivalent to the known static solution. We
briefly discuss its physical properties.


  The problem of belief tracking in the presence of stochastic actions and
observations is pervasive and yet computationally intractable. In this work we
show however that probabilistic beliefs can be maintained in factored form
exactly and efficiently across a number of causally closed beams, when the
state variables that appear in more than one beam obey a form of backward
determinism. Since computing marginals from the factors is still
computationally intractable in general, and variables appearing in several
beams are not always backward-deterministic, the basic formulation is extended
with two approximations: forms of belief propagation for computing marginals
from factors, and sampling of non-backward-deterministic variables for making
such variables backward-deterministic given their sampled history. Unlike,
Rao-Blackwellized particle-filtering, the sampling is not used for making
inference tractable but for making the factorization sound. The resulting
algorithm involves sampling and belief propagation or just one of them as
determined by the structure of the model.


  In this paper, we propose a novel framework of air traffic management (ATM).
The framework is in particular characterized by the trajectory planning of
weakly supervised aircraft; the air traffic control (ATC) does not completely
determine the trajectory of each aircraft unlike conventional planning methods,
but determines allowable safe sets of trajectories. ATC requests pilots to
select their own trajectories from the sets, and the pilots determine ones by
pursuing their own aims. For example, the selection can be based on pilot
preferences and airline strategies. This two stage ATM system contributes to
simultaneously achieve the both objectives of the ATC and pilots such as fuel
cost minimization under safety management. The effectiveness of the proposed
ATM system is demonstrated in a simulation using actual air traffic data.


  Activism can take a multitude of forms, including protests, social media
campaigns, and even public art. The uniqueness of public art lies in that both
the act of creation and the artifacts created can serve as activism.
Furthermore, public art is often site-specific and can be created with (e.g.,
commissioned murals) or without permission (e.g., graffiti art) of the site's
owner. However, the majority of public art is inaccessible to blind and low
vision people, excluding them from political and social action. In this
position paper, we build on a prior crowdsourced mural description project and
describe the design of one potential process artifact, ARtivism, for making
public art more accessible via augmented reality. We then discuss tensions that
may occur at the intersection of public art, activism, and technology.


  We analyze three sets of gauge ensembles in our extended physics program of a
particularly important BSM gauge theory with a fermion doublet in the two-index
symmetric (sextet) representation of the SU(3) BSM color gauge group. Our
investigations include chiral symmetry breaking $\rm{(\chi SB)}$ in the
p-regime and $\epsilon$-regime, the mass of the composite ${\rm 0^{++}}$
scalar, resonance spectroscopy, new physics from gauge anomaly constraints, and
the role of stable sextet BSM baryons with Electroweak interactions in dark
matter searches. Important new goals include studies of the ${\rm 0^{++}}$
scalar entangled with Goldstone dynamics in the p-regime and the
$\epsilon$-regime, the resonance spectrum with particular attention to emerging
LHC signals, like recent hints for diphoton excess at 750 GeV or diboson
anomalies in the 2 TeV range. All results reported here are preliminary before
journal publication including some post-conference material for the discussion.


  The formation of high-mass stars is usually accompanied by powerful
protostellar outflows. Such high-mass outflows are not simply scaled-up
versions of their lower-mass counterparts, since observations suggest that the
collimation degree degrades with stellar mass. Theoretically, the origins of
massive outflows remain open to question because radiative feedback and
fragmentation of the accretion flow around the most massive stars, with M > 15
M_Sun, may impede the driving of magnetic disk winds. We here present a
three-dimensional simulation of the early stages of core fragmentation and
massive star formation that includes a subgrid-scale model for protostellar
outflows. We find that stars that form in a common accretion flow tend to have
aligned outflow axes, so that the individual jets of multiple stars can combine
to form a collective outflow. We compare our simulation to observations with
synthetic H_2 and CO observations and find that the morphology and kinematics
of such a collective outflow resembles some observed massive outflows, such as
Cepheus A and DR 21. We finally compare physical quantities derived from
simulated observations of our models to the actual values in the models to
examine the reliability of standard methods for deriving physical quantities,
demonstrating that those methods indeed recover the actual values to within a
factor of 2-3.


  We provide a novel reduction from swap-regret minimization to external-regret
minimization, which improves upon the classical reductions of Blum-Mansour
[BM07] and Stolz-Lugosi [SL05] in that it does not require finiteness of the
space of actions. We show that, whenever there exists a no-external-regret
algorithm for some hypothesis class, there must also exist a no-swap-regret
algorithm for that same class. For the problem of learning with expert advice,
our result implies that it is possible to guarantee that the swap regret is
bounded by {\epsilon} after $\log(N)^{O(1/\epsilon)}$ rounds and with $O(N)$
per iteration complexity, where $N$ is the number of experts, while the
classical reductions of Blum-Mansour and Stolz-Lugosi require $O(N/\epsilon^2)$
rounds and at least $\Omega(N^2)$ per iteration complexity. Our result comes
with an associated lower bound, which -- in contrast to that in [BM07] -- holds
for oblivious and $\ell_1$-constrained adversaries and learners that can employ
distributions over experts, showing that the number of rounds must be
$\tilde\Omega(N/\epsilon^2)$ or exponential in $1/\epsilon$.
  Our reduction implies that, if no-regret learning is possible in some game,
then this game must have approximate correlated equilibria, of arbitrarily good
approximation. This strengthens the folklore implication of no-regret learning
that approximate coarse correlated equilibria exist. Importantly, it provides a
sufficient condition for the existence of correlated equilibrium which vastly
extends the requirement that the action set is finite, thus answering a
question left open by [DG22; Ass+23]. Moreover, it answers several outstanding
questions about equilibrium computation and learning in games.


