  We describe how the semiclassical theory of radical pair recombination
reactions recently introduced by two of us [D. E. Manolopoulos and P. J. Hore,
J. Chem. Phys. 139, 124106 (2013)] can be generalised to allow for different
singlet and triplet recombination rates. This is a non-trivial generalisation
because when the recombination rates are different the recombination process is
dynamically coupled to the coherent electron spin dynamics of the radical pair.
Furthermore, because the recombination operator is a two-electron operator, it
is no longer sufficient simply to consider the two electrons as classical
vectors: one has to consider the complete set of 16 two-electron spin operators
as independent classical variables. The resulting semiclassical theory is first
validated by comparison with exact quantum mechanical results for a model
radical pair containing 12 nuclear spins. It is then used to shed light on the
spin dynamics of a carotenoid-porphyrin-fullerene (CPF) triad containing
considerably more nuclear spins which has recently been used to establish a
'proof of principle' for the operation of a chemical compass [K. Maeda et al.,
Nature 453, 387 (2008)]. We find in particular that the intriguing biphasic
behaviour that has been observed in the effect of an Earth-strength magnetic
field on the time-dependent survival probability of the photo-excited C+PF-
radical pair arises from a delicate balance between its asymmetric
recombination and the relaxation of the electron spin in the carotenoid
radical.


  With the recent success of computer vision and deep learning, remarkable
progress has been achieved on automatic personal recognition using vein
biometrics. However, collecting large-scale real-world training data for palm
vein recognition has turned out to be challenging, mainly due to the noise and
irregular variations included at the time of acquisition. Meanwhile, existing
palm vein recognition datasets are usually collected under near-infrared light,
lacking detailed annotations on attributes (e.g., pose), so the influences of
different attributes on vein recognition have been poorly investigated.
Therefore, this paper examines the suitability of synthetic vein images
generated to compensate for the urgent lack of publicly available large-scale
datasets. Firstly, we present an overview of recent research progress on palm
vein recognition, from the basic background knowledge to vein anatomical
structure, data acquisition, public database, and quality assessment
procedures. Then, we focus on the state-of-the-art methods that have allowed
the generation of vascular structures for biometric purposes and the modeling
of biological networks with their respective application domains. In addition,
we review the existing research on the generation of style transfer and
biological nature-based synthetic palm vein image algorithms. Afterward, we
formalize a general flowchart for the creation of a synthetic database
comparing real palm vein images and generated synthetic samples to obtain some
understanding into the development of the realistic vein imaging system.
Ultimately, we conclude by discussing the challenges, insights, and future
perspectives in generating synthetic palm vein images for further works.


  Deep learning has emerged as a technique of choice for rapid feature
extraction across imaging disciplines, allowing rapid conversion of the data
streams to spatial or spatiotemporal arrays of features of interest. However,
applications of deep learning in experimental domains are often limited by the
out-of-distribution drift between the experiments, where the network trained
for one set of imaging conditions becomes sub-optimal for different ones. This
limitation is particularly stringent in the quest to have an automated
experiment setting, where retraining or transfer learning becomes impractical
due to the need for human intervention and associated latencies. Here we
explore the reproducibility of deep learning for feature extraction in
atom-resolved electron microscopy and introduce workflows based on ensemble
learning and iterative training to greatly improve feature detection. This
approach both allows incorporating uncertainty quantification into the deep
learning analysis and also enables rapid automated experimental workflows where
retraining of the network to compensate for out-of-distribution drift due to
subtle change in imaging conditions is substituted for a human operator or
programmatic selection of networks from the ensemble. This methodology can be
further applied to machine learning workflows in other imaging areas including
optical and chemical imaging.


  A measurement of the charge asymmetry in the production of top quark pairs in
the semileptonic decay channel has been performed. A dataset corresponding to
an integrated luminosity of 1.04 inverse femtobarn, obtained at a
centre-of-mass energy of 7 TeV with the ATLAS experiment at the LHC, was used.
After performing a selection of events with one isolated lepton, at least four
jets and missing transverse energy, a kinematic fit was performed to
reconstruct the top-antitop event topology. The charge asymmetry was determined
using the differential distribution of the difference of absolute reconstructed
rapidities of the top and antitop quark. An unfolding procedure was applied to
correct for detector acceptance and resolution effects and to obtain the
corresponding distribution at parton level. The total charge asymmetry after
unfolding was measured to be A_C = -0.018 +/- 0.028 (stat.) +/- 0.023 (syst.)
in agreement with the Standard Model prediction. In addition, a simultaneous
unfolding in the difference of absolute reconstructed rapidities of the top and
antitop quark and the invariant mass of the top-antitop pair was performed.


  The main function of IDS (Intrusion Detection System) is to protect the
system, analyze and predict the behaviors of users. Then these behaviors will
be considered an attack or a normal behavior. Though IDS has been developed for
many years, the large number of return alert messages makes managers maintain
system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM
(Support Vector Machine) to detect intrusions. First, RST is used to preprocess
the data and reduce the dimensions. Next, the features were selected by RST
will be sent to SVM model to learn and test respectively. The method is
effective to decrease the space density of data. The experiments will compare
the results with different methods and show RST and SVM schema could improve
the false positive rate and accuracy.


  We consider model of a complex particle that consists of a rigid shell and a
nucleus with spatial asymmetric interaction. The particle's dynamics with the
nucleus driven by a periodic excitation is considered. It is shown that
unidirectional self-propulsed particle motion arises in the absence of spatial
and temporary asymmetry of external potentials and influences. Transport modes
are the general case of complex particle dynamics in the presence of nonlinear
friction or periodic external potential. The changes of average transport
velocity and direction of transport are determined by qualitative changes of
internal dynamics regimes: local attractor bifurcations in the internal phase
space of the complex particle. Finally, microbiological relevance of the
proposed model is briefly discussed.


  Particle ratios are important observables used to constrain models of
particle production in heavy-ion collisions. In this work we report on a
measurement of the p/pi ratio in the transverse momentum range 2.0 <
p_{T,assoc} < 4.0 GeV/c, associated with a charged trigger particle of 5.0 <
p_{T,trig} < 10.0 GeV/c, in 0-10% central Pb--Pb collisions at
sqrt{s_{NN}}=2.76 TeV. The ratio is measured in the jet peak and in a region at
large Delta eta separation from the peak (bulk region). The presented results
are based on 14M minimum-bias Pb--Pb collisions, recorded by the ALICE
detector. It is observed that the p/pi ratio in the bulk region is compatible
with the p/pi ratio of an inclusive measurement, and is much larger than the
p/pi ratio in the jet peak. The p/pi ratio in the jet peak is compatible with a
PYTHIA reference, in which fragmentation in the vacuum is the dominant
mechanism of particle production.


  We report the discovery of PSR J1838-0537, a gamma-ray pulsar found through a
blind search of data from the Fermi Large Area Telescope (LAT). The pulsar has
a spin frequency of 6.9 Hz and a frequency derivative of -2.2e-11 Hz/s,
implying a young characteristic age of 4970 years and a large spin-down power
of 5.9e36 erg/s. Follow-up observations with radio telescopes detected no
pulsations, thus PSR J1838-0537 appears radio-quiet as viewed from Earth. In
September 2009 the pulsar suffered the largest glitch so far seen in any
gamma-ray-only pulsar, causing a relative increase in spin frequency of about
5.5e-6. After the glitch, during a putative recovery period, the timing
analysis is complicated by the sparsity of the LAT photon data, the weakness of
the pulsations, and the reduction in average exposure from a coincidental,
contemporaneous change in the LAT's sky-survey observing pattern. The pulsar's
sky position is coincident with the spatially extended TeV source HESS
J1841-055 detected by the High Energy Stereoscopic System (H.E.S.S.). The
inferred energetics suggest that HESS J1841-055 contains a pulsar wind nebula
powered by the pulsar.


  Coherent quantum optics, where the interaction of a photon with an emitter
does not scramble phase coherence, lies at the heart of many quantum optical
effects and emerging technologies. Solid-state emitters coupled to nanophotonic
waveguides are a promising platform for quantum devices, as this combination is
scalable. Yet, reaching full coherence in these systems is challenging due to
the dynamics of the solid-state environment of the emitters. Here, we review
progress towards coherent light-matter interactions with solid-state quantum
emitters coupled to nanophotonic waveguides. We first lay down the theoretical
foundation for coherent and nonlinear light-matter interactions of a two-level
system in a quasi-one-dimensional system, and then benchmark experimental
realizations. We then discuss higher-order nonlinearities that arise due to the
addition of photons of different frequencies, more complex energy-level schemes
of the emitters, and the coupling of multiple emitters via a shared photonic
mode. Throughout, we highlight protocols for applications and novel effects
that are based on these coherent interactions, the steps taken towards their
realization, and the challenges that remain to be overcome.


  Quantum applications transmit and receive data through quantum and classical
communication channels. Channel capacity, the distance and the photon path
between transmitting and receiving parties and the speed of the computation
links play an essential role in timely synchronization and delivery of
information using classical and quantum channels. In this study, we analyze and
optimize the parameters of the communication channels needed for the quantum
application to successfully operate. We also develop algorithms for
synchronizing data delivery on classical and quantum channels.


  Navigating in environments where the GPS signal is unavailable, weak,
purposefully blocked, or spoofed has become crucial for a wide range of
applications. A prime example is autonomous navigation for drones in indoor
environments: to fly fully or partially autonomously, drones demand accurate
and frequent updates of their locations. This paper proposes a Robust Acoustic
Indoor Localization (RAIL) scheme for drones designed explicitly for GPS-denied
environments. Instead of depending on GPS, RAIL leverages ultrasonic acoustic
signals to achieve precise localization using a novel hybrid Frequency Hopping
Code Division Multiple Access (FH-CDMA) technique. Contrary to previous
approaches, RAIL is able to both overcome the multi-path fading effect and
provide precise signal separation in the receiver. Comprehensive simulations
and experiments using a prototype implementation demonstrate that RAIL provides
high-accuracy three-dimensional localization with an average error of less than
$1.5$~cm.


