  Swarming patterns that emerge from the interaction of many mobile agents are
a subject of great interest in fields ranging from biology to physics and
robotics. In some application areas, multiple swarms effectively interact and
collide, producing complex spatiotemporal patterns. Recent studies have begun
to address swarm-on-swarm dynamics, and in particular the scattering of two
large, colliding swarms with nonlinear interactions. To build on early
numerical insights, we develop a mean-field approach that can be used to
predict the parameters under which colliding swarms are expected to form a
milling state. Our analytical method relies on the assumption that, upon
collision, two swarms oscillate near a limit-cycle, where each swarm rotates
around the other while maintaining an approximately constant and uniform
density. Using this approach we are able to predict the critical swarm-on-swarm
interaction coupling, below which two colliding swarms merely scatter, for near
head-on collisions as a function of control parameters. We show that the
critical coupling corresponds to a saddle-node bifurcation of a stable limit
cycle in the uniform, constant density approximation. Our results are tested
and found to agree with large multi-agent simulations.


  Gravitational scattering in the ADD-model is studied and it is argued that no
cut-off is needed for the exchange of virtual Kaluza--Klein modes. By
introduction of a small coordinate in the extra dimensions a unique form of the
Kaluza--Klein-summed propagator is found for an odd number of extra dimensions.
The matrix element corresponding to this propagator can also (as opposed to the
cut-offed version) be Fourier transformed to position space, giving back the
extra-dimensional version of Newton's law. For an even number of extra
dimensions the propagator is found by requiring that Newton's law should be
recovered.


  We present an advance towards accurately predicting the arrivals of coronal
mass ejections (CMEs) at the terrestrial planets, including Earth. For the
first time, we are able to assess a CME prediction model using data over 2/3 of
a solar cycle of observations with the Heliophysics System Observatory. We
validate modeling results of 1337 CMEs observed with the Solar Terrestrial
Relations Observatory (STEREO) heliospheric imagers (HI) (science data) from 8
years of observations by 5 in situ observing spacecraft. We use the
self-similar expansion model for CME fronts assuming 60 degree longitudinal
width, constant speed and constant propagation direction. With these
assumptions we find that 23%-35% of all CMEs that were predicted to hit a
certain spacecraft lead to clear in situ signatures, so that for 1 correct
prediction, 2 to 3 false alarms would have been issued. In addition, we find
that the prediction accuracy does not degrade with the HI longitudinal
separation from Earth. Predicted arrival times are on average within 2.6 +/-
16.6 hours difference of the in situ arrival time, similar to analytical and
numerical modeling, and a true skill statistic of 0.21. We also discuss various
factors that may improve the accuracy of space weather forecasting using
wide-angle heliospheric imager observations. These results form a first order
approximated baseline of the prediction accuracy that is possible with HI and
other methods used for data by an operational space weather mission at the
Sun-Earth L5 point.


  We study the electron/hole transport in puddle-disordered and rough graphene
samples which are subject to in-plane magnetic fields. Previous treatments,
mostly devoted to regimes where the electron/hole scattering wavelengths are
larger than the surface height correlation length, are based on the use of
transport equations with appropriate forms for the collision term. We point out
in this work, as a counterpoint, that classical Lorentz force effects, which
are expected to hold when the Fermi level is far enough away from the charge
neutral point, can be heuristically assessed through disordered Boltzmann
equations that contain magnetic-field dependent material derivatives, and keep
the zero magnetic-field structure of the collision term. It turns out that the
electric conductivity tensor gets a peculiar nondiagonal component, induced by
the in-plane magnetic field that crosses the rough topography of the graphene
sheet, even if the projected random transverse magnetic field vanishes in the
mean. Numerical estimates of the transverse conductivities suggest that they
are suitable of observation under conditions which are within the reach of
up-to-date experimental methods.


  We present a simple model of inactivation decoding for LT codes which can be
used to estimate the decoding complexity as a function of the LT code degree
distribution. The model is shown to be accurate in variety of settings of
practical importance. The proposed method allows to perform a numerical
optimization on the degree distribution of a LT code aiming at minimizing the
number of inactivations required for decoding.


  It is expected that the Gross-Neveu-Yukawa (GNY) chiral Ising transition of
$N$ Majorana (or $N_f=N/4$ four-component Dirac) fermions coupled with scalar
field in (2+1)D will be the first fermionic quantum critical point that various
methods such as conformal bootstrap [1], perturbative renormalization group [2]
and quantum Monte Carlo (QMC) simulations [3], would yield the converged
critical exponents -- serving the same textbook role as the Ising and $O(N)$
models in the statistical and quantum phase transition. However, such
expectation has not been fully realized from the lattice QMC simulations due to
the obstacles introduced by the UV finite size effect. In this work, by means
of the elective-momentum ultra-size (EMUS) QMC method [4], we compute the
critical exponents of the GNY $N=8$ chiral Ising transition on a 2D $\pi$-flux
fermion lattice model between Dirac semimetal and quantum spin Hall insulator
phases [3, 5]. With the matching of fermionic and bosonic momentum transfer and
collective update in momentum space, our QMC results provide the fully
consistent exponents with those obtained from the bootstrap and perturbative
approaches. In this way, the Emus now live happily on the $N=8$ island and
could explore the Gross-Neveu-Yukawa archipelago [1] with ease.


  The Wigner function of a finite-dimensional system can be constructed via
dual pairing of a density matrix with the Stratonovich-Weyl kernel. Following
Kenfack and $\dot{\text{Z}}$yczkowski, we consider the indicator of
nonclassicality of a finite-dimensional quantum system which depends on the
volume of the negative part of the Wigner function. This indicator is defined
over the unitary non-equivalent classes of quantum states, i.e. represents an
invariant, but since for a given quantum system there is no unique Wigner
function it turns to be sensitive to the choice of representations for the
Wigner function. Based on the explicit parameterization of the moduli space of
the Wigner functions, we compute the corresponding
Kenfack-$\dot{\text{Z}}$yczkowski indicators of a 3-level system for
degenerate, unitary non-equivalent Stratonovich-Weyl kernels.


  We examine Gaussian-basis Monte Carlo method (GBMC) introduced by Corney and
Drummond. This method is based on an expansion of the density-matrix operator
rho by means of the coherent Gaussian-type operator basis Lambda and does not
suffer from the minus sign problem. The original method, however, often fails
in reproducing the true ground state and causes systematic errors of calculated
physical quantities because the samples are often trapped in some metastable or
symmetry broken states. To overcome this difficulty, we combine the
quantum-number projection scheme proposed by Assaad, Werner, Corboz, Gull and
Troyer in conjunction with the importance sampling of the original GBMC method.
This improvement allows us to carry out the importance sampling in the
quantum-number-projected phase-space. Some comparisons with the previous
quantum-number projection scheme indicate that, in our method, the convergence
with the ground state is accelerated, which makes it possible to extend the
applicability and widen the range of tractable parameters in the GBMC method.
The present scheme offers an efficient practical way of computation for
strongly correlated electron systems beyond the range of system sizes,
interaction strengths and lattice structures tractable by other computational
methods such as the quantum Monte Carlo method.


  Organic light emitting diodes (OLEDs) based on thermally activated delayed
fluorescence (TADF) can be highly efficient because of the conversion of
non-radiative triplet to radiative singlet states by reverse intersystem
crossing (RISC). Even highly efficient TADF OLEDs are limited by long excited
state lifetimes though, which limit current densities and cause device
degradation. When singlet-triplet energy gaps are comparable to thermal
energies (~tens of millielectronvolts), RISC is fast and limited only by
bottlenecks due to spin-selection rules. We have studied this phenomenon under
device operating conditions using pulsed electrically detected magnetic
resonance spectroscopy (pEDMR) in OLEDs based on the donor:acceptor combination
m MTDATA:BPhen (4,4',4''-tris[phenyl(m-tolyl)amino]triphenylamine : 4,7
diphenyl-1,10-phenanthroline). These experiments showed magnetic resonance
signatures of emissive exciplex states at the donor:acceptor interface, yet
these signals did not reveal coherent spin propagation effects. Instead, the
intensity of these signals scales linearly with the energy dose of the applied
microwave pulses. This observation excludes the direct involvement of the
resonantly prepared coherent spin states and indicates that the observed
current response is due to magnetic resonant heating. This implies that the
studied TADF blend is not limited by spin-forbidden RISC, but rather by the
thermal activation step.


  We give a necessary condition for a closed subset of $R^3$ to be the set of
critical points of some smooth function. In particular we obtain that for
example neither the Whitehead continuum nor the p-adic solenoid are such a
critical sets.


  A contemporary feed application usually provides blended results of organic
items and sponsored items~(ads) to users. Conventionally, ads are exposed at
fixed positions. Such a static exposure strategy is inefficient due to ignoring
users' personalized preferences towards ads. To this end, adaptive ad exposure
has become an appealing strategy to boost the overall performance of the feed.
However, existing approaches to implementing the adaptive ad exposure still
suffer from several limitations: 1) they usually fall into sub-optimal
solutions because of only focusing on request-level optimization without
consideration of the long-term application-level performance and constraints,
2) they neglect the necessity of keeping the game-theoretical properties of ad
auctions, which may lead to anarchy in bidding, and 3) they can hardly be
deployed in large-scale applications due to high computational complexity. In
this paper, we focus on long-term performance optimization under hierarchical
constraints in feeds and formulate the adaptive ad exposure as a Dynamic
Knapsack Problem. We propose an effective approach: Hierarchically Constrained
Adaptive Ad Exposure~(HCA2E). We present that HCA2E possesses desired
game-theoretical properties, computational efficiency, and performance
robustness. Comprehensive offline and online experiments on a leading
e-commerce application demonstrate the significant performance superiority of
HCA2E over representative baselines. HCA2E has also been deployed on this
application to serve millions of daily users.


  The implications of a QCD phase transition at high temperatures and densities
for core-collapse supernovae are discussed. For a strong first order phase
transition to quark matter, various scenarios have been put forward in the
literature. Here, detailed numerical simulations including neutrino transport
are presented, where it is found that a second shock wave due to the QCD phase
transition emerges shortly after bounce. It is demonstrated that such a
supernova banging twice results in a second peak in the antineutrino spectrum.
This second peak is clearly detectable in present neutrino detectors for a
galactic supernova.


