  Vehicle-sharing systems -- such as bike-, car-, or motorcycle-sharing systems
-- have become increasingly popular in big cities in recent years. On the one
hand, they provide a cheaper and environmentally friendlier means of
transportation than private cars, and on the other hand, they satisfy the
individual mobility demands of citizens better than traditional public
transport systems. One of their advantages in this regard is their
availability, e.g., the possibility of taking (or leaving) a vehicle almost
anywhere in a city. This availability obviously depends on different strategic
and operational management decisions and policies, such as the dimension of the
fleet or the (re)distribution of vehicles. Agglutination problems -- where, due
to usage patterns, available vehicles are concentrated in certain areas,
whereas no vehicles are available in others -- are quite common in such
systems, and need to be dealt with. Research has been dedicated to this
problem, specifying different techniques to reduce imbalanced situations. In
this paper, we present and compare strategies for recommending stations to
users who wish to rent or return bikes in station-based bike-sharing systems.
Our first contribution is a novel recommendation strategy based on queuing
theory that recommends stations based on their utility to the user in terms of
lower distance and higher probability of finding a bike or slot. Then, we go
one step further, defining a strategy that recommends stations by combining the
utility of a particular user with the utility of the global system, measured in
terms of the improvement in the distribution of bikes and slots with respect to
the expected future demand, with the aim of implicitly avoiding or alleviating
balancing problems. We present several experiments to evaluate our proposal
with real data from the bike sharing system BiciMAD in Madrid.


  The drastic increase of data quantity often brings the severe decrease of
data quality, such as incorrect label annotations, which poses a great
challenge for robustly training Deep Neural Networks (DNNs). Existing learning
\mbox{methods} with label noise either employ ad-hoc heuristics or restrict to
specific noise assumptions. However, more general situations, such as
instance-dependent label noise, have not been fully explored, as scarce studies
focus on their label corruption process. By categorizing instances into
confusing and unconfusing instances, this paper proposes a simple yet universal
probabilistic model, which explicitly relates noisy labels to their instances.
The resultant model can be realized by DNNs, where the training procedure is
accomplished by employing an alternating optimization algorithm. Experiments on
datasets with both synthetic and real-world label noise verify that the
proposed method yields significant improvements on robustness over
state-of-the-art counterparts.


  Let $\mathbb{k}$ be a characteristic zero domain. For a locally unital
$\mathbb{k}$-superalgebra $A$ with distinguished idempotents $I$and even
subalgebra $a \subseteq A_{\bar 0}$, we define and study an associated
diagrammatic monoidal $\mathbb{k}$-linear supercategory $\mathbf{Web}^{A,a}_I$.
This supercategory yields a diagrammatic description of the generalized Schur
algebras $T^A_a(n,d)$. We also show there is an asymptotically faithful functor
from $\mathbf{Web}^{A,a}_I$ to the monoidal supercategory of
$\mathfrak{gl}_n(A)$-modules generated by symmetric powers of the natural
module. When this functor is full, the single diagrammatic supercategory
$\mathbf{Web}^{A,a}_I$ provides a combinatorial description of this module
category for all $n \geq 1$. We also use these results to establish Howe
dualities between $\mathfrak{gl}_{m}(A)$ and $\mathfrak{gl}_{n}(A)$ when $A$ is
semisimple.


  Quantum emitters respond to resonant illumination by radiating
electromagnetic fields. A component of these fields is phase-coherent with the
driving tone, while another one is incoherent, consisting of spontaneously
emitted photons and forming the fluorescence signal. Atoms and molecules are
routinely detected by their fluorescence at optical frequencies, with important
applications in quantum technology and microscopy. Spins, on the other hand,
are usually detected by {their coherent response} at radio- or microwave
frequencies, either in continuous-wave or pulsed magnetic resonance. Indeed,
fluorescence detection of spins is hampered {by their low spontaneous emission
rate} and by the lack of single-photon detectors in this frequency range. Here,
using superconducting quantum devices, we demonstrate the detection of a small
ensemble of donor spins in silicon by their fluorescence at microwave frequency
and millikelvin temperatures. We enhance the spin radiative decay rate by
coupling them to a high-quality-factor and small-mode-volume superconducting
resonator, and we connect the device output to a newly-developed microwave
single-photon counter based on a superconducting qubit. We discuss the
potential of fluorescence detection as a novel method for magnetic resonance
spectroscopy of small numbers of spins.


  In this paper we extend previous work on calculating massless boundary Ramond
sector spectra of open strings to include cases with nonzero flat B fields. In
such cases, D-branes are no longer well-modelled precisely by sheaves, but
rather they are replaced by `twisted' sheaves, reflecting the fact that gauge
transformations of the B field act as affine translations of the Chan-Paton
factors. As in previous work, we find that the massless boundary Ramond sector
states are counted by Ext groups -- this time, Ext groups of twisted sheaves.
As before, the computation of BRST cohomology relies on physically realizing
some spectral sequences. Subtleties that cropped up in previous work also
appear here.


  We study the consequences of nonzero neutrino masses for black holes
evaporating by the emission of Hawking radiation. We find that the evolution of
small, hot, black holes may be unaffected (if neutrinos are Majorana
particles), or may show an increase in neutrino luminosity and a decrease in
lifetime by up to a factor of 1.85 (if neutrinos are Dirac particles). However,
for sufficiently large (e.g., stellar mass) black holes, neutrino emission is
largely or entirely suppressed, resulting in a decrease in emitted power and an
increase in lifetime by up to a factor of 7.5.


  Many applications in data analysis rely on the decomposition of a data matrix
into a low-rank and a sparse component. Existing methods that tackle this task
use the nuclear norm and L1-cost functions as convex relaxations of the rank
constraint and the sparsity measure, respectively, or employ thresholding
techniques. We propose a method that allows for reconstructing and tracking a
subspace of upper-bounded dimension from incomplete and corrupted observations.
It does not require any a priori information about the number of outliers. The
core of our algorithm is an intrinsic Conjugate Gradient method on the set of
orthogonal projection matrices, the so-called Grassmannian. Non-convex sparsity
measures are used for outlier detection, which leads to improved performance in
terms of robustly recovering and tracking the low-rank matrix. In particular,
our approach can cope with more outliers and with an underlying matrix of
higher rank than other state-of-the-art methods.


  This report is a serious call to scientists, innovators, investors, and
policymakers to invest in the development of biophysics in Africa. The complex
problems of our day demand multidisciplinary approaches, and biophysics offers
training in much-needed multi- and cross-disciplinary thinking. Biophysics is a
research field at the forefront of modern science because it provides a
powerful scientific platform that addresses many of the critical challenges
humanity faces today and in the future. It is a vital source of innovation for
any country interested in developing a high-tech economy. However, there is
woefully little biophysics educational and research activity in Africa,
representing a critical gap that must be addressed with urgency. This report
suggests key research areas that African biophysicists should focus on,
identifies major challenges to growing biophysics in Africa, and underscores
the high-priority needs that must be addressed.


  The Herschel Extragalactic Legacy Project (HELP) focuses to publish an
astronomical multiwavelength catalogue of millions of objects over 1300~deg$^2$
of the Herschel Space Observatory survey fields. Millions of galaxies with
ultraviolet--far infrared photometry {make} HELP a perfect sample for testing
spectral energy distribution fitting models, and to prepare tools for
next-generation data. In the frame of HELP collaboration we estimated the main
physical properties of all galaxies from the HELP database and we checked a new
procedure to select peculiar galaxies from large galaxy sample and we
investigated the influence of used modules for stellar mass estimation.


  We determine the group of reductive cohomological degree $3$ invariants of
all split semisimple groups of types $B$, $C$, and $D$. We also present a
complete description of the cohomological invariants. As an application, we
show that the group of degree $3$ unramified cohomology of the classifying
space $BG$ is trivial for all split semisimple groups $G$ of types $B$, $C$,
and $D$.


  Long, high-quality time-series data provided by previous space-missions such
as CoRoT and $\mathit{Kepler}$ have made it possible to derive the evolutionary
state of red-giant stars, i.e. whether the stars are hydrogen-shell burning
around an inert helium core or helium-core burning, from their individual
oscillation modes. We utilise data from the $\mathit{Kepler}$ mission to
develop a tool to classify the evolutionary state for the large number of stars
being observed in the current era of K2, TESS and for the future PLATO mission.
These missions provide new challenges for evolutionary state classification
given the large number of stars being observed and the shorter observing
duration of the data. We propose a new method, $\mathtt{Clumpiness}$, based
upon a supervised classification scheme that uses "summary statistics" of the
time series, combined with distance information from the Gaia mission to
predict the evolutionary state. Applying this to red giants in the APOKASC
catalogue, we obtain a classification accuracy of ~91% for the full 4 years of
$\mathit{Kepler}$ data, for those stars that are either only hydrogen-shell
burning or also helium-core burning. We also applied the method to shorter
$\mathit{Kepler}$ datasets, mimicking CoRoT, K2 and TESS achieving an accuracy
>91% even for the 27 day time series. This work paves the way towards fast,
reliable classification of vast amounts of relatively short-time-span data with
a few, well-engineered features.


  With data durability, high access speed, low power efficiency and byte
addressability, NVMe and SSD, which are acknowledged representatives of
emerging storage technologies, have been applied broadly in many areas.
However, one key issue with high-performance adoption of these technologies is
how to properly define intelligent cache layers such that the performance gap
between emerging technologies and main memory can be well bridged. To this end,
we propose Phoebe, a reuse-aware reinforcement learning framework for the
optimal online caching that is applicable for a wide range of emerging storage
models. By continuous interacting with the cache environment and the data
stream, Phoebe is capable to extract critical temporal data dependency and
relative positional information from a single trace, becoming ever smarter over
time. To reduce training overhead during online learning, we utilize periodical
training to amortize costs. Phoebe is evaluated on a set of Microsoft cloud
storage workloads. Experiment results show that Phoebe is able to close the gap
of cache miss rate from LRU and a state-of-the-art online learning based cache
policy to the Belady's optimal policy by 70.3% and 52.6%, respectively.


