  We show analytically using a macrospin approximation that easy-plane spin
Hall nano-oscillators excited by a spin-current polarized perpendicularly to
the easy-plane have phase dynamics analogous to that of Josephson junctions.
Similarly to Josephson junctions, they can reproduce the spiking behavior of
biological neurons that is appropriate for neuromorphic computing. We perform
micromagnetic simulations of such oscillators realized in the nano-constriction
geometry and show that the easy-plane spiking dynamics is preserved in an
experimentally feasible architecture. Finally we simulate two elementary neural
network blocks that implement operations essential for neuromorphic computing.
First, we show that output spikes energies from two neurons can be summed and
injected into a following layer neuron and second, we demonstrate that outputs
can be multiplied by synaptic weights implemented by locally modifying the
anisotropy.


  Bottom-Up (BU) saliency models do not perform well in complex interactive
environments where humans are actively engaged in tasks (e.g., sandwich making
and playing the video games). In this paper, we leverage Reinforcement Learning
(RL) to highlight task-relevant locations of input frames. We propose a soft
attention mechanism combined with the Deep Q-Network (DQN) model to teach an RL
agent how to play a game and where to look by focusing on the most pertinent
parts of its visual input. Our evaluations on several Atari 2600 games show
that the soft attention based model could predict fixation locations
significantly better than bottom-up models such as Itti-Kochs saliency and
Graph-Based Visual Saliency (GBVS) models.


  In the following paper we investigate the critical temperature $T_c$ behavior
in the two-dimensional S/TI (S denotes superconductor and TI - topological
insulator) junction with a proximity induced in-plane helical magnetization in
the TI surface. The calculations of $T_c$ are performed using the general
self-consistent approach based on the Usadel equations in Matsubara Green's
functions technique. We show that the presence of the helical magnetization
leads to the nonmonotonic behavior of the critical temperature as a function of
the topological insulator layer thickness.


  Developing ways to affordably deliver broadband connectivity is one of the
major issues of our time. In challenging deployment locations with irregular
terrain, traditional Clear-Line-Of-Sight (CLOS) wireless links can be
uneconomical to deploy, as the number of required towers make infrastructure
investment unviable. With new research focusing on developing wireless
diffractive backhaul technologies to provide Non-Line-Of-Sight (NLOS) links,
this paper evaluates the engineering-economic implications. A Three-Dimensional
(3D) techno-economic assessment framework is developed, utilizing a combination
of remote sensing and viewshed geospatial techniques, in order to quantify the
impact of different wireless backhaul strategies. This framework is applied to
assess both Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight strategies
for deployment in Peru, as well as the islands of Kalimantan and Papua, in
Indonesia. The results find that a hybrid strategy combining the use of
Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight links produces a 9-45
percent cost-efficiency saving, relative to only using traditional
Clear-Line-Of-Sight wireless backhaul links.


  To enhance flexibility and facilitate resource cooperation, a novel
fully-decoupled radio access network (FD-RAN) architecture is proposed for 6G.
However, the decoupling of uplink (UL) and downlink (DL) in FD-RAN makes the
existing feedback mechanism ineffective. To this end, we propose an end-to-end
data-driven MIMO solution without the conventional channel feedback procedure.
Data-driven MIMO can alleviate the drawbacks of feedback including overheads
and delay, and can provide customized precoding design for different BSs based
on their historical channel data. It essentially learns a mapping from
geolocation to MIMO transmission parameters. We first present a codebook-based
approach, which selects transmission parameters from the statistics of discrete
channel state information (CSI) values and utilizes integer interpolation for
spatial inference. We further present a non-codebook-based approach, which 1)
derives the optimal precoder from the singular value decomposition (SVD) of the
channel; 2) utilizes variational autoencoder (VAE) to select the representative
precoder from the latent Gaussian representations; and 3) exploits Gaussian
process regression (GPR) to predict unknown precoders in the space domain.
Extensive simulations are performed on a link-level 5G simulator using
realistic ray-tracing channel data. The results demonstrate the effectiveness
of data-driven MIMO, showcasing its potential for application in FD-RAN and 6G.


  In this paper, we characterize the maximal violation of Ardehali's inequality
of $n$ qubits by showing that GHZ's states and the states obtained from them by
local unitary transformations are the unique states that maximally violate the
Ardehali's inequalities. This concludes that Ardehali's inequalities can be
used to characterize maximally entangled states of $n$ qubits, as the same as
Mermin's and Bell-Klyshko's inequalities.


  We study the problems of finding a minimum cycle basis (a minimum weight set
of cycles that form a basis for the cycle space) and a minimum homology basis
(a minimum weight set of cycles that generates the $1$-dimensional
($\mathbb{Z}_2$)-homology classes) of an undirected graph embedded on a
surface. The problems are closely related, because the minimum cycle basis of a
graph contains its minimum homology basis, and the minimum homology basis of
the $1$-skeleton of any graph is exactly its minimum cycle basis.
  For the minimum cycle basis problem, we give a deterministic
$O(n^\omega+2^{2g}n^2+m)$-time algorithm for graphs embedded on an orientable
surface of genus $g$. The best known existing algorithms for surface embedded
graphs are those for general graphs: an $O(m^\omega)$ time Monte Carlo
algorithm and a deterministic $O(nm^2/\log n + n^2 m)$ time algorithm. For the
minimum homology basis problem, we give a deterministic $O((g+b)^3 n \log n +
m)$-time algorithm for graphs embedded on an orientable or non-orientable
surface of genus $g$ with $b$ boundary components, assuming shortest paths are
unique, improving on existing algorithms for many values of $g$ and $n$. The
assumption of unique shortest paths can be avoided with high probability using
randomization or deterministically by increasing the running time of the
homology basis algorithm by a factor of $O(\log n)$.


  We show within the saturation framework that measurements of exclusive vector
meson production at high energy provide evidence for strong geometric
fluctuations of the proton. In comparison, the effect of saturation scale and
color charge fluctuations is weak. This knowledge will allow detailed future
measurements of the incoherent cross section to tightly constrain the
fluctuating geometry of the proton as a function of the parton momentum
fraction $x$.


  We show how a medium, under the influece of a coherent control field which is
resonant or close to resonance to an appropriate atomic transition, can lead to
very strong asymmetries in the propagation of unpolarized light when the
direction of the magnetic field is reversed. We show how EIT can be used to
mimic effects occuring in natural systems and that EIT can produce very large
asymmetries as we use electric dipole allowed transitions. Using density matrix
calculations we present results for the breakdown of the magnetic field
reversal symmetry for two different atomic configurations.


  We measure the ground and excited states for B mesons in the static limit
using maximally variance reduced estimators for light quark propagators.
Because of the large number of propagators we are able to measure accurately
also orbitally excited P, D and F states. We also present some results for
Lambda_b.


  Due to its strong interpretability, linear regression is widely used in
social science, from which significance test provides the significance level of
models or coefficients in the traditional statistical inference. However,
linear regression methods rely on the linear assumptions of the ground truth
function, which do not necessarily hold in practice. As a result, even for
simple non-linear cases, linear regression may fail to report the correct
significance level.
  In this paper, we present a simple and effective assumption-free method for
linear approximation in both linear and non-linear scenarios. First, we apply a
machine learning method to fit the ground truth function on the training set
and calculate its linear approximation. Afterward, we get the estimator by
adding adjustments based on the validation set. We prove the concentration
inequalities and asymptotic properties of our estimator, which leads to the
corresponding significance test. Experimental results show that our estimator
significantly outperforms linear regression for non-linear ground truth
functions, indicating that our estimator might be a better tool for the
significance test.


  We prove the global existence of the unique mild solution for the Cauchy
problem of the cut-off Boltzmann equation for soft potential model $\gamma=2-N$
with initial data small in $L^N_{x,v}$ where $N=2,3$ is the dimension. The
proof relies on the existing inhomogeneous Strichartz estimates for the kinetic
equation by Ovcharov and convolution-like estimates for the gain term of the
Boltzmann collision operator by Alonso, Carneiro and Gamba. The global dynamics
of the solution is also characterized by showing that the small global solution
scatters with respect to the kinetic transport operator in $L^N_{x,v}$. Also
the connection between function spaces and cut-off soft potential model
$-N<\gamma<2-N$ is characterized in the local well-posedness result for the
Cauchy problem with large initial data.


  Recent advances in self-supervised learning (SSL) have largely closed the gap
with supervised ImageNet pretraining. Despite their success these methods have
been primarily applied to unlabeled ImageNet images, and show marginal gains
when trained on larger sets of uncurated images. We hypothesize that current
SSL methods perform best on iconic images, and struggle on complex scene images
with many objects. Analyzing contrastive SSL methods shows that they have poor
visual grounding and receive poor supervisory signal when trained on scene
images. We propose Contrastive Attention-Supervised Tuning(CAST) to overcome
these limitations. CAST uses unsupervised saliency maps to intelligently sample
crops, and to provide grounding supervision via a Grad-CAM attention loss.
Experiments on COCO show that CAST significantly improves the features learned
by SSL methods on scene images, and further experiments show that CAST-trained
models are more robust to changes in backgrounds.


  The role of the final state interactions (FSI) in inclusive electro
disintegration of the deuteron, D(e,e')X, is being investigated within
different approaches. A detailed comparison between an improved Glauber method
and the standard Schroedinger approach is presented. It is shown that both
methods become inadequate at large values of Q^2, where the virtuality of the
hit nucleon after photon absorbtion is very high. The concept of finite
formation time (FFT) required by the hit hadron to reach its asymptotic form is
introduced by a Feynman diagram approach and by explicitly taking into account
the dependence of the ejected nucleon on its virtuality. The approach has been
applied both at x_{Bj} ~= 1, as well as in the so called cumulative region i.e.
at x_{Bj} > 1. Numerical calculations show that the effects of the FFT almost
completely cancel the contribution from rescattering processes. In the
cumulative region the color transparency or finite formation time effects
become fairly visible.