  Dynamical systems whose symplectic structure degenerates, becoming
noninvertible at some points along the orbits are analyzed. It is shown that
for systems with a finite number of degrees of freedom, like in classical
mechanics, the degeneracy occurs on domain walls that divide phase space into
nonoverlapping regions each one describing a nondegenerate system, causally
disconnected from each other. These surfaces are characterized by the sign of
the Liouville's flux density on them, behaving as sources or sinks of orbits.
In this latter case, once the system reaches the domain wall, it acquires a new
gauge invariance and one degree of freedom is dynamically frozen, while the
remaining degrees of freedom evolve regularly thereafter.


  The laser flash method for measuring thermal diffusivity of solids involves
subjecting the front face of a small sample to a heat pulse of radiant energy
and recording the resulting temperature rise on the opposite (rear) surface.
For the adiabatic case, the widely-used standard approach estimates the thermal
diffusivity from the rear-surface temperature rise history by calculating the
half rise time: the time required for the temperature rise to reach one half of
its maximum value. In this article, we develop a novel alternative approach by
expressing the thermal diffusivity exactly in terms of the area enclosed by the
rear-surface temperature rise curve and the steady-state temperature over time.
Approximating this integral numerically leads to a simple formula for the
thermal diffusivity involving the rear-surface temperature rise history. Using
synthetic experimental data we demonstrate that the new formula produces
estimates of the thermal diffusivity - for a typical test case - that are more
accurate and less variable than the standard approach. The article concludes by
briefly commenting on extension of the new method to account for heat losses
from the sample.


  We present the appearance of negative differential resistance (NDR) in
spin-dependent electron transport through a few-atom spin-chain. A chain of
three antiferromagnetically coupled Fe atoms(Fe trimer) was positioned on a
Cu2N/Cu(100) surface and contacted with the spin-polarized tip of a scanning
tunneling microscope, thus coupling the Fe trimer to one non-magnetic and one
magnetic lead. Pronounced NDR appears at the low bias of 7 mV where inelastic
electron tunneling dynamically locks the atomic spin in a long-lived excited
state. This causes a rapid increase of the magnetoresistance between
spin-polarized tip and Fe trimer and quenches elastic tunneling. By varying the
coupling strength between tip and Fe trimer we find that in this transport
regime the dynamic locking of the Fe trimer competes with magnetic exchange
interaction, which statically forces the Fe trimer into the
high-magnetoresistance state and removes the NDR.


  Defects intentionally introduced into magnetic materials often have a
profound effect on the physical properties. Specifically tailored neutron
spectroscopic experiments can provide detailed information on both the local
exchange interactions and the local distances between the magnetic atoms around
the defects. This is demonstrated for manganese dimer excitations observed for
the magnetically diluted three-, two- and one-dimensional compounds
KMnxZn1-xF3, K2MnxZn1-xF4 and CsMnxMg1-xBr3, respectively, with x=0.10. The
resulting local exchange interactions deviate up to 10% from the average, and
the local Mn-Mn distances are found to vary stepwise with increasing internal
chemical pressure due to the Mn/Zn or Mn/Mg substitution. Our analysis
qualitatively supports the theoretically predicted decay of atomic
displacements according to 1/r**2, 1/r and constant (for three-, two- and
one-dimensional compounds, respectively) where r denotes the distance of the
displaced atoms from the defect.


  We prove that the following problem is decidable: given a finite set of
relations, decide whether this set admits a near-unanimity function.


  The efficiency of microscopic heat engines in a thermally heterogenous
environment is considered. We show that, as a consequence of the recently
discovered entropic anomaly, quasi-static engines, whose efficiency is maximal
in a fluid at uniform temperature, have in fact vanishing efficiency in
presence of temperature gradients. For slow cycles the efficiency falls off as
the inverse of the period. The maximum efficiency is reached at a finite value
of the cycle period that is inversely proportional to the square root of the
gradient intensity. The relative loss in maximal efficiency with respect to the
thermally homogeneous case grows as the square root of the gradient. As an
illustration of these general results, we construct an explicit, analytically
solvable example of a Carnot stochastic engine. In this thought experiment, a
Brownian particle is confined by a harmonic trap and immersed in a fluid with a
linear temperature profile. This example may serve as a template for the design
of real experiments in which the effect of the entropic anomaly can be
measured.


  The production of a single top quark in the $t$-channel and its subsequent
decay is studied at NLO accuracy in QCD, augmented with the relevant
dimension-6 effective operators from the Standard Model Effective Theory. We
examine various kinematic and angular distributions for proton-proton
collisions at the LHC at 13 TeV, in order to assess the sensitivity to these
operators, both with and without the top quark narrow width approximation. Our
results will be helpful when devising strategies to establish bounds on their
coefficients, including the amount of CP violation of the weak dipole operator.


  A mathematical model of figure-ground articulation is presented, taking into
account both local and global gestalt laws. The model is compatible with the
functional architecture of the primary visual cortex (V1). Particularly the
local gestalt law of good continuity is described by means of suitable
connectivity kernels, that are derived from Lie group theory and are neurally
implemented in long range connectivity in V1. Different kernels are compatible
with the geometric structure of cortical connectivity and they are derived as
the fundamental solutions of the Fokker Planck, the Sub-Riemannian Laplacian
and the isotropic Laplacian equations. The kernels are used to construct
matrices of connectivity among the features present in a visual stimulus.
Global gestalt constraints are then introduced in terms of spectral analysis of
the connectivity matrix, showing that this processing can be cortically
implemented in V1 by mean field neural equations. This analysis performs
grouping of local features and individuates perceptual units with the highest
saliency. Numerical simulations are performed and results are obtained applying
the technique to a number of stimuli.