  We establish a new equivalent condition for the Grand Riemann Hypothesis for
L-functions in a wide subclass of the Selberg class in terms of canonical
systems of differential equations. A canonical system is determined by a real
symmetric matrixvalued function called a Hamiltonian. To establish the
equivalent condition, we use an inverse problem for canonical systems of a
special type.


  The Lagoon Nebula (M8) is host to multiple regions with recent and ongoing
massive star formation. With M8-Main and M8 East, two prominent regions of
massive star formation have been studied in detail over the past years, while
large parts of the nebula have received little attention. These largely
unexplored regions comprise a large sample of molecular clumps that are
affected by the presence of massive O- and B-type stars. We establish an
inventory of species observed towards 37 known molecular clumps in M8 by
conducting an unbiased line survey for each clump. For this, we used APEX and
the IRAM 30m telescope for pointed on-off observations on the clumps. These
observations cover bandwidths of 53GHz and 40GHz in frequency ranges from
210GHz to 280GHz and from 70GHz to 117GHz, respectively. Temperatures are
derived from rotational transitions of CH3CN, CH3C2H and para-H2CO. Additional
archival data from the Spitzer, Herschel, MSX, APEX, WISE, JCMT and AKARI
telescopes are used to derive physical parameters of the dust emission by
fitting spectral energy distributions to the observed flux densities. Across
the observed M8 region, we identify 346 transitions from 70 different molecular
species, including isotopologues. We detect tracers of photo-dissociation
regions across all the clumps and 38% of these clumps show signs of star
formation. We find that PDR tracers are most abundant in clumps with relatively
lower H2 column densities. When comparing M8 clumps to ATLASGAL sources at
similar distances, we find them to be slightly less massive and have compatible
luminosities and radii. This possibly indicates a fragmentation of the gas
caused by the O- and B-type stars. In contrast, dust temperatures of the clumps
in M8 are found to be increased by approximately 5K (25%) indicating
substantial external heating of the clumps by radiation of the present massive
stars.


  Thermodynamical equilibrium is considered as an effect of quantum entangling
of the vacuum state of a system. An explicit mathematical model of multi-
particle entangled pure quantum states is developed and analyzed. In the
framework, the process of measurement results in probability distributions that
exactly correspond to the heat equilibrium of a system in a thermostat.


  We show how the twisting of spectral triples induces a transition from an
euclidean to a lorentzian noncommutative geometry, at the level of the
fermionic action. More specifically, we compute the fermionic action for the
twisting of a closed euclidean manifold, then that of a two-sheet euclidean
manifold, and finally the twisting of the spectral triple of electrodynamics in
euclidean signature. We obtain the Weyl and the Dirac equations in lorentzian
signature (and in the temporal gauge). The twisted fermionic action is then
shown to be invariant under an action of the Lorentz group. This permits to
interprete the field of 1-form that parametrizes the twisted fluctuation of a
manifold as the (dual) of the energy momentum 4-vector.


  Infrared and visible image fusion aims to extract complementary features to
synthesize a single fused image. Many methods employ convolutional neural
networks (CNNs) to extract local features due to its translation invariance and
locality. However, CNNs fail to consider the image's non-local self-similarity
(NLss), though it can expand the receptive field by pooling operations, it
still inevitably leads to information loss. In addition, the transformer
structure extracts long-range dependence by considering the correlativity among
all image patches, leading to information redundancy of such transformer-based
methods. However, graph representation is more flexible than grid (CNN) or
sequence (transformer structure) representation to address irregular objects,
and graph can also construct the relationships among the spatially repeatable
details or texture with far-space distance. Therefore, to address the above
issues, it is significant to convert images into the graph space and thus adopt
graph convolutional networks (GCNs) to extract NLss. This is because the graph
can provide a fine structure to aggregate features and propagate information
across the nearest vertices without introducing redundant information.
Concretely, we implement a cascaded NLss extraction pattern to extract NLss of
intra- and inter-modal by exploring interactions of different image pixels in
intra- and inter-image positional distance. We commence by preforming GCNs on
each intra-modal to aggregate features and propagate information to extract
independent intra-modal NLss. Then, GCNs are performed on the concatenate
intra-modal NLss features of infrared and visible images, which can explore the
cross-domain NLss of inter-modal to reconstruct the fused image. Ablation
studies and extensive experiments illustrates the effectiveness and superiority
of the proposed method on three datasets.


  The 85-cm telescope at the Xinglong station is a well-operated prime focus
system with high science outputs. The telescope has been upgraded since 2014
with new corrector, filters and camera, which are provided by Beijing Normal
University (BNU). The filter set is Johnson-Cousins UBVRI system. We report the
test results of the new system including the bias, dark current, linearity,
gain and readout noise of the CCD camera . Then we derive accurate instrumental
calibration coefficients in UBVRI bands with Landolt standard stars in the
photometric nights. Finally, we give the limiting magnitudes with various
exposure time and signal-to-noise ratio for observers as references.


  Partitioning a graph into balanced blocks such that few edges run between
blocks is a key problem for large-scale distributed processing. A current trend
for partitioning huge graphs are streaming algorithms, which use low
computational resources. In this work, we present a shared-memory streaming
multi-recursive partitioning scheme that performs recursive multi-sections on
the fly without knowing the overall input graph. Our approach has a
considerably lower running time complexity in comparison with state-of-the-art
non-buffered one-pass partitioning algorithms for the standard graph
partitioning case. Moreover, if the topology of a distributed system is known,
it is possible to further optimize the communication costs by mapping
partitions onto processing elements. Our experiments indicate that our
algorithm is both faster and produces better process mappings than competing
tools. In case of graph partitioning, our framework is up to two orders of
magnitude faster at the cost of 5% more cut edges compared to Fennel.


  The complement of a hyperplane arrangement in $\mathbb{C}^n$ deformation
retracts onto an $n$-dimensional cell complex, but the known procedures only
apply to complexifications of real arrangements (Salvetti) or the cell complex
produced depends on an initial choice of coordinates (Bj\"orner-Ziegler). In
this article we consider the unique complex euclidean reflection group acting
cocompactly by isometries on $\mathbb{C}^2$ whose linear part is the finite
complex reflection group known as $G_4$ in the Shephard-Todd classification and
we construct a choice-free deformation retraction from its hyperplane
complement onto an elegant $2$-dimensional complex $K$ where every $2$-cell is
a euclidean equilateral triangle and every vertex link is a M\"obius-Kantor
graph. Since $K$ is non-positively curved, the corresponding braid group is a
CAT(0) group, despite the fact that there are non-regular points in the
hyperplane complement, the action of the reflection group on $K$ is not free,
and the braid group is not torsion-free.


  We show that the location of the first singularity of the Upsilon function of
an algebraic knot is determined by the first term of its Puiseux characteristic
sequence. In many cases this gives better bounds than the tau invariant on the
genus of a cobordism between algebraic knots.


  A novel regime of the saturation of the Pierce-type ion-sound instability in
bounded ion-beam-plasma system is revealed in 1D PIC simulations. It is found
that the saturation of the instability is mediated by the oscillating virtual
anode potential structure. The periodically oscillating potential barrier
separates the incoming beam ions into two groups. One component forms a
supersonic beam which is accelerated to an energy exceeding the energy of the
initial cold ion beam. The other component is organized as a self-consistent
phase space structure of trapped ions with a wide energy spread - the ion hole.
The effective temperature (energy spread) of the ions trapped in the hole is
lower than the initial beam energy. In the final stage the ion hole expands
over the whole system length.


  Hot subdwarf B stars (sdBs) are evolved core helium-burning stars with very
thin hydrogen envelopes. In order to form an sdB, the progenitor has to lose
almost all of its hydrogen envelope right at the tip of the red giant branch.
In close binary systems, mass transfer to the companion provides the
extraordinary mass loss required for their formation. However, apparently
single sdBs exist as well and their formation is unclear since decades. The
merger of helium white dwarfs leading to an ignition of core helium-burning or
the merger of a helium core and a low mass star during the common envelope
phase have been proposed. Here we report the discovery of SB 290 as the first
apparently single fast rotating sdB star located on the extreme horizontal
branch indicating that those stars may form from mergers.


  Understanding how few distributed areas can steer large-scale brain activity
is a fundamental question that has practical implications, which range from
inducing specific patterns of behavior to counteracting disease. Recent
endeavors based on network controllability provided fresh insights into the
potential ability of single regions to influence whole brain dynamics through
the underlying structural connectome. However, controlling the entire brain
activity is often unfeasible and might not always be necessary. The question
whether single areas can control specific target subsystems remains crucial,
albeit still poorly explored. Furthermore, the structure of the brain network
exhibits progressive changes across the lifespan, but little is known about the
possible consequences in the controllability properties. To address these
questions, we adopted a novel target controllability approach that quantifies
the centrality of brain nodes in controlling specific target anatomo-functional
systems. We then studied such target control centrality in human connectomes
obtained from healthy individuals aged from 5 to 85. Main results showed that
the sensorimotor system has a high influencing capacity, but it is difficult
for other areas to influence it. Furthermore, we reported that target control
centrality varies with age and that temporal-parietal regions, whose cortical
thinning is crucial in dementia-related diseases, exhibit lower values in older
people. By simulating targeted attacks, such as those 19 occurring in focal
stroke, we showed that the ipsilesional hemisphere is the most affected one
regardless of the damaged area. Notably, such degradation in target control
centrality was more evident in younger people, thus supporting
early-vulnerability hypotheses after stroke.


  This paper presents an approach of using methods of process mining and
rule-based artificial intelligence to analyze and understand study paths of
students based on campus management system data and study program models.
Process mining techniques are used to characterize successful study paths, as
well as to detect and visualize deviations from expected plans. These insights
are combined with recommendations and requirements of the corresponding study
programs extracted from examination regulations. Here, event calculus and
answer set programming are used to provide models of the study programs which
support planning and conformance checking while providing feedback on possible
study plan violations. In its combination, process mining and rule-based
artificial intelligence are used to support study planning and monitoring by
deriving rules and recommendations for guiding students to more suitable study
paths with higher success rates. Two applications will be implemented, one for
students and one for study program designers.