  We present an attempt to probe the underlying structure of the quark-gluon
plasma (QGP) at high resolution, based on the extracted jet transport
coefficient $\hat{q}$. We argue that the exchanged momentum $k$ between the
hard parton and the medium varies over a range of scales, and for $k\geq$ 1
GeV, $\hat{q}$ can be expressed in terms of a parton distribution function
(PDF). Because the mass of a QGP constituent is unknown, we define a scaling
variable $x_N$ to represent the ratio of the parton momentum to the momentum of
a self-contained section of the plasma which has a mass of 1 GeV. This scaling
variable is used to parametrize the QGP-PDF. Calculations, based on this
reconstructed $\hat{q}$ are compared to data sensitive to the hardcore of jets
$i.e.,$ the single hadron suppression in terms of the nuclear modification
factor $R_{AA}$ and the azimuthal anisotropy parameter $v_{2}$, as a function
of transverse momentum $p_{\mathrm{T}}$, centrality and energy of the
collision. It is demonstrated that the scale evolution of the QGP-PDF is
responsible for the reduction in the normalization of $\hat{q}$ between fits to
Relativistic Heavy-Ion Collider (RHIC) and Large Hadron Collider (LHC) data; a
puzzle, first discovered by the JET collaboration.


  A deterministic finite automaton (DFA) separates two strings $w$ and $x$ if
it accepts $w$ and rejects $x$. The minimum number of states required for a DFA
to separate $w$ and $x$ is denoted by $sep(w,x)$. The present paper shows that
the difference $|sep(w,x)-sep(w^R,x^R)|$ is unbounded for a binary alphabet;
here $w^R$ stands for the mirror image of $w$. This solves an open problem
stated in [Demaine, Eisenstat, Shallit, Wilson: Remarks on separating words.
DCFS 2011. LNCS vol. 6808, pp. 147-157.]


  In February 2007 the MAGIC Air Cherenkov Telescope for gamma ray astronomy
was fully upgraded with a fast 2 GSamples/s digitization system. The upgraded
readout system uses a novel fiber-optic multiplexing technique. It consists of
10-bit 2 GSamples/s FADCs to digitize 16 channels consecutively and optical
fibers to delay the analog signals. A distributed data acquisition system using
GBit Ethernet and FiberChannel technology allows to read out the 100 kByte
events with a continuous rate of up to 1 kHz.


  The series of equilibrium states reached by disordered packings of rigid,
frictionless discs in two dimensions, under gradually varying stress, are
studied by numerical simulations. Statistical properties of trajectories in
configuration space are found to be independent of specific assumptions ruling
granular dynamics, and determined by geometry only. A monotonic increase in
some macroscopic loading parameter causes a discrete sequence of
rearrangements. For a biaxial compression, we show that, due to the statistical
importance of such events of large magnitudes, the dependence of the resulting
strain on stress direction is a Levy flight in the thermodynamic limit.


  Characterisations of interval graphs, comparability graphs, co-comparability
graphs, permutation graphs, and split graphs in terms of linear orderings of
the vertex set are presented. As an application, it is proved that interval
graphs, co-comparability graphs, AT-free graphs, and split graphs have
bandwidth bounded by their maximum degree.


  An NQR experiment revealed superconductivity of
Pr$_2$Ba$_4$Cu$_7$O$_{15-\delta}$ (Pr247) to be realized on CuO double chain
layers and suggests possibility of novel one-dimensional(1D) superconductivity.
To clarify the nature of the 1D superconductivity, we calculate the band
dispersions of Pr247 by using the generalized gradient approximation(GGA). It
indicates that Fermi surface of CuO double chains is well described to the
electronic structure of a quasi-1D system.
  Assuming the zigzag Hubbard chain model to be an effective model of the
system, we derive tight binding parameters of the model from a fit to the
result of GGA. Based on the Tomonaga-Luttinger liquid theory, we estimate
transition temperature ($T_c$) of the quasi-1D zigzag Hubbard model from the
calculated value of the Luttinger liquid parameter $K_{\rho}$. The result of
$T_c$ is consistent with that of experiments in Pr247 and it suggests that the
mechanism of the superconductivity is well understood within the concept of the
Tomonaga-Luttinger liquid.


  Control over transition rates between spin states of emitters is crucial in a
wide variety of fields ranging from quantum information science to the
nanochemistry of free radicals. We present an approach to drive a both electric
and magnetic dipole-forbidden transition of a spin emitter by placing it in a
nanomagnonic cavity, requiring a description of both the spin emitter beyond
the point dipole approximation and the vacuum magnetic fields of the
nanomagnonic cavity with a large spatial gradient over the volume of the spin
emitter. We specifically study the SiV$^-$ defect in diamond, whose
Zeeman-split ground states comprise a logical qubit for solid-state quantum
information processing, coupled to a magnetic nanoparticle serving as a model
nanomagnonic cavity capable of concentrating microwave magnetic fields into
deeply subwavelength volumes. Through first principles modeling of the SiV$^-$
spin orbitals, we calculate the spin transition densities of magnetic
dipole-allowed and -forbidden transitions and calculate their coupling rates to
various multipolar modes of the nanomagnonic cavity. We envision using such a
framework for quantum state transduction and state preparation of spin qubits
at GHz frequency scales.


  Artificial intelligence has emerged as promising tool to decode a phase image
transmitted through a multimode fiber (MMF) by applying deep learning
techniques. By transmitting tens of thousands of images through the MMF, deep
neural networks (DNNs) are capable of learning how to decipher the seemingly
random output speckle patterns and unveil the intrinsic input-output
relationship. High fidelity reconstruction is obtained for datasets with a
large degree of homogeneity, which underutilizes the capacity of the combined
MMF-DNN system. Here, we show that holographic modulation can be employed to
encode an additional layer of variance on the output speckle pattern, improving
the overall transmissive capabilities of the system. Operatively we have
implemented this by adding a holographic label to the original dataset and
injecting the resulting phase image into the fiber facet through a Fourier
transform lens. The resulting speckle pattern dataset can be clustered
primarily by holographic label (rather than the image data), and can be
reconstructed without loss of fidelity. As an application, we describe how
colour images may be segmented into RGB components and each colour component
may then be labelled by distinct hologram. A UNET architecture was then used to
decode each class of speckle patterns and reconstruct the colour image without
the need for temporal synchronisation between sender and receiver


  We calculate the electronic structure for a modulation doped and gated
T-shaped quantum wire using density functional theory. We calculate the bandgap
renormalization as a function of the density of conduction band electrons,
induced by the donor layer and/or the gate, for the translationally invariant
wire, incorporating all growth and geometric properties of the structure
completely. We show that most of the bandgap renormalization arises from
exchange-correlation effects, but that a small shift also results from the
difference of wave function evolution between electrons and holes. We calculate
the binding energy of excitons in the wire, which breaks translational
invariance, using a simpler, cylindrical model of the wire. For a single hole
and a one dimensional electron gas of density n_e, screening of the exciton
binding energy is shown to approximately compensate for bandgap
renormalization, suggesting that the recombination energy remains approximately
constant with n_e, in agreement with experiment. We find that the nature of
screening, as treated within our non-linear model, is significantly different
from that of the various linear screening treatments, and the orthogonality of
free carrier states with the bound electron states has a profound effect on the
screening charge. We find that the electron and hole remain bound for all
densities up to about 3 x 10^6 cm^{-1} and that, as n_e increases from zero,
trion and even ``quadron'' formation becomes allowed.


  This paper considers nonlinear systems with full state feedback, a central
controller and distributed sensors not co-located with the central controller.
We present a methodology for designing decentralized asynchronous
event-triggers, which utilize only locally available information, for
determining the time instants of transmission from the sensors to the central
controller. The proposed design guarantees a positive lower bound for the
inter-transmission times of each sensor, while ensuring asymptotic stability of
the origin of the system with an arbitrary, but priorly fixed, compact region
of attraction. In the special case of Linear Time Invariant (LTI) systems,
global asymptotic stability is guaranteed and scale invariance of
inter-transmission times is preserved. A modified design method is also
proposed for nonlinear systems, with the addition of event-triggered
communication from the controller to the sensors, that promises to
significantly increase the average sensor inter-transmission times compared to
the case where the controller does not transmit data to the sensors. The
proposed designs are illustrated through simulations of a linear and a
nonlinear example.


  Half-metallic ferromagnets show 100% spin-polarization at the Fermi level and
are ideal candidates for spintronic applications. Despite the extensive
research in the field, very few materials have been discovered so far. Here we
present results of electronic band structure calculations based on density
functional theory and extensive physical-property measurements for Fe3Se4
revealing signatures of half-metallicity. The spin-polarized electronic band
structure calculations predict half-metallic ferrimagnetism for Fe3Se4. The
electrical resistivity follows exponentially suppressed electron-magnon
scattering mechanism in the low-temperature regime and show a magnetoresistance
effect that changes the sign from negative to positive with decreasing
temperature around 100 K. Other intriguing observations include the anomalous
behavior of Hall resistance below 100 K and an anomalous Hall coefficient that
roughly follows the \r{ho}2 behavior.


  In Direct Sum problems [KRW], one tries to show that for a given
computational model, the complexity of computing a collection of finite
functions on independent inputs is approximately the sum of their individual
complexities. In this paper, by contrast, we study the diversity of ways in
which the joint computational complexity can behave when all the functions are
evaluated on a common input. We focus on the deterministic decision tree model,
with depth as the complexity measure; in this model we prove a result to the
effect that the 'obvious' constraints on joint computational complexity are
essentially the only ones.
  The proof uses an intriguing new type of cryptographic data structure called
a `mystery bin' which we construct using a small polynomial separation between
deterministic and unambiguous query complexity shown by Savicky. We also pose a
variant of the Direct Sum Conjecture of [KRW] which, if proved for a single
family of functions, could yield an analogous result for models such as the
communication model.


  Motivated by the nonlinear star formation efficiency found in recent
numerical simulations by a number of workers, we perform high-resolution
adaptive mesh refinement simulations of star formation in self-gravitating
turbulently driven gas. As we follow the collapse of this gas, we find that the
character of the flow changes at two radii, the disk radius $r_d$, and the
radius $r_*$ where the enclosed gas mass exceeds the stellar mass. Accretion
starts at large scales and works inwards. In line with recent analytical work,
we find that the density evolves to a fixed attractor, $\rho(r,t ) \rightarrow
\rho(r)$, for $r_d<r<r_*$; mass flows through this structure onto a
sporadically gravitationally unstable disk, and from thence onto the star. In
the bulk of the simulation box we find that the random motions $v_T \sim r^p$
with $p \sim 0.5$, in agreement with Larson's size-linewidth relation. In the
vicinity of massive star forming regions we find $ p \sim 0.2-0.3$, as seen in
observations. For $r<r_*$, $v_T$ increases inward, with $p=-1/2$. Finally, we
find that the total stellar mass $M_*(t)\sim t^2$ in line with previous
numerical and analytic work that suggests a nonlinear rate of star formation.