  Phase estimation protocols provide a fundamental benchmark for the field of
quantum metrology. The latter represents one of the most relevant applications
of quantum theory, potentially enabling the capability of measuring unknown
physical parameters with improved precision over classical strategies. Within
this context, most theoretical and experimental studies have focused on
determining the fundamental bounds and how to achieve them in the asymptotic
regime where a large number of resources is employed. However, in most
applications it is necessary to achieve optimal precisions by performing only a
limited number of measurements. To this end, machine learning techniques can be
applied as a powerful optimization tool. Here, we implement experimentally
single-photon adaptive phase estimation protocols enhanced by machine learning,
showing the capability of reaching optimal precision after a small number of
trials. In particular, we introduce a new approach for Bayesian estimation that
exhibit best performances for very low number of photons N. Furthermore, we
study the resilience to noise of the tested methods, showing that the optimized
Bayesian approach is very robust in the presence of imperfections. Application
of this methodology can be envisaged in the more general multiparameter case,
that represents a paradigmatic scenario for several tasks including imaging or
Hamiltonian learning.


  High-efficiency point cloud 3D object detection operated on embedded systems
is important for many robotics applications including autonomous driving. Most
previous works try to solve it using anchor-based detection methods which come
with two drawbacks: post-processing is relatively complex and computationally
expensive; tuning anchor parameters is tricky. We are the first to address
these drawbacks with an anchor free and Non-Maximum Suppression free one stage
detector called AFDet. The entire AFDet can be processed efficiently on a CNN
accelerator or a GPU with the simplified post-processing. Without bells and
whistles, our proposed AFDet performs competitively with other one stage
anchor-based methods on KITTI validation set and Waymo Open Dataset validation
set.


  Selecting the optimal material for a part designed through topology
optimization is a complex problem. The shape and properties of the Pareto front
plays an important role in this selection. In this paper we show that the
compliance-volume fraction Pareto fronts of some topology optimization problems
in linear elasticity share some useful properties. These properties provide an
interesting point of view on the efficiency of topology optimization compared
to other design approaches such as parametric structural optimization. We
construct a simple meta-model which requires only one full topology
optimization to fit the whole Pareto fronts. Precise Pareto fronts are obtained
independently. The fast meta-model constructed has a maximum error of 6.4% with
respect to these precise Pareto fronts, on the different problems tested. The
selection of the optimal material is then successfully tested on the mass
minimization of an MBB beam with an illustrative choice of 4 materials.


  We study the transient response of an electrolytic cell subject to a small,
suddenly applied temperature increase at one of its two bounding electrode
surfaces. An inhomogeneous temperature profile then develops, causing, via the
Soret effect, ionic rearrangements towards a state of polarized ionic charge
density $q$ and local salt density $c$. For the case of equal cationic and
anionic diffusivities, we derive analytical approximations to $q, c$, and the
thermovoltage $V_{T}$ for early ($t\ll\tau_{T}$) and late ($t\gg\tau_{T}$)
times as compared to the relaxation time $\tau_{T}$ of the temperature. We
challenge the conventional wisdom that the typically large Lewis number, the
ratio $a/D$ of thermal to ionic diffusivities, of most liquids implies a
quickly reached steady-state temperature profile onto which ions relax slowly.
Though true for the evolution of $c$, it turns out that $q$ (and $V_{T}$) can
respond much faster. Particularly when the cell is much bigger than the Debye
length, a significant portion of the transient response of the cell falls in
the $t\ll\tau_{T}$ regime, for which our approximated $q$ (corroborated by
numerics) exhibits a density wave that has not been discussed before in this
context. For electrolytes with unequal ionic diffusivities, $V_{T}$ exhibits a
two-step relaxation process, in agreement with experimental data of Bonetti et
al. [J. Chem. Phys. 142, 244708 (2015)].


  Accurate pedestrian orientation estimation of autonomous driving helps the
ego vehicle obtain the intentions of pedestrians in the related environment,
which are the base of safety measures such as collision avoidance and
prewarning. However, because of relatively small sizes and high-level
deformation of pedestrians, common pedestrian orientation estimation models
fail to extract sufficient and comprehensive information from them, thus having
their performance restricted, especially monocular ones which fail to obtain
depth information of objects and related environment. In this paper, a novel
monocular pedestrian orientation estimation model, called FFNet, is proposed.
Apart from camera captures, the model adds the 2D and 3D dimensions of
pedestrians as two other inputs according to the logic relationship between
orientation and them. The 2D and 3D dimensions of pedestrians are determined
from the camera captures and further utilized through two feedforward links
connected to the orientation estimator. The feedforward links strengthen the
logicality and interpretability of the network structure of the proposed model.
Experiments show that the proposed model has at least 1.72% AOS increase than
most state-of-the-art models after identical training processes. The model also
has competitive results in orientation estimation evaluation on KITTI dataset.


  The semiclassical propagation of spin coherent states is considered in
complex phase space. For two time-independent systems we find the appropriate
classical trajectories and show that their combined contributions are able to
describe quantum interference with great accuracy. Not only the modulus but
also the phase of the quantum propagator, both dynamical and geometric terms
combined, are accurately reproduced.


  We give simply exponential lower bounds on the probabilities of a given
strongly Rayleigh distribution, depending only on its expectation. This
resolves a weak version of a problem left open by Karlin-Klein-Oveis Gharan in
their recent breakthrough work on metric TSP, and this resolution leads to a
minor improvement of their approximation factor for metric TSP. Our results
also allow for a more streamlined analysis of the algorithm.
  To achieve these new bounds, we build upon the work of Gurvits-Leake on the
use of the productization technique for bounding the capacity of a real stable
polynomial. This technique allows one to reduce certain inequalities for real
stable polynomials to products of affine linear forms, which have an underlying
matrix structure. In this paper, we push this technique further by
characterizing the worst-case polynomials via bipartitioned forests. This rigid
combinatorial structure yields a clean induction argument, which implies our
stronger bounds.
  In general, we believe the results of this paper will lead to further
improvement and simplification of the analysis of various combinatorial and
probabilistic bounds and algorithms.


  X-ray computed tomographic infrastructures are medical imaging modalities
that rely on the acquisition of rays crossing examined objects while measuring
their intensity decrease. Physical measurements are post-processed by
mathematical reconstruction algorithms that may offer weaker or top-notch
consistency guarantees on the computed volumetric field. Superior results are
provided on the account of an abundance of low-noise measurements being
supplied. Nonetheless, such a scanning process would expose the examined body
to an undesirably large-intensity and long-lasting ionising radiation, imposing
severe health risks. One main objective of the ongoing research is the
reduction of the number of projections while keeping the quality performance
stable. Due to the under-sampling, the noise occurring inherently because of
photon-electron interactions is now supplemented by reconstruction artifacts.
Recently, deep learning methods, especially fully convolutional networks have
been extensively investigated and proven to be efficient in filtering such
deviations. In this report algorithms are presented that take as input a slice
of a low-quality reconstruction of the volume in question and aim to map it to
the reconstruction that is considered ideal, the ground truth. Above that, the
first system comprises two additional elements: firstly, it ensures the
consistency with the measured sinogram, secondly it adheres to constraints
proposed in classical compressive sampling theory. The second one, inspired by
classical ways of solving the inverse problem of reconstruction, takes an
iterative approach to regularise the hypothesis in the direction of the correct
result.


  We propose a string theory construction for the system of two heavy quarks
and two light antiquarks. The potential of the system is a function of
separation between the quarks. We define a critical separation distance below
which the system can be thought of mainly as a compact tetraquark. The results
show the universality of the string tension and factorization at small
separations expected from heavy quark-diquark symmetry. Our estimate of the
screening length is in the range of lattice QCD. We also make a comparison with
the potential of the $QQq$ system. The potentials look very similar at small
quark separations but at larger separations they differ. The reason for this is
that the flattening of the potentials happens at two well-separated scales as
follows from the two different mechanisms: string breaking by light quarks for
$QQq$ and string junction annihilation for $QQ\bar q\bar q$. Moreover, a
similar construction can also be applied to the $\bar Q\bar Q qq$ system.


  The Faulkes Telescope Project is an educational and research arm of the Las
Cumbres Observatory Global Telescope Network (LCOGTN). It has two 2-metre
robotic telescopes, located at Haleakala on Maui (FT North) and Siding Spring
in Australia (FT South). It is planned for these telescopes to be complemented
by a research network of eighteen 1-metre telescopes, along with an educational
network of twenty-eight 0.4-metre telescopes, providing 24 hour coverage of
both northern and southern hemispheres.
  We have been conducting a monitoring project of 13 low-mass X-ray binaries
(LMXBs) using FT North since early 2006. The introduction of FT South has
allowed us to extend this to monitor a total of 30 LMXBs. New instrumentation
will allow us to expand this project to include both infrared wavelengths (z
and y band) and spectroscopy. Brighter targets (~ 16 - 18 mag.) are imaged
weekly in V, R and i' band (SNR ~ 50), while fainter ones (> 18 mag.) are
observed only in i' band (SNR ~ 20). We alter this cadence in response to our
own analysis or Astronomers Telegrams (ATels).


  We establish a hierarchy of quantum dilogarithm identities associated to a
sequence of triangular shaped quivers. The tetrahedron equation plays a key
role in our construction.


  We present a model for the squeezed dark matter bispectrum, where the short
modes are deep in the non-linear regime. We exploit the consistency relations
for large-scale structures combined with a response function approach to write
the squeezed bispectrum in terms of a few unknown functions of the short modes.
We provide an ansatz for a fitting function for these response functions,
checking that the resulting model is reliable when compared to the one-loop
squeezed bispectrum. We then test the model against measured bispectra from
numerical simulations for short modes ranging between $k \sim 0.1 \, h/$Mpc,
and $k \sim 0.7 \, h/$Mpc at redshift $z=0$. To evaluate the goodness of the
fit of our model we implement a non-Gaussian covariance and find agreement
within $1$-$\sigma$ standard deviation of the simulated data.