  We identify a class of $U(1)_X$ models which can explain the $R_K$ anomaly
and the neutrino mixing pattern, by using a bottom-up approach. The different
$X$-charges of lepton generations account for the lepton universality violation
required to explain $R_K$. In addition to the three right-handed neutrinos
needed for the Type-I seesaw mechanism, these minimal models only introduce an
additional doublet Higgs and a singlet scalar. While the former helps in
reproducing the quark mixing structure, the latter gives masses to neutrinos
and the new gauge boson $Z^\prime$. Our bottom-up approach determines the
$X$-charges of all particles using theoretical consistency and experimental
constraints. We find the parameter space allowed by the constraints from
neutral meson mixing, rare $b\to s$ decays and direct collider searches for
$Z^\prime$. Such a $Z^\prime$ may be observable at the ongoing run of the Large
Hadron Collider with a few hundred fb$^{-1}$ of integrated luminosity.


  The coupling of electronic degrees of freedom in materials to create
hybridized functionalities is a holy grail of modern condensed matter physics
that may produce novel mechanisms of control. Correlated electron systems often
exhibit coupled degrees of freedom with a high degree of tunability which
sometimes lead to hybridized functionalities based on external stimuli.
However, the mechanisms of tunability and the sensitivity to external stimuli
are determined by intrinsic material properties which are not always
controllable. A Mott metal-insulator transition, which is technologically
attractive due to the large changes in resistance, can be tuned by doping,
strain, electric fields, and orbital occupancy but cannot be, in and of itself,
controlled externally with light. Here we present a new approach to produce
hybridized functionalities using a properly engineered
photoconductor/strongly-correlated hybrid heterostructure, showing control of
the Metal-to-Insulator transition (MIT) using optical means. This approach
combines a photoconductor, which does not exhibit an MIT, with a strongly
correlated oxide, which is not photoconducting. Due to the close proximity
between the two materials, the heterostructure exhibits large volatile and
nonvolatile, photoinduced resistivity changes and substantial photoinduced
shifts in the MIT transition temperatures. This approach can potentially be
extended to other judiciously chosen combinations of strongly correlated
materials with systems which exhibit optically, electrically or magnetically
controllable behavior.


  Our motivation comes from the work of Engel and Schneider (1980). Their main
theorem implies that two symmetric matrices have equal corresponding principal
minors of all orders if and only if they are diagonally similar. This study was
continued by Hartfiel and Loewy (1984). They found sufficient conditions under
which two $n\times n$ matrices\ $A$ and $B$ have equal corresponding principal
minors of all orders if and only if $B$ or its transpose $B^{t}$ is diagonally
similar to $A$. In this paper, we give a new way to construct a pair of
skew-symmetric having equal corresponding principal minors of all orders.


  Atomically thin two-dimensional materials such as graphene exhibit extremely
high-pressure sensitivity compared to the commercially used pressure sensors
due to their high surface-to-volume ratio and excellent mechanical properties.
The smaller piezoresistance of graphene across different transport regimes
limits its pressure sensitivity compared to other two-dimensional materials.
Using membrane theory and thin-film adhesivity model, we show miniaturization
as means to enhance theoverall performance of graphene pressure sensors. Our
findings reveal that ballistic graphene canbe configured to measure ultra-high
pressure (10^9 Pa) with many-fold higher sensitivity per unit area than
quasi-ballistic graphene, diffusive graphene, and thin layers of transition
metal dichalcogenides. Based on these findings, we propose an array of
ballistic graphene sensors with extremely high-pressure sensitivity and ultra
high-pressure range that will find applications in next-generation NEMS
pressure sensors. The performance parameters of the array sensors can be
further enhancedby reducing the size of graphene membranes and increasing the
number of sensors in the array. The methodology developed in this paper can be
used to explore similar applications using other two-dimensional materials.


  Janus materials have attracted much interest due to their intrinsic electric
dipole moment and Rashba band splitting. We show that, by building bilayers of
MoSeTe and WSeTe with different chalcogen atom sequences and different stacking
patterns, one can modulate the net dipole moment strength and the Rashba
effect, as well as the band alignment of the MoSeTe/WSeTe bilayer. Type-II band
alignment is found which can be exploited to create long-lived interlayer
excitons. Moreover, it is shown that the atomic sequence and stacking play
pivotal roles in the interlayer distance of MoSeTe/WSeTe and thus its
electronic structure and vibrational, especially low-frequency,
characteristics. The long-range dispersion forces between atoms are treated
with a conventional additive pairwise, as well as a many-body-dispersion
method. It is shown that under the many-body dispersion method, more clear and
rational thermodynamic trends of bilayer stacking are realized and interface
distances are estimated more accurately. Vibrational spectra of the bilayers
are calculated using first-principles phonon calculations and the fingerprints
of monolayer attraction and repulsion are identified. An anti-correlation
between distance and the shearing mode frequency of the rigid monolayers is
demonstrated which agrees well with experimental findings. The results suggest
that the judicious selection of the atomic sequence and stacking helps to widen
the scope of the low-dimensional materials by adding or enhancing properties
for specific applications, e.g. for spintronics or valleytronics devices.


  We argue that the Raman intensity in a spin S two-leg spin-ladder has a
pseudo-resonance peak, whose width is very small at large S. The
pseudo-resonance originates from the existence of a local minimum in the magnon
excitation spectrum, and is located slightly below twice the magnon energy at
the minimum. The physics behind the peak is similar to the excitonic scenario
for the neutron and Raman resonances in a d-wave superconductor.


  We study higher-order asymptotic expansions of eigenvalues in perturbed
transfer operators, of the corresponding eigenfunctions and of the
corresponding eigenvectors of the dual operators. In our main result, we give
explicit expressions of these coefficients and these remainders under mild
conditions of linear operators and of (there is even no norm) linear spaces.
Moreover we investigate sufficient conditions for convergence of the remainders
under weak conditions in the sense that the eigenvalue of perturbed operators
does not have a uniform spectrum gap. As our main application, we apply our
result to the asymptotic behaviour of Gibbs measures associated with the
Hausdorff dimension of the limit set of the so-called graph directed Markov
systems. Moreover, we demonstrate the asymptotic behaviours of Ruelle transfer
operators without a uniform spectral gap. In another example, we mention that
the conditions of our main results are satisfied under the conditions of
abstract asymptotic theory given by Gou\"ezel and Liverani and under mild
conditions.


  Boundary value problem for a fractional power of an elliptic operator is
considered. An integral representation by means of a standard solution problem
for parabolic equations is used to solve such problems. Quadrature generalized
Gauss-Laguerre formulas are constructed. We examine the effect of key
parameters on the accuracy of the approximate solution: the number of nodes of
the quadrature and fractional power of the operator. Computational experiments
were performed to model two-dimensional problem with a fractional power of an
elliptic operator.


  We extend the treatment of neutrinos within the context of SIM(2) Very
Special Relativity (VSR) by adding a new discrete symmetry that we call Light
Cone Reflection (LCR). We construct a Lagrangian that exhibits both VSR and LCR
symmetry, and find that the spectrum involves two neutrinos, one tachyonic and
the other not, with the same absolute value of the mass parameter. We argue
that LCR symmetry offers a rationale for introducing tachyonic neutrinos.


  This paper conducts a comparative study of proximal gradient methods (PGMs)
and proximal DC algorithms (PDCAs) for sparse regression problems which can be
cast as Difference-of-two-Convex-functions (DC) optimization problems. It has
been shown that for DC optimization problems, both General Iterative Shrinkage
and Thresholding algorithm (GIST), a modified version of PGM, and PDCA converge
to critical points. Recently some enhanced versions of PDCAs are shown to
converge to d-stationary points, which are stronger necessary condition for
local optimality than critical points. In this paper we claim that without any
modification, PGMs converge to a d-stationary point not only to DC problems but
also to more general nonsmooth nonconvex problems under some technical
assumptions. While the convergence to d-stationary points is known for the case
where the step size is small enough, the finding of this paper is valid also
for extended versions such as GIST and its alternating optimization version,
which is to be developed in this paper. Numerical results show that among
several algorithms in the two categories, modified versions of PGM perform best
among those not only in solution quality but also in computation time.


  (Talk presented at the XVth Workshop on Geometric Methods in Physics,
Quantizations, Deformations and Coherent States, in Bialowieza, Poland, July
1-7, 1996.) The aim of this article is to introduce some basic notions of
Topological Quantum Field Theory (TQFT) and to consider a modification of TQFT,
applicable to embedded manifolds. After an introduction based around a simple
example (Section 1) the notion of a d-dimensional TQFT is defined in
category-theoretical terms, as a certain type of functor from a category of
d-dimensional cobordisms to the category of vector spaces (Section 2). A
construction due to Turaev, an operator-valued invariant of tangles, is
discussed in Section 3. It bears a strong resemblance to 1-dimensional TQFTs,
but carries much richer structure due to the fact that the 1-dimensional
manifolds involved are embedded in a 3-dimensional space. This leads us, in
Section 4, to propose a class of TQFT-like theories, appropriate to embedded,
rather than pure, manifolds.