  An upper bound on the error probability of specific lattices, based on their
distance-spectrum, is constructed. The derivation is accomplished using a
simple alternative to the Minkowski-Hlawka mean-value theorem of the geometry
of numbers. In many ways, the new bound greatly resembles the Shulman-Feder
bound for linear codes. Based on the new bound, an error-exponent is derived
for specific lattice sequences (of increasing dimension) over the AWGN channel.
Measuring the sequence's gap to capacity, using the new exponent, is
demonstrated.


  In this paper we prove, by expressing the energy as a function of the wave
propagation speed, it is highlighted the existence of an equivalent mass of the
wave, as well as of an Einstein type relations between the energy and this
mass. Also, we establish a relation between angular frequency and energy
similar to that of the Planck relation. For the propagating wave, there is a de
Broglie type relationship between the linear momentum and the action variable
(the angular momentum), i.e. the wave linear momentum is proportional to the
wave number, the proportionality coefficient being the action.


  We consider viscosity and thermal conductivity as dissipation mechanisms to
derive a general dispersion relation for MHD waves propagating in a homogeneous
plasma. We show that the actual dispersion relation for MHD waves in a
homogeneous medium must be six-order. The finding is in agreement (except some
coefficients) with the results of Porter et al. (1994) but it is in
disagreement with the previous results obtained by Kumar et al. (2006). We also
discuss in detail differences between our approach and those considered by
other authors.
  Accepted, BASI


  Understanding and predicting the hydrodynamics of gas bubbles and
particle-laden phase in fluidized beds is essential for the successful design
and efficient operation of this type of reactor. In this work, we used
real-time magnetic resonance imaging (MRI) to investigate the effect of baffles
on gas bubble behavior and particle motion in a fluidized bed model with an
inner diameter of 190 mm. MRI time series of the local particle density and
velocity were acquired and used to study the size, number, and shape of gas
bubbles as well as the motion of the particle phase. The superficial gas
velocity was varied between 1 and 2 Umf . We found that baffles decreased the
average equivalent bubble diameter with a simultaneous increase in the total
number of bubbles. Moreover, baffles promoted bubble splitting and the
formation of air cushions below the baffles and decreased the average particle
velocity and acceleration in the bed. For two of the three investigated baffle
types, the particle velocity distribution became wider compared to the bed
without internal.


  We investigate the LF in the very relaxed cluster Abell 496. Our analysis is
based on deep images obtained at CFHT with MegaPrime/MegaCam in four bands
(ugri) covering a 1x1 deg2 region, which is centered on the cluster Abell 496
and extends to near its virial radius. The LFs are estimated by statistically
subtracting a reference field taken as the mean of the 4 Deep fields of the
CFHTLS survey. Background contamination is minimized by cutting out galaxies
redder than the observed Red Sequence in the g-i versus i colour-magnitude
diagram. In Abell 496, the global LFs show a faint-end slope alpha=-1.55+/-0.06
and vary little with observing band. Without colour cuts, the LFs are much
noisier but not significantly steeper. The faint-end slopes show a
statistically significant steepening from alpha=-1.4+/-0.1 in the central
region (extending to half a virial radius) to -1.8+/-0.1 in the Southern
envelope of the cluster. Cosmic variance and uncertain star-galaxy separation
are our main limiting factors in measuring the faint-end of the LFs. The
large-scale environment of Abell 496, probed with the fairly complete 6dFGS
catalogue, shows a statistically significant 36 Mpc long filament at PA=137
deg, but we do not find an enhanced LF along this axis. Our LFs do not display
the large number of dwarf galaxies (alpha ~ -2) inferred by several authors,
whose analyses may suffer from field contamination caused by inexistent or
inadequate colour cuts. Alternatively, different clusters may have different
faint-end slopes, but this is hard to reconcile with the wide range of slopes
found for given clusters and for wide sets of clusters.


  The double well oscillator is used as a QCD-like model for studying the
relationship between large order graphs and the instanton-antiinstanton
solution. We derive an equation for the perturbative coefficients of the ground
state energy when the number of 3 and/or 4-vertices is fixed and large. These
coefficients are determined in terms of an exact``bounce'' solution. When the
number of 4-vertices is analytically continued to be near the negative of half
the number of 3-vertices the bounce solution approaches the
instanton-antiinstanton solution and detremines leading Borel singularity.


  Considering an arbitrary dimensional FLRW universe in the framework of a
generalized S\'{a}ez--Ballester (SB) theory, we establish a noncommutative (NC)
cosmological model. We concentrate on the predictions of NC model and compare
them with their commutative counterparts in both the classical and quantum
regimes. For the classic case, taking a very small NC parameter, we apply two
different methods to analyze the model features. First, we show through
numerical analysis that our NC model is a successful inflationary model capable
of overcoming the graceful exit and horizon problems. Furthermore, the NC
traces are visible the late time, which supports the UV/IR mixing
characteristic of the NC models. In the second method, we show that our NC
model can correspond to the previously developed NC inflationary models. In the
commutative quantum case, we obtain an exact wave function and then use the WKB
approximation to show that the solutions of the corresponding classical regime
are recovered. Finally, with regard to the NC quantum level, we focus on the
special case for which we show that a constant of motion exists. The latter
helps us to conveniently transform the corresponding complicated NC-WDW
equation into an ordinary differential equation, which can be easily solved
numerically for the general case or analytically for some special cases. The
resultant solutions show a damping behavior in the wave function associated
with the proposed NC model, which may be important in determining the viable
initial states for the very early universe.


  Cosmic strings can be created in the early universe during symmetry-breaking
phase transitions, such as might arise if the gauge structure of the standard
model is extended by additional U(1) factors at high energies. Cosmic strings
present in the early universe form a network of long horizon-length segments,
as well as a population of closed string loops. The closed loops are unstable
against decay, and can be a source of non-thermal particle production. In this
work we compute the density of WIMP dark matter formed by the decay of gauge
theory cosmic string loops derived from a network of long strings in the
scaling regime or under the influence of frictional forces. We find that for
symmetry breaking scales larger than 10^10 GeV, this mechanism has the
potential to account for the observed relic density of dark matter. For
symmetry breaking scales lower than this, the density of dark matter created by
loop decays from a scaling string network lies below the observed value. In
particular, the cosmic strings originating from a U(1) gauge symmetry broken
near the electroweak scale, that could lead to a massive Z' gauge boson
observable at the LHC, produce a negligibly small dark matter relic density by
this mechanism.


  In this study, we independently test the presence of an exoplanet around the
binary KIC 9472174, which is composed of a red dwarf and a pulsating type B
subdwarf. We also present the results of our search for Jupiter-mass objects
orbiting near to the eclipsing binary KIC 7975824, which is composed of a white
dwarf and type B subdwarf, and the pulsating white dwarf KIC 8626021. The goal
is to test analytical techniques and prepare the ground for a larger search for
possible substellar survivors on tight orbits around post-common envelope
binaries and stars at the end of their evolution, that is, extended horizontal
branch stars and white dwarfs. We, therefore, mainly focus on substellar bodies
orbiting these stars within the range of the host's former red-giant or
asymptotic-giant phase envelopes. Due to the methods we use, the quest is
restricted to single-pulsating type B subdwarf and white dwarf stars and
short-period eclipsing binaries containing a white dwarf or a subdwarf
component.
  Results. Based on the three objects studied in this paper, we demonstrate
that these methods can be used to detect giant exoplanets orbiting around
pulsating white dwarf or type B subdwarf stars as well as short-period binary
systems, at distances which fall within the range of the former red-giant
envelope of a single star or the common envelope of a binary. Using our
analysis techniques, we reject the existence of a Jupiter-mass exoplanet around
the binary KIC 9472174 at the distance and orbital period previously suggested
in the literature. We also found that the eclipse timing variations observed in
the binary might depend on the reduction and processing of the Kepler data. The
other two objects analyzed in this work do not have Jupiter mass exoplanets
orbiting within 0.7 - 1.4 AU from them, or larger-mass objects on closer orbits
(the given mass limits are minimum masses).


  Despite considerable progress in genome- and proteome-based high-throughput
screening methods and in rational drug design, the increase in approved drugs
in the past decade did not match the increase of drug development costs.
Network description and analysis not only give a systems-level understanding of
drug action and disease complexity, but can also help to improve the efficiency
of drug design. We give a comprehensive assessment of the analytical tools of
network topology and dynamics. The state-of-the-art use of chemical similarity,
protein structure, protein-protein interaction, signaling, genetic interaction
and metabolic networks in the discovery of drug targets is summarized. We
propose that network targeting follows two basic strategies. The central hit
strategy selectively targets central nodes/edges of the flexible networks of
infectious agents or cancer cells to kill them. The network influence strategy
works against other diseases, where an efficient reconfiguration of rigid
networks needs to be achieved by targeting the neighbors of central nodes or
edges. It is shown how network techniques can help in the identification of
single-target, edgetic, multi-target and allo-network drug target candidates.
We review the recent boom in network methods helping hit identification, lead
selection optimizing drug efficacy, as well as minimizing side-effects and drug
toxicity. Successful network-based drug development strategies are shown
through the examples of infections, cancer, metabolic diseases,
neurodegenerative diseases and aging. Summarizing more than 1200 references we
suggest an optimized protocol of network-aided drug development, and provide a
list of systems-level hallmarks of drug quality. Finally, we highlight
network-related drug development trends helping to achieve these hallmarks by a
cohesive, global approach.


  The Space multi-band Variable Object Monitor (SVOM) is a proposed Chinese
astronomical satellite, dedicated to the detection, localization and
measurement of the gamma-ray bursts (GRBs) on the cosmological scale. An
efficient algorithm is developed for the purpose of onboard star extraction
from the CCD images obtained with the Visible Telescope (VT) on board SVOM. The
CCD pixel coordinates of the reference stars will be used to refine the
astronomical position of the satellite, which will facilitate trigger rapid
ground-based follow-up obsevations of the GRBs. In this algorithm, the image is
divided into a number of grid cells and the "global" pixel-value maximum within
each cell is taken as the first-guess position of a "bright" star. The correct
center postion of a star is then computed using a simple iterative method.
Applying two additional strategies, i.e., scanning the image only by even (or
odd) lines or in a black-white chess board mode, are proposed to further reduce
the time to extract the stars. To examine the efficiency of the above
aglorithms, we applied them to the experimental images obtained with a
ground-based telescope. We find that the accuracy of the astronomical
positioning achieved by our method is comparable to that derived by using the
conventional star-extraction method, while the former needs CPU time about 25
times less than the latter. This will significantly improve the performance of
the SVOM VT mission.