  Resource leak bugs in Android apps are pervasive and can cause serious
performance degradation and system crashes. In recent years, several resource
leak detection techniques have been proposed to assist Android developers in
correctly managing system resources. Yet, there exist no common bug benchmarks
for effectively and reliably comparing such techniques and quantitatively
evaluating their strengths and weaknesses. This paper describes our initial
contribution towards constructing such a benchmark. To locate real resource
leak bugs, we mined 124,215 code revisions of 34 large-scale open-source
Android apps. We successfully found 298 fixed resource leaks, which cover a
diverse set of resource classes, from 32 out of the 34 apps. To understand the
characteristics of these bugs, we conducted an empirical study, which revealed
the root causes of frequent resource leaks in Android apps and common patterns
of faults made by developers. With our findings, we further implemented a
static checker to detect a common pattern of resource leaks in Android apps.
Experiments showed that the checker can effectively locate real resource leaks
in popular Android apps, confirming the usefulness of our work.


  The aim of this study is to probe the sub-mJy polarized source population
with LOFAR. We present the method used to stack LOFAR polarization datasets,
the resulting catalog of polarized sources, and the derived polarized source
counts. The ELAIS-N1 field was selected for a polarimetric study at 114.9-177.4
MHz. A total area of 25 deg2 was imaged at 6"- resolution in the Stokes Q and U
parameters. Alignment of polarization angles was done both in frequency and in
Faraday space before stacking datasets from 19 eight-hour-long epochs. A search
for polarized sources was carried out in the final, stacked dataset, and the
properties of the detected sources were examined. The depolarization level of
sources known to be polarized at 1.4 GHz was quantified. A one-sigma noise
level of 19 {\mu}Jy/beam was reached in the central part of the field after
stacking. Twenty-five polarized sources were detected above 8\sigma, five of
which had not been detected in polarization at any other radio frequencies
before. Seven additional polarized components were found by lowering the
threshold to 6\sigma at positions corresponding to sources known to be
polarized at 1.4 GHz. In two radio galaxies, polarization was detected from
both radio lobes, so the final number of associated radio continuum sources is
31. The detected sources are weakly polarized, with a median degree of
polarization of 1.75% for the sample of sources detected in polarized emission.
The sources previously detected in polarization at 1.4 GHz are significantly
depolarized at 150 MHz. The catalog is used to derive the polarized source
counts at 150 MHz. This is the deepest and highest-resolution polarization
study at 150 MHz to date.


  The way infants use auditory cues to learn to speak despite the acoustic
mismatch of their vocal apparatus is a hot topic of scientific debate. The
simulation of early vocal learning using articulatory speech synthesis offers a
way towards gaining a deeper understanding of this process. One of the crucial
parameters in these simulations is the choice of features and a metric to
evaluate the acoustic error between the synthesised sound and the reference
target. We contribute with evaluating the performance of a set of 40
feature-metric combinations for the task of optimising the production of static
vowels with a high-quality articulatory synthesiser. Towards this end we assess
the usability of formant error and the projection of the feature-metric error
surface in the normalised F1-F2 formant space. We show that this approach can
be used to evaluate the impact of features and metrics and also to offer
insight to perceptual results.


  Most convolutional neural networks in chest radiology use only the frontal
posteroanterior (PA) view to make a prediction. However the lateral view is
known to help the diagnosis of certain diseases and conditions. The recently
released PadChest dataset contains paired PA and lateral views, allowing us to
study for which diseases and conditions the performance of a neural network
improves when provided a lateral x-ray view as opposed to a frontal
posteroanterior (PA) view. Using a simple DenseNet model, we find that using
the lateral view increases the AUC of 8 of the 56 labels in our data and
achieves the same performance as the PA view for 21 of the labels. We find that
using the PA and lateral views jointly doesn't trivially lead to an increase in
performance but suggest further investigation.


  We calculate the finite temperature self-energy for neutrinos in the presence
of a constant magnetic field in a medium in the unbroken $SU(2) \otimes U(1)$
model. We obtain the exact dispersion relation for such neutrinos and find that
the thermal effective mass is modified by the magnetic field. We also find a
simple analytic expression for the dispersion relation and obtain the index of
refraction for large neutrino momentum.


  We study the interplay between the flow and hydrodynamic gradients in jet
quenching at first order in opacity. We find that the mixed flow-gradient
contributions in jet quenching are enhanced by the medium length, and survive
in the eikonal limit, dominating over other medium evolution effects. The
resulting modification to the jet quenching parameter and energy loss rate can
be substantial, leading to ample phenomenological implications. We also compute
the leading corrections to the jet broadening due to the flow velocity
gradients, and consider the leading gradient effects in the medium-induced
branching for general kinematics, extending the recent considerations of jets
in inhomogeneous media. These results can be straightforwardly coupled to
matter simulations, providing new opportunities for jet tomography in heavy-ion
collisions.


  We propose a procedure to demodulate analog signals encoded by a multicarrier
modulator, with slowly-varying carrier shapes. We prove that the asymptotic
demodulation error can be made arbitrarily small. The intended application is
the "sensorless" control of AC electric motors at or near standstill, through
the decoding of the PWM-induced current ripple.


  Training populations of agents has demonstrated great promise in
Reinforcement Learning for stabilizing training, improving exploration and
asymptotic performance, and generating a diverse set of solutions. However,
population-based training is often not considered by practitioners as it is
perceived to be either prohibitively slow (when implemented sequentially), or
computationally expensive (if agents are trained in parallel on independent
accelerators). In this work, we compare implementations and revisit previous
studies to show that the judicious use of compilation and vectorization allows
population-based training to be performed on a single machine with one
accelerator with minimal overhead compared to training a single agent. We also
show that, when provided with a few accelerators, our protocols extend to large
population sizes for applications such as hyperparameter tuning. We hope that
this work and the public release of our code will encourage practitioners to
use population-based learning more frequently for their research and
applications.


  The present density functional theory study addresses the question whether
the presence of H2O influences the catalytic activity of small gold clusters,
Au1-4/MgO(100), towards the oxidation of carbon monoxide. To this end, we
studied the (co-)adsorption of H2O and CO/O2 on these gold clusters. The ground
state structures in the presence of all three molecular species, that we found,
are Au1O2/MgO and Au2-4CO/MgO with H2O adsorbed on the surface in the proximity
of the clusters-molecule complex. In this configuration the catalytic activity
of Au1-4/MgO is indifferent to the presence of H2O. We also found that a
stable, highly activated hydroperoxyl-hydroxyl complex, O2H\dot\dot OH, can be
formed on Au1,3/MgO. For the catalytic active system Au8/MgO, it has been
predicted that this complex opens an alternative catalytic reaction pathway
towards CO oxidation. Our results suggest that this water mediated catalytic
cycle is unlikely to occur on Au1,3/MgO. In the case of Au1/MgO the cycle is
interrupted by the dissociation of the remaining (OH)2 complex after forming
the first CO2 molecule. On Au3 /MgO the O2 H\dot\dot OH complex is likely to
decompose upon the impact of a CO molecule, since three of its bond
dissociation energies are comparable to the reaction barrier of the CO to CO2
oxidation.


  Financial institutions use clients' payment transactions in numerous banking
applications. Transactions are very personal and rich in behavioural patterns,
often unique to individuals, which make them equivalent to personally
identifiable information in some cases. In this paper, we generate synthetic
transactional profiles using machine learning techniques with the goal to
preserve both data utility and privacy. A challenge we faced was to deal with
sparse vectors due to the few spending categories a client uses compared to all
the ones available. We measured data utility by calculating common insights
used by the banking industry on both the original and the synthetic data-set.
Our approach shows that neural network models can generate valuable synthetic
data in such context. Finally, we tried privacy-preserving techniques and
observed its effect on models' performances.


  In structural rigidity, one studies frameworks of bars and joints in
Euclidean space. Such a framework is an articulated structure consisting of
rigid bars, joined together at joints around which the bars may rotate. In this
paper, we will describe articulated motions of realisations of incidence
geometries that uses the terminology of graph of groups, and describe the
motions of such a framework using group theory. Our approach allows to model a
variety of situations, such as parallel redrawings, scenes, polytopes,
realisations of graphs on surfaces, and even unique colourability of graphs.
This approach allows a concise description of various dualities in rigidity
theory. We also provide a lower bound on the dimension of the infinitesimal
motions of such a framework in the special case when the underlying group is a
Lie group.


  We study the cosmological history of the classical conformal $B-L$ gauge
extension of the standard model, in which the physical scales are generated via
the Coleman-Weinberg-type symmetry breaking. Especially, we consider the
thermal phase transition of the U$(1)_{B-L}$ symmetry in the early universe and
resulting gravitational-wave production. Due to the classical conformal
invariance, the phase transition tends to be a first-order one with
ultra-supercooling, which enhances the strength of the produced gravitational
waves. We show that, requiring (1) U$(1)_{B-L}$ is broken after the reheating,
(2) the $B-L$ gauge coupling does not blow up below the Planck scale, (3) the
thermal phase transition completes in almost all the patches in the universe,
the gravitational wave spectrum can be as large as $\Omega_{\rm GW} \sim
10^{-8}$ at the frequency $f \sim 0.01$-$1$Hz for some model parameters, and a
vast parameter region can be tested by future interferometer experiments such
as eLISA, LISA, BBO and DECIGO.


  We study a magnetic phase diagram of CsCuCl3 by a spin wave theory. We
clarify an existence of new magnetic phases, i.e. up-up-down (uud) phase and Y
coplanar phase under a high pressure. We also discuss a magnetic
field(H)-temperature(T) phase diagram under ambient and high pressures.


  Wireless systems that carry delay-sensitive information (such as speech
and/or video signals) typically transmit with fixed data rates, but may
occasionally suffer from transmission outages caused by the random nature of
the fading channels. If the transmitter has instantaneous channel state
information (CSI) available, it can compensate for a significant portion of
these outages by utilizing power allocation. In this paper, we consider optimal
power allocation for a conventional dual-hop bidirectional decode-and-forward
(DF) relaying system with a three-phase transmission protocol. The proposed
strategy minimizes the average power consumed by the end nodes and the relay,
subject to some maximum allowable system outage probability (OP), or
equivalently, minimizes the system OP while meeting average power constraints
at the end nodes and the relay. We show that in the proposed power allocation
scheme, the end nodes and the relay adjust their output powers to the minimum
level required to avoid outages, but will sometimes be silent, in order to
conserve power and prolong their lifetimes. For the proposed scheme, the end
nodes use the instantaneous CSI of their respective source-relay links and the
relay uses the instantaneous CSI of both links.