  Advancements in deep learning are often associated with increasing model
sizes. The model size dramatically affects the deployment cost and latency of
deep models. For instance, models like BERT cannot be deployed on edge devices
and mobiles due to their sheer size. As a result, most advances in Deep
Learning are yet to reach the edge. Model compression has sought much-deserved
attention in literature across natural language processing, vision, and
recommendation domains. This paper proposes a model-agnostic, cache-friendly
model compression approach: Random Operation Access Specific Tile (ROAST)
hashing. ROAST collapses the parameters by clubbing them through a lightweight
mapping. Notably, while clubbing these parameters, ROAST utilizes cache
hierarchies by aligning the memory access pattern with the parameter access
pattern. ROAST is up to $\sim 25 \times$ faster to train and $\sim 50 \times$
faster to infer than the popular parameter sharing method HashedNet.
Additionally, ROAST introduces global weight sharing, which is empirically and
theoretically superior to local weight sharing in HashedNet, and can be of
independent interest in itself. With ROAST, we present the first compressed
BERT, which is $100\times - 1000\times$ smaller but does not result in quality
degradation. These compression levels on universal architecture like
transformers are promising for the future of SOTA model deployment on
resource-constrained devices like mobile and edge devices


  Discrete Ginzburg-Landau (DGL) equations with non-local nonlinearities have
been established as significant inherently discrete models in numerous physical
contexts, similar to their counterparts with local nonlinear terms. We study
two prototypical examples of non-local and local DGLs on the one-dimensional
infinite lattice. For the non-local DGL, we identify distinct scenarios for the
asymptotic behavior of the globally existing in time solutions depending on
certain parametric regimes. One of these scenarios is associated with a
restricted compact attractor according to J. K. Hale's definition. We also
prove the closeness of the solutions of the two models in the sense of a
"continuous dependence on their initial data" in the $l^2$ metric under general
conditions on the intrinsic linear gain or loss incorporated in the model. As a
consequence of the closeness results, in the dissipative regime we establish
the congruence of the attractors possessed by the semiflows of the non-local
and of the local model respectively, for initial conditions in a suitable
domain of attraction defined by the non-local system.


  We report the results of a search for class II methanol masers at 37.7, 38.3
and 38.5 GHz towards a sample of 70 high-mass star formation regions. We
primarily searched towards regions known to show emission either from the 107
GHz class II methanol maser transition, or from the 6.035 GHz excited OH
transition. We detected maser emission from 13 sources in the 37.7 GHz
transition, eight of these being new detections. We detected maser emission
from three sources in the 38 GHz transitions, one of which is a new detection.
We find that 37.7 GHz methanol masers are only associated with the most
luminous 6.7 and 12.2 GHz methanol maser sources, which in turn are
hypothesised to be the oldest class II methanol sources. We suggest that the
37.7 GHz methanol masers are associated with a brief evolutionary phase (of
1000-4000 years) prior to the cessation of class II methanol maser activity in
the associated high-mass star formation region.


  We study the formation and detection at the next linear e^+e^- collider of
bound states of level-1 quark Kaluza-Klein excitations B_KK within a scenario
of universal extra-dimensions (UED). The interactions of such Kaluza-Klein
excitations are modeled by an alpha_s driven Coulomb potential. In order to
obtain the threshold cross-section, we employ the Green function method which
is known to properly describe the peaks below threshold and to yield a net
increase in the continuum region (above threshold) relative to the naive Born
cross-section. We study such effect at different values of the scale (R^-1) of
the extra-dimensions with an explicit calculation of the mass spectrum as given
by radiative corrections. The overall effect is roughly 2.7 at R^-1=300 GeV and
goes down to 2.2 at R^-1=1000 GeV and a relatively large number of events is
expected from N_events ~ 2.5*10^4 at R^-1=300 GeV down to N_events ~ 10^3 at
R^-1=1000 GeV at the anticipated annual integrated luminosity of L_0= 100
fb^-1. We finally discuss some potentially observable signatures such as the
multilepton channels 2j + 2l + missing energy, and 2j + 4l + missing energy for
which we estimate statistical significance >~ 2 for R^-1 up to 600 ~ 700 GeV.


  We have discovered a novel feature in the plasmon excitations for a pair of
Coulomb-coupled non- concentric spherical two-dimensional electron gases
(S2DEGs). Our results show that the plasmon excitations for such pairs depend
on the orientation with respect to the external electromagnetic probe field.
The origin of this anisotropy of the inter-sphere Coulomb interaction is due to
the directional asymmetry of the electrostatic coupling of electrons in excited
states which depend on both the angular momentum quantum number L and its
projection M on the axis of quantization taken as the probe E-field direction.
We demonstrate the anisotropic inter-sphere Coulomb coupling in space and
present semi-analytic results in the random-phase approximation both
perpendicular and parallel to the axis of quantization. For the incidence of
light with a finite orbital or spin angular momentum, the magnetic field
generated from an induced oscillating electric dipole on one sphere can couple
to an induced magnetic dipole on another sphere in a way depending on the
direction parallel or perpendicular to the probe E field. Such an effect from
the plasmon spatial correlation is expected to be experimentally observable by
employing circularly-polarized light or a helical light beam for incidence. The
S2DEG serves as a simple model for fullerenes as well as metallic dimers, when
the energy bands are far apart.


  Graph-based data present unique challenges and opportunities for machine
learning. Graph Neural Networks (GNNs), and especially those algorithms that
capture graph topology through message passing for neighborhood aggregation,
have been a leading solution. However, these networks often require substantial
computational resources and may not optimally leverage the information
contained in the graph's topology, particularly for large-scale or complex
graphs. We propose Topology Coordinate Neural Network (TCNN) and Directional
Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives
to message passing GNNs, that directly leverage the graph's topology,
sidestepping the computational challenges presented by competing algorithms.
Our proposed methods can be viewed as a reprise of classic techniques for graph
embedding for neural network feature engineering, but they are novel in that
our embedding techniques leverage ideas in Graph Coordinates (GC) that are
lacking in current practice. Experimental results, benchmarked against the Open
Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve
competitive or superior performance to message passing GNNs. For similar levels
of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters
than contenders of the OGBN Leaderboard. The proposed TCNN architecture
requires fewer parameters than any neural network method currently listed in
the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets.
Conversely, our methods achieve higher performance for a similar number of
trainable parameters. By providing an efficient and effective alternative to
message passing GNNs, our work expands the toolbox of techniques for
graph-based machine learning.


  Quantum size effect-induced heat capacity of metal nanoparticles at low
temperatures was predicted 79 years ago to be exponential. This, however, has
not been reported until date. In defiance, we demonstrate here observation of
exponentially decaying heat capacity, below 45.2 K, associated with quantum
jumps, exceptionally in 4 nm naturally assembled hexagonal closed packed (hcp)
lattice of nickel nanoparticles; high magnetic fields have negligible effect on
these features. Magnetic susceptibilities in contrast reveal evolution of
quantum size effects with decrease in particle size. They exhibit sharp rise
below about 30 K and vestiges of saturations below 5 K. The former is explained
by Curie-like characteristics of odd electrons while the latter tend towards
the orthogonal even-like case. These characteristics, ascribed to the ensembles
of Ni nanoparticles, will give a new direction in understanding this crucial
thermodynamic phenome-non.


  The Mid Infra Red Instrument (MIRI) is one of the four instruments onboard
the James Webb Space Telescope (JWST), providing imaging, coronagraphy and
spectroscopy over the 5-28 microns band. To verify the optical performance of
the instrument, extensive tests were performed at CEA on the flight model (FM)
of the Mid-InfraRed IMager (MIRIM) at cryogenic temperatures and in the
infrared. This paper reports on the point spread function (PSF) measurements at
5.6 microns, the shortest operating wavelength for imaging. At 5.6 microns the
PSF is not Nyquist-sampled, so we use am original technique that combines a
microscanning measurement strategy with a deconvolution algorithm to obtain an
over-resolved MIRIM PSF. The microscanning consists in a sub-pixel scan of a
point source on the focal plane. A data inversion method is used to reconstruct
PSF images that are over-resolved by a factor of 7 compared to the native
resolution of MIRI. We show that the FWHM of the high-resolution PSFs were
5-10% wider than that obtained with Zemax simulations. The main cause was
identified as an out-of-specification tilt of the M4 mirror. After correction,
two additional test campaigns were carried out, and we show that the shape of
the PSF is conform to expectations. The FWHM of the PSFs are 0.18-0.20 arcsec,
in agreement with simulations. 56.1-59.2% of the total encircled energy
(normalized to a 5 arcsec radius) is contained within the first dark Airy ring,
over the whole field of view. At longer wavelengths (7.7-25.5 microns), this
percentage is 57-68%. MIRIM is thus compliant with the optical quality
requirements. This characterization of the MIRIM PSF, as well as the
deconvolution method presented here, are of particular importance, not only for
the verification of the optical quality and the MIRI calibration, but also for
scientific applications.


  We show that couplings of the goldstino field, appearing when global
supersymmetry is broken, can always be described in superspace by means of a
nilpotent chiral superfield $X$, satisfying $X^2=0$. This applies to both
F-term and D-term supersymmetry breaking, even when the supersymmetry breaking
model admits a linear description.


  Network infrastructures are essential for the distribution of resources such
as electricity and water. Typical strategies to assess their resilience focus
on the impact of a sequence of random or targeted failures of network nodes or
links. Here we consider a more realistic scenario, where elements fail based on
their usage. We propose a dynamic model of transport based on the
Bak-Tang-Wiesenfeld sandpile model where links fail after they have transported
more than an amount $\mu$ (threshold) of the resource and we investigate it on
the square lattice. As we deal with a new model, we provide insight on its
fundamental behavior and dependence on parameters. We observe that for low
values of the threshold due to a positive feedback of link failure, an
avalanche develops that leads to an abrupt collapse of the lattice. By
contrast, for high thresholds the lattice breaks down in an uncorrelated
fashion. We determine the critical threshold $\mu^*$ separating these two
regimes and show how it depends on the toppling threshold of the nodes and the
mass increment added stepwise to the system. We find that the time of major
disconnection is well described with a linear dependence on $\mu$. Furthermore,
we propose a lower bound for $\mu^*$ by measuring the strength of the dynamics
leading to abrupt collapses.


  Zero energy states in the Dirac spectrum with U(1) symmetric massive vortices
of various underlying insulating orders in strained graphene are constructed in
the presence of the magnetic field. An easy plane vortex of antiferromagnet and
quantum spin Hall orders host two zero energy states, however, with two
different length scales. Such inequivalent zero modes can lead to oscillatory
charge and magnetization, and their usual quantizations get restored only far
from the vortex core. Otherwise, these zero modes can be delocalized from each
other by tuning the mutual strength of two fields. One can, therefore,
effectively bind a single zero mode in the vortex core. A possible experimental
set up to capture signature of this theory in real graphene as well as in
optical honeycomb lattices is mentioned. Generalization of this scenario with
underlying topological defects of Kekule superconductors can localize a single
Majorana mode in the vicinity of the defect-core.