  Spurred by recent advances in Large Language Models (LLMs), virtual
assistants are poised to take a leap forward in terms of their dialogue
capabilities. Yet a major bottleneck to achieving genuinely transformative
task-oriented dialogue capabilities remains the scarcity of high quality data.
Existing datasets, while impressive in scale, have limited domain coverage and
contain few genuinely challenging conversational phenomena; those which are
present are typically unlabelled, making it difficult to assess the strengths
and weaknesses of models without time-consuming and costly human evaluation.
Moreover, creating high quality dialogue data has until now required
considerable human input, limiting both the scale of these datasets and the
ability to rapidly bootstrap data for a new target domain. We aim to overcome
these issues with LUCID, a modularised and highly automated LLM-driven data
generation system that produces realistic, diverse and challenging dialogues.
We use LUCID to generate a seed dataset of 4,277 conversations across 100
intents to demonstrate its capabilities, with a human review finding
consistently high quality labels in the generated data.


  Light antinuclei may be formed in relativistic heavy ion collisions via final
state coalescence of antinucleons. The yields of antinuclei are sensitive to
primordial antinucleon production, the volume of the system at kinetic
freeze-out, and space-momentum correlations among antinucleons at freeze-out.
We report here preliminary STAR results on antideuteron and antihelion
production in 130A GeV Au+Au collisions. These results are examined in a
coalescence framework to elucidate the space-time structure of the antinucleon
source.


  We present a novel surface convolution operator acting on vector fields that
is based on a simple observation: instead of combining neighboring features
with respect to a single coordinate parameterization defined at a given point,
we have every neighbor describe the position of the point within its own
coordinate frame. This formulation combines intrinsic spatial convolution with
parallel transport in a scattering operation while placing no constraints on
the filters themselves, providing a definition of convolution that commutes
with the action of isometries, has increased descriptive potential, and is
robust to noise and other nuisance factors. The result is a rich notion of
convolution which we call field convolution, well-suited for CNNs on surfaces.
Field convolutions are flexible, straight-forward to incorporate into surface
learning frameworks, and their highly discriminating nature has cascading
effects throughout the learning pipeline. Using simple networks constructed
from residual field convolution blocks, we achieve state-of-the-art results on
standard benchmarks in fundamental geometry processing tasks, such as shape
classification, segmentation, correspondence, and sparse matching.


  The criticality of the low-frequency conductivity for the bilayer quantum
Heisenberg model was investigated numerically. The dynamical conductivity
(associated with the O$(3)$ symmetry) displays the inductor $\sigma (\omega)
=(i\omega L)^{-1}$ and capacitor $i \omega C$ behaviors for the ordered and
disordered phases, respectively. Both constants, $C$ and $L$, have the same
scaling dimension as that of the reciprocal paramagnetic gap $\Delta^{-1}$.
Then, there arose a question to fix the set of critical amplitude ratios among
them. So far, the O$(2)$ case has been investigated in the context of the
boson-vortex duality. In this paper, we employ the exact diagonalization
method, which enables us to calculate the paramagnetic gap $\Delta$ directly.
Thereby, the set of critical amplitude ratios as to $C$, $L$ and $\Delta$ are
estimated with the finite-size-scaling analysis for the cluster with $N \le 34$
spins.


  We study twisted cohomologies with paracompactifying families of supports.
The Kunneth theorems, Leray-Hirsch theorems and self-intersection formulae are
established. Based on these results, we eventually give explicit expressions of
complex blow-up formulae for twisted Dolbeault cohomology on arbitrary complex
manifolds and the ones of generalized blow-ups formulae for twisted de Rham
cohomology on arbitrary oriented smooth manifolds. These expressions are
induced by the morphisms of (simple or double) complexes of spaces of forms and
currents rather than just the maps between cohomologies, which help us to
obtain the corresponding results for twisted Bott-Chern, Aeppli cohomologies
and hypercohomologies of truncated twisted holomorphic de Rham complexes.


  Statistical isotropy (SI) is one of the fundamental assumptions made in
cosmological model building. This assumption is now being rigorously tested
using the almost full sky measurements of the CMB anisotropies. A major hurdle
in any such analysis is to handle the large biases induced due to the process
of masking. We have developed a new method of analysis, using the bipolar
spherical harmonic basis functions, in which we semi-analytically evaluate the
modifications to SI violation induced by the mask. The method developed here is
generic and can be potentially used to search for any arbitrary form of SI
violation. We specifically demonstrate the working of this method by recovering
the Doppler boost signal from a set of simulated, masked CMB skies.


  Ptychography is a lensless imaging technique, which considers reconstruction
from a set of far-field diffraction patterns obtained by illuminating small
overlapping regions of the specimen. In many cases, a distribution of light
inside the illuminated region is unknown and has to be estimated along with the
object of interest. This problem is referred to as blind ptychography. While in
ptychography the illumination is commonly assumed to have a point spectrum, in
this paper we consider an alternative scenario with non-trivial light spectrum
known as blind polychromatic ptychography.
  Firstly, we show that non-blind polychromatic ptychography can be seen as a
recovery from quadratic measurements. Then, a reconstruction from such
measurements can be performed by a variant of Amplitude Flow algorithm, which
has guaranteed sublinear convergence to a critical point. Secondly, we address
recovery from blind polychromatic ptychographic measurements by devising an
alternating minimization version of Amplitude Flow and showing that it
converges to a critical point at a sublinear rate.
  Keywords: ptychography, phase retrieval, blind, alternating minimization,
gradient descent.


  The domain wall-magnetic tunnel junction (DW-MTJ) is a versatile device that
can simultaneously store data and perform computations. These three-terminal
devices are promising for digital logic due to their nonvolatility, low-energy
operation, and radiation hardness. Here, we augment the DW-MTJ logic gate with
voltage controlled magnetic anisotropy (VCMA) to improve the reliability of
logical concatenation in the presence of realistic process variations. VCMA
creates potential wells that allow for reliable and repeatable localization of
domain walls. The DW-MTJ logic gate supports different fanouts, allowing for
multiple inputs and outputs for a single device without affecting area. We
simulate a systolic array of DW-MTJ Multiply-Accumulate (MAC) units with 4-bit
and 8-bit precision, which uses the nonvolatility of DW-MTJ logic gates to
enable fine-grained pipelining and high parallelism. The DW-MTJ systolic array
provides comparable throughput and efficiency to state-of-the-art CMOS systolic
arrays while being radiation-hard. These results improve the feasibility of
using domain wall-based processors, especially for extreme-environment
applications such as space.


  We have proposed a model based upon flocking on a complex network, and then
developed two clustering algorithms on the basis of it. In the algorithms,
firstly a \textit{k}-nearest neighbor (knn) graph as a weighted and directed
graph is produced among all data points in a dataset each of which is regarded
as an agent who can move in space, and then a time-varying complex network is
created by adding long-range links for each data point. Furthermore, each data
point is not only acted by its \textit{k} nearest neighbors but also \textit{r}
long-range neighbors through fields established in space by them together, so
it will take a step along the direction of the vector sum of all fields. It is
more important that these long-range links provides some hidden information for
each data point when it moves and at the same time accelerate its speed
converging to a center. As they move in space according to the proposed model,
data points that belong to the same class are located at a same position
gradually, whereas those that belong to different classes are away from one
another. Consequently, the experimental results have demonstrated that data
points in datasets are clustered reasonably and efficiently, and the rates of
convergence of clustering algorithms are fast enough. Moreover, the comparison
with other algorithms also provides an indication of the effectiveness of the
proposed approach.


  Motivated by recent theoretical work on tidal disruption events and other
peculiar transients, we present moving-mesh radiation-hydrodynamic simulations
of radiative luminosity emitted by a central source being reprocessed by a
wind-like outflow. We couple the moving-mesh hydrodynamic code JET with our
newly-developed radiation module based on mixed-frame grey flux-limited
diffusion with implicit timestep update. This allows us to study the
self-consistent multi-dimensional radiation-hydrodynamic evolution over more
than ten orders of magnitude in both space and time in a single run. We
simulate an optically-thick spherical wind with constant or evolving mass-loss
rate, which is irradiated by a central isotropic or angularly-dependent
radiation source. Our spherically-symmetric simulations confirm previous
analytic results by identifying different stages of radiation reprocessing:
radiation trapped in the wind, diffusing out through the wind, and reaching
constant maximum attenuation. We find that confining the central radiation
source in a cone with moderate opening angles decrease up to one order of
magnitude the early flux along sightlines oriented away from the direction of
radiation injection but that the reprocessed radiation becomes isotropic
roughly after one lateral diffusion time through the ejecta. We discuss further
applications and guidelines for the use of our novel radiation-hydrodynamics
tool in the context of transient modelling.


  We present a modeling framework for dynamical and bursty contact networks
made of agents in social interaction. We consider agents' behavior at short
time scales, in which the contact network is formed by disconnected cliques of
different sizes. At each time a random agent can make a transition from being
isolated to being part of a group, or vice-versa. Different distributions of
contact times and inter-contact times between individuals are obtained by
considering transition probabilities with memory effects, i.e. the transition
probabilities for each agent depend both on its state (isolated or interacting)
and on the time elapsed since the last change of state. The model lends itself
to analytical and numerical investigations. The modeling framework can be
easily extended, and paves the way for systematic investigations of dynamical
processes occurring on rapidly evolving dynamical networks, such as the
propagation of an information, or spreading of diseases.


  Solving the Plateau problem means to find the surface with minimal area among
all surfaces with a given boundary. Part of the problem actually consists of
giving a suitable definition to the notions of 'surface', 'area' and
'boundary'. The sliding boundary condition has been introduced by David in
order to study the boundary regularity of minimal sets. In order to do that an
important step is to know the list of minimal boundary cones, that is to say
tangent cones on boundary points of minimal surfaces. In this paper we focus on
cones contained in an half-space and whose boundary can slide along the
bounding hyperplane. After giving a classification of one-dimensional minimal
cones in the half-plane we provide four new two-dimensional minimal cones in
the three-dimensional half space (which cannot be obtained as the Cartesian
product of the real line with one of the previous cones).