  We report the flexible on-target delivery of 800 nm wavelength, 5 GW peak
power, 40 fs duration laser pulses through an evacuated and tightly coiled 10 m
long hollow-core nested anti-resonant fiber by positively chirping the input
pulses to compensate for the anomalous dispersion of the fiber.
Near-transform-limited output pulses with high beam quality and a guided peak
intensity of 3 PW/cm2 were achieved by suppressing plasma effects in the
residual gas by pre-pumping the fiber after evacuation. This appears to cause a
long-term removal of molecules from the fiber core. Identifying the fluence at
the fiber core-wall interface as the damage origin, we scaled the coupled
energy to 2.1 mJ using a short piece of larger-core fiber to obtain 20 GW at
the fiber output. This scheme can pave the way towards the integration of
anti-resonant fibers in mJ-level nonlinear optical experiments and laser-source
development.


  It is shown that the Jacobi problem of geodesics on ellipsoid may be reduced
to more simple one, namely to the special case of the Clebsch problem. The last
one may be solved directly by using Weber's approach in terms of
multi-dimensional theta functions


  We regard pre-trained residual networks (ResNets) as nonlinear systems and
use linearization, a common method used in the qualitative analysis of
nonlinear systems, to understand the behavior of the networks under small
perturbations of the input images. We work with ResNet-56 and ResNet-110
trained on the CIFAR-10 data set. We linearize these networks at the level of
residual units and network stages, and the singular value decomposition is used
in the stability analysis of these components. It is found that most of the
singular values of the linearizations of residual units are 1 and, in spite of
the fact that the linearizations depend directly on the activation maps, the
singular values differ only slightly for different input images. However,
adjusting the scaling of the skip connection or the values of the weights in a
residual unit has a significant impact on the singular value distributions.
Inspection of how random and adversarial perturbations of input images
propagate through the network reveals that there is a dramatic jump in the
magnitude of adversarial perturbations towards the end of the final stage of
the network that is not present in the case of random perturbations. We attempt
to gain a better understanding of this phenomenon by projecting the
perturbations onto singular vectors of the linearizations of the residual
units.


  There is a recommended body posture and hand position for playing every
musical instrument, allowing efficient and quick movements without blockage.
Due to humans' limited cognitive capabilities, they struggle to concentrate on
several things simultaneously and thus sometimes lose the correct position
while playing their instrument. Incorrect positions when playing an instrument
can lead to injuries and movement disorders in the long run. Previous work in
HCI mainly focused on developing systems to assist in learning an instrument.
However, the design space for posture correction when playing a musical
instrument has not yet been explored. In this position paper, we present our
vision of providing subtle vibrotactile or thermal feedback to guide the focus
of attention back to the correct posture when playing a musical instrument. We
discuss our concept with a focus on motion recognition and feedback modalities.
Finally, we outline the next steps for future research.


  Let $\mathfrak D$ be a residually finite Dedekind domain, $a\in \mathfrak D$
be a nonzero element and $\mathfrak n$ be a nonzero ideal of $\mathfrak D$. In
this paper we describe the dynamics of the map $x\mapsto ax$ over the quotient
ring $\mathfrak D/\mathfrak n$. We further present some applications of our
main result.


  A Hilbert space operator $S\in\B$ is left $m$-invertible by $T\in\B$ if
$$\sum_{j=0}^m{(-1)^{m-j}\left(\begin{array}{clcr}m\\j\end{array}\right)T^jS^j}=0,$$
$S$ is $m$-isometric if
$$\sum_{j=0}^m{(-1)^{m-j}\left(\begin{array}{clcr}m\\j\end{array}\right){S^*}^jS^j}=0$$
and $S$ is $(m,C)$-isometric for some conjugation $C$ of $\H$ if
$$\sum_{j=0}^m{(-1)^{m-j}\left(\begin{array}{clcr}m\\j\end{array}\right){S^*}^jCS^jC}=0.$$
If a power bounded operator $S$ is left invertible by a power bounded operator
$T$, then $S$ (also, $T^*$) is similar to an isometry. Translated to
$m$-isometric and $(m,C)$-isometric operators $S$ this implies that $S$ is
$1$-isometric, equivalently isometric, and (respectively) $(1,C)$-isometric.


  This paper is devoted to the error analysis of a time-spectral algorithm for
fractional diffusion problems of order $\alpha$ ($0 < \alpha < 1$). The
solution regularity in the Sobolev space is revisited, and new regularity
results in the Besov space are established. A time-spectral algorithm is
developed which adopts a standard spectral method and a conforming linear
finite element method for temporal and spatial discretizations, respectively.
Optimal error estimates are derived with nonsmooth data. Particularly, a sharp
temporal convergence rate $1+2\alpha$ is shown theoretically and numerically.


  Let $n \geq 3$ be an integer. In this paper, we study the average behavior of
the $2$-torsion in class groups of number fields cut out by integral binary
$n$-ic forms having any fixed odd leading coefficient. Specifically, we compute
upper bounds on the average size of the $2$-torsion in the class groups of such
number fields. Conditional on a uniformity estimate, we further prove that each
of these upper bounds is in fact an equality.
  Our theorems extend recent work of Bhargava-Hanke-Shankar in the cubic case
and of Siad in the monic case to binary forms of any degree with any fixed odd
leading coefficient. When $n$ is odd, we find that fixing the leading
coefficient increases the average $2$-torsion in the class group, relative to
the prediction of Cohen-Lenstra-Martinet-Malle. When $n$ is even, such
predictions are yet to be formulated; along with Siad's results in the monic
case, our theorems are the first of their kind to describe the average behavior
of the $p$-torsion in class groups of degree-$n$ fields where $p \mid n > 2$.
  To prove these theorems, we first answer a question of Ellenberg by
parametrizing square roots of the class of the inverse different of a ring cut
out by a binary form in terms of the integral orbits of a certain coregular
representation. This parametrization has a range of interesting applications,
from studying $2$-parts of class groups to studying $2$-Selmer groups of
hyperelliptic Jacobians.


  Motivated by topological bidimensional quantum models for distinguishable
particles, and by Haldane's definition of mutual statistics for different
species of particles, we propose a new class of one-dimensional $1/r_{ij}^2$
Calogero model with coupling constants $g_{ij}$ depending on the labels of the
particles. We solve the groundstate problem, and show how to build some classes
of excited states.


  We derive a formula for the halo/galaxy bispectrum on the basis of the
integrated Perturbation Theory (iPT). In addition to the gravity-induced
non-Gaussianity, we consider the non-Gaussianity of the primordial curvature
perturbations, and investigate in detail the effect of such primordial
non-Gaussianity on the large-scale halo/galaxy bispectrum. In iPT, the effects
of primordial non-Gaussianity are wholly encapsulated in the linear
(primordial) polyspectra, and we systematically calculate the contributions to
the large-scale behaviors arising from the three types of primordial bispectrum
(local-, equilateral-, and orthogonal-types), and primordial trispectrum of the
local-type non-Gaussianity. We find that the equilateral- and orthogonal-type
non-Gaussianities show distinct scale-dependent behaviors which can dominate
the gravity-induced non-Gaussianity at very large scales. For the local-type
non-Gaussianity, higher-order loop corrections are found to give a
significantly large contribution to the halo/galaxy bispectrum of the squeezed
shape, and eventually dominate over the other contributions on large scales. A
diagrammatic approach based on the iPT helps us to systematically investigate
an impact of such higher-order contributions to the large-scale halo/galaxy
bispectrum.


  In this paper, we consider a challenging but realistic continual learning
(CL) problem, Few-Shot Continual Active Learning (FoCAL), where a CL agent is
provided with unlabeled data for a new or a previously learned task in each
increment and the agent only has limited labeling budget available. Towards
this, we build on the continual learning and active learning literature and
develop a framework that can allow a CL agent to continually learn new object
classes from a few labeled training examples. Our framework represents each
object class using a uniform Gaussian mixture model (GMM) and uses
pseudo-rehearsal to mitigate catastrophic forgetting. The framework also uses
uncertainty measures on the Gaussian representations of the previously learned
classes to find the most informative samples to be labeled in an increment. We
evaluate our approach on the CORe-50 dataset and on a real humanoid robot for
the object classification task. The results show that our approach not only
produces state-of-the-art results on the dataset but also allows a real robot
to continually learn unseen objects in a real environment with limited labeling
supervision provided by its user.


  In the trial \cite{BNL} of measuring the proton electric moment, storage
rings with electrostatic lattice have been considered. Here an overview is
given about the main parameters regarding such a kind of focusing. Beyond
confirming all the issues regarding this subject, a non-null element $M_{31}$
is introduced in all the $3\times 3$ matrices which deal with the vector
$(x,x',\Delta p/p)$ and its role is discussed.


  We calculate the one-photon loop radiative corrections to charged pion
Compton scattering, $\pi^- \gamma \to \pi^- \gamma $. Ultraviolet and infrared
divergencies are both treated in dimensional regularization. Analytical
expressions for the ${\cal O}(\alpha)$ corrections to the invariant Compton
scattering amplitudes, $A(s,u)$ and $B(s,u)$, are presented for 11 classes of
contributing one-loop diagrams. Infrared finiteness of the virtual radiative
corrections is achieved (in the standard way) by including soft photon
radiation below an energy threshold $\lambda$, and its relation to the
experimental detection threshold is discussed. We find that the radiative
corrections are maximal in backward directions, reaching e.g. -2.4% for a
center-of-mass energy of $\sqrt{s}=4m_\pi$ and $\lambda=5 $MeV. Furthermore, we
extend our calculation of the radiative corrections by including the leading
pion structure effect (at low energies) in form of its electric and magnetic
polarizability difference, $\alpha_\pi - \beta_\pi \simeq 6\cdot 10^{-4}
$fm$^3$. We find that this structure effect does not change the relative size
and angular dependence of the radiative corrections to pion Compton scattering.
Our results are particularly relevant for analyzing the COMPASS experiment at
CERN which aims at measuring the pion electric and magnetic polarizabilities
with high statistics using the Primakoff effect.