  A straightforward application of semi-supervised machine learning to the
problem of treatment effect estimation would be to consider data as "unlabeled"
if treatment assignment and covariates are observed but outcomes are
unobserved. According to this formulation, large unlabeled data sets could be
used to estimate a high dimensional propensity function and causal inference
using a much smaller labeled data set could proceed via weighted estimators
using the learned propensity scores. In the limiting case of infinite unlabeled
data, one may estimate the high dimensional propensity function exactly.
However, longstanding advice in the causal inference community suggests that
estimated propensity scores (from labeled data alone) are actually preferable
to true propensity scores, implying that the unlabeled data is actually useless
in this context. In this paper we examine this paradox and propose a simple
procedure that reconciles the strong intuition that a known propensity
functions should be useful for estimating treatment effects with the previous
literature suggesting otherwise. Further, simulation studies suggest that
direct regression may be preferable to inverse-propensity weight estimators in
many circumstances.


  The plane wave decomposition method (PWDM) is one of the most popular
strategies for numerical solution of the quantum billiard problem. The method
is based on the assumption that each eigenstate in a billiard can be
approximated by a superposition of plane waves at a given energy. By the
classical results on the theory of differential operators this can indeed be
justified for billiards in convex domains. On the contrary, in the present work
we demonstrate that eigenstates of non-convex billiards, in general, cannot be
approximated by any solution of the Helmholtz equation regular everywhere in
$\R^2$ (in particular, by linear combinations of a finite number of plane waves
having the same energy). From this we infer that PWDM cannot be applied to
billiards in non-convex domains. Furthermore, it follows from our results that
unlike the properties of integrable billiards, where each eigenstate can be
extended into the billiard exterior as a regular solution of the Helmholtz
equation, the eigenstates of non-convex billiards, in general, do not admit
such an extension.


  A large class of Laboratory, Space, and Astrophysical plasmas is nearly
collisionless. When a localized energy or particle sink, for example, in the
form of a radiative cooling spot or a black hole, is introduced into such a
plasma, it can trigger a plasma thermal collapse, also known as a thermal
quench in tokamak fusion. Here we show that the electron thermal conduction in
such a nearly collisionless plasma follows the convective energy transport
scaling in itself or in its spatial gradient, due to the constraint of
ambipolar transport. As the result, a robust cooling flow aggregates mass
toward the cooling spot and the thermal collapse of the surrounding plasma
takes the form of four propagating fronts that originate from the radiative
cooling spot, along the magnetic field line in a magnetized plasma. The slowest
one, which is responsible for deep cooling, is a shock front.


  We determine the Galois representations inside the $l$-adic cohomology of
some quaternionic and related unitary Shimura varieties at ramified places. The
main results generalize the previous works of Reimann and Kottwitz in this
setting to arbitrary levels at $p$, and confirm the expected description of the
cohomology due to Langlands and Kottwitz.


  Motivation. Association studies have been widely used to search for
associations between common genetic variants observations and a given
phenotype. However, it is now generally accepted that genes and environment
must be examined jointly when estimating phenotypic variance. In this work we
consider two types of biological markers: genotypic markers, which characterize
an observation in terms of inherited genetic information, and metagenomic
marker which are related to the environment. Both types of markers are
available in their millions and can be used to characterize any observation
uniquely. Objective. Our focus is on detecting interactions between groups of
genetic and metagenomic markers in order to gain a better understanding of the
complex relationship between environment and genome in the expression of a
given phenotype. Contributions. We propose a novel approach for efficiently
detecting interactions between complementary datasets in a high-dimensional
setting with a reduced computational cost. The method, named SICOMORE, reduces
the dimension of the search space by selecting a subset of supervariables in
the two complementary datasets. These supervariables are given by a weighted
group structure defined on sets of variables at different scales. A Lasso
selection is then applied on each type of supervariable to obtain a subset of
potential interactions that will be explored via linear model testing. Results.
We compare SICOMORE with other approaches in simulations, with varying sample
sizes, noise, and numbers of true interactions. SICOMORE exhibits convincing
results in terms of recall, as well as competitive performances with respect to
running time. The method is also used to detect interaction between genomic
markers in Medicago truncatula and metagenomic markers in its rhizosphere
bacterial community. Software availability. A R package is available, along
with its documentation and associated scripts, allowing the reader to reproduce
the results presented in the paper.


  We improve bump conditions for the two-weight boundedness of
Calder\'on-Zygmund operators introduced recently by R. Rahm and S. Spencer.


  We present first-principles theoretical determination of the three-body
polarizability and the third dielectric virial coefficient of helium.
Coupled-cluster theory and the full configuration interaction procedure were
used to perform required electronic structure calculations. The mean absolute
relative uncertainty of the trace of the three-body polarizability tensor,
resulting from the incompleteness of orbital basis set, was determined using
extrapolation techniques. Additional uncertainty due to approximate treatment
of triple and the neglect of higher excitations was estimated using full
configuration interaction calculations. An analytic function was developed to
describe the behavior of the polarizability and its asymptotic decay to
three-atomic and atom-diatom fragmentation channels. We also developed an
analytic function describing the local behavior of the total uncertainty of our
calculations. Using both fits we calculated the third dielectric virial
coefficient and its uncertainty using the classical and semiclassical
Feynman-Hibbs approaches. The results of our calculations were compared with
available experimental data and with recent Path-Integral Monte Carlo (PIMC)
calculations employing the so-called superposition approximation of the
three-body polarizability. For temperatures above 200 K we observed significant
discrepancy between the classical results obtained using either the
superposition approximation or the ab initio computed polarizability. The
theoretical data reported in this work eliminate the main accuracy bottleneck
of the development of optical pressure standard and are expected to facilitate
further progress in the field of quantum thermal metrology


  We consider a new interaction between a heavy Majorana neutrino (N) and a
charged Higgs boson (H+) and show that it can have drastic implications on
lepton number violating (LNV) signal of same-sign dileptons at the LHC. The LNV
signal of heavy Majorana neutrinos previously considered at the LHC, pp -> l+ N
-> l+ l+ W-, may be overwhelmed by pp -> l+ N -> l+ l+ H-. With the subsequent
decays H- -> b t(bar) or H- -> W- H^0, the heavy Majorana neutrino production
leads to the spectacular events of l+ l+ b b(bar) + 2 jets. We also explore the
case m_N < m_{H+}, where the decay H+ -> l+ N can become the dominant
N-production mechanism at the LHC. In particular, we show that the process g b
-> t H- followed by t -> b W+ and H- -> l- N -> l- l- W+ could lead to another
type of spectacular events of l- l- b + 4 jets.


  We introduce collisions of solids as a new and efficient ionization mechanism
for gas in protoplanetary disks, which especially operates in the dense
midplane of protoplanetary disks. This idea is sparked by laboratory
experiments where we found that charge, which is exchanged by grains in mutual
collision (tribocharging), is not tied to their surfaces alone. As kind of
collateral effect, charges also become entrained into the gas phase, i.e.
collisions ionize the protoplanetary disk. Therefore, solids are not only sinks
of charges in disks but also sources. A first estimate shows that ionization
rates in the midplane at 1 AU in the range of $10^{-19} ... 10^{-15} \rm \,
s^{-1}$ seem feasible depending on the assumption of rather calm or highly
turbulent conditions with radial particle pile up.


  Chiral corrections to the delta axial charge are determined using heavy
baryon chiral perturbation theory. Knowledge of this axial coupling is
necessary to assess virtual-delta contributions to nucleon and delta
observables. We give isospin relations useful for a lattice determination of
the axial coupling. Furthermore we detail partially quenched chiral
corrections, which are relevant to address partial quenching and/or mixed
action errors in lattice calculations of the delta axial charge.


  We provide bounds on the compression size of the solutions to 22 problems in
computer science. For each problem, we show that solutions exist with high
probability, for some simple probability measure. Once this is proven,
derandomization can be used to prove the existence of a simple solution.


  Urban environments offer a challenging scenario for autonomous driving.
Globally localizing information, such as a GPS signal, can be unreliable due to
signal shadowing and multipath errors. Detailed a priori maps of the
environment with sufficient information for autonomous navigation typically
require driving the area multiple times to collect large amounts of data,
substantial post-processing on that data to obtain the map, and then
maintaining updates on the map as the environment changes. This paper addresses
the issue of autonomous driving in an urban environment by investigating
algorithms and an architecture to enable fully functional autonomous driving
with limited information. An algorithm to autonomously navigate urban roadways
with little to no reliance on an a priori map or GPS is developed. Localization
is performed with an extended Kalman filter with odometry, compass, and sparse
landmark measurement updates. Navigation is accomplished by a compass-based
navigation control law. Key results from Monte Carlo studies show success rates
of urban navigation under different environmental conditions. Experiments
validate the simulated results and demonstrate that, for given test conditions,
an expected range can be found for a given success rate.


  We present an overview of the electrical, mechanical, and thermal properties
of polycrystalline graphene. Most global properties of this material, such as
the charge mobility, thermal conductivity, or Young's modulus, are sensitive to
its microstructure, for instance the grain size and the presence of line or
point defects. Both the local and global features of polycrystalline graphene
have been investigated by a variety of simulations and experimental
measurements. In this review, we summarize the properties of polycrystalline
graphene, and by establishing a perspective on how the microstructure impacts
its large-scale physical properties, we aim to provide guidance for further
optimization and improvement of applications based on this material, such as
flexible and wearable electronics, and high-frequency or spintronic devices.


  Image-based sequence recognition has been a long-standing research topic in
computer vision. In this paper, we investigate the problem of scene text
recognition, which is among the most important and challenging tasks in
image-based sequence recognition. A novel neural network architecture, which
integrates feature extraction, sequence modeling and transcription into a
unified framework, is proposed. Compared with previous systems for scene text
recognition, the proposed architecture possesses four distinctive properties:
(1) It is end-to-end trainable, in contrast to most of the existing algorithms
whose components are separately trained and tuned. (2) It naturally handles
sequences in arbitrary lengths, involving no character segmentation or
horizontal scale normalization. (3) It is not confined to any predefined
lexicon and achieves remarkable performances in both lexicon-free and
lexicon-based scene text recognition tasks. (4) It generates an effective yet
much smaller model, which is more practical for real-world application
scenarios. The experiments on standard benchmarks, including the IIIT-5K,
Street View Text and ICDAR datasets, demonstrate the superiority of the
proposed algorithm over the prior arts. Moreover, the proposed algorithm
performs well in the task of image-based music score recognition, which
evidently verifies the generality of it.