  We apply a perturbative approach to evaluate the Casimir energy for a
massless real scalar field in 3+1 dimensions, subject to Dirichlet boundary
conditions on two surfaces. One of the surfaces is assumed to be flat, while
the other corresponds to a small deformation, described by a single function
$\eta$, of a flat mirror. The perturbative expansion is carried out up to the
fourth order in the deformation $\eta$, and the results are applied to the
calculation of the Casimir energy for corrugated mirrors in front of a plane.
We also reconsider the proximity force approximation within the context of this
expansion.


  Recently Lewis Bowen introduced a notion of entropy for measure-preserving
actions of a countable sofic group on a standard probability space admitting a
generating partition with finite entropy. By applying an operator algebra
perspective we develop a more general approach to sofic entropy which produces
both measure and topological dynamical invariants, and we establish the
variational principle in this context. In the case of residually finite groups
we use the variational principle to compute the topological entropy of
principal algebraic actions whose defining group ring element is invertible in
the full group C*-algebra.


  Ab initio approach is used to describe the time evolution of the amplitudes
of whispering gallery modes in a system of coupled microdisk resonators with
Kerr nonlinearity. It is shown that this system demonstrates a transition
between Josephson-like nonlinear oscillations and self-trapping behavior.
Manifestation of this transition in the dynamics of radiative losses is
studied.


  In the race to achieve climate goals, many governments and organizations are
encouraging the local development of Renewable Energy Technology (RET). The
spatial innovation dynamics of the development of a technology partly depends
on the characteristics of the knowledge base on which this technology builds,
in particular the analyticity and cumulativeness of knowledge. Theoretically,
greater analyticity and lesser cumulativeness are positively associated with
more widespread development. In this study, we first empirically evaluate these
relations for general technology and then systematically determine the
knowledge base characteristics for a set of 14 different RETs. We find that,
while several RETs (photovoltaics, fuel-cells, energy storage) have a highly
analytic knowledge base and develop more widespread, there are also important
RETs (wind turbines, solar thermal, geothermal and hydro energy) for which the
knowledge base is less analytic and which develop less widespread. Likewise,
the technological cumulativeness tends to be lower for the former than for the
latter group. This calls for regional and country-level policies to be specific
for different RETs, taking for a given RET into account both the type of
knowledge it builds on as well as the local presence of this knowledge.


  We study dynamics of a locally conserved energy in ergodic, local many-body
quantum systems on a lattice with no additional symmetry. The resulting
dynamics is well approximated by a coarse grained, classical linear functional
diffusion equation for the probability of all spatial configurations of energy.
This is equivalent to nonlinear stochastic hydrodynamics, describing the
diffusion of energy in physical spacetime. We find the absence of
non-hydrodynamic slow degrees of freedom, a nonlinear fluctuation-dissipation
theorem, and the emergence of a (weakly interacting) kinetic theory for
hydrodynamic modes near thermal equilibrium. The observable part of the
microscopic entropy obeys the local second law of thermodynamics, and
quantitatively agrees with the phenomenological predictions of hydrodynamics.
Our approach naturally generalizes to ergodic systems with additional
symmetries, may lead to numerical algorithms to calculate diffusion constants
for lattice models, and implies sufficiency conditions for a rigorous
derivation of hydrodynamics in quantum systems.


  We obtain a sharp estimate on the norm of the differential of a harmonic map
from the unit disc $\mathbb D$ in $\mathbb C$ into the unit ball $\mathbb B^n$
in $\mathbb R^n$, $n\ge 2$, at any point where the map is conformal. In
dimension $n=2$, this generalizes the classical Schwarz-Pick lemma, and for
$n\ge 3$ it gives the optimal Schwarz-Pick lemma for conformal minimal discs
$\mathbb D\to \mathbb B^n$. This implies that conformal harmonic immersions $M
\to \mathbb B^n$ from any hyperbolic conformal surface are distance-decreasing
in the Poincar$\mathrm{\'e}$ metric on $M$ and the Cayley-Klein metric on the
ball $\mathbb B^n$, and the extremal maps are precisely the conformal
embeddings of the disc $\mathbb D$ onto affine discs in $\mathbb B^n$. By using
these results, we lay the foundations of the hyperbolicity theory for domains
in $\mathbb R^n$ based on minimal surfaces.


  We discuss semiclassical Nariai black holes in the framework of
$f(T)$-gravity. For a diagonal choice of tetrades, stable Nariai metrics can be
found, emitting Bekenstein-Hawking radiation in semiclassical limit. However,
for a non-diagonal choice of tetrades, evaporation and antievaporation
instabilities are turned on. In turn, this causes a back-reaction effect
suppressing the Bekenstein-Hawking radiation. In particular, evaporation
instabilities produce a new radiation -- different by Bekenstein-Hawking
emission -- non-violating unitarity in particle physics sector.


  To gain insight in the quantum nature of the big bang, we study the dual
field theory description of asymptotically anti-de Sitter solutions of
supergravity that have cosmological singularities. The dual theories do not
appear to have a stable ground state. One regularization of the theory causes
the cosmological singularities in the bulk to turn into giant black holes with
scalar hair. We interpret these hairy black holes in the dual field theory and
use them to compute a finite temperature effective potential. In our study of
the field theory evolution, we find no evidence for a "bounce" from a big
crunch to a big bang. Instead, it appears that the big bang is a rare
fluctuation from a generic equilibrium quantum gravity state.


  We first review asymptotic twistor theory with its real subspace of null
asymptotic twistors. This is followed by a description of an asymptotic version
of the Kerr theorem that produces regular asymptotically shear free null
geodesic congruences in arbitrary asymptotically flat Einstein or
Einstein-Maxwell spacetimes.


  Computing the probability of a formula given the probabilities or weights
associated with other formulas is a natural extension of logical inference to
the probabilistic setting. Surprisingly, this problem has received little
attention in the literature to date, particularly considering that it includes
many standard inference problems as special cases. In this paper, we propose
two algorithms for this problem: formula decomposition and conditioning, which
is an exact method, and formula importance sampling, which is an approximate
method. The latter is, to our knowledge, the first application of model
counting to approximate probabilistic inference. Unlike conventional
variable-based algorithms, our algorithms work in the dual realm of logical
formulas. Theoretically, we show that our algorithms can greatly improve
efficiency by exploiting the structural information in the formulas.
Empirically, we show that they are indeed quite powerful, often achieving
substantial performance gains over state-of-the-art schemes.


  The human islet amyloid polypeptide (hIAPP) co-operates with insulin to
maintain glycemic balance. It also constitutes the amyloid plaques that
aggregate in the pancreas of type-II diabetic patients. We have performed
extensive in silico investigations to analyse the structural landscape of
monomeric hIAPP, which is presumed to be intrinsically disordered. For this we
construct from first principles a highly predictive energy function that
describes a monomeric hIAPP observed in a NMR experiment, as a local energy
minimum. We subject our theoretical model of hIAPP to repeated heating and
cooling simulations, back and forth between a high temperature regime where the
conformation resembles a random walker and a low temperature limit where no
thermal motions prevail. We find that the final low temperature conformations
display a high level of degeneracy, in a manner which is fully in line with the
presumed intrinsically disordered character of hIAPP. In particular, we
identify an isolated family of alpha-helical conformations that might cause the
transition to amyloidosis, by nucleation.


  We calculate a Ricci curvature lower bound for some classical examples of
random walks, namely, a chain on a slice of the n-dimensional discrete cube
(the so-called Bernoulli-Laplace model) and the random transposition shuffle of
the symmetric group of permutations on n letters.


  We explore the field of wildlife re-identification by implementing an MLP
from scratch using NumPy, A DCNN using Keras, and a binary classifier with
LightGBM for the purpose of learning for an assignment. Analyzing the
performance of multiple models on multiple datasets. We attempt to replicate
prior research in metric learning for wildlife re-identification. Firstly, we
find that the usage of MLPs trained for classification, then removing the
output layer and using the second last layer as an embedding was not a
successful strategy for similar learning; it seems like losses designed for
embeddings such as triplet loss are required. The DCNNS performed well on some
datasets but poorly on others, which did not align with findings in previous
literature. The LightGBM classifier overfitted too heavily and was not
significantly better than a constant model when trained and evaluated on all
pairs using accuracy as a metric. The technical implementations used seem to
match standards according to comparisons with documentation examples and good
results on certain datasets. However, there is still more to explore in regards
to being able to fully recreate past literature.


  In this article I review the origin and the effects of chirally enhanced
loop-corrections in the MSSM based on Refs. [1-3]. Chiral enhancement is
related to fermion-Higgs couplings (or self-energies when the Higgs field is
replaced by its vev). I describe the resummation of these chirally-enhanced
corrections to all orders in perturbation theory and the calculation of the
effective fermion-Higgs and gaugino(higgsino)-fermion vertices. As an
application a model with radiative flavor-violation is discussed which can
solve the SUSY-CP and the SUSY-flavor problem while it is still capable of
explaining the observed deviation from the SM in the Bs mixing phase.


  We consider generalized quantum Ising models, including those which could
describe disordered materials or quantum annealers, and we prove that for all
temperatures above a system-size independent threshold the path integral Monte
Carlo method based on worldline heat-bath updates always mixes to stationarity
in time $\mathcal{O}(n \log n)$ for an $n$ qubit system, and therefore provides
a fully polynomial-time approximation scheme for the partition function. This
result holds whenever the temperature is greater than four plus twice the
maximum interaction degree (valence) over all qubits, measured in units of the
local coupling strength. For example, this implies that the classical
simulation of the thermal state of a superconducting device modeling a
frustrated quantum Ising model with maximum valence of 6 and coupling strengths
of 1 GHz is always possible at temperatures above 800 mK. Despite the quantum
system being at high temperature, the classical spin system resulting from the
quantum-to-classical mapping contains strong couplings which cause the
single-site Glauber dynamics to mix slowly, therefore this result depends on
the use of worldline updates (which are a form of cluster updates that can be
implemented efficiently). This result places definite constraints on the
temperatures required for a quantum advantage in analog quantum simulation with
various NISQ devices based on equilibrium states of quantum Ising models.