  Treatment effect estimation from observational data is a fundamental problem
in causal inference. There are two very different schools of thought that have
tackled this problem. On one hand, Pearlian framework commonly assumes
structural knowledge (provided by an expert) in form of directed acyclic graphs
and provides graphical criteria such as back-door criterion to identify valid
adjustment sets. On other hand, potential outcomes (PO) framework commonly
assumes that all observed features satisfy ignorability (i.e., no hidden
confounding), which in general is untestable. In prior works that attempted to
bridge these frameworks, there is an observational criteria to identify an
anchor variable and if a subset of covariates (not involving the anchor
variable) passes a suitable conditional independence criteria, then that subset
is a valid back-door. Our main result strengthens these prior results by
showing that under a different expert-driven structural knowledge -- that one
variable is a direct causal parent of treatment variable -- remarkably, testing
for subsets (not involving the known parent variable) that are valid back-doors
is equivalent to an invariance test. Importantly, we also cover the non-trivial
case where entire set of observed features is not ignorable (generalizing the
PO framework) without requiring knowledge of all parents of treatment variable.
Our key technical idea involves generation of a synthetic sub-sampling (or
environment) variable that is a function of the known parent variable. In
addition to designing an invariance test, this sub-sampling variable allows us
to leverage Invariant Risk Minimization, and thus, connects finding valid
adjustments (in non-ignorable observational setting) to representation
learning. We demonstrate effectiveness and tradeoffs of our approaches on a
variety of synthetic data as well as real causal effect estimation benchmarks.


  We report the first detection of hard (>10 keV) X-ray emission simultaneous
with gamma rays in a nova eruption. Observations of the nova V5855 Sgr carried
out with the NuSTAR satellite on Day 12 of the eruption revealed faint, highly
absorbed thermal X-rays. The extreme equivalent hydrogen column density towards
the X-ray emitting region (~3 x 10$^{24}$ cm$^{-2}$) indicates that the shock
producing the X-rays was deeply embedded within the nova ejecta. The slope of
the X-ray spectrum favors a thermal origin for the bulk of the emission, and
the constraints of the temperature in the shocked region suggest a shock
velocity compatible with the ejecta velocities inferred from optical
spectroscopy. While we do not claim the detection of non-thermal X-rays, the
data do not allow us to rule out an additional, fainter component dominating at
energy above 20 keV, for which we obtained upper limits. The inferred
luminosity of the thermal X-rays is too low to be consistent with the gamma-ray
luminosities if both are powered by the same shock under standard assumptions
regarding the efficiency of non-thermal particle acceleration and the
temperature distribution of the shocked gas.


  We present improved upper and lower bounds on the spanning ratio of
$\theta$-graphs with at least six cones. Given a set of points in the plane, a
$\theta$-graph partitions the plane around each vertex into $m$ disjoint cones,
each having aperture $\theta=2\pi/m$, and adds an edge to the `closest' vertex
in each cone. We show that for any integer $k \geq 1$, $\theta$-graphs with
$4k+2$ cones have a spanning ratio of $1+2\sin(\theta/2)$ and we provide a
matching lower bound, showing that this spanning ratio tight.
  Next, we show that for any integer $k \geq 1$, $\theta$-graphs with $4k+4$
cones have spanning ratio at most
$1+2\sin(\theta/2)/(\cos(\theta/2)-\sin(\theta/2))$. We also show that
$\theta$-graphs with $4k+3$ and $4k+5$ cones have spanning ratio at most
$\cos(\theta/4)/(\cos(\theta/2)-\sin(3\theta/4))$. This is a significant
improvement on all families of $\theta$-graphs for which exact bounds are not
known. For example, the spanning ratio of the $\theta$-graph with 7 cones is
decreased from at most 7.5625 to at most 3.5132. These spanning proofs also
imply improved upper bounds on the competitiveness of the $\theta$-routing
algorithm. In particular, we show that the $\theta$-routing algorithm is
$(1+2\sin(\theta/2)/(\cos(\theta/2)-\sin(\theta/2)))$-competitive on
$\theta$-graphs with $4k+4$ cones and that this ratio is tight.
  Finally, we present improved lower bounds on the spanning ratio of these
graphs. Using these bounds, we provide a partial order on these families of
$\theta$-graphs. In particular, we show that $\theta$-graphs with $4k+4$ cones
have spanning ratio at least $1+2\tan(\theta/2)+2\tan^2(\theta/2)$. This is
somewhat surprising since, for equal values of $k$, the spanning ratio of
$\theta$-graphs with $4k+4$ cones is greater than that of $\theta$-graphs with
$4k+2$ cones, showing that increasing the number of cones can make the spanning
ratio worse.


  Neural nets are powerful function approximators, but the behavior of a given
neural net, once trained, cannot be easily modified. We wish, however, for
people to be able to influence neural agents' actions despite the agents never
training with humans, which we formalize as a human-assisted decision-making
problem. Inspired by prior art initially developed for model explainability, we
develop a method for updating representations in pre-trained neural nets
according to externally-specified properties. In experiments, we show how our
method may be used to improve human-agent team performance for a variety of
neural networks from image classifiers to agents in multi-agent reinforcement
learning settings.


  In this paper, we consider the patient similarity matching problem over a
cancer cohort of more than 220,000 patients. Our approach first leverages on
Word2Vec framework to embed ICD codes into vector-valued representation. We
then propose a sequential algorithm for case-control matching on this
representation space of diagnosis codes. The novel practice of applying the
sequential matching on the vector representation lifted the matching accuracy
measured through multiple clinical outcomes. We reported the results on a
large-scale dataset to demonstrate the effectiveness of our method. For such a
large dataset where most clinical information has been codified, the new method
is particularly relevant.


  We present optical and near-infrared searches for afterglow emission from the
first four Swift bursts with accurate positions from the X-ray Telescope (XRT).
Using telescopes at Las Campanas, Keck, and Palomar observatories we rapidly
identified and followed up afterglows for three of the four bursts. The burst
positions were also observed with the Very Large Array, but no radio afterglow
emission was detected. The optical/NIR afterglows are fainter than about 75% of
all afterglows detected to date, with GRB 050126 being the faintest, and were
identified thanks to accurate and rapid positions from the XRT and rapid
response with >1-m telescopes. This suggests that the fraction of dust-obscured
bursts is small, <10% when combined with afterglows localized by the HETE-2
Soft X-ray Camera. The X-ray fluxes are typical of the known population, with
the exception of GRB 050126 which has the faintest X-ray afterglow to date
(normalized to t=10 hr), and was detected thanks to a response time of only 130
s after the burst. Finally, we find that all three optical/NIR afterglows are
located <2 arcsec away from the nominal XRT positions, suggesting that the XRT
is capable of delivering highly accurate positions, which will revolutionize
afterglow studies.


  Let $(\hat{X}, T^{1,0} \hat{X})$ be a compact orientable CR embeddable three
dimensional strongly pseudoconvex CR manifold, where $T^{1,0} \hat{X}$ is a CR
structure on $\hat{X}$. Fix a point $p \in \hat{X}$ and take a global contact
form $\hat{\theta}$ so that $\hat{\theta}$ is asymptotically flat near $p$.
Then $(\hat{X}, T^{1,0} \hat{X}, \hat{\theta})$ is a pseudohermitian
$3$-manifold. Let $G_p \in C^{\infty} (\hat{X} \setminus \{p\})$, $G_p > 0$,
with $G_p(x) \sim \vartheta(x,p)^{-2}$ near $p$, where $\vartheta(x,y)$ denotes
the natural pseudohermitian distance on $\hat{X}$. Consider the new
pseudohermitian $3$-manifold with a blow-up of contact form $(\hat{X} \setminus
\{p\}, T^{1,0} \hat{X}, G^2_p \hat{\theta})$ and let $\Box_{b}$ denote the
corresponding Kohn Laplacian on $\hat{X} \setminus \{p\}$.
  In this paper, we prove that the weighted Kohn Laplacian $G^2_p \Box_b$ has
closed range in $L^2$ with respect to the weighted volume form $G^2_p
\hat{\theta} \wedge d\hat{\theta}$, and that the associated partial inverse and
the Szeg\"{o} projection enjoy some regularity properties near $p$. As an
application, we prove the existence of some special functions in the kernel of
$\Box_{b}$ that grow at a specific rate at $p$. The existence of such functions
provides an important ingredient for the proof of a positive mass theorem in
3-dimensional CR geometry by Cheng-Malchiodi-Yang.


  Embryo quality assessment after in vitro fertilization (IVF) is primarily
done visually by embryologists. Variability among assessors, however, remains
one of the main causes of the low success rate of IVF. This study aims to
develop an automated embryo assessment based on a deep learning model. This
study includes a total of 1084 images from 1226 embryos. The images were
captured by an inverted microscope at day 3 after fertilization. The images
were labelled based on Veeck criteria that differentiate embryos to grade 1 to
5 based on the size of the blastomere and the grade of fragmentation. Our deep
learning grading results were compared to the grading results from trained
embryologists to evaluate the model performance. Our best model from
fine-tuning a pre-trained ResNet50 on the dataset results in 91.79% accuracy.
The model presented could be developed into an automated embryo assessment
method in point-of-care settings.


  This overview describes the official results of the CL-SciSumm Shared Task
2018 -- the first medium-scale shared task on scientific document summarization
in the computational linguistics (CL) domain. This year, the dataset comprised
60 annotated sets of citing and reference papers from the open access research
papers in the CL domain. The Shared Task was organized as a part of the 41st
Annual Conference of the Special Interest Group in Information Retrieval
(SIGIR), held in Ann Arbor, USA in July 2018. We compare the participating
systems in terms of two evaluation metrics. The annotated dataset and
evaluation scripts can be accessed and used by the community from:
\url{https://github.com/WING-NUS/scisumm-corpus}.


  In this paper, we investigate the dynamics of two coupled quantum degenerate
atomic fields (BEC) interacting with two classical optical fields in the
nonlinear atom optics regime. Two photon interaction produces entangled
atom-atom pairs which exhibit nonclassical correlations. Since the system
involves the creation of two correlated atom pairs, we call it the
nondegenerate dual atomic parametric amplifier.