  Feedback by Active Galactic Nuclei is often divided into quasar and radio
mode, powered by radiation or radio jets, respectively. Both are fundamental in
galaxy evolution, especially in late-type galaxies, as shown by cosmological
simulations and observations of jet-ISM interactions in these systems. We
compare AGN feedback by radiation and by collimated jets through a suite of
simulations, in which a central AGN interacts with a clumpy, fractal galactic
disc. We test AGN of $10^{43}$ and $10^{46}$ erg/s, considering jets
perpendicular or parallel to the disc. Mechanical jets drive the more powerful
outflows, exhibiting stronger mass and momentum coupling with the dense gas,
while radiation heats and rarifies the gas more. Radiation and perpendicular
jets evolve to be quite similar in outflow properties and effect on the cold
ISM, while inclined jets interact more efficiently with all the disc gas,
removing the densest $20\%$ in $20$ Myr, and thereby reducing the amount of
cold gas available for star formation. All simulations show small-scale inflows
of $0.01-0.1$ M$_\odot$/yr, which can easily reach down to the Bondi radius of
the central supermassive black hole (especially for radiation and perpendicular
jets), implying that AGN modulate their own duty cycle in a feedback/feeding
cycle.


  R-mode oscillations in a rotating star produce characteristic signatures in a
Fourier amplitude spectrum at frequencies related with the rotation frequency,
which can be, in turn, used to obtain the surface rotation rate of the star.
Some binary stars observed by Kepler indicate the presence of r~modes that are
probably excited by the tidal effect. In this paper, we have obtained stellar
rotation periods in 20 eccentric (heartbeat) binaries with r-mode signatures.
The majority of the rotation periods are found to be comparable to
pseudo-synchronous periods, in which the angular velocity of rotation is
similar to the angular orbital motion of the companion at periastron. In
particular, for the heartbeat stars with orbital periods longer than about
8\,d, all but one agree with pseudo-synchronous rotation. In contrast to a
previous investigation by Zimmerman et al., our result supports the
pseudo-synchronisation theory developed by Hut.


  At Crypto'07, Goyal introduced the concept of Accountable Authority
Identity-Based Encryption as a convenient tool to reduce the amount of trust in
authorities in Identity-Based Encryption. In this model, if the Private Key
Generator (PKG) maliciously re-distributes users' decryption keys, it runs the
risk of being caught and prosecuted. Goyal proposed two constructions: the
first one is efficient but can only trace well-formed decryption keys to their
source; the second one allows tracing obfuscated decryption boxes in a model
(called weak black-box model) where cheating authorities have no decryption
oracle. The latter scheme is unfortunately far less efficient in terms of
decryption cost and ciphertext size. In this work, we propose a new
construction that combines the efficiency of Goyal's first proposal with a very
simple weak black-box tracing mechanism. Our scheme is described in the
selective-ID model but readily extends to meet all security properties in the
adaptive-ID sense, which is not known to be true for prior black-box schemes.


  A geometry of two sets (GTS) is given on manifolds $\mathfrak{M}$ and
$\mathfrak{N}$ by a metric (two-point) function $f:\mathfrak{M\times N}\to R$.
Its phenomenological symmetry (PS) means that for some numbers of points from
each manifold all the reciprocal distances are tied to some equation. Such
simplest geometry on one-dimensional manifolds was discovered by Yu.I. Kulakov
when he was analyzing the structure of Newton's 2nd law. In this note the PS
G2S of rank (3,2) that springs up in the process of analysis the structure of
Ohm's law is precisely defined and analyzed using a new approach.


  Human-centric perception tasks, e.g., pedestrian detection, skeleton-based
action recognition, and pose estimation, have wide industrial applications,
such as metaverse and sports analysis. There is a recent surge to develop
human-centric foundation models that can benefit a broad range of human-centric
perception tasks. While many human-centric foundation models have achieved
success, they did not explore 3D and vision-language tasks for human-centric
and required task-specific finetuning. These limitations restrict their
application to more downstream tasks and situations. To tackle these problems,
we present Hulk, the first multimodal human-centric generalist model, capable
of addressing 2D vision, 3D vision, skeleton-based, and vision-language tasks
without task-specific finetuning. The key to achieving this is condensing
various task-specific heads into two general heads, one for discrete
representations, e.g., languages, and the other for continuous representations,
e.g., location coordinates. The outputs of two heads can be further stacked
into four distinct input and output modalities. This uniform representation
enables Hulk to treat diverse human-centric tasks as modality translation,
integrating knowledge across a wide range of tasks. Comprehensive evaluations
of Hulk on 12 benchmarks covering 8 human-centric tasks demonstrate the
superiority of our proposed method, achieving state-of-the-art performance in
11 benchmarks. The code is available on https://github.com/OpenGVLab/Hulk.


  We consider unsupervised domain adaptation (UDA), where labeled data from a
source domain (e.g., photographs) and unlabeled data from a target domain
(e.g., sketches) are used to learn a classifier for the target domain.
Conventional UDA methods (e.g., domain adversarial training) learn
domain-invariant features to improve generalization to the target domain. In
this paper, we show that contrastive pre-training, which learns features on
unlabeled source and target data and then fine-tunes on labeled source data, is
competitive with strong UDA methods. However, we find that contrastive
pre-training does not learn domain-invariant features, diverging from
conventional UDA intuitions. We show theoretically that contrastive
pre-training can learn features that vary subtantially across domains but still
generalize to the target domain, by disentangling domain and class information.
Our results suggest that domain invariance is not necessary for UDA. We
empirically validate our theory on benchmark vision datasets.


  We investigated electron phases in the $f^2$-configuration system with the
$\Gamma_1$ crystalline-electric-field (CEF) ground state in cubic symmetry. An
extended three-orbital periodic Anderson model, which consists of $j=5/2$
states, is evaluated by using the rotationally invariant slave boson formalism.
As a result, we found three phases at zero temperature: the $\Gamma_1$ CEF
singlet phase, and two kinds of Fermi liquid (FL). One of the FL arises owing
to the presence of the $\Gamma_5$ CEF state, while the other forms a
quasi-degenerated CEF state. All these phases are separated by first-order
transitions: the charge-transfer transition and the CEF energy-level crossing.
We also found heavy quasiparticle behaviors in the intermediate valence region
between the $f^2$ and $f^3$ configurations. In this region, the mass
enhancement factor exhibits a peak structure by increasing the hybridization.
Physical properties of these phases and transitions are discussed.


  The $\ell_0$-constrained empirical risk minimization ($\ell_0$-ERM) is a
promising tool for high-dimensional statistical estimation. The existing
analysis of $\ell_0$-ERM estimator is mostly on parameter estimation and
support recovery consistency. From the perspective of statistical learning,
another fundamental question is how well the $\ell_0$-ERM estimator would
perform on unseen samples. The answer to this question is important for
understanding the learnability of such a non-convex (and also NP-hard)
M-estimator but still relatively under explored.
  In this paper, we investigate this problem and develop a generalization
theory for $\ell_0$-ERM. We establish, in both white-box and black-box
statistical regimes, a set of generalization gap and excess risk bounds for
$\ell_0$-ERM to characterize its sparse prediction and optimization capability.
Our theory mainly reveals three findings: 1) tighter generalization bounds can
be attained by $\ell_0$-ERM than those of $\ell_2$-ERM if the risk function is
(with high probability) restricted strongly convex; 2) tighter uniform
generalization bounds can be established for $\ell_0$-ERM than the conventional
dense ERM; and 3) sparsity level invariant bounds can be established by
imposing additional strong-signal conditions to ensure the stability of
$\ell_0$-ERM. In light of these results, we further provide generalization
guarantees for the Iterative Hard Thresholding (IHT) algorithm which serves as
one of the most popular greedy pursuit methods for approximately solving
$\ell_0$-ERM. Numerical evidence is provided to confirm our theoretical
predictions when implied to sparsity-constrained linear regression and logistic
regression models.


  Barrier function-based inequality constraints are a means to enforce safety
specifications for control systems. When used in conjunction with a convex
optimization program, they provide a computationally efficient method to
enforce safety for the general class of control-affine systems. One of the main
assumptions when taking this approach is the a priori knowledge of the barrier
function itself, i.e., knowledge of the safe set. In the context of navigation
through unknown environments where the locally safe set evolves with time, such
knowledge does not exist. This manuscript focuses on the synthesis of a zeroing
barrier function characterizing the safe set based on safe and unsafe sample
measurements, e.g., from perception data in navigation applications. Prior work
formulated a supervised machine learning algorithm whose solution guaranteed
the construction of a zeroing barrier function with specific level-set
properties. However, it did not explore the geometry of the neural network
design used for the synthesis process. This manuscript describes the specific
geometry of the neural network used for zeroing barrier function synthesis, and
shows how the network provides the necessary representation for splitting the
state space into safe and unsafe regions.


  We study a 1D chain of non-interacting bosonic cavities which are subject to
nearest-neighbour parametric driving. With a suitable choice of drive phases,
this model is strongly analogous to the celebrated Kitaev chain model of a 1D
p-wave superconductor. The system exhibits phase-dependent chirality: photons
propagate and are amplified in a direction that is determined by the phase of
the initial drive or excitation. Further, we find a drastic sensitivity to
boundary conditions: for a range of parameters, the boundary-less system has
only delocalized, dynamically unstable modes, while a finite open chain is
described by localized, dynamically stable modes. While our model is described
by a Hermitian Hamiltonian, we show that it has a surprising connection to
non-Hermitian asymmetric-hopping models.