  We study perturbatively the (conformal) WZNW model. At one loop we compute
one-particle irreducible two- and three-point current correlation functions,
both in the conventional version and in the classically equivalent, chiral,
nonlocal, induced version of the model. At two loops we compute the two-point
function and find that it vanishes (modulo infrared-induced logarithms). We use
dimensional regularization and the $R^*$ operation for removing infrared
divergences. The outcome of the calculations is insensitive to the treatment of
the $\varepsilon^{\m\n}$ tensor as a two-dimensional or $d$-dimensional object.
Our results indicate that the one-particle irreducible current correlation
functions constitute an effective action equal to the original WZNW action with
the familiar level shift, $k\to k+\tilde h$.


  This paper presents a robust secondary control strategy for frequency
synchronization and active power sharing for inverter-based microgrids. The
problem is addressed in a multi-agent fashion where the local controllers of
the distributed generators play the role of agents, and communication is
affected by time-varying delays. {The approach is fully distributed and based
on a synergic combination of linear consensus and integral sliding-mode
control. Lyapunovanalysis is presented to assess the stability properties of
the closed loop.} Delay-dependent stability conditions are expressed as a set
of linear matrix inequalities whose solution yields appropriate control gains
such that frequency restoration is achieved despite delays and active power
sharing constraints. Simulations confirm the effectiveness of the proposed
control strategy.


  Predicting user churn and taking personalized measures to retain users is a
set of common and effective practices for online game operators. However,
different from the traditional user churn relevant researches that can involve
demographic, economic, and behavioral data, most online games can only obtain
logs of user behavior and have no access to users' latent feelings. There are
mainly two challenges in this work: 1. The latent feelings, which cannot be
directly observed in this work, need to be estimated and verified; 2. User
churn needs to be predicted with only behavioral data. In this work, a
Recurrent Neural Network(RNN) called LaFee (Latent Feeling) is proposed, which
can get the users' latent feelings while predicting user churn. Besides, we
proposed a method named BMM-UCP (Behavior-based Modeling Method for User Churn
Prediction) to help models predict user churn with only behavioral data. The
latent feelings are names as satisfaction and aspiration in this work. We
designed experiments on a real dataset and the results show that our methods
outperform baselines and are more suitable for long-term sequential learning.
The latent feelings learned are fully discussed and proven meaningful.


  The main result can be given a short and elementary proof which has been
incorporated into Lemma 3.2 of arXiv:1206.5775


  While the axiomatic introduction of a probability distribution over a space
is common, its use for making predictions, using physical theories and prior
knowledge, suffers from a lack of formalization. We propose to introduce, in
the space of all probability distributions, two operations, the OR and the AND
operation, that bring to the space the necessary structure for making
inferences on possible values of physical parameters. While physical theories
are often asumed to be analytical, we argue that consistent inference needs to
replace analytical theories by probability distributions over the parameter
space, and we propose a systematic way of obtaining such "theoretical
correlations", using the OR operation on the results of physical experiments.
Predicting the outcome of an experiment or solving "inverse problems" are then
examples of the use of the AND operation. This leads to a simple and complete
mathematical basis for general physical inference.


  We show that all primitive idempotents for the Brauer algebra B_n(w) can be
found by evaluating a rational function in several variables which has the form
of a product of R-matrix type factors. This provides an analogue of the fusion
procedure for B_n(w).


  The observation of lepton flavour violation (LFV) in the charged lepton
sector would be an unambiguous sign of physics beyond the Standard Model (BSM),
and thus, it is the channel of choice for many BSM searches. LFV searches in
muon decays in particular benefit from the fact that muons can be easily
produced at high rates. There is a global effort to search for LFV at
high-intensity muon sources to which the upcoming Mu3e experiment at the Paul
Scherrer Institute (PSI) will contribute. The Mu3e Collaboration aims to
perform a background-free search for the LFV decay $\mu^+\to e^+e^-e^+$ with an
unprecedented sensitivity in the order of $10^{-15}$ in the first phase of
operation and $10^{-16}$ in the final phase - an improvement over the preceding
SINDRUM experiment by four orders of magnitude. The high muon stopping rates
and low momenta of the decay electrons make high demands on momentum and time
resolution and on the data acquisition. The innovative experimental concept is
based on a tracking detector built from novel ultra-thin silicon pixel sensors
and scintillating fibres and tiles as well as online event reconstruction and
filtering in real time.


  The Dark Matter Particle Explorer (DAMPE) recently released measurements of
the electron spectrum with a hint of a narrow peak at about 1.4 TeV. We
investigate dark matter (DM) models that could produce such a signal by
annihilation in a nearby subhalo whilst simultaneously satisfying constraints
from DM searches. In our model-independent approach, we consider all
renormalizable interactions via a spin 0 or 1 mediator between spin 0 or 1/2 DM
particles and the Standard Model leptons. We find that of the 20 combinations,
10 are ruled out by velocity or helicity suppression of the annihilation cross
section to fermions. The remaining 10 models, though, evade constraints from
the relic density, collider and direct detection searches, and include models
of spin 0 and 1/2 DM coupling to a spin 0 or 1 mediator. We delineate the
regions of mediator mass and couplings that could explain the DAMPE excess. In
all cases the mediator is required to be heaver than about 2 TeV by LEP limits.


  Fourier transform deep level transient spectroscopy has been performed
between 80 K and 550 K in five n-type ZnO samples grown by different
techniques. The capture cross section and ionization energy of four electron
traps have been deduced from Arrhenius diagrams. A trap 1 eV below the
conduction band edge is systematically observed in the five samples with a
large apparent capture cross section for electrons (1.6 $\pm$ 0.4 x 10 - 13
cm2) indicating a donor character. The assignment of this deep level to the
oxygen vacancy is discussed on the basis of available theoretical predictions.


  In this paper we show that a conjecture of Stephen Yau on highest weights of
invariant Jacobians is true for arbitrary connected semisimple algebraic
groups.