  Relativistic interaction of short-pulse lasers with underdense plasmas has
recently led to the emergence of a novel generation of femtosecond x-ray
sources. Based on radiation from electrons accelerated in plasma, these sources
have the common properties to be compact and to deliver collimated, incoherent
and femtosecond radiation. In this article we review, within a unified
formalism, the betatron radiation of trapped and accelerated electrons in the
so-called bubble regime, the synchrotron radiation of laser-accelerated
electrons in usual meter-scale undulators, the nonlinear Thomson scattering
from relativistic electrons oscillating in an intense laser field, and the
Thomson backscattered radiation of a laser beam by laser-accelerated electrons.
The underlying physics is presented using ideal models, the relevant parameters
are defined, and analytical expressions providing the features of the sources
are given. Numerical simulations and a summary of recent experimental results
on the different mechanisms are also presented. Each section ends with the
foreseen development of each scheme. Finally, one of the most promising
applications of laser-plasma accelerators is discussed: the realization of a
compact free-electron laser in the x-ray range of the spectrum. In the
conclusion, the relevant parameters characterizing each sources are summarized.
Considering typical laser-plasma interaction parameters obtained with currently
available lasers, examples of the source features are given. The sources are
then compared to each other in order to define their field of applications.


  A Kerr microresonator frequency comb has enabled the generation of
low-phase-noise millimeter- and terahertz-waves in conjunction with an
ultrafast photodiode. It is intriguing to employ the new light source in
wireless communication at above 100 GHz band, where a carrier signal with a
high signal-to-noise ratio is desired to achieve higher data rates. In this
study, we demonstrate two simple and efficient architectures of wireless links
based on a microresonator comb. We show experimentally that simultaneous
modulation and detection of multiple comb lines result in >10 times stronger
modulation signal strength than two-line detection at a receiver. Successful
transmission of complex modulation format up to 64 quadrature amplitude
modulation proves that a microresonator comb and the proposed modulation method
are effective in modern wireless communication.


  Current-correlations are a very sensitive probe of the fluctuations of small
conductors. For non-interacting particles injected from thermal sources there
is a simple connection between the sign of correlations and statistics:
current-current correlations of Fermions are negative, intensity-intensity
correlations of Bosons can be positive. In contrast to photons, electrons are
interacting entities, and we can expect the simple connection between
statistics and the sign of current-current correlations to be broken, if
interactions play a crucial role. We present a number of examples in which
interactions are important. At a voltage probe the potential fluctuates to
maintain zero current. It is shown that there are geometries for which these
fluctuations lead to positive correlations. Displacement currents at
capacitively coupled contacts are also positively correlated if both contacts
contribute to screening of the same excess charge fluctuation. Hybrid normal
superconducting systems provide another example which permits positive
correlations. The conditions for positive correlations differ strongly
depending on whether the normal conductor is open and well coupled to the
superconductor or is only weakly coupled via a barrier to the superconductor.
In latter case, positive correlations result if the partition noise generated
by Cooper pairs is overcome by pairs which are broken up and emit one electron
into the contacts of interest.


  The Breit interaction is implemented in the no-pair variational Dirac-Coulomb
(DC) framework using an explicitly correlated Gaussian basis reported in the
previous paper [Paper I: P. Jeszenszki, D. Ferenc, and E. M\'atyus (2022)].
Both a perturbative and a fully variational inclusion of the Breit term is
considered. The no-pair DC plus perturbative Breit as well as the no-pair
Dirac-Coulomb-Breit energies are compared with perturbation theory results
including the Breit-Pauli Hamiltonian and leading-order non-radiative quantum
electrodynamics corrections for low $Z$ values. Possible reasons for the
observed deviations are discussed.


  While numerous architectures for long-range language models (LRLMs) have
recently been proposed, a meaningful evaluation of their discourse-level
language understanding capabilities has not yet followed. To this end, we
introduce ChapterBreak, a challenge dataset that provides an LRLM with a long
segment from a narrative that ends at a chapter boundary and asks it to
distinguish the beginning of the ground-truth next chapter from a set of
negative segments sampled from the same narrative. A fine-grained human
annotation reveals that our dataset contains many complex types of chapter
transitions (e.g., parallel narratives, cliffhanger endings) that require
processing global context to comprehend. Experiments on ChapterBreak show that
existing LRLMs fail to effectively leverage long-range context, substantially
underperforming a segment-level model trained directly for this task. We
publicly release our ChapterBreak dataset to spur more principled future
research into LRLMs.


  Linear optics provides versatile means to simulate and control the dynamics
of open quantum systems. Here, the polarization and frequency of photons often
represent the system and its environment, respectively. Recently, the photon's
path degree of freedom and the effects of interference have been considered as
well, giving rise to the concept of "open system interference". Such models
have been shown to beat traditional parameter estimation schemes in certain
scenarios. In this work, we study the sensitivity of non-Markovian memory
effects arising from an open system Mach-Zehnder interferometer. Our protocol
provides an alternative way to get around the quantum Cram\'er-Rao theorem,
proving itself a noteworthy option for more conventional parameter estimation
protocols.


  As music has become more available especially on music streaming platforms,
people have started to have distinct preferences to fit to their varying
listening situations, also known as context. Hence, there has been a growing
interest in considering the user's situation when recommending music to users.
Previous works have proposed user-aware autotaggers to infer situation-related
tags from music content and user's global listening preferences. However, in a
practical music retrieval system, the autotagger could be only used by assuming
that the context class is explicitly provided by the user. In this work, for
designing a fully automatised music retrieval system, we propose to
disambiguate the user's listening information from their stream data. Namely,
we propose a system which can generate a situational playlist for a user at a
certain time 1) by leveraging user-aware music autotaggers, and 2) by
automatically inferring the user's situation from stream data (e.g. device,
network) and user's general profile information (e.g. age). Experiments show
that such a context-aware personalized music retrieval system is feasible, but
the performance decreases in the case of new users, new tracks or when the
number of context classes increases.


  As the one-chip integration of HW-modules designed by different companies
becomes more and more popular reliability of a HW-design and evaluation of the
timing behavior during the prototype stage are absolutely necessary. One way to
guarantee reliability is the use of robust design styles, e.g.,
delay-insensitivity. For early timing evaluation two aspects must be
considered: a) The timing needs to be proportional to technology variations and
b) the implemented architecture should be identical for prototype and target.
The first can be met also by delay-insensitive implementation. The latter one
is the key point. A unified architecture is needed for prototyping as well as
implementation. Our new approach to rapid prototyping of signal processing
tasks is based on a configurable, delay-insensitive implemented processor
called Flysig. In essence, the Flysig processor can be understood as a complex
FPGA where the CLBs are substituted by bit-serial operators. In this paper the
general concept is detailed and first experimental results are given for
demonstration of the main advantages: delay-insensitive design style, direct
correspondence between prototyping and target architecture, high performance
and reasonable shortening of the design cycle.


  Given a graph $G=(V,E)$ with two distinguished vertices $s,t\in V$ and an
integer $L$, an {\em $L$-bounded flow} is a flow between $s$ and $t$ that can
be decomposed into paths of length at most $L$. In the {\em maximum $L$-bounded
flow problem} the task is to find a maximum $L$-bounded flow between a given
pair of vertices in the input graph.
  The problem can be solved in polynomial time using linear programming.
However, as far as we know, no polynomial-time combinatorial algorithm for the
$L$-bounded flow is known. The only attempt, that we are aware of, to describe
a combinatorial algorithm for the maximum $L$-bounded flow problem was done by
Koubek and \v{R}\'i ha in 1981. Unfortunately, their paper contains
substantional flaws and the algorithm does not work; in the first part of this
paper, we describe these problems.
  In the second part of this paper we describe a combinatorial algorithm based
on the exponential length method that finds a $(1+\epsilon)$-approximation of
the maximum $L$-bounded flow in time $O(\epsilon^{-2}m^2 L\log L)$ where $m$ is
the number of edges in the graph. Moreover, we show that this approach works
even for the NP-hard generalization of the maximum $L$-bounded flow problem in
which each edge has a length.


  Distinguishing quarks from gluons based on low-level detector output is one
of the most challenging applications of multi-variate and machine learning
techniques at the LHC. We first show the performance of our 4-vector-based LoLa
tagger without and after considering detector effects. We then discuss two
benchmark applications, mono-jet searches with a gluon-rich signal and di-jet
resonances with a quark-rich signal. In both cases an immediate benefit
compared to the standard event-level analysis exists.


  The Covid-19 pandemic has had a deep impact on the lives of the entire world
population, inducing a participated societal debate. As in other contexts, the
debate has been the subject of several d/misinformation campaigns; in a quite
unprecedented fashion, however, the presence of false information has seriously
put at risk the public health. In this sense, detecting the presence of
malicious narratives and identifying the kinds of users that are more prone to
spread them represent the first step to limit the persistence of the former
ones. In the present paper we analyse the semantic network observed on Twitter
during the first Italian lockdown (induced by the hashtags contained in
approximately 1.5 millions tweets published between the 23rd of March 2020 and
the 23rd of April 2020) and study the extent to which various discursive
communities are exposed to d/misinformation arguments. As observed in other
studies, the recovered discursive communities largely overlap with traditional
political parties, even if the debated topics concern different facets of the
management of the pandemic. Although the themes directly related to
d/misinformation are a minority of those discussed within our semantic
networks, their popularity is unevenly distributed among the various discursive
communities.


  Low light conditions not only degrade human visual experience, but also
reduce the performance of downstream machine analytics. Although many works
have been designed for low-light enhancement or domain adaptive machine
analytics, the former considers less on high-level vision, while the latter
neglects the potential of image-level signal adjustment. How to restore
underexposed images/videos from the perspective of machine vision has long been
overlooked. In this paper, we are the first to propose a learnable illumination
enhancement model for high-level vision. Inspired by real camera response
functions, we assume that the illumination enhancement function should be a
concave curve, and propose to satisfy this concavity through discrete integral.
With the intention of adapting illumination from the perspective of machine
vision without task-specific annotated data, we design an asymmetric
cross-domain self-supervised training strategy. Our model architecture and
training designs mutually benefit each other, forming a powerful unsupervised
normal-to-low light adaptation framework. Comprehensive experiments demonstrate
that our method surpasses existing low-light enhancement and adaptation methods
and shows superior generalization on various low-light vision tasks, including
classification, detection, action recognition, and optical flow estimation.
Project website: https://daooshee.github.io/SACC-Website/


  We prove an almost sure invariance principle that is valid for general
classes of nonuniformly expanding and nonuniformly hyperbolic dynamical
systems. Discrete time systems and flows are covered by this result. In
particular, the result applies to the planar periodic Lorentz flow with finite
horizon. Statistical limit laws such as the central limit theorem, the law of
the iterated logarithm, and their functional versions, are immediate
consequences.