  In this paper we propose a novel observer-based method to improve the safety
and security of connected and automated vehicle (CAV) transportation. The
proposed method combines model-based signal filtering and anomaly detection
methods. Specifically, we use adaptive extended Kalman filter (AEKF) to smooth
sensor readings of a CAV based on a nonlinear car-following motion model. Under
the assumption of a car-following model, the subject vehicle utilizes its
leading vehicle's information to detect sensor anomalies by employing
previously-trained One Class Support Vector Machine (OCSVM) models. This
approach allows the AEKF to estimate the state of a vehicle not only based on
the vehicle's location and speed, but also by taking into account the state of
the surrounding traffic. A communication time delay factor is considered in the
car-following model to make it more suitable for real-world applications. Our
experiments show that compared with the AEKF with a traditional
$\chi^2$-detector, our proposed method achieves a better anomaly detection
performance. We also demonstrate that a larger time delay factor has a negative
impact on the overall detection performance.


  This paper presents a specific man-in-the-middle exploit: Ambient Tactical
Deception (ATD) in online communication, realized via a malicious web browser
extension. Extensions manipulate web content in unobtrusive ways as ambient
intermediaries of the overall browsing experience. In our previous work, we
demonstrated that it is possible to employ tactical deception by making covert
changes in the text content of a web page, regardless of the source. In this
work, we investigated the application of ATD in a web-based email discourse
where the objective is to manipulate the interpersonal perception without the
knowledge of the involved parties. We focus on web-based email text because it
is asynchronous and usually revised for clarity and politeness. Previous
research has demonstrated that people's perception of politeness in online
communication is based on three factors: the degree of imposition, the power of
the receiver over the sender, and the social distance between them. We
interviewed participants about their perception of these factors to establish
the plausibility of ATD for email discourse. The results indicate that by
covertly altering the politeness strategy in an email, it is possible for an
ATD attacker to manipulate the receiver's perception on all of the politeness
factors. Our findings support the Brown and Levinson's politeness theory and
Walther's hyperpersonal model of email communication.


  We showcase applications of nonlinear algebra in the sciences and
engineering. Our review is organized into eight themes: polynomial
optimization, partial differential equations, algebraic statistics, integrable
systems, configuration spaces of frameworks, biochemical reaction networks,
algebraic vision, and tensor decompositions. Conversely, developments on these
topics inspire new questions and algorithms for algebraic geometry.


  We consider the sin(phi) single target-spin asymmetry in deep-inelastic
pi^{+} inclusive electroproduction off a longitudinally polarized target. We
show that at larger transverse momentum of the outgoing hadron the evaluated
asymmetry decreases if one takes into account the first order alpha_S
perturbative contribution to the cross section, integrated over the azimuthal
angle. This leads to good agreement with recent HERMES data.


  Cryptography is the science of encrypting the information so that it is
rendered unreadable for an intruder. Cryptographic techniques are of utmost
importance in today's world as the information to be sent might be of
invaluable importance to both the sender and the receiver. Various
cryptographic techniques ensure that even if an intruder intercepts the sent
information, he is not able to decipher it thus render ending it useless for
the intruder. Cryptography can be grouped into two types, that is Symmetric key
cryptography and Asymmetric key cryptography. Symmetric key cryptography uses
the same key for encryption as well as decryption thus making it faster
compared to Asymmetric Key cryptography which uses different keys for
encryption and decryption. Generation of dynamic keys for Symmetric key
cryptography is an interesting field and in this we have tapped this field so
as to generate dynamic keys for symmetric key cryptography. In this work, we
have devised an algorithm for generating dynamic keys for sending messages over
a communication channel and also solving key refreshment problem.


  The construction of a dendogram on a set of individuals is a key component of
a genomewide association study. However even with modern sequencing
technologies the distances on the individuals required for the construction of
such a structure may not always be reliable making it tempting to exclude them
from an analysis. This, in turn, results in an input set for dendogram
construction that consists of only partial distance information which raises
the following fundamental question. For what subset of its leaf set can we
reconstruct uniquely the dendogram from the distances that it induces on that
subset. By formalizing a dendogram in terms of an edge-weighted, rooted
phylogenetic tree on a pre-given finite set X with |X|>2 whose edge-weighting
is equidistant and a set of partial distances on X in terms of a set L of
2-subsets of X, we investigate this problem in terms of when such a tree is
lassoed, that is, uniquely determined by the elements in L. For this we
consider four different formalizations of the idea of "uniquely determining"
giving rise to four distinct types of lassos. We present characterizations for
all of them in terms of the child-edge graphs of the interior vertices of such
a tree. Our characterizations imply in particular that in case the tree in
question is binary then all four types of lasso must coincide.


  We discuss the excitation of a chiral dipolar mode in an achiral silicon
nanoparticle. In particular, we make use of the electric and magnetic
polarizabilities of the silicon nanoparticle to construct this chiral
electromagnetic mode which is conceptually similar to the fundamental modes of
3D chiral nanostructures or molecules. We describe the chosen tailored
excitation with a beam carrying neither spin nor orbital angular momentum and
investigate the emission characteristics of the chiral dipolar mode in the
helicity basis, consisting of parallel electric and magnetic dipole moments,
phase shifted by $\pm \pi/2$. We demonstrate the wavelength dependence and
measure the spin and orbital angular momentum in the emission of the excited
chiral mode.


  One issue limiting the adaption of large-scale multi-region segmentation is
the sometimes prohibitive memory requirements. This is especially troubling
considering advances in massively parallel computing and commercial graphics
processing units because of their already limited memory compared to the
current random access memory used in more traditional computation. To address
this issue in the field of continuous max-flow segmentation, we have developed
a \textit{pseudo-flow} framework using the theory of Bregman proximal
projections and entropic distances which implicitly represents flow variables
between labels and designated source and sink nodes. This reduces the memory
requirements for max-flow segmentation by approximately 20\% for Potts models
and approximately 30\% for hierarchical max-flow (HMF) and directed acyclic
graph max-flow (DAGMF) models. This represents a great improvement in the
state-of-the-art in max-flow segmentation, allowing for much larger problems to
be addressed and accelerated using commercially available graphics processing
hardware.


  Intensity interferometry is a well known method in astronomy. Recently, a
related method called incoherent diffractive imaging (IDI) was proposed to
apply intensity correlations of x-ray fluorescence radiation to determine the
3D arrangement of the emitting atoms in a sample. Here we discuss inherent
sources of noise affecting IDI and derive a model to estimate the dependence of
the signal to noise ratio (SNR) on the photon counts per pixel, the temporal
coherence (or number of modes), and the shape of the imaged object. Simulations
in two- and three-dimensions have been performed to validate the predictions of
the model. We find that contrary to coherent imaging methods, higher
intensities and higher detected counts do not always correspond to a larger
SNR. Also, larger and more complex objects generally yield a poorer SNR despite
the higher measured counts. The framework developed here should be a valuable
guide to future experimental design.


  In order to reproduce the statistical properties of the observed exoplanets,
population synthesis models have shown that the migration of protoplanets
should be significantly slowed down, and that processes stalling migration
should be at work. Much current theoretical efforts have thus been dedicated to
find physical effects that slow down, halt or even reverse migration. Many of
these studies rely on the horseshoe drag, whose long term-evolution (saturated
or not) is intimately related to the disk viscosity in laminar disk models. We
investigate how the horseshoe drag exerted on a low-mass planet is altered by a
more realistic treatment of the turbulence in protoplanetary disks.
Two-dimensional hydrodynamic simulations are performed with a turbulence model
that reproduces the main turbulence properties of three-dimensional
magnetohydrodynamic calculations. We find that the horseshoe drag can remain
unsaturated on the long term, depending on the turbulence strength. We show
that the desaturation of the horseshoe drag by turbulence can be modeled by
vortensity diffusion across the time-averaged planet's horseshoe region. At
low-turbulence, the running-time averaged torque is in good agreement with the
total torque obtained for an equivalent laminar model, with a similar
vortensity's diffusion coefficient. At high-turbulence, differences arise due
to the time-evolution of the averaged density profile with turbulence.


  A non perturbative numerical method for determining the discrete spectra is
deduced from the classical analogue of the Schrodinger's equation. The energy
eigenvalues coincide with the bifurcation parameters for the classical orbits.


  The field of Continual Learning (CL) has inspired numerous researchers over
the years, leading to increasingly advanced countermeasures to the issue of
catastrophic forgetting. Most studies have focused on the single-class
scenario, where each example comes with a single label. The recent literature
has successfully tackled such a setting, with impressive results. Differently,
we shift our attention to the multi-label scenario, as we feel it to be more
representative of real-world open problems. In our work, we show that existing
state-of-the-art CL methods fail to achieve satisfactory performance, thus
questioning the real advance claimed in recent years. Therefore, we assess both
old-style and novel strategies and propose, on top of them, an approach called
Selective Class Attention Distillation (SCAD). It relies on a knowledge
transfer technique that seeks to align the representations of the student
network -- which trains continuously and is subject to forgetting -- with the
teacher ones, which is pretrained and kept frozen. Importantly, our method is
able to selectively transfer the relevant information from the teacher to the
student, thereby preventing irrelevant information from harming the student's
performance during online training. To demonstrate the merits of our approach,
we conduct experiments on two different multi-label datasets, showing that our
method outperforms the current state-of-the-art Continual Learning methods. Our
findings highlight the importance of addressing the unique challenges posed by
multi-label environments in the field of Continual Learning. The code of SCAD
is available at https://github.com/aimagelab/SCAD-LOD-2024.


  We obtained SDSS spectra for a set of 37 radio-quiet quasars (RQQSOs) that
had been previously examined for rapid small scale optical variations, or
microvariability. Their H-beta and Mg II emission lines were carefully fit to
determine line widths (FWHM) as well as equivalent widths (EW) due to the broad
emission line components. The line widths were used to estimate black hole
masses and Eddington ratios, ell. Both EW and FWHM are anticorrelated with ell.
The EW distributions provide no evidence for the hypothesis that a weak jet
component in the RQQSOs is responsible for their microvariability.


  This paper investigates the online motion coordination problem for a group of
mobile robots moving in a shared workspace. Based on the realistic assumptions
that each robot is subject to both velocity and input constraints and can have
only local view and local information, a fully distributed multi-robot motion
coordination strategy is proposed. Building on top of a cell decomposition, a
conflict detection algorithm is presented first. Then, a rule is proposed to
assign dynamically a planning order to each pair of neighboring robots, which
is deadlock-free. Finally, a two-step motion planning process that combines
fixed-path planning and trajectory planning is designed. The effectiveness of
the resulting solution is verified by a simulation example.