  The algebraic method for computing the complete point symmetry group of a
system of differential equations is extended to finding the complete
equivalence group of a class of such systems. The extended method uses the
knowledge of the corresponding equivalence algebra. Two versions of the method
are presented, where the first involves the automorphism group of this algebra
and the second is based on a list of its megaideals. We illustrate the
megaideal-based version of the method with the computation of the complete
equivalence group of a class of nonlinear wave equations with applications in
nonlinear elasticity.


  Recently, the progress of learning-by-synthesis has proposed a training model
for synthetic images, which can effectively reduce the cost of human and
material resources. However, due to the different distribution of synthetic
images compared to real images, the desired performance cannot still be
achieved. Real images consist of multiple forms of light orientation, while
synthetic images consist of a uniform light orientation. These features are
considered to be characteristic of outdoor and indoor scenes, respectively. To
solve this problem, the previous method learned a model to improve the realism
of the synthetic image. Different from the previous methods, this paper takes
the first step to purify real images. Through the style transfer task, the
distribution of outdoor real images is converted into indoor synthetic images,
thereby reducing the influence of light. Therefore, this paper proposes a
real-time style transfer network that preserves image content information (eg,
gaze direction, pupil center position) of an input image (real image) while
inferring style information (eg, image color structure, semantic features) of
style image (synthetic image). In addition, the network accelerates the
convergence speed of the model and adapts to multi-scale images. Experiments
were performed using mixed studies (qualitative and quantitative) methods to
demonstrate the possibility of purifying real images in complex directions.
Qualitatively, it compares the proposed method with the available methods in a
series of indoor and outdoor scenarios of the LPW dataset. In quantitative
terms, it evaluates the purified image by training a gaze estimation model on
the cross data set. The results show a significant improvement over the
baseline method compared to the raw real image.


  A model of nonlinear electrodynamics is proposed and investigated in general
relativity. We consider the magnetic black hole and find a regular solution
which gives corrections into the Reissner-Nordstr\"{o}m solution. At
$r\rightarrow\infty$ the asymptotic spacetime becomes flat. The magnetic mass
of the black hole is calculated and the metric function is obtained. At some
values of the model parameter there can be one, two or no horizons.
Thermodynamics of black holes is studied and we calculate the Hawking
temperature and heat capacity of black holes. It is demonstrated that there is
a phase transition of second order. At some parameters of the model black holes
are thermodynamically stable.


  Cyberbullying, identified as intended and repeated online bullying behavior,
has become increasingly prevalent in the past few decades. Despite the
significant progress made thus far, the focus of most existing work on
cyberbullying detection lies in the independent content analysis of different
comments within a social media session. We argue that such leading notions of
analysis suffer from three key limitations: they overlook the temporal
correlations among different comments; they only consider the content within a
single comment rather than the topic coherence across comments; they remain
generic and exploit limited interactions between social media users. In this
work, we observe that user comments in the same session may be inherently
related, e.g., discussing similar topics, and their interaction may evolve over
time. We also show that modeling such topic coherence and temporal interaction
are critical to capture the repetitive characteristics of bullying behavior,
thus leading to better predicting performance. To achieve the goal, we first
construct a unified temporal graph for each social media session. Drawing on
recent advances in graph neural network, we then propose a principled
graph-based approach for modeling the temporal dynamics and topic coherence
throughout user interactions. We empirically evaluate the effectiveness of our
approach with the tasks of session-level bullying detection and comment-level
case study. Our code is released to public.


  (LaNiO3)n/(LaMnO3)2 superlattices were grown using ozone-assisted molecular
beam epitaxy, where LaNiO3 is a paramagnetic metal and LaMnO3 is an
antiferromagnetic insulator. The superlattices exhibit excellent crystallinity
and interfacial roughness of less than 1 unit cell. X-ray spectroscopy and
dichroism measurements indicate that electrons are transferred from the LaMnO3
to the LaNiO3, inducing magnetism in LaNiO3. Magnetotransport measurements
reveal a transition from metallic to insulating behavior as the LaNiO3 layer
thickness is reduced from 5 unit cells to 2 unit cells and suggest a modulated
magnetic structure within LaNiO3.


  Recidivism prediction instruments provide decision makers with an assessment
of the likelihood that a criminal defendant will reoffend at a future point in
time. While such instruments are gaining increasing popularity across the
country, their use is attracting tremendous controversy. Much of the
controversy concerns potential discriminatory bias in the risk assessments that
are produced. This paper discusses a fairness criterion originating in the
field of educational and psychological testing that has recently been applied
to assess the fairness of recidivism prediction instruments. We demonstrate how
adherence to the criterion may lead to considerable disparate impact when
recidivism prevalence differs across groups.


  In this work, we propose a motion robust and high-speed detection pipeline
which better leverages the event data. First, we design an event stream
representation called temporal active focus (TAF), which efficiently utilizes
the spatial-temporal asynchronous event stream, constructing event tensors
robust to object motions. Then, we propose a module called the bifurcated
folding module (BFM), which encodes the rich temporal information in the TAF
tensor at the input layer of the detector. Following this, we design a
high-speed lightweight detector called agile event detector (AED) plus a simple
but effective data augmentation method, to enhance the detection accuracy and
reduce the model's parameter. Experiments on two typical real-scene event
camera object detection datasets show that our method is competitive in terms
of accuracy, efficiency, and the number of parameters. By classifying objects
into multiple motion levels based on the optical flow density metric, we
further illustrated the robustness of our method for objects with different
velocities relative to the camera. The codes and trained models are available
at https://github.com/HarmoniaLeo/FRLW-EvD .


  The dynamics of grain ejection consecutive to a sphere impacting a granular
material is investigated experimentally and the variations of the
characteristics of grain ejection with the control parameters are
quantitatively studied. The time evolution of the corona formed by the ejected
grains is reported, mainly in terms of its diameter and height, and favourably
compared with a simple ballistic model. A key characteristic of the granular
corona is that the angle formed by its edge with the horizontal granular
surface remains constant during the ejection process, which again can be
reproduced by the ballistic model. The number and the kinetic energy of the
ejected grains is evaluated and allows for the calculation of an effective
restitution coefficient characterizing the complex collision process between
the impacting sphere and the fine granular target. The effective restitution
coefficient is found to be constant when varying the control parameters.


  Trotignon and Vuskovic completely characterized graphs that do not contain
cycles with exactly one chord. In particular, they show that such a graph G has
chromatic number at most max(3,w(G)). We generalize this result to the class of
graphs that do not contain cycles with exactly two chords and the class of
graphs that do not contain cycles with exactly three chords. More precisely we
prove that graphs with no cycle with exactly two chords have chromatic number
at most 6. And a graph G with no cycle with exactly three chords have chromatic
number at most max(96,w(G)+1).


  We describe a simple and effective method (Spectral Attribute removaL; SAL)
to remove private or guarded information from neural representations. Our
method uses matrix decomposition to project the input representations into
directions with reduced covariance with the guarded information rather than
maximal covariance as factorization methods normally use. We begin with linear
information removal and proceed to generalize our algorithm to the case of
nonlinear information removal using kernels. Our experiments demonstrate that
our algorithm retains better main task performance after removing the guarded
information compared to previous work. In addition, our experiments demonstrate
that we need a relatively small amount of guarded attribute data to remove
information about these attributes, which lowers the exposure to sensitive data
and is more suitable for low-resource scenarios. Code is available at
https://github.com/jasonshaoshun/SAL.


  In a recent paper "Head-on collision of electron acoustic solitary waves in a
plasma with nonextensive hot electrons" [Astrophys. Space Sci. 338, 271-278
(2012)] Eslami, Mottaghizadeh and Pakzad deal with the problem of the head-on
collisions between two weakly nonlinear electron-acoustic solitary waves.
Unfortunately, their treatment is deficient and leads to erroneous conclusions.


  We investigate broad absorption line (BAL) disappearance and emergence using
a 470 BAL-quasar sample over < 0.10-5.25 rest-frame years with at least three
spectroscopic epochs for each quasar from the Sloan Digital Sky Survey. We
identify 14 disappearing BALs over < 1.73-4.62 rest-frame years and 18 emerging
BALs over < 1.46-3.66 rest-frame years associated with the CIV 1548,1550 and/or
SiIV 1393,1402 doublets, and report on their variability behavior. BAL quasars
in our dataset exhibit disappearing/emerging CIV BALs at a rate of 2.3 and 3.0
per cent, respectively, and the frequency for BAL to non-BAL quasar transitions
is 1.7 per cent. We detect four re-emerging BALs over < 3.88 rest-frame years
on average and three re-disappearing BALs over < 4.15 rest-frame years on
average, the first reported cases of these types. We infer BAL lifetimes along
the line of sight to be nominally < 100-1000 yr using disappearing CIV BALs in
our sample. Interpretations of (re-)emerging and (re-)disappearing BALs reveal
evidence that collectively supports both transverse-motion and
ionization-change scenarios to explain BAL variations. We constrain a nominal
CIV/SiIV BAL outflow location of < 100 pc from the central source and a radial
size of > 1x10^-7 pc (0.02 au) using the ionization-change scenario, and
constrain a nominal outflow location of < 0.5 pc and a transverse size of ~0.01
pc using the transverse-motion scenario. Our findings are consistent with
previous work, and provide evidence in support of BALs tracing compact flow
geometries with small filling factors.


  This paper aims at the study of controllability properties and induced
controllability metrics on complex networks governed by a class of (discrete
time) linear decision processes with mul-tiplicative noise. The dynamics are
given by a couple consisting of a Markov trend and a linear decision process
for which both the "deterministic" and the noise components rely on
trend-dependent matrices. We discuss approximate, approximate null and exact
null-controllability. Several examples are given to illustrate the links
between these concepts and to compare our results with their continuous-time
counterpart (given in [16]). We introduce a class of backward stochastic
Riccati difference schemes (BSRDS) and study their solvability for particular
frameworks. These BSRDS allow one to introduce Gramian-like controllability
metrics. As application of these metrics, we propose a minimal
intervention-targeted reduction in the study of gene networks.