  We propose that the kinematical observations of dwarf galaxies can be used to
constrain the primordial black hole's (PBH) abundance in dark matter since the
presence of primordial black holes in star clusters will lead to the radial
velocity dispersion of the system. For instance, using the velocity dispersion
observations from Leo I we show that the primordial black hole fraction $f_{\rm
PBH}\gtrsim 2.0\times(1~M_{\odot}/m_{\rm PBH})^2$ is ruled out at a 99.99\%
confidence level. This method yields the most stringent limits on the PBH
abundance at the mass scales $\sim (1-10^3)~M_{\odot}$ and tightly constrains
the primordial origin of gravitational wave events observed by the LIGO
experiments.


  The longitudinal proton structure function (FL) has been measured at the HERA
collider in positron-proton deep inelastic scattering collisions with the H1
and ZEUS detectors. This measurement is achieved by using multiple
center-of-mass energies (sqrt(s)) via a reduction of the proton beam energy.
The energies used for this measurement are sqrt(s) = 318, 251, 225 GeV. The
kinematic region studied is 2.5<Q^2<800 GeV^2. H1 and ZEUS data have been
combined to increase statistical precision and reduce systematic effects. The
impact of the low proton beam energy cross sections to the proton PDFs is being
investigated.


  Disordered topological insulator (TI) films have gained intense interest by
benefiting from both the TIs exotic transport properties and the advantage of
mass production by sputtering. Here, we report on the clear evidence of
spin-charge conversion (SCC) in amorphous Gd-alloyed BixSe1-x (BSG)/CoFeB
bilayers fabricated by sputtering, which could be related to the amorphous TI
surface states. Two methods have been employed to study SCC in BSG/CoFeB(5 nm)
bilayers with different BSG thicknesses. Firstly, spin pumping is used to
generate a spin current in CoFeB and to detect SCC by inverse Edelstein effect.
The maximum SCC efficiency (SCE) is measured as large as 0.035 nm in a 6 nm
thick BSG sample, which shows a strong decay when tBSG increases due to the
increase of BSG surface roughness. The second method is the THz time-domain
spectroscopy, which reveals a small tBSG dependence of SCE, validating the
occurrence of a pure interface state related SCC. Furthermore, our
angle-resolved photoemission spectroscopy data show dispersive two-dimensional
surface states that cross the bulk gap until to the Fermi level, strengthening
the possibility of SCC due to the amorphous TI states. Our studies provide a
new experimental direction towards the search for topological systems in the
amorphous solids.


  In the framework of quantum thermodynamics, we propose a method to
quantitatively describe thermodynamic quantities for out-of-equilibrium
interacting many-body systems. The method is articulated in various
approximation protocols which allow to achieve increasing levels of accuracy,
it is relatively simple to implement even for medium and large number of
interactive particles, and uses tools and concepts from density functional
theory. We test the method on the driven Hubbard dimer at half filling, and
compare exact and approximate results. We show that the proposed method
reproduces the average quantum work to high accuracy: for a very large region
of parameter space (which cuts across all dynamical regimes) estimates are
within 10% of the exact results.


  Convex optimization over the spectrahedron, i.e., the set of all real
$n\times n$ positive semidefinite matrices with unit trace, has important
applications in machine learning, signal processing and statistics, mainly as a
convex relaxation for optimization problems with low-rank matrices. It is also
one of the most prominent examples in the theory of first-order methods for
convex optimization in which non-Euclidean methods can be significantly
preferable to their Euclidean counterparts. In particular, the desirable choice
is the Matrix Exponentiated Gradient (MEG) method which is based on the Bregman
distance induced by the (negative) von Neumann entropy. Unfortunately,
implementing MEG requires a full SVD computation on each iteration, which is
not scalable to high-dimensional problems. In this work we propose an efficient
implementations of MEG, both with deterministic and stochastic gradients, which
are tailored for optimization with low-rank matrices, and only use a single
low-rank SVD computation on each iteration. We also provide
efficiently-computable certificates for the correct convergence of our methods.
Mainly, we prove that under a strict complementarity condition, the suggested
methods converge from a ``warm-start" initialization with similar rates to
their full-SVD-based counterparts. Finally, we bring empirical experiments
which both support our theoretical findings and demonstrate the practical
appeal of our methods.


  In heterogeneous distributed computing (HC) systems, diversity can exist in
both computational resources and arriving tasks. In an inconsistently
heterogeneous computing system, task types have different execution times on
heterogeneous machines. A method is required to map arriving tasks to machines
based on machine availability and performance, maximizing the number of tasks
meeting deadlines (defined as robustness). For tasks with hard deadlines (eg
those in live video streaming), tasks that miss their deadlines are dropped.
The problem investigated in this research is maximizing the robustness of an
oversubscribed HC system. A way to maximize this robustness is to prune (ie
defer or drop) tasks with low probability of meeting their deadlines to
increase the probability of other tasks meeting their deadlines. In this paper,
we first provide a mathematical model to estimate a task's probability of
meeting its deadline in the presence of task dropping. We then investigate
methods for engaging probabilistic dropping and we find thresholds for dropping
and deferring. Next, we develop a pruning-aware mapping heuristic and extend it
to engender fairness across various task types. We show the cost benefit of
using probabilistic pruning in an HC system. Simulation results, harnessing a
selection of mapping heuristics, show efficacy of the pruning mechanism in
improving robustness (on average by 25%) and cost in an oversubscribed HC
system by up to 40%.


  The $P$-partition generating function of a (naturally labeled) poset $P$ is a
quasisymmetric function enumerating order-preserving maps from $P$ to
$\mathbb{Z}^+$. Using the Hopf algebra of posets, we give necessary conditions
for two posets to have the same generating function. In particular, we show
that they must have the same number of antichains of each size, as well as the
same shape (as defined by Greene). We also discuss which shapes guarantee
uniqueness of the $P$-partition generating function and give a method of
constructing pairs of non-isomorphic posets with the same generating function.