  For a finite subgroup $G$ of $\operatorname{GL}(2, \mathbb C)$, we consider
the moduli space ${\mathcal M}_\theta$ of $G$-constellations. It depends on the
stability parameter $\theta$ and if $\theta$ is generic it is a resolution of
singularities of $\mathbb C^2/G$. In this paper, we show that a resolution $Y$
of $\mathbb C^2/G$ is isomorphic to ${\mathcal M}_\theta$ for some generic
$\theta$ if and only if $Y$ is dominated by the maximal resolution under the
assumption that $G$ is abelian or small.


  We discuss the art and science of producing conformally correct euclidean and
hyperbolic tilings of compact surfaces. As an example, we present a tiling of
the Chmutov surface by hyperbolic (2, 4, 6) triangles.


  We prove the optimal $W^{2, \infty }$ regularity for fully nonlinear elliptic
equations with convex gradient constraints. We do not assume any regularity
about the constraints; so the constraints need not be $C^1$ or strictly convex.
We also show that the optimal regularity holds up to the boundary. Our approach
is to show that these elliptic equations with gradient constraints are related
to some fully nonlinear double obstacle problems. Then we prove the optimal
$W^{2, \infty }$ regularity for the double obstacle problems. In this process,
we also employ the monotonicity property for the second derivative of
obstacles, which we have obtained in a previous work.


  Recently, two-dimensional band insulators with a topologically nontrivial
(almost) flat band has been studied extensively, which can realize integer and
fractional quantum Hall effect in a system without an orbital magnetic field.
Realizing a topological flat band generally requires longer range hoppings in a
lattice Hamiltonian. It is natural to ask what is the minimal hopping range
required.% for a topological flat-band Hamiltonian. In this paper, we prove
that the mean hopping range of the flat-band Hamiltonian with Chern number
$C_1$ and total number of bands $N$ has a universal lower bound of
$\sqrt{4|C_1|/\pi N}$. Furthermore, for the Hamiltonians that reach this lower
bound, the Bloch wavefunctions of the topological flat band are instanton
solutions of a $CP(N-1)$ non-linear $\sigma$ model on the Brillouin zone torus,
which are elliptic functions up to a normalization factor.


  The backreaction equations for the linearized quantum fluctuations in an
acoustic black hole are given. The solution near the horizon, obtained within a
dimensional reduction, indicates that acoustic black holes, unlike
Schwarzschild ones, get cooler as they radiate phonons. They show remarkable
analogies with near-extremal Reissner-Nordstrom black holes.


  A major challenge in deploying the smallest of Micro Aerial Vehicle (MAV)
platforms (< 100 g) is their inability to carry sensors that provide
high-resolution metric depth information (e.g., LiDAR or stereo cameras).
Current systems rely on end-to-end learning or heuristic approaches that
directly map images to control inputs, and struggle to fly fast in unknown
environments. In this work, we ask the following question: using only a
monocular camera, optical odometry, and offboard computation, can we create
metrically accurate maps to leverage the powerful path planning and navigation
approaches employed by larger state-of-the-art robotic systems to achieve
robust autonomy in unknown environments? We present MonoNav: a fast 3D
reconstruction and navigation stack for MAVs that leverages recent advances in
depth prediction neural networks to enable metrically accurate 3D scene
reconstruction from a stream of monocular images and poses. MonoNav uses
off-the-shelf pre-trained monocular depth estimation and fusion techniques to
construct a map, then searches over motion primitives to plan a collision-free
trajectory to the goal. In extensive hardware experiments, we demonstrate how
MonoNav enables the Crazyflie (a 37 g MAV) to navigate fast (0.5 m/s) in
cluttered indoor environments. We evaluate MonoNav against a state-of-the-art
end-to-end approach, and find that the collision rate in navigation is
significantly reduced (by a factor of 4). This increased safety comes at the
cost of conservatism in terms of a 22% reduction in goal completion.


  Over recent years a lot of research papers and studies have been published on
the development of effective approaches that benefit from a large amount of
user-generated content and build intelligent predictive models on top of them.
This research applies machine learning-based approaches to tackle the hurdles
that come with Persian user-generated textual content. Unfortunately, there is
still inadequate research in exploiting machine learning approaches to
classify/cluster Persian text. Further, analyzing Persian text suffers from a
lack of resources; specifically from datasets and text manipulation tools.
Since the syntax and semantics of the Persian language is different from
English and other languages, the available resources from these languages are
not instantly usable for Persian. In addition, recognition of nouns and
pronouns, parts of speech tagging, finding words' boundary, stemming or
character manipulations for Persian language are still unsolved issues that
require further studying. Therefore, efforts have been made in this research to
address some of the challenges. This presented approach uses a
machine-translated datasets to conduct sentiment analysis for the Persian
language. Finally, the dataset has been rehearsed with different classifiers
and feature engineering approaches. The results of the experiments have shown
promising state-of-the-art performance in contrast to the previous efforts; the
best classifier was Support Vector Machines which achieved a precision of
91.22%, recall of 91.71%, and F1 score of 91.46%.


  Money laundering is a crime that makes it possible to finance other crimes,
for this reason, it is important for criminal organizations and their combat is
prioritized by nations around the world. The anti-money laundering process has
not evolved as expected because it has prioritized only the signaling of
suspicious transactions. The constant increasing in the volume of transactions
has overloaded the indispensable human work of final evaluation of the
suspicions. This article presents a multiagent system that aims to go beyond
the capture of suspicious transactions, seeking to assist the human expert in
the analysis of suspicions. The agents created use data mining techniques to
create transactional behavioral profiles; apply rules generated in learning
process in conjunction with specific rules based on legal aspects and profiles
created to capture suspicious transactions; and analyze these suspicious
transactions indicating to the human expert those that require more detailed
analysis.


  For a homeomorphism $T$ on a compact metric space $X$, a $T$-invariant Borel
probability measure $\mu$ on $X$ and a measure-theoretic quasifactor
$\widetilde{\mu}$ of $\mu$, we study the relationship between the local entropy
of the system $(X,\mu,T)$ and of its induced system
$(\mathcal{M}(X),\widetilde{\mu},\widetilde{T})$, where $\widetilde{T}$ is the
homeomorphism induced by $T$ on the space $\mathcal{M}(X)$ of all Borel
probability measures defined on $X$.


  These are lectures for students at the summer school conducted by the Faculty
of Fundamental Sciences, Bauman Moscow State Technical University at 2022.


  The X-ray Background has been resolved in the 0.5-5keV band and found to
consist mostly of both unabsorbed and absorbed AGN with column densities <1e23.
This contrasts with the local AGN population where the column density range
extends to Compton-thick objects and beyond (>1e24). Stacking analysis of the
integrated emission of sources detected by XMM-Newton in the Lockman Hole, and
by Chandra in the CDF-N and S reveals that the resolved fraction of the X-ray
Background drops above 6keV and is about 50% above 8keV. The missing flux has
the spectrum of highly absorbed AGN, making it likely that the range of column
density at redshift one is similar to that locally, and that many AGN are as
yet undetected in well-studied fields.


  This study is done in the context of the project titled Interplanetary Laser
Tri-lateration Network (ILTN) proposed by \cite{2018P&SS..153..127S} and
investigated more in details by \cite{2022P&SS..21405415B} and
\cite{2022P&SS..21505423B}. The original idea was to propose interplanetary
measurements (in this case between Venus, Mars and the earth) as a way to
measure the solar system expansion. But some recent interests on the
measurement of asteroid masses and more generally the study of the mass
distribution in the outer solar system appear with the ILTN. In this work, we
are investigating how different possible configurations of interplanetary
measurements of distances can be introduced in planetary ephemeris construction
and how they improve our knowledge of planet orbits and other related
parameters.


  We develop an accurate, highly efficient and scalable random batch Ewald
(RBE) method to conduct simulations in the isothermal-isobaric ensemble (the
NPT ensemble) for charged particles in a periodic box. After discretizing the
Langevin equations of motion derived using suitable Lagrangians, the RBE method
builds the mini-batch strategy into the Fourier space in the Ewald summation
for the pressure and forces so the computational cost is reduced from
$\mathcal{O}(N^2)$ to $\mathcal{O}(N)$ per time step. We implement the method
in the LAMMPS package and report accurate simulation results for both dynamical
quantities and statistics for equilibrium for typical systems including
all-atom bulk water and a semi-isotropic membrane system. Numerical simulations
on massive supercomputing cluster are also performed to show promising CPU
efficiency of RBE.


  We report new Chandra hard X-ray ($>2\rm~keV$) and JVLA C-band observations
of the nuclear superbubble of NGC 3079, an analog of the "Fermi bubble" in our
Milky Way. We detect extended hard X-ray emission on the SW side of the
galactic nucleus with coherent multi-wavelength features in radio, H$\alpha$,
and soft X-ray. The hard X-ray feature has a cone shape with possibly a weak
cap, forming a bubble-like structure with a diameter of $\sim1.1\rm~kpc$. A
similar extended feature, however, is not detected on the NE side, which is
brighter in all other wavelengths such as radio, H$\alpha$, and soft X-ray.
Scattered photons from the nuclear region or other nearby point-like X-ray
bright sources, inverse Compton emission from cosmic ray electrons via
interaction with the cosmic microwave background, or any individually faint
stellar X-ray source populations, cannot explain the extended hard X-ray
emission on the SW side and the strongly NE/SW asymmetry. A synchrotron
emission model, plus a thermal component accounting for the excess at
$\sim1\rm~keV$, can well characterize the broadband radio/hard X-ray spectra.
The broadband synchrotron spectra do not show any significant cutoff, and even
possibly slightly flatten at higher energy. This rules out a loss-limited
scenario in the acceleration of the cosmic ray electrons in or around this
superbubble. As the first detection of kpc-scale extended hard X-ray emission
associated with a galactic nuclear superbubble, the spatial and spectral
properties of the multi-wavelength emissions indicate that the cosmic ray
leptons responsible for the broad-band synchrotron emission from the SW bubble
must be accelerated in situ, instead of transported from the nuclear region of
the galaxy.