  We define an simple invariant of an embedded nullhomologous Lagrangian torus
and use this invariant to show that many symplectic 4-manifolds have infinitely
many pairwise symplectically inequivalent nullhomologous Lagrangian tori. We
further show that for a large class of examples that lambda(T) is actually a
C-infinity invariant. In addition, this invariant is used to show that many
symplectic 4-manifolds have nontrivial homology classes which are represented
by infinitely many pairwise inequivalent Lagrangian tori, a result first proved
by S Vidussi for the homotopy K3-surface obtained from knot surgery using the
trefoil knot in [Lagrangian surfaces in a fixed homology class: existence of
knotted Lagrangian tori, J. Diff. Geom. (to appear)].


  The origin of large-scale magnetic field in the universe is one of the
greatest mysteries in modern cosmology. We present a new mechanism for
generation of large-scale magnetic field, based on the power-counting
renormalizable theory of gravitation recently proposed by Horava. Contrary to
the usual case in general relativity, the U(1) gauge symmetry of a Maxwell
action in this theory permits terms breaking conformal invariance in the
ultraviolet. Moreover, for high frequency modes, the anisotropic scaling
intrinsic to the theory inevitably makes the sound horizon far outside the
Hubble horizon. Consequently, non-inflationary cosmic expansion in the early
universe naturally generates super-horizon quantum fluctuations of the magnetic
field. Specializing our consideration to the case with the dynamical critical
exponent $z=3$, we show an explicit set of parameters for which (i) the
amplitude of generated magnetic field is large enough as a seed for the dynamo
mechanism; (ii) backreaction to the cosmic expansion is small enough; and (iii)
the high-energy dispersion relation is consistent with the most recent
observational limits from MAGIC and FERMI.


  A local weighted discontinuous Galerkin gradient discretization method for
solving elliptic equations is introduced. The local scheme is based on a coarse
grid and successively improves the solution solving a sequence of local
elliptic problems in high gradient regions. Using the gradient discretization
framework we prove convergence of the scheme for linear and quasilinear
equations under minimal regularity assumptions. The error due to artificial
boundary conditions is also analyzed, shown to be of higher order and shown to
depend only locally on the regularity of the solution. Numerical experiments
illustrate our theoretical findings and the local method's accuracy is compared
against the non local approach


  Sepsis affects millions of people worldwide each year. It occurs when a
normal human immune response to a bacterial, viral or fungal infection becomes
dysfunctional and triggers widespread inflammation that results in severe
tissue damage that leads to organ failure, shock, and death. Sepsis, requires
immediate treatment and has a high readmission rate for survivors. It is also
one of the most expensive conditions to treat. In 2013, there were more than
1.6 million cases of sepsis in the United States with a financial cost of more
than $23 billion. Sepsis was first described in antiquity, and given its
current name, by the ancient Greek physician Hippocrates. Despite its long
medical history, severity, and financial burden, the causes of sepsis are not
well understood, and there is no standard approach to diagnosis and treatment.
The definition of sepsis, the characterization of its clinical stages, and
sepsis monitoring tools have changed three times in the past 25 years, most
recently in March 2016. The universal adoption of this latest definition,
sepsis-3, and a screening tool, qSOFA, are currently under debate in the
medical community. A means to rapidly identify and treat sepsis could reduce
the five million deaths due to sepsis each year worldwide. This paper reviews
the evolution of the definition of sepsis and the controversy surrounding the
sepsis-3 definition and the sepsis screening tool, qSOFA.


  The mathematical concept of a "Banchoff index" associated with discrete Morse
functions for oriented triangular meshes has been shown to correspond to the
height assignments of nodes in adinkras. In recent work there has been
introduced the concept of "Banchoff matrices" leading to HYMNs - height
yielding matrix numbers. HYMNs map the shape of an adinkra to a set of
eigenvalues derived from Banchoff matrices. In the context of some examples of
four-color, minimal five-color, and minimal six-color adinkras, properties of
the HYMNs are explored.


  Convolutional neural networks have been successfully extended to operate on
graphs, giving rise to Graph Neural Networks (GNNs). GNNs combine information
from adjacent nodes by successive applications of graph convolutions. GNNs have
been implemented successfully in various learning tasks while the theoretical
understanding of their generalization capability is still in progress. In this
paper, we leverage manifold theory to analyze the statistical generalization
gap of GNNs operating on graphs constructed on sampled points from manifolds.
We study the generalization gaps of GNNs on both node-level and graph-level
tasks. We show that the generalization gaps decrease with the number of nodes
in the training graphs, which guarantees the generalization of GNNs to unseen
points over manifolds. We validate our theoretical results in multiple
real-world datasets.


  Three specializations of a set of orthogonal polynomials with ``8 different
q's'' are given. The polynomials are identified as $q$-analogues of Laguerre
polynomials, and the combinatorial interpretation of the moments give
infinitely many new Mahonian statistics on permutations.


  The phase transformation kinetics of
LaFe$_{11.41}$Mn$_{0.30}$Si$_{1.29}$-H$_{1.65}$ magnetocaloric compound is
addressed by low rate calorimetry experiments. Scans at 1 mK/s show that its
first order phase transitions are made by multiple heat fllux avalanches.
Getting very close to the critical point, the step-like discontinuous behavior
associated with avalanches is smoothed out and thermal hysteresis disappears.
This result is confirmed by magneto-resistivity measurements and allows to
measure accurate values of the zero field hysteresis ($\Delta T_{hyst}$ = 0.37
K) and of the critical field (H$_c$ = 1.19 T). The number and magnitude of heat
flux avalanches change with magnetic field, showing the interplay between the
intrinsic energy barrier between phases and the microstructural disorder of the
sample.


  Possible interpretation of highest energy neutrino never detected by IceCube
as cosmological neutrinos boosted by the first accelerated protons in the
Universe: the Inverse Fermi scattering $p\nu\rightarrow p'\nu'$.


  The Kerr metric is known to present issues when trying to find an interior
solution. In this work we continue in our efforts to construct a more realistic
exterior metric for astrophysical objects. A new approximate metric
representing the spacetime of a charged, rotating and slightly-deformed body is
obtained by perturbing the Kerr-Newman metric to include the mass-quadrupole
and quadrupole-quadrupole orders. It has a simple form, because is
Kerr-Newman-like. Its post-linear form without charge coincides with
post-linear quadrupole-quadrupole metrics already found.


  Finding good solutions for Multi-objective Optimization (MOPs) Problems is
considered a hard problem, especially when considering MOPs with constraints.
Thus, most of the works in the context of MOPs do not explore in-depth how
different constraints affect the performance of MOP solvers. Here, we focus on
exploring the effects of different Constraint Handling Techniques (CHTs) on
MOEA/D, a commonly used MOP solver when solving complex real-world MOPs.
Moreover, we introduce a simple and effective CHT focusing on the exploration
of the decision space, the Three Stage Penalty. We explore each of these CHTs
in MOEA/D on two simulated MOPs and six analytic MOPs (eight in total). The
results of this work indicate that while the best CHT is problem-dependent, our
new proposed Three Stage Penalty achieves competitive results and remarkable
performance in terms of hypervolume values in the hard simulated car design
MOP.


  Topological insulators are non-trivial quantum states of matter which exhibit
a gap in the electronic structure of their bulk form, but a gapless metallic
electronic spectrum at the surface. Here, we predict a uniaxial strain induced
electronic topological transition (ETT) from a band to topological insulating
state in the rhombohedral phase (space group: R$\bar{3}$m) of As$_2$Te$_3$
($\beta$-As$_2$Te$_3$) through \textit{first-principles} calculations including
spin-orbit coupling within density functional theory. The ETT in
$\beta$-As$_2$Te$_3$ is shown to occur at the uniaxial strain $\epsilon_{zz}$ =
-0.05 ($\sigma_{zz}$=1.77 GPa), passing through a Weyl metallic state with a
single Dirac cone in its electronic structure at the $\Gamma$ point. We
demonstrate the ETT through band inversion and reversal of parity of the top of
the valence and bottom of the conduction bands leading to change in the
$\mathbb{Z}_2$ topological invariant $\nu_0$ from 0 to 1 across the transition.
Based on its electronic structure and phonon dispersion, we propose ultra-thin
films of As$_2$Te$_3$ to be promising for use in ultra-thin stress sensors,
charge pumps and thermoelectrics.


  We have measured spin transfer-induced dynamics in magnetic nanocontact
devices having a perpendicularly magnetized Co/Ni free layer and an in-plane
magnetized CoFe fixed layer. The frequencies and powers of the excitations
agree well with the predictions of the single-domain model and indicate that
the excited dynamics correspond to precessional orbits with angles ranging from
zero to 90 degrees as the applied current is increased at a fixed field. From
measurements of the onset current as a function of applied field strength we
estimate the magnitude of the spin torque asymmetry parameter lambda ~ 1.5. By
combining these with spin torque ferromagnetic resonance measurements, we also
estimate the spin wave radiation loss in these devices.


  Autonomous vehicles are in an intensive research and development stage, and
the organizations developing these systems are targeting to deploy them on
public roads in a very near future. One of the expectations from
fully-automated vehicles is never to cause an accident. However, an automated
vehicle may not be able to avoid all collisions, e.g., the collisions caused by
other road occupants. Hence, it is important for the system designers to
understand the boundary case scenarios where an autonomous vehicle can no
longer avoid a collision. In this paper, an automated test generation approach
that utilizes Rapidly-exploring Random Trees is presented. A comparison of the
proposed approach with an optimization-guided falsification approach from the
literature is provided. Furthermore, a cost function that guides the test
generation toward almost-avoidable collisions or near-misses is proposed.


  We consider large-scale dynamics of non-equilibrium dense soliton gas for the
Korteweg-de Vries (KdV) equation in the special "condensate" limit. We prove
that in this limit the integro-differential kinetic equation for the spectral
density of states reduces to the $N$-phase KdV-Whitham modulation equations
derived by Flaschka, Forest and McLaughlin (1980) and Lax and Levermore (1983).
We consider Riemann problems for soliton condensates and construct explicit
solutions of the kinetic equation describing generalized rarefaction and
dispersive shock waves. We then present numerical results for "diluted" soliton
condensates exhibiting rich incoherent behaviours associated with integrable
turbulence.