  We propose an original adaptive wavefront holographic setup based on the
photorefractive effect (PR), to make real-time measurements of acousto-optic
signals in thick scattering media, with a high flux collection at high rates
for breast tumor detection. We describe here our present state of art and
understanding on the problem of breast imaging with PR detection of the
acousto-optic signal.


  The ability to semantically interpret hand-drawn line sketches, although very
challenging, can pave way for novel applications in multimedia. We propose
SketchParse, the first deep-network architecture for fully automatic parsing of
freehand object sketches. SketchParse is configured as a two-level fully
convolutional network. The first level contains shared layers common to all
object categories. The second level contains a number of expert sub-networks.
Each expert specializes in parsing sketches from object categories which
contain structurally similar parts. Effectively, the two-level configuration
enables our architecture to scale up efficiently as additional categories are
added. We introduce a router layer which (i) relays sketch features from shared
layers to the correct expert (ii) eliminates the need to manually specify
object category during inference. To bypass laborious part-level annotation, we
sketchify photos from semantic object-part image datasets and use them for
training. Our architecture also incorporates object pose prediction as a novel
auxiliary task which boosts overall performance while providing supplementary
information regarding the sketch. We demonstrate SketchParse's abilities (i) on
two challenging large-scale sketch datasets (ii) in parsing unseen,
semantically related object categories (iii) in improving fine-grained
sketch-based image retrieval. As a novel application, we also outline how
SketchParse's output can be used to generate caption-style descriptions for
hand-drawn sketches.


  Deep reinforcement learning methods have been widely used in recent years for
autonomous vehicle's decision-making. A key issue is that deep neural networks
can be fragile to adversarial attacks or other unseen inputs. In this paper, we
address the latter issue: we focus on generating socially acceptable
perturbations (SAP), so that the autonomous vehicle (AV agent), instead of the
challenging vehicle (attacker), is primarily responsible for the crash. In our
process, one attacker is added to the environment and trained by deep
reinforcement learning to generate the desired perturbation. The reward is
designed so that the attacker aims to fail the AV agent in a socially
acceptable way. After training the attacker, the agent policy is evaluated in
both the original naturalistic environment and the environment with one
attacker. The results show that the agent policy which is safe in the
naturalistic environment has many crashes in the perturbed environment.


  Achieving efficient and stable ultraviolet emission is a challenging goal in
optoelectronic devices. Herein, we investigate the UV luminescence of zinc
germanate Zn2GeO4 microwires by means of photoluminescence measurements as a
function of temperature and excitation conditions. The emitted UV light is
composed of two bands (a broad one and a narrow one) associated with the native
defects structure. In addition, with the aid of density functional theory (DFT)
calculations, the energy positions of the electronic levels related to native
defects in Zn2GeO4 have been calculated. In particular, our results support
that zinc interstitials are the responsible for the narrow UV band, which is,
in turn, split into two components with different temperature dependence
behaviour. The origin of the two components is explained on the basis of the
particular location of Zn_i in the lattice and agrees with DFT calculations.
Furthermore, a kinetic luminescence model is proposed to ascertain the
temperature evolution of this UV emission. These results pave the way to
exploit defect engineering in achieving functional optoelectronic devices to
operate in the UV region.


  We study the dynamics of the relative phase following the connection of two
independently formed Bose-Einstein condensates. Dissipation is assumed to be
due to the creation of quasiparticles induced by a fluctuating condensate
particle number. The coherence between different values of the phase, which is
characteristic of the initial Fock state, is quickly lost after the net
exchange of a few atoms has taken place. This process effectively measures the
phase and marks the onset of a semiclassical regime in which the system
undergoes Bloch oscillations around the initial particle number. These fast
oscillations excite quasiparticles within each condensate and the system
relaxes at a longer time scale until it displays low-energy, damped Josephson
plasma oscillations, eventually coming to a halt when the equilibrium
configuration is finally reached.


  Experiments done with single photon in the early 1990's produced a surprising
result: that single photon pass through a photon tunnel barrier with a group
velocity faster than the vacuum speed of light. Recently, a series of
experiments revealed that electromagnetic wave was able to travel at a group
velocity faster than $c$. These phenomena have been observed in dispersive
media. We think all particles can be divided into three kinds in nature: The
first kind of particle is its velocity in the range of $0\leq v < c$, e.g.
electron, atom and so on. The second kind of particle is its velocity in the
range of $0\leq v < c_{m}$, e.g. photon. The third kind of particle is its
velocity in the range of $c\leq v < c_{m}$ ($c_{m}$ is the maximum velocity in
universe), e.g. tachyon. The first kind of particle is described by the special
relativity. In this paper, we give some new kinematic and dynamic equations to
describe the second and third kinds particles.


  Non-relativistic potential models are considered of the pure power V(r) =
sgn(q) r^q and logarithmic V(r) = ln(r) types. Envelope representations and
kinetic potentials are employed to show that these potentials are actually in a
single family. The log spectra can be obtained from the power spectra by the
limit q --> 0 taken in a smooth representation P_{n\ell}(q) for the eigenvalues
E_{n\ell}(q). A simple approximation formula is developed which yields the
first thirty eigenvalues with error < 0.04%. Extensions to potentials with
linear combinations of terms such as -a/r + br and applications to
spatially-symmetric few-body problems are discussed.


  We consider the effect of backreaction of quantized massive fields on the
metric of extreme black holes (EBH). We find the analytical approximate
expression for the stress-energy tensor for a scalar (with an arbitrary
coupling), spinor and vector fields near an event horizon. We show that,
independent of a concrete type of EBH, the energy measured by a freely falling
observer is finite on the horizon, so that quantum backreaction is consistent
with the existence of EBH. For the Reissner-Nordstrom EBH with a total mass
M_{tot} and charge Q we show that for all cases of physical interest M_{tot}<
Q. We also discuss different types of quantum-corrected Bertotti-Robinson
spacetimes, find for them exact self-consistent solutions and consider
situations in which tiny quantum corrections lead to the qualitative change of
the classical geometry and topology. In all cases one should start not from a
classical background with further adding quantum corrections but from the
quantum-corrected self-consistent geometries from the very beginning.