  We find a remarkable time persistence of various proxies for the kurtosis
(p-kurtosis) of the intraday returns distribution for the S&P500 index and this
permits a significant measure of their evolution from 1983 to 2004. There
appears a long time scale dramatic variation of the p-kurtosis uncorrelated
with the variation of the volatility thus falsifying any hypothesis of a
universal shape for the probability distribution of the returns. A large
increase in the kurtosis anticipates the October 87 crash. During the years
1991-2003 it continuously decreases even when the volatility grows during the
dot-com bubble. We propose some speculative interpretations of these results.


  The merging of two soap bubbles is a fundamental fluid mechanical process in
foam formation. In the present experimental study the liquid films from two
soap bubbles are brought together. Once the liquid layers initially separated
by a gas sheet are bridged on a single spot the rapid merging of the two liquid
films proceed. Thereby the connecting rim is rapidly accelerated into the
separating gas layer. We show that due to the dimple formation the velocity is
not uniform and the high acceleration causes initially a Rayleigh-Taylor
instability of the liquid rim. At later times, the rim takes heals into a
circular shape. However for sufficient high concentrations of the surfactant
the unstable rim pinches off microbubbles resulting in a fractal dendritic
structure after coalescence.


  After a thorough treatment of all algebraic structures involved, we address
two dimensional holonomy operators with values in crossed modules of Hopf
algebras and in crossed modules of associative algebras (called here crossed
modules of bare algebras.) In particular, we will consider two general
formulations of the two-dimensional holonomy of a (fully primitive) Hopf
2-connection (exact and blur), the first being multiplicative the second being
additive, proving that they coincide in a certain natural quotient (defining
what we called the fuzzy holonomy of a fully primitive Hopf 2-connection).


  A semi-analytic method is proposed for the generation of realizations of a
multivariate process of a given linear correlation structure and marginal
distribution. This is an extension of a similar method for univariate
processes, transforming the autocorrelation of the non-Gaussian process to that
of a Gaussian process based on a piece-wise linear marginal transform from
non-Gaussian to Gaussian marginal. The extension to multivariate processes
involves the derivation of the autocorrelation matrix from the marginal
transforms, which determines the generating vector autoregressive process. The
effectiveness of the approach is demonstrated on systems designed under
different scenarios of autocovariance and marginals.


  We give a new derivation of mean-field percolation critical behaviour from
the triangle condition that is quantitatively much better than previous proofs
when the triangle diagram $\nabla_{p_c}$ is large. In contrast to earlier
methods, our approach continues to yield bounds of reasonable order when the
triangle diagram $\nabla_p$ is unbounded but diverges slowly as $p \uparrow
p_c$, as is expected to occur in percolation on $\mathbb{Z}^d$ at the
upper-critical dimension $d=6$. Indeed, we show in particular that if the
triangle diagram diverges polylogarithmically as $p \uparrow p_c$ then
mean-field critical behaviour holds to within a polylogarithmic factor. We
apply the methods we develop to deduce that for long-range percolation on the
hierarchical lattice, mean-field critical behaviour holds to within
polylogarithmic factors at the upper-critical dimension.
  As part of the proof, we introduce a new method for comparing diagrammatic
sums on general transitive graphs that may be of independent interest.


  It is well-known that modern neural networks are vulnerable to adversarial
examples. To mitigate this problem, a series of robust learning algorithms have
been proposed. However, although the robust training error can be near zero via
some methods, all existing algorithms lead to a high robust generalization
error. In this paper, we provide a theoretical understanding of this puzzling
phenomenon from the perspective of expressive power for deep neural networks.
Specifically, for binary classification problems with well-separated data, we
show that, for ReLU networks, while mild over-parameterization is sufficient
for high robust training accuracy, there exists a constant robust
generalization gap unless the size of the neural network is exponential in the
data dimension $d$. This result holds even if the data is linear separable
(which means achieving standard generalization is easy), and more generally for
any parameterized function classes as long as their VC dimension is at most
polynomial in the number of parameters. Moreover, we establish an improved
upper bound of $\exp({\mathcal{O}}(k))$ for the network size to achieve low
robust generalization error when the data lies on a manifold with intrinsic
dimension $k$ ($k \ll d$). Nonetheless, we also have a lower bound that grows
exponentially with respect to $k$ -- the curse of dimensionality is inevitable.
By demonstrating an exponential separation between the network size for
achieving low robust training and generalization error, our results reveal that
the hardness of robust generalization may stem from the expressive power of
practical models.


  We present a detailed investigation of different approaches to modeling
feedback in simulations of galaxy formation. Gas-dynamic forces are evaluated
using Smoothed Particle Hydrodynamics (SPH) while star formation and supernova
feedback are included using a three parameter model which determines the star
formation rate normalization, feedback energy and lifetime of feedback regions.
The star formation rate is calculated using a Lagrangian Schmidt Law for all
gas particles which satisfy temperature, density and convergent flow criteria.
Feedback is incorporated as thermal heating of the ISM. We compare the effects
of distributing this energy over the smoothing scale or depositing it on a
single particle. Radiative losses are prevented from heated particles by
adjusting the density used in radiative cooling. We test the models on the
formation of galaxies from cosmological initial conditions and also on isolated
Milky Way and dwarf galaxies. Extremely violent feedback is necessary to
produce a gas disk with angular momentum remotely close to that of observed
disk galaxies. This is a result of the extreme central concentration of the
dark halos in the sCDM model, and the pervasiveness of the core-halo angular
momentum transport mechanism. We emphasize that the disks formed in
hierarchical simulations are partially a numerical artifact produced by the
minimum mass scale of the simulation acting as a highly efficient `support'
mechanism. Disk formation is strongly affected by the treatment of dense
regions in SPH, which along with the difficulty of representing the
hierarchical formation process, means that realistic simulations of galaxy
formation require far higher resolution than currently used.


  Eye movements are a behavioral response that can be involved in tasks as
complicated as natural image classification. This report confirms that pro- and
anti-saccades can be used by a volunteer to designate target (animal) or
non-target images that were centered 16 degrees off the fixation point. With
more than 86% correct responses, 11 participants responded to targets in 470
milliseconds on average, starting as quick as 245 milliseconds. Furthermore,
tracking the gaze position is considered a powerful method in the studies of
recognition as the saccade response times, ocular dynamics and the events
around the response time can be calculated from the data sampled 240 times per
second. The Hilbert transform is applied to obtain the analytic signal from the
horizontal gaze position. Its amplitude and phase are used to describe
differences between saccades that may testify to the recognition process.


  The authors report on the use of carbon nanofiber nanoemitters to ionize
deuterium atoms for the generation of neutrons in a deuterium-deuterium
reaction in a preloaded target. Acceleration voltages in the range of 50-80 kV
are used. Field emission of electrons is investigated to characterize the
emitters. The experimental setup and sample preparation are described and first
data of neutron production are presented. Ongoing experiments to increase
neutron production yields by optimizing the field emitter geometry and surface
conditions are discussed.


  We show that the groupoids of two directed graphs are isomorphic if and only
if the two graphs are orbit equivalent by an orbit equivalence that preserves
isolated eventually periodic points. We also give a complete description of the
(topological) isolated points of the boundary path space of a graph. As a
result, we are able to show that the groupoids of two directed graphs with
finitely many vertices and no sinks are isomorphic if and only if the two
graphs are orbit equivalent, and that the groupoids of the stabilisations of
two such graphs are isomorphic if and only if the stabilisations of the graphs
are orbit equivalent.


  Context. Gaia DR2 offers unparalleled precision on stars' parallaxes and
proper motions. This allows the prediction of microlensing events for which the
lens stars (and any planets they possess) are nearby and may be well studied
and characterised. Aims. We identify a number of potential microlensing events
that will occur before the year 2035.5, 20 years from the Gaia DR2 reference
epoch. Methods. We query Gaia DR2 for potential lenses within 100 pc, extract
parallaxes and proper motions of the lenses and background sources, and
identify potential lensing events. We estimate the lens masses from Priam
effective temperatures, and use these to calculate peak magnifications and the
size of the Einstein radii relative to the lens stars' habitable zones.
Results. We identify 7 future events with a probability > 10% of an alignment
within one Einstein radius. Of particular interest is DR2 5918299904067162240
(WISE J175839.20-583931.6), magnitude G = 14.9, which will lens a G = 13.9
background star in early 2030, with a median 23% net magnification. Other pairs
are typically fainter, hampering characterisation of the lens (if the lens is
faint) or the ability to accurately measure the magnification (if the source is
much fainter than the lens). Of timely interest is DR2 4116504399886241792
(2MASS J17392440-2327071), which will lens a background star in July 2020,
albeit with weak net magnification (0.03%). Median magnifications for the other
5 high-probability events range from 0.3% to 5.3%. The Einstein radii for these
lenses are 1-10 times the radius of the habitable zone, allowing these lensing
events to pick out cold planets around the ice line, and filling a gap between
transit and current microlensing detections of planets around very low-mass
stars. Conclusions. We provide a catalogue of the predicted events to aid
future characterisation efforts... [abridged]


  Kepler-16 is an eccentric low-mass eclipsing binary with a circumbinary
transiting planet. Here we investigate the angular momentum of the primary
star, based on Kepler photometry and Keck spectroscopy. The primary star's
rotation period is 35.1 +/- 1.0 days, and its projected obliquity with respect
to the stellar binary orbit is 1.6 +/- 2.4 degrees. Therefore the three largest
sources of angular momentum---the stellar orbit, the planetary orbit, and the
primary's rotation---are all closely aligned. This finding supports a formation
scenario involving accretion from a single disk. Alternatively, tides may have
realigned the stars despite their relatively wide separation (0.2 AU), a
hypothesis that is supported by the agreement between the measured rotation
period and the "pseudosynchronous" period of tidal evolution theory. The
rotation period, chromospheric activity level, and fractional light variations
suggest a main-sequence age of 2-4 Gyr. Evolutionary models of low-mass stars
can match the observed masses and radii of the primary and secondary stars to
within about 3%.