  We study, using Monte Carlo simulations, the interaction between charged
colloidal particles confined to the air-water interface. The dependence of
force on ionic strength and counterion valence is explored. For 1:1
electrolyte, we find that the electrostatic interaction at the interface is
very close to the one observed in the bulk. On the other hand, for salts with
multivalent counterions, an interface produces an enhanced attraction between
like charged colloids. Finally, we explore the effect of induced surface charge
at the air-water interface on the interaction between colloidal particles.


  Sentiment analysis in software engineering (SE) has shown promise to analyze
and support diverse development activities. We report the results of an
empirical study that we conducted to determine the feasibility of developing an
ensemble engine by combining the polarity labels of stand-alone SE-specific
sentiment detectors. Our study has two phases. In the first phase, we pick five
SE-specific sentiment detection tools from two recently published papers by Lin
et al. [31, 32], who first reported negative results with standalone sentiment
detectors and then proposed an improved SE-specific sentiment detector, POME
[31]. We report the study results on 17,581 units (sentences/documents) coming
from six currently available sentiment benchmarks for SE. We find that the
existing tools can be complementary to each other in 85-95% of the cases, i.e.,
one is wrong, but another is right. However, a majority voting-based ensemble
of those tools fails to improve the accuracy of sentiment detection. We develop
Sentisead, a supervised tool by combining the polarity labels and bag of words
as features. Sentisead improves the performance (F1-score) of the individual
tools by 4% (over Senti4SD [5]) - 100% (over POME [31]). In a second phase, we
compare and improve Sentisead infrastructure using Pre-trained Transformer
Models (PTMs). We find that a Sentisead infrastructure with RoBERTa as the
ensemble of the five stand-alone rule-based and shallow learning SE-specific
tools from Lin et al. [31, 32] offers the best F1-score of 0.805 across the six
datasets, while a stand-alone RoBERTa shows an F1-score of 0.801.


  In multitype lattice gas models with hard-core interaction of
Widom--Rowlinson type, there is a competition between the entropy due to the
large number of types, and the positional energy and geometry resulting from
the exclusion rule and the activity of particles. We investigate this
phenomenon in four different models on the square lattice: the multitype
Widom-Rowlinson model with diamond-shaped resp. square-shaped exclusion between
unlike particles, a Widom-Rowlinson model with additional molecular exclusion,
and a continuous-spin Widom-Rowlinson model. In each case we show that this
competition leads to a first-order phase transition at some critical value of
the activity, but the number and character of phases depend on the geometry of
the model. Our technique is based on reflection positivity and the chessboard
estimate.


  We present some cosmological solutions of Brans-Dicke theory, characterized
by a decaying vacuum energy density and by a constant relative matter density.
With these features, they shed light on the cosmological constant problems,
leading to a presently small vacuum term, and to a constant ratio between the
vacuum and matter energy densities. By fixing the only free parameter of our
solutions, we obtain cosmological parameters in accordance with observations of
the relative matter density, the universe age and redshift-distance relations.


  The nuclear polarization of $\mathrm{H}_2$ molecules formed by recombination
of nuclear polarized H atoms on the surface of a storage cell initially coated
with a silicon-based polymer has been measured by using the longitudinal
double-spin asymmetry in deep-inelastic positron-proton scattering. The
molecules are found to have a substantial nuclear polarization, which is
evidence that initially polarized atoms retain their nuclear polarization when
absorbed on this type of surface


  We present the analysis of photospheric emission for a set of hydrodynamic
simulations of long duration gamma-ray burst jets from massive compact stars.
The results are obtained by using the Monte Carlo Radiation Transfer code
(MCRaT) to simulate thermal photons scattering through the collimated outflows.
MCRaT allows us to study explicitly the time evolution of the photosphere
within the photospheric region, as well as the gradual decoupling of the photon
and matter counterparts of the jet. The results of the radiation transfer
simulations are also used to construct light curves and time resolved spectra
at various viewing angles, which are then used to make comparisons with
observed data, and outline the agreement and strain points between the
photospheric model and long duration gamma-ray burst observations. We find that
our fitted time resolved spectral Band $\beta$ parameters are in agreement with
observations, even though we do not consider the effects of non-thermal
particles. Finally, the results are found to be consistent with the Yonetoku
correlation, but bear some strain with the Amati correlation.


  The sequence of rings $H^n, n\ge 0,$ introduced in math.QA/0103190, controls
categorification of the quantum sl(2) invariant of tangles. We prove that the
center of $H^n$ is isomorphic to the cohomology ring of the (n,n) Springer
variety and show that the braid group action in the derived category of
$H^n$-modules descends to the Springer action of the symmetric group.


  We study the phenomenology of the heavy charged gauge boson and obtain the
lower bounds on its mass with the early LHC data at 7 TeV center-of-mass energy
in the nonuniversal gauge interaction model, in which the electroweak SU(2)
gauge group depends upon the fermion family. We found that the direct bound on
the mass of the $W'$ boson is compatible to the indirect bound with only the
early data of the LHC.


  This paper describes the generation of initial conditions for numerical
simulations in cosmology with multiple levels of resolution, or multiscale
simulations. We present the theory of adaptive mesh refinement of Gaussian
random fields followed by the implementation and testing of a computer code
package performing this refinement called GRAFIC2. This package is available to
the computational cosmology community at http://arcturus.mit.edu/grafic/ or by
email from the author.


  We present a line of research aimed at investigating holographic dualities in
the context of three dimensional quantum gravity within finite bounded regions.
The bulk quantum geometrodynamics is provided by the Ponzano-Regge state-sum
model, which defines 3d quantum gravity as a discrete topological quantum field
theory (TQFT). This formulation provides an explicit and detailed definition of
the quantum boundary states, which allows a rich correspondence between quantum
boundary conditions and boundary theories, thereby leading to holographic
dualities between 3d quantum gravity and 2d statistical models as used in
condensed matter. After presenting the general framework, we focus on the
concrete example of the coherent twisted torus boundary, which allows for a
direct comparison with other approaches to 3d/2d holography at asymptotic
infinity. We conclude with the most interesting questions to pursue in this
framework.


  We show that a $C^*$-algebra $\mathfrak{A}$ which is stably isomorphic to a
unital graph $C^*$-algebra, is isomorphic to a graph $C^*$-algebra if and only
if it admits an approximate unit of projections. As a consequence, a hereditary
$C^*$-subalgebra of a unital real rank zero graph $C^*$-algebra is isomorphic
to a graph $C^*$-algebra. Furthermore, if a $C^*$-algebra $\mathfrak{A}$ admits
an approximate unit of projections, then its minimal unitization is isomorphic
to a graph $C^*$-algebra if and only if $\mathfrak{A}$ is stably isomorphic to
a unital graph $C^*$-algebra.


  We study quasinormal spectrum of massive scalar field in the $D$-dimensional
black hole background. We found the qualitatively different dependence on the
field mass of the fundamental modes for $D\geq6$. The behaviour of higher modes
is qualitatively the same for all $D$. Thus for some particular values of mass
(of the field and of the black hole) the spectrum has two dominating
oscillations with a very long lifetime. Also we show that the asymptotically
high overtones do not depend on the field mass. In addition, we present the
generalisation of the Nollert improvement of the continued fraction technique
for the numerical calculation of quasi-normal frequencies of $D$-dimensional
black holes.


  We propose a new superspace formulation for N=(4,4) conformal supergravity in
two dimensions. This is based on a geometry where the structure group of the
curved superspace is chosen to be SO(1,1) x SU(2)_L x SU(2)_R. The off-shell
supergravity multiplet possesses super-Weyl transformations generated by an
unconstrained real scalar superfield. The new supergravity formulation turns
out to be an extension of the minimal multiplet introduced in 1988 by Gates et.
al. and it allows the existence of various off-shell matter supermultiplets.
Covariant twisted-II and twisted-I multiplets respectively describe the field
strength of an Abelian vector multiplet and its prepotential. Moreover, we
introduce covariant bi-projective superfields. These define a large class of
matter multiplets coupled to 2D N=(4,4) conformal supergravity. They are the
analogue of the covariant projective superfields recently introduced for 4D and
5D matter-coupled supergravity but they differ by the fact that bi-projective
superfields are defined with the use of two CP^1 instead of one. We conclude by
giving a manifestly locally supersymmetric and super-Weyl invariant action
principle in bi-projective superspace.


  In the Bayesian approach to probability theory, probability quantifies a
degree of belief for a single trial, without any a priori connection to
limiting frequencies. In this paper we show that, despite being prescribed by a
fundamental law, probabilities for individual quantum systems can be understood
within the Bayesian approach. We argue that the distinction between classical
and quantum probabilities lies not in their definition, but in the nature of
the information they encode. In the classical world, maximal information about
a physical system is complete in the sense of providing definite answers for
all possible questions that can be asked of the system. In the quantum world,
maximal information is not complete and cannot be completed. Using this
distinction, we show that any Bayesian probability assignment in quantum
mechanics must have the form of the quantum probability rule, that maximal
information about a quantum system leads to a unique quantum-state assignment,
and that quantum theory provides a stronger connection between probability and
measured frequency than can be justified classically. Finally we give a
Bayesian formulation of quantum-state tomography.


  We use analytic continuation to extend the gauge/gravity duality
nonperturbative description of the strong force coupling into the transition,
near-perturbative, regime where perturbative effects become important. By
excluding the unphysical region in coupling space from the flow of
singularities in the complex plane, we derive a specific relation between the
scales relevant at large and short distances; this relation is uniquely fixed
by requiring maximal analyticity. The unified effective coupling model gives an
accurate description of the data in the nonperturbative and the
near-perturbative regions.


  In this paper, we prove existence and regularity of positive solutions for
singular quasilinear elliptic systems involving gradient terms. Our approach is
based on comparison properties, a priori estimates and Schauder's fixed point
theorem.