  We analyze the cosmic-ray variations during a significant Forbush decrease
observed with world-wide networks of ground-based neutron monitors and muon
detectors during November 3-5, 2021. Utilizing the difference between primary
cosmic-ray rigidities monitored by neutron monitors and muon detectors, we
deduce the rigidity spectra of the cosmic-ray density (or omnidirectional
intensity) and the first- and second-order anisotropies separately, for each
hour of data. A clear two-step decrease is seen in the cosmic-ray density with
the first $\sim2\%$ decrease after the interplanetary shock arrival followed by
the second $\sim5\%$ decrease inside the magnetic flux rope (MFR) at 15 GV.
Most strikingly, a large bidirectional streaming along the magnetic field is
observed in the MFR with a peak amplitude of $\sim5\%$ at 15 GV which is
comparable to the total density decrease inside the MFR. The bidirectional
streaming could be explained by adiabatic deceleration and/or focusing in the
expanding MFR, which have stronger effects for pitch angles near 90$^\circ$, or
by selective entry of GCRs along a leg of the MFR. The peak anisotropy and
density depression in the flux rope both decrease with increasing rigidity. The
spectra vary dynamically indicating that the temporal variations of density and
anisotropy appear different in neutron monitor and muon detector data.


  We recall the notion of nearest integer continued fractions over the
Euclidean imaginary quadratic fields $K$ and characterize the "badly
approximable" numbers, ($z$ such that there is a $C(z)>0$ with $|z-p/q|\geq
C/|q|^2$ for all $p/q\in K$), by boundedness of the partial quotients in the
continued fraction expansion of $z$. Applying this algorithm to "tagged"
indefinite integral binary Hermitian forms demonstrates the existence of entire
circles in $\mathbb{C}$ whose points are badly approximable over $K$, with
effective constants.
  By other methods (the Dani correspondence), we prove the existence of circles
of badly approximable numbers over any imaginary quadratic field, with loss of
effectivity. Among these badly approximable numbers are algebraic numbers of
every even degree over $\mathbb{Q}$, which we characterize. All of the examples
we consider are associated with cocompact Fuchsian subgroups of the Bianchi
groups $SL_2(\mathcal{O})$, where $\mathcal{O}$ is the ring of integers in an
imaginary quadratic field.


  When making decisions under risk, people often exhibit behaviors that
classical economic theories cannot explain. Newer models that attempt to
account for these irrational behaviors often lack neuroscience bases and
require the introduction of subjective and problem-specific constructs. Here,
we present a decision-making model inspired by the prediction error signals and
introspective neuronal replay reported in the brain. In the model, decisions
are chosen based on anticipated surprise, defined by a nonlinear average of the
differences between individual outcomes and a reference point. The reference
point is determined by the expected value of the possible outcomes, which can
dynamically change during the mental simulation of decision-making problems
involving sequential stages. Our model elucidates the contribution of each
stage to the appeal of available options in a decision-making problem. This
allows us to explain several economic paradoxes and gambling behaviors. Our
work could help bridge the gap between decision-making theories in economics
and neurosciences.


  Optimal transportation theory and the related $p$-Wasserstein distance
($W_p$, $p\geq 1$) are widely-applied in statistics and machine learning. In
spite of their popularity, inference based on these tools has some issues. For
instance, it is sensitive to outliers and it may not be even defined when the
underlying model has infinite moments. To cope with these problems, first we
consider a robust version of the primal transportation problem and show that it
defines the {robust Wasserstein distance}, $W^{(\lambda)}$, depending on a
tuning parameter $\lambda > 0$. Second, we illustrate the link between $W_1$
and $W^{(\lambda)}$ and study its key measure theoretic aspects. Third, we
derive some concentration inequalities for $W^{(\lambda)}$. Fourth, we use
$W^{(\lambda)}$ to define minimum distance estimators, we provide their
statistical guarantees and we illustrate how to apply the derived concentration
inequalities for a data driven selection of $\lambda$. Fifth, we provide the
{dual} form of the robust optimal transportation problem and we apply it to
machine learning problems (generative adversarial networks and domain
adaptation). Numerical exercises provide evidence of the benefits yielded by
our novel methods.


  Equipping agents with the capacity to justify made decisions using supporting
evidence represents a cornerstone of accountable decision-making. Furthermore,
ensuring that justifications are in line with human expectations and societal
norms is vital, especially in high-stakes situations such as healthcare. In
this work, we propose the use of a debate-based reward model for reinforcement
learning agents, where the outcome of a zero-sum debate game quantifies the
justifiability of a decision in a particular state. This reward model is then
used to train a justifiable policy, whose decisions can be more easily
corroborated with supporting evidence. In the debate game, two argumentative
agents take turns providing supporting evidence for two competing decisions.
Given the proposed evidence, a proxy of a human judge evaluates which decision
is better justified. We demonstrate the potential of our approach in learning
policies for prescribing and justifying treatment decisions of septic patients.
We show that augmenting the reward with the feedback signal generated by the
debate-based reward model yields policies highly favored by the judge when
compared to the policy obtained solely from the environment rewards, while
hardly sacrificing any performance. Moreover, in terms of the overall
performance and justifiability of trained policies, the debate-based feedback
is comparable to the feedback obtained from an ideal judge proxy that evaluates
decisions using the full information encoded in the state. This suggests that
the debate game outputs key information contained in states that is most
relevant for evaluating decisions, which in turn substantiates the practicality
of combining our approach with human-in-the-loop evaluations. Lastly, we
showcase that agents trained via multi-agent debate learn to propose evidence
that is resilient to refutations and closely aligns with human preferences.