  It is known that several variations of the axiom of determinacy play
important roles in the study of reverse mathematics, and the relation between
the hierarchy of determinacy and comprehension are revealed by Tanaka, Nemoto,
Montalb\'an, Shore, and others. We prove variations of a result by
Ko{\l}odziejczyk and Michalewski relating determinacy of arbitrary boolean
combinations of $\Sigma^0_2$ sets and reflection in second-order arithmetic.
Specifically, we prove that: over $\mathsf{ACA}_0$,
$\Pi^1_2$-$\mathsf{Ref}(\mathsf{ACA}_0)$ is equivalent to $\forall
n.(\Sigma^0_1)_n$-$\mathsf{Det}^*_0$;
$\Pi^1_3$-$\mathsf{Ref}(\Pi^1_1$-$\mathsf{CA}_0)$ is equivalent to $\forall
n.(\Sigma^0_1)_n$-$\mathsf{Det}$; and
$\Pi^1_3$-$\mathsf{Ref}(\Pi^1_2$-$\mathsf{CA}_0)$ is equivalent to $\forall
n.(\Sigma^0_2)_n$-$\mathsf{Det}$. We also restate results by Montalb\'an and
Shore to show that $\Pi^1_3$-$\mathsf{Ref}(\mathsf{Z}_2)$ is equivalent to
$\forall n.(\Sigma^0_3)_n$-$\mathsf{Det}$ over $\mathsf{ACA}_0$.


  We discuss deep inelastic scattering at low $x$ as a critical phenomenon in
2+1 space-time dimensions. QCD (SU2) near the light cone becomes a critical
theory in the limit of $\lim x \to 0$ with a correlation mass $m(x) \propto
x^{\nu/2}$. We conjecture that the perturbative dipole wave function of the
virtual photon in the region $1/Q<x_{\bot}<1/m$ obeys correlation scaling $\Psi
\propto (x_{\bot})^{-(1+n)}$ before exponentially decaying for distances larger
than the inverse correlation mass. This behavior combined with an $x$
-independent dipole proton cross section gives a longitudinal structure
function which shows the dominant features of the experimental data. For SU3
QCD a similar second order phase transition is possible in the presence of
quark zero modes on the light cone.


  We present a study of the kinematics of 101 stars observed with VIMOS around
Whiting1, a globular cluster embedded in the Sagittarius tidal stream. The
obtained velocity distribution shows the presence of two wraps of that halo
substructure at the same heliocentric distance as that of the cluster and with
well differentiated mean radial velocities. The most prominent velocity
component seems to be associated with the trailing arm of Sagittarius with
$<v_{\rm r}>$ ~ -130 km/s, which is consistent with the velocity of Whiting1.
This result supports that this globular cluster was formed in Sagittarius and
recently accreted by the Milky Way. The second component with $<v_{\rm r}>$ ~
120 km/s might correspond to the leading arm of Sagittarius, which has been
predicted by numerical simulations but with no conclusive observational
evidence of its existence presented so far. This detection of the old leading
wrap of Sagittarius in the southern hemisphere may be used to confirm and
further constrain the models for its orbit and evolution.


  We present similarity solutions for mass-loaded adiabatic bubbles that are
blown by winds having time dependent mechanical luminosities. We consider
mass-loading through conductive evaporation of clumps. In the limit of little
mass loading a similarity solution found by Dyson (1973) for expansion into a
smooth ambient medium is recovered. We find that the Mach number in the shocked
mass-loaded wind shows a radial dependence that varies qualitatively from
solution to solution. In some cases it is everywhere less than unity in the
frame of the clumps being evaporated, while in others it is everywhere greater
than unity. In some solutions the mass-loaded shocked wind undergoes one or two
sonic transitions in the clump frame. Maximum possible values of the ratio of
evaporated mass to stellar wind mass are found as a consequence of the
evaporation rates dependence on temperature and the lowering of the temperature
by mass-loading. Mass-loading tends to reduce the emissivity in the interior of
the bubble relative to its limb, whilst simultaneously increasing the central
temperature relative to the limb temperature.


  Current-induced magnetization switching through spin-orbit torques (SOTs) is
the fundamental building block of spin-orbitronics. The SOTs generally arise
from the spin-orbit coupling of heavy metals. However, even in a
heterostructure where a metallic magnet is sandwiched by two different
insulators, a nonzero current-induced SOT is expected because of the broken
inversion symmetry; an electrical insulator can be a spin-torque generator.
Here, we demonstrate current-induced magnetization switching using an
insulator. We show that oxygen incorporation into the most widely used
spintronic material, Pt, turns the heavy metal into an electrically-insulating
generator of the SOTs, enabling the electrical switching of perpendicular
magnetization in a ferrimagnet sandwiched by electrically-insulating oxides. We
further found that the SOTs generated from the Pt oxide can be controlled
electrically through voltage-driven oxygen migration. These findings open a
route towards energy-efficient, voltage-programmable spin-orbit devices based
on solid-state switching of heavy metal oxidation.


  The procedure of the dimensional reduction related to the partition function
of a quantum field living in curved space-time which is the warp product of a
symmetric space is investigated.


  We investigate the problem of dimension reduction for plates in nonlinear
magnetoelasticity. The model features a mixed Eulerian-Lagrangian formulation,
as magnetizations are defined on the deformed set in the actual space. We
consider low-energy configurations by rescaling the elastic energy according to
the linearized von K\'{a}rm\'{a}n regime. First, we identify a reduced model by
computing the $\Gamma$-limit of the magnetoelastic energy, as the thickness of
the plate goes to zero. This extends a previous result obtained by the first
author in the incompressible case to the compressible one. Then, we introduce
applied loads given by mechanical forces and external magnetic fields and we
prove that, under clamped boundary conditions, sequences of almost minimizes of
the total energy converge to minimizers of the corresponding energy in the
reduced model. Subsequently, we study quasistatic evolutions driven by
time-dependent applied loads and a rate-independent dissipation. We prove that
solutions to the approximate incremental minimization problem at the bulk
converge to energetic solutions to the reduced model. This result provides a
further justification of the latter in the spirit of evolutionary
$\Gamma$-convergence.


  We give an explicit recipe for determining iterated local cohomology groups
with support in ideals of minors of a generic matrix in characteristic zero,
expressing them as direct sums of indecomposable D-modules. For non-square
matrices these indecomposables are simple, but this is no longer true for
square matrices where the relevant indecomposables arise from the pole order
filtration associated with the determinant hypersurface. Specializing our
results to a single iteration, we determine the Lyubeznik numbers for all
generic determinantal rings, thus answering a question of Hochster.


  The fact that the number of users of open source software (OSS) is
practically un-limited and that ultimately the software quality is determined
by end users experience, makes the usability an even more critical quality
attribute than it is for proprietary software. With the sharp increase in use
of open source projects by both individuals and organizations, the level of
usability and related issues must be addressed more seriously. The research
model of this empirical investigation studies and establishes the relationship
between the key usability factors from contributors perspective and OSS
usability. A data set of 78 OSS contributors that includes architects,
designers, developers, testers and users from 22 open source projects of varied
size has been used to study the research model. The results of this study
provide empirical evidence by indicating that the highlighted key factors play
a significant role in improving OSS usability.


  The rising demand for sustainable IoT has promoted the adoption of
battery-free devices intermittently powered by ambient energy for sensing.
However, the intermittency poses significant challenges in sensing data
collection. Despite recent efforts to enable one-to-one communication, routing
data across multiple intermittently-powered battery-free devices, a crucial
requirement for a sensing system, remains a formidable challenge.
  This paper fills this gap by introducing Swift, which enables seamless data
routing in intermittently-powered battery-free sensing systems. Swift overcomes
the challenges posed by device intermittency and heterogeneous energy
conditions through three major innovative designs. First, Swift incorporates a
reliable node synchronization protocol backed by number theory, ensuring
successful synchronization regardless of energy conditions. Second, Swift
adopts a low-latency message forwarding protocol, allowing continuous message
forwarding without repeated synchronization. Finally, Swift features a simple
yet effective mechanism for routing path construction, enabling nodes to obtain
the optimal path to the sink node with minimum hops. We implement Swift and
perform large-scale experiments representing diverse realworld scenarios. The
results demonstrate that Swift achieves an order of magnitude reduction in
end-to-end message delivery time compared with the state-of-the-art approaches
for intermittentlypowered battery-free sensing systems.


  Stellar spectroscopic classification has been successfully automated by a
number of groups. Automated classification and parameterization work best when
applied to a homogeneous data set, and thus these techniques primarily have
been developed for and applied to large surveys. While most ongoing large
spectroscopic surveys target extragalactic objects, many stellar spectra have
been and will be obtained. We briefly summarize past work on automated
classification and parameterization, with emphasis on the work done in our
group. Accurate automated classification in the spectral type domain and
parameterization in the temperature domain have been relatively easy. Automated
parameterization in the metallicity domain, formally outside the MK system, has
also been effective. Due to the subtle effects on the spectrum, automated
classification in the luminosity domain has been somewhat more difficult, but
still successful. In order to extend the use of automated techniques beyond a
few surveys, we present our current efforts at building a web-based automated
stellar spectroscopic classification and parameterization machine. Our proposed
machinery would provide users with MK classifications as well as the
astrophysical parameters of effective temperature, surface gravity, mean
abundance, abundance anomalies, and microturbulence.


  We study $[0,1]$-valued logics that are closed under the
{\L}ukasiewicz-Pavelka connectives; our primary examples are the the continuous
logic framework of Ben Yaacov and Usvyatsov \cite{Ben-Yaacov-Usvyatsov:2010}
and the {\L}ukasziewicz-Pavelka logic itself. The main result of the paper is a
characterization of these logics in terms of a model-theoretic property,
namely, an extension of the omitting types theorem to uncountable languages.


  Let $\mathcal{H}iggs_{ss}$ be the stack of semistable rank two Higgs bundles
on $\mathbf{P}^{1}$ with value in $O(n)$ where $n\geq 2$. In this paper we will
construct the Poincar\'e sheaf $\mathcal{P}$ on
$\mathcal{H}iggs_{ss}\times_{H}\mathcal{H}iggs_{ss}$ which is maximal
Cohen-Macaulay and flat over $\mathcal{H}iggs_{ss}$.