  Based on the Sturm-Liouville eigenvalue problem, Banerjee \emph{et al.}
proposed a perturbative approach to analytically investigate the properties of
the ($2+1$)-dimensional superconductor with Born-Infeld electrodynamics [Phys.
Rev. D {\bf 87}, 104001 (2013)]. By introducing an iterative procedure, we will
further improve the analytical results and the consistency with the numerical
findings, and can easily extend the analytical study to the higher-dimensional
superconductor with Born-Infeld electrodynamics. We observe that the higher
Born-Infeld corrections make it harder for the condensation to form but do not
affect the critical phenomena of the system. Our analytical results can be used
to back up the numerical computations for the holographic superconductors with
various condensates in Born-Infeld electrodynamics.


  We present recent results obtained by the BRAHMS experiment at the
Relativistic Heavy Ion Collider (RHIC) for the systems of Au + Au and Cu + Cu
at \rootsnn{200} and at 62.4 GeV, and p + p at \rootsnn{200}. Nuclear
modification factors for Au + Au and Cu + Cu collisions are presented. Analysis
of anti-particle to particle ratios as a function of rapidity and collision
energy reveal that particle populations at the chemical freeze-out stage for
heavy-ion reactions at and above SPS energies are controlled by the baryon
chemical potential. From the particle spectra we deduce significant radial
expansion ($\beta \approx $ 0.75), as expected for systems created with a large
initial energy density. We also measure the elliptic flow parameter $v_2$
versus rapidity and \ptn. We present rapidity dependent $p/\pi$ ratios within
$0 < y < 3$ for Au + Au and Cu + Cu at \rootsnn{200}. \Raa is found to increase
with decreasing collision energy, decreasing system size, and when going
towards more peripheral collisions. However, \Raa shows only a very weak
dependence on rapidity (for $0 < y < 3.2$), both for pions and protons.


  We investigate the anisotropic spin motive force in
$\alpha$-(BEDT-TTF)$_2$I$_3$, which is a multi-layered massless Dirac fermion
system under pressure. Assuming the interlayer antiferromagnetic interaction
and the interlayer anisotropic ferromagnetic interaction, we numerically
examine the spin ordered state of the ground state using the steepest descent
method. The anisotropic interaction leads to the anisotropic spin ordered
state. We calculate the spin motive force produced by the anisotropic spin
texture. The result quantitatively agrees with the experiment.


  This paper proves the approximate intermediate value theorem, constructively
and from notably weak hypotheses: from pointwise rather than uniform
continuity, without assuming that reals are presented with rational
approximants, and without using countable choice. The theorem is that if a
pointwise continuous function has both a negative and a positive value, then it
has values arbitrarily close to 0. The proof builds on the usual classical
proof by bisection, which repeatedly selects the left or right half of an
interval; the algorithm here selects an interval of half the size in a
continuous way, interpolating between those two possibilities.


  Transcranial ultrasonic stimulation (TUS) is an emerging technology for
non-invasive brain stimulation. In a series of meetings, the International
Consortium for Transcranial Ultrasonic Stimulation Safety and Standards
(ITRUSST) has established expert consensus on considerations for the
biophysical safety of TUS, drawing upon the relevant diagnostic ultrasound
literature and regulations. This report reflects a consensus expert opinion and
can inform but not replace regulatory guidelines or official international
standards. Their establishment by international and national commissions will
follow expert consensus. Similarly, this consensus will inform but not replace
ethical evaluation, which will consider aspects beyond biophysical safety
relevant to burden, risk, and benefit, such as physiological effects and
disease-specific interactions. Here, we assume the application of TUS to
persons who are not at risk for thermal or mechanical damage, and without
ultrasound contrast agents. In this context, we present a concise yet
comprehensive set of levels for a nonsignificant risk of TUS application. For
mechanical effects, it is safe if the mechanical index (MI) or the mechanical
index for transcranial application (MItc) does not exceed 1.9. For thermal
effects, it is safe if any of the following three levels are met: a temperature
rise less than 2 C, a thermal dose less than 0.25 CEM43, or specific values of
the thermal index (TI) for a given exposure time. We review literature relevant
to our considerations and discuss limitations and future developments of our
approach.


  The muon anomalous magnetic moment $a_\mu$ is investigated in the MSSM for
$\tan\beta\to\infty$. This is an attractive example of radiative muon mass
generation with completely different qualitative parameter dependence compared
to the MSSM with the usual, finite $\tan\beta$. The observed, positive
difference between the experimental and Standard Model value can only be
explained if there are mass splittings, such that bino contributions dominate
over wino ones. The two most promising cases are characterized either by large
Higgsino mass $\mu$ or by large left-handed smuon mass $m_L$. The required mass
splittings and the resulting $a_\mu^\text{SUSY}$ are studied in detail. It is
shown that the current discrepancy in $a_\mu$ can be explained even in cases
where all SUSY masses are at the TeV scale. The paper also presents useful
analytical formulas, approximations for limiting cases, and benchmark points.


  Using Bridgeland stability conditions we give sufficient criteria for a
stable vector bundle on a surface to remain stable when restricted to a curve.
We give a stronger criterion when the vector bundle is a general vector bundle
on the plane. As an application, we compute the cohomology of such bundles for
curves that lie in the plane or on Hirzebruch surfaces.


  We study the motivic Serre invariant of a smoothly bounded algebraic or rigid
variety $X$ over a complete discretely valued field $K$ with perfect residue
field $k$. If $K$ has characteristic zero, we extend the definition to
arbitrary $K$-varieties using Bittner's presentation of the Grothendieck ring
and a process of N\'eron smoothening of pairs of varieties.
  The motivic Serre invariant can be considered as a measure for the set of
unramified points on $X$. Under certain tameness conditions, it admits a
cohomological interpretation by means of a trace formula. In the curve case, we
use T. Saito's geometric criterion for cohomological tameness to obtain more
detailed results. We discuss some applications to Weil-Ch\^atelet groups, Chow
motives, and the structure of the Grothendieck ring.


  In this paper, we propose a novel equilibrium solution notion for the
time-inconsistent stochastic linear-quadratic optimal control problem. This
notion is called the mixed equilibrium solution, which consists of two parts: a
pure-feedback-strategy part and an open-loop-control part. When the
pure-feedback-strategy part is zero or the open-loop-control part does not
depend on the initial state, the mixed equilibrium solution reduces to the
open-loop equilibrium control and the feedback equilibrium strategy,
respectively. Using a maximum-principle-like methodology with forward-backward
stochastic difference equations, a necessary and sufficient condition is
established to characterize the existence of a mixed equilibrium solution.
Then, by decoupling the forward-backward stochastic difference equations, three
sets of difference equations, which together portray the existence of a mixed
equilibrium solution, are obtained. Moreover, the case with a fixed time-state
initial pair and the case with all the initial pairs are separately
investigated. Furthermore, an example is constructed to show that the mixed
equilibrium solution exists for all the initial pairs, although neither the
open-loop equilibrium control nor the feedback equilibrium strategy exists for
some initial pairs.


  We measure the magnetic field produced by a linear octupole formed by four
magnets placed in such a way that both their magnetic moments and their
quadrupole moments cancel each other. The magnetic field is measured with the
magnetic sensor of a smartphone together with a suitable app, using the Android
operating system. The main objective is to determine the dependence of the
x-component of the magnetic field with distance and compare with the
theoretical result that tells us that this dependence is a quintic decrease.


  Based on the locality principle of insulating superlattices, we apply the
method of Wu {\it et al} [Phys. Rev. Letter {\bf 101}, 087610 (2008)] to the
piezoelectric strains of individual layers under fixed displacement field. For
a superlattice of arbitrary stacking sequence an accurate model is acquired for
predicting piezoelectricity. By applying the model in the superlattices where
ferroelectric and antiferrodistortive modes are in competition, functional
piezoelectricity can be achieved. A strong nonlinear effect is observed and can
be further engineered in the PbTiO$_3$/SrTiO$_3$ superlattice and an interface
enhancement of piezoelectricity is found in the BaTiO$_3$/CaTiO$_3$
superlattice.


  This thesis explores two specific topics of discrete geometry, the
multitriangulations and the polytopal realizations of products, whose
connection is the problem of finding polytopal realizations of a given
combinatorial structure.
  A k-triangulation is a maximal set of chords of the convex n-gon such that no
k+1 of them mutually cross. We propose a combinatorial and geometric study of
multitriangulations based on their stars, which play the same role as triangles
of triangulations. This study leads to interpret multitriangulations by duality
as pseudoline arrangements with contact points covering a given support. We
exploit finally these results to discuss some open problems on
multitriangulations, in particular the question of the polytopal realization of
their flip graphs.
  We study secondly the polytopality of Cartesian products. We investigate the
existence of polytopal realizations of cartesian products of graphs, and we
study the minimal dimension that can have a polytope whose k-skeleton is that
of a product of simplices.


  Trench defects in multi-quantum wells (MQWs) have been considered as flawed
structures that severely degrade the internal quantum efficiency of
light-emitting diodes (LEDs) in the past. In this research, trench defects are
innovatively modulated into the structure to enhance the efficiency of red
InGaN LEDs. Specifically, dual-color MQWs structures are grown with green MQWs
at the bottom and red MQWs at the top. When high-density trench defects are
introduced into the green MQWs, the upper red MQWs exhibit a significant
wavelength redshift of 68 nm and approximately 6-fold luminescence enhancement
compared to those without trench defects. The wavelength redshift is attributed
to the increased indium incorporation due to the strain relaxation effect of
trench defects. Moreover, the luminescence enhancement originates from the
strong emission of the red MQWs inside trench defects. The mechanisms behind
the superior luminescent properties of red MQWs within trench defects are
explored in detail. Red InGaN LEDs with an internal quantum efficiency of 16.4%
are achieved by modulating the trench defects. The method of achieving
InGaN-based red emission by introducing trench defects is simple and
reproducible, requiring no additional substrate designs. This research provides
a novel pathway toward achieving high-efficiency red InGaN LEDs.