  We present a comprehensive study of TeV black hole events in Earth's
atmosphere originated by cosmic rays of very high energy. An advanced fortran
Monte Carlo code is developed and used to simulate black hole extensive air
showers from ultrahigh-energy neutrino-nucleon interactions. We investigate the
characteristics of these events, compare the black hole air showers to standard
model air showers, and test different theoretical and phenomenological models
of black hole formation and evolution. The main features of black hole air
showers are found to be independent of the model considered. No significant
differences between models are likely to be observed at fluorescence telescopes
and/or ground arrays. We also discuss the tau ``double bang'' signature in
black hole air showers. We find that the energy deposited in the second bang is
too small to produce a detectable peak. Our results show that the theory of
TeV-scale black holes in ultrahigh-energy cosmic rays leads to robust
predictions, but the fine prints of new physics are hardly to be investigated
through atmospheric black hole events in the near future.


  We propose to estimate a metamodel and the sensitivity indices of a complex
model m in the Gaussian regression framework. Our approach combines methods for
sensitivity analysis of complex models and statistical tools for sparse
non-parametric estimation in multivariate Gaussian regression model. It rests
on the construction of a metamodel for aproximating the Hoeffding-Sobol
decomposition of m. This metamodel belongs to a reproducing kernel Hilbert
space constructed as a direct sum of Hilbert spaces leading to a functional
ANOVA decomposition. The estimation of the metamodel is carried out via a
penalized least-squares minimization allowing to select the subsets of
variables that contribute to predict the output. It allows to estimate the
sensitivity indices of m. We establish an oracle-type inequality for the risk
of the estimator, describe the procedure for estimating the metamodel and the
sensitivity indices, and assess the performances of the procedure via a
simulation study.


  Automatic meeting summarization is becoming increasingly popular these days.
The ability to automatically summarize meetings and to extract key information
could greatly increase the efficiency of our work and life. In this paper, we
experiment with different approaches to improve the performance of query-based
meeting summarization. We started with HMNet\cite{hmnet}, a hierarchical
network that employs both a word-level transformer and a turn-level
transformer, as the baseline. We explore the effectiveness of pre-training the
model with a large news-summarization dataset. We investigate adding the
embeddings of queries as a part of the input vectors for query-based
summarization. Furthermore, we experiment with extending the
locate-then-summarize approach of QMSum\cite{qmsum} with an intermediate
clustering step. Lastly, we compare the performance of our baseline models with
BART, a state-of-the-art language model that is effective for summarization. We
achieved improved performance by adding query embeddings to the input of the
model, by using BART as an alternative language model, and by using clustering
methods to extract key information at utterance level before feeding the text
into summarization models.


  The complexity underlying real-world systems implies that standard
statistical hypothesis testing methods may not be adequate for these peculiar
applications. Specifically, we show that the likelihood-ratio test's
null-distribution needs to be modified to accommodate the complexity found in
multi-edge network data. When working with independent observations, the
p-values of likelihood-ratio tests are approximated using a $\chi^2$
distribution. However, such an approximation should not be used when dealing
with multi-edge network data. This type of data is characterized by multiple
correlations and competitions that make the standard approximation unsuitable.
We provide a solution to the problem by providing a better approximation of the
likelihood-ratio test null-distribution through a Beta distribution. Finally,
we empirically show that even for a small multi-edge network, the standard
$\chi^2$ approximation provides erroneous results, while the proposed Beta
approximation yields the correct p-value estimation.


  Automatic prediction of amyotrophic lateral sclerosis (ALS) disease
progression provides a more efficient and objective alternative than manual
approaches. We propose ALS longitudinal speech transformer (ALST), a neural
network-based automatic predictor of ALS disease progression from longitudinal
speech recordings of ALS patients. By taking advantage of high-quality
pretrained speech features and longitudinal information in the recordings, our
best model achieves 91.0\% AUC, improving upon the previous best model by 5.6\%
relative on the ALS TDI dataset. Careful analysis reveals that ALST is capable
of fine-grained and interpretable predictions of ALS progression, especially
for distinguishing between rarer and more severe cases. Code is publicly
available.


  The Relativistic Heavy Ion Collider (RHIC) facility at Brookhaven National
Laboratory is the first accelerator specifically constructed for the study of
very hot and dense nuclear matter. At sufficiently high temperature, nuclear
matter is expected to undergo a phase transition to a quark-gluon plasma. It is
the specific goal of the field to study the nature of this plasma and
understand the phase transitions between different states. The RHIC accelerator
along with four experiments BRAHMS, PHENIX, PHOBOS, and STAR were commissioned
last year with first collisions occurring in June 2000. Presented here are the
first results from low luminosity beam in Run I. They are a glimpse of the
wealth of physics to be extracted from the RHIC program over the next several
years.


  Driver attention prediction is currently becoming the focus in safe driving
research community, such as the DR(eye)VE project and newly emerged Berkeley
DeepDrive Attention (BDD-A) database in critical situations. In safe driving,
an essential task is to predict the incoming accidents as early as possible.
BDD-A was aware of this problem and collected the driver attention in
laboratory because of the rarity of such scenes. Nevertheless, BDD-A focuses
the critical situations which do not encounter actual accidents, and just faces
the driver attention prediction task, without a close step for accident
prediction. In contrast to this, we explore the view of drivers' eyes for
capturing multiple kinds of accidents, and construct a more diverse and larger
video benchmark than ever before with the driver attention and the driving
accident annotation simultaneously (named as DADA-2000), which has 2000 video
clips owning about 658,476 frames on 54 kinds of accidents. These clips are
crowd-sourced and captured in various occasions (highway, urban, rural, and
tunnel), weather (sunny, rainy and snowy) and light conditions (daytime and
nighttime). For the driver attention representation, we collect the maps of
fixations, saccade scan path and focusing time. The accidents are annotated by
their categories, the accident window in clips and spatial locations of the
crash-objects. Based on the analysis, we obtain a quantitative and positive
answer for the question in this paper.