  We study the privacy-utility trade-off in the context of metric differential
privacy. Ghosh et al. introduced the idea of universal optimality to
characterise the best mechanism for a certain query that simultaneously
satisfies (a fixed) $\epsilon$-differential privacy constraint whilst at the
same time providing better utility compared to any other
$\epsilon$-differentially private mechanism for the same query. They showed
that the Geometric mechanism is "universally optimal" for the class of counting
queries. On the other hand, Brenner and Nissim showed that outside the space of
counting queries, and for the Bayes risk loss function, no such universally
optimal mechanisms exist. In this paper we use metric differential privacy and
quantitative information flow as the fundamental principle for studying
universal optimality. Metric differential privacy is a generalisation of both
standard (i.e., central) differential privacy and local differential privacy,
and it is increasingly being used in various application domains, for instance
in location privacy and in privacy preserving machine learning. Using this
framework we are able to clarify Nissim and Brenner's negative results, showing
(a) that in fact all privacy types contain optimal mechanisms relative to
certain kinds of non-trivial loss functions, and (b) extending and generalising
their negative results beyond Bayes risk specifically to a wide class of
non-trivial loss functions. We also propose weaker universal benchmarks of
utility called "privacy type capacities". We show that such capacities always
exist and can be computed using a convex optimisation algorithm.


  We prove that whenever a sequence of invertible and bounded operators
$(A_m)_{m\in \mathbb{Z}}$ acting on a Banach space $X$ admits an exponential
dichotomy and a sequence of differentiable maps $f_m \colon X\to X$, $m\in
\mathbb{Z}$, has bounded and H\"{o}lder derivatives, the nonautonomous dynamics
given by $x_{m+1}=A_mx_m+f_m(x_m)$, $m\in \mathbb{Z}$ has various shadowing
properties. Hence, we extend recent results of Bernardes Jr. et al. in several
directions. As a nontrivial application of our results, we give a new proof of
the nonautonomous Grobman-Hartman theorem.


  We study properties of continuous finite group actions on topological
manifolds that hold true, for any finite group action, after possibly passing
to a subgroup of index bounded above by a constant depending only on the
manifold. These include the Jordan property, the almost fixed point property,
as well as bounds on the discrete symmetry group. Most of our results apply to
manifolds satisfying some restriction such as having nonzero Euler
characteristic or having the integral homology of a sphere. For an arbitrary
topological manifold $X$ such that $H_*(X;{\mathbf Z})$ is finitely generated,
we prove the existence a constant $C$ with the property that for any continuous
action of a finite group $G$ on $X$ such that every $g\in G$ fixes at least on
point of $X$, there is a subgroup $H\leq G$ satisfying $[G:H]\leq C$ and a
point $x\in X$ which is fixed by all elements of $H$.


  Normal behavior of the thermodynamic properties of a Fermi gas in $d>2$
dimensions, integer or not, means monotonically increasing or decreasing of its
specific heat, chemical potential or isothermal sound velocity, all as
functions of temperature. However, for $0<d<2$ dimensions these properties
develop a ``hump'' (or ``trough'') which increases (or deepens) as $d\to 0$.
Though not the phase transition signaled by the sharp features (``cusp'' or
``jump'') in those properties for the ideal Bose gas in $d>2$ (known as the
Bose-Einstein condensation), it is nevertheless an intriguing structural
anomaly which we exhibit in detail.


  Similarity search approaches based on graph walks have recently attained
outstanding speed-accuracy trade-offs, taking aside the memory requirements. In
this paper, we revisit these approaches by considering, additionally, the
memory constraint required to index billions of images on a single server. This
leads us to propose a method based both on graph traversal and compact
representations. We encode the indexed vectors using quantization and exploit
the graph structure to refine the similarity estimation.
  In essence, our method takes the best of these two worlds: the search
strategy is based on nested graphs, thereby providing high precision with a
relatively small set of comparisons. At the same time it offers a significant
memory compression. As a result, our approach outperforms the state of the art
on operating points considering 64-128 bytes per vector, as demonstrated by our
results on two billion-scale public benchmarks.


  We present a scheme for tuning two quantum point contacts as a
quantum-limited charge detector. Based on the scattering matrix approach, we
analyze a general condition of quantum-limited detection with a single-channel
quantum detector possessing time-reversal symmetry. From this analysis we find
that quantum-limited detection can be easily realized with two quantum point
contacts connected in series, which is not possible if only a single quantum
point contact is used. We also discuss the sensitivity enhancement due to
multiple reflections of the two point contacts.


  This paper introduces a novel framework for end-to-end learned video coding.
Image compression is generalized through conditional coding to exploit
information from reference frames, allowing to process intra and inter frames
with the same coder. The system is trained through the minimization of a
rate-distortion cost, with no pre-training or proxy loss. Its flexibility is
assessed under three coding configurations (All Intra, Low-delay P and Random
Access), where it is shown to achieve performance competitive with the
state-of-the-art video codec HEVC.


  We propose a novel adversarial multi-task learning scheme, aiming at actively
curtailing the inter-talker feature variability while maximizing its senone
discriminability so as to enhance the performance of a deep neural network
(DNN) based ASR system. We call the scheme speaker-invariant training (SIT). In
SIT, a DNN acoustic model and a speaker classifier network are jointly
optimized to minimize the senone (tied triphone state) classification loss, and
simultaneously mini-maximize the speaker classification loss. A
speaker-invariant and senone-discriminative deep feature is learned through
this adversarial multi-task learning. With SIT, a canonical DNN acoustic model
with significantly reduced variance in its output probabilities is learned with
no explicit speaker-independent (SI) transformations or speaker-specific
representations used in training or testing. Evaluated on the CHiME-3 dataset,
the SIT achieves 4.99% relative word error rate (WER) improvement over the
conventional SI acoustic model. With additional unsupervised speaker
adaptation, the speaker-adapted (SA) SIT model achieves 4.86% relative WER gain
over the SA SI acoustic model.


  The magnetization $|\Omega_{\mathrm e}|/\omega_{\mathrm{e}}$ is an important
parameter in plasma astrophysics, where $\Omega_{\mathrm e}$ and
$\omega_{\mathrm{e}}$ are the electron gyro-frequency and electron plasma
frequency, respectively. It only depends on the mass ratio $m_{\mathrm
i}/m_{\mathrm e}$ and the light-to-Alfv\'en speed ratio $c/v_{\mathrm{Ai}}$,
where $m_{\mathrm i}$ ($m_{\mathrm e}$) is the ion (electron) mass, $c$ is the
speed of light, and $v_{\mathrm{Ai}}$ is the ion Alfv\'en speed. Nonlinear
numerical plasma models such as particle-in-cell simulations must often assume
unrealistic values for $m_{\mathrm i}/m_{\mathrm e}$ and for
$c/v_{\mathrm{Ai}}$. Because linear theory yields exact results for parametric
scalings of wave properties at small amplitudes, we use linear theory to
investigate the dispersion relations of Alfv\'en/ion-cyclotron and
fast-magnetosonic/whistler waves as prime examples for collective plasma
behaviour depending on $m_{\mathrm i}/m_{\mathrm e}$ and $c/v_{\mathrm{Ai}}$.
We analyse their dependence on $m_{\mathrm i}/m_{\mathrm e}$ and
$c/v_{\mathrm{Ai}}$ in quasi-parallel and quasi-perpendicular directions of
propagation with respect to the background magnetic field for a plasma with
$\beta_j\sim1$, where $\beta_j$ is the ratio of the thermal to magnetic
pressure for species $j$. Although their dispersion relations are largely
independent of $c/v_{\mathrm{Ai}}$ for $c/v_{\mathrm{Ai}}\gtrsim 10$, the mass
ratio $m_{\mathrm i}/m_{\mathrm e}$ has a strong effect at scales smaller than
the ion inertial length. Moreover, we study the impact of relativistic electron
effects on the dispersion relations. Based on our results, we recommend aiming
for a more realistic value of $m_{\mathrm i}/m_{\mathrm e}$ than for a more
realistic value of $c/v_{\mathrm{Ai}}$ in non-relativistic plasma simulations
if such a choice is necessary, although $\dots$


  We consider Constant Proportion Portfolio Insurance (CPPI) and its dynamic
extension, which may be called Dynamic Proportion Portfolio Insurance (DPPI).
It is shown that these investment strategies work within the setting of
F\"ollmer's pathwise It\^o calculus, which makes no probabilistic assumptions
whatsoever. This shows, on the one hand, that CPPI and DPPI are completely
independent of any choice of a particular model for the dynamics of asset
prices. They even make sense beyond the class of semimartingale sample paths
and can be successfully defined for models admitting arbitrage, including some
models based on fractional Brownian motion. On the other hand, the result can
be seen as a case study for the general issue of robustness in the face of
model uncertainty in finance.


  Quantization of a system constrained to move on a sphere is considered by
taking a square root of the ``on sphere condition''. We arrive at the fibre
bundle structure of the Hopf map in the cases of $S^{2} $and $S^{4}$. This
leads to more geometrical understanding of monopole and instanton gauge
structures that emerge in the course of quantization.


  In this note we offer some log-concavity properties of certain functions
related to Bessel functions of the first kind and modified Bessel functions of
the first and second kind, by solving partially a recent conjecture on the
log-convexity/log-concavity properties for modified Bessel functions of the
first kind and their derivatives. Moreover, we give an application of the
mentioned results by extending two inequalities of van der Corput to Bessel and
modified Bessel functions of the first kind. Similar inequalities are proved
also for modified Bessel functions of the second kind, as well as for
log-concave probability density functions.


  We present results for the hyperon vector form factor f_1 for $\Xi^0
\rightarrow \Sigma^+ l\bar{\nu}$ and $\Sigma^- \rightarrow n l\bar{\nu}$
semileptonic decays from dynamical lattice QCD with domain-wall quarks.
Simulations are performed on the 2+1 flavor gauge configurations generated by
the RBC and UKQCD Collaborations with a lattice cutoff of 1/a = 1.7 GeV. Our
preliminary results, which are calculated at the lightest sea quark mass (pion
mass down to approximately 330 MeV), show that a sign of the second-order
correction of SU(3) breaking on hyperon vector coupling f_1(0) is likely
negative.