  The current research on credit risk is primarily focused on modeling default
probabilities. Recovery rates are often treated as an afterthought; they are
modeled independently, in many cases they are even assumed constant. This is
despite of their pronounced effect on the tail of the loss distribution. Here,
we take a step back, historically, and start again from the Merton model, where
defaults and recoveries are both determined by an underlying process. Hence,
they are intrinsically connected. For the diffusion process, we can derive the
functional relation between expected recovery rate and default probability.
This relation depends on a single parameter only. In Monte Carlo simulations we
find that the same functional dependence also holds for jump-diffusion and
GARCH processes. We discuss how to incorporate this structural recovery rate
into reduced form models, in order to restore essential structural information
which is usually neglected in the reduced-form approach.


  The broad sharing of research data is widely viewed as of critical importance
for the speed, quality, accessibility, and integrity of science. Despite
increasing efforts to encourage data sharing, both the quality of shared data,
and the frequency of data reuse, remain stubbornly low. We argue here that a
major reason for this unfortunate state of affairs is that the organization of
research results in the findable, accessible, interoperable, and reusable
(FAIR) form required for reuse is too often deferred to the end of a research
project, when preparing publications, by which time essential details are no
longer accessible. Thus, we propose an approach to research informatics that
applies FAIR principles continuously, from the very inception of a research
project, and ubiquitously, to every data asset produced by experiment or
computation. We suggest that this seemingly challenging task can be made
feasible by the adoption of simple tools, such as lightweight identifiers (to
ensure that every data asset is findable), packaging methods (to facilitate
understanding of data contents), data access methods, and metadata organization
and structuring tools (to support schema development and evolution). We use an
example from experimental neuroscience to illustrate how these methods can work
in practice.


  We consider motion of a particle in a one-dimensional tilted ratchet
potential subject to two-sided tempered stable L\'{e}vy noise characterised by
strength $\Omega$, fractional index $\alpha$, skew $\theta$ and tempering
$\lambda$. We derive analytic solutions to the corresponding Fokker-Planck
L\'{e}vy equations for the probability density. Due to the periodicity of the
potential, we carry out reduction to a compact domain and solve for the
analogue there of steady-state solutions which we represent as wrapped
probability density functions. By solving for the expected value of the current
associated with the particle motion, we are able to determine threshold for
metastability of the system, namely when the particle stabilises in a well of
the potential and when the particle is in motion, for example as a consequence
of the tilt of the potential. Because the noise may be asymmetric, we examine
the relationship between skew of the noise and the tilt of the potential. With
tempering, we find two remarkable regimes where the current may be reversed in
a direction opposite to the tilt or where the particle may be stabilised in a
well in circumstances where deterministically it should flow with the tilt.


  Using Density Functional Theory we theoretically study the orientational
properties of uniform phases of hard kites -- two isosceles triangles joined by
their common base. Two approximations are used: Scaled Particle Theory, and a
new approach which better approximates third virial coefficients of
two-dimensional hard particles. By varying some of their geometrical parameters
kites can be transformed into squares, rhombuses, triangles, and also very
elongated particles, even reaching the hard-needle limit. Thus a fluid of hard
kites, depending on the particle shape, can stabilize isotropic, nematic,
tetratic and triatic phases. Different phase diagrams are calculated, including
those of rhombuses, and kites with two of their equal interior angles fixed to
$90^{\circ}$, $60^{\circ}$ and $75^{\circ}$. Kites with one of their unequal
angles fixed to $72^{\circ}$, which have been recently studied via Monte Carlo
simulations, are also considered. We find that rhombuses and kites with two
equal right angles and not too large anisometry stabilise the tetratic phase
but the latter stabilize it to a much higher degree. By contrast, kites with
two equal interior angles fixed to $60^{\circ}$ stabilize the triatic phase to
some extent, although it is very sensitive to changes in particle geometry.
Kites with the two equal interior angles fixed to $75^{\circ}$ have a phase
diagram with both tetratic and triatic phases, but we show the nonexistence of
a particle shape for which both phases are stable at different densities.
Finally the success of the new theory in the description of orientational order
in kites is shown by comparing with Monte Carlo simulations for the case where
one of the unequal angles is fixed to $72^{\circ}$. These particles also
present phase diagrams with stable tetratic and triatic phases.


  We have stabilized the {\alpha}-Fe1.6Ga0.4O3 (Ga doped {\alpha}-Fe2O3) system
in rhombohedral structure. The system has shown magnetically canted
ferromagnetic state and ferroelectric properties at room temperature. In first
time, we confirm the existence of magneto-electric coupling and
multiferro-electric properties at room temperature in the Ga doped
{\alpha}-Fe2O3 system based on the experimental observation of magnetic field
controlled electric polarization and electric field controlled magnetization.
The {\alpha}-Fe2O3 system does not exhibit electric field controlled magnetic
exchange bias effect, where as Ga doped {\alpha}-Fe2O3 showed exchange bias
shift up to the value of 370 Oe. We have recorded the response of
current-voltage characteristics and in-field magnetic relaxation of the system
under the simultaneous application of magnetic and electric fields. The
magnetization of the system is found highly sensitive to the ON and OFF modes,
as well as change of the polarity of external electric field. The system is a
new addition in the list of non-traditional
magneto-electrics/multi-ferroelectrics so far reported in literature. Such
novel materials, where magnetization and electric polarization can be
controlled by simultaneous application of magnetic and electric fields, is in
the increasing demand for potential applications in the field of next
generation magnetic sensor, switching, non-volatile memory and spintronic
devices.


  For unsupervised pretraining, mask-reconstruction pretraining (MRP)
approaches, e.g. MAE and data2vec, randomly mask input patches and then
reconstruct the pixels or semantic features of these masked patches via an
auto-encoder. Then for a downstream task, supervised fine-tuning the pretrained
encoder remarkably surpasses the conventional ``supervised learning'' (SL)
trained from scratch. However, it is still unclear 1) how MRP performs semantic
feature learning in the pretraining phase and 2) why it helps in downstream
tasks. To solve these problems, we first theoretically show that on an
auto-encoder of a two/one-layered convolution encoder/decoder, MRP can capture
all discriminative features of each potential semantic class in the pretraining
dataset. Then considering the fact that the pretraining dataset is of huge size
and high diversity and thus covers most features in downstream dataset, in
fine-tuning phase, the pretrained encoder can capture as much features as it
can in downstream datasets, and would not lost these features with theoretical
guarantees. In contrast, SL only randomly captures some features due to lottery
ticket hypothesis. So MRP provably achieves better performance than SL on the
classification tasks. Experimental results testify to our data assumptions and
also our theoretical implications.


  The engineering of the optical response of materials is a paradigm that
demands microscopic-level accuracy and reliable predictive theoretical tools.
Here we compare and contrast the dispersive permittivity tensor, using both a
low-energy effective model and density functional theory (DFT). As a
representative material, phosphorene subject to strain is considered. Employing
a low-energy model Hamiltonian with a Green's function current-current
correlation function, we compute the dynamical optical conductivity and its
associated permittivity tensor. For the DFT approach, first-principles
calculations make use of the first-order random-phase approximation. Our
results reveal that although the two models are generally in agreement within
the low-strain and low-frequency regime, the intricate features associated with
the fundamental physical properties of the system and optoelectronic device
implementation such as band gap, Drude absorption response, vanishing real
part, absorptivity, and sign of permittivity over the frequency range show
significant discrepancies. Our results suggest that the random-phase
approximation employed in widely used DFT packages should be revisited and
improved to be able to predict these fundamental electronic characteristics of
a given material with confidence. Furthermore, employing the permittivity
results from both models, we uncover the pivotal role that phosphorene can play
in optoelectronics devices to facilitate highly programable perfect absorption
of electromagnetic waves by manipulating the chemical potential and exerting
strain and illustrate how reliable predictions for the dielectric response of a
given material are crucial to precise device design.


  Fanaroff-Riley II low-excitation radio galaxies (FRII-LERGs) are
characterized by weak nuclear excitation on pc-scales and by properties typical
of powerful FRIIs (defined as high-excitation, hereafter HERGs/BLRGs) on
kp-scales. Since a link between the accretion properties and the power of the
produced jets is expected both from theory and observations, their nature is
still debated. In this work we investigate the X-ray properties of a complete
sample of 19 FRII-LERGs belonging to the 3CR catalog, exploiting Chandra and
XMM-Newton archival data. We also analyze 32 FRII-HERGs/BLRGs with Chandra data
as a control sample. We compared FRII-LERG and FRII-HERG/BLRG X-ray properties
and optical data available in literature to obtain a wide outlook of their
behavior. The low accretion rate estimates for FRII-LERGs, from both X-ray and
optical bands, allow us to firmly reject the hypothesis for that they are the
highly obscured counterpart of powerful FRII-HERGs/BLRGs. Therefore, at least
two hypothesis can be invoked to explain the FRII-LERGs nature: (i) they are
evolving from classical FRIIs because of the depletion of accreting cold gas in
the nuclear region, while the extended radio emission is the heritage of a past
efficiently accreting activity; (ii) they are an intrinsically distinct class
of objects with respect to classical FRIs/FRIIs. Surprisingly, in this
direction a correlation between accretion rates and environmental richness is
found in our sample. The richer the environment, the more inefficient is the
accretion. In this framework, the FRII-LERGs are intermediate between FRIs and
FRII-HERGs/BLRGs both in terms of accretion rate and environment.


  This paper investigates the application of a unified non-orthogonal multiple
access framework in beam hopping (U-NOMA-BH) based satellite communication
systems. More specifically, the proposed U-NOMA-BH framework can be applied to
code-domain NOMA based BH (CD-NOMA-BH) and power-domain NOMA based BH
(PD-NOMA-BH) systems. To satisfy dynamic-uneven traffic demands, we formulate
the optimization problem to minimize the square of discrete difference by
jointly optimizing power allocation, carrier assignment and beam scheduling.
The non-convexity of the objective function and the constraint condition is
solved through Dinkelbach's transform and variable relaxation. As a further
development, the closed-from and asymptotic expressions of outage probability
are derived for CD/PD-NOMA-BH systems. Based on approximated results, the
diversity orders of a pair of users are obtained in detail. In addition, the
system throughput of U-NOMA-BH is discussed in delay-limited transmission mode.
Numerical results verify that: i) The gap between traffic requests of
CD/PD-NOMA-BH systems appears to be more closely compared with orthogonal
multiple access based BH (OMA-BH); ii) The CD-NOMA-BH system is capable of
providing the enhanced traffic request and capacity provision; and iii) The
outage behaviors of CD/PD-NOMA-BH are better than that of OMA-BH.