  We present radiative transfer models of the circumstellar environment of
classical T Tauri stars, concentrating on the formation of the H-alpha
emission. The wide variety of line profiles seen in observations are indicative
of both inflow and outflow, and we therefore employ a circumstellar structure
that includes both magnetospheric accretion and a disc wind. We perform
systematic investigations of the model parameters for the wind and the
magnetosphere to search for possible geometrical and physical conditions which
lead to the types of profiles seen in observations. We find that the hybrid
models can reproduce the wide range of profile types seen in observations, and
that the most common profile types observed occupy a large volume of parameter
space. Conversely, the most infrequently observed profile morphologies require
a very specific set of models parameters. We find our model profiles are
consistent with the canonical value of the mass-loss rate to mass-accretion
rate ratio (mu=0.1) found in earlier magneto-hydrodynamic calculations and
observations, but the models with 0.05<mu<0.2 are still in accord with observed
H-alpha profiles. We investigate the wind contribution to the line profile as a
function of model parameters, and examine the reliability of H-alpha as a mass
accretion diagnostic. Finally, we examine the H-alpha spectroscopic
classification used by Reipurth et. al, and discuss the basic physical
conditions that are required to reproduce the profiles in each classified type.


  Let F be the complete flag variety over Spec(Z) with the tautological
filtration 0 \subset E_1 \subset E_2 \subset ... \subset E_n=E of the trivial
bundle E over F. The trivial hermitian metric on E(\C) induces metrics on the
quotient line bundles L_i(\C). Let \hat{c}_1(L_i) be the first Chern class of
L_i in the arithmetic Chow ring \hat{CH}(F) and x_i = -\hat{c}_1(L_i). Let
h(X_1,...,X_n) be a polynomial with integral coefficients in the ideal
<e_1,...,e_n> generated by the elementary symmetric polynomials e_i. We give an
effective algorithm for computing the arithmetic intersection h(x_1,...,x_n) in
\hat{CH}(F), as the class of a SU(n)-invariant differential form on F(\C). In
particular we show that all the arithmetic Chern numbers one obtains are
rational numbers. The results are true for partial flag varieties and
generalize those of Maillot for grassmannians. An `arithmetic Schubert
calculus' is established for an `invariant arithmetic Chow ring' which
specializes to the Arakelov Chow ring in the grassmannian case.


  We study entropy-regularized constrained Markov decision processes (CMDPs)
under the soft-max parameterization, in which an agent aims to maximize the
entropy-regularized value function while satisfying constraints on the expected
total utility. By leveraging the entropy regularization, our theoretical
analysis shows that its Lagrangian dual function is smooth and the Lagrangian
duality gap can be decomposed into the primal optimality gap and the constraint
violation. Furthermore, we propose an accelerated dual-descent method for
entropy-regularized CMDPs. We prove that our method achieves the global
convergence rate $\widetilde{\mathcal{O}}(1/T)$ for both the optimality gap and
the constraint violation for entropy-regularized CMDPs. A discussion about a
linear convergence rate for CMDPs with a single constraint is also provided.


  Many software engineering tasks, such as testing, and anomaly detection can
benefit from the ability to infer a behavioral model of the software.Most
existing inference approaches assume access to code to collect execution
sequences. In this paper, we investigate a black-box scenario, where the system
under analysis cannot be instrumented, in this granular fashion.This scenario
is particularly prevalent with control systems' log analysis in the form of
continuous signals. In this situation, an execution trace amounts to a
multivariate time-series of input and output signals, where different states of
the system correspond to different `phases` in the time-series. The main
challenge is to detect when these phase changes take place. Unfortunately, most
existing solutions are either univariate, make assumptions on the data
distribution, or have limited learning power.Therefore, we propose a hybrid
deep neural network that accepts as input a multivariate time series and
applies a set of convolutional and recurrent layers to learn the non-linear
correlations between signals and the patterns over time.We show how this
approach can be used to accurately detect state changes, and how the inferred
models can be successfully applied to transfer-learning scenarios, to
accurately process traces from different products with similar execution
characteristics. Our experimental results on two UAV autopilot case studies
indicate that our approach is highly accurate (over 90% F1 score for state
classification) and significantly improves baselines (by up to 102% for change
point detection).Using transfer learning we also show that up to 90% of the
maximum achievable F1 scores in the open-source case study can be achieved by
reusing the trained models from the industrial case and only fine tuning them
using as low as 5 labeled samples, which reduces the manual labeling effort by
98%.


  In this paper, we introduce the generalised Knutson Index and compute it for
the special linear groups and projective special linear groups of degree two by
computing the lowest common multiple of the degrees of their irreducible
representations. We also classify all alternating and symmetric groups such
that the lowest common multiple of the degrees of their irreducible
representations equals the order groups, which yields a lower bound on the
generalised Knutson Indices of these groups.


  In this work, we study the effect of doping the Rutile-type dioxide SnO2
material by the non-metal Sulfur (S) atoms on the electronic properties. In
fact, we have used the ab-initio method applied on the basis of the Density
Functional Theory (DFT) using the Quantum Espresso code. Through the density of
states and the band structure calculations for different concentrations have
been deduced. When doping the SnO2 material with 6% of Sulfur (S), we found a
perfect symmetry between up and down spin states in the total DOS confirming
the non-magnetic behavior of this material doped SnO2 with 6% of Sulfur. It is
also worth to note that the SnO2 material doped with 6% of Sulfur, exhibits a
semiconductor of the P-type. Moreover the band gap decreases when increasing
the concentration of doping the SnO2 by Sulfur. Our results are in good
agreement with the existing literature both experimental and theoretical.