  Nowadays event extraction systems mainly deal with a relatively small amount
of information about temporal and modal qualifications of situations, primarily
processing assertive sentences in the past tense. However, systems with a wider
coverage of tense, aspect and mood can provide better analyses and can be used
in a wider range of text analysis applications. This thesis develops such a
system for Turkish language. This is accomplished by extending Open Source
Information Mining and Analysis (OPTIMA) research group's event extraction
software, by implementing appropriate extensions in the semantic representation
format, by adding a partial grammar which improves the TAM (Tense, Aspect and
Mood) marker, adverb analysis and matching functions of ExPRESS, and by
constructing an appropriate lexicon in the standard of CORLEONE. These
extensions are based on iv the theory of anchoring relations (Tem\"urc\"u,
2007, 2011) which is a crosslinguistically applicable semantic framework for
analyzing tense, aspect and mood related categories. The result is a system
which can, in addition to extracting basic event structures, classify sentences
given in news reports according to their temporal, modal and
volitional/illocutionary values. Although the focus is on news reports of
natural disasters, disease outbreaks and man-made disasters in Turkish
language, the approach can be adapted to other languages, domains and genres.
This event extraction and classification system, with further developments, can
provide a basis for automated browsing systems for preventing environmental and
humanitarian risk.


  It is widely accepted that the number of reported cases during the first
stages of the COVID-19 pandemic severely underestimates the number of actual
cases. We leverage delay embedding theorems of Whitney and Takens and use
Gaussian Process regression to estimate the number of cases during the first
2020 wave based on the second wave of the epidemic in several European
countries, South Korea, and Brazil. We assume that the second wave was more
accurately monitored and hence that it can be trusted. We then construct a
manifold diffeomorphic to that of the implied original dynamical system, using
fatalities or hospitalizations only. Finally, we restrict the diffeomorphism to
the reported cases coordinate of the dynamical system. Our main finding is that
in the European countries studied, the actual cases are under-reported by as
much as 50\%. On the other hand, in South Korea -- which had an exemplary and
proactive mitigation approach -- a far smaller discrepancy between the actual
and reported cases is predicted, with an approximately 17\% predicted
under-estimation. We believe that our backcasting framework is applicable to
other epidemic outbreaks where (due to limited or poor quality data) there is
uncertainty around the actual cases.


  We present results on simulations of a stock market with heterogeneous,
cumulative information setup. We find a non-monotonic behaviour of traders'
returns as a function of their information level. Particularly, the average
informed agents underperform random traders; only the most informed agents are
able to beat the market. We also study the effect of a strategy updating
mechanism, when traders have the possibility of using other pieces of
information than the fundamental value. These results corroborate the latter
ones: it is only for the most informed player that it is rewarding to stay
fundamentalist. The simulations reproduce some stylized facts of tick-by-tick
stock-exchange data and globally show informational efficiency.


  We present MPWide, a platform independent communication library for
performing message passing between computers. Our library allows coupling of
several local MPI applications through a long distance network and is
specifically optimized for such communications. The implementation is
deliberately kept light-weight, platform independent and the library can be
installed and used without administrative privileges. The only requirements are
a C++ compiler and at least one open port to a wide area network on each site.
In this paper we present the library, describe the user interface, present
performance tests and apply MPWide in a large scale cosmological N-body
simulation on a network of two computers, one in Amsterdam and the other in
Tokyo.


  Inspired by the human visual perception system, hexagonal image processing in
the context of machine learning deals with the development of image processing
systems that combine the advantages of evolutionary motivated structures based
on biological models. While conventional state-of-the-art image processing
systems of recording and output devices almost exclusively utilize square
arranged methods, their hexagonal counterparts offer a number of key advantages
that can benefit both researchers and users. This contribution serves as a
general application-oriented approach the synthesis of the therefore designed
hexagonal image processing framework, called Hexnet, the processing steps of
hexagonal image transformation, and dependent methods. The results of our
created test environment show that the realized framework surpasses current
approaches of hexagonal image processing systems, while hexagonal artificial
neural networks can benefit from the implemented hexagonal architecture. As
hexagonal lattice format based deep neural networks, also called H-DNN, can be
compared to their square counterparts by transforming classical square lattice
based data sets into their hexagonal representation, they can also result in a
reduction of trainable parameters as well as result in increased training and
test rates.


  Based on recent well-posedness results in Sobolev (or Besov spaces) for
periodic solutions to the Fornberg-Whitham equations we investigate here the
questions of wave breaking and blow-up for these solutions. We show first that
finite maximal life time of a solution necessarily leads to wave breaking.
Second, we prove that for a certain class of initial wave profiles the
corresponding solutions do indeed blow-up in finite time.


  First-principles calculations based on density-functional theory in the
pseudo-potential approach have been performed for the total energy, crystal
structure and cell polarization for SrTaO$_2$N/SrTiO$_3$ heterostructures.
Different heterojunctions were analyzed in terms of the termination atoms at
the interface plane, and periodic or non-periodic stacking in the perpendicular
direction. The calculations show that the SrTaO$_2$N layer is compressed along
the $ab$-plane, while the SrTiO$_3$ is elongated, thus favoring the formation
of P4mm local environment on both sides of the interface, leading to net
macroscopic polarization. The analysis of the local polarization as a function
of the distance to the interface, for each individual unit cell was found to
depend on the presence of a N or an O atom at the interface, and also on the
asymmetric and not uniform $c$-axis deformation due to the induced strain in
the $ab$-plane. The resulting total polarization in the periodic array was $
\approx 0.54$ C/m$^2$, which makes this type of arrangement suitable for
microelectronic applications.


  The GEM-TPC detector will be part of the standard Super-FRS detection system,
as tracker detectors at several focal stations along the separator and its
three branches.


  We present a provably more efficient implementation of the Minimum Norm Point
Algorithm conceived by Fujishige than the one presented in \cite{FUJI06}. The
algorithm solves the minimization problem for a class of functions known as
submodular. Many important functions, such as minimum cut in the graph, have
the so called submodular property \cite{FUJI82}. It is known that the problem
can also be efficiently solved in strongly polynomial time \cite{IWAT01},
however known theoretical bounds are far from being practical. We present an
improved implementation of the algorithm, for which unfortunately no worst case
bounds are know, but which performs very well in practice. With the
modifications presented, the algorithm performs an order of magnitude faster
for certain submodular functions.


  This paper evaluates the difference between human pathway curation and
current NLP systems. We propose graph analysis methods for quantifying the gap
between human curated pathway maps and the output of state-of-the-art automatic
NLP systems. Evaluation is performed on the popular mTOR pathway. Based on
analyzing where current systems perform well and where they fail, we identify
possible avenues for progress.


  The structure of an optical waveguide determines the characteristics of its
guided modes, such as their spatial profile and group index. General features
are shared by modes regardless of the waveguiding structure; for example, modal
dispersion is inevitable in multimode waveguides, every mode experiences
group-velocity dispersion, and higher-order modes are usually slower than their
lower-order counterparts. We show here that such trends can be fundamentally
altered -- altogether severing the link between modal order and group index
hybrid and eliminating dispersion -- by exploiting hybrid guided space-time
modes in a planar multimode waveguide. Such modes are confined in one-dimension
by the waveguide and in the other by the spatio-temporal spectral structure of
the field itself. Direct measurements of the modal group delays confirm that
the group index for low-loss, dispersion-free, hybrid space-time modes can be
each tuned away from the group index of the conventional mode of same order,
and that the transverse size of these hybrid modes can be varied independently
of the modal order and group index. These findings are verified in a few-mode
planar waveguide consisting of a 25.5-mm-long, 4-$\mu$m-thick silica film
deposited on a MgF$_2$ substrate.


  We give an overview of how to construct continued fractions on the Heisenberg
group $\mathbb{H}$, the projective and planar Siegel models of the group, and
how to perform computations on the group using matrices. We discuss and work
with some recent results of Vandehey on the classification of which elements of
the Heisenberg group have eventually periodic continued fraction
expansions.Then we examine and work with major theorems in ergodic theory to
explore results concerning growth of denominators and digit frequency, relating
the results to hyperbolic geometry.


  Quasiperiodic Fibonacci-like and fractal Cantor-like single- and multiple-row
nanopillar waveguides are investigated theoretically employing the finite
difference time domain (FDTD) method. It is shown that resonant modes of the
Fibonacci and Cantor waveguides can have a Q-factor comparable with that of a
point-defect resonator embedded in a periodic nanopillar waveguide, while the
radiation is preferably emitted into the waveguide direction, thus improving
coupling to an unstructured dielectric waveguide located along the structure
axis. This is especially so when the dielectric waveguide introduces a small
perturbation in the aperiodic structure, breaking the structure symmetry while
staying well apart from the main localization area of the resonant mode. The
high Q-factor and increased coupling with external dielectric waveguide suggest
using the proposed deterministically aperiodic nanopillar waveguides in
photonic integrated circuits.


  Children learning their first language face multiple problems of induction:
how to learn the meanings of words, and how to build meaningful phrases from
those words according to syntactic rules. We consider how children might solve
these problems efficiently by solving them jointly, via a computational model
that learns the syntax and semantics of multi-word utterances in a grounded
reference game. We select a well-studied empirical case in which children are
aware of patterns linking the syntactic and semantic properties of words ---
that the properties picked out by base nouns tend to be related to shape, while
prenominal adjectives tend to refer to other properties such as color. We show
that children applying such inductive biases are accurately reflecting the
statistics of child-directed speech, and that inducing similar biases in our
computational model captures children's behavior in a classic adjective
learning experiment. Our model incorporating such biases also demonstrates a
clear data efficiency in learning, relative to a baseline model that learns
without forming syntax-sensitive overhypotheses of word meaning. Thus solving a
more complex joint inference problem may make the full problem of language
acquisition easier, not harder.


  This paper presents the case study of a non-intrusive porting of a monolithic
C++ library for real-time 3D hand tracking, to the domain of edge-based
computation. Towards a proof of concept, the case study considers a pair of
workstations, a computationally powerful and a computationally weak one. By
wrapping the C++ library in Java container and by capitalizing on a Java-based
offloading infrastructure that supports both CPU and GPGPU computations, we are
able to establish automatically the required server-client workflow that best
addresses the resource allocation problem in the effort to execute from the
weak workstation. As a result, the weak workstation can perform well at the
task, despite lacking the sufficient hardware to do the required computations
locally. This is achieved by offloading computations which rely on GPGPU, to
the powerful workstation, across the network that connects them. We show the
edge-based computation challenges associated with the information flow of the
ported algorithm, demonstrate how we cope with them, and identify what needs to
be improved for achieving even better performance.