  Free-floating substellar candidates with estimated theoretical masses of as
low as ~5 Jupiter masses have been found in the ~3 Myr old sigma Orionis open
cluster. As the overlap with the planetary mass domain increases, the question
of how these objects form becomes important. The determination of their number
density and whether a mass cut-off limit exists is crucial to understanding
their formation. We propose to search for objects of yet lower masses in the
cluster and determine the shape of the mass function at low mass. Using new-
and (re-analysed) published IZJHKs[3.6]-[8.0]-band data of an area of 840
arcmin2, we performed a search for LT-type cluster member candidates in the
magnitude range J=19.5-21.5 mag, based on their expected magnitudes and
colours. Besides recovering the T type object S Ori 70 and two other known
objects, we find three new cluster member candidates, S Ori 72-74, with J=21
mag and within 12 arcmin of the cluster centre. They have theoretical masses of
4 (-2,+3) M_Jup and are among the least massive free-floating objects detected
by direct imaging outside the Solar System. The photometry in archival Spitzer
[3.6]-[5.8]-band images infers that S Ori 72 is an L/T transition candidate and
S Ori 73 a T-type candidate, following the expected cluster sequence in the
mid-infrared. Finally, the L-type candidate S Ori 74 with lower quality
photometry is located at 11.8 arcsec (~4250 AU) of a stellar member of sigma
Orionis and could be a companion. After contaminant correction in the area
complete to J=21.1 mag, we estimate that there remain between zero and two
cluster members in the mass interval 6-4 M_Jup. Our result suggests a possible
turnover in the substellar mass spectrum below ~6 Jupiter masses, which could
be investigated further by wider and deeper photometric surveys.


  We obtain existence and conormal Sobolev regularity of strong solutions to
the 3D compressible isentropic Navier-Stokes system on the half-space with a
Navier boundary condition, over a time that is uniform with respect to the
viscosity parameters when these are small. These solutions then converge
globally and strongly in $L^2$ towards the solution of the compressible
isentropic Euler system when the viscosity parameters go to zero.


  Conventionally, the Ih symmetry of fullerene C60 is accepted which is
supported by numerous calculations. However, this conclusion results from the
consideration of the molecule electron system, of its odd electrons in
particular, in a close-shell approximation without taking the electron spin
into account. Passing to the open-shell approximation has lead to both the
energy and the symmetry lowering up to Ci. Seemingly contradicting to a
high-symmetry pattern of experimental recording, particularly concerning the
molecule electronic spectra, the finding is considered in the current paper
from the continuous symmetry viewpoint. Exploiting both continuous symmetry
measure and continuous symmetry content, was shown that formal Ci symmetry of
the molecule is by 99.99% Ih. A similar continuous symmetry analysis of the
fullerene monoderivatives gives a reasonable explanation of a large variety of
their optical spectra patterns within the framework of the same C1 formal
symmetry exhibiting a strong stability of the C60 skeleton.


  We analyze the fragmentation behavior of random clusters on the lattice under
a process where bonds between neighboring sites are successively broken.
Modeling such structures by configurations of a generalized Potts or
random-cluster model allows us to discuss a wide range of systems with fractal
properties including trees as well as dense clusters. We present exact results
for the densities of fragmenting edges and the distribution of fragment sizes
for critical clusters in two dimensions. Dynamical fragmentation with a size
cutoff leads to broad distributions of fragment sizes. The resulting power laws
are shown to encode characteristic fingerprints of the fragmented objects.


  Social media users generate tremendous amounts of data. To better serve
users, it is required to share the user-related data among researchers,
advertisers and application developers. Publishing such data would raise more
concerns on user privacy. To encourage data sharing and mitigate user privacy
concerns, a number of anonymization and de-anonymization algorithms have been
developed to help protect privacy of social media users. In this work, we
propose a new adversarial attack specialized for social media data. We further
provide a principled way to assess effectiveness of anonymizing different
aspects of social media data. Our work sheds light on new privacy risks in
social media data due to innate heterogeneity of user-generated data which
require striking balance between sharing user data and protecting user privacy.


  We address the problem of defining a network graph on a large collection of
classes. Each class is comprised of a collection of data points, sampled in a
non i.i.d. way, from some unknown underlying distribution. The application we
consider in this paper is a large scale high dimensional survey of people
living in the US, and the question of how similar or different are the various
counties in which these people live. We use a co-clustering diffusion metric to
learn the underlying distribution of people, and build an approximate earth
mover's distance algorithm using this data adaptive transportation cost.


  We outline reasoning that led to the current theory of quasars and look at
George Contopoulos's place in the long history of the N-body problem. Following
Newton we find new exactly soluble N-body problems with multibody forces and
give a strange eternally pulsating system that in its other degrees of freedom
reaches statistical equilibrium.


  Analyses of heavy mesons and baryons hadronic charmless decays using the
flavor SU(3) symemtry can be formulated in two different forms. One is to
construct the SU(3) irreducible representation amplitude (IRA) by decomposing
effective Hamiltonian, and the other is to draw the topological diagrams (TDA).
In the flavor SU(3) limit, we study various $B/D\to PP,VP,VV$, $B_c\to DP/DV$
decays, and two-body nonleptonic decays of beauty/charm baryons, and
demonstrate that when all terms are included these two ways of analyzing the
decay amplitudes are completely equivalent. Furthermore we clarify some
confusions in drawing topological diagrams using different ways of describing
beauty/charm baryons.


  A search for single production of vector-like quarks, T and B, decaying into
a Z boson and a top or a bottom quark, respectively, is presented. The search
is performed using data collected by the CMS experiment at the LHC in
proton-proton collisions at sqrt(s) = 13 TeV, corresponding to an integrated
luminosity of 2.3 inverse femtobarns. An exotic T quark production mode through
the decay of a heavy Z' resonance is also considered. The search is performed
in events with a Z boson decaying leptonically, accompanied by a bottom or a
top quark decaying hadronically. No excess of events is observed over the
standard model background expectation. Products of production cross section and
branching fraction for T and B quarks from 1.26 and 0.13 pb are excluded at 95%
confidence level for the range of resonance mass considered, which is between
0.7 and 1.7 TeV. Limits on the product of the Z' boson production cross section
and branching fraction, with the Z' boson decaying to the Tt final state, are
set between 0.31 and 0.13 pb, for Z' boson masses in the range from 1.5 to 2.5
TeV. This is the first search at 13 TeV for single production of vector-like
quarks in events with a Z boson decaying leptonically accompanied by boosted
jets.


  The recent development of all-electrical electron spin resonance (ESR) in a
scanning tunneling microscope (STM) setup has opened the door to vast
applications. Despite the fast growing number of experimental works on STM-ESR,
the fundamental principles remains unclear. By using a cotunneling picture, we
show that the spin resonance signal can be explained as a time-dependent
variation of the tunnel barrier induced by the alternating electric driving
field. We demonstrate how this variation translates into the resonant frequency
response of the direct current. Our cotunneling theory explains the main
experimental findings. Namely, the linear dependence of the Rabi flop rate with
the alternating bias amplitude, the absence of resonant response for
spin-unpolarized currents, and the weak dependence on the actual atomic
species.


  The intermediate transport regime in nanoscale transistors between the fully
ballistic case and the quasi equilibrium case described by the drift-diffusion
model is still an open modeling issue. Analytical approaches to the problem
have been proposed, based on the introduction of a backscattering coefficient,
or numerical approaches consisting in the MonteCarlo solution of the Boltzmann
transport equation or in the introduction of dissipation in quantum transport
descriptions. In this paper we propose a very simple analytical model to
seamlessly cover the whole range of transport regimes in generic quasi-one
dimensional field-effect transistors, and apply it to silicon nanowire
transistors. The model is based on describing a generic transistor as a chain
of ballistic nanowire transistors in series, or as the series of a ballistic
transistor and a drift-diffusion transistor operating in the triode region. As
an additional result, we find a relation between the mobility and the mean free
path, that has deep consequences on the understanding of transport in nanoscale
devices.


  For any odd prime power q we provide a quick construction of a complete
family of q(q-1) mutually orthogonal sudoku squares of order q^2.


  Let alpha \in (1, 2] and X be an R^d-valued alpha-stable process with
independent and symmetric components starting in 0. We consider the closure S_t
of the path described by X on the interval [0, t] and its convex hull Z_t. The
first result of this paper provides a formula for certain mean mixed volumes of
Z_t and in particular for the expected first intrinsic volume of Z_t. The
second result deals with the asymptotics of the expected volume of the stable
sausage Z_t+B (where B is an arbitrary convex body with interior points) as t
\to 0.


  Electric resistance in conducting media is related to heat (or entropy)
production in presence of electric fields. In this paper, by using Araki's
relative entropy for states, we mathematically define and analyze the heat
production of free fermions within external potentials. More precisely, we
investigate the heat production of the non-autonomous C*-dynamical system
obtained from the fermionic second quantization of a discrete Schr\"odinger
operator with bounded static potential in presence of an electric field that is
time- and space-dependent. It is a first preliminary step towards a
mathematical description of transport properties of fermions from thermal
considerations. This program will be carried out in several papers. The regime
of small and slowly varying in space electric fields is important in this
context, and is studied the present paper. We use tree-decay bounds of the
$n$-point, $n\in 2\mathbb{N}$, correlations of the many-fermion system to
analyze this regime. We verify below the 1st law of thermodynamics for the
system under consideration. The latter implies, for systems doing no work, that
the heat produced by the electromagnetic field is exactly the increase of the
internal energy resulting from the modification of the (infinite volume) state
of the fermion system. The identification of heat production with an energy
increment is, among other things, technically convenient. We initially focus
our study on non-interacting (or free) fermions, but our approach will be later
applied to weakly interacting fermions.


  Let K denote an algebraically closed field. We study the relation between an
ideal I in K[x1,...,xn] and its cross sections I_a=I+<x1-a>. In particular, we
study under what conditions I can be recovered from the set I_S={(a,I_a):a in
S} with S a subset of K. For instance, we show that an ideal I=cap_i Q_i, where
Q_i is primary and Q_i cap K[x1]={0}, is uniquely determined by I_S when S is
infinite. Moreover, there exists a function B(d,n) such that, if I is generated
by polynomials of degree at most d, then I is uniquely determined by I_S when
|S|>=B(d,n). If I is also known to be principal, the reconstruction can be done
when |S|>=2d, and in this case, we prove that the bound is sharp.


  Cepheid-based distances to seven Type Ia supernovae (SNe)-host galaxies have
been derived using the standard HST Key Project on the Extragalactic Distance
Scale pipeline. For the first time, this allows for a transparent comparison of
data accumulated as part of three different HST projects, the Key Project, the
Sandage et al. Type Ia SNe program, and the Tanvir et al. Leo I Group study.
Re-analyzing the Tanvir et al. galaxy and six Sandage et al. galaxies we find a
mean (weighted) offset in true distance moduli of 0.12+/-0.07 mag -- i.e., 6%
in linear distance -- in the sense of reducing the distance scale, or
increasing H0. Adopting the reddening-corrected Hubble relations of Suntzeff et
al. (1999), tied to a zero point based upon SNe~1990N, 1981B, 1998bu, 1989B,
1972E and 1960F and the photometric calibration of Hill et al. (1998), leads to
a Hubble constant of H0=68+/-2(random)+/-5(systematic) km/s/Mpc. Adopting the
Kennicutt et al. (1998) Cepheid period-luminosity-metallicity dependency
decreases the inferred H0 by 4%. The H0 result from Type Ia SNe is now in good
agreement, to within their respective uncertainties, with that from the
Tully-Fisher and surface brightness fluctuation relations.


  We consider fractional Hartree and cubic nonlinear Schr\"odinger equations on
Euclidean space $\mathbb R^d$ and on torus $\mathbb T^d$. We establish norm
inflation (a stronger phenomena than standard ill-posedness) at every initial
data in Fourier amalgam spaces with negative regularity. In particular, these
spaces include Fourier-Lebesgue, modulation and Sobolev spaces. We further show
that this can be even worse by exhibiting norm inflation with an infinite loss
of regularity. To establish these phenomena, we employ a Fourier analytic
approach and introduce new resonant sets corresponding to the fractional
dispersion $(-\Delta)^{\alpha/2}$. In particular, when dispersion index
$\alpha$ is large enough, we obtain norm inflation {above} scaling critical
regularity in some of these spaces. It turns out that our approach could treat
both equations (Hartree and power-type NLS) in a unified manner. The method
should also work for a broader range of nonlinear equations with Hartree-type
nonlinearity.


  We compute the spectrum of the Dirac operator on 3-dimensional Heisenberg
manifolds. The behavior under collapse to the 2-torus is studied. Depending on
the spin structure either all eigenvalues tend to $\pm\infty$ or there are
eigenvalues converging to those of the torus. This is shown to be true in
general for collapsing circle bundles with totally geodesic fibers. Using the
Hopf fibration we use this fact to compute the Dirac eigenvalues on complex
projective space including the multiplicities.
  Finally, we show that there are 1-parameter families of Riemannian
nilmanifolds such that the Laplacian on functions and the Dirac operator for
certain spin structures have constant spectrum while the Laplacian on 1-forms
and the Dirac operator for the other spin structures have nonconstant spectrum.
The marked length spectrum is also constant for these families.


  The recent trend for multi-camera 3D object detection is through the unified
bird's-eye view (BEV) representation. However, directly transforming features
extracted from the image-plane view to BEV inevitably results in feature
distortion, especially around the objects of interest, making the objects blur
into the background. To this end, we propose OA-BEV, a network that can be
plugged into the BEV-based 3D object detection framework to bring out the
objects by incorporating object-aware pseudo-3D features and depth features.
Such features contain information about the object's position and 3D
structures. First, we explicitly guide the network to learn the depth
distribution by object-level supervision from each 3D object's center. Then, we
select the foreground pixels by a 2D object detector and project them into 3D
space for pseudo-voxel feature encoding. Finally, the object-aware depth
features and pseudo-voxel features are incorporated into the BEV representation
with a deformable attention mechanism. We conduct extensive experiments on the
nuScenes dataset to validate the merits of our proposed OA-BEV. Our method
achieves consistent improvements over the BEV-based baselines in terms of both
average precision and nuScenes detection score. Our codes will be published.


  Newtonian dynamical systems which accept the normal shift on an arbitrary
Riemannian manifold are considered. For them the determinating equations making
the weak normality condition are derived. The expansion for the algebra of
tensor fields is constructed.


  By using exact quantum Monte-Carlo methods we calculate the ground-state
properties of the liquid phase in one-dimensional Bose mixtures with contact
interactions. We find that the liquid state can be formed if the ratio of
coupling strengths between inter-species attractive and intraspecies repulsive
interactions exceeds a critical value. As a function of this ratio we determine
the density where the energy per particle has a minimum and the one where the
compressibility diverges, thereby identifying the equilibrium density and the
spinodal point in the phase diagram of the homogeneous liquid. Furthermore, in
the stable liquid state, we calculate the chemical potential, the speed of
sound, as well as structural and coherence properties such as the pair
correlation function, the static structure factor and the one-body density
matrix, thus providing a detailed description of the bulk region in self-bound
droplets.


  We discuss the possibility to observe matter effects in atmospheric neutrino
oscillations. The main conclusion is that an impact on the nu_mu survival
probability requires the action of the MSW resonance, which becomes visible for
baselines above ~7000 km. The associated muon charge asymmetry carries
information on theta_{13} and the sign of Dm^2_{31}.


  We develop a class of data-driven generative models that approximate the
solution operator for parameter-dependent partial differential equations (PDE).
We propose a novel probabilistic formulation of the operator learning problem
based on recently developed generative denoising diffusion probabilistic models
(DDPM) in order to learn the input-to-output mapping between problem parameters
and solutions of the PDE. To achieve this goal we modify DDPM to supervised
learning in which the solution operator for the PDE is represented by a class
of conditional distributions. The probabilistic formulation combined with DDPM
allows for an automatic quantification of confidence intervals for the learned
solutions. Furthermore, the framework is directly applicable for learning from
a noisy data set. We compare computational performance of the developed method
with the Fourier Network Operators (FNO). Our results show that our method
achieves comparable accuracy and recovers the noise magnitude when applied to
data sets with outputs corrupted by additive noise.


  The Tur\'an number $\text{ex}(n,H)$ of a graph $H$ is the maximal number of
edges in an $H$-free graph on $n$ vertices. In $1983$ Chung and Erd\H{o}s asked
which graphs $H$ with $e$ edges minimize $\text{ex}(n,H)$. They resolved this
question asymptotically for most of the range of $e$ and asked to complete the
picture. In this paper we answer their question by resolving all remaining
cases. Our result translates directly to the setting of universality, a
well-studied notion of finding graphs which contain every graph belonging to a
certain family. In this setting we extend previous work done by Babai, Chung,
Erd\H{o}s, Graham and Spencer, and by Alon and Asodi.


  Dilute impurities and growth conditions can drastically affect the transport
properties of TiSe$_2$, especially below the charge density wave transition. In
this paper, we discuss the effects of cooling rate, annealing time and
annealing temperature on the transport properties of TiSe$_2$: slow cooling of
polycrystalline TiSe$_2$ post-synthesis drastically increases the low
temperature resistivity, which is in contrast to the metallic behavior of
single-crystalline TiSe$_2$ due to charge doping from the residual iodine
transport agent. A logarithmic increase of resistivity upon cooling and
negative magnetoresistance with a sharp cusp around zero field are observed for
the first time for the polycrystalline TiSe$_2$ samples, pointing to
weak-localization effects due to low dimensionality. Annealing at low
temperatures has a similar, but less drastic effect. Furthermore, rapid
quenching of the polycrystalline samples from high temperatures freezes in
disorder, leading to a decrease in the low temperature resistivity.


  As the size of transistors approaches the mesoscopic scale, existing energy
consumption analysis methods exhibit various limits, especially when being
applied to describe the non-equilibrium information processing of transistors
at ultra-low voltages. The stochastic thermodynamics offers a theoretic tool to
analyze the energy consumption of transistor during the non-equilibrium
information processing. Based on this theory, an information energy ratio of
XOR gate composed of single-electron transistors is proposed at the mesoscopic
scale, which can be used to quantify the exchange between the information and
energy at XOR gates. Furthermore, the energy efficiency of the parity check
circuit is proposed to analyze the energy consumption of digital signal
processing systems. Compared with the energy efficiency of parity check circuit
adopting the 7 nm semiconductor process supply voltage, simulation results show
that the energy efficiency of the parity check circuit is improved by 266% when
the supply voltage is chosen at a specified value.


  Despite the significant improvements in speaker recognition enabled by deep
neural networks, unsatisfactory performance persists under noisy environments.
In this paper, we train the speaker embedding network to learn the "clean"
embedding of the noisy utterance. Specifically, the network is trained with the
original speaker identification loss with an auxiliary within-sample
variability-invariant loss. This auxiliary variability-invariant loss is used
to learn the same embedding among the clean utterance and its noisy copies and
prevents the network from encoding the undesired noises or variabilities into
the speaker representation. Furthermore, we investigate the data preparation
strategy for generating clean and noisy utterance pairs on-the-fly. The
strategy generates different noisy copies for the same clean utterance at each
training step, helping the speaker embedding network generalize better under
noisy environments. Experiments on VoxCeleb1 indicate that the proposed
training framework improves the performance of the speaker verification system
in both clean and noisy conditions.


  Multivariate time series is a very active topic in the research community and
many machine learning tasks are being used in order to extract information from
this type of data. However, in real-world problems data has missing values,
which may difficult the application of machine learning techniques to extract
information. In this paper we focus on the task of imputation of time series.
Many imputation methods for time series are based on regression methods.
Unfortunately, these methods perform poorly when the variables are categorical.
To address this case, we propose a new imputation method based on Expectation
Maximization over dynamic Bayesian networks. The approach is assessed with
synthetic and real data, and it outperforms several state-of-the art methods.


  On montre qu'une fonction holomorphe non-constante $f$ definie sur un
sous-espace analytique de $\CC_p$ est injective si et seulement si on a $$ |
\frac{f(x) - f(y)}{{x - y)} |^2 = |f'(x) f'(y)|,$$ pour tous $x$ et $y$
distincts.
  Cette caracterisation demontre l'analogue, pour les fonctions holomorphes,
d'une conjecture de A. Escassut et M.C. Sarmant.
  D'ature part on donne une contre-exemple a cette conjecture, qui concerne les
elements bi-analytiques.


  In this note, a short-cut is proposed to shorten the proof which has been
previously presented for non existence of periodic solutions in time invariant
fractional order systems.


  This paper deals with the robust force and position control problems of
Series Elastic Actuators. It is shown that a Series Elastic Actuator's force
control problem can be described by a second-order dynamic model which suffers
from only matched disturbances. However, the position control dynamics of a
Series Elastic Actuator is of fourth-order and includes matched and mismatched
disturbances. In other words, a Series Elastic Actuator's position control is
more complicated than its force control, particularly when disturbances are
considered. A novel robust motion controller is proposed for Series Elastic
Actuators by using Disturbance Observer and Sliding Mode Control. When the
proposed robust motion controller is implemented, a Series Elastic Actuator can
precisely track desired trajectories and safely contact with an unknown and
dynamic environment. The proposed motion controller does not require precise
dynamic models of the actuator and environment. Therefore, it can be applied to
many different advanced robotic systems such as compliant humanoids and
exoskeletons. The validity of the motion controller is experimentally verified.


  There is a gap that has been left open since the formulation of general
relativity in terms of Ashtekar's new variables namely the treatment of
asymptotically flat field configurations that are general enough to be able to
define the generators of the Lorentz subgroup of the asymptotical Poincar\'e
group. While such a formulation already exists for the old geometrodynamical
variables, up to now only the generators of the translation subgroup could be
defined because the function spaces of the fields considered earlier are taken
too special. The transcription of the framework from the ADM variables to
Ashtekar's variables turns out not to be straightforward due to the freedom to
choose the internal SO(3) frame at spatial infinity and due to the fact that
the non-trivial reality conditions of the Ashtekar framework reenter the game
when imposing suitable boundary conditions on the fields and the Lagrange
multipliers.


  In this paper, a comprehensive examination of the temperature- and
bias-dependent diffusion regimes of underdamped Brownian particles is
presented. A temperature threshold for a transition between anomalous and
normal diffusive behaviors is located, yielding a new phase diagram for the
system. In the low-temperature regime, the system exhibits an apparent negative
differential mobility due to persistent, long-time subdiffusion at low-bias; at
high temperature (or critical bias,) the system rapidly approaches normal
diffusion below an intermediate barrier height, $U_o \sim k_B T$. By
consideration of numerical results, comparison to the overdamped case, and the
related Kramers multistable escape problems, it is demonstrated that the
low-bias non-monotonic temperature dependence of the diffusivity, persistent
subdiffusion, and negative differential mobility can be traced to inertial
effects, which are evident in the oscillatory modes of the velocity power
spectra at low bias. In the giant diffusion regime, the velocity power spectra
exhibit coupling between the ``locked" and ``running" states, with a
characteristic frequency corresponding to the principal frequency of the limit
cycles of a damped, driven plane pendulum near critical bias. Non-linear second
harmonic generation, corresponding to oscillatory transient anomalous
diffusivity, is observed with increasing bias and decreasing temperature,
further emphasizing that the low-noise diffusion problem converges to
noise-free dynamics, complementing analytic results for the average velocity
[L. Cheng and N.K. Yip, Physica D, 2015].


  We present an optimal, combinatorial 1-1/e approximation algorithm for
monotone submodular optimization over a matroid constraint. Compared to the
continuous greedy algorithm (Calinescu, Chekuri, Pal and Vondrak, 2008), our
algorithm is extremely simple and requires no rounding. It consists of the
greedy algorithm followed by local search. Both phases are run not on the
actual objective function, but on a related non-oblivious potential function,
which is also monotone submodular. Our algorithm runs in randomized time
O(n^8u), where n is the rank of the given matroid and u is the size of its
ground set. We additionally obtain a 1-1/e-eps approximation algorithm running
in randomized time O (eps^-3n^4u). For matroids in which n = o(u), this
improves on the runtime of the continuous greedy algorithm. The improvement is
due primarily to the time required by the pipage rounding phase, which we avoid
altogether. Furthermore, the independence of our algorithm from pipage rounding
techniques suggests that our general approach may be helpful in contexts such
as monotone submodular maximization subject to multiple matroid constraints.
  Our approach generalizes to the case where the monotone submodular function
has restricted curvature. For any curvature c, we adapt our algorithm to
produce a (1-e^-c)/c approximation. This result complements results of Vondrak
(2008), who has shown that the continuous greedy algorithm produces a
(1-e^-c)/c approximation when the objective function has curvature c. He has
also proved that achieving any better approximation ratio is impossible in the
value oracle model.


  The most unique advantage of multipass SAR interferometry (InSAR) is the
retrieval of long term geophysical parameters, e.g. linear deformation rates,
over large areas. Recently, an object-based multipass InSAR framework has been
proposed in [1], as an alternative to the typical single-pixel methods, e.g.
Persistent Scatterer Interferometry (PSI), or pixel-cluster-based methods, e.g.
SqueeSAR. This enables the exploitation of inherent properties of InSAR phase
stacks on an object level. As a followon, this paper investigates the inherent
low rank property of such phase tensors, and proposes a Robust Multipass InSAR
technique via Object-based low rank tensor decomposition (RoMIO). We
demonstrate that the filtered InSAR phase stacks can improve the accuracy of
geophysical parameters estimated via conventional multipass InSAR techniques,
e.g. PSI, by a factor of ten to thirty in typical settings. The proposed method
is particularly effective against outliers, such as pixels with unmodeled
phases. These merits in turn can effectively reduce the number of images
required for a reliable estimation. The promising performance of the proposed
method is demonstrated using high-resolution TerraSAR-X image stacks.


  For a long time, the minimal supersymmetric standard model (MSSM) with light
masses for the supersymmetric states was considered as the most natural
extension of the Standard Model of particle physics. Consequently, a valid
approximation was to match the MSSM to the precision measurement directly at
the electroweak scale. This approach was also utilized by all dedicated
spectrum generators for the MSSM. However, the higher the supersymmetric (SUSY)
scale is, the bigger the uncertainties which are introduced by this matching.
We point out important consequences of a two-scale matching, where the running
parameters within the SM are calculated at $M_Z$ and evaluated up to the SUSY
scale, where they are matched to the full model. We show the impact on gauge
coupling unification as well as the SUSY mass spectrum. Also the Higgs mass
prediction for large supersymmetric masses has been improved by performing the
calculation within an effective SM. The approach presented here is now also
available in the spectrum generator SPheno. Moreover, also SARAH was extended
accordingly and gives the possibility to study these effects now in many
different supersymmetric models.


  We define a Gauss map $\gamma:M\rightarrow\mathbb{S}^{6}$ of an oriented
hypersurface $M$ of the unit sphere $\mathbb{S}^{7}$ and prove that $\gamma$ is
harmonic if and only if $M$ has CMC. Results on the geometry and topology of
CMC hypersurfaces of $\mathbb{S}^{7}$, under hypothesis on the image of
$\gamma$, are then obtained. By a Hopf symmetrization process we define a Gauss
map for hypersurfaces of $\mathbb{CP}^{3}$ and obtain similar results for CMC
hypersurfaces of this space.


  We compute the number of connected components in a generic real double Bruhat
cell for series $B_n$ and $C_n$ and an exceptional group $F_4$.


  HOOMD-blue is a particle simulation engine designed for nano- and
colloidal-scale molecular dynamics and hard particle Monte Carlo simulations.
It has been actively developed since March 2007 and available open source since
August 2008. HOOMD-blue is a Python package with a high performance C++/CUDA
backend that we built from the ground up for GPU acceleration. The Python
interface allows users to combine HOOMD-blue with with other packages in the
Python ecosystem to create simulation and analysis workflows. We employ
software engineering practices to develop, test, maintain, and expand the code.


  Coverage path planning is a major application for mobile robots, which
requires robots to move along a planned path to cover the entire map. For
large-scale tasks, coverage path planning benefits greatly from multiple
robots. In this paper, we describe Turn-minimizing Multirobot Spanning Tree
Coverage Star(TMSTC*), an improved multirobot coverage path planning (mCPP)
algorithm based on the MSTC*. Our algorithm partitions the map into minimum
bricks as tree's branches and thereby transforms the problem into finding the
maximum independent set of bipartite graph. We then connect bricks with greedy
strategy to form a tree, aiming to reduce the number of turns of corresponding
circumnavigating coverage path. Our experimental results show that our approach
enables multiple robots to make fewer turns and thus complete terrain coverage
tasks faster than other popular algorithms.


  This paper addresses the problem of matching pedestrians across multiple
camera views, known as person re-identification. Variations in lighting
conditions, environment and pose changes across camera views make
re-identification a challenging problem. Previous methods address these
challenges by designing specific features or by learning a distance function.
We propose a hierarchical feature learning framework that learns invariant
representations from labeled image pairs. A mapping is learned such that the
extracted features are invariant for images belonging to same individual across
views. To learn robust representations and to achieve better generalization to
unseen data, the system has to be trained with a large amount of data.
Critically, most of the person re-identification datasets are small. Manually
augmenting the dataset by partial corruption of input data introduces
additional computational burden as it requires several training epochs to
converge. We propose a hierarchical network which incorporates a
marginalization technique that can reap the benefits of training on large
datasets without explicit augmentation. We compare our approach with several
baseline algorithms as well as popular linear and non-linear metric learning
algorithms and demonstrate improved performance on challenging publicly
available datasets, VIPeR, CUHK01, CAVIAR4REID and iLIDS. Our approach also
achieves the stateof-the-art results on these datasets.


  Developing effective methods to measure the enantiomeric excess of the chiral
mixture is one of the major topics in chiral molecular researches, yet remains
challenging. Enantiodetection method via two-dimensional (2D) spectroscopy
based on a four level model, containing a cyclic three-level system (CTLS), of
chiral molecules was recently proposed and demonstrated, yet with a strict
condition of the one-photon resonance (where three driving fields are exactly
resonantly coupled to the three electric-dipole transitions, respectively) in
the CTLS and narrowband probe pulse assumption. Here, we extend the 2D
spectroscopy method to more general experimental conditions, with three-photon
resonance (where the sum of the two smaller frequencies among the three driving
fields equals to the third one) and broadband probe pulse. Our method remains
effective on enantiodetection with the help of experimental techniques, such as
the chop detection method, which is used to eliminate the influence of the
other redundant levels existing in the real system of chiral molecules. Under
these more general conditions, the enantiomeric excess of the chiral mixture is
estimated by taking an easily available standard sample (usually the racemic
mixture) as